Dynamics in geometrical confinement
Kremer, Friedrich
2014-01-01
This book describes the dynamics of low molecular weight and polymeric molecules when they are constrained under conditions of geometrical confinement. It covers geometrical confinement in different dimensionalities: (i) in nanometer thin layers or self supporting films (1-dimensional confinement) (ii) in pores or tubes with nanometric diameters (2-dimensional confinement) (iii) as micelles embedded in matrices (3-dimensional) or as nanodroplets.The dynamics under such conditions have been a much discussed and central topic in the focus of intense worldwide research activities within the last two decades. The present book discusses how the resulting molecular mobility is influenced by the subtle counterbalance between surface effects (typically slowing down molecular dynamics through attractive guest/host interactions) and confinement effects (typically increasing the mobility). It also explains how these influences can be modified and tuned, e.g. through appropriate surface coatings, film thicknesses or pore...
A geometrical approach to control and controllability of nonlinear dynamical networks.
Wang, Le-Zhi; Su, Ri-Qi; Huang, Zi-Gang; Wang, Xiao; Wang, Wen-Xu; Grebogi, Celso; Lai, Ying-Cheng
2016-04-14
In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control.
Geometric integrators for stochastic rigid body dynamics
Tretyakov, Mikhail
2016-01-05
Geometric integrators play an important role in simulating dynamical systems on long time intervals with high accuracy. We will illustrate geometric integration ideas within the stochastic context, mostly on examples of stochastic thermostats for rigid body dynamics. The talk will be mainly based on joint recent work with Rusland Davidchak and Tom Ouldridge.
Geometric phases in discrete dynamical systems
Energy Technology Data Exchange (ETDEWEB)
Cartwright, Julyan H.E., E-mail: julyan.cartwright@csic.es [Instituto Andaluz de Ciencias de la Tierra, CSIC–Universidad de Granada, E-18100 Armilla, Granada (Spain); Instituto Carlos I de Física Teórica y Computacional, Universidad de Granada, E-18071 Granada (Spain); Piro, Nicolas, E-mail: nicolas.piro@epfl.ch [École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne (Switzerland); Piro, Oreste, E-mail: piro@imedea.uib-csic.es [Departamento de Física, Universitat de les Illes Balears, E-07122 Palma de Mallorca (Spain); Tuval, Idan, E-mail: ituval@imedea.uib-csic.es [Mediterranean Institute for Advanced Studies, CSIC–Universitat de les Illes Balears, E-07190 Mallorca (Spain)
2016-10-14
In order to study the behaviour of discrete dynamical systems under adiabatic cyclic variations of their parameters, we consider discrete versions of adiabatically-rotated rotators. Parallelling the studies in continuous systems, we generalize the concept of geometric phase to discrete dynamics and investigate its presence in these rotators. For the rotated sine circle map, we demonstrate an analytical relationship between the geometric phase and the rotation number of the system. For the discrete version of the rotated rotator considered by Berry, the rotated standard map, we further explore this connection as well as the role of the geometric phase at the onset of chaos. Further into the chaotic regime, we show that the geometric phase is also related to the diffusive behaviour of the dynamical variables and the Lyapunov exponent. - Highlights: • We extend the concept of geometric phase to maps. • For the rotated sine circle map, we demonstrate an analytical relationship between the geometric phase and the rotation number. • For the rotated standard map, we explore the role of the geometric phase at the onset of chaos. • We show that the geometric phase is related to the diffusive behaviour of the dynamical variables and the Lyapunov exponent.
Fisher metric, geometric entanglement, and spin networks
Chirco, Goffredo; Mele, Fabio M.; Oriti, Daniele; Vitale, Patrizia
2018-02-01
Starting from recent results on the geometric formulation of quantum mechanics, we propose a new information geometric characterization of entanglement for spin network states in the context of quantum gravity. For the simple case of a single-link fixed graph (Wilson line), we detail the construction of a Riemannian Fisher metric tensor and a symplectic structure on the graph Hilbert space, showing how these encode the whole information about separability and entanglement. In particular, the Fisher metric defines an entanglement monotone which provides a notion of distance among states in the Hilbert space. In the maximally entangled gauge-invariant case, the entanglement monotone is proportional to a power of the area of the surface dual to the link thus supporting a connection between entanglement and the (simplicial) geometric properties of spin network states. We further extend such analysis to the study of nonlocal correlations between two nonadjacent regions of a generic spin network graph characterized by the bipartite unfolding of an intertwiner state. Our analysis confirms the interpretation of spin network bonds as a result of entanglement and to regard the same spin network graph as an information graph, whose connectivity encodes, both at the local and nonlocal level, the quantum correlations among its parts. This gives a further connection between entanglement and geometry.
Geometric symmetries in superfluid vortex dynamics
Kozik, Evgeny; Svistunov, Boris
2010-10-01
Dynamics of quantized vortex lines in a superfluid feature symmetries associated with the geometric character of the complex-valued field, w(z)=x(z)+iy(z) , describing the instant shape of the line. Along with a natural set of Noether’s constants of motion, which—apart from their rather specific expressions in terms of w(z) —are nothing but components of the total linear and angular momenta of the fluid, the geometric symmetry brings about crucial consequences for kinetics of distortion waves on the vortex lines, the Kelvin waves. It is the geometric symmetry that renders Kelvin-wave cascade local in the wave-number space. Similar considerations apply to other systems with purely geometric degrees of freedom.
Geometrical Methods for Power Network Analysis
Bellucci, Stefano; Gupta, Neeraj
2013-01-01
This book is a short introduction to power system planning and operation using advanced geometrical methods. The approach is based on well-known insights and techniques developed in theoretical physics in the context of Riemannian manifolds. The proof of principle and robustness of this approach is examined in the context of the IEEE 5 bus system. This work addresses applied mathematicians, theoretical physicists and power engineers interested in novel mathematical approaches to power network theory.
Introduction to Dynamical Systems and Geometric Mechanics
Maruskin, Jared M.
2012-01-01
Introduction to Dynamical Systems and Geometric Mechanics provides a comprehensive tour of two fields that are intimately entwined: dynamical systems is the study of the behavior of physical systems that may be described by a set of nonlinear first-order ordinary differential equations in Euclidean space, whereas geometric mechanics explores similar systems that instead evolve on differentiable manifolds. In the study of geometric mechanics, however, additional geometric structures are often present, since such systems arise from the laws of nature that govern the motions of particles, bodies, and even galaxies. In the first part of the text, we discuss linearization and stability of trajectories and fixed points, invariant manifold theory, periodic orbits, PoincarÃ© maps, Floquet theory, the PoincarÃ©-Bendixson theorem, bifurcations, and chaos. The second part of the text begins with a self-contained chapter on differential geometry that introduces notions of manifolds, mappings, vector fields, the Jacobi-Lie bracket, and differential forms. The final chapters cover Lagrangian and Hamiltonian mechanics from a modern geometric perspective, mechanics on Lie groups, and nonholonomic mechanics via both moving frames and fiber bundle decompositions. The text can be reasonably digested in a single-semester introductory graduate-level course. Each chapter concludes with an application that can serve as a springboard project for further investigation or in-class discussion.
Geometrical dynamics of Born-Infeld objects
International Nuclear Information System (INIS)
Cordero, Ruben; Molgado, Alberto; Rojas, Efrain
2007-01-01
We present a geometrically inspired study of the dynamics of Dp-branes. We focus on the usual non-polynomial Dirac-Born-Infeld action for the worldvolume swept out by the brane in its evolution in general background spacetimes. We emphasize the form of the resulting equations of motion which are quite simple and resemble Newton's second law, complemented with a conservation law for a worldvolume bicurrent. We take a closer look at the classical Hamiltonian analysis which is supported by the ADM framework of general relativity. The constraints and their algebra are identified as well as the geometrical role they play in phase space. In order to illustrate our results, we review the dynamics of a D1-brane immersed in a AdS 3 x S 3 background spacetime. We exhibit the mechanical properties of Born-Infeld objects paving the way to a consistent quantum formulation
Geometrical dynamics of Born-Infeld objects
Energy Technology Data Exchange (ETDEWEB)
Cordero, Ruben [Departamento de Fisica, Escuela Superior de Fisica y Matematicas del I.P.N., Unidad Adolfo Lopez Mateos, Edificio 9, 07738 Mexico, D.F. (Mexico); Molgado, Alberto [Facultad de Ciencias, Universidad de Colima, Bernal DIaz del Castillo 340, Col. Villas San Sebastian, Colima (Mexico); Rojas, Efrain [Facultad de Fisica e Inteligencia Artificial, Universidad Veracruzana, 91000 Xalapa, Veracruz (Mexico)
2007-03-21
We present a geometrically inspired study of the dynamics of Dp-branes. We focus on the usual non-polynomial Dirac-Born-Infeld action for the worldvolume swept out by the brane in its evolution in general background spacetimes. We emphasize the form of the resulting equations of motion which are quite simple and resemble Newton's second law, complemented with a conservation law for a worldvolume bicurrent. We take a closer look at the classical Hamiltonian analysis which is supported by the ADM framework of general relativity. The constraints and their algebra are identified as well as the geometrical role they play in phase space. In order to illustrate our results, we review the dynamics of a D1-brane immersed in a AdS{sub 3} x S{sup 3} background spacetime. We exhibit the mechanical properties of Born-Infeld objects paving the way to a consistent quantum formulation.
Geometric theory of discrete nonautonomous dynamical systems
Pötzsche, Christian
2010-01-01
Nonautonomous dynamical systems provide a mathematical framework for temporally changing phenomena, where the law of evolution varies in time due to seasonal, modulation, controlling or even random effects. Our goal is to provide an approach to the corresponding geometric theory of nonautonomous discrete dynamical systems in infinite-dimensional spaces by virtue of 2-parameter semigroups (processes). These dynamical systems are generated by implicit difference equations, which explicitly depend on time. Compactness and dissipativity conditions are provided for such problems in order to have attractors using the natural concept of pullback convergence. Concerning a necessary linear theory, our hyperbolicity concept is based on exponential dichotomies and splittings. This concept is in turn used to construct nonautonomous invariant manifolds, so-called fiber bundles, and deduce linearization theorems. The results are illustrated using temporal and full discretizations of evolutionary differential equations.
Geometric methods for discrete dynamical systems
Easton, Robert W
1998-01-01
This book looks at dynamics as an iteration process where the output of a function is fed back as an input to determine the evolution of an initial state over time. The theory examines errors which arise from round-off in numerical simulations, from the inexactness of mathematical models used to describe physical processes, and from the effects of external controls. The author provides an introduction accessible to beginning graduate students and emphasizing geometric aspects of the theory. Conley''s ideas about rough orbits and chain-recurrence play a central role in the treatment. The book will be a useful reference for mathematicians, scientists, and engineers studying this field, and an ideal text for graduate courses in dynamical systems.
Geometrical shock dynamics for magnetohydrodynamic fast shocks
Mostert, W.
2016-12-12
We describe a formulation of two-dimensional geometrical shock dynamics (GSD) suitable for ideal magnetohydrodynamic (MHD) fast shocks under magnetic fields of general strength and orientation. The resulting area–Mach-number–shock-angle relation is then incorporated into a numerical method using pseudospectral differentiation. The MHD-GSD model is verified by comparison with results from nonlinear finite-volume solution of the complete ideal MHD equations applied to a shock implosion flow in the presence of an oblique and spatially varying magnetic field ahead of the shock. Results from application of the MHD-GSD equations to the stability of fast MHD shocks in two dimensions are presented. It is shown that the time to formation of triple points for both perturbed MHD and gas-dynamic shocks increases as (Formula presented.), where (Formula presented.) is a measure of the initial Mach-number perturbation. Symmetry breaking in the MHD case is demonstrated. In cylindrical converging geometry, in the presence of an azimuthal field produced by a line current, the MHD shock behaves in the mean as in Pullin et al. (Phys. Fluids, vol. 26, 2014, 097103), but suffers a greater relative pressure fluctuation along the shock than the gas-dynamic shock. © 2016 Cambridge University Press
Network modelling of physical systems: a geometric approach
van der Schaft, Arjan; Maschke, B.M.; Ortega, Romeo; Banos, A.; Lamnabhi-lagarrigue, F; Montoya, F.J.
2001-01-01
It is discussed how network modeling of lumped-parameter physical systems naturally leads to a geometrically defined class of systems, called port-controlled Hamiltonian systems (with dissipation). The structural properties of these systems are investigated, in particular the existence of Casimir
Maslennikov, O. V.; Nekorkin, V. I.
2017-10-01
Dynamical networks are systems of active elements (nodes) interacting with each other through links. Examples are power grids, neural structures, coupled chemical oscillators, and communications networks, all of which are characterized by a networked structure and intrinsic dynamics of their interacting components. If the coupling structure of a dynamical network can change over time due to nodal dynamics, then such a system is called an adaptive dynamical network. The term ‘adaptive’ implies that the coupling topology can be rewired; the term ‘dynamical’ implies the presence of internal node and link dynamics. The main results of research on adaptive dynamical networks are reviewed. Key notions and definitions of the theory of complex networks are given, and major collective effects that emerge in adaptive dynamical networks are described.
Geometric analysis of nondeterminacy in dynamical systems
DEFF Research Database (Denmark)
Wisniewski, Rafal; Raussen, Martin Hubert
2007-01-01
This article intends to provide some new insights into concurrency using ideas from the theory of dynamical systems. Inherently discrete concurrency corresponds to a parallel continuous concept: a discrete state space corresponds to a differential manifold, an execution path corresponds to a flow...
Network-aware SuperPeers-Peers Geometric Overlay Network
Lua, E.K.; Zhou, X.
2007-01-01
Peer-to-Peer (P2P) overlay networks can be utilized to deploy massive Internet overlay services such as multicast, content distribution, file sharing, etc. efficiently without any underlying network support. The crucial step to meet this objective is to design network-aware overlay network
Geometrical Similarity Transformations in Dynamic Geometry Environment Geogebra
Andraphanova, Natalia V.
2015-01-01
The subject of the article is usage of modern computer technologies through the example of interactive geometry environment Geogebra as an innovative technology of representing and studying of geometrical material which involves such didactical opportunities as vizualisation, simulation and dynamics. There is shown a classification of geometric…
A geometric view on learning Bayesian network structures
Czech Academy of Sciences Publication Activity Database
Studený, Milan; Vomlel, Jiří; Hemmecke, R.
2010-01-01
Roč. 51, č. 5 (2010), s. 578-586 ISSN 0888-613X. [PGM 2008] R&D Projects: GA AV ČR(CZ) IAA100750603; GA MŠk(CZ) 1M0572; GA ČR GA201/08/0539 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : learning Bayesian networks * standard imset * inclusion neighborhood * geometric neighborhood * GES algorithm Subject RIV: BA - General Mathematics Impact factor: 1.679, year: 2010 http://library.utia.cas.cz/separaty/2010/MTR/studeny-0342804.pdf
Geometric detection of coupling directions by means of inter-system recurrence networks
Energy Technology Data Exchange (ETDEWEB)
Feldhoff, Jan H. [Potsdam Institute for Climate Impact Research, P.O. Box 60 12 03, 14412 Potsdam (Germany); Department of Physics, Humboldt University, Newtonstr. 15, 12489 Berlin (Germany); Donner, Reik V., E-mail: reik.donner@pik-potsdam.de [Potsdam Institute for Climate Impact Research, P.O. Box 60 12 03, 14412 Potsdam (Germany); Donges, Jonathan F. [Potsdam Institute for Climate Impact Research, P.O. Box 60 12 03, 14412 Potsdam (Germany); Department of Physics, Humboldt University, Newtonstr. 15, 12489 Berlin (Germany); Marwan, Norbert [Potsdam Institute for Climate Impact Research, P.O. Box 60 12 03, 14412 Potsdam (Germany); Kurths, Jürgen [Potsdam Institute for Climate Impact Research, P.O. Box 60 12 03, 14412 Potsdam (Germany); Department of Physics, Humboldt University, Newtonstr. 15, 12489 Berlin (Germany); Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB243UE (United Kingdom)
2012-10-15
We introduce a geometric method for identifying the coupling direction between two dynamical systems based on a bivariate extension of recurrence network analysis. Global characteristics of the resulting inter-system recurrence networks provide a correct discrimination for weakly coupled Rössler oscillators not yet displaying generalised synchronisation. Investigating two real-world palaeoclimate time series representing the variability of the Asian monsoon over the last 10,000 years, we observe indications for a considerable influence of the Indian summer monsoon on climate in Eastern China rather than vice versa. The proposed approach can be directly extended to studying K>2 coupled subsystems.
Naming games in two-dimensional and small-world-connected random geometric networks
Lu, Qiming; Korniss, G.; Szymanski, B. K.
2008-01-01
We investigate a prototypical agent-based model, the naming game, on two-dimensional random geometric networks. The naming game [Baronchelli , J. Stat. Mech.: Theory Exp. (2006) P06014] is a minimal model, employing local communications that captures the emergence of shared communication schemes (languages) in a population of autonomous semiotic agents. Implementing the naming games with local broadcasts on random geometric graphs, serves as a model for agreement dynamics in large-scale, autonomously operating wireless sensor networks. Further, it captures essential features of the scaling properties of the agreement process for spatially embedded autonomous agents. Among the relevant observables capturing the temporal properties of the agreement process, we investigate the cluster-size distribution and the distribution of the agreement times, both exhibiting dynamic scaling. We also present results for the case when a small density of long-range communication links are added on top of the random geometric graph, resulting in a “small-world”-like network and yielding a significantly reduced time to reach global agreement. We construct a finite-size scaling analysis for the agreement times in this case.
Naming games in two-dimensional and small-world-connected random geometric networks.
Lu, Qiming; Korniss, G; Szymanski, B K
2008-01-01
We investigate a prototypical agent-based model, the naming game, on two-dimensional random geometric networks. The naming game [Baronchelli, J. Stat. Mech.: Theory Exp. (2006) P06014] is a minimal model, employing local communications that captures the emergence of shared communication schemes (languages) in a population of autonomous semiotic agents. Implementing the naming games with local broadcasts on random geometric graphs, serves as a model for agreement dynamics in large-scale, autonomously operating wireless sensor networks. Further, it captures essential features of the scaling properties of the agreement process for spatially embedded autonomous agents. Among the relevant observables capturing the temporal properties of the agreement process, we investigate the cluster-size distribution and the distribution of the agreement times, both exhibiting dynamic scaling. We also present results for the case when a small density of long-range communication links are added on top of the random geometric graph, resulting in a "small-world"-like network and yielding a significantly reduced time to reach global agreement. We construct a finite-size scaling analysis for the agreement times in this case.
Geometric de-noising of protein-protein interaction networks.
Directory of Open Access Journals (Sweden)
Oleksii Kuchaiev
2009-08-01
Full Text Available Understanding complex networks of protein-protein interactions (PPIs is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H, tandem affinity purification (TAP and other high-throughput methods for protein-protein interaction (PPI detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise.We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising.
A geometrical approach to dynamical decoupling with smooth pulses
Zeng, Junkai; Deng, Xiuhao; Barnes, Edwin
In order to perform high-fidelity quantum information processing, reducing the effects of noise is an essential task. It is well known that a system can be decoupled from noise dynamically by using carefully designed pulse sequences based on delta-function or square waveforms such as spin echo or CPMG. However, such ideal pulses are often challenging to implement experimentally with high fidelity. We present an analytical approach that enables one to generate an unlimited number of smooth, experimentally feasible pulses that perform dynamical decoupling or dynamically corrected gates. Our method is based on a simple geometric picture that facilitates the identification of driving fields that cancel errors in the single-qubit evolution operator to second order or beyond. We demonstrate that this scheme can significantly enhance the fidelity of single-qubit gates in the case of noise with a 1/f power spectrum.
Evaporation dynamics of completely wetting drops on geometrically textured surfaces
Mekhitarian, Loucine; Sobac, Benjamin; Dehaeck, Sam; Haut, Benoît; Colinet, Pierre
2017-10-01
This study deals with the evaporation dynamics of completely wetting and highly volatile drops deposited on geometrically textured but chemically homogeneous surfaces. The texturation consists in a cylindrical pillars array with a square pitch. The triple line dynamics and the drop shape are characterized by an interferometric method. A parametric study is realized by varying the radius and the height of the pillars (at fixed interpillar distance), allowing to distinguish three types of dynamics: i) an evaporation-dominated regime with a receding triple line; ii) a spreading-dominated regime with an initially advancing triple line; iii) a cross-over region with strong pinning effects. The overall picture is in qualitative agreement with a mathematical model showing that the selected regime mostly depends on the value of a dimensionless parameter comparing the time scales for evaporation and spreading into the substrate texture.
Dynamics and Control of a Quadrotor with Active Geometric Morphing
Wallace, Dustin A.
Quadrotors are manufactured in a wide variety of shapes, sizes, and performance levels to fulfill a multitude of roles. Robodub Inc. has patented a morphing quadrotor which will allow active reconfiguration between various shapes for performance optimization across a wider spectrum of roles. The dynamics of the system are studied and modeled using Newtonian Mechanics. Controls are developed and simulated using both Linear Quadratic and Numerical Nonlinear Optimal control for a symmetric simplificiation of the system dynamics. Various unique vehicle capabilities are investigated, including novel single-throttle flight control using symmetric geometric morphing, as well as recovery from motor loss by reconfiguring into a trirotor configuration. The system dynamics were found to be complex and highly nonlinear. All attempted control strategies resulted in controllability, suggesting further research into each may lead to multiple viable control strategies for a physical prototype.
Geometrical Origins of Contractility in Disordered Actomyosin Networks
Directory of Open Access Journals (Sweden)
Martin Lenz
2014-10-01
Full Text Available Movement within eukaryotic cells largely originates from localized forces exerted by myosin motors on scaffolds of actin filaments. Although individual motors locally exert both contractile and extensile forces, large actomyosin structures at the cellular scale are overwhelmingly contractile, suggesting that the scaffold serves to favor contraction over extension. While this mechanism is well understood in highly organized striated muscle, its origin in disordered networks such as the cell cortex is unknown. Here, we develop a mathematical model of the actin scaffold’s local two- or three-dimensional mechanics and identify four competing contraction mechanisms. We predict that one mechanism dominates, whereby local deformations of the actin break the balance between contraction and extension. In this mechanism, contractile forces result mostly from motors plucking the filaments transversely rather than buckling them longitudinally. These findings shed light on recent in vitro experiments and provide a new geometrical understanding of contractility in the myriad of disordered actomyosin systems found in vivo.
Geometric Modeling of Construction Communications with Specified Dynamic Properties
Korotkiy, V. A.; Usmanova, E. A.; Khmarova, L. I.
2017-11-01
Among many construction communications the pipelines designed for the organized supply or removal of liquid or loose working bodies are distinguished for their functional purpose. Such communications should have dynamic properties which allow one to reduce losses on friction and vortex formation. From the point of view of geometric modeling, the given dynamic properties of the projected communication mean the required degree of smoothness of its center line. To model the axial line (flat or spatial), it is proposed to use composite curve lines consisting of the curve arcs of the second order or from their quadratic images. The advantage of the proposed method is that the designer gets the model of a given curve not as a set of coordinates of its points but in the form of a matrix of coefficients of the canonical equations for each arc.
Unconstrained Finite Element for Geometrical Nonlinear Dynamics of Shells
Directory of Open Access Journals (Sweden)
Humberto Breves Coda
2009-01-01
Full Text Available This paper presents a positional FEM formulation to deal with geometrical nonlinear dynamics of shells. The main objective is to develop a new FEM methodology based on the minimum potential energy theorem written regarding nodal positions and generalized unconstrained vectors not displacements and rotations. These characteristics are the novelty of the present work and avoid the use of large rotation approximations. A nondimensional auxiliary coordinate system is created, and the change of configuration function is written following two independent mappings from which the strain energy function is derived. This methodology is called positional and, as far as the authors' knowledge goes, is a new procedure to approximated geometrical nonlinear structures. In this paper a proof for the linear and angular momentum conservation property of the Newmark algorithm is provided for total Lagrangian description. The proposed shell element is locking free for elastic stress-strain relations due to the presence of linear strain variation along the shell thickness. The curved, high-order element together with an implicit procedure to solve nonlinear equations guarantees precision in calculations. The momentum conserving, the locking free behavior, and the frame invariance of the adopted mapping are numerically confirmed by examples.
A network approach to the geometric structure of shallow cloud fields
Glassmeier, F.; Feingold, G.
2017-12-01
The representation of shallow clouds and their radiative impact is one of the largest challenges for global climate models. While the bulk properties of cloud fields, including effects of organization, are a very active area of research, the potential of the geometric arrangement of cloud fields for the development of new parameterizations has hardly been explored. Self-organized patterns are particularly evident in the cellular structure of Stratocumulus (Sc) clouds so readily visible in satellite imagery. Inspired by similar patterns in biology and physics, we approach pattern formation in Sc fields from the perspective of natural cellular networks. Our network analysis is based on large-eddy simulations of open- and closed-cell Sc cases. We find the network structure to be neither random nor characteristic to natural convection. It is independent of macroscopic cloud fields properties like the Sc regime (open vs closed) and its typical length scale (boundary layer height). The latter is a consequence of entropy maximization (Lewis's Law with parameter 0.16). The cellular pattern is on average hexagonal, where non-6 sided cells occur according to a neighbor-number distribution variance of about 2. Reflecting the continuously renewing dynamics of Sc fields, large (many-sided) cells tend to neighbor small (few-sided) cells (Aboav-Weaire Law with parameter 0.9). These macroscopic network properties emerge independent of the Sc regime because the different processes governing the evolution of closed as compared to open cells correspond to topologically equivalent network dynamics. By developing a heuristic model, we show that open and closed cell dynamics can both be mimicked by versions of cell division and cell disappearance and are biased towards the expansion of smaller cells. This model offers for the first time a fundamental and universal explanation for the geometric pattern of Sc clouds. It may contribute to the development of advanced Sc parameterizations
Auxiliary fields in the geometrical relativistic particle dynamics
International Nuclear Information System (INIS)
Amador, A; Bagatella, N; Rojas, E; Cordero, R
2008-01-01
We describe how to construct the dynamics of relativistic particles, following either timelike or null curves, by means of an auxiliary variables method instead of the standard theory of deformations for curves. There are interesting physical particle models governed by actions that involve higher order derivatives of the embedding functions of the worldline. We point out that the mechanical content of such models can be extracted wisely from a lower order action, which can be performed by implementing in the action a finite number of constraints that involve the geometrical relationship structures inherent to a curve and by using a covariant formalism. We emphasize our approach for null curves. For such systems, the natural time parameter is a pseudo-arclength whose properties resemble those of the standard proper time. We illustrate the formalism by applying it to some models for relativistic particles
Auxiliary fields in the geometrical relativistic particle dynamics
Energy Technology Data Exchange (ETDEWEB)
Amador, A; Bagatella, N; Rojas, E [Departamento de Fisica, Facultad de Fisica e Inteligencia Artificial, Universidad Veracruzana, 91000 Xalapa, Veracruz (Mexico); Cordero, R [Departamento de Fisica, Escuela Superior de Fisica y Matematicas del I.P.N, Edificio 9, 07738 Mexico D.F (Mexico)], E-mail: aramador@gmail.com, E-mail: nbagatella@uv.mx, E-mail: cordero@esfm.ipn.mx, E-mail: efrojas@uv.mx
2008-03-21
We describe how to construct the dynamics of relativistic particles, following either timelike or null curves, by means of an auxiliary variables method instead of the standard theory of deformations for curves. There are interesting physical particle models governed by actions that involve higher order derivatives of the embedding functions of the worldline. We point out that the mechanical content of such models can be extracted wisely from a lower order action, which can be performed by implementing in the action a finite number of constraints that involve the geometrical relationship structures inherent to a curve and by using a covariant formalism. We emphasize our approach for null curves. For such systems, the natural time parameter is a pseudo-arclength whose properties resemble those of the standard proper time. We illustrate the formalism by applying it to some models for relativistic particles.
Critical space-time networks and geometric phase transitions from frustrated edge antiferromagnetism
Trugenberger, Carlo A.
2015-12-01
Recently I proposed a simple dynamical network model for discrete space-time that self-organizes as a graph with Hausdorff dimension dH=4 . The model has a geometric quantum phase transition with disorder parameter (dH-ds) , where ds is the spectral dimension of the dynamical graph. Self-organization in this network model is based on a competition between a ferromagnetic Ising model for vertices and an antiferromagnetic Ising model for edges. In this paper I solve a toy version of this model defined on a bipartite graph in the mean-field approximation. I show that the geometric phase transition corresponds exactly to the antiferromagnetic transition for edges, the dimensional disorder parameter of the former being mapped to the staggered magnetization order parameter of the latter. The model has a critical point with long-range correlations between edges, where a continuum random geometry can be defined, exactly as in Kazakov's famed 2D random lattice Ising model but now in any number of dimensions.
Gol Tabaghi, Shiva; Sinclair, Nathalie
2013-01-01
This article analyses students' thinking as they interacted with a dynamic geometric sketch designed to explore eigenvectors and eigenvalues. We draw on the theory of instrumental genesis and, in particular, attend to the different dragging modalities used by the students throughout their explorations. Given the kinaesthetic and dynamic…
Complex networks: Dynamics and security
Indian Academy of Sciences (India)
cusing on how dynamics may affect network security under attacks. In particular, we review two related ... portation network etc., are an essential part of a modern society. The security of such a network under ... communication (or information flow) within the network, is changed under random or intentional attacks [7–10].
Hodograph: A useful geometrical tool for solving some difficult problems in dynamics
Apostolatos, Theocharis A.
2003-03-01
The hodograph is very useful for solving complicated problems in dynamics. By simple geometrical arguments students can directly obtain the answer to problems that would otherwise be complicated exercises in algebra. Although beyond the level of undergraduates, we also use the hodograph to calculate by variational geometrical techniques, the well-known brachistochrone curve, thus illustrating this approach.
Complex Dynamics in Communication Networks
Kocarev, Ljupco
2005-01-01
Computer and communication networks are among society's most important infrastructures. The internet, in particular, is a giant global network of networks without central control or administration. It is a paradigm of a complex system, where complexity may arise from different sources: topological structure, network evolution, connection and node diversity, or dynamical evolution. The present volume is the first book entirely devoted to the new and emerging field of nonlinear dynamics of TCP/IP networks. It addresses both scientists and engineers working in the general field of communication networks.
Studying Dynamics in Business Networks
DEFF Research Database (Denmark)
Andersen, Poul Houman; Anderson, Helen; Havila, Virpi
1998-01-01
This paper develops a theory on network dynamics using the concepts of role and position from sociological theory. Moreover, the theory is further tested using case studies from Denmark and Finland......This paper develops a theory on network dynamics using the concepts of role and position from sociological theory. Moreover, the theory is further tested using case studies from Denmark and Finland...
Complex networks: Dynamics and security
Indian Academy of Sciences (India)
and nonlinear physics, applied mathematics, and social science has emerged, which brings novel concepts and approaches to the study of complex networks. Issues such as the characterization of the network architecture, dynamics on complex net- works, and the effect of attacks on network operation have begun to be ...
Dynamic training algorithm for dynamic neural networks
International Nuclear Information System (INIS)
Tan, Y.; Van Cauwenberghe, A.; Liu, Z.
1996-01-01
The widely used backpropagation algorithm for training neural networks based on the gradient descent has a significant drawback of slow convergence. A Gauss-Newton method based recursive least squares (RLS) type algorithm with dynamic error backpropagation is presented to speed-up the learning procedure of neural networks with local recurrent terms. Finally, simulation examples concerning the applications of the RLS type algorithm to identification of nonlinear processes using a local recurrent neural network are also included in this paper
Directory of Open Access Journals (Sweden)
2006-01-01
Full Text Available In a wireless ad hoc network, messages are transmitted, received, and forwarded in a finite geometrical region and the transmission of messages is highly dependent on the locations of the nodes. Therefore the study of geometrical relationship between nodes in wireless ad hoc networks is of fundamental importance in the network architecture design and performance evaluation. However, most previous works concentrated on the networks deployed in the two-dimensional region or in the infinite three-dimensional space, while in many cases wireless ad hoc networks are deployed in the finite three-dimensional space. In this paper, we analyze the geometrical characteristics of the three-dimensional wireless ad hoc network in a finite space in the framework of random graph and deduce an expression to calculate the distance probability distribution between network nodes that are independently and uniformly distributed in a finite cuboid space. Based on the theoretical result, we present some meaningful results on the finite three-dimensional network performance, including the node degree and the max-flow capacity. Furthermore, we investigate some approximation properties of the distance probability distribution function derived in the paper.
Rashvand, Habib
2013-01-01
Motivated by the exciting new application paradigm of using amalgamated technologies of the Internet and wireless, the next generation communication networks (also called 'ubiquitous', 'complex' and 'unstructured' networking) are changing the way we develop and apply our future systems and services at home and on local, national and global scales. Whatever the interconnection - a WiMAX enabled networked mobile vehicle, MEMS or nanotechnology enabled distributed sensor systems, Vehicular Ad hoc Networking (VANET) or Mobile Ad hoc Networking (MANET) - all can be classified under new networking s
Cognitive Dynamic Optical Networks
DEFF Research Database (Denmark)
de Miguel, Ignacio; Duran, Ramon J.; Lorenzo, Ruben M.
2013-01-01
Cognitive networks are a promising solution for the control of heterogeneous optical networks. We review their fundamentals as well as a number of applications developed in the framework of the EU FP7 CHRON project.......Cognitive networks are a promising solution for the control of heterogeneous optical networks. We review their fundamentals as well as a number of applications developed in the framework of the EU FP7 CHRON project....
Assimilation Dynamic Network (ADN), Phase II
National Aeronautics and Space Administration — The Assimilation Dynamic Network (ADN) is a dynamic inter-processor communication network that spans heterogeneous processor architectures, unifying components,...
Country neighborhood network on territory and its geometrical model
Xuan, Qi; Wu, Tie-Jun
2009-04-01
The country neighborhood network, where nodes represent countries and two nodes are considered linked if the corresponding countries are neighbors on territory, is created and its giant component, the Asia, Europe, and Africa (AEA) cluster, is carefully studied in this paper. It is found that, as common, the degree distribution and the clustering function of the AEA cluster are both compatible with scale-free property, besides, the AEA cluster presents a little disassortativity, and its near power-law country area-degree relationship with the exponent close to 1.7 may imply a fractal dimension close to 1.2 of country borderlines in the AEA continent. It is also revealed that the average difference of population density between two countries obeys an approximately increasing function of the shortest path length between them, which may suggest a gradual consensus of population density in the AEA cluster. A simple unity rule is then adopted to model the AEA cluster and such model explains the AEA cluster very well in most aspects, e.g., power-law domain area distribution and fractal domain borderlines, etc., except that the network derived by the model has stronger disassortativity, which may be explained by the fact that, in the evolution history of countries, unbalanced neighbors are more likely to be united as one than balanced neighbors. Additionally, the network evolving process can be divided into three periods, defined as formation period, growth period, and combination period, and there are indications that the AEA cluster is in its third period.
Cognitive Dynamic Optical Networks
DEFF Research Database (Denmark)
de Miguel, Ignacio; Duran, Ramon J.; Jimenez, Tamara
2013-01-01
The use of cognition is a promising element for the control of heterogeneous optical networks. Not only are cognitive networks able to sense current network conditions and act according to them, but they also take into account the knowledge acquired through past experiences; that is, they include...... learning with the aim of improving performance. In this paper, we review the fundamentals of cognitive networks and focus on their application to the optical networking area. In particular, a number of cognitive network architectures proposed so far, as well as their associated supporting technologies......, are reviewed. Moreover, several applications, mainly developed in the framework of the EU FP7 Cognitive Heterogeneous Reconfigurable Optical Network (CHRON) project, are also described....
Entropy of dynamical social networks
Zhao, Kun; Karsai, Marton; Bianconi, Ginestra
2012-02-01
Dynamical social networks are evolving rapidly and are highly adaptive. Characterizing the information encoded in social networks is essential to gain insight into the structure, evolution, adaptability and dynamics. Recently entropy measures have been used to quantify the information in email correspondence, static networks and mobility patterns. Nevertheless, we still lack methods to quantify the information encoded in time-varying dynamical social networks. In this talk we present a model to quantify the entropy of dynamical social networks and use this model to analyze the data of phone-call communication. We show evidence that the entropy of the phone-call interaction network changes according to circadian rhythms. Moreover we show that social networks are extremely adaptive and are modified by the use of technologies such as mobile phone communication. Indeed the statistics of duration of phone-call is described by a Weibull distribution and is significantly different from the distribution of duration of face-to-face interactions in a conference. Finally we investigate how much the entropy of dynamical social networks changes in realistic models of phone-call or face-to face interactions characterizing in this way different type human social behavior.
Students' Geometrical Perception on a Task-Based Dynamic Geometry Platform
Leung, Allen; Lee, Arthur Man Sang
2013-01-01
This paper describes a task-based dynamic geometry platform that is able to record student responses in a collective fashion to pre-designed dragging tasks. The platform provides a new type of data and opens up a quantitative dimension to interpret students' geometrical perception in dynamic geometry environments. The platform is capable of…
Nonlinear Dynamics on Interconnected Networks
Arenas, Alex; De Domenico, Manlio
2016-06-01
Networks of dynamical interacting units can represent many complex systems, from the human brain to transportation systems and societies. The study of these complex networks, when accounting for different types of interactions has become a subject of interest in the last few years, especially because its representational power in the description of users' interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.) [1], or in representing different transportation modes in urban networks [2,3]. The general name coined for these networks is multilayer networks, where each layer accounts for a type of interaction (see Fig. 1).
National Research Council Canada - National Science Library
Schott, Brian
2004-01-01
...: Declarative Languages and Execution Environment includes topographical soldier interface and a sensor network simulation environment for algorithm development, deployment planning, and operational support. Finally, Task 3...
Geometrical Models of the Phase Space Structures Governing Reaction Dynamics
2009-08-01
of Mathematical Sciences . Springer, Berlin. [Child & Pollak(1980)] Child, M. S. & Pollak, E. (1980). Analytical reaction dynamics: Origin and implica...state region, i.e. the phase space point at which a trajectory enters the transition state region can be mapped analytically to the phase space point...Neishtadt, A. I. (1988). Mathematical aspects of classical and celestial mechanics. In V. I. Arnol’d, editor, Dynamical Systems III, volume 3 of Encyclopaedia
Dynamic and interacting complex networks
Dickison, Mark E.
This thesis employs methods of statistical mechanics and numerical simulations to study some aspects of dynamic and interacting complex networks. The mapping of various social and physical phenomena to complex networks has been a rich field in the past few decades. Subjects as broad as petroleum engineering, scientific collaborations, and the structure of the internet have all been analyzed in a network physics context, with useful and universal results. In the first chapter we introduce basic concepts in networks, including the two types of network configurations that are studied and the statistical physics and epidemiological models that form the framework of the network research, as well as covering various previously-derived results in network theory that are used in the work in the following chapters. In the second chapter we introduce a model for dynamic networks, where the links or the strengths of the links change over time. We solve the model by mapping dynamic networks to the problem of directed percolation, where the direction corresponds to the time evolution of the network. We show that the dynamic network undergoes a percolation phase transition at a critical concentration pc, that decreases with the rate r at which the network links are changed. The behavior near criticality is universal and independent of r. We find that for dynamic random networks fundamental laws are changed: i) The size of the giant component at criticality scales with the network size N for all values of r, rather than as N2/3 in static network, ii) In the presence of a broad distribution of disorder, the optimal path length between two nodes in a dynamic network scales as N1/2, compared to N1/3 in a static network. The third chapter consists of a study of the effect of quarantine on the propagation of epidemics on an adaptive network of social contacts. For this purpose, we analyze the susceptible-infected-recovered model in the presence of quarantine, where susceptible
On unified field theories, dynamical torsion and geometrical models: II
International Nuclear Information System (INIS)
Cirilo-Lombardo, D.J.
2011-01-01
We analyze in this letter the same space-time structure as that presented in our previous reference (Part. Nucl, Lett. 2010. V.7, No.5. P.299-307), but relaxing now the condition a priori of the existence of a potential for the torsion. We show through exact cosmological solutions from this model, where the geometry is Euclidean RxO 3 ∼ RxSU(2), the relation between the space-time geometry and the structure of the gauge group. Precisely this relation is directly connected with the relation of the spin and torsion fields. The solution of this model is explicitly compared with our previous ones and we find that: i) the torsion is not identified directly with the Yang-Mills type strength field, ii) there exists a compatibility condition connected with the identification of the gauge group with the geometric structure of the space-time: this fact leads to the identification between derivatives of the scale factor a with the components of the torsion in order to allow the Hosoya-Ogura ansatz (namely, the alignment of the isospin with the frame geometry of the space-time), and iii) of two possible structures of the torsion the 'tratorial' form (the only one studied here) forbid wormhole configurations, leading only to cosmological instanton space-time in eternal expansion
Geometric Methods for Infinite-Dimensional Dynamical Systems
2012-08-27
thermohaline circulation due to sea ice processes Qiliang Wu (University of Minnesota) Dynamics near Turing patterns in RD systems ...of them covering multiple themes. Ample time (90 minutes) was devoted to the poster session, so that participants could circulate to all of the
Interfacial Dynamics of Abelian Domains: Differential Geometric Methods
International Nuclear Information System (INIS)
Owczarek, Robert M.; Makaruk, Hanna E.
1997-11-01
The equation: ReF'(T,Z)ZF'(T,Z) = 1 for conformal maps f(t,z) is important in interfacial dynamics. We extend the results by Gustafsson on existence and uniqueness of solutions of this equation from the case when f(t,z) is a rational function of z to the case when the spatial derivative f'(t,z) is rational
Innovative education networking aimed at multimedia tools for geometrical optics learning
García-Martínez, P.; Zapata-Rodríguez, C. J.; Ferreira, C.; Fernández, I.; Pastor, D.; Nasenpour, M.; Moreno, I.; Sánchez-López, M. M.; Espinosa, J.; Mas, D.; Miret, J. J.
2015-10-01
We present a purposeful initiative to open new grounds for teaching Geometrical Optics. It is based on the creation of an innovative education networking involving academic staff from three Spanish universities linked together around Optics. Nowadays, students demand online resources such as innovative multimedia tools for complementing the understanding of their studies. Geometrical Optics relies on basics of light phenomena like reflection and refraction and the use of simple optical elements such as mirrors, prisms, lenses, and fibers. The mathematical treatment is simple and the equations are not too complicated. But from our long time experience in teaching to undergraduate students, we realize that important concepts are missed by these students because they do not work ray tracing as they should do. Moreover, Geometrical Optics laboratory is crucial by providing many short Optics experiments and thus stimulating students interest in the study of such a topic. Multimedia applications help teachers to cover those student demands. In that sense, our educational networking shares and develops online materials based on 1) video-tutorials of laboratory experiences and of ray tracing exercises, 2) different online platforms for student self-examinations and 3) computer assisted geometrical optics exercises. That will result in interesting educational synergies and promote student autonomy for learning Optics.
2004-03-01
ecosystems, endangered species , forest fires, and disaster sites. The primary interest in wireless sensor networks is due to their ability to monitor...unsupervised sensing and actuation. Typical tasks include condition-based maintenance in factories, monitoring remote ecosystems, endangered species , forest...temperature of node #27563’ or ‘is there a rhino near node #85396’, but rather ‘where is the temperature higher than 60 degrees’ or ‘notify me of any
Complex networks: Structure and dynamics
Boccaletti, S.; Latora, V.; Moreno, Y.; Chavez, M.; Hwang, D.-U.
2006-02-01
Coupled biological and chemical systems, neural networks, social interacting species, the Internet and the World Wide Web, are only a few examples of systems composed by a large number of highly interconnected dynamical units. The first approach to capture the global properties of such systems is to model them as graphs whose nodes represent the dynamical units, and whose links stand for the interactions between them. On the one hand, scientists have to cope with structural issues, such as characterizing the topology of a complex wiring architecture, revealing the unifying principles that are at the basis of real networks, and developing models to mimic the growth of a network and reproduce its structural properties. On the other hand, many relevant questions arise when studying complex networks’ dynamics, such as learning how a large ensemble of dynamical systems that interact through a complex wiring topology can behave collectively. We review the major concepts and results recently achieved in the study of the structure and dynamics of complex networks, and summarize the relevant applications of these ideas in many different disciplines, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.
Network Dynamics of Innovation Processes
Iacopini, Iacopo; Milojević, Staša; Latora, Vito
2018-01-01
We introduce a model for the emergence of innovations, in which cognitive processes are described as random walks on the network of links among ideas or concepts, and an innovation corresponds to the first visit of a node. The transition matrix of the random walk depends on the network weights, while in turn the weight of an edge is reinforced by the passage of a walker. The presence of the network naturally accounts for the mechanism of the "adjacent possible," and the model reproduces both the rate at which novelties emerge and the correlations among them observed empirically. We show this by using synthetic networks and by studying real data sets on the growth of knowledge in different scientific disciplines. Edge-reinforced random walks on complex topologies offer a new modeling framework for the dynamics of correlated novelties and are another example of coevolution of processes and networks.
Complex networks: Dynamics and security
Lai, Ying-Cheng; Motter, Adilson; Nishikawa, Takashi; Park, Kwangho; Zhao, Liang
2005-04-01
This paper presents a perspective in the study of complex networks by focusing on how dynamics may affect network security under attacks. In particular, we review two related problems: attack-induced cascading breakdown and range-based attacks on links. A cascade in a network means the failure of a substantial fraction of the entire network in a cascading manner, which can be induced by the failure of or attacks on only a few nodes. These have been reported for the internet and for the power grid (e.g., the August 10, 1996 failure of the western United States power grid). We study a mechanism for cascades in complex networks by constructing a model incorporating the flows of information and physical quantities in the network. Using this model we can also show that the cascading phenomenon can be understood as a phase transition in terms of the key parameter characterizing the node capacity. For a parameter value below the phase-transition point, cascading failures can cause the network to disintegrate almost entirely. We will show how to obtain a theoretical estimate for the phase-transition point. The second problem is motivated by the fact that most existing works on the security of complex networks consider attacks on nodes rather than on links. We address attacks on links. Our investigation leads to the finding that many scale-free networks are more sensitive to attacks on short-range than on long-range links. Considering that the small-world phenomenon in complex networks has been identified as being due to the presence of long-range links, i.e., links connecting nodes that would otherwise be separated by a long node-to-node distance, our result, besides its importance concerning network efficiency and security, has the striking implication that the small-world property of scale-free networks is mainly due to short-range links.
Computational fluid dynamics simulation and geometric design of hydraulic turbine draft tube
Directory of Open Access Journals (Sweden)
JB Sosa
2015-10-01
Full Text Available Any hydraulic reaction turbine is installed with a draft tube that impacts widely the entire turbine performance, on which its functions are as follows: drive the flux in appropriate manner after it releases its energy to the runner; recover the suction head by a suction effect; and improve the dynamic energy in the runner outlet. All these functions are strongly linked to the geometric definition of the draft tube. This article proposes a geometric parametrization and analysis of a Francis turbine draft tube. Based on the parametric definition, geometric changes in the draft tube are proposed and the turbine performance is modeled by computational fluid dynamics; the boundary conditions are set by measurements performed in a hydroelectric power plant. This modeling allows us to see the influence of the draft tube shape on the entire turbine performance. The numerical analysis is based on the steady-state solution of the turbine component flows for different guide vanes opening and multiple modified draft tubes. The computational fluid dynamics predictions are validated using hydroelectric plant measurements. The prediction of the turbine performance is successful and it is linked to the draft tube geometric features; therefore, it is possible to obtain a draft tube parameter value that results in a desired turbine performance.
Competitive Dynamics on Complex Networks
Zhao, Jiuhua; Liu, Qipeng; Wang, Xiaofan
2014-07-01
We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states, and is independent of the initial states of agents. This implies that the competition result is fully determined by the network structure and positions of competitors in the network. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another agent in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with seven node centrality measures to predict the winner. We find that the competitor with higher Katz Centrality in an undirected network or higher PageRank in a directed network is most likely to be the winner. These findings may shed new light on the role of network structure in competition and to what extent could competitors adjust network structure so as to win the competition.
Directory of Open Access Journals (Sweden)
Khasanov Zimfir
2018-01-01
Full Text Available The article reviews the capabilities and particularities of the approach to the improvement of metrological characteristics of fiber-optic pressure sensors (FOPS based on estimation estimation of dynamic errors in laser optoelectronic dimension gauges for geometric measurement of details. It is shown that the proposed criteria render new methods for conjugation of optoelectronic converters in the dimension gauge for geometric measurements in order to reduce the speed and volume requirements for the Random Access Memory (RAM of the video controller which process the signal. It is found that the lower relative error, the higher the interrogetion speed of the CCD array. It is shown that thus, the maximum achievable dynamic accuracy characteristics of the optoelectronic gauge are determined by the following conditions: the parameter stability of the electronic circuits in the CCD array and the microprocessor calculator; linearity of characteristics; error dynamics and noise in all electronic circuits of the CCD array and microprocessor calculator.
Dynamical vs. geometric anisotropy in relativistic heavy-ion collisions. Which one prevails?
Energy Technology Data Exchange (ETDEWEB)
Bravina, L.V. [University of Oslo, Department of Physics, Oslo (Norway); National Research Nuclear University ' ' MEPhI' ' (Moscow Engineering Physics Institute), Moscow (Russian Federation); Lokhtin, I.P.; Malinina, L.V.; Petrushanko, S.V.; Snigirev, A.M. [Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Moscow (Russian Federation); Zabrodin, E.E. [Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Moscow (Russian Federation); University of Oslo, Department of Physics, Oslo (Norway); National Research Nuclear University ' ' MEPhI' ' (Moscow Engineering Physics Institute), Moscow (Russian Federation)
2017-11-15
We study the influence of geometric and dynamical anisotropies on the development of flow harmonics and, simultaneously, on the second- and third-order oscillations of femtoscopy radii. The analysis is done within the Monte Carlo event generator HYDJET++, which was extended to dynamical triangular deformations. It is shown that the merely geometric anisotropy provides the results which anticorrelate with the experimental observations of either v{sub 2} (or v{sub 3}) or second-order (or third-order) oscillations of the femtoscopy radii. Decays of resonances significantly increase the emitting areas but do not change the phases of the radii oscillations. In contrast to the spatial deformations, the dynamical anisotropy alone provides the correct qualitative description of the flow and the femtoscopy observables simultaneously. However, one needs both types of the anisotropy to match quantitatively the experimental data. (orig.)
Decoding network dynamics in cancer
DEFF Research Database (Denmark)
Linding, Rune
2014-01-01
Biological systems are composed of highly dynamic and interconnected molecular networks that drive biological decision processes. The goal of network biology is to describe, quantify and predict the information flow and functional behaviour of living systems in a formal language...... of predicting cellular trajectories in time, space or disease. The development of high-throughput methodologies has further enhanced our ability to obtain quantitative genomic, proteomic and phenotypic readouts for many genes/proteins simultaneously. Here, I will discuss how it is now possible to derive network...... models through computational integration of systematic, large-scale, high-dimensional quantitative data sets. I will review our latest advances in methods for exploring phosphorylation networks. In particular I will discuss how the combination of quantitative mass-spectrometry, systems...
Directory of Open Access Journals (Sweden)
Zhaoyuan Yu
2015-12-01
Full Text Available Passive infrared (PIR motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks.
Yu, Zhaoyuan; Yuan, Linwang; Luo, Wen; Feng, Linyao; Lv, Guonian
2015-12-30
Passive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks.
Error performance analysis in K-tier uplink cellular networks using a stochastic geometric approach
Afify, Laila H.
2015-09-14
In this work, we develop an analytical paradigm to analyze the average symbol error probability (ASEP) performance of uplink traffic in a multi-tier cellular network. The analysis is based on the recently developed Equivalent-in-Distribution approach that utilizes stochastic geometric tools to account for the network geometry in the performance characterization. Different from the other stochastic geometry models adopted in the literature, the developed analysis accounts for important communication system parameters and goes beyond signal-to-interference-plus-noise ratio characterization. That is, the presented model accounts for the modulation scheme, constellation type, and signal recovery techniques to model the ASEP. To this end, we derive single integral expressions for the ASEP for different modulation schemes due to aggregate network interference. Finally, all theoretical findings of the paper are verified via Monte Carlo simulations.
Centrality Measures of Dynamic Social Networks
2012-11-01
Centrality Measures of Dynamic Social Networks by Allison Moore ARL-TN-0513 November 2012...Centrality Measures of Dynamic Social Networks Allison Moore Computational and Information Sciences Directorate, ARL...Centrality Measures of Dynamic Social Networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Allison Moore
Pedestrian dynamics via Bayesian networks
Venkat, Ibrahim; Khader, Ahamad Tajudin; Subramanian, K. G.
2014-06-01
Studies on pedestrian dynamics have vital applications in crowd control management relevant to organizing safer large scale gatherings including pilgrimages. Reasoning pedestrian motion via computational intelligence techniques could be posed as a potential research problem within the realms of Artificial Intelligence. In this contribution, we propose a "Bayesian Network Model for Pedestrian Dynamics" (BNMPD) to reason the vast uncertainty imposed by pedestrian motion. With reference to key findings from literature which include simulation studies, we systematically identify: What are the various factors that could contribute to the prediction of crowd flow status? The proposed model unifies these factors in a cohesive manner using Bayesian Networks (BNs) and serves as a sophisticated probabilistic tool to simulate vital cause and effect relationships entailed in the pedestrian domain.
Directory of Open Access Journals (Sweden)
Daniel Andres Dos Santos
2014-06-01
Full Text Available Since the tendon is composed by collagen fibrils of various sizes connected between them through molecular cross-links, it sounds logical to model it via a heterogeneous network of fibrils. Using cross sectional images, that network is operatively inferred from the respective Gabriel graph of the fibril mass centers. We focus on network percolation characteristics under an ordered activation of fibrils (progressive recruitment going from the smallest to the largest fibril. Analyses of percolation were carried out on a repository of images of digital flexor tendons obtained from samples of lizards and frogs. Observed percolation thresholds were compared against values derived from hypothetical scenarios of random activation of nodes. Strikingly, we found a significant delay for the occurrence of percolation in actual data. We interpret this finding as the consequence of some non-random packing of fibrillar units into a size-constrained geometric pattern. We erect an ideal geometric model of balanced interspersion of polymorphic units that accounts for the delayed percolating instance. We also address the circumstance of being percolation curves mirrored by the empirical curves of stress-strain obtained from the same studied tendons. By virtue of this isomorphism, we hypothesize that the inflection points of both curves are different quantitative manifestations of a common transitional process during mechanical load transference.
DEFF Research Database (Denmark)
Baldi, P.; Blanke, Mogens; Castaldi, P.
2015-01-01
This paper suggests a novel diagnosis scheme for detection, isolation and estimation of faults affecting satellite reaction wheels. Both spin rate measurements and actuation torque defects are dealt with. The proposed system consists of a fault detection and isolation module composed by a bank...... of residual filters organized in a generalized scheme, followed by a fault estimation module consisting of a bank of adaptive estimation filters. The residuals are decoupled from aerodynamic disturbances thanks to the Nonlinear Geometric Approach. The use of Radial Basis Function Neural Networks is shown...
International Nuclear Information System (INIS)
Ozevin, Didem; Harding, James
2012-01-01
Time dependent aging and instantaneous threats can cause the initiation of damage in the buried and on-ground pipelines. Damage may propagate all through the structural thickness and cause leaking. The leakage detection in oil, water, gas or steam pipeline networks before it becomes structurally instable is important to prevent any catastrophic failures. The leak in pressurized pipelines causes turbulent flow at its location, which generates solid particles or gas bubbles impacting on the pipeline material. The impact energy causes propagating elastic waves that can be detected by the sensors mounted on the pipeline. The method is called Acoustic Emission, which can be used for real time detection of damage caused by unintentional or intentional sources in the pipeline networks. In this paper, a new leak localization approach is proposed for pipeline networks spread in a two dimensional configuration. The approach is to determine arrival time differences using cross correlation function, and introduce the geometric connectivity in order to identify the path that the leak waves should propagate to reach the AE sensors. The leak location in multi-dimensional space is identified in an effective approach using an array of sensors spread on the pipeline network. The approach is successfully demonstrated on laboratory scale polypropylene pipeline networks. - Highlights: ► Leak is identified in 2D using the 1D algorithm and geometric connectivity. ► The methodology is applicable if the source to sensor path is not straight. ► The hit sequence based on average signal level improves the source location. ► The leak localization in viscoelastic materials is high due to attenuation.
Anomaly Detection in Dynamic Networks
Energy Technology Data Exchange (ETDEWEB)
Turcotte, Melissa [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2014-10-14
Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. A second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the
Geometric universality of currents in an open network of interacting particles
International Nuclear Information System (INIS)
Sinitsyn, Nikolai A.; Chernyak, Vladimir Y.; Chertkov, Michael
2010-01-01
We discuss a non-equilibrium statistical system on a graph or network. Identical particles are injected, interact with each other, traverse, and leave the graph in a stochastic manner described in terms of Poisson rates, possibly dependent on time and instantaneous occupation numbers at the nodes of the graph. We show that under the assumption of the relative rates constancy, the system demonstrates a profound statistical symmetry, resulting in geometric universality of the particle currents statistics. The phenomenon applies broadly to many man-made and natural open stochastic systems, such as queuing of packages over internet, transport of electrons and quasi-particles in mesoscopic systems, and chains of reactions in bio-chemical networks. We illustrate the utility of the general approach using two enabling examples from the two latter disciplines.
A network dynamics approach to chemical reaction networks
van der Schaft, Abraham; Rao, S.; Jayawardhana, B.
2016-01-01
A treatment of chemical reaction network theory is given from the perspective of nonlinear network dynamics, in particular of consensus dynamics. By starting from the complex-balanced assumption the reaction dynamics governed by mass action kinetics can be rewritten into a form which allows for a
Directory of Open Access Journals (Sweden)
Gil Rama
2017-04-01
Full Text Available Composite laminates consisting of passive and multi-functional materials represent a powerful material system. Passive layers could be made of isotropic materials or fiber-reinforced composites, while piezoelectric ceramics are considered here as a multi-functional material. The paper is focused on linear and geometrically nonlinear dynamic analysis of smart structures made of such a material system. For this purpose, a linear 3-node shell element is used. It employs the Mindlin-Reissner kinematics and the discrete shear gap (DSG technique to alleviate the transverse shear locking effects. The electric potential is assumed to vary linearly through the thickness for each piezoelectric layer. A co-rotational formulation is used to handle the geometrically nonlinear effects. A number of examples involving actuator and sensor application of piezoelectric layers are considered. For the validation purposes, the results available in the literature and those computed in Abaqus are used as a reference.
Factorial graphical lasso for dynamic networks
Wit, E. C.; Abbruzzo, A.
2012-01-01
Dynamic networks models describe a growing number of important scientific processes, from cell biology and epidemiology to sociology and finance. There are many aspects of dynamical networks that require statistical considerations. In this paper we focus on determining network structure. Estimating
DEFF Research Database (Denmark)
Bisdom, Kevin; Bertotti, Giovanni; Nick, Hamid
2016-01-01
a geometrically based method for calculating the shear-induced hydraulic aperture, that is, an aperture of up to 0.5 mm (0.02 in.) that can result from shear displacement along irregular fracture walls. The geometrically based method does not require numerical simulations, but it can instead be directly applied...... to DFNs using the fracture orientation and spacing distributions in combination with an estimate of the regional stress tensor and orientation. The frequency distribution of hydraulic aperture from the geometrically based method is compared with finite-element models constructed from five real fracture...... networks, digitized from outcropping pavements. These networks cover a wide range of possible geometries and spatial distributions. The geometrically based method predicts the average hydraulic aperture and equivalent permeability of fractured porous media with error margins of less than 5%....
Scaling behaviours in the growth of networked systems and their geometric origins.
Zhang, Jiang; Li, Xintong; Wang, Xinran; Wang, Wen-Xu; Wu, Lingfei
2015-04-29
Two classes of scaling behaviours, namely the super-linear scaling of links or activities, and the sub-linear scaling of area, diversity, or time elapsed with respect to size have been found to prevail in the growth of complex networked systems. Despite some pioneering modelling approaches proposed for specific systems, whether there exists some general mechanisms that account for the origins of such scaling behaviours in different contexts, especially in socioeconomic systems, remains an open question. We address this problem by introducing a geometric network model without free parameter, finding that both super-linear and sub-linear scaling behaviours can be simultaneously reproduced and that the scaling exponents are exclusively determined by the dimension of the Euclidean space in which the network is embedded. We implement some realistic extensions to the basic model to offer more accurate predictions for cities of various scaling behaviours and the Zipf distribution reported in the literature and observed in our empirical studies. All of the empirical results can be precisely recovered by our model with analytical predictions of all major properties. By virtue of these general findings concerning scaling behaviour, our models with simple mechanisms gain new insights into the evolution and development of complex networked systems.
Universality in geometric properties of german road networks: Empirical analysis and modelling
Energy Technology Data Exchange (ETDEWEB)
Chan, Sonic; Donner, Reik; Laemmer, Stefan [TU Dresden (Germany); Helbing, Dirk [ETH Zuerich (Switzerland)
2009-07-01
In order to understand the development of urban road networks, we have investigated the structural properties of a variety of German cities. A considerable degree of universality is found in simple geometric features such as the distributions of link lengths, cell areas and cell degrees. In particular, German cities are mainly characterized by perpendicular intersections and splittings of straight roads, deviations of the link angle distributions from the rectangular pattern follow in good approximation stretched exponential distributions. It is shown that most empirical features of the studied road networks can be surprisingly well reproduced by a simple self-organizing evolving network model. For this purpose, we suggest a two-step procedure with a stochastic generation of new nodes in the presence of a sophisticated interaction potential, which is followed by the establishment of new links according to some deterministic rules. In this model, rectangular patterns naturally emerge due to basic economic considerations. It will be further discussed to which extent similar mechanisms do significantly contribute also in other technological or biological transportation networks.
Boyer, Frédéric; Porez, Mathieu
2015-03-26
This article presents a set of generic tools for multibody system dynamics devoted to the study of bio-inspired locomotion in robotics. First, archetypal examples from the field of bio-inspired robot locomotion are presented to prepare the ground for further discussion. The general problem of locomotion is then stated. In considering this problem, we progressively draw a unified geometric picture of locomotion dynamics. For that purpose, we start from the model of discrete mobile multibody systems (MMSs) that we progressively extend to the case of continuous and finally soft systems. Beyond these theoretical aspects, we address the practical problem of the efficient computation of these models by proposing a Newton-Euler-based approach to efficient locomotion dynamics with a few illustrations of creeping, swimming, and flying.
Study of static and dynamic stability of flexible rods in a geometrically nonlinear statement
Annin, B. D.; Vlasov, A. Yu.; Zakharov, Yu. V.; Okhotkin, K. G.
2017-07-01
We study static and dynamic stability problems for a thin flexible rod subjected to axial compression with the geometric nonlinearity explicitly taken into account. In the case of static action of a force, the critical load and the bending shapes of the rod were determined by Euler. Lavrent'ev and Ishlinsky discovered that, in the case of rod dynamic loading significantly greater than the Euler static critical load, there arise buckling modes with a large number of waves in the longitudinal direction. Lavrent'ev and Ishlinsky referred to the first loading threshold discovered by Euler as the static threshold, and the subsequent ones were called dynamic thresholds; they can be attained under impact loading if the pulse growth time is less than the system relaxation time. Later, the buckling mechanism in this case and the arising parametric resonance were studied in detail by Academician Morozov and his colleagues. In this paper, we complete and develop the approach to studying dynamic rod systems suggested by Morozov; in particular, we construct exact and approximate analytic solutions by using a system of special functions generalizing the Jacobi elliptic functions. We obtain approximate analytic solutions of the nonlinear dynamic problem of flexible rod deformation under longitudinal loading with regard to the boundary conditions and show that the analytic solution of static rod system stability problems in a geometrically nonlinear statement permits exactly determining all possible shapes of the bent rod and the complete system of buckling thresholds. The study of approximate analytic solutions of dynamic problems of nonlinear vibrations of rod systems loaded by lumped forces after buckling in the deformed state allows one to determine the vibration frequencies and then the parametric resonance thresholds.
DEFF Research Database (Denmark)
Bisdom, Kevin; Bertotti, Giovanni; Nick, Hamid
2016-01-01
Modeling of fluid flow in naturally fractured reservoirs is often done through modeling and upscaling of discrete fracture networks (DFNs). The two-dimensional fracture geometry required for DFNs is obtained from subsurface and outcropping analog data. However, these data provide little information...... networks, digitized from outcropping pavements. These networks cover a wide range of possible geometries and spatial distributions. The geometrically based method predicts the average hydraulic aperture and equivalent permeability of fractured porous media with error margins of less than 5%....
Geometric quantum gates in liquid-state NMR based on a cancellation of dynamical phases
Ota, Yukihiro; Goto, Yoshito; Kondo, Yasushi; Nakahara, Mikio
2009-11-01
A proposal for applying nonadiabatic geometric phases to quantum computing, called double-loop method [S.-L. Zhu and Z. D. Wang, Phys. Rev. A 67, 022319 (2003)], is demonstrated in a liquid-state nuclear magnetic-resonance quantum computer. Using a spin-echo technique, the original method is modified so that quantum gates are implemented in a standard high-precision nuclear magnetic-resonance system for chemical analysis. We show that a dynamical phase is successfully eliminated and a one-qubit quantum gate is realized although the gate fidelity is not high.
Neural dynamics in superconducting networks
Segall, Kenneth; Schult, Dan; Crotty, Patrick; Miller, Max
2012-02-01
We discuss the use of Josephson junction networks as analog models for simulating neuron behaviors. A single unit called a ``Josephson Junction neuron'' composed of two Josephson junctions [1] displays behavior that shows characteristics of single neurons such as action potentials, thresholds and refractory periods. Synapses can be modeled as passive filters and can be used to connect neurons together. The sign of the bias current to the Josephson neuron can be used to determine if the neuron is excitatory or inhibitory. Due to the intrinsic speed of Josephson junctions and their scaling properties as analog models, a large network of Josephson neurons measured over typical lab times contains dynamics which would essentially be impossible to calculate on a computer We discuss the operating principle of the Josephson neuron, coupling Josephson neurons together to make large networks, and the Kuramoto-like synchronization of a system of disordered junctions.[4pt] [1] ``Josephson junction simulation of neurons,'' P. Crotty, D. Schult and K. Segall, Physical Review E 82, 011914 (2010).
Tourism-planning network knowledge dynamics
DEFF Research Database (Denmark)
Dredge, Dianne
2014-01-01
This chapter explores the characteristics and functions of tourism networks as a first step in understanding how networks facilitate and reproduce knowledge. A framework to progress understandings of knowledge dynamics in tourism networks is presented that includes four key dimensions: context......, network agents, network boundaries and network resources. A case study of the development of the Next Generation Tourism Handbook (Queensland, Australia), a policy initiative that sought to bring tourism and land use planning knowledge closer together is presented. The case study illustrates...... that the tourism policy and land use planning networks operate in very different spheres and that context, network agents, network boundaries and network resources have a significant influence not only on knowledge dynamics but also on the capacity of network agents to overcome barriers to learning and to innovate....
Synchronization of coupled chaotic dynamics on networks
Indian Academy of Sciences (India)
www.ias.ac.in/article/fulltext/pram/064/03/0455-0464. Keywords. Dynamical systems; linear stability analysis; floating nodes. Abstract. We review some recent work on the synchronization of coupled dynamical systems on a variety of networks.
Influence of a geometrical perturbation on the ion dynamics in a 3D Paul trap
Joshi, Manoj Kumar; Satyajit, K. T.; Rao, Pushpa M.
2015-11-01
In ion traps, the purity of the Quadrupole potential is essential. Any perturbations in potential caused by geometrical imperfections or the presence of other ions alter the dynamics of trapped ions. In this paper the effect of a particular perturbation on dynamics of trapped ions in a 3-D quadrupole trap is analyzed and we have compared the experimental findings with simulations. To see the effect of geometrical perturbation, the position of a filament reaching into the trap (which acts as the perturbing element) is altered. The equi-frequency line on the a - q Mathieu plot was scanned at different filament insertion heights. We studied the effect of filament current and the duration of loading. The distorted potential within this trap has been theoretically calculated and simulated using SIMION. An analytical expression for potential inside the trap is fitted to the potential values obtained from the simulation and the numerical solutions of the equation of motion of trapped ions is obtained. The motional frequencies are then calculated from the Fourier transformation of the simulated ion trajectories at any given trapping potential and compared with our experimental findings. The shift in the secular frequency with respect to the level of insertion of the filament within the trap is evaluated.
Koffel, Thomas; Daufresne, Tanguy; Massol, François; Klausmeier, Christopher A
2016-10-21
Contemporary niche theory is a powerful structuring framework in theoretical ecology. First developed in the context of resource competition, it has been extended to encompass other types of regulating factors such as shared predators, parasites or inhibitors. A central component of contemporary niche theory is a graphical approach popularized by Tilman that illustrates the different outcomes of competition along environmental gradients, like coexistence and competitive exclusion. These food web modules have been used to address species sorting in community ecology, as well as adaptation and coexistence on eco-evolutionary time scales in adaptive dynamics. Yet, the associated graphical approach has been underused so far in the evolutionary context. In this paper, we provide a rigorous approach to extend this graphical method to a continuum of interacting strategies, using the geometrical concept of the envelope. Not only does this approach provide community and eco-evolutionary bifurcation diagrams along environmental gradients, it also sheds light on the similarities and differences between those two perspectives. Adaptive dynamics naturally merges with this ecological framework, with a close correspondence between the classification of singular strategies and the geometrical properties of the envelope. Finally, this approach provides an integrative tool to study adaptation between levels of organization, from the individual to the ecosystem. Copyright © 2016 Elsevier Ltd. All rights reserved.
Learning dynamic Bayesian networks with mixed variables
DEFF Research Database (Denmark)
Bøttcher, Susanne Gammelgaard
This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learn....... An automated procedure for specifying prior distributions for the parameters in a dynamic Bayesian network is presented. It is a simple extension of the procedure for the ordinary Bayesian networks. Finally the W¨olfer?s sunspot numbers are analyzed....
The dynamics of networked power in a concentrated business network
Olsen, Per Ingvar; Prenkert, Frans; Hoholm, Thomas; Harrison, Debbie
2014-01-01
This is the authors' accepted and refereed manuscript to the article The purpose of this paper is to investigate the dynamics of networked power in a concentrated business network. Power is a long standing theme in inter-organisational research, yet there is a paucity of studies about how power emerges and is constructed over time at the network level. The paper adopts process, systems and network theory to interpret a rich single case study from the food industry. Three power mechanism...
Local Dynamics in Trained Recurrent Neural Networks
Rivkind, Alexander; Barak, Omri
2017-06-01
Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.
Local Dynamics in Trained Recurrent Neural Networks.
Rivkind, Alexander; Barak, Omri
2017-06-23
Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.
Forced synchronization of autonomous dynamical Boolean networks
International Nuclear Information System (INIS)
Rivera-Durón, R. R.; Campos-Cantón, E.; Campos-Cantón, I.; Gauthier, Daniel J.
2015-01-01
We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enable future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics
Forced synchronization of autonomous dynamical Boolean networks
Energy Technology Data Exchange (ETDEWEB)
Rivera-Durón, R. R., E-mail: roberto.rivera@ipicyt.edu.mx; Campos-Cantón, E., E-mail: eric.campos@ipicyt.edu.mx [División de Matemáticas Aplicadas, Instituto Potosino de Investigación Científica y Tecnológica A. C., Camino a la Presa San José 2055, Col. Lomas 4 Sección, C.P. 78216, San Luis Potosí, S.L.P. (Mexico); Campos-Cantón, I. [Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Álvaro Obregón 64, C.P. 78000, San Luis Potosí, S.L.P. (Mexico); Gauthier, Daniel J. [Department of Physics and Center for Nonlinear and Complex Systems, Duke University, Box 90305, Durham, North Carolina 27708 (United States)
2015-08-15
We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enable future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics.
Energy Technology Data Exchange (ETDEWEB)
Marseguerra, M. [Department of Nuclear Engineering, Polytechnic of Milan, Via Ponzio 34/3, 20133 Milan (Italy)]. E-mail: marzio.marseguerra@polimi.it; Zoia, A. [Department of Nuclear Engineering, Polytechnic of Milan, Via Ponzio 34/3, 20133 Milan (Italy)
2005-07-15
In complex and risky plants, such as the nuclear reactors, the analysis of the signals released by the many sensors which monitor the plant represents a difficult task due to the high-dimensionality of the data. This paper is the first of two in which we tackle the problem of the dimensionality reduction by the nonlinear principal components analysis as performed by an autoassociative neural network (AANN). This network filters the many input data and releases at the bottleneck output a relatively small number of signals which capture the significant properties of the original data, thus realizing the data reduction. In the present paper, we show that the network ability in correctly reproducing as output the given input after a passage through the bottleneck layer (which by definition should have fewer nodes than either input or output layers) could be conceived as a topological mapping between abstract spaces. Apart from the less critical choice of the number of nodes in the mapping and demapping layers, the topological mapping will be successful - and the AANN will be able to perform the required data reconstruction - provided that the number of nodes of the bottleneck layer is related to the dimensionality d of the abstract projection space. We show how to obtain a numerical estimate d* for the real dimension d. This numerical estimate will firmly base the choice of the number of nodes f of the bottleneck layer, thus avoiding the usual troubling trial-and-error procedure. The power of the proposed approach is demonstrated firstly on a few geometrical cases and then on the analysis of nuclear transients simulated by the classic Chernick's model.
Inferring network topology from complex dynamics
International Nuclear Information System (INIS)
Shandilya, Srinivas Gorur; Timme, Marc
2011-01-01
Inferring the network topology from dynamical observations is a fundamental problem pervading research on complex systems. Here, we present a simple, direct method for inferring the structural connection topology of a network, given an observation of one collective dynamical trajectory. The general theoretical framework is applicable to arbitrary network dynamical systems described by ordinary differential equations. No interference (external driving) is required and the type of dynamics is hardly restricted in any way. In particular, the observed dynamics may be arbitrarily complex; stationary, invariant or transient; synchronous or asynchronous and chaotic or periodic. Presupposing a knowledge of the functional form of the dynamical units and of the coupling functions between them, we present an analytical solution to the inverse problem of finding the network topology from observing a time series of state variables only. Robust reconstruction is achieved in any sufficiently long generic observation of the system. We extend our method to simultaneously reconstructing both the entire network topology and all parameters appearing linear in the system's equations of motion. Reconstruction of network topology and system parameters is viable even in the presence of external noise that distorts the original dynamics substantially. The method provides a conceptually new step towards reconstructing a variety of real-world networks, including gene and protein interaction networks and neuronal circuits.
Sensitive Dependence of Optimal Network Dynamics on Network Structure
Directory of Open Access Journals (Sweden)
Takashi Nishikawa
2017-11-01
Full Text Available The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important long-standing problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here, we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, using diffusively coupled systems as examples, we demonstrate that the stability of a dynamical state can exhibit sensitivity to unweighted structural perturbations (i.e., link removals and node additions for undirected optimal networks and to weighted perturbations (i.e., small changes in link weights for directed optimal networks. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of undirected optimal networks and the prevalence of eigenvector degeneracy in directed optimal networks. These findings establish a unified characterization of networks optimized for dynamical stability, which we illustrate using Turing instability in activator-inhibitor systems, synchronization in power-grid networks, network diffusion, and several other network processes. Our results suggest that the network structure of a complex system operating near an optimum can potentially be fine-tuned for a significantly enhanced stability compared to what one might expect from simple extrapolation. On the other hand, they also suggest constraints on how close to the optimum the system can be in practice. Finally, the results have potential implications for biophysical networks, which have evolved under the competing pressures of optimizing fitness while remaining robust against perturbations.
Suramlishvili, Nugzar; Eggers, Jens; Fontelos, Marco
2014-11-01
We are concerned with singularities of the shock fronts of converging perturbed shock waves. Our considerations are based on Whitham's theory of geometrical shock dynamics. The recently developed method of local analysis is applied in order to determine generic singularities. In this case the solutions of partial differential equations describing the geometry of the shock fronts are presented as families of smooth maps with state variables and the set of control parameters dependent on Mach number, time and initial conditions. The space of control parameters of the singularities is analysed, the unfoldings describing the deformations of the canonical germs of shock front singularities are found and corresponding bifurcation diagrams are constructed. Research is supported by the Leverhulme Trust, Grant Number RPG-2012-568.
International Nuclear Information System (INIS)
Li Qing; Wang Tianshu; Ma Xingrui
2009-01-01
Flexible-body modeling with geometric nonlinearities remains a hot topic of research by applications in multibody system dynamics undergoing large overall motions. However, the geometric nonlinear effects on the impact dynamics of flexible multibody systems have attracted significantly less attention. In this paper, a point-surface impact problem between a rigid ball and a pivoted flexible beam is investigated. The Hertzian contact law is used to describe the impact process, and the dynamic equations are formulated in the floating frame of reference using the assumed mode method. The two important geometric nonlinear effects of the flexible beam are taken into account, i.e., the longitudinal foreshortening effect due to the transverse deformation, and the stress stiffness effect due to the axial force. The simulation results show that good consistency can be obtained with the nonlinear finite element program ABAQUS/Explicit if proper geometric nonlinearities are included in the floating frame formulation. Specifically, only the foreshortening effect should be considered in a pure transverse impact for efficiency, while the stress stiffness effect should be further considered in an oblique case with much more computational effort. It also implies that the geometric nonlinear effects should be considered properly in the impact dynamic analysis of more general flexible multibody systems
Temporal fidelity in dynamic social networks
DEFF Research Database (Denmark)
Stopczynski, Arkadiusz; Sapiezynski, Piotr; Pentland, Alex ‘Sandy’
2015-01-01
of the network dynamics can be used to inform the process of measuring social networks. The details of measurement are of particular importance when considering dynamic processes where minute-to-minute details are important, because collection of physical proximity interactions with high temporal resolution......It has recently become possible to record detailed social interactions in large social systems with high resolution. As we study these datasets, human social interactions display patterns that emerge at multiple time scales, from minutes to months. On a fundamental level, understanding...... is difficult and expensive. Here, we consider the dynamic network of proximity-interactions between approximately 500 individuals participating in the Copenhagen Networks Study. We show that in order to accurately model spreading processes in the network, the dynamic processes that occur on the order...
Pinning Synchronization of Switched Complex Dynamical Networks
Directory of Open Access Journals (Sweden)
Liming Du
2015-01-01
Full Text Available Network topology and node dynamics play a key role in forming synchronization of complex networks. Unfortunately there is no effective synchronization criterion for pinning synchronization of complex dynamical networks with switching topology. In this paper, pinning synchronization of complex dynamical networks with switching topology is studied. Two basic problems are considered: one is pinning synchronization of switched complex networks under arbitrary switching; the other is pinning synchronization of switched complex networks by design of switching when synchronization cannot achieved by using any individual connection topology alone. For the two problems, common Lyapunov function method and single Lyapunov function method are used respectively, some global synchronization criteria are proposed and the designed switching law is given. Finally, simulation results verify the validity of the results.
Psychology and social networks: a dynamic network theory perspective.
Westaby, James D; Pfaff, Danielle L; Redding, Nicholas
2014-04-01
Research on social networks has grown exponentially in recent years. However, despite its relevance, the field of psychology has been relatively slow to explain the underlying goal pursuit and resistance processes influencing social networks in the first place. In this vein, this article aims to demonstrate how a dynamic network theory perspective explains the way in which social networks influence these processes and related outcomes, such as goal achievement, performance, learning, and emotional contagion at the interpersonal level of analysis. The theory integrates goal pursuit, motivation, and conflict conceptualizations from psychology with social network concepts from sociology and organizational science to provide a taxonomy of social network role behaviors, such as goal striving, system supporting, goal preventing, system negating, and observing. This theoretical perspective provides psychologists with new tools to map social networks (e.g., dynamic network charts), which can help inform the development of change interventions. Implications for social, industrial-organizational, and counseling psychology as well as conflict resolution are discussed, and new opportunities for research are highlighted, such as those related to dynamic network intelligence (also known as cognitive accuracy), levels of analysis, methodological/ethical issues, and the need to theoretically broaden the study of social networking and social media behavior. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
Protaziuk, Elżbieta
2016-06-01
Satellite measurements become competitive in many tasks of engineering surveys, however, in many requiring applications possibilities to apply such solutions are still limited. The possibility to widely apply satellite technologies for displacements measurements is related with new challenges; the most important of them relate to increasing requirements concerning the accuracy, reliability and continuity of results of position determination. One of the solutions is a ground augmentation of satellite network, which intention is to improve precision of positioning, ensure comparable accuracy of coordinates and reduce precision fluctuations over time. The need for augmentation of GNSS is particularly significant in situations: where the visibility of satellites is poor because of terrain obstacles, when the determined position is not precise enough or a satellites constellation does not allow for reliable positioning. Ground based source/sources of satellite signal placed at a ground, called pseudosatellites, or pseudolites were intensively investigated during the last two decades and finally were developed into groundbased, time-synchronized transceivers, that can transmit and receive a proprietary positioning signal. The paper presents geometric aspects of the ground based augmentation of the satellite networks using various quality measures of positioning geometry, which depends on access to the constellation of satellites and the conditions of the observation environment. The issue of minimizing these measures is the key problem that allows to obtain the position with high accuracy. For this purpose, the use of an error ellipsoid is proposed and compared with an error ellipse. The paper also describes the results of preliminary accuracy analysis obtained at test area and a comparison of various measures of the quality of positioning geometry.
A geometric network model of intrinsic grey-matter connectivity of the human brain
Lo, Yi-Ping; O'Dea, Reuben; Crofts, Jonathan J.; Han, Cheol E.; Kaiser, Marcus
2015-10-01
Network science provides a general framework for analysing the large-scale brain networks that naturally arise from modern neuroimaging studies, and a key goal in theoretical neuroscience is to understand the extent to which these neural architectures influence the dynamical processes they sustain. To date, brain network modelling has largely been conducted at the macroscale level (i.e. white-matter tracts), despite growing evidence of the role that local grey matter architecture plays in a variety of brain disorders. Here, we present a new model of intrinsic grey matter connectivity of the human connectome. Importantly, the new model incorporates detailed information on cortical geometry to construct ‘shortcuts’ through the thickness of the cortex, thus enabling spatially distant brain regions, as measured along the cortical surface, to communicate. Our study indicates that structures based on human brain surface information differ significantly, both in terms of their topological network characteristics and activity propagation properties, when compared against a variety of alternative geometries and generative algorithms. In particular, this might help explain histological patterns of grey matter connectivity, highlighting that observed connection distances may have arisen to maximise information processing ability, and that such gains are consistent with (and enhanced by) the presence of short-cut connections.
Parker, Jeffrey B.
2018-05-01
Zonal flows have been observed to appear spontaneously from turbulence in a number of physical settings. A complete theory for their behavior is still lacking. Recently, a number of studies have investigated the dynamics of zonal flows using quasilinear (QL) theories and the statistical framework of a second-order cumulant expansion (CE2). A geometrical-optics (GO) reduction of CE2, derived under an assumption of separation of scales between the fluctuations and the zonal flow, is studied here numerically. The reduced model, CE2-GO, has a similar phase-space mathematical structure to the traditional wave-kinetic equation, but that wave-kinetic equation has been shown to fail to preserve enstrophy conservation and to exhibit an ultraviolet catastrophe. CE2-GO, in contrast, preserves nonlinear conservation of both energy and enstrophy. We show here how to retain these conservation properties in a pseudospectral simulation of CE2-GO. We then present nonlinear simulations of CE2-GO and compare with direct simulations of quasilinear (QL) dynamics. We find that CE2-GO retains some similarities to QL. The partitioning of energy that resides in the zonal flow is in good quantitative agreement between CE2-GO and QL. On the other hand, the length scale of the zonal flow does not follow the same qualitative trend in the two models. Overall, these simulations indicate that CE2-GO provides a simpler and more tractable statistical paradigm than CE2, but CE2-GO is missing important physics.
Fundamental structures of dynamic social networks
DEFF Research Database (Denmark)
Sekara, Vedran; Stopczynski, Arkadiusz; Jørgensen, Sune Lehmann
2016-01-01
Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships...... and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection......, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals...
Evolutionary dynamics of complex communications networks
Karyotis, Vasileios; Papavassiliou, Symeon
2013-01-01
Until recently, most network design techniques employed a bottom-up approach with lower protocol layer mechanisms affecting the development of higher ones. This approach, however, has not yielded fascinating results in the case of wireless distributed networks. Addressing the emerging aspects of modern network analysis and design, Evolutionary Dynamics of Complex Communications Networks introduces and develops a top-bottom approach where elements of the higher layer can be exploited in modifying the lowest physical topology-closing the network design loop in an evolutionary fashion similar to
Modeling the Dynamics of Compromised Networks
Energy Technology Data Exchange (ETDEWEB)
Soper, B; Merl, D M
2011-09-12
Accurate predictive models of compromised networks would contribute greatly to improving the effectiveness and efficiency of the detection and control of network attacks. Compartmental epidemiological models have been applied to modeling attack vectors such as viruses and worms. We extend the application of these models to capture a wider class of dynamics applicable to cyber security. By making basic assumptions regarding network topology we use multi-group epidemiological models and reaction rate kinetics to model the stochastic evolution of a compromised network. The Gillespie Algorithm is used to run simulations under a worst case scenario in which the intruder follows the basic connection rates of network traffic as a method of obfuscation.
Information Dynamics in Networks: Models and Algorithms
2016-09-13
Information Dynamics in Networks: Models and Algorithms In this project, we investigated how network structure interplays with higher level processes in...Models and Algorithms Report Title In this project, we investigated how network structure interplays with higher level processes in online social...Received Paper 1.00 2.00 3.00 . A Note on Modeling Retweet Cascades on Twitter, Workshop on Algorithms and Models for the Web Graph. 09-DEC-15
Complex networks: Dynamics and security
Indian Academy of Sciences (India)
We study a mechanism for cascades in complex networks by constructing a model incorporating the flows of information and physical quantities in the network. Using this model we can also show that the cascading phenomenon can be understood as a phase transition in terms of the key parameter characterizing the node ...
Complex networks: Dynamics and security
Indian Academy of Sciences (India)
1996-08-10
These have been reported for the internet and for the power grid (e.g., the August 10, 1996 failure of the western United States power grid). We study a mechanism for cascades in complex networks by constructing a model incorporating the flows of information and physical quantities in the network. Using this model we can ...
Network Physiology: How Organ Systems Dynamically Interact.
Bartsch, Ronny P; Liu, Kang K L; Bashan, Amir; Ivanov, Plamen Ch
2015-01-01
We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems.
Dynamic Image Networks for Action Recognition
Bilen, H.; Fernando, B.; Gavves, E.; Vedaldi, A.; Gould, S.
2016-01-01
We introduce the concept of dynamic image, a novel compact representation of videos useful for video analysis especially when convolutional neural networks (CNNs) are used. The dynamic image is based on the rank pooling concept and is obtained through the parameters of a ranking machine that encodes
Markovian dynamics on complex reaction networks
International Nuclear Information System (INIS)
Goutsias, J.; Jenkinson, G.
2013-01-01
Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underlying population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions and the large size of the underlying state-spaces, computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples
Dynamic Cognitive Networks, Fundamentals and Applications
Directory of Open Access Journals (Sweden)
Márcio Mendonça
2014-02-01
Full Text Available This study presents the proposal of dynamic cognitive networks (DCN, and also the evolution of Cognitive Maps and Fuzzy Cognitive Maps. Fuzzy Cognitive Maps (FCM can be applied in several areas of knowledge; however, it presents some restrictions in dynamic systems. Due to these restrictions, some architectures proposals are based on FCM and also classical proposals for cognitive models based on these concepts are available in the literature. Dynamic Cognitive Networks is one of these approaches. Hence, this study presents an original proposal with background for the construction of DCN and applications in process control and autonomous navigation.
Cognitive radio networks dynamic resource allocation schemes
Wang, Shaowei
2014-01-01
This SpringerBrief presents a survey of dynamic resource allocation schemes in Cognitive Radio (CR) Systems, focusing on the spectral-efficiency and energy-efficiency in wireless networks. It also introduces a variety of dynamic resource allocation schemes for CR networks and provides a concise introduction of the landscape of CR technology. The author covers in detail the dynamic resource allocation problem for the motivations and challenges in CR systems. The Spectral- and Energy-Efficient resource allocation schemes are comprehensively investigated, including new insights into the trade-off
Dynamic Protection of Optical Networks
DEFF Research Database (Denmark)
Ruepp, Sarah Renée
2008-01-01
This thesis deals with making optical networks resilient to failures. The recovery performance of path, segment and span restoration is evaluated in a network with limited wavelength conversion capability using both standard and enhanced wavelength assignment schemes. The enhanced wavelength...... stubs at the failure adjacent nodes. Both modifcations have a positive influence on the recovery percentage. The recovery enhancements are applicable in both single and multi-domain network environments. Stub release, where the still working parts of a failure affected connection are released prior...... of the modularity of capacity units is investigated for resilient network design. Different span upgrading strategies and algorithms for finding restoration paths are evaluated. Furthermore, the capacity effciency of constraining restoration requests for the same destination node to the same restoration path...
Evolution of Cooperation on Stochastic Dynamical Networks
Wu, Bin; Zhou, Da; Fu, Feng; Luo, Qingjun; Wang, Long; Traulsen, Arne
2010-01-01
Cooperative behavior that increases the fitness of others at a cost to oneself can be promoted by natural selection only in the presence of an additional mechanism. One such mechanism is based on population structure, which can lead to clustering of cooperating agents. Recently, the focus has turned to complex dynamical population structures such as social networks, where the nodes represent individuals and links represent social relationships. We investigate how the dynamics of a social network can change the level of cooperation in the network. Individuals either update their strategies by imitating their partners or adjust their social ties. For the dynamics of the network structure, a random link is selected and breaks with a probability determined by the adjacent individuals. Once it is broken, a new one is established. This linking dynamics can be conveniently characterized by a Markov chain in the configuration space of an ever-changing network of interacting agents. Our model can be analytically solved provided the dynamics of links proceeds much faster than the dynamics of strategies. This leads to a simple rule for the evolution of cooperation: The more fragile links between cooperating players and non-cooperating players are (or the more robust links between cooperators are), the more likely cooperation prevails. Our approach may pave the way for analytically investigating coevolution of strategy and structure. PMID:20614025
Comparison of geometrical shock dynamics and kinematic models for shock-wave propagation
Ridoux, J.; Lardjane, N.; Monasse, L.; Coulouvrat, F.
2018-03-01
Geometrical shock dynamics (GSD) is a simplified model for nonlinear shock-wave propagation, based on the decomposition of the shock front into elementary ray tubes. Assuming small changes in the ray tube area, and neglecting the effect of the post-shock flow, a simple relation linking the local curvature and velocity of the front, known as the A{-}M rule, is obtained. More recently, a new simplified model, referred to as the kinematic model, was proposed. This model is obtained by combining the three-dimensional Euler equations and the Rankine-Hugoniot relations at the front, which leads to an equation for the normal variation of the shock Mach number at the wave front. In the same way as GSD, the kinematic model is closed by neglecting the post-shock flow effects. Although each model's approach is different, we prove their structural equivalence: the kinematic model can be rewritten under the form of GSD with a specific A{-}M relation. Both models are then compared through a wide variety of examples including experimental data or Eulerian simulation results when available. Attention is drawn to the simple cases of compression ramps and diffraction over convex corners. The analysis is completed by the more complex cases of the diffraction over a cylinder, a sphere, a mound, and a trough.
Energy Technology Data Exchange (ETDEWEB)
Saha, Sourav, E-mail: ssaha09@me.buet.ac.bd; Mojumder, Satyajit; Mahboob, Monon [Department of Mechanical Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000 (Bangladesh); Islam, M. Zahabul [Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802 (United States)
2016-07-12
Tungsten is a promising material and has potential use as battery anode. Tungsten nanowires are gaining attention from researchers all over the world for this wide field of application. In this paper, we investigated effect of temperature and geometric parameters (diameter and aspect ratio) on elastic properties of Tungsten nanowire. Aspect ratios (length to diameter ratio) considered are 8:1, 10:1, and 12:1 while diameter of the nanowire is varied from 1-4 nm. For 2 nm diameter sample (aspect ratio 10:1), temperature is varied (10 K ~ 1500 K) to observe elastic behavior of Tungsten nanowire under uniaxial tensile loading. EAM potential is used for molecular dynamic simulation. We applied constant strain rate of 10{sup 9} s{sup −1} to deform the nanowire. Elastic behavior is expressed through stress vs. strain plot. We also investigated the fracture mechanism of tungsten nanowire and radial distribution function. Investigation suggests peculiar behavior of Tungsten nanowire in nano-scale with double peaks in stress vs. strain diagram. Necking before final fracture suggests that actual elastic behavior of the material is successfully captured through atomistic modeling.
Effect of ablation geometry on the dynamics, composition, and geometrical shape of thin film plasma
Mondal, Alamgir; Singh, R. K.; Kumar, Ajai
2018-01-01
The characteristics of plasma plume produced by front and back ablation of thin films have been investigated using fast imaging and optical emission spectroscopy. Ablation geometry dependence of the plume dynamics, its geometrical aspect and composition is emphasized. Also, the effect of an ambient environment and the beam diameter of an ablating laser on the front and back ablations is briefly discussed. Analysis of time resolved images and plasma parameters indicates that the energetic and spherical plasma formed by front ablation is strikingly different in comparison to the slow and nearly cylindrical plasma plume observed in the case of back ablation. Further shock formation, plume confinement, thermalization and validity of different expansion models in these two ablation geometries are also presented. The present study demonstrates the manipulation of kinetic energy, shape, ion/neutral compositions and directionality of the expanding plume by adjusting the experimental configuration, which is highly relevant to its utilization in various applications e.g., generation of energetic particles, tokamak edge plasma diagnostics, thin film deposition, etc.
Quantum three-body reaction dynamics including the geometric phase effect
International Nuclear Information System (INIS)
Wu, Y.S.M.
1992-01-01
Accurate quantum mechanical reactive scattering calculations within the framework of symmetrized hyperspherical coordinate techniques are presented for several processes involving collisions of an electron with a hydrogen atom and an atom with a diatomic molecule in three-dimensional space, and the collinear collision of an atom with a diatomic molecule. In addition to the interest of the processes themselves, the results are compared with previous experimental and theoretical results in such a way as to provide tests of the general usefulness of the methods used. The general theory for the calculation of accurate differential cross sections in the reactive collision of an atom with a diatomic molecule including the geometric phase effect in three-dimensional space is described. This methodology has permitted, for the first time, the calculation of integral and differential cross sections over a significantly larger range of collision energies (up to 2.6 eV total energy) than previously possible for the system H + H 2 . The authors present numerical solutions of the quantum mechanical streamlines of probability current density for collinear atom-diatom reactions. It is used to study the barrier height dependence of dynamics on the Cl + HCl reaction
Xie, Changjian; Malbon, Christopher L; Yarkony, David R; Guo, Hua
2017-07-28
The incorporation of the geometric phase in single-state adiabatic dynamics near a conical intersection (CI) seam has so far been restricted to molecular systems with high symmetry or simple model Hamiltonians. This is due to the fact that the ab initio determined derivative coupling (DC) in a multi-dimensional space is not curl-free, thus making its line integral path dependent. In a recent work [C. L. Malbon et al., J. Chem. Phys. 145, 234111 (2016)], we proposed a new and general approach based on an ab initio determined diabatic representation consisting of only two electronic states, in which the DC is completely removable, so that its line integral is path independent in the simply connected domains that exclude the CI seam. Then with the CIs included, the line integral of the single-valued DC can be used to construct the complex geometry-dependent phase needed to exactly eliminate the double-valued character of the real-valued adiabatic electronic wavefunction. This geometry-dependent phase gives rise to a vector potential which, when included in the adiabatic representation, rigorously accounts for the geometric phase in a system with an arbitrary locus of the CI seam and an arbitrary number of internal coordinates. In this work, we demonstrate this approach in a three-dimensional treatment of the tunneling facilitated dissociation of the S 1 state of phenol, which is affected by a C s symmetry allowed but otherwise accidental seam of CI. Here, since the space is three-dimensional rather than two-dimensional, the seam is a curve rather than a point. The nodal structure of the ground state vibronic wavefunction is shown to map out the seam of CI.
Feedforward Approximations to Dynamic Recurrent Network Architectures.
Muir, Dylan R
2018-02-01
Recurrent neural network architectures can have useful computational properties, with complex temporal dynamics and input-sensitive attractor states. However, evaluation of recurrent dynamic architectures requires solving systems of differential equations, and the number of evaluations required to determine their response to a given input can vary with the input or can be indeterminate altogether in the case of oscillations or instability. In feedforward networks, by contrast, only a single pass through the network is needed to determine the response to a given input. Modern machine learning systems are designed to operate efficiently on feedforward architectures. We hypothesized that two-layer feedforward architectures with simple, deterministic dynamics could approximate the responses of single-layer recurrent network architectures. By identifying the fixed-point responses of a given recurrent network, we trained two-layer networks to directly approximate the fixed-point response to a given input. These feedforward networks then embodied useful computations, including competitive interactions, information transformations, and noise rejection. Our approach was able to find useful approximations to recurrent networks, which can then be evaluated in linear and deterministic time complexity.
Competing dynamic phases of active polymer networks
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.
Control theory of digitally networked dynamic systems
Lunze, Jan
2013-01-01
The book gives an introduction to networked control systems and describes new modeling paradigms, analysis methods for event-driven, digitally networked systems, and design methods for distributed estimation and control. Networked model predictive control is developed as a means to tolerate time delays and packet loss brought about by the communication network. In event-based control the traditional periodic sampling is replaced by state-dependent triggering schemes. Novel methods for multi-agent systems ensure complete or clustered synchrony of agents with identical or with individual dynamic
Dynamics of rumor spreading in complex networks.
Moreno, Yamir; Nekovee, Maziar; Pacheco, Amalio F
2004-06-01
We derive the mean-field equations characterizing the dynamics of a rumor process that takes place on top of complex heterogeneous networks. These equations are solved numerically by means of a stochastic approach. First, we present analytical and Monte Carlo calculations for homogeneous networks and compare the results with those obtained by the numerical method. Then, we study the spreading process in detail for random scale-free networks. The time profiles for several quantities are numerically computed, which allows us to distinguish among different variants of rumor spreading algorithms. Our conclusions are directed to possible applications in replicated database maintenance, peer-to-peer communication networks, and social spreading phenomena.
Dynamical Adaptation in Terrorist Cells/Networks
DEFF Research Database (Denmark)
Hussain, Dil Muhammad Akbar; Ahmed, Zaki
2010-01-01
Typical terrorist cells/networks have dynamical structure as they evolve or adapt to changes which may occur due to capturing or killing of a member of the cell/network. Analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long...... and followers etc. In this research we analyze and predict the most likely role a particular node can adapt once a member of the network is either killed or caught. The adaptation is based on computing Bayes posteriori probability of each node and the level of the said node in the network structure....
Dynamic simulation of regulatory networks using SQUAD
Directory of Open Access Journals (Sweden)
Xenarios Ioannis
2007-11-01
Full Text Available Abstract Background The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. Results We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. Conclusion The simulation of regulatory networks aims at predicting the behavior of a whole system when subject
Innovation networking between stability and political dynamics
DEFF Research Database (Denmark)
Koch, Christian
2004-01-01
This contribution views innovation as a social activity of building networks, using software product development in multicompany alliances and networks as example. Innovation networks are frequently understood as quite stable arrangements characterised by high trust among the participants. The aim...... of the contribution is to challenge and transcend these notions and develop an understanding of innovation networks as an interplay between stable and dynamic elements, where political processes in innovation are much more than a disruptive and even a counterproductive feature. It reviews the growing number...... is a segment-collaboration between a few manufacturing companies and a software house, the other a complex and extensive innovation network. These studies show how negotiations, shifting positions of players, mobilising stable elements of the network, when developing new ones, and interplays between internal...
The dynamics of transmission and the dynamics of networks.
Farine, Damien
2017-05-01
A toy example depicted here highlighting the results of a study in this issue of the Journal of Animal Ecology that investigates the impact of network dynamics on potential disease outbreaks. Infections (stars) that spread by contact only (left) reduce the predicted outbreak size compared to situations where individuals can become infected by moving through areas that previously contained infected individuals (right). This is potentially important in species where individuals, or in this case groups, have overlapping ranges (as depicted on the top right). Incorporating network dynamics that maintain information about the ordering of contacts (central blocks; including the ordering of spatial overlap as noted by the arrows that highlight the blue group arriving after the red group in top-right of the figure) is important for capturing how a disease might not have the opportunity to spread to all individuals. By contrast, a static or 'average' network (lower blocks) does not capture any of these dynamics. Interestingly, although static networks generally predict larger outbreak sizes, the authors find that in cases when transmission probability is low, this prediction can switch as a result of changes in the estimated intensity of contacts among individuals. [Colour figure can be viewed at wileyonlinelibrary.com]. Springer, A., Kappeler, P.M. & Nunn, C.L. (2017) Dynamic vs. static social networks in models of parasite transmission: Predicting Cryptosporidium spread in wild lemurs. Journal of Animal Ecology, 86, 419-433. The spread of disease or information through networks can be affected by several factors. Whether and how these factors are accounted for can fundamentally change the predicted impact of a spreading epidemic. Springer, Kappeler & Nunn () investigate the role of different modes of transmission and network dynamics on the predicted size of a disease outbreak across several groups of Verreaux's sifakas, a group-living species of lemur. While some factors
Traffic Dynamics of Computer Networks
Fekete, Attila
2008-10-01
Two important aspects of the Internet, namely the properties of its topology and the characteristics of its data traffic, have attracted growing attention of the physics community. My thesis has considered problems of both aspects. First I studied the stochastic behavior of TCP, the primary algorithm governing traffic in the current Internet, in an elementary network scenario consisting of a standalone infinite-sized buffer and an access link. The effect of the fast recovery and fast retransmission (FR/FR) algorithms is also considered. I showed that my model can be extended further to involve the effect of link propagation delay, characteristic of WAN. I continued my thesis with the investigation of finite-sized semi-bottleneck buffers, where packets can be dropped not only at the link, but also at the buffer. I demonstrated that the behavior of the system depends only on a certain combination of the parameters. Moreover, an analytic formula was derived that gives the ratio of packet loss rate at the buffer to the total packet loss rate. This formula makes it possible to treat buffer-losses as if they were link-losses. Finally, I studied computer networks from a structural perspective. I demonstrated through fluid simulations that the distribution of resources, specifically the link bandwidth, has a serious impact on the global performance of the network. Then I analyzed the distribution of edge betweenness in a growing scale-free tree under the condition that a local property, the in-degree of the "younger" node of an arbitrary edge, is known in order to find an optimum distribution of link capacity. The derived formula is exact even for finite-sized networks. I also calculated the conditional expectation of edge betweenness, rescaled for infinite networks.
Spiking, Bursting, and Population Dynamics in a Network of Growth Transform Neurons.
Gangopadhyay, Ahana; Chakrabartty, Shantanu
2017-04-27
This paper investigates the dynamical properties of a network of neurons, each of which implements an asynchronous mapping based on polynomial growth transforms. In the first part of this paper, we present a geometric approach for visualizing the dynamics of the network where each of the neurons traverses a trajectory in a dual optimization space, whereas the network itself traverses a trajectory in an equivalent primal optimization space. We show that as the network learns to solve basic classification tasks, different choices of primal-dual mapping produce unique but interpretable neural dynamics like noise shaping, spiking, and bursting. While the proposed framework is general enough, in this paper, we demonstrate its use for designing support vector machines (SVMs) that exhibit noise-shaping properties similar to those of ΣΔ modulators, and for designing SVMs that learn to encode information using spikes and bursts. It is demonstrated that the emergent switching, spiking, and burst dynamics produced by each neuron encodes its respective margin of separation from a classification hyperplane whose parameters are encoded by the network population dynamics. We believe that the proposed growth transform neuron model and the underlying geometric framework could serve as an important tool to connect well-established machine learning algorithms like SVMs to neuromorphic principles like spiking, bursting, population encoding, and noise shaping.
Dynamics-based centrality for directed networks.
Masuda, Naoki; Kori, Hiroshi
2010-11-01
Determining the relative importance of nodes in directed networks is important in, for example, ranking websites, publications, and sports teams, and for understanding signal flows in systems biology. A prevailing centrality measure in this respect is the PageRank. In this work, we focus on another class of centrality derived from the Laplacian of the network. We extend the Laplacian-based centrality, which has mainly been applied to strongly connected networks, to the case of general directed networks such that we can quantitatively compare arbitrary nodes. Toward this end, we adopt the idea used in the PageRank to introduce global connectivity between all the pairs of nodes with a certain strength. Numerical simulations are carried out on some networks. We also offer interpretations of the Laplacian-based centrality for general directed networks in terms of various dynamical and structural properties of networks. Importantly, the Laplacian-based centrality defined as the stationary density of the continuous-time random walk with random jumps is shown to be equivalent to the absorption probability of the random walk with sinks at each node but without random jumps. Similarly, the proposed centrality represents the importance of nodes in dynamics on the original network supplied with sinks but not with random jumps.
Critical dynamics in associative memory networks
Directory of Open Access Journals (Sweden)
Maximilian eUhlig
2013-07-01
Full Text Available Critical behavior in neural networks is characterized by scale-free avalanche size distributions and can be explained by self-regulatory mechanisms. Theoretical and experimental evidence indicates that information storage capacity reaches its maximum in the critical regime. We study the effect of structural connectivity formed by Hebbian learning on the criticality of network dynamics. The network endowed with Hebbian learning only does not allow for simultaneous information storage and criticality. However, the critical regime is can be stabilized by short-term synaptic dynamics in the form of synaptic depression and facilitation or, alternatively, by homeostatic adaptation of the synaptic weights. We show that a heterogeneous distribution of maximal synaptic strengths does not preclude criticality if the Hebbian learning is alternated with periods of critical dynamics recovery. We discuss the relevance of these findings for the flexibility of memory in aging and with respect to the recent theory of synaptic plasticity.
Dynamic Dilution Effects in Polymeric Networks
DEFF Research Database (Denmark)
Skov, Anne Ladegaard; Sommer-Larsen, Peter; Hassager, Ole
2006-01-01
The relaxation processes occurring in slightly and well-entangled polydimetylsiloxane ( PDMS) networks are investigated. Swelling experiments are performed in order to determine the sol fractions. The low-frequency linear rheology of the two types of networks reveal two significant relaxation pro...... by the change in the amount of dangling arms and solubles with stoichiometry. The star arm relaxation is suppressed by washing out the sol fraction which is seen as a clear example of the dynamic dilution effect arising from the small amount of non-reactive PDMS....... processes, namely the reptation of linear species within the network and the arm withdrawal process of star arms in the sol fraction and of dangling single-chain ends attached to the network. The relaxation spectra are influenced by the stoichiometry to a large extent due to dynamic dilution effects caused...
Agent-based modeling and network dynamics
Namatame, Akira
2016-01-01
The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...
Hydrogen application dynamics and networks
Energy Technology Data Exchange (ETDEWEB)
Schmidt, E. [Air Liquide Large Industries, Champigny-sur-Marne (France)
2010-12-30
The Chemical Industry consumes large volumes of hydrogen as raw material for the manufacture of numerous products (e.g. polyamides and polyurethanes account for 60% of hydrogen demand). The hydrogen demand was in the recent past and will continue to be driven by the polyurethane family. China will host about 60% of new hydrogen needs over the period 2010-2015 becoming the first hydrogen market next year and reaching 25% of market share by 2015 (vs. only 4% in 2001). Air Liquide supplies large volumes of Hydrogen (and other Industrial Gases) to customers by on-site plants and through pipeline networks which offer significant benefits such as higher safety, reliability and flexibility of supply. Thanks to its long term strategy and heavy investment in large units and pipeline networks, Air Liquide is the Industrial Gas leader in most of the world class Petrochemical basins (Rotterdam, Antwerp, US Gulf Coast, Yosu, Caojing,..) (orig.)
International Nuclear Information System (INIS)
Ozkanlar, Abdullah; Zhou, Tiecheng; Clark, Aurora E.
2014-01-01
The definition of a hydrogen bond (H-bond) is intimately related to the topological and dynamic properties of the hydrogen bond network within liquid water. The development of a universal H-bond definition for water is an active area of research as it would remove many ambiguities in the network properties that derive from the fixed definition employed to assign whether a water dimer is hydrogen bonded. This work investigates the impact that an electronic-structure based definition, an energetic, and a geometric definition of the H-bond has upon both topological and dynamic network behavior of simulated water. In each definition, the use of a cutoff (either geometric or energetic) to assign the presence of a H-bond leads to the formation of transiently bonded or broken dimers, which have been quantified within the simulation data. The relative concentration of transient species, and their duration, results in two of the three definitions sharing similarities in either topological or dynamic features (H-bond distribution, H-bond lifetime, etc.), however no two definitions exhibit similar behavior for both classes of network properties. In fact, two networks with similar local network topology (as indicated by similar average H-bonds) can have dramatically different global network topology (as indicated by the defect state distributions) and altered H-bond lifetimes. A dynamics based correction scheme is then used to remove artificially transient H-bonds and to repair artificially broken bonds within the network such that the corrected network exhibits the same structural and dynamic properties for two H-bond definitions (the properties of the third definition being significantly improved). The algorithm described represents a significant step forward in the development of a unified hydrogen bond network whose properties are independent of the original hydrogen bond definition that is employed
Mean field methods for cortical network dynamics
DEFF Research Database (Denmark)
Hertz, J.; Lerchner, Alexander; Ahmadi, M.
2004-01-01
We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrate- and-fire neurons, we can show how the firing irregularity, as measured by the Fano factor, increases with the stren......We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrate- and-fire neurons, we can show how the firing irregularity, as measured by the Fano factor, increases...
Complex networks under dynamic repair model
Chaoqi, Fu; Ying, Wang; Kun, Zhao; Yangjun, Gao
2018-01-01
Invulnerability is not the only factor of importance when considering complex networks' security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair.
perception of communication network fraud dynamics by network ...
African Journals Online (AJOL)
ES Obe
extremely costly incidents of vulnerabilities, there remains a remarkable level of compla- cency on the part of .... network fraud dynamics, fraud de- tection techniques in place, changing patterns of fraudsters, in the study area, ..... test is carried out, and it is a critical fac- tor in deciding whether to accept or reject a Hypothesis.
Discrete dynamic modeling of cellular signaling networks.
Albert, Réka; Wang, Rui-Sheng
2009-01-01
Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.
Dynamics of High-Resolution Networks
DEFF Research Database (Denmark)
Sekara, Vedran
NETWORKS are everywhere. From the smallest confines of the cells within our bodies to the webs of social relations across the globe. Networks are not static, they constantly change, adapt, and evolve to suit new conditions. In order to understand the fundamental laws that govern networks we need...... the unprecedented amounts of information collected by mobile phones to gain detailed insight into the dynamics of social systems. This dissertation presents an unparalleled data collection campaign, collecting highly detailed traces for approximately 1000 people over the course of multiple years. The availability...
DEFF Research Database (Denmark)
Baldi, P.; Blanke, Mogens; Castaldi, P.
2016-01-01
This paper presents a novel scheme for diagnosis of faults affecting the sensors measuring the satellite attitude, body angular velocity and flywheel spin rates as well as defects related to the control torques provided by satellite reaction wheels. A nonlinear geometric design is used to avoid t...
Fundamental structures of dynamic social networks.
Sekara, Vedran; Stopczynski, Arkadiusz; Lehmann, Sune
2016-09-06
Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision.
Spreading dynamics in complex networks
International Nuclear Information System (INIS)
Pei, Sen; Makse, Hernán A
2013-01-01
Searching for influential spreaders in complex networks is an issue of great significance for applications across various domains, ranging from epidemic control, innovation diffusion, viral marketing, and social movement to idea propagation. In this paper, we first display some of the most important theoretical models that describe spreading processes, and then discuss the problem of locating both the individual and multiple influential spreaders respectively. Recent approaches in these two topics are presented. For the identification of privileged single spreaders, we summarize several widely used centralities, such as degree, betweenness centrality, PageRank, k-shell, etc. We investigate the empirical diffusion data in a large scale online social community—LiveJournal. With this extensive dataset, we find that various measures can convey very distinct information of nodes. Of all the users in the LiveJournal social network, only a small fraction of them are involved in spreading. For the spreading processes in LiveJournal, while degree can locate nodes participating in information diffusion with higher probability, k-shell is more effective in finding nodes with a large influence. Our results should provide useful information for designing efficient spreading strategies in reality. (paper)
Spreading dynamics in complex networks
Pei, Sen; Makse, Hernán A.
2013-12-01
Searching for influential spreaders in complex networks is an issue of great significance for applications across various domains, ranging from epidemic control, innovation diffusion, viral marketing, and social movement to idea propagation. In this paper, we first display some of the most important theoretical models that describe spreading processes, and then discuss the problem of locating both the individual and multiple influential spreaders respectively. Recent approaches in these two topics are presented. For the identification of privileged single spreaders, we summarize several widely used centralities, such as degree, betweenness centrality, PageRank, k-shell, etc. We investigate the empirical diffusion data in a large scale online social community—LiveJournal. With this extensive dataset, we find that various measures can convey very distinct information of nodes. Of all the users in the LiveJournal social network, only a small fraction of them are involved in spreading. For the spreading processes in LiveJournal, while degree can locate nodes participating in information diffusion with higher probability, k-shell is more effective in finding nodes with a large influence. Our results should provide useful information for designing efficient spreading strategies in reality.
Synchronization of coupled chaotic dynamics on networks
Indian Academy of Sciences (India)
We review some recent work on the synchronization of coupled dynamical systems on a variety of networks. When nodes show synchronized behaviour, two ... [5], congregations of synchronously flashing fireflies [6], and cricket that chirp in unison [7]. Coupled oscillators were first studied by Winfree [8] and Kuramoto [9].
Discerning connectivity from dynamics in climate networks
Czech Academy of Sciences Publication Activity Database
Paluš, Milan; Hartman, David; Hlinka, Jaroslav; Vejmelka, Martin
2011-01-01
Roč. 18, č. 5 (2011), s. 751-763 ISSN 1023-5809 R&D Projects: GA ČR GCP103/11/J068 Institutional research plan: CEZ:AV0Z10300504 Keywords : complex networks * climate dynamics * connectivity * North Atlantic Oscillation * solar activity Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.597, year: 2011
Dynamical networks with topological self-organization
Zak, M.
2001-01-01
Coupled evolution of state and topology of dynamical networks is introduced. Due to the well organized tensor structure, the governing equations are presented in a canonical form, and required attractors as well as their basins can be easily implanted and controlled.
Dynamic Network Formation Using Ant Colony Optimization
2009-03-01
Problem (DVRP) ............................................ 36 2.7.2 Dynamic Traveling Salesman Problem (DTSP) ....................................... 41...47 2.8.3 Distributed Traveling Salesman Problem ................................................. 48 2.8.4 FIRE Ant...uses the fixed cost of the network in its calculation and commodities are not included in the problem formulation . Using a probabilistic undirected
Dynamical systems on networks a tutorial
Porter, Mason A
2016-01-01
This volume is a tutorial for the study of dynamical systems on networks. It discusses both methodology and models, including spreading models for social and biological contagions. The authors focus especially on “simple” situations that are analytically tractable, because they are insightful and provide useful springboards for the study of more complicated scenarios. This tutorial, which also includes key pointers to the literature, should be helpful for junior and senior undergraduate students, graduate students, and researchers from mathematics, physics, and engineering who seek to study dynamical systems on networks but who may not have prior experience with graph theory or networks. Mason A. Porter is Professor of Nonlinear and Complex Systems at the Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, UK. He is also a member of the CABDyN Complexity Centre and a Tutorial Fellow of Somerville College. James P. Gleeson is Professor of Industrial and Appli...
Shape, connectedness and dynamics in neuronal networks.
Comin, Cesar Henrique; da Fontoura Costa, Luciano
2013-11-15
The morphology of neurons is directly related to several aspects of the nervous system, including its connectedness, health, development, evolution, dynamics and, ultimately, behavior. Such interplays of the neuronal morphology can be understood within the more general shape-function paradigm. The current article reviews, in an introductory way, some key issues regarding the role of neuronal morphology in the nervous system, with emphasis on works developed in the authors' group. The following topics are addressed: (a) characterization of neuronal shape; (b) stochastic synthesis of neurons and neuronal systems; (c) characterization of the connectivity of neuronal networks by using complex networks concepts; and (d) investigations of influences of neuronal shape on network dynamics. The presented concepts and methods are useful also for several other multiple object systems, such as protein-protein interaction, tissues, aggregates and polymers. Copyright © 2013 Elsevier B.V. All rights reserved.
Power Aware Dynamic Provisioning of HPC Networks
Energy Technology Data Exchange (ETDEWEB)
Groves, Taylor [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Grant, Ryan [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-10-01
Future exascale systems are under increased pressure to find power savings. The network, while it consumes a considerable amount of power is often left out of the picture when discussing total system power. Even when network power is being considered, the references are frequently a decade or older and rely on models that lack validation on modern inter- connects. In this work we explore how dynamic mechanisms of an Infiniband network save power and at what granularity we can engage these features. We explore this within the context of the host controller adapter (HCA) on the node and for the fabric, i.e. switches, using three different mechanisms of dynamic link width, frequency and disabling of links for QLogic and Mellanox systems. Our results show that while there is some potential for modest power savings, real world systems need to improved responsiveness to adjustments in order to fully leverage these savings. This page intentionally left blank.
A dynamic chemical network for cystinuria diagnosis.
Lafuente, Maria; Solà, Jordi; Alfonso, Ignacio
2018-04-12
The study of molecular networks represents a conceptual revolution in chemistry. Building on previous knowledge and after understanding the rules of non-covalent interactions, the design of stimulus-responsive chemical systems is possible. Here we report a new strategy, based on the reorganization of a dynamic chemical network that generates new fluorescent associations in the presence of cysteine or cystine. The binding and sensing units are encoded in the components that dynamically assemble and disassemble responding to external stimuli as a successful tool to detect both cysteine and cystine in aqueous media. Moreover, the dynamic sensing system works in human urine, as a prospective application for cystinuria diagnosis. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Marchetti, Barbara; Karsili, Tolga N. V.; Cipriani, Maicol; Hansen, Christopher S.; Ashfold, Michael N. R.
2017-07-01
The near ultraviolet spectroscopy and photodissociation dynamics of two families of asymmetrically substituted thiophenols (2- and 3-YPhSH, with Y = F and Me) have been investigated experimentally (by H (Rydberg) atom photofragment translational spectroscopy) and by ab initio electronic structure calculations. Photoexcitation in all cases populates the 11ππ* and/or 11πσ* excited states and results in S-H bond fission. Analyses of the experimentally obtained total kinetic energy release (TKER) spectra yield the respective parent S-H bond strengths, estimates of ΔE(A ˜ -X ˜ ), the energy splitting between the ground (X ˜ ) and first excited (A ˜ ) states of the resulting 2-(3-)YPhS radicals, and reveal a clear propensity for excitation of the C-S in-plane bending vibration in the radical products. The companion theory highlights roles for both geometric (e.g., steric effects and intramolecular H-bonding) and electronic (i.e., π (resonance) and σ (inductive)) effects in determining the respective parent minimum energy geometries, and the observed substituent and position-dependent trends in S-H bond strength and ΔE(A ˜ -X ˜ ). 2-FPhSH shows some clear spectroscopic and photophysical differences. Intramolecular H-bonding ensures that most 2-FPhSH molecules exist as the syn rotamer, for which the electronic structure calculations return a substantial barrier to tunnelling from the photoexcited 11ππ* state to the 11πσ* continuum. The 11ππ* ← S0 excitation spectrum of syn-2-FPhSH thus exhibits resolved vibronic structure, enabling photolysis studies with a greater parent state selectivity. Structure apparent in the TKER spectrum of the H + 2-FPhS products formed when exciting at the 11ππ* ← S0 origin is interpreted by assuming unintended photoexcitation of an overlapping resonance associated with syn-2-FPhSH(v33 = 1) molecules. The present data offer tantalising hints that such out-of-plane motion influences non-adiabatic coupling in the vicinity
Dynamic analysis of biochemical network using complex network method
Directory of Open Access Journals (Sweden)
Wang Shuqiang
2015-01-01
Full Text Available In this study, the stochastic biochemical reaction model is proposed based on the law of mass action and complex network theory. The dynamics of biochemical reaction system is presented as a set of non-linear differential equations and analyzed at the molecular-scale. Given the initial state and the evolution rules of the biochemical reaction system, the system can achieve homeostasis. Compared with random graph, the biochemical reaction network has larger information capacity and is more efficient in information transmission. This is consistent with theory of evolution.
Propagation, cascades, and agreement dynamics in complex communication and social networks
Lu, Qiming
Many modern and important technological, social, information and infrastructure systems can be viewed as complex systems with a large number of interacting components. Models of complex networks and dynamical interactions, as well as their applications are of fundamental interests in many aspects. Here, several stylized models of multiplex propagation and opinion dynamics are investigated on complex and empirical social networks. We first investigate cascade dynamics in threshold-controlled (multiplex) propagation on random geometric networks. We find that such local dynamics can serve as an efficient, robust, and reliable prototypical activation protocol in sensor networks in responding to various alarm scenarios. We also consider the same dynamics on a modified network by adding a few long-range communication links, resulting in a small-world network. We find that such construction can further enhance and optimize the speed of the network's response, while keeping energy consumption at a manageable level. We also investigate a prototypical agent-based model, the Naming Game, on two-dimensional random geometric networks. The Naming Game [A. Baronchelli et al., J. Stat. Mech.: Theory Exp. (2006) P06014.] is a minimal model, employing local communications that captures the emergence of shared communication schemes (languages) in a population of autonomous semiotic agents. Implementing the Naming Games with local broadcasts on random geometric graphs, serves as a model for agreement dynamics in large-scale, autonomously operating wireless sensor networks. Further, it captures essential features of the scaling properties of the agreement process for spatially-embedded autonomous agents. Among the relevant observables capturing the temporal properties of the agreement process, we investigate the cluster-size distribution and the distribution of the agreement times, both exhibiting dynamic scaling. We also present results for the case when a small density of long
Bozkurt, Ali
2018-01-01
This study examined pre-service teachers' accuracy for geometric constructions with dynamic geometry software, their justification for the accuracy of geometric figures, and their awareness they gained throughout the process. The data come from a sample of 71 elementary grade pre-service teachers activity form completed as a part of geometry…
Innovation networking between stability and political dynamics
DEFF Research Database (Denmark)
Koch, Christian
2004-01-01
of the contribution is to challenge and transcend these notions and develop an understanding of innovation networks as an interplay between stable and dynamic elements, where political processes in innovation are much more than a disruptive and even a counterproductive feature. It reviews the growing number......This contribution views innovation as a social activity of building networks, using software product development in multicompany alliances and networks as example. Innovation networks are frequently understood as quite stable arrangements characterised by high trust among the participants. The aim...... of studies that highlight the political aspect of innovation. The paper reports on a study of innovation processes conducted within the EU—TSER-programme and a study made under the banner of management of technology. Intensive field studies in two constellations of enterprises were carried out. One...
Nonparametric inference of network structure and dynamics
Peixoto, Tiago P.
The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among
Volunteerism: Social Network Dynamics and Education
Ajrouch, Kristine J.; Antonucci, Toni C.; Webster, Noah J.
2016-01-01
Objectives . We examine how changes in social networks influence volunteerism through bridging (diversity) and bonding (spending time) mechanisms. We further investigate whether social network change substitutes or amplifies the effects of education on volunteerism. Methods . Data (n = 543) are drawn from a two-wave survey of Social Relations and Health over the Life Course (SRHLC). Zero-inflated negative binomial regressions were conducted to test competing hypotheses about how changes in social network characteristics alone and in conjunction with education level predict likelihood and frequency of volunteering. Results . Changes in social networks were associated with volunteerism: as the proportion of family members decreased and the average number of network members living within a one-hour drive increased over time, participants reported higher odds of volunteering. The substitution hypothesis was supported: social networks that exhibited more geographic proximity and greater contact frequency over-time compensated for lower levels of education to predict volunteering more hours. Discussion . The dynamic role of social networks and the ways in which they may work through bridging and bonding to influence both likelihood and frequency of volunteering are discussed. The potential benefits of volunteerism in light of longer life expectancies and smaller families are also considered. PMID:25512570
Keshavarz, M; Mojra, A
2015-05-01
Geometrical features of a cancerous tumor embedded in biological soft tissue, including tumor size and depth, are a necessity in the follow-up procedure and making suitable therapeutic decisions. In this paper, a new socio-politically motivated global search strategy which is called imperialist competitive algorithm (ICA) is implemented to train a feed forward neural network (FFNN) to estimate the tumor's geometrical characteristics (FFNNICA). First, a viscoelastic model of liver tissue is constructed by using a series of in vitro uniaxial and relaxation test data. Then, 163 samples of the tissue including a tumor with different depths and diameters are generated by making use of PYTHON programming to link the ABAQUS and MATLAB together. Next, the samples are divided into 123 samples as training dataset and 40 samples as testing dataset. Training inputs of the network are mechanical parameters extracted from palpation of the tissue through a developing noninvasive technology called artificial tactile sensing (ATS). Last, to evaluate the FFNNICA performance, outputs of the network including tumor's depth and diameter are compared with desired values for both training and testing datasets. Deviations of the outputs from desired values are calculated by a regression analysis. Statistical analysis is also performed by measuring Root Mean Square Error (RMSE) and Efficiency (E). RMSE in diameter and depth estimations are 0.50 mm and 1.49, respectively, for the testing dataset. Results affirm that the proposed optimization algorithm for training neural network can be useful to characterize soft tissue tumors accurately by employing an artificial palpation approach. Copyright © 2015 John Wiley & Sons, Ltd.
Advances in dynamic network modeling in complex transportation systems
Ukkusuri, Satish V
2013-01-01
This book focuses on the latest in dynamic network modeling, including route guidance and traffic control in transportation systems and other complex infrastructure networks. Covers dynamic traffic assignment, flow modeling, mobile sensor deployment and more.
Dynamic social networks based on movement
Scharf, Henry; Hooten, Mevin B.; Fosdick, Bailey K.; Johnson, Devin S.; London, Joshua M.; Durban, John W.
2016-01-01
Network modeling techniques provide a means for quantifying social structure in populations of individuals. Data used to define social connectivity are often expensive to collect and based on case-specific, ad hoc criteria. Moreover, in applications involving animal social networks, collection of these data is often opportunistic and can be invasive. Frequently, the social network of interest for a given population is closely related to the way individuals move. Thus, telemetry data, which are minimally invasive and relatively inexpensive to collect, present an alternative source of information. We develop a framework for using telemetry data to infer social relationships among animals. To achieve this, we propose a Bayesian hierarchical model with an underlying dynamic social network controlling movement of individuals via two mechanisms: an attractive effect and an aligning effect. We demonstrate the model and its ability to accurately identify complex social behavior in simulation, and apply our model to telemetry data arising from killer whales. Using auxiliary information about the study population, we investigate model validity and find the inferred dynamic social network is consistent with killer whale ecology and expert knowledge.
Dynamic motifs in socio-economic networks
Zhang, Xin; Shao, Shuai; Stanley, H. Eugene; Havlin, Shlomo
2014-12-01
Socio-economic networks are of central importance in economic life. We develop a method of identifying and studying motifs in socio-economic networks by focusing on “dynamic motifs,” i.e., evolutionary connection patterns that, because of “node acquaintances” in the network, occur much more frequently than random patterns. We examine two evolving bi-partite networks: i) the world-wide commercial ship chartering market and ii) the ship build-to-order market. We find similar dynamic motifs in both bipartite networks, even though they describe different economic activities. We also find that “influence” and “persistence” are strong factors in the interaction behavior of organizations. When two companies are doing business with the same customer, it is highly probable that another customer who currently only has business relationship with one of these two companies, will become customer of the second in the future. This is the effect of influence. Persistence means that companies with close business ties to customers tend to maintain their relationships over a long period of time.
Modeling Insurgent Network Structure and Dynamics
Gabbay, Michael; Thirkill-Mackelprang, Ashley
2010-03-01
We present a methodology for mapping insurgent network structure based on their public rhetoric. Indicators of cooperative links between insurgent groups at both the leadership and rank-and-file levels are used, such as joint policy statements or joint operations claims. In addition, a targeting policy measure is constructed on the basis of insurgent targeting claims. Network diagrams which integrate these measures of insurgent cooperation and ideology are generated for different periods of the Iraqi and Afghan insurgencies. The network diagrams exhibit meaningful changes which track the evolution of the strategic environment faced by insurgent groups. Correlations between targeting policy and network structure indicate that insurgent targeting claims are aimed at establishing a group identity among the spectrum of rank-and-file insurgency supporters. A dynamical systems model of insurgent alliance formation and factionalism is presented which evolves the relationship between insurgent group dyads as a function of their ideological differences and their current relationships. The ability of the model to qualitatively and quantitatively capture insurgent network dynamics observed in the data is discussed.
Personality traits and ego-network dynamics.
Directory of Open Access Journals (Sweden)
Simone Centellegher
Full Text Available Strong and supportive social relationships are fundamental to our well-being. However, there are costs to their maintenance, resulting in a trade-off between quality and quantity, a typical strategy being to put a lot of effort on a few high-intensity relationships while maintaining larger numbers of less close relationships. It has also been shown that there are persistent individual differences in this pattern; some individuals allocate their efforts more uniformly across their networks, while others strongly focus on their closest relationships. Furthermore, some individuals maintain more stable networks than others. Here, we focus on how personality traits of individuals affect this picture, using mobile phone calls records and survey data from the Mobile Territorial Lab (MTL study. In particular, we look at the relationship between personality traits and the (i persistence of social signatures, namely the similarity of the social signature shape of an individual measured in different time intervals; (ii the turnover in egocentric networks, that is, differences in the set of alters present at two consecutive temporal intervals; and (iii the rank dynamics defined as the variation of alter rankings in egocentric networks in consecutive intervals. We observe that some traits have effects on the stability of the social signatures as well as network turnover and rank dynamics. As an example, individuals who score highly in the Openness to Experience trait tend to have higher levels of network turnover and larger alter rank variations. On broader terms, our study shows that personality traits clearly affect the ways in which individuals maintain their personal networks.
Dynamic Trust Management for Mobile Networks and Its Applications
Bao, Fenye
2013-01-01
Trust management in mobile networks is challenging due to dynamically changing network environments and the lack of a centralized trusted authority. In this dissertation research, we "design" and "validate" a class of dynamic trust management protocols for mobile networks, and demonstrate the utility of dynamic trust management…
Activating and inhibiting connections in biological network dynamics
Directory of Open Access Journals (Sweden)
Knight Rob
2008-12-01
Full Text Available Abstract Background Many studies of biochemical networks have analyzed network topology. Such work has suggested that specific types of network wiring may increase network robustness and therefore confer a selective advantage. However, knowledge of network topology does not allow one to predict network dynamical behavior – for example, whether deleting a protein from a signaling network would maintain the network's dynamical behavior, or induce oscillations or chaos. Results Here we report that the balance between activating and inhibiting connections is important in determining whether network dynamics reach steady state or oscillate. We use a simple dynamical model of a network of interacting genes or proteins. Using the model, we study random networks, networks selected for robust dynamics, and examples of biological network topologies. The fraction of activating connections influences whether the network dynamics reach steady state or oscillate. Conclusion The activating fraction may predispose a network to oscillate or reach steady state, and neutral evolution or selection of this parameter may affect the behavior of biological networks. This principle may unify the dynamics of a wide range of cellular networks. Reviewers Reviewed by Sergei Maslov, Eugene Koonin, and Yu (Brandon Xia (nominated by Mark Gerstein. For the full reviews, please go to the Reviewers' comments section.
System crash as dynamics of complex networks.
Yu, Yi; Xiao, Gaoxi; Zhou, Jie; Wang, Yubo; Wang, Zhen; Kurths, Jürgen; Schellnhuber, Hans Joachim
2016-10-18
Complex systems, from animal herds to human nations, sometimes crash drastically. Although the growth and evolution of systems have been extensively studied, our understanding of how systems crash is still limited. It remains rather puzzling why some systems, appearing to be doomed to fail, manage to survive for a long time whereas some other systems, which seem to be too big or too strong to fail, crash rapidly. In this contribution, we propose a network-based system dynamics model, where individual actions based on the local information accessible in their respective system structures may lead to the "peculiar" dynamics of system crash mentioned above. Extensive simulations are carried out on synthetic and real-life networks, which further reveal the interesting system evolution leading to the final crash. Applications and possible extensions of the proposed model are discussed.
Method of Geometric Connected Disk Cover Problem for UAV realy network deployment
Directory of Open Access Journals (Sweden)
Chuang Liu
2017-01-01
Full Text Available Aiming at the problem of the effective connectivity of a large number of mobile combat units in the future aeronautic swarm operation, this paper proposes an idea of using UAV(Unmanned Aerial Vehicle to build, and studies the deployment of the network. User coverage and network connectivity are important for a relay network planning which are studied separately in traditional ways. In order to effectively combine these two factors while the network’s survivability is taken into account. Firstly, the concept of node aggregation degree is proposed. Secondly, a performance evaluation parameter for UAV relay network is proposed based on node aggregation degree, then analyzes the lack of deterministic deployment and presents one a PSO (VFA-PSO deployment algorithm based on virtual force. Finally, compared with the existing algorithms, the validity and stability of the algorithm are verified. The experimental results show that the VFA-PSO algorithm can effectively improve the network coverage and the survivability of the network under the premise of ensuring the network connectivity, and has better deployment effect.
An Enhanced Tire Model for Dynamic Simulation based on Geometrically Exact Shells
Directory of Open Access Journals (Sweden)
Roller Michael
2016-06-01
Full Text Available In the present work, a tire model is derived based on geometrically exact shells. The discretization is done with the help of isoparametric quadrilateral finite elements. The interpolation is performed with bilinear Lagrangian polynomials for the mid-surface as well as for the director field. As time stepping method for the resulting differential algebraic equation a backward differentiation formula is chosen. A multilayer material model for geometrically exact shells is introduced, to describe the anisotropic behavior of the tire material. To handle the interaction with a rigid road surface, a unilateral frictional contact formulation is introduced. Therein a special surface to surface contact element is developed, which rebuilds the shape of the tire.
Dynamics of the ethanolamine glycerophospholipid remodeling network.
Directory of Open Access Journals (Sweden)
Lu Zhang
Full Text Available Acyl chain remodeling in lipids is a critical biochemical process that plays a central role in disease. However, remodeling remains poorly understood, despite massive increases in lipidomic data. In this work, we determine the dynamic network of ethanolamine glycerophospholipid (PE remodeling, using data from pulse-chase experiments and a novel bioinformatic network inference approach. The model uses a set of ordinary differential equations based on the assumptions that (1 sn1 and sn2 acyl positions are independently remodeled; (2 remodeling reaction rates are constant over time; and (3 acyl donor concentrations are constant. We use a novel fast and accurate two-step algorithm to automatically infer model parameters and their values. This is the first such method applicable to dynamic phospholipid lipidomic data. Our inference procedure closely fits experimental measurements and shows strong cross-validation across six independent experiments with distinct deuterium-labeled PE precursors, demonstrating the validity of our assumptions. In contrast, fits of randomized data or fits using random model parameters are worse. A key outcome is that we are able to robustly distinguish deacylation and reacylation kinetics of individual acyl chain types at the sn1 and sn2 positions, explaining the established prevalence of saturated and unsaturated chains in the respective positions. The present study thus demonstrates that dynamic acyl chain remodeling processes can be reliably determined from dynamic lipidomic data.
Cooperation, clustering, and assortative mixing in dynamic networks.
Melamed, David; Harrell, Ashley; Simpson, Brent
2018-01-30
Humans' propensity to cooperate is driven by our embeddedness in social networks. A key mechanism through which networks promote cooperation is clustering. Within clusters, conditional cooperators are insulated from exploitation by noncooperators, allowing them to reap the benefits of cooperation. Dynamic networks, where ties can be shed and new ties formed, allow for the endogenous emergence of clusters of cooperators. Although past work suggests that either reputation processes or network dynamics can increase clustering and cooperation, existing work on network dynamics conflates reputations and dynamics. Here we report results from a large-scale experiment (total n = 2,675) that embedded participants in clustered or random networks that were static or dynamic, with varying levels of reputational information. Results show that initial network clustering predicts cooperation in static networks, but not in dynamic ones. Further, our experiment shows that while reputations are important for partner choice, cooperation levels are driven purely by dynamics. Supplemental conditions confirmed this lack of a reputation effect. Importantly, we find that when participants make individual choices to cooperate or defect with each partner, as opposed to a single decision that applies to all partners (as is standard in the literature on cooperation in networks), cooperation rates in static networks are as high as cooperation rates in dynamic networks. This finding highlights the importance of structured relations for sustained cooperation, and shows how giving experimental participants more realistic choices has important consequences for whether dynamic networks promote higher levels of cooperation than static networks.
Directory of Open Access Journals (Sweden)
Changho Yun
2016-01-01
Full Text Available The nonnegligible propagation delay of acoustic signals causes spatiotemporal uncertainty that occasionally enables simultaneous, collision-free packet transmission among underwater nodes (UNs. These transmissions can be handled by efficiently managing the channel access of the UNs in the data-link layer. To this end, Geometric Spatial Reuse-TDMA (GSR-TDMA, a new TDMA-based MAC protocol, is designed for use in centralized, multihop underwater acoustic sensor networks (UASNs, and in this case all UNs are periodically scheduled after determining a geometric map according to the information on their location. The scheduling strategy increases the number of UNs that send packets coincidentally via two subscheduling configurations (i.e., interhop and intrahop scheduling. Extensive simulations are used to investigate the reception success rate (RSR and the multihop delay (MHD of GSR-TDMA, and the results are compared to those of previous approaches, including C-MAC and HSR-TDMA. GSR-TDMA outperforms C-MAC; the RSR of GSR-TDMA is 15% higher than that of C-MAC, and the MHD of GSR-TDMA is 30% lower than that of C-MAC at the most. In addition, GSR-TDMA provides even better performance improvements over HSR-TDMA; the RSR of GSR-TDMA is 50% higher than that of HSR-TDMA, and the MHD of GSR-TDMA is an order of 102 lower than that of HSR-TDMA at the most.
International Nuclear Information System (INIS)
Joubert-Doriol, Loïc; Ryabinkin, Ilya G.; Izmaylov, Artur F.
2013-01-01
In molecular systems containing conical intersections (CIs), a nontrivial geometric phase (GP) appears in the nuclear and electronic wave functions in the adiabatic representation. We study GP effects in nuclear dynamics of an N-dimensional linear vibronic coupling (LVC) model. The main impact of GP on low-energy nuclear dynamics is reduction of population transfer between the local minima of the LVC lower energy surface. For the LVC model, we proposed an isometric coordinate transformation that confines non-adiabatic effects within a two-dimensional subsystem interacting with an N − 2 dimensional environment. Since environmental modes do not couple electronic states, all GP effects originate from nuclear dynamics within the subsystem. We explored when the GP affects nuclear dynamics of the isolated subsystem, and how the subsystem-environment interaction can interfere with GP effects. Comparing quantum dynamics with and without GP allowed us to devise simple rules to determine significance of the GP for nuclear dynamics in this model
International Nuclear Information System (INIS)
Civalek, Ö.
2014-01-01
In the present study nonlinear static and dynamic responses of shallow spherical shells resting on Winkler–Pasternak elastic foundations are carried out. The formulation of the shells is based on the Donnell theory. The nonlinear governing equations of motion of shallow shells are discretized in space and time domains using the discrete singular convolution and the differential quadrature methods, respectively. The validity of the present method is demonstrated by comparing the present results with those available in the open literature. The effects of the Winkler and Pasternak foundation parameters on nonlinear static and dynamic response of shells are investigated. Some results are also presented for circular plate as special case. Damping effect on nonlinear dynamic response of shells is studied. It is important to state that the increase in damping parameter causes decrease in the dynamic response of the shells. It is shown that the shear parameter of the foundation has a significant influence on the dynamic and static response of the shells. Also, the response of the shell is decreased with the increasing value of the shear parameter of the foundation. Parametric studies considering different geometric variables have also been investigated. -- Highlights: • Nonlinear responses of shallow spherical shells are presented. • The effects of foundation parameters are investigated. • Damping effect on nonlinear dynamic response of shells is also studied
Stochastic Geometric Coverage Analysis in mmWave Cellular Networks with a Realistic Channel Model
DEFF Research Database (Denmark)
Rebato, Mattia; Park, Jihong; Popovski, Petar
2017-01-01
Millimeter-wave (mmWave) bands have been attracting growing attention as a possible candidate for next-generation cellular networks, since the available spectrum is orders of magnitude larger than in current cellular allocations. To precisely design mmWave systems, it is important to examine mmWa...
Discrete Opinion Dynamics on Online Social Networks
Hu, Yan-Li; Bai, Liang; Zhang, Wei-Ming
2013-01-01
This paper focuses on the dynamics of binary opinions {+1, -1} on online social networks consisting of heterogeneous actors. In our model, actors update their opinions under the interplay of social influence and self- affirmation, which leads to rich dynamical behaviors on online social networks. We find that the opinion leading to the consensus features an advantage of the initially weighted fraction based on actors' strength over the other, instead of the population. For the role of specific actors, the consensus converges towards the opinion that a small fraction of high-strength actors hold, and individual diversity of self-affirmation slows down the ordering process of consensus. These indicate that high-strength actors play an essential role in opinion formation with strong social influence as well as high persistence. Further investigations show that the initial fraction of high-strength actors to dominate the evolution depends on the heterogeneity of the strength distribution, and less high-strength actors are needed in the case of a smaller exponent of power-law distribution of actors' strength. Our study provides deep insights into the role of social influence and self-affirmation on opinion formation on online social networks.
Discrete Opinion Dynamics on Online Social Networks
International Nuclear Information System (INIS)
Hu Yan-Li; Bai Liang; Zhang Wei-Ming
2013-01-01
This paper focuses on the dynamics of binary opinions {+1, −1} on online social networks consisting of heterogeneous actors. In our model, actors update their opinions under the interplay of social influence and self- affirmation, which leads to rich dynamical behaviors on online social networks. We find that the opinion leading to the consensus features an advantage of the initially weighted fraction based on actors' strength over the other, instead of the population. For the role of specific actors, the consensus converges towards the opinion that a small fraction of high-strength actors hold, and individual diversity of self-affirmation slows down the ordering process of consensus. These indicate that high-strength actors play an essential role in opinion formation with strong social influence as well as high persistence. Further investigations show that the initial fraction of high-strength actors to dominate the evolution depends on the heterogeneity of the strength distribution, and less high-strength actors are needed in the case of a smaller exponent of power-law distribution of actors' strength. Our study provides deep insights into the role of social influence and self-affirmation on opinion formation on online social networks. (general)
A `snowflake' geometrical representation for optimised degree six 3-modified chordal ring networks
Chien, S. L. E.; Farah, R. N.; Othman, M.
2017-05-01
The performance parameters and properties of chordal rings have been researched extensively as models for parallel and distributed interconnection topology models since their founding in 1981. A chordal ring is modelled after a circulant graph, where its vertices represent processor nodes and its edges represent the links between them. Hence, its performance and properties of connectivity can be studied through graph theory. This research was aimed at the investigation of a new degree six chordal ring, the optimised degree six 3-modified chordal ring CHR6o3. A tree visualisation was constructed based on its connectivity to enable the generation of formulae for optimal diameter and average optimal path lengths. As the numbers of nodes further increased with its layers, the visualisation was found to be more accurately represented in a table where all the combinations of different links for each node were listed, compared to drawing it out. Redundant nodes were also more easily found by using this representation. Furthermore, the `snowflake' geometrical representation was proposed to illustrate the connectivity of nodes in CHR6o3 as well as to aid the proving of some properties involving its Hamiltonicity. The results of this research are important in developing a routing algorithm for CHR6o3.
Opinion dynamics on an adaptive random network
Benczik, I. J.; Benczik, S. Z.; Schmittmann, B.; Zia, R. K. P.
2009-04-01
We revisit the classical model for voter dynamics in a two-party system with two basic modifications. In contrast to the original voter model studied in regular lattices, we implement the opinion formation process in a random network of agents in which interactions are no longer restricted by geographical distance. In addition, we incorporate the rapidly changing nature of the interpersonal relations in the model. At each time step, agents can update their relationships. This update is determined by their own opinion, and by their preference to make connections with individuals sharing the same opinion, or rather with opponents. In this way, the network is built in an adaptive manner, in the sense that its structure is correlated and evolves with the dynamics of the agents. The simplicity of the model allows us to examine several issues analytically. We establish criteria to determine whether consensus or polarization will be the outcome of the dynamics and on what time scales these states will be reached. In finite systems consensus is typical, while in infinite systems a disordered metastable state can emerge and persist for infinitely long time before consensus is reached.
Collective Dynamics in Physical and Social Networks
Isakov, Alexander
We study four systems where individual units come together to display a range of collective behavior. First, we consider a physical system of phase oscillators on a network that expands the Kuramoto model to include oscillator-network interactions and the presence of noise: using a Hebbian-like learning rule, oscillators that synchronize in turn strengthen their connections to each other. We find that the average degree of connectivity strongly affects rates of flipping between aligned and anti-aligned states, and that this result persists to the case of complex networks. Turning to a fully multi-player, multi-strategy evolutionary dynamics model of cooperating bacteria that change who they give resources to and take resources from, we find several regimes that give rise to high levels of collective structure in the resulting networks. In this setting, we also explore the conditions in which an intervention that affects cooperation itself (e.g. "seeding the network with defectors") can lead to wiping out an infection. We find a non-monotonic connection between the percent of disabled cooperation and cure rate, suggesting that in some regimes a limited perturbation can lead to total population collapse. At a larger scale, we study how the locomotor system recovers after amputation in fruit flies. Through experiment and a theoretical model of multi-legged motion controlled by neural oscillators, we find that proprioception plays a role in the ability of flies to control leg forces appropriately to recover from a large initial turning bias induced by the injury. Finally, at the human scale, we consider a social network in a traditional society in Africa to understand how social ties lead to group formation for collective action (stealth raids). We identify critical and distinct roles for both leadership (important for catalyzing a group) and friendship (important for final composition). We conclude with prospects for future work.
A dynamic evidential network for fall detection.
Aguilar, Paulo Armando Cavalcante; Boudy, Jerome; Istrate, Dan; Dorizzi, Bernadette; Mota, Joao Cesar Moura
2014-07-01
This study is part of the development of a remote home healthcare monitoring application designed to detect distress situations through several types of sensors. The multisensor fusion can provide more accurate and reliable information compared to information provided by each sensor separately. Furthermore, data from multiple heterogeneous sensors present in the remote home healthcare monitoring systems have different degrees of imperfection and trust. Among the multisensor fusion methods, Dempster-Shafer theory (DST) is currently considered the most appropriate for representing and processing the imperfect information. Based on a graphical representation of the DST called evidential networks, a structure of heterogeneous data fusion from multiple sensors for fall detection has been proposed. The evidential networks, implemented on our remote medical monitoring platform, are also proposed in this paper to maximize the performance of automatic fall detection and thus make the system more reliable. However, the presence of noise, the variability of recorded signals by the sensors, and the failing or unreliable sensors may thwart the evidential networks performance. In addition, the sensors signals nonstationary nature may degrade the experimental conditions. To compensate the nonstationary effect, the time evolution is considered by introducing the dynamic evidential network which was evaluated by the simulated fall scenarios corresponding to various use cases.
Creative Cognition and Brain Network Dynamics
Beaty, Roger E.; Benedek, Mathias; Silvia, Paul J.; Schacter, Daniel L.
2015-01-01
Creative thinking is central to the arts, sciences, and everyday life. How does the brain produce creative thought? A series of recently published papers has begun to provide insight into this question, reporting a strikingly similar pattern of brain activity and connectivity across a range of creative tasks and domains, from divergent thinking to poetry composition to musical improvisation. This research suggests that creative thought involves dynamic interactions of large-scale brain systems, with the most compelling finding being that the default and executive control networks, which can show an antagonistic relationship, actually cooperate during creative cognition and artistic performance. These findings have implications for understanding how brain networks interact to support complex cognitive processes, particularly those involving goal-directed, self-generated thought. PMID:26553223
Dynamics of neural networks with continuous attractors
Fung, C. C. Alan; Wong, K. Y. Michael; Wu, Si
2008-10-01
We investigate the dynamics of continuous attractor neural networks (CANNs). Due to the translational invariance of their neuronal interactions, CANNs can hold a continuous family of stationary states. We systematically explore how their neutral stability facilitates the tracking performance of a CANN, which is believed to have wide applications in brain functions. We develop a perturbative approach that utilizes the dominant movement of the network stationary states in the state space. We quantify the distortions of the bump shape during tracking, and study their effects on the tracking performance. Results are obtained on the maximum speed for a moving stimulus to be trackable, and the reaction time to catch up an abrupt change in stimulus.
Exponential Synchronization of Uncertain Complex Dynamical Networks with Delay Coupling
International Nuclear Information System (INIS)
Wang Lifu; Kong Zhi; Jing Yuanwei
2010-01-01
This paper studies the global exponential synchronization of uncertain complex delayed dynamical networks. The network model considered is general dynamical delay networks with unknown network structure and unknown coupling functions but bounded. Novel delay-dependent linear controllers are designed via the Lyapunov stability theory. Especially, it is shown that the controlled networks are globally exponentially synchronized with a given convergence rate. An example of typical dynamical network of this class, having the Lorenz system at each node, has been used to demonstrate and verify the novel design proposed. And, the numerical simulation results show the effectiveness of proposed synchronization approaches. (general)
Dynamic load modeling using neural networks
Energy Technology Data Exchange (ETDEWEB)
Ferreira, C.; Silva, A.P. Alves da; Torres, G. Lambert [Escola Federal de Engenharia de Itajuba, MG (Brazil). Inst. de Engenharia Eletrica
1996-07-01
Accurate dynamic load models allow more precise calculations of power system controls and stability limits. System identification methods can be applied to estimate load models based on measurements. Parametric and nonparametric are the two classes in system identification methods. The parametric approach has been the only one used for load modeling so far. In this paper, the performance of a nonparametric load model based on the functional polynomial artificial neural network is compared with a linear model and with the popular Zip model. The impact of clustering different load compositions is also investigated. Substation buses (138 kV) from the Brazilian system feeding important industrial consumers have been modeled. (author)
Prompting Teacher Geometric Reasoning through Coaching in a Dynamic Geometry Software Context
Knapp, Andrea K.; Barrett, Jeffrey E.; Moore, Cynthia J.
2016-01-01
This study investigated the ways in which four middle grades teachers developed mathematical knowledge for teaching (MKT) geometry as they implemented dynamic geometry software in their classrooms with the assistance of a coach. Teachers developed various components of MKT by observing coaches teach, by dynamic discourse with students, which is…
Compact analysis of 3D bipedal gait using geometric dynamics of simplified models
Stramigioli, Stefano; Duindam, V.; van Oort, Gijs; Goswami, Asok
2009-01-01
The large number of degrees of freedom in legged robots give rise to complicated dynamics equations. Analyzing these equations or using them for control can therefore be a difficult and non-intuitive task. A simplification of the complex multi-body dynamics can be achieved by instantaneously re-
Reliable dynamics in Boolean and continuous networks
International Nuclear Information System (INIS)
Ackermann, Eva; Drossel, Barbara; Peixoto, Tiago P
2012-01-01
We investigate the dynamical behavior of a model of robust gene regulatory networks which possess ‘entirely reliable’ trajectories. In a Boolean representation, these trajectories are characterized by being insensitive to the order in which the nodes are updated, i.e. they always go through the same sequence of states. The Boolean model for gene activity is compared with a continuous description in terms of differential equations for the concentrations of mRNA and proteins. We found that entirely reliable Boolean trajectories can be reproduced perfectly in the continuous model when realistic Hill coefficients are used. We investigate to what extent this high correspondence between Boolean and continuous trajectories depends on the extent of reliability of the Boolean trajectories, and we identify simple criteria that enable the faithful reproduction of the Boolean dynamics in the continuous description. (paper)
Tabassum, Hina
2014-07-01
This paper presents a novel framework to derive the statistics of the interference considering dedicated and shared spectrum access for uplink transmission in two-tier small cell networks such as the macrocell-femtocell networks. The framework exploits the distance distributions from geometric probability theory to characterize the uplink interference while considering a traditional grid-model set-up for macrocells along with the randomly deployed femtocells. The derived expressions capture the impact of path-loss, composite shadowing and fading, uniform and non-uniform traffic loads, spatial distribution of femtocells, and partial and full spectral reuse among femtocells. Considering dedicated spectrum access, first, we derive the statistics of co-tier interference incurred at both femtocell and macrocell base stations (BSs) from a single interferer by approximating generalized- K composite fading distribution with the tractable Gamma distribution. We then derive the distribution of the number of interferers considering partial spectral reuse and moment generating function (MGF) of the cumulative interference for both partial and full spectral reuse scenarios. Next, we derive the statistics of the cross-tier interference at both femtocell and macrocell BSs considering shared spectrum access. Finally, we utilize the derived expressions to analyze the capacity in both dedicated and shared spectrum access scenarios. The derived expressions are validated by the Monte Carlo simulations. Numerical results are generated to assess the feasibility of shared and dedicated spectrum access in femtocells under varying traffic load and spectral reuse scenarios. © 2014 IEEE.
Koppensteiner, Matthias; Zangerl, Christian
2017-04-01
considered, the consistency is obvious. Scanline measurements and analyses provide siginificant results for discontinuity properties under the described circumstances. Considering sampling biases, the obtained dataset is even benefiting from the randomized sampling process, due to the natural terrain. The scanline survey provides a statistical database which can be used for rock mass characterization. Geometrical rock mass characterization is essential to model the in-situ block size distribution, to estimate the degree of fracturing and rock mass anisotropy for quarry oder tunnelling projects or define the mechanical rock mass properties based on classifications systems. The study should contribute a reference for the development and application of other methods for investigating discontinuity properties in instable rock masses.
International Nuclear Information System (INIS)
Yahaqi, E.; Movafeghi, A.; Hosseini- Ashrafi, M.E.
2004-01-01
Given the number of possible combinations of different setting in radiotherapy such as the number of fields etc., arriving at an optimum treatment plan with a completely conventional solution would require an unacceptable number of interaction. Using a priori information whether of a qualitative or quantitative nature has the potential of greatly reducing amount of calculation required in any optimization procedure. Having extracted the outline of the body counter line the treatment area, the sensitive organ and any in- homogeneity present in the given cross section quantitative information in the form of moments is calculated for each treatment case. An artificial neural network classifier is then developed using group of sample treatment case and applied to arrive at initial treatment plan for any new case. The approach has been shown to have strong potential for greatly reducing the number of choices in selecting the optimum answer in treatment planning
Information diversity in structure and dynamics of simulated neuronal networks.
Mäki-Marttunen, Tuomo; Aćimović, Jugoslava; Nykter, Matti; Kesseli, Juha; Ruohonen, Keijo; Yli-Harja, Olli; Linne, Marja-Leena
2011-01-01
Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviors are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses. We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.
Stochastic Online Learning in Dynamic Networks under Unknown Models
2016-08-02
Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for...12211 Research Triangle Park, NC 27709-2211 Online learning , multi-armed bandit, dynamic networks REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S... Online Learning in Dynamic Networks under Unknown Models Report Title This research aims to develop fundamental theories and practical algorithms for
Google matrix, dynamical attractors, and Ulam networks.
Shepelyansky, D L; Zhirov, O V
2010-03-01
We study the properties of the Google matrix generated by a coarse-grained Perron-Frobenius operator of the Chirikov typical map with dissipation. The finite-size matrix approximant of this operator is constructed by the Ulam method. This method applied to the simple dynamical model generates directed Ulam networks with approximate scale-free scaling and characteristics being in certain features similar to those of the world wide web with approximate scale-free degree distributions as well as two characteristics similar to the web: a power-law decay in PageRank that mirrors the decay of PageRank on the world wide web and a sensitivity to the value alpha in PageRank. The simple dynamical attractors play here the role of popular websites with a strong concentration of PageRank. A variation in the Google parameter alpha or other parameters of the dynamical map can drive the PageRank of the Google matrix to a delocalized phase with a strange attractor where the Google search becomes inefficient.
Complex Dynamical Network Control for Trajectory Tracking Using Delayed Recurrent Neural Networks
Directory of Open Access Journals (Sweden)
Jose P. Perez
2014-01-01
Full Text Available In this paper, the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results, we present a tracking simulation of a dynamical network with each node being just one Lorenz’s dynamical system and three identical Chen’s dynamical systems.
International Nuclear Information System (INIS)
Bouchard, Bruno; Vu, Thanh Nam
2010-01-01
We provide an obstacle version of the Geometric Dynamic Programming Principle of Soner and Touzi (J. Eur. Math. Soc. 4:201-236, 2002) for stochastic target problems. This opens the doors to a wide range of applications, particularly in risk control in finance and insurance, in which a controlled stochastic process has to be maintained in a given set on a time interval [0,T]. As an example of application, we show how it can be used to provide a viscosity characterization of the super-hedging cost of American options under portfolio constraints, without appealing to the standard dual formulation from mathematical finance. In particular, we allow for a degenerate volatility, a case which does not seem to have been studied so far in this context.
Acoustic leak detection at complicated geometrical structures using fuzzy logic and neural networks
International Nuclear Information System (INIS)
Hessel, G.; Schmitt, W.; Weiss, F.P.
1993-10-01
An acoustic method based on pattern recognition is being developed. During the learning phase, the localization classifier is trained with sound patterns that are generated with simulated leaks at all locations endangered by leak. The patterns are extracted from the signals of an appropriate sensor array. After training unknown leak positions can be recognized through comparison with the training patterns. The experimental part is performed at an acoustic 1:3 model of the reactor vessel and head and at an original VVER-440 reactor in the former NPP Greifswald. The leaks were simulated at the vessel head using mobile sound sources driven either by compressed air, a piezoelectric transmitter or by a thin metal blade excited through a jet of compressed air. The sound patterns of the simulated leaks are simultaneously detected with an AE-sensor array and with high frequency microphones measuring structure-borne sound and airborne sound, respectively. Pattern classifiers based on Fuzzy Pattern Classification (FPC) and Artificial Neural Networks (ANN) are currently tested for validation of the acoustic emission-sensor array (FPC), leak localization via structure-borne sound (FPC) and the leak localization using microphones (ANN). The initial results show the used classifiers principally to be capable of detecting and locating leaks, but they also show that further investigations are necessary to develop a reliable method applicable at NPPs. (orig./HP)
International Nuclear Information System (INIS)
Na, Jonggeol; Jung, Ikhwan; Kshetrimayum, Krishnadash S.; Park, Seongho; Park, Chansaem; Han, Chonghun
2014-01-01
Driven by both environmental and economic reasons, the development of small to medium scale GTL(gas-to-liquid) process for offshore applications and for utilizing other stranded or associated gas has recently been studied increasingly. Microchannel GTL reactors have been preferred over the conventional GTL reactors for such applications, due to its compactness, and additional advantages of small heat and mass transfer distance desired for high heat transfer performance and reactor conversion. In this work, multi-microchannel reactor was simulated by using commercial CFD code, ANSYS FLUENT, to study the geometric effect of the microchannels on the heat transfer phenomena. A heat generation curve was first calculated by modeling a Fischer-Tropsch reaction in a single-microchannel reactor model using Matlab-ASPEN integration platform. The calculated heat generation curve was implemented to the CFD model. Four design variables based on the microchannel geometry namely coolant channel width, coolant channel height, coolant channel to process channel distance, and coolant channel to coolant channel distance, were selected for calculating three dependent variables namely, heat flux, maximum temperature of coolant channel, and maximum temperature of process channel. The simulation results were visualized to understand the effects of the design variables on the dependent variables. Heat flux and maximum temperature of cooling channel and process channel were found to be increasing when coolant channel width and height were decreased. Coolant channel to process channel distance was found to have no effect on the heat transfer phenomena. Finally, total heat flux was found to be increasing and maximum coolant channel temperature to be decreasing when coolant channel to coolant channel distance was decreased. Using the qualitative trend revealed from the present study, an appropriate process channel and coolant channel geometry along with the distance between the adjacent
Interestingness-Driven Diffusion Process Summarization in Dynamic Networks
DEFF Research Database (Denmark)
Qu, Qiang; Liu, Siyuan; Jensen, Christian Søndergaard
2014-01-01
tool in this regard is data summarization. However, few existing studies aim to summarize graphs/networks for dynamics. Dynamic networks raise new challenges not found in static settings, including time sensitivity and the needs for online interestingness evaluation and summary traceability, which...... render existing techniques inapplicable. We study the topic of dynamic network summarization: how to summarize dynamic networks with millions of nodes by only capturing the few most interesting nodes or edges over time, and we address the problem by finding interestingness-driven diffusion processes......The widespread use of social networks enables the rapid diffusion of information, e.g., news, among users in very large communities. It is a substantial challenge to be able to observe and understand such diffusion processes, which may be modeled as networks that are both large and dynamic. A key...
Spatial Dynamics of Multilayer Cellular Neural Networks
Wu, Shi-Liang; Hsu, Cheng-Hsiung
2018-02-01
The purpose of this work is to study the spatial dynamics of one-dimensional multilayer cellular neural networks. We first establish the existence of rightward and leftward spreading speeds of the model. Then we show that the spreading speeds coincide with the minimum wave speeds of the traveling wave fronts in the right and left directions. Moreover, we obtain the asymptotic behavior of the traveling wave fronts when the wave speeds are positive and greater than the spreading speeds. According to the asymptotic behavior and using various kinds of comparison theorems, some front-like entire solutions are constructed by combining the rightward and leftward traveling wave fronts with different speeds and a spatially homogeneous solution of the model. Finally, various qualitative features of such entire solutions are investigated.
Magnetoencephalography from signals to dynamic cortical networks
Aine, Cheryl
2014-01-01
"Magnetoencephalography (MEG) provides a time-accurate view into human brain function. The concerted action of neurons generates minute magnetic fields that can be detected---totally noninvasively---by sensitive multichannel magnetometers. The obtained millisecond accuracycomplements information obtained by other modern brain-imaging tools. Accurate timing is quintessential in normal brain function, often distorted in brain disorders. The noninvasiveness and time-sensitivityof MEG are great assets to developmental studies, as well. This multiauthored book covers an ambitiously wide range of MEG research from introductory to advanced level, from sensors to signals, and from focal sources to the dynamics of cortical networks. Written by active practioners of this multidisciplinary field, the book contains tutorials for newcomers and chapters of new challenging methods and emerging technologies to advanced MEG users. The reader will obtain a firm grasp of the possibilities of MEG in the study of audition, vision...
Attractor dynamics in local neuronal networks
Directory of Open Access Journals (Sweden)
Jean-Philippe eThivierge
2014-03-01
Full Text Available Patterns of synaptic connectivity in various regions of the brain are characterized by the presence of synaptic motifs, defined as unidirectional and bidirectional synaptic contacts that follow a particular configuration and link together small groups of neurons. Recent computational work proposes that a relay network (two populations communicating via a third, relay population of neurons can generate precise patterns of neural synchronization. Here, we employ two distinct models of neuronal dynamics and show that simulated neural circuits designed in this way are caught in a global attractor of activity that prevents neurons from modulating their response on the basis of incoming stimuli. To circumvent the emergence of a fixed global attractor, we propose a mechanism of selective gain inhibition that promotes flexible responses to external stimuli. We suggest that local neuronal circuits may employ this mechanism to generate precise patterns of neural synchronization whose transient nature delimits the occurrence of a brief stimulus.
Integrated 6-DOF Orbit-Attitude Dynamical Modeling and Control Using Geometric Mechanics
Directory of Open Access Journals (Sweden)
Ling Jiang
2017-01-01
Full Text Available The integrated 6-DOF orbit-attitude dynamical modeling and control have shown great importance in various missions, for example, formation flying and proximity operations. The integrated approach yields better performances than the separate one in terms of accuracy, efficiency, and agility. One challenge in the integrated approach is to find a unified representation for the 6-DOF motion with configuration space SE(3. Recently, exponential coordinates of SE(3 have been used in dynamics and control of the 6-DOF motion, however, only on the kinematical level. In this paper, we will improve the current method by adopting exponential coordinates on the dynamical level, by giving the relation between the second-order derivative of exponential coordinates and spacecraft’s accelerations. In this way, the 6-DOF motion in terms of exponential coordinates can be written as a second-order system with a quite compact form, to which a broader range of control theories, such as higher-order sliding modes, can be applied. For a demonstration purpose, a simple asymptotic tracking control law with almost global convergence is designed. Finally, the integrated modeling and control are applied to the body-fixed hovering over an asteroid and verified by a simulation, in which absolute motions of the spacecraft and asteroid are simulated separately.
Dynamics of subway networks based on vehicles operation timetable
Xiao, Xue-mei; Jia, Li-min; Wang, Yan-hui
2017-05-01
In this paper, a subway network is represented as a dynamic, directed and weighted graph, in which vertices represent subway stations and weights of edges represent the number of vehicles passing through the edges by considering vehicles operation timetable. Meanwhile the definitions of static and dynamic metrics which can represent vertices' and edges' local and global attributes are proposed. Based on the model and metrics, standard deviation is further introduced to study the dynamic properties (heterogeneity and vulnerability) of subway networks. Through a detailed analysis of the Beijing subway network, we conclude that with the existing network structure, the heterogeneity and vulnerability of the Beijing subway network varies over time when the vehicle operation timetable is taken into consideration, and the distribution of edge weights affects the performance of the network. In other words, although the vehicles operation timetable is restrained by the physical structure of the network, it determines the performances and properties of the Beijing subway network.
The stochastic network dynamics underlying perceptual discrimination
Directory of Open Access Journals (Sweden)
Genis Prat-Ortega
2015-04-01
Full Text Available The brain is able to interpret streams of high-dimensional ambiguous information and yield coherent percepts. The mechanisms governing sensory integration have been extensively characterized using time-varying visual stimuli (Britten et al. 1996; Roitman and Shadlen 2002, but some of the basic principles regarding the network dynamics underlying this process remain largely unknown. We captured the basic features of a neural integrator using three canonical one-dimensional models: (1 the Drift Diffusion Model (DDM, (2 the Perfect Integrator (PI which is a particular case of the DDM where the bounds are set to infinity and (3 the double-well potential (DW which captures the dynamics of the attractor networks (Wang 2002; Roxin and Ledberg 2008. Although these models has been widely studied (Bogacz et al. 2006; Roxin and Ledberg 2008; Gold and Shadlen 2002, it has been difficult to experimentally discriminate among them because most of the observables measured are only quantitatively different among these models (e.g. psychometric curves. Here we aim to find experimentally measurable quantities that can yield qualitatively different behaviors depending on the nature of the underlying network dynamics. We examined the categorization dynamics of these models in response to fluctuating stimuli of different duration (T. On each time step, stimuli are drawn from a Gaussian distribution N(μ, σ and the two stimulus categories are defined by μ > 0 and μ < 0. Psychometric curves can therefore be obtained by quantifying the probability of the integrator to yield one category versus μ . We find however that varying σ can reveal more clearly the differences among the different integrators. In the small σ regime, both the DW and the DDM perform transient integration and exhibit a decaying stimulus reverse correlation kernel revealing a primacy effect (Nienborg and Cumming 2009; Wimmer et al. 2015 . In the large σ regime, the integration in the DDM
Filtering in Hybrid Dynamic Bayesian Networks
Andersen, Morten Nonboe; Andersen, Rasmus Orum; Wheeler, Kevin
2000-01-01
We implement a 2-time slice dynamic Bayesian network (2T-DBN) framework and make a 1-D state estimation simulation, an extension of the experiment in (v.d. Merwe et al., 2000) and compare different filtering techniques. Furthermore, we demonstrate experimentally that inference in a complex hybrid DBN is possible by simulating fault detection in a watertank system, an extension of the experiment in (Koller & Lerner, 2000) using a hybrid 2T-DBN. In both experiments, we perform approximate inference using standard filtering techniques, Monte Carlo methods and combinations of these. In the watertank simulation, we also demonstrate the use of 'non-strict' Rao-Blackwellisation. We show that the unscented Kalman filter (UKF) and UKF in a particle filtering framework outperform the generic particle filter, the extended Kalman filter (EKF) and EKF in a particle filtering framework with respect to accuracy in terms of estimation RMSE and sensitivity with respect to choice of network structure. Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. Furthermore, we investigate the influence of data noise in the watertank simulation using UKF and PFUKD and show that the algorithms are more sensitive to changes in the measurement noise level that the process noise level. Theory and implementation is based on (v.d. Merwe et al., 2000).
Dynamics of seed magnetic island formation due to geometrically coupled perturbations
International Nuclear Information System (INIS)
Hegna, C.C.; Callen, J.D.; LaHaye, R.J.
1998-06-01
Seed magnetic island formation due to a dynamically growing external source in toroidal confinement devices is modeled as an initial value forced reconnection problem. For an external source whose amplitude grows on a time scale quickly compared to the Sweet-Parker time of resistive magnetohydrodynamics, the induced reconnection is characterized by a current sheet and a reconnected flux amplitude which lags in time the source amplitude. This suggests that neoclassical tearing modes, whose excitation requires a seed magnetic island, are more difficult to cause in high Lundquist number plasmas
Sparse dynamical Boltzmann machine for reconstructing complex networks with binary dynamics
Chen, Yu-Zhong; Lai, Ying-Cheng
2018-03-01
Revealing the structure and dynamics of complex networked systems from observed data is a problem of current interest. Is it possible to develop a completely data-driven framework to decipher the network structure and different types of dynamical processes on complex networks? We develop a model named sparse dynamical Boltzmann machine (SDBM) as a structural estimator for complex networks that host binary dynamical processes. The SDBM attains its topology according to that of the original system and is capable of simulating the original binary dynamical process. We develop a fully automated method based on compressive sensing and a clustering algorithm to construct the SDBM. We demonstrate, for a variety of representative dynamical processes on model and real world complex networks, that the equivalent SDBM can recover the network structure of the original system and simulates its dynamical behavior with high precision.
Geometric nonlinear dynamic analysis of curved beams using curved beam element
Pan, Ke-Qi; Liu, Jin-Yang
2011-12-01
Instead of using the previous straight beam element to approximate the curved beam, in this paper, a curvilinear coordinate is employed to describe the deformations, and a new curved beam element is proposed to model the curved beam. Based on exact nonlinear strain-displacement relation, virtual work principle is used to derive dynamic equations for a rotating curved beam, with the effects of axial extensibility, shear deformation and rotary inertia taken into account. The constant matrices are solved numerically utilizing the Gauss quadrature integration method. Newmark and Newton-Raphson iteration methods are adopted to solve the differential equations of the rigid-flexible coupling system. The present results are compared with those obtained by commercial programs to validate the present finite method. In order to further illustrate the convergence and efficiency characteristics of the present modeling and computation formulation, comparison of the results of the present formulation with those of the ADAMS software are made. Furthermore, the present results obtained from linear formulation are compared with those from nonlinear formulation, and the special dynamic characteristics of the curved beam are concluded by comparison with those of the straight beam.
Rumor diffusion in an interests-based dynamic social network.
Tang, Mingsheng; Mao, Xinjun; Guessoum, Zahia; Zhou, Huiping
2013-01-01
To research rumor diffusion in social friend network, based on interests, a dynamic friend network is proposed, which has the characteristics of clustering and community, and a diffusion model is also proposed. With this friend network and rumor diffusion model, based on the zombie-city model, some simulation experiments to analyze the characteristics of rumor diffusion in social friend networks have been conducted. The results show some interesting observations: (1) positive information may evolve to become a rumor through the diffusion process that people may modify the information by word of mouth; (2) with the same average degree, a random social network has a smaller clustering coefficient and is more beneficial for rumor diffusion than the dynamic friend network; (3) a rumor is spread more widely in a social network with a smaller global clustering coefficient than in a social network with a larger global clustering coefficient; and (4) a network with a smaller clustering coefficient has a larger efficiency.
Modelling flow dynamics in water distribution networks using ...
African Journals Online (AJOL)
One such approach is the Artificial Neural Networks (ANNs) technique. The advantage of ANNs is that they are robust and can be used to model complex linear and non-linear systems without making implicit assumptions. ANNs can be trained to forecast flow dynamics in a water distribution network. Such flow dynamics ...
Long term behavior of dynamic equilibria in fluid queuing networks
R. Cominetti (Roberto); J. Correa (José); N.K. Olver (Neil)
2017-01-01
textabstractA fluid queuing network constitutes one of the simplest models in which to study flow dynamics over a network. In this model we have a single source-sink pair and each link has a per-time-unit capacity and a transit time. A dynamic equilibrium (or equilibrium flow over time) is a flow
Directory of Open Access Journals (Sweden)
Anatolij K. Prykarpatski
2017-12-01
Full Text Available The classical Lagrange-d’Alembert principle had a decisive influence on formation of modern analytical mechanics which culminated in modern Hamilton and Poisson mechanics. Being mainly interested in the geometric interpretation of this principle, we devoted our review to its deep relationships to modern Lie-algebraic aspects of the integrability theory of nonlinear heavenly type dynamical systems and its so called Lax-Sato counterpart. We have also analyzed old and recent investigations of the classical M. A. Buhl problem of describing compatible linear vector field equations, its general M.G. Pfeiffer and modern Lax-Sato type special solutions. Especially we analyzed the related Lie-algebraic structures and integrability properties of a very interesting class of nonlinear dynamical systems called the dispersionless heavenly type equations, which were initiated by Plebański and later analyzed in a series of articles. As effective tools the AKS-algebraic and related R -structure schemes are used to study the orbits of the corresponding co-adjoint actions, which are intimately related to the classical Lie-Poisson structures on them. It is demonstrated that their compatibility condition coincides with the corresponding heavenly type equations under consideration. It is also shown that all these equations originate in this way and can be represented as a Lax-Sato compatibility condition for specially constructed loop vector fields on the torus. Typical examples of such heavenly type equations, demonstrating in detail their integrability via the scheme devised herein, are presented.
Greathouse, James S.; Schwing, Alan M.
2015-01-01
This paper explores use of computational fluid dynamics to study the e?ect of geometric porosity on static stability and drag for NASA's Multi-Purpose Crew Vehicle main parachute. Both of these aerodynamic characteristics are of interest to in parachute design, and computational methods promise designers the ability to perform detailed parametric studies and other design iterations with a level of control previously unobtainable using ground or flight testing. The approach presented here uses a canopy structural analysis code to define the inflated parachute shapes on which structured computational grids are generated. These grids are used by the computational fluid dynamics code OVERFLOW and are modeled as rigid, impermeable bodies for this analysis. Comparisons to Apollo drop test data is shown as preliminary validation of the technique. Results include several parametric sweeps through design variables in order to better understand the trade between static stability and drag. Finally, designs that maximize static stability with a minimal loss in drag are suggested for further study in subscale ground and flight testing.
Dynamics and universal scaling law in geometrically-controlled sessile drop evaporation
Sáenz, P. J.; Wray, A. W.; Che, Z.; Matar, O. K.; Valluri, P.; Kim, J.; Sefiane, K.
2017-01-01
The evaporation of a liquid drop on a solid substrate is a remarkably common phenomenon. Yet, the complexity of the underlying mechanisms has constrained previous studies to spherically symmetric configurations. Here we investigate well-defined, non-spherical evaporating drops of pure liquids and binary mixtures. We deduce a universal scaling law for the evaporation rate valid for any shape and demonstrate that more curved regions lead to preferential localized depositions in particle-laden drops. Furthermore, geometry induces well-defined flow structures within the drop that change according to the driving mechanism. In the case of binary mixtures, geometry dictates the spatial segregation of the more volatile component as it is depleted. Our results suggest that the drop geometry can be exploited to prescribe the particle deposition and evaporative dynamics of pure drops and the mixing characteristics of multicomponent drops, which may be of interest to a wide range of industrial and scientific applications. PMID:28294114
Dynamic Interbank Network Analysis Using Latent Space Models
Linardi, F.; Diks, C.; van der Leij, M.; Lazier, I.
2017-01-01
Longitudinal network data are increasingly available, allowing researchers to model how networks evolve over time and to make inference on their dependence structure. In this paper, a dynamic latent space approach is used to model directed networks of monthly interbank exposures. In this model, each
Optical-router-based dynamically reconfigurable photonic access network
Roy, R.
2014-01-01
The Broadband photonics (BBP) project under the Freeband consortium of projects investigated the design of a dynamically reconfigurable photonic access network. Access networks form a key link in ensuring optimal bandwidth to the end user without which any improvements deeper in the network in the
Dynamic Mobile IP routers in ad hoc networks
Kock, B.A.; Schmidt, J.R.
2005-01-01
This paper describes a concept combining mobile IP and ad hoc routing to create a robust mobile network. In this network all nodes are mobile and globally and locally reachable under the same IP address. Essential for implementing this network are the dynamic mobile IP routers. They act as gateways
Gender, Friendship Networks, and Delinquency: A Dynamic Network Approach**
Haynie, Dana L.; Doogan, Nathan J.; Soller, Brian
2014-01-01
Researchers have examined selection and influence processes in shaping delinquency similarity among friends, but little is known about the role of gender in moderating these relationships. Our objective is to examine differences between adolescent boys and girls regarding delinquency-based selection and influence processes. Using longitudinal network data from adolescents attending two large schools in AddHealth (N = 1,857) and stochastic actor-oriented models, we evaluate whether girls are influenced to a greater degree by friends' violence or delinquency than boys (influence hypothesis) and whether girls are more likely to select friends based on violent or delinquent behavior than boys (selection hypothesis). The results indicate that girls are more likely than boys to be influenced by their friends' involvement in violence. Although a similar pattern emerges for nonviolent delinquency, the gender differences are not significant. Some evidence shows that boys are influenced toward increasing their violence or delinquency when exposed to more delinquent or violent friends but are immune to reducing their violence or delinquency when associating with less violent or delinquent friends. In terms of selection dynamics, although both boys and girls have a tendency to select friends based on friends' behavior, girls have a stronger tendency to do so, suggesting that among girls, friends' involvement in violence or delinquency is an especially decisive factor for determining friendship ties. PMID:26097241
Topology Identification of General Dynamical Network with Distributed Time Delays
International Nuclear Information System (INIS)
Zhao-Yan, Wu; Xin-Chu, Fu
2009-01-01
General dynamical networks with distributed time delays are studied. The topology of the networks are viewed as unknown parameters, which need to be identified. Some auxiliary systems (also called the network estimators) are designed to achieve this goal. Both linear feedback control and adaptive strategy are applied in designing these network estimators. Based on linear matrix inequalities and the Lyapunov function method, the sufficient condition for the achievement of topology identification is obtained. This method can also better monitor the switching topology of dynamical networks. Illustrative examples are provided to show the effectiveness of this method. (general)
THE EFFECT OF TOPOLOGY ON TEMPORAL NETWORK DYNAMICS
Directory of Open Access Journals (Sweden)
Valentina Yu. Guleva
2016-11-01
Full Text Available The effect of initial network topology on a temporal network dynamics is studied. An example of interbank exposures network is considered. It is modeled with a graph, where banks are represented by nodes and interbank lending is represented by edges. The dynamical processes in аtemporal network are defined by state changes of nodes and lie in edges and nodes addition and deletion in a graph, and modification of node states contribute to network evolution. The algorithm of network modification over the whole evolution period is fixed. We present parameters of random, scale free and small world generative models corresponding to different simulation results with fixed modification algorithms. The influence of initial graph topologies on temporal network dynamics is demonstrated. The results obtained give the possibility to assess time interval before the attainment of unstable topology state, and to estimate an optimal topology for the transition to a steady state under fixed modification algorithms.
Adaptive Dynamics, Control, and Extinction in Networked Populations
2015-07-09
extinction . VI. CONCLUSIONS We have presented a method for predicting extinction in stochastic network systems by analyzing a pair-based proxy model...including games on networks (e.g., [40], [41]). Further, we expect that our method of continuously varying a parameter while tracking the path to extinction ...Adaptive Dynamics, Control, and Extinction in Networked Populations Ira B. Schwartz US Naval Research Laboratory Code 6792 Nonlinear System Dynamics
Arresting Strategy Based on Dynamic Criminal Networks Changing over Time
Directory of Open Access Journals (Sweden)
Junqing Yuan
2013-01-01
Full Text Available We investigate a sequence of dynamic criminal networks on a time series based on the dynamic network analysis (DNA. According to the change of networks’ structure, networks’ variation trend is analyzed to forecast its future structure. Finally, an optimal arresting time and priority list are designed based on our analysis. Better results can be expected than that based on social network analysis (SNA.
Soda, K J; Slice, D E; Naylor, G J P
2017-01-01
Over the past few decades, geometric morphometric methods have become increasingly popular and powerful tools to describe morphological data while over the same period artificial neural networks have had a similar rise in the classification of specimens to preconceived groups. However, there has been little research into how well these two systems operate together, particularly in comparison to preexisting techniques. In this study, geometric morphometric data and multilayer perceptrons, a style of artificial neural network, were used to classify shark teeth from the genus Carcharhinus to species. Three datasets of varying size and species differences were used. We compared the performance of this combination with geometric morphometric data in a linear discriminate function analysis, linear measurements in a linear discriminate function analysis, and a preexisting methodology from the literature that incorporates linear measurements and a two-layered discriminate function analysis. Across datasets, geometric morphometric data in a multilayer perceptron tended to yield modest accuracies but accuracies that varied less across species whereas other methods were able to achieve higher accuracies in some species at the expense of lower accuracies in others. Further, the performance of the two-layered discriminate function analysis illustrates that constraining what material is classified can increase the accuracy of a method. Based on this tradeoff, the best methodology will then depend on the scope of the study and the amount of material available. J. Morphol. 278:131-141, 2017. ©© 2016 Wiley Periodicals,Inc. © 2016 Wiley Periodicals, Inc.
Major component analysis of dynamic networks of physiologic organ interactions
International Nuclear Information System (INIS)
Liu, Kang K L; Ma, Qianli D Y; Ivanov, Plamen Ch; Bartsch, Ronny P
2015-01-01
The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function. (paper)
Major component analysis of dynamic networks of physiologic organ interactions
Liu, Kang K. L.; Bartsch, Ronny P.; Ma, Qianli D. Y.; Ivanov, Plamen Ch
2015-09-01
The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function.
Identify Dynamic Network Modules with Temporal and Spatial Constraints
Energy Technology Data Exchange (ETDEWEB)
Jin, R; McCallen, S; Liu, C; Almaas, E; Zhou, X J
2007-09-24
Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of cellular activity remains a difficult task. The reason is that most biological data are static, or only correspond to snapshots of cellular activity. In this study, we explicitly attempt to detangle the temporal complexity of biological networks by using compilations of time-series gene expression profiling data.We define a dynamic network module to be a set of proteins satisfying two conditions: (1) they form a connected component in the protein-protein interaction (PPI) network; and (2) their expression profiles form certain structures in the temporal domain. We develop the first efficient mining algorithm to discover dynamic modules in a temporal network, as well as frequently occurring dynamic modules across many temporal networks. Using yeast as a model system, we demonstrate that the majority of the identified dynamic modules are functionally homogeneous. Additionally, many of them provide insight into the sequential ordering of molecular events in cellular systems. We further demonstrate that identifying frequent dynamic network modules can significantly increase the signal to noise separation, despite the fact that most dynamic network modules are highly condition-specific. Finally, we note that the applicability of our algorithm is not limited to the study of PPI systems, instead it is generally applicable to the combination of any type of network and time-series data.
Nishida, Masahiro; Yamane, Takashi
A monopivot magnetic suspension blood pump has been developed in our laboratory. The flow patterns within the pump should be carefully examined in order to prevent thrombogenesis, especially around the pivot bearing. Therefore, the effects of the pump geometry on the local flow were analyzed using computational fluid dynamics together with the experimental flow visualization. The engineering goal was to reduce the area of stagnation around the pivot in order to prevent thrombus formation. As a result, the stagnation area and the flow rate through the washout holes were found to be highly affected by the size and geometry of the washout holes. Secondary flow was revealed to form a jet-like wash against the pivot, thus preventing thrombus formation. The flow rate through the washout holes was estimated to be up to one fifth of the pump flow rate, depending on the cross-sectional areas of the washout holes. Furthermore, an anti-thrombogenic effect was attained by removing a small gap between the male and female pivots.
Adaptive Dynamics of Regulatory Networks: Size Matters
Directory of Open Access Journals (Sweden)
Martinetz Thomas
2009-01-01
Full Text Available To accomplish adaptability, all living organisms are constructed of regulatory networks on different levels which are capable to differentially respond to a variety of environmental inputs. Structure of regulatory networks determines their phenotypical plasticity, that is, the degree of detail and appropriateness of regulatory replies to environmental or developmental challenges. This regulatory network structure is encoded within the genotype. Our conceptual simulation study investigates how network structure constrains the evolution of networks and their adaptive abilities. The focus is on the structural parameter network size. We show that small regulatory networks adapt fast, but not as good as larger networks in the longer perspective. Selection leads to an optimal network size dependent on heterogeneity of the environment and time pressure of adaptation. Optimal mutation rates are higher for smaller networks. We put special emphasis on discussing our simulation results on the background of functional observations from experimental and evolutionary biology.
Mathematical model for spreading dynamics of social network worms
International Nuclear Information System (INIS)
Sun, Xin; Liu, Yan-Heng; Han, Jia-Wei; Liu, Xue-Jie; Li, Bin; Li, Jin
2012-01-01
In this paper, a mathematical model for social network worm spreading is presented from the viewpoint of social engineering. This model consists of two submodels. Firstly, a human behavior model based on game theory is suggested for modeling and predicting the expected behaviors of a network user encountering malicious messages. The game situation models the actions of a user under the condition that the system may be infected at the time of opening a malicious message. Secondly, a social network accessing model is proposed to characterize the dynamics of network users, by which the number of online susceptible users can be determined at each time step. Several simulation experiments are carried out on artificial social networks. The results show that (1) the proposed mathematical model can well describe the spreading dynamics of social network worms; (2) weighted network topology greatly affects the spread of worms; (3) worms spread even faster on hybrid social networks
Bistable responses in bacterial genetic networks: Designs and dynamical consequences
Tiwari, Abhinav; Ray, J. Christian J.; Narula, Jatin; Igoshin, Oleg A.
2011-01-01
A key property of living cells is their ability to react to stimuli with specific biochemical responses. These responses can be understood through the dynamics of underlying biochemical and genetic networks. Evolutionary design principles have been well studied in networks that display graded responses, with a continuous relationship between input signal and system output. Alternatively, biochemical networks can exhibit bistable responses so that over a range of signals the network possesses two stable steady states. In this review, we discuss several conceptual examples illustrating network designs that can result in a bistable response of the biochemical network. Next, we examine manifestations of these designs in bacterial master-regulatory genetic circuits. In particular, we discuss mechanisms and dynamic consequences of bistability in three circuits: two-component systems, sigma-factor networks, and a multistep phosphorelay. Analyzing these examples allows us to expand our knowledge of evolutionary design principles for networks with bistable responses. PMID:21385588
International Nuclear Information System (INIS)
Zhang Li-Sheng; Mi Yuan-Yuan; Gu Wei-Feng; Hu Gang
2014-01-01
All dynamic complex networks have two important aspects, pattern dynamics and network topology. Discovering different types of pattern dynamics and exploring how these dynamics depend on network topologies are tasks of both great theoretical importance and broad practical significance. In this paper we study the oscillatory behaviors of excitable complex networks (ECNs) and find some interesting dynamic behaviors of ECNs in oscillatory probability, the multiplicity of oscillatory attractors, period distribution, and different types of oscillatory patterns (e.g., periodic, quasiperiodic, and chaotic). In these aspects, we further explore strikingly sharp differences among network dynamics induced by different topologies (random or scale-free topologies) and different interaction structures (symmetric or asymmetric couplings). The mechanisms behind these differences are explained physically. (interdisciplinary physics and related areas of science and technology)
Opinion competition dynamics on multiplex networks
Amato, R.; Kouvaris, N. E.; San Miguel, M.; Díaz-Guilera, A.
2017-12-01
Multilayer and multiplex networks represent a good proxy for the description of social phenomena where social structure is important and can have different origins. Here, we propose a model of opinion competition where individuals are organized according to two different structures in two layers. Agents exchange opinions according to the Abrams–Strogatz model in each layer separately and opinions can be copied across layers by the same individual. In each layer a different opinion is dominant, so each layer has a different absorbing state. Consensus in one opinion is not the only possible stable solution because of the interaction between the two layers. A new mean field solution has been found where both opinions coexist. In a finite system there is a long transient time for the dynamical coexistence of both opinions. However, the system ends in a consensus state due to finite size effects. We analyze sparse topologies in the two layers and the existence of positive correlations between them, which enables the coexistence of inter-layer groups of agents sharing the same opinion.
Opinion dynamics in activity-driven networks
Li, Dandan; Han, Dun; Ma, Jing; Sun, Mei; Tian, Lixin; Khouw, Timothy; Stanley, H. Eugene
2017-10-01
Social interaction between individuals constantly affects the development of their personal opinions. Previous models such as the Deffuant model and the Hegselmann-Krause (HK) model have assumed that individuals only update their opinions after interacting with neighbors whose opinions are similar to their own. However, people are capable of communicating widely with all of their neighbors to gather their ideas and opinions, even if they encounter a number of opposing attitudes. We propose a model in which agents listen to the opinions of all their neighbors. Continuous opinion dynamics are investigated in activity-driven networks with a tolerance threshold. We study how the initial opinion distribution, tolerance threshold, opinion-updating speed, and activity rate affect the evolution of opinion. We find that when the initial fraction of positive opinion is small, all opinions become negative by the end of the simulation. As the initial fraction of positive opinions rises above a certain value —about 0.45— the final fraction of positive opinions sharply increases and eventually equals 1. Increased tolerance threshold δ is found to lead to a more varied final opinion distribution. We also find that if the negative opinion has an initial advantage, the final fraction of negative opinion increases and reaches its peak as the updating speed λ approaches 0.5. Finally we show that the lower the activity rate of individuals, the greater the fluctuation range of their opinions.
Robustness and dynamics of networks of coupled modules
Bagrow, James; Ahn, Yong-Yeol; Lehmann, Sune
2011-03-01
Many systems, from power grids and the internet, to the brain and society, can be modeled using networks of coupled overlapping modules. The elements of these networks perform individual and collective tasks such as generating and consuming electrical load or transmitting data. We study the robustness of these systems using percolation theory: a random fraction of the elements fail which may cause the network to lose global connectivity. We show that the modules themselves can become isolated or uncoupled (non-overlapping) well before the network falls apart. This has important structural and dynamical consequences for these networks and may explain how missing information hides pervasive overlap between communities in real networks.
Synthesis of recurrent neural networks for dynamical system simulation.
Trischler, Adam P; D'Eleuterio, Gabriele M T
2016-08-01
We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.
On investigating social dynamics in tactical opportunistic mobile networks
Gao, Wei; Li, Yong
2014-06-01
The efficiency of military mobile network operations at the tactical edge is challenging due to the practical Disconnected, Intermittent, and Limited (DIL) environments at the tactical edge which make it hard to maintain persistent end-to-end wireless network connectivity. Opportunistic mobile networks are hence devised to depict such tactical networking scenarios. Social relations among warfighters in tactical opportunistic mobile networks are implicitly represented by their opportunistic contacts via short-range radios, but were inappropriately considered as stationary over time by the conventional wisdom. In this paper, we develop analytical models to probabilistically investigate the temporal dynamics of this social relationship, which is critical to efficient mobile communication in the battlespace. We propose to formulate such dynamics by developing various sociological metrics, including centrality and community, with respect to the opportunistic mobile network contexts. These metrics investigate social dynamics based on the experimentally validated skewness of users' transient contact distributions over time.
Complex systems and networks dynamics, controls and applications
Yu, Xinghuo; Chen, Guanrong; Yu, Wenwu
2016-01-01
This elementary book provides some state-of-the-art research results on broad disciplinary sciences on complex networks. It presents an in-depth study with detailed description of dynamics, controls and applications of complex networks. The contents of this book can be summarized as follows. First, the dynamics of complex networks, for example, the cluster dynamic analysis by using kernel spectral methods, community detection algorithms in bipartite networks, epidemiological modeling with demographics and epidemic spreading on multi-layer networks, are studied. Second, the controls of complex networks are investigated including topics like distributed finite-time cooperative control of multi-agent systems by applying homogenous-degree and Lyapunov methods, composite finite-time containment control for disturbed second-order multi-agent systems, fractional-order observer design of multi-agent systems, chaos control and anticontrol of complex systems via Parrondos game and many more. Third, the applications of ...
Structure-based control of complex networks with nonlinear dynamics
Zanudo, Jorge G. T.; Yang, Gang; Albert, Reka
What can we learn about controlling a system solely from its underlying network structure? Here we use a framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system towards any of its natural long term dynamic behaviors, regardless of the dynamic details and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of classical structural control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case, but not in specific model instances. This work was supported by NSF Grants PHY 1205840 and IIS 1160995. JGTZ is a recipient of a Stand Up To Cancer - The V Foundation Convergence Scholar Award.
Modular networks with hierarchical organization: The dynamical ...
Indian Academy of Sciences (India)
constraint optimization as shown by us previously. Keywords. Modular network; hierarchical organization; stability; robustness. PACS Nos 89.75.Hc; 05.45.-a; 89.75.Fb. 1. Introduction. Structural patterns in complex networks occurring in biological, ...
Catching homologies by geometric entropy
Felice, Domenico; Franzosi, Roberto; Mancini, Stefano; Pettini, Marco
2018-02-01
A geometric entropy is defined in terms of the Riemannian volume of the parameter space of a statistical manifold associated with a given network. As such it can be a good candidate for measuring networks complexity. Here we investigate its ability to single out topological features of networks proceeding in a bottom-up manner: first we consider small size networks by analytical methods and then large size networks by numerical techniques. Two different classes of networks, the random graphs and the scale-free networks, are investigated computing their Betti numbers and then showing the capability of geometric entropy of detecting homologies.
A User Driven Dynamic Circuit Network Implementation
Energy Technology Data Exchange (ETDEWEB)
Guok, Chin; Robertson, David; Chaniotakis, Evangelos; Thompson, Mary; Johnston, William; Tierney, Brian
2008-10-01
The requirements for network predictability are becoming increasingly critical to the DoE science community where resources are widely distributed and collaborations are world-wide. To accommodate these emerging requirements, the Energy Sciences Network has established a Science Data Network to provide user driven guaranteed bandwidth allocations. In this paper we outline the design, implementation, and secure coordinated use of such a network, as well as some lessons learned.
Network rewiring dynamics with convergence towards a star network.
Whigham, P A; Dick, G; Parry, M
2016-10-01
Network rewiring as a method for producing a range of structures was first introduced in 1998 by Watts & Strogatz ( Nature 393 , 440-442. (doi:10.1038/30918)). This approach allowed a transition from regular through small-world to a random network. The subsequent interest in scale-free networks motivated a number of methods for developing rewiring approaches that converged to scale-free networks. This paper presents a rewiring algorithm (RtoS) for undirected, non-degenerate, fixed size networks that transitions from regular, through small-world and scale-free to star-like networks. Applications of the approach to models for the spread of infectious disease and fixation time for a simple genetics model are used to demonstrate the efficacy and application of the approach.
Energy Efficiency Analysis for Dynamic Routing in Optical Transport Networks
DEFF Research Database (Denmark)
Vizcaíno, Jorge López; Ye, Yabin; Tafur Monroy, Idelfonso
2012-01-01
The energy efficiency in telecommunication networks is gaining more relevance as the Internet traffic is growing. The introduction of OFDM and dynamic operation opens new horizons in the operation of optical networks, improving the network flexibility and its efficiency. In this paper, we compare...... the performance in terms of energy efficiency of a flexible-grid OFDM-based solution with a fixed-grid WDM network in a dynamic scenario with time-varying connections. We highlight the benefits that the bandwidth elasticity and the flexibility of selecting different modulation formats can offer compared...
International Nuclear Information System (INIS)
Burghelea, Manuela; Verellen, Dirk; Poels, Kenneth; Gevaert, Thierry; Depuydt, Tom; Tournel, Koen; Hung, Cecilia; Simon, Viorica; Hiraoka, Masahiro; Ridder, Mark de
2015-01-01
Purpose: The purpose of this study was to define an independent verification method based on on-board orthogonal fluoroscopy to determine the geometric accuracy of synchronized gantry–ring (G/R) rotations during dynamic wave arc (DWA) delivery available on the Vero system. Methods and Materials: A verification method for DWA was developed to calculate O-ring-gantry (G/R) positional information from ball-bearing positions retrieved from fluoroscopic images of a cubic phantom acquired during DWA delivery. Different noncoplanar trajectories were generated in order to investigate the influence of path complexity on delivery accuracy. The G/R positions detected from the fluoroscopy images (DetPositions) were benchmarked against the G/R angulations retrieved from the control points (CP) of the DWA RT plan and the DWA log files recorded by the treatment console during DWA delivery (LogActed). The G/R rotational accuracy was quantified as the mean absolute deviation ± standard deviation. The maximum G/R absolute deviation was calculated as the maximum 3-dimensional distance between the CP and the closest DetPositions. Results: In the CP versus DetPositions comparison, an overall mean G/R deviation of 0.13°/0.16° ± 0.16°/0.16° was obtained, with a maximum G/R deviation of 0.6°/0.2°. For the LogActed versus DetPositions evaluation, the overall mean deviation was 0.08°/0.15° ± 0.10°/0.10° with a maximum G/R of 0.3°/0.4°. The largest decoupled deviations registered for gantry and ring were 0.6° and 0.4° respectively. No directional dependence was observed between clockwise and counterclockwise rotations. Doubling the dose resulted in a double number of detected points around each CP, and an angular deviation reduction in all cases. Conclusions: An independent geometric quality assurance approach was developed for DWA delivery verification and was successfully applied on diverse trajectories. Results showed that the Vero system is capable of following complex
Employing Deceptive Dynamic Network Topology Through Software-Defined Networking
2014-03-01
Request For Comments RIP Routing Information Protocol RIPE NCC Reseaux IP Europeans Network Coordination Center RIR Regional Internet Registries RTT...Another large research project in building Internet measurement infrastructure is from the Reseaux IP Europeans Network Coordination Center (RIPE NCC) [24
Perception of Communication Network Fraud Dynamics by Network ...
African Journals Online (AJOL)
In considering the implications of the varied nature of the potential targets, the paper identifies the view to develop effective intelligence analysis methodologies for network fraud detection and prevention by network administrators and stakeholders. The paper further notes that organizations are fighting an increasingly ...
Structure-based control of complex networks with nonlinear dynamics
Zañudo, Jorge Gomez Tejeda; Yang, Gang; Albert, Réka
2017-01-01
What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework’s applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances. PMID:28655847
Epidemic dynamics and endemic states in complex networks
International Nuclear Information System (INIS)
Pastor-Satorras, Romualdo; Vespignani, Alessandro
2001-01-01
We study by analytical methods and large scale simulations a dynamical model for the spreading of epidemics in complex networks. In networks with exponentially bounded connectivity we recover the usual epidemic behavior with a threshold defining a critical point below that the infection prevalence is null. On the contrary, on a wide range of scale-free networks we observe the absence of an epidemic threshold and its associated critical behavior. This implies that scale-free networks are prone to the spreading and the persistence of infections whatever spreading rate the epidemic agents might possess. These results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks
Characterization of Static/Dynamic Topological Routing For Grid Networks
DEFF Research Database (Denmark)
Gutierrez Lopez, Jose Manuel; Cuevas, Ruben; Riaz, M. Tahir
2009-01-01
Grid or 2D Mesh structures are becoming one of the most attractive network topologies to study. They can be used in many different fields raging from future broadband networks to multiprocessors structures. In addition, the high requirements of future services and applications demand more flexible...... and adaptive networks. Topological routing in grid networks is a simple and efficient alternative to traditional routing techniques, e.g. routing tables, and the paper extends this kind of routing providing a "Dynamic" attribute. This new property attempts to improve the overall network performance for future...
The Graph Laplacian and the Dynamics of Complex Networks
Energy Technology Data Exchange (ETDEWEB)
Thulasidasan, Sunil [Los Alamos National Laboratory
2012-06-11
In this talk, we explore the structure of networks from a spectral graph-theoretic perspective by analyzing the properties of the Laplacian matrix associated with the graph induced by a network. We will see how the eigenvalues of the graph Laplacian relate to the underlying network structure and dynamics and provides insight into a phenomenon frequently observed in real world networks - the emergence of collective behavior from purely local interactions seen in the coordinated motion of animals and phase transitions in biological networks, to name a few.
Recovery time after localized perturbations in complex dynamical networks
International Nuclear Information System (INIS)
Mitra, Chiranjit; Kittel, Tim; Kurths, Jürgen; Donner, Reik V; Choudhary, Anshul
2017-01-01
Maintaining the synchronous motion of dynamical systems interacting on complex networks is often critical to their functionality. However, real-world networked dynamical systems operating synchronously are prone to random perturbations driving the system to arbitrary states within the corresponding basin of attraction, thereby leading to epochs of desynchronized dynamics with a priori unknown durations. Thus, it is highly relevant to have an estimate of the duration of such transient phases before the system returns to synchrony, following a random perturbation to the dynamical state of any particular node of the network. We address this issue here by proposing the framework of single-node recovery time (SNRT) which provides an estimate of the relative time scales underlying the transient dynamics of the nodes of a network during its restoration to synchrony. We utilize this in differentiating the particularly slow nodes of the network from the relatively fast nodes, thus identifying the critical nodes which when perturbed lead to significantly enlarged recovery time of the system before resuming synchronized operation. Further, we reveal explicit relationships between the SNRT values of a network, and its global relaxation time when starting all the nodes from random initial conditions. Earlier work on relaxation time generally focused on investigating its dependence on macroscopic topological properties of the respective network. However, we employ the proposed concept for deducing microscopic relationships between topological features of nodes and their respective SNRT values. The framework of SNRT is further extended to a measure of resilience of the different nodes of a networked dynamical system. We demonstrate the potential of SNRT in networks of Rössler oscillators on paradigmatic topologies and a model of the power grid of the United Kingdom with second-order Kuramoto-type nodal dynamics illustrating the conceivable practical applicability of the proposed
Dynamic Trust Models between Users over Social Networks
2016-03-30
the- art hTrust and its variants for solving the trust -link prediction problem. In addition to the above main research results, we developed a...AFRL-AFOSR-JP-TR-2016-0039 Dynamic Trust Models between Users over Social Networks Kazumi Saito University Of Shizuoka Final Report 04/05/2016...2013 to 30-03-2016 4. TITLE AND SUBTITLE (134042) Dynamic Trust Models between Users over Social Networks 5a. CONTRACT NUMBER FA2386-13-1
Complexity functions for networks: Dynamical hubs and complexity clusters
Afraimovich, Valentin; Dmitrichev, Aleksei; Shchapin, Dmitry; Nekorkin, Vladimir
2018-02-01
A method for studying the behavior of the elements of dynamical networks is introduced. We measure the amount of instability stored at each element according to the value of the mean complexity related to this element. Elements with close values of the mean complexity can be unified into complexity clusters; elements with the smallest values of complexities form dynamical hubs. The effectiveness of the method is manifested by its successive application to networks of coupled Lorenz systems.
Complex networks: when random walk dynamics equals synchronization
International Nuclear Information System (INIS)
Kriener, Birgit; Anand, Lishma; Timme, Marc
2012-01-01
Synchrony prevalently emerges from the interactions of coupled dynamical units. For simple systems such as networks of phase oscillators, the asymptotic synchronization process is assumed to be equivalent to a Markov process that models standard diffusion or random walks on the same network topology. In this paper, we analytically derive the conditions for such equivalence for networks of pulse-coupled oscillators, which serve as models for neurons and pacemaker cells interacting by exchanging electric pulses or fireflies interacting via light flashes. We find that the pulse synchronization process is less simple, but there are classes of, e.g., network topologies that ensure equivalence. In particular, local dynamical operators are required to be doubly stochastic. These results provide a natural link between stochastic processes and deterministic synchronization on networks. Tools for analyzing diffusion (or, more generally, Markov processes) may now be transferred to pin down features of synchronization in networks of pulse-coupled units such as neural circuits. (paper)
Network evolution driven by dynamics applied to graph coloring
International Nuclear Information System (INIS)
Wu Jian-She; Li Li-Guang; Yu Xin; Jiao Li-Cheng; Wang Xiao-Hua
2013-01-01
An evolutionary network driven by dynamics is studied and applied to the graph coloring problem. From an initial structure, both the topology and the coupling weights evolve according to the dynamics. On the other hand, the dynamics of the network are determined by the topology and the coupling weights, so an interesting structure-dynamics co-evolutionary scheme appears. By providing two evolutionary strategies, a network described by the complement of a graph will evolve into several clusters of nodes according to their dynamics. The nodes in each cluster can be assigned the same color and nodes in different clusters assigned different colors. In this way, a co-evolution phenomenon is applied to the graph coloring problem. The proposed scheme is tested on several benchmark graphs for graph coloring
Bray, Hubert L; Mazzeo, Rafe; Sesum, Natasa
2015-01-01
This volume includes expanded versions of the lectures delivered in the Graduate Minicourse portion of the 2013 Park City Mathematics Institute session on Geometric Analysis. The papers give excellent high-level introductions, suitable for graduate students wishing to enter the field and experienced researchers alike, to a range of the most important areas of geometric analysis. These include: the general issue of geometric evolution, with more detailed lectures on Ricci flow and Kähler-Ricci flow, new progress on the analytic aspects of the Willmore equation as well as an introduction to the recent proof of the Willmore conjecture and new directions in min-max theory for geometric variational problems, the current state of the art regarding minimal surfaces in R^3, the role of critical metrics in Riemannian geometry, and the modern perspective on the study of eigenfunctions and eigenvalues for Laplace-Beltrami operators.
Simulating market dynamics: interactions between consumer psychology and social networks.
Janssen, Marco A; Jager, Wander
2003-01-01
Markets can show different types of dynamics, from quiet markets dominated by one or a few products, to markets with continual penetration of new and reintroduced products. In a previous article we explored the dynamics of markets from a psychological perspective using a multi-agent simulation model. The main results indicated that the behavioral rules dominating the artificial consumer's decision making determine the resulting market dynamics, such as fashions, lock-in, and unstable renewal. Results also show the importance of psychological variables like social networks, preferences, and the need for identity to explain the dynamics of markets. In this article we extend this work in two directions. First, we will focus on a more systematic investigation of the effects of different network structures. The previous article was based on Watts and Strogatz's approach, which describes the small-world and clustering characteristics in networks. More recent research demonstrated that many large networks display a scale-free power-law distribution for node connectivity. In terms of market dynamics this may imply that a small proportion of consumers may have an exceptional influence on the consumptive behavior of others (hubs, or early adapters). We show that market dynamics is a self-organized property depending on the interaction between the agents' decision-making process (heuristics), the product characteristics (degree of satisfaction of unit of consumption, visibility), and the structure of interactions between agents (size of network and hubs in a social network).
Dynamic Network Security Control Using Software Defined Networking
2016-03-24
rapidly respond to host level security events using SDN flow table updates, role-based flow classes , and Advanced Messaging Queuing Protocol to auto...the success of most organizations. One approach is to apply host and network-based security systems, which typically come in the form of antivirus or...intrusion detection/prevention products to man- age these threats. However, since traditional networks require manual configuration, an antivirus alert
Infection dynamics on spatial small-world network models
Iotti, Bryan; Antonioni, Alberto; Bullock, Seth; Darabos, Christian; Tomassini, Marco; Giacobini, Mario
2017-11-01
The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex networks.
Multiple dynamical time-scales in networks with hierarchically ...
Indian Academy of Sciences (India)
Multiple dynamical time-scales in networks with hierarchically nested modular organization ... http://www.ias.ac.in/article/fulltext/pram/077/05/0833-0842 ... Many natural and engineered complex networks have intricate mesoscopic organization, e.g., the clustering of the constituent nodes into several communities or ...
Large maneuverable flight control using neural networks dynamic inversion
Yang, Enquan; Gao, Jinyuan
2003-09-01
An adaptive dynamic-inversion-based neural network is applied to aircraft large maneuverable flight control. Neural network is used to cancel the inversion error which may arise from imperfect modeling or approximate inversion. Simulation results for an aircraft model are presented to illustrate the performance of the flight control system.
Popularity and Adolescent Friendship Networks : Selection and Influence Dynamics
Dijkstra, Jan Kornelis; Cillessen, Antonius H. N.; Borch, Casey
This study examined the dynamics of popularity in adolescent friendship networks across 3 years in middle school. Longitudinal social network modeling was used to identify selection and influence in the similarity of popularity among friends. It was argued that lower status adolescents strive to
Dynamic Relaying in 3GPP LTE-Advanced Networks
DEFF Research Database (Denmark)
Teyeb, Oumer Mohammed; Van Phan, Vinh; Redana, Simone
2009-01-01
Relaying is one of the proposed technologies for LTE-Advanced networks. In order to enable a flexible and reliable relaying support, the currently adopted architectural structure of LTE networks has to be modified. In this paper, we extend the LTE architecture to enable dynamic relaying, while ma...
Non-homogeneous dynamic Bayesian networks for continuous data
Grzegorczyk, Marco; Husmeier, Dirk
Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with non-homogeneous temporal processes. Various approaches to relax the homogeneity assumption have recently been proposed. The present paper presents a combination of a Bayesian network with
Terrestrial-Satellite Integration in Dynamic 5G Backhaul Networks
Artiga, Xavier; Núñez-Martínez, José; Pérez-Neira, Ana Isabel; Lendrino Vela, Gorka Juan; Faré García, Juan Mario; Ziaragkas, Georgios
2016-01-01
This paper presents a dynamic backhaul network in order to face some of the main 5G challenges such as 100% coverage, improved capacity or reduction in energy consumption. The proposed solution, elaborated within the SANSA H2020 project, is based on the seamless integration of the satellite component in a terrestrial network capable of reconfiguring its topology according to the traffic demands. The paper highlights the benefits of this hybrid network and describes the technology enablers to ...
Traffic allocation strategies in WSS-based dynamic optical networks
Shakeri, Ali; Garrich, Miquel; Bravalheri, Anderson; Careglio, Davide; Solé Pareta, Josep; Fumagalli, Andrea
2017-01-01
Elastic optical networking (EON) is a viable solution to meet future dynamic capacity requirements of Internet service provider and inter-datacenter networks. At the core of EON, wavelength selective switches (WSSs) are applied to individually route optical circuits, while assigning an arbitrary bandwidth to each circuit. Critically, the WSS control scheme and configuration time may delay the creation time of each circuit in the network. In this paper, we first detail the WSS-based optical da...
Developing a dynamic control system for mine compressed air networks
Van Heerden, S.W.; Pelzer, R.; Marais, J.H.
2014-01-01
Mines in general, make use of compressed air systems for daily operational activities. Compressed air on mines is traditionally distributed via compressed air ring networks where multiple shafts are supplied with compressed air from an integral system. These compressed air networks make use of a number of compressors feeding the ring from various locations in the network. While these mines have sophisticated control systems to control these compressors, they are not dynamic systems. Compresso...
International Nuclear Information System (INIS)
Montani, S.; Portinale, L.; Bobbio, A.; Codetta-Raiteri, D.
2008-01-01
In this paper, we present RADYBAN (Reliability Analysis with DYnamic BAyesian Networks), a software tool which allows to analyze a dynamic fault tree relying on its conversion into a dynamic Bayesian network. The tool implements a modular algorithm for automatically translating a dynamic fault tree into the corresponding dynamic Bayesian network and exploits classical algorithms for the inference on dynamic Bayesian networks, in order to compute reliability measures. After having described the basic features of the tool, we show how it operates on a real world example and we compare the unreliability results it generates with those returned by other methodologies, in order to verify the correctness and the consistency of the results obtained
Modular networks with hierarchical organization: The dynamical ...
Indian Academy of Sciences (India)
terms hierarchy and modularity have been used almost interchangeably, although, as shown in figure 1, they represent distinct properties of the network. However, it is interesting to note that these two properties have been found to coexist in many networks occurring in real life [3–6], including the Internet [7,8] and the ...
Cytoskeletal Network Morphology Regulates Intracellular Transport Dynamics
Ando, David; Korabel, Nickolay; Huang, Kerwyn Casey; Gopinathan, Ajay
2015-01-01
Intracellular transport is essential for maintaining proper cellular function in most eukaryotic cells, with perturbations in active transport resulting in several types of disease. Efficient delivery of critical cargos to specific locations is accomplished through a combination of passive diffusion and active transport by molecular motors that ballistically move along a network of cytoskeletal filaments. Although motor-based transport is known to be necessary to overcome cytoplasmic crowding and the limited range of diffusion within reasonable timescales, the topological features of the cytoskeletal network that regulate transport efficiency and robustness have not been established. Using a continuum diffusion model, we observed that the time required for cellular transport was minimized when the network was localized near the nucleus. In simulations that explicitly incorporated network spatial architectures, total filament mass was the primary driver of network transit times. However, filament traps that redirect cargo back to the nucleus caused large variations in network transport. Filament polarity was more important than filament orientation in reducing average transit times, and transport properties were optimized in networks with intermediate motor on and off rates. Our results provide important insights into the functional constraints on intracellular transport under which cells have evolved cytoskeletal structures, and have potential applications for enhancing reactions in biomimetic systems through rational transport network design. PMID:26488648
Cytoskeletal Network Morphology Regulates Intracellular Transport Dynamics.
Ando, David; Korabel, Nickolay; Huang, Kerwyn Casey; Gopinathan, Ajay
2015-10-20
Intracellular transport is essential for maintaining proper cellular function in most eukaryotic cells, with perturbations in active transport resulting in several types of disease. Efficient delivery of critical cargos to specific locations is accomplished through a combination of passive diffusion and active transport by molecular motors that ballistically move along a network of cytoskeletal filaments. Although motor-based transport is known to be necessary to overcome cytoplasmic crowding and the limited range of diffusion within reasonable timescales, the topological features of the cytoskeletal network that regulate transport efficiency and robustness have not been established. Using a continuum diffusion model, we observed that the time required for cellular transport was minimized when the network was localized near the nucleus. In simulations that explicitly incorporated network spatial architectures, total filament mass was the primary driver of network transit times. However, filament traps that redirect cargo back to the nucleus caused large variations in network transport. Filament polarity was more important than filament orientation in reducing average transit times, and transport properties were optimized in networks with intermediate motor on and off rates. Our results provide important insights into the functional constraints on intracellular transport under which cells have evolved cytoskeletal structures, and have potential applications for enhancing reactions in biomimetic systems through rational transport network design. Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.
DAWN: Dynamic Ad-hoc Wireless Network
2016-06-19
Wireless Network 1 Introduction The network-centric battlefield includes sensors, troop carriers, unmanned air vehicle (UAV), aircraft, smart ...Bellman Control Heritage Award. • Honorary Doctorate at Technical University of Crete. • Best paper award at 2008 IEEE International Conference on Mobile Ad-hoc and Sensor Systems.
Towards Memristive Dynamic Adaptive Neural Network Arrays
2016-03-17
Memories,” in Proc. of International Symposium on Circuits and Systems (ISCAS), Rio de Janeiro , Brazil, May, 2011. 9. Q. Xia, W. Robinett, et al...network’s outputs fare with the given inputs. The EO then generates an initial population of random networks, and gradually evolves the population
Discriminating lysosomal membrane protein types using dynamic neural network.
Tripathi, Vijay; Gupta, Dwijendra Kumar
2014-01-01
This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.
Functional clustering in hippocampal cultures: relating network structure and dynamics
International Nuclear Information System (INIS)
Feldt, S; Dzakpasu, R; Olariu, E; Żochowski, M; Wang, J X; Shtrahman, E
2010-01-01
In this work we investigate the relationship between gross anatomic structural network properties, neuronal dynamics and the resultant functional structure in dissociated rat hippocampal cultures. Specifically, we studied cultures as they developed under two conditions: the first supporting glial cell growth (high glial group), and the second one inhibiting it (low glial group). We then compared structural network properties and the spatio-temporal activity patterns of the neurons. Differences in dynamics between the two groups could be linked to the impact of the glial network on the neuronal network as the cultures developed. We also implemented a recently developed algorithm called the functional clustering algorithm (FCA) to obtain the resulting functional network structure. We show that this new algorithm is useful for capturing changes in functional network structure as the networks evolve over time. The FCA detects changes in functional structure that are consistent with expected dynamical differences due to the impact of the glial network. Cultures in the high glial group show an increase in global synchronization as the cultures age, while those in the low glial group remain locally synchronized. We additionally use the FCA to quantify the amount of synchronization present in the cultures and show that the total level of synchronization in the high glial group is stronger than in the low glial group. These results indicate an interdependence between the glial and neuronal networks present in dissociated cultures
Vannitsem, Stéphane; Lucarini, Valerio
2016-06-01
We study a simplified coupled atmosphere-ocean model using the formalism of covariant Lyapunov vectors (CLVs), which link physically-based directions of perturbations to growth/decay rates. The model is obtained via a severe truncation of quasi-geostrophic equations for the two fluids, and includes a simple yet physically meaningful representation of their dynamical/thermodynamical coupling. The model has 36 degrees of freedom, and the parameters are chosen so that a chaotic behaviour is observed. There are two positive Lyapunov exponents (LEs), sixteen negative LEs, and eighteen near-zero LEs. The presence of many near-zero LEs results from the vast time-scale separation between the characteristic time scales of the two fluids, and leads to nontrivial error growth properties in the tangent space spanned by the corresponding CLVs, which are geometrically very degenerate. Such CLVs correspond to two different classes of ocean/atmosphere coupled modes. The tangent space spanned by the CLVs corresponding to the positive and negative LEs has, instead, a non-pathological behaviour, and one can construct robust large deviations laws for the finite time LEs, thus providing a universal model for assessing predictability on long to ultra-long scales along such directions. Interestingly, the tangent space of the unstable manifold has substantial projection on both atmospheric and oceanic components. The results show the difficulties in using hyperbolicity as a conceptual framework for multiscale chaotic dynamical systems, whereas the framework of partial hyperbolicity seems better suited, possibly indicating an alternative definition for the chaotic hypothesis. They also suggest the need for an accurate analysis of error dynamics on different time scales and domains and for a careful set-up of assimilation schemes when looking at coupled atmosphere-ocean models.
Dynamics of slow and fast systems on complex networks
Indian Academy of Sciences (India)
In this study, we present the emergent collective behavior in a network of nonlinear dynamical systems, where the heterogeneity arises only from the difference in the time scales of nodal dynamics. To make this a spe- cific feature and bring out effects of time-scale mismatch of connected systems, we consider an otherwise ...
Maritime piracy situation modelling with dynamic Bayesian networks
CSIR Research Space (South Africa)
Dabrowski, James M
2015-05-01
Full Text Available A generative model for modelling maritime vessel behaviour is proposed. The model is a novel variant of the dynamic Bayesian network (DBN). The proposed DBN is in the form of a switching linear dynamic system (SLDS) that has been extended into a...
Classification of networks of automata by dynamical mean field theory
International Nuclear Information System (INIS)
Burda, Z.; Jurkiewicz, J.; Flyvbjerg, H.
1990-01-01
Dynamical mean field theory is used to classify the 2 24 =65,536 different networks of binary automata on a square lattice with nearest neighbour interactions. Application of mean field theory gives 700 different mean field classes, which fall in seven classes of different asymptotic dynamics characterized by fixed points and two-cycles. (orig.)
Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.
Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu
2017-10-01
This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.
Dynamics of rumor propagation on small-world networks.
Zanette, Damián H
2002-04-01
We study the dynamics of an epidemiclike model for the spread of a rumor on a small-world network. It has been shown that this model exhibits a transition between regimes of localization and propagation at a finite value of the network randomness. Here, by numerical means, we perform a quantitative characterization of the evolution in the two regimes. The variant of dynamic small worlds, where the quenched disorder of small-world networks is replaced by randomly changing connections between individuals, is also analyzed in detail and compared with a mean-field approximation.
Dynamic baseline detection method for power data network service
Chen, Wei
2017-08-01
This paper proposes a dynamic baseline Traffic detection Method which is based on the historical traffic data for the Power data network. The method uses Cisco's NetFlow acquisition tool to collect the original historical traffic data from network element at fixed intervals. This method uses three dimensions information including the communication port, time, traffic (number of bytes or number of packets) t. By filtering, removing the deviation value, calculating the dynamic baseline value, comparing the actual value with the baseline value, the method can detect whether the current network traffic is abnormal.
The architecture of dynamic reservoir in the echo state network
Cui, Hongyan; Liu, Xiang; Li, Lixiang
2012-09-01
Echo state network (ESN) has recently attracted increasing interests because of its superior capability in modeling nonlinear dynamic systems. In the conventional echo state network model, its dynamic reservoir (DR) has a random and sparse topology, which is far from the real biological neural networks from both structural and functional perspectives. We hereby propose three novel types of echo state networks with new dynamic reservoir topologies based on complex network theory, i.e., with a small-world topology, a scale-free topology, and a mixture of small-world and scale-free topologies, respectively. We then analyze the relationship between the dynamic reservoir structure and its prediction capability. We utilize two commonly used time series to evaluate the prediction performance of the three proposed echo state networks and compare them to the conventional model. We also use independent and identically distributed time series to analyze the short-term memory and prediction precision of these echo state networks. Furthermore, we study the ratio of scale-free topology and the small-world topology in the mixed-topology network, and examine its influence on the performance of the echo state networks. Our simulation results show that the proposed echo state network models have better prediction capabilities, a wider spectral radius, but retain almost the same short-term memory capacity as compared to the conventional echo state network model. We also find that the smaller the ratio of the scale-free topology over the small-world topology, the better the memory capacities.
Scalable Approaches to Control Network Dynamics: Prospects for City Networks
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...
Intrinsic dynamics induce global symmetry in network controllability
Zhao, Chen; Wang, Wen-Xu; Liu, Yang-Yu; Slotine, Jean-Jacques
2015-02-01
Controlling complex networked systems to desired states is a key research goal in contemporary science. Despite recent advances in studying the impact of network topology on controllability, a comprehensive understanding of the synergistic effect of network topology and individual dynamics on controllability is still lacking. Here we offer a theoretical study with particular interest in the diversity of dynamic units characterized by different types of individual dynamics. Interestingly, we find a global symmetry accounting for the invariance of controllability with respect to exchanging the densities of any two different types of dynamic units, irrespective of the network topology. The highest controllability arises at the global symmetry point, at which different types of dynamic units are of the same density. The lowest controllability occurs when all self-loops are either completely absent or present with identical weights. These findings further improve our understanding of network controllability and have implications for devising the optimal control of complex networked systems in a wide range of fields.
Actin dynamics and the elasticity of cytoskeletal networks
Directory of Open Access Journals (Sweden)
2009-09-01
Full Text Available The structural integrity of a cell depends on its cytoskeleton, which includes an actin network. This network is transient and depends upon the continual polymerization and depolymerization of actin. The degradation of an actin network, and a corresponding reduction in cell stiffness, can indicate the presence of disease. Numerical simulations will be invaluable for understanding the physics of these systems and the correlation between actin dynamics and elasticity. Here we develop a model that is capable of generating actin network structures. In particular, we develop a model of actin dynamics which considers the polymerization, depolymerization, nucleation, severing, and capping of actin filaments. The structures obtained are then fed directly into a mechanical model. This allows us to qualitatively assess the effects of changing various parameters associated with actin dynamics on the elasticity of the material.
Congested Link Inference Algorithms in Dynamic Routing IP Network
Directory of Open Access Journals (Sweden)
Yu Chen
2017-01-01
Full Text Available The performance descending of current congested link inference algorithms is obviously in dynamic routing IP network, such as the most classical algorithm CLINK. To overcome this problem, based on the assumptions of Markov property and time homogeneity, we build a kind of Variable Structure Discrete Dynamic Bayesian (VSDDB network simplified model of dynamic routing IP network. Under the simplified VSDDB model, based on the Bayesian Maximum A Posteriori (BMAP and Rest Bayesian Network Model (RBNM, we proposed an Improved CLINK (ICLINK algorithm. Considering the concurrent phenomenon of multiple link congestion usually happens, we also proposed algorithm CLILRS (Congested Link Inference algorithm based on Lagrangian Relaxation Subgradient to infer the set of congested links. We validated our results by the experiments of analogy, simulation, and actual Internet.
Multinephron dynamics on the renal vascular network
DEFF Research Database (Denmark)
Marsh, Donald J; Wexler, Anthony S; Brazhe, Alexey
2012-01-01
Tubuloglomerular feedback (TGF) and the myogenic mechanism combine in each nephron to regulate blood flow and glomerular filtration rate. Both mechanisms are non-linear, generate self-sustained oscillations, and interact as their signals converge on arteriolar smooth muscle, forming a regulatory...... ensemble. Ensembles may synchronize. Smooth muscle cells in the ensemble depolarize periodically, generating electrical signals that propagate along the vascular network. We developed a mathematical model of a nephron-vascular network, with 16 versions of a single nephron model containing representations...... of both mechanisms in the regulatory ensemble, to examine the effects of network structure on nephron synchronization. Symmetry, as a property of a network, facilitates synchronization. Nephrons received blood from a symmetric electrically conductive vascular tree. Symmetry was created by using identical...
Dynamic Virtual LANs for Adaptive Network Security
National Research Council Canada - National Science Library
Merani, Diego; Berni, Alessandro; Leonard, Michel
2004-01-01
The development of Network-Enabled capabilities in support of undersea research requires architectures for the interconnection and data sharing that are flexible, scalable, and built on open standards...
Railway Network Timetabling and Dynamic Traffic Management
Hansen, I.A.
2009-01-01
The paper discusses the current state of research concerning railway network timetabling and traffic management. Timetable effectiveness is governed by frequency, regularity, accurate running, recovery and layover times, as well as minimal headway, buffer times and waiting times. Analytic (queuing)
Connectivity, topology and dynamics in climate networks
Czech Academy of Sciences Publication Activity Database
Paluš, Milan; Hartman, David; Hlinka, Jaroslav; Vejmelka, Martin
2012-01-01
Roč. 14, - (2012), s. 8397 ISSN 1607-7962. [European Geosciences Union General Assembly 2012. 22.04.2012-27.04.2012, Vienna] R&D Projects: GA ČR GCP103/11/J068 Institutional support: RVO:67985807 Keywords : complex networks * climate network * connectivity * entropy rate * El Nino Southern Oscillation * North Atlantic Oscillation Subject RIV: BB - Applied Statistics, Operational Research
Network Reconstruction of Dynamic Biological Systems
Asadi, Behrang
2013-01-01
Inference of network topology from experimental data is a central endeavor in biology, since knowledge of the underlying signaling mechanisms a requirement for understanding biological phenomena. As one of the most important tools in bioinformatics area, development of methods to reconstruct biological networks has attracted remarkable attention in the current decade. Integration of different data types can lead to remarkable improvements in our ability to identify the connectivity of differe...
Social network dynamics in international students' learning
Cox, A.M.; Taha, N.
2010-01-01
The potential for the internationalisation of UK HE to bring diverse viewpoints and perspectives into the curriculum has not been fully realised. One of the many obstacles to this may be our lack of understanding of how international students use and build social networks for learning, information sharing and support, and how this impacts on engagement and learning. The literature suggests various ways in which network positions and learning might be associated. In this study we used a range ...
Dynamical complexity in the perception-based network formation model
Jo, Hang-Hyun; Moon, Eunyoung
2016-12-01
Many link formation mechanisms for the evolution of social networks have been successful to reproduce various empirical findings in social networks. However, they have largely ignored the fact that individuals make decisions on whether to create links to other individuals based on cost and benefit of linking, and the fact that individuals may use perception of the network in their decision making. In this paper, we study the evolution of social networks in terms of perception-based strategic link formation. Here each individual has her own perception of the actual network, and uses it to decide whether to create a link to another individual. An individual with the least perception accuracy can benefit from updating her perception using that of the most accurate individual via a new link. This benefit is compared to the cost of linking in decision making. Once a new link is created, it affects the accuracies of other individuals' perceptions, leading to a further evolution of the actual network. As for initial actual networks, we consider both homogeneous and heterogeneous cases. The homogeneous initial actual network is modeled by Erdős-Rényi (ER) random networks, while we take a star network for the heterogeneous case. In any cases, individual perceptions of the actual network are modeled by ER random networks with controllable linking probability. Then the stable link density of the actual network is found to show discontinuous transitions or jumps according to the cost of linking. As the number of jumps is the consequence of the dynamical complexity, we discuss the effect of initial conditions on the number of jumps to find that the dynamical complexity strongly depends on how much individuals initially overestimate or underestimate the link density of the actual network. For the heterogeneous case, the role of the highly connected individual as an information spreader is also discussed.
State-dependent intrinsic predictability of cortical network dynamics.
Directory of Open Access Journals (Sweden)
Leila Fakhraei
Full Text Available The information encoded in cortical circuit dynamics is fleeting, changing from moment to moment as new input arrives and ongoing intracortical interactions progress. A combination of deterministic and stochastic biophysical mechanisms governs how cortical dynamics at one moment evolve from cortical dynamics in recently preceding moments. Such temporal continuity of cortical dynamics is fundamental to many aspects of cortex function but is not well understood. Here we study temporal continuity by attempting to predict cortical population dynamics (multisite local field potential based on its own recent history in somatosensory cortex of anesthetized rats and in a computational network-level model. We found that the intrinsic predictability of cortical dynamics was dependent on multiple factors including cortical state, synaptic inhibition, and how far into the future the prediction extends. By pharmacologically tuning synaptic inhibition, we obtained a continuum of cortical states with asynchronous population activity at one extreme and stronger, spatially extended synchrony at the other extreme. Intermediate between these extremes we observed evidence for a special regime of population dynamics called criticality. Predictability of the near future (10-100 ms increased as the cortical state was tuned from asynchronous to synchronous. Predictability of the more distant future (>1 s was generally poor, but, surprisingly, was higher for asynchronous states compared to synchronous states. These experimental results were confirmed in a computational network model of spiking excitatory and inhibitory neurons. Our findings demonstrate that determinism and predictability of network dynamics depend on cortical state and the time-scale of the dynamics.
Identifying and tracking dynamic processes in social networks
Chung, Wayne; Savell, Robert; Schütt, Jan-Peter; Cybenko, George
2006-05-01
The detection and tracking of embedded malicious subnets in an active social network can be computationally daunting due to the quantity of transactional data generated in the natural interaction of large numbers of actors comprising a network. In addition, detection of illicit behavior may be further complicated by evasive strategies designed to camouflage the activities of the covert subnet. In this work, we move beyond traditional static methods of social network analysis to develop a set of dynamic process models which encode various modes of behavior in active social networks. These models will serve as the basis for a new application of the Process Query System (PQS) to the identification and tracking of covert dynamic processes in social networks. We present a preliminary result from application of our technique in a real-world data stream-- the Enron email corpus.
Cytoskeleton dynamics: Fluctuations within the network
International Nuclear Information System (INIS)
Bursac, Predrag; Fabry, Ben; Trepat, Xavier; Lenormand, Guillaume; Butler, James P.; Wang, Ning; Fredberg, Jeffrey J.; An, Steven S.
2007-01-01
Out-of-equilibrium systems, such as the dynamics of a living cytoskeleton (CSK), are inherently noisy with fluctuations arising from the stochastic nature of the underlying biochemical and molecular events. Recently, such fluctuations within the cell were characterized by observing spontaneous nano-scale motions of an RGD-coated microbead bound to the cell surface [Bursac et al., Nat. Mater. 4 (2005) 557-561]. While these reported anomalous bead motions represent a molecular level reorganization (remodeling) of microstructures in contact with the bead, a precise nature of these cytoskeletal constituents and forces that drive their remodeling dynamics are largely unclear. Here, we focused upon spontaneous motions of an RGD-coated bead and, in particular, assessed to what extent these motions are attributable to (i) bulk cell movement (cell crawling), (ii) dynamics of focal adhesions, (iii) dynamics of lipid membrane, and/or (iv) dynamics of the underlying actin CSK driven by myosin motors
Dynamic Network-Based Epistasis Analysis: Boolean Examples
Azpeitia, Eugenio; Benítez, Mariana; Padilla-Longoria, Pablo; Espinosa-Soto, Carlos; Alvarez-Buylla, Elena R.
2011-01-01
In this article we focus on how the hierarchical and single-path assumptions of epistasis analysis can bias the inference of gene regulatory networks. Here we emphasize the critical importance of dynamic analyses, and specifically illustrate the use of Boolean network models. Epistasis in a broad sense refers to gene interactions, however, as originally proposed by Bateson, epistasis is defined as the blocking of a particular allelic effect due to the effect of another allele at a different locus (herein, classical epistasis). Classical epistasis analysis has proven powerful and useful, allowing researchers to infer and assign directionality to gene interactions. As larger data sets are becoming available, the analysis of classical epistasis is being complemented with computer science tools and system biology approaches. We show that when the hierarchical and single-path assumptions are not met in classical epistasis analysis, the access to relevant information and the correct inference of gene interaction topologies is hindered, and it becomes necessary to consider the temporal dynamics of gene interactions. The use of dynamical networks can overcome these limitations. We particularly focus on the use of Boolean networks that, like classical epistasis analysis, relies on logical formalisms, and hence can complement classical epistasis analysis and relax its assumptions. We develop a couple of theoretical examples and analyze them from a dynamic Boolean network model perspective. Boolean networks could help to guide additional experiments and discern among alternative regulatory schemes that would be impossible or difficult to infer without the elimination of these assumption from the classical epistasis analysis. We also use examples from the literature to show how a Boolean network-based approach has resolved ambiguities and guided epistasis analysis. Our article complements previous accounts, not only by focusing on the implications of the hierarchical and
Directory of Open Access Journals (Sweden)
Eliska Vohradska
Full Text Available Study of genetic networks has moved from qualitative description of interactions between regulators and regulated genes to the analysis of the interaction dynamics. This paper focuses on the analysis of dynamics of one particular network--the yeast cyclins network. Using a dedicated mathematical model of gene expression and a procedure for computation of the parameters of the model from experimental data, a complete numerical model of the dynamics of the cyclins genetic network was attained. The model allowed for performing virtual experiments on the network and observing their influence on the expression dynamics of the genes downstream in the regulatory cascade. Results show that when the network structure is more complicated, and the regulatory interactions are indirect, results of gene deletion are highly unpredictable. As a consequence of quantitative behavior of the genes and their connections within the network, causal relationship between a regulator and target gene may not be discovered by gene deletion. Without including the dynamics of the system into the network, its functional properties cannot be studied and interpreted correctly.
Tourist activated networks: Implications for dynamic packaging systems in tourism
DEFF Research Database (Denmark)
Zach, Florian; Gretzel, Ulrike; Fesenmaier, Daniel R.
2008-01-01
This paper discusses tourist activated networks as a concept to inform technological applications supporting dynamic bundling and en-route recommendations. Empirical data was collected from travellers who visited a regional destination in the US and then analyzed with respect to its network...... structure. The results indicate that the tourist activated network for the destination is rather sparse and that there are clearly differences in core and peripheral nodes. The findings illustrate the structure of a tourist activated network and provide implications for technology design and tourism...
Rumor Diffusion in an Interests-Based Dynamic Social Network
Directory of Open Access Journals (Sweden)
Mingsheng Tang
2013-01-01
Full Text Available To research rumor diffusion in social friend network, based on interests, a dynamic friend network is proposed, which has the characteristics of clustering and community, and a diffusion model is also proposed. With this friend network and rumor diffusion model, based on the zombie-city model, some simulation experiments to analyze the characteristics of rumor diffusion in social friend networks have been conducted. The results show some interesting observations: (1 positive information may evolve to become a rumor through the diffusion process that people may modify the information by word of mouth; (2 with the same average degree, a random social network has a smaller clustering coefficient and is more beneficial for rumor diffusion than the dynamic friend network; (3 a rumor is spread more widely in a social network with a smaller global clustering coefficient than in a social network with a larger global clustering coefficient; and (4 a network with a smaller clustering coefficient has a larger efficiency.
Irrelevant stimulus processing in ADHD: catecholamine dynamics and attentional networks
Aboitiz, Francisco; Ossandón, Tomás; Zamorano, Francisco; Palma, Bárbara; Carrasco, Ximena
2014-01-01
A cardinal symptom of attention deficit and hyperactivity disorder (ADHD) is a general distractibility where children and adults shift their attentional focus to stimuli that are irrelevant to the ongoing behavior. This has been attributed to a deficit in dopaminergic signaling in cortico-striatal networks that regulate goal-directed behavior. Furthermore, recent imaging evidence points to an impairment of large scale, antagonistic brain networks that normally contribute to attentional engagement and disengagement, such as the task-positive networks and the default mode network (DMN). Related networks are the ventral attentional network (VAN) involved in attentional shifting, and the salience network (SN) related to task expectancy. Here we discuss the tonic–phasic dynamics of catecholaminergic signaling in the brain, and attempt to provide a link between this and the activities of the large-scale cortical networks that regulate behavior. More specifically, we propose that a disbalance of tonic catecholamine levels during task performance produces an emphasis of phasic signaling and increased excitability of the VAN, yielding distractibility symptoms. Likewise, immaturity of the SN may relate to abnormal tonic signaling and an incapacity to build up a proper executive system during task performance. We discuss different lines of evidence including pharmacology, brain imaging and electrophysiology, that are consistent with our proposal. Finally, restoring the pharmacodynamics of catecholaminergic signaling seems crucial to alleviate ADHD symptoms; however, the possibility is open to explore cognitive rehabilitation strategies to top-down modulate network dynamics compensating the pharmacological deficits. PMID:24723897
Social Insects: A Model System for Network Dynamics
Charbonneau, Daniel; Blonder, Benjamin; Dornhaus, Anna
Social insect colonies (ants, bees, wasps, and termites) show sophisticated collective problem-solving in the face of variable constraints. Individuals exchange information and materials such as food. The resulting network structure and dynamics can inform us about the mechanisms by which the insects achieve particular collective behaviors and these can be transposed to man-made and social networks. We discuss how network analysis can answer important questions about social insects, such as how effective task allocation or information flow is realized. We put forward the idea that network analysis methods are under-utilized in social insect research, and that they can provide novel ways to view the complexity of collective behavior, particularly if network dynamics are taken into account. To illustrate this, we present an example of network tasks performed by ant workers, linked by instances of workers switching from one task to another. We show how temporal network analysis can propose and test new hypotheses on mechanisms of task allocation, and how adding temporal elements to static networks can drastically change results. We discuss the benefits of using social insects as models for complex systems in general. There are multiple opportunities emergent technologies and analysis methods in facilitating research on social insect network. The potential for interdisciplinary work could significantly advance diverse fields such as behavioral ecology, computer sciences, and engineering.
Irrelevant stimulus processing in ADHD: catecholamine dynamics and attentional networks
Directory of Open Access Journals (Sweden)
Francisco eAboitiz
2014-03-01
Full Text Available A cardinal symptom of Attenion Deficit and Hyperactivity Disorder (ADHD is a general distractibility where children and adults shift their attentional focus to stimuli that are irrelevant to the ongoing behavior. This has been attributed to a deficit in dopaminergic signaling in cortico-striatal networks that regulate goal-directed behavior. Furthermore, recent imaging evidence points to an impairment of large scale, antagonistic brain networks that normally contribute to attentional engagement and disengagement, such as the task-positive networks and the Default Mode Network (DMN. Related networks are the ventral attentional network (VAN involved in attentional shifting, and the salience network (SN related to task expectancy. Here we discuss the tonic-phasic dynamics of catecholaminergic signaling in the brain, and attempt to provide a link between this and the activities of the large-scale cortical networks that regulate behavior. More specifically, we propose that a disbalance of tonic catecholamine levels during task performance produce an emphasis of phasic signaling and increased excitability of the VAN, yielding distractibility symptoms. Likewise, immaturity of the SN may relate to abnormal tonic signaling and an incapacity to build up a proper executive system during task performance. We discuss different lines of evidence including pharmacology, brain imaging and electrophysiology, that are consistent with our proposal. Finally, restoring the pharmacodynamics of catecholaminergic signaling seems crucial to alleviate ADHD symptoms; however, the possibility is open to explore cognitive rehabilitation strategies to top-down modulate network dynamics compensating the pharmacological deficits.
Muniz Oliva, Waldyr
2002-01-01
Geometric Mechanics here means mechanics on a pseudo-riemannian manifold and the main goal is the study of some mechanical models and concepts, with emphasis on the intrinsic and geometric aspects arising in classical problems. The first seven chapters are written in the spirit of Newtonian Mechanics while the last two ones as well as two of the four appendices describe the foundations and some aspects of Special and General Relativity. All the material has a coordinate free presentation but, for the sake of motivation, many examples and exercises are included in order to exhibit the desirable flavor of physical applications.
Dynamics of TCP traffic over ATM networks
Romanow, Allyn; Floyd, Sally
1995-05-01
We investigate the performance of TCP connections over ATM networks without ATM-level congestion control and compare it to the performance of TCP over packet-based networks. For simulations of congested networks, the effective throughput of TCP over ATM can be quite low when cells are dropped at the congested ATM switch. The low throughput is due to wasted bandwidth as the congested link transmits cells from 'corrupted' packets, i.e., packets in which at least one cell is dropped by the switch. We investigate two packet-discard strategies that alleviate the effects of fragmentation. Partial packet discard, in which remaining cells are discarded after one cell has been dropped from a packet, somewhat improves throughput. We introduce early packet discard, a strategy in which the switch drops whole packets prior to buffer overflow. This mechanism prevents fragmentation and restores throughput to maximal levels.
Recruitment dynamics in adaptive social networks
International Nuclear Information System (INIS)
Shkarayev, Maxim S; Shaw, Leah B; Schwartz, Ira B
2013-01-01
We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean-field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime). (paper)
Cortical electrophysiological network dynamics of feedback learning
Cohen, M.X.; Wilmes, K.A.; van de Vijver, I.
2011-01-01
Understanding the neurophysiological mechanisms of learning is important for both fundamental and clinical neuroscience. We present a neurophysiologically inspired framework for understanding cortical mechanisms of feedback-guided learning. This framework is based on dynamic changes in systems-level
Reliability-based Dynamic Network Design with Stochastic Networks
Li, H.
2009-01-01
Transportation systems are stochastic and dynamic systems. The road capacities and the travel demand are fluctuating from time to time within a day and at the same time from day to day. For road users, the travel time and travel costs experienced over time and space are stochastic, thus desire
The topology and dynamics of complex networks
Dezso, Zoltan
We start with a brief introduction about the topological properties of real networks. Most real networks are scale-free, being characterized by a power-law degree distribution. The scale-free nature of real networks leads to unexpected properties such as the vanishing epidemic threshold. Traditional methods aiming to reduce the spreading rate of viruses cannot succeed on eradicating the epidemic on a scale-free network. We demonstrate that policies that discriminate between the nodes, curing mostly the highly connected nodes, can restore a finite epidemic threshold and potentially eradicate the virus. We find that the more biased a policy is towards the hubs, the more chance it has to bring the epidemic threshold above the virus' spreading rate. We continue by studying a large Web portal as a model system for a rapidly evolving network. We find that the visitation pattern of a news document decays as a power law, in contrast with the exponential prediction provided by simple models of site visitation. This is rooted in the inhomogeneous nature of the browsing pattern characterizing individual users: the time interval between consecutive visits by the same user to the site follows a power law distribution, in contrast with the exponential expected for Poisson processes. We show that the exponent characterizing the individual user's browsing patterns determines the power-law decay in a document's visitation. Finally, we turn our attention to biological networks and demonstrate quantitatively that protein complexes in the yeast, Saccharomyces cerevisiae, are comprised of a core in which subunits are highly coexpressed, display the same deletion phenotype (essential or non-essential) and share identical functional classification and cellular localization. The results allow us to define the deletion phenotype and cellular task of most known complexes, and to identify with high confidence the biochemical role of hundreds of proteins with yet unassigned functionality.
Predictive coding of dynamical variables in balanced spiking networks.
Boerlin, Martin; Machens, Christian K; Denève, Sophie
2013-01-01
Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated.
Recurrence networks to study dynamical transitions in a turbulent combustor
Godavarthi, V.; Unni, V. R.; Gopalakrishnan, E. A.; Sujith, R. I.
2017-06-01
Thermoacoustic instability and lean blowout are the major challenges faced when a gas turbine combustor is operated under fuel lean conditions. The dynamics of thermoacoustic system is the result of complex nonlinear interactions between the subsystems—turbulent reactive flow and the acoustic field of the combustor. In order to study the transitions between the dynamical regimes in such a complex system, the time series corresponding to one of the dynamic variables is transformed to an ɛ-recurrence network. The topology of the recurrence network resembles the structure of the attractor representing the dynamics of the system. The transitions in the thermoacoustic system are then captured as the variation in the topological characteristics of the network. We show the presence of power law degree distribution in the recurrence networks constructed from time series acquired during the occurrence of combustion noise and during the low amplitude aperiodic oscillations prior to lean blowout. We also show the absence of power law degree distribution in the recurrence networks constructed from time series acquired during the occurrence of thermoacoustic instability and during the occurrence of intermittency. We demonstrate that the measures derived from recurrence network can be used as tools to capture the transitions in the turbulent combustor and also as early warning measures for predicting impending thermoacoustic instability and blowout.
Dynamic hydro-climatic networks in pristine and regulated rivers
Botter, G.; Basso, S.; Lazzaro, G.; Doulatyari, B.; Biswal, B.; Schirmer, M.; Rinaldo, A.
2014-12-01
Flow patterns observed at-a-station are the dynamical byproduct of a cascade of processes involving different compartments of the hydro-climatic network (e.g., climate, rainfall, soil, vegetation) that regulates the transformation of rainfall into streamflows. In complex branching rivers, flow regimes result from the heterogeneous arrangement around the stream network of multiple hydrologic cascades that simultaneously occur within distinct contributing areas. As such, flow regimes are seen as the integrated output of a complex "network of networks", which can be properly characterized by its degree of temporal variability and spatial heterogeneity. Hydrologic networks that generate river flow regimes are dynamic in nature. In pristine rivers, the time-variance naturally emerges at multiple timescales from climate variability (namely, seasonality and inter-annual fluctuations), implying that the magnitude (and the features) of the water flow between two nodes may be highly variable across different seasons and years. Conversely, the spatial distribution of river flow regimes within pristine rivers involves scale-dependent transport features, as well as regional climatic and soil use gradients, which in small and meso-scale catchments (A Human-impacted rivers, instead, constitute hybrid networks where observed spatio-temporal patterns are dominated by anthropogenic shifts, such as landscape alterations and river regulation. In regulated rivers, the magnitude and the features of water flows from node to node may change significantly through time due to damming and withdrawals. However, regulation may impact river regimes in a spatially heterogeneous manner (e.g. in localized river reaches), with a significant decrease of spatial correlations and network connectivity. Provided that the spatial and temporal dynamics of flow regimes in complex rivers may strongly impact important biotic processes involved in the river food web (e.g. biofilm and riparian vegetation
Cell fate reprogramming by control of intracellular network dynamics
Zanudo, Jorge G. T.; Albert, Reka
Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Although the topic of controlling the dynamics of a system has a long history in control theory, most of this work is not directly applicable to intracellular networks. Here we present a network control method that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our control method takes advantage of certain function-dependent network components and their relation to steady states in order to identify control targets, which are guaranteed to drive any initial state to the target state with 100% effectiveness and need to be applied only transiently for the system to reach and stay in the desired state. We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments. This work was supported by NSF Grant PHY 1205840.
Extracting hierarchical organization of complex networks by dynamics towards synchronization
Wang, Xiao-Hua; Jiao, Li-Cheng; Wu, Jian-She
2009-07-01
Based on the dynamics towards synchronization in hierarchical networks, we present an efficient method for extracting hierarchical organization in complex network. In the synchronization process, hierarchical structures corresponding to well defined communities of nodes emerge in different time scales, ordered in a hierarchical way. Thus, a new strategy for quantifying the dissimilarity between a pair of nodes in networks is introduced according to their time scales of synchronization. Then, using such a dissimilarity measure in conjunction with a hierarchical clustering method, our extracting method is proposed. The performance of our approach is tested on a set of computer generated and real-world networks with known hierarchical organization. The results demonstrate that our method enables us to offer insight into the complex networks with a multi-scale description. In addition, using a criterion of modularity, the method can also accurately find community structures in complex networks.
Social Network Dynamics and Psychological Adjustment among University Students
Directory of Open Access Journals (Sweden)
Yasuyuki Fukukawa
2013-04-01
Full Text Available Abstract The present study investigated the social network structure in a university class and how it changed over time. In addition, student rankings of social status in the class based on different network centrality measures were compared, and associations between students’ social status and psychological adjustment were evaluated. One university seminar class in which ten juniors and ten seniors were enrolled was followed for six months. Although the class network consisted of some disconnected subgroups at baseline, it became a single group at followup. In addition to these structural changes, measures of network integration (density and transitivity also increased from baseline to follow-up. Comparisons of centrality measures indicated that the information centrality measure best captured the network infrastructure compared to the betweenness, closeness, and degree centrality measures. Furthermore, among the centrality measures, information centrality had the most stable positive association with psychological adjustment. Theoretical and practical implications of these peer network dynamics and adjustment issues are discussed.
Analysing Stagecoach Network Problem Using Dynamic ...
African Journals Online (AJOL)
The stagecoach problem is a special type of network analysis problem in which the cities (nodes) are arranged in stages. By such human or natural arrangement, a journey from City 1 in stage 1 to City n in stage n involves visiting only one city in each intermediate stage. The stagecoach problem involves the determination ...
Dynamic intelligent paging in mobile telecommunication network
Indian Academy of Sciences (India)
S R PARIJA
2018-03-10
Mar 10, 2018 ... Mobility management; telecommunication network; intelligent profile-based paging; call data ... Bhattacharya and Das [9] studied the human mobility ...... Theory Eng. 2(4):. 581–585, https://doi.org/10.7763/ijcte.2010.v2.205. [20] Zahran A H and Liang B 2007 A generic framework for mobility modeling and ...
Dynamic intelligent paging in mobile telecommunication network
Indian Academy of Sciences (India)
S R PARIJA
2018-03-10
Mar 10, 2018 ... An illustrative scenario demonstrates the proposed approach with synthetic data. The novelty of this work is that instead of using theoretically predicted data it uses actual CDR data to profile the users. Keywords. Mobility management; telecommunication network; intelligent profile-based paging; call data.
Information space dynamics for neural networks
de Almeida, R. M.; Idiart, M. A.
2002-06-01
We propose a coupled map lattice defined on a hypercube in M dimensions, the information space, to model memory retrieval by a neural network. We consider that both neuronal activity and the spiking phase may carry information. In this model the state of the network at a given time t is completely determined by a function y(σ-->,t) of the bit strings σ-->=(σ1,σ2,...,σM), where σi=+/-1 with i=1,2,...,M, that gives the intensity with which the information σ--> is being expressed by the network. As an example, we consider logistic maps, coupled in the information space, to describe the evolution of the intensity function y(σ-->,t). We propose an interpretation of the maps in terms of the physiological state of the neurons and the coupling between them, obtain Hebb-like learning rules, show that the model works as an associative memory, numerically investigate the capacity of the network and the size of the basins of attraction, and estimate finite size effects. We finally show that the model, when exposed to sequences of uncorrelated stimuli, shows recency and latency effects that depend on the noise level, delay time of measurement, and stimulus intensity.
Content Dynamics Over the Network Cloud
2015-11-04
Ferragut, F. Paganini, "Averting Speed Inefficiency in Rate-Diverse WiFi Networks through Queueing and Aggregation", in Proc. IEEE Globecom, Anaheim, CA...Teletraffic Congress, Krakow, Poland, Sept 2012, pp. 145- 152. 2. M. Zubeldía, A. Ferragut, F. Paganini, "Averting Speed Inefficiency in Rate-Diverse WiFi
Dynamic Optical Networks for Future Internet Environments
Matera, Francesco
2014-05-01
This article reports an overview on the evolution of the optical network scenario taking into account the exponential growth of connected devices, big data, and cloud computing that is driving a concrete transformation impacting the information and communication technology world. This hyper-connected scenario is deeply affecting relationships between individuals, enterprises, citizens, and public administrations, fostering innovative use cases in practically any environment and market, and introducing new opportunities and new challenges. The successful realization of this hyper-connected scenario depends on different elements of the ecosystem. In particular, it builds on connectivity and functionalities allowed by converged next-generation networks and their capacity to support and integrate with the Internet of Things, machine-to-machine, and cloud computing. This article aims at providing some hints of this scenario to contribute to analyze impacts on optical system and network issues and requirements. In particular, the role of the software-defined network is investigated by taking into account all scenarios regarding data centers, cloud computing, and machine-to-machine and trying to illustrate all the advantages that could be introduced by advanced optical communications.
Connection Dynamics in Learning Networks: Games, Agents and Social Network Visualization
Angehrn, Albert; Maxwell, Katrina; Sereno, Bertrand
2007-01-01
This paper addresses the challenge of enhancing social interaction through value-adding connections among the online members of Learning Networks. We report on our exploration of three types of connection dynamics: (1) features enabling network member to visualize and browse through relationship
Tahmassebi, Amirhessam; Pinker-Domenig, Katja; Wengert, Georg; Lobbes, Marc; Stadlbauer, Andreas; Romero, Francisco J.; Morales, Diego P.; Castillo, Encarnacion; Garcia, Antonio; Botella, Guillermo; Meyer-Bäse, Anke
2017-05-01
Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links. In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system's eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.
Modeling and interpreting mesoscale network dynamics.
Khambhati, Ankit N; Sizemore, Ann E; Betzel, Richard F; Bassett, Danielle S
2017-06-20
Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its role in cognition and disease. Such understanding necessitates a description of the raw observations, and a delineation of computational models and mathematical theories that accurately capture fundamental principles behind the observations. Here we review recent advances in a range of modeling approaches that embrace the temporally-evolving interconnected structure of the brain and summarize that structure in a dynamic graph. We describe recent efforts to model dynamic patterns of connectivity, dynamic patterns of activity, and patterns of activity atop connectivity. In the context of these models, we review important considerations in statistical testing, including parametric and non-parametric approaches. Finally, we offer thoughts on careful and accurate interpretation of dynamic graph architecture, and outline important future directions for method development. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
Exploring the evolution of node neighborhoods in Dynamic Networks
Orman, Günce Keziban; Labatut, Vincent; Naskali, Ahmet Teoman
2017-09-01
Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of neighborhood event, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes.
A mathematical programming approach for sequential clustering of dynamic networks
Silva, Jonathan C.; Bennett, Laura; Papageorgiou, Lazaros G.; Tsoka, Sophia
2016-02-01
A common analysis performed on dynamic networks is community structure detection, a challenging problem that aims to track the temporal evolution of network modules. An emerging area in this field is evolutionary clustering, where the community structure of a network snapshot is identified by taking into account both its current state as well as previous time points. Based on this concept, we have developed a mixed integer non-linear programming (MINLP) model, SeqMod, that sequentially clusters each snapshot of a dynamic network. The modularity metric is used to determine the quality of community structure of the current snapshot and the historical cost is accounted for by optimising the number of node pairs co-clustered at the previous time point that remain so in the current snapshot partition. Our method is tested on social networks of interactions among high school students, college students and members of the Brazilian Congress. We show that, for an adequate parameter setting, our algorithm detects the classes that these students belong more accurately than partitioning each time step individually or by partitioning the aggregated snapshots. Our method also detects drastic discontinuities in interaction patterns across network snapshots. Finally, we present comparative results with similar community detection methods for time-dependent networks from the literature. Overall, we illustrate the applicability of mathematical programming as a flexible, adaptable and systematic approach for these community detection problems. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.
Distributed dynamic simulations of networked control and building performance applications.
Yahiaoui, Azzedine
2018-02-01
The use of computer-based automation and control systems for smart sustainable buildings, often so-called Automated Buildings (ABs), has become an effective way to automatically control, optimize, and supervise a wide range of building performance applications over a network while achieving the minimum energy consumption possible, and in doing so generally refers to Building Automation and Control Systems (BACS) architecture. Instead of costly and time-consuming experiments, this paper focuses on using distributed dynamic simulations to analyze the real-time performance of network-based building control systems in ABs and improve the functions of the BACS technology. The paper also presents the development and design of a distributed dynamic simulation environment with the capability of representing the BACS architecture in simulation by run-time coupling two or more different software tools over a network. The application and capability of this new dynamic simulation environment are demonstrated by an experimental design in this paper.
An Efficient Mesh Generation Method for Fractured Network System Based on Dynamic Grid Deformation
Directory of Open Access Journals (Sweden)
Shuli Sun
2013-01-01
Full Text Available Meshing quality of the discrete model influences the accuracy, convergence, and efficiency of the solution for fractured network system in geological problem. However, modeling and meshing of such a fractured network system are usually tedious and difficult due to geometric complexity of the computational domain induced by existence and extension of fractures. The traditional meshing method to deal with fractures usually involves boundary recovery operation based on topological transformation, which relies on many complicated techniques and skills. This paper presents an alternative and efficient approach for meshing fractured network system. The method firstly presets points on fractures and then performs Delaunay triangulation to obtain preliminary mesh by point-by-point centroid insertion algorithm. Then the fractures are exactly recovered by local correction with revised dynamic grid deformation approach. Smoothing algorithm is finally applied to improve the quality of mesh. The proposed approach is efficient, easy to implement, and applicable to the cases of initial existing fractures and extension of fractures. The method is successfully applied to modeling of two- and three-dimensional discrete fractured network (DFN system in geological problems to demonstrate its effectiveness and high efficiency.
Node-Dependence-Based Dynamic Incentive Algorithm in Opportunistic Networks
Directory of Open Access Journals (Sweden)
Ruiyun Yu
2014-01-01
Full Text Available Opportunistic networks lack end-to-end paths between source nodes and destination nodes, so the communications are mainly carried out by the “store-carry-forward” strategy. Selfish behaviors of rejecting packet relay requests will severely worsen the network performance. Incentive is an efficient way to reduce selfish behaviors and hence improves the reliability and robustness of the networks. In this paper, we propose the node-dependence-based dynamic gaming incentive (NDI algorithm, which exploits the dynamic repeated gaming to motivate nodes relaying packets for other nodes. The NDI algorithm presents a mechanism of tolerating selfish behaviors of nodes. Reward and punishment methods are also designed based on the node dependence degree. Simulation results show that the NDI algorithm is effective in increasing the delivery ratio and decreasing average latency when there are a lot of selfish nodes in the opportunistic networks.
Triadic closure dynamics drives scaling laws in social multiplex networks
International Nuclear Information System (INIS)
Klimek, Peter; Thurner, Stefan
2013-01-01
Social networks exhibit scaling laws for several structural characteristics, such as degree distribution, scaling of the attachment kernel and clustering coefficients as a function of node degree. A detailed understanding if and how these scaling laws are inter-related is missing so far, let alone whether they can be understood through a common, dynamical principle. We propose a simple model for stationary network formation and show that the three mentioned scaling relations follow as natural consequences of triadic closure. The validity of the model is tested on multiplex data from a well-studied massive multiplayer online game. We find that the three scaling exponents observed in the multiplex data for the friendship, communication and trading networks can simultaneously be explained by the model. These results suggest that triadic closure could be identified as one of the fundamental dynamical principles in social multiplex network formation. (paper)
Dynamic Relaying in 3GPP LTE-Advanced Networks
Directory of Open Access Journals (Sweden)
Van Phan Vinh
2009-01-01
Full Text Available Relaying is one of the proposed technologies for LTE-Advanced networks. In order to enable a flexible and reliable relaying support, the currently adopted architectural structure of LTE networks has to be modified. In this paper, we extend the LTE architecture to enable dynamic relaying, while maintaining backward compatibility with LTE Release 8 user equipments, and without limiting the flexibility and reliability expected from relaying. With dynamic relaying, relays can be associated with base stations on a need basis rather than in a fixed manner which is based only on initial radio planning. Proposals are also given on how to further improve a relay enhanced LTE network by enabling multiple interfaces between the relay nodes and their controlling base stations, which can possibly be based on technologies different from LTE, so that load balancing can be realized. This load balancing can be either between different base stations or even between different networks.
Enabling dynamic network analysis through visualization in TVNViewer
Directory of Open Access Journals (Sweden)
Curtis Ross E
2012-08-01
Full Text Available Abstract Background Many biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development ofTVNViewer (http://sailing.cs.cmu.edu/tvnviewer, a visualization tool for dynamic network analysis. Results In this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets. Conclusions TVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space.
Enabling dynamic network analysis through visualization in TVNViewer
2012-01-01
Background Many biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development ofTVNViewer (http://sailing.cs.cmu.edu/tvnviewer), a visualization tool for dynamic network analysis. Results In this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets. Conclusions TVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space. PMID:22897913
An Efficient Dynamic Trust Evaluation Model for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Zhengwang Ye
2017-01-01
Full Text Available Trust evaluation is an effective method to detect malicious nodes and ensure security in wireless sensor networks (WSNs. In this paper, an efficient dynamic trust evaluation model (DTEM for WSNs is proposed, which implements accurate, efficient, and dynamic trust evaluation by dynamically adjusting the weights of direct trust and indirect trust and the parameters of the update mechanism. To achieve accurate trust evaluation, the direct trust is calculated considering multitrust including communication trust, data trust, and energy trust with the punishment factor and regulating function. The indirect trust is evaluated conditionally by the trusted recommendations from a third party. Moreover, the integrated trust is measured by assigning dynamic weights for direct trust and indirect trust and combining them. Finally, we propose an update mechanism by a sliding window based on induced ordered weighted averaging operator to enhance flexibility. We can dynamically adapt the parameters and the interactive history windows number according to the actual needs of the network to realize dynamic update of direct trust value. Simulation results indicate that the proposed dynamic trust model is an efficient dynamic and attack-resistant trust evaluation model. Compared with existing approaches, the proposed dynamic trust model performs better in defending multiple malicious attacks.
LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS.
Almquist, Zack W; Butts, Carter T
2014-08-01
Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach.
Generative modelling of regulated dynamical behavior in cultured neuronal networks
Volman, Vladislav; Baruchi, Itay; Persi, Erez; Ben-Jacob, Eshel
2004-04-01
The spontaneous activity of cultured in vitro neuronal networks exhibits rich dynamical behavior. Despite the artificial manner of their construction, the networks’ activity includes features which seemingly reflect the action of underlying regulating mechanism rather than arbitrary causes and effects. Here, we study the cultured networks dynamical behavior utilizing a generative modelling approach. The idea is to include the minimal required generic mechanisms to capture the non-autonomous features of the behavior, which can be reproduced by computer modelling, and then, to identify the additional features of biotic regulation in the observed behavior which are beyond the scope of the model. Our model neurons are composed of soma described by the two Morris-Lecar dynamical variables (voltage and fraction of open potassium channels), with dynamical synapses described by the Tsodyks-Markram three variables dynamics. The model neuron satisfies our self-consistency test: when fed with data recorded from a real cultured networks, it exhibits dynamical behavior very close to that of the networks’ “representative” neuron. Specifically, it shows similar statistical scaling properties (approximated by similar symmetric Lévy distribution with finite mean). A network of such M-L elements spontaneously generates (when weak “structured noise” is added) synchronized bursting events (SBEs) similar to the observed ones. Both the neuronal statistical scaling properties within the bursts and the properties of the SBEs time series show generative (a new discussed concept) agreement with the recorded data. Yet, the model network exhibits different structure of temporal variations and does not recover the observed hierarchical temporal ordering, unless fed with recorded special neurons (with much higher rates of activity), thus indicating the existence of self-regulation mechanisms. It also implies that the spontaneous activity is not simply noise-induced. Instead, the
Neural network with dynamically adaptable neurons
Tawel, Raoul (Inventor)
1994-01-01
This invention is an adaptive neuron for use in neural network processors. The adaptive neuron participates in the supervised learning phase of operation on a co-equal basis with the synapse matrix elements by adaptively changing its gain in a similar manner to the change of weights in the synapse IO elements. In this manner, training time is decreased by as much as three orders of magnitude.
Dynamics in a delayed-neural network
International Nuclear Information System (INIS)
Yuan Yuan
2007-01-01
In this paper, we consider a neural network of four identical neurons with time-delayed connections. Some parameter regions are given for global, local stability and synchronization using the theory of functional differential equations. The root distributions in the corresponding characteristic transcendental equation are analyzed, Pitchfork bifurcation, Hopf and equivariant Hopf bifurcations are investigated by revealing the center manifolds and normal forms. Numerical simulations are shown the agreements with the theoretical results
A new dynamical layout algorithm for complex biochemical reaction networks
Kummer Ursula; Wegner Katja
2005-01-01
Abstract Background To study complex biochemical reaction networks in living cells researchers more and more rely on databases and computational methods. In order to facilitate computational approaches, visualisation techniques are highly important. Biochemical reaction networks, e.g. metabolic pathways are often depicted as graphs and these graphs should be drawn dynamically to provide flexibility in the context of different data. Conventional layout algorithms are not sufficient for every k...
SEWER NETWORK DISCHARGE OPTIMIZATION USING THE DYNAMIC PROGRAMMING
Directory of Open Access Journals (Sweden)
Viorel MINZU
2015-12-01
Full Text Available It is necessary to adopt an optimal control that allows an efficient usage of the existing sewer networks, in order to avoid the building of new retention facilities. The main objective of the control action is to minimize the overflow volume of a sewer network. This paper proposes a method to apply a solution obtained by discrete dynamic programming through a realistic closed loop system.
Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks
Pyle, Ryan; Rosenbaum, Robert
2017-01-01
Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.
Neural networks for nonlinear dynamic system modelling and identification
Chen, S.; Billings, S. A.
1992-01-01
Many real-world systems exhibit complex non-linear characteristics and cannot be treated satisfactorily using linear systems theory. A neural network which has the ability to learn sophisticated non-linear relationships provides an ideal means of modelling complicated non-linear systems. This paper addresses the issues related to the identification of non-linear discrete-time dynamic systems using neural networks..........
Stochastic population dynamic models as probability networks
M.E. and D.C. Lee. Borsuk
2009-01-01
The dynamics of a population and its response to environmental change depend on the balance of birth, death and age-at-maturity, and there have been many attempts to mathematically model populations based on these characteristics. Historically, most of these models were deterministic, meaning that the results were strictly determined by the equations of the model and...
Robust adaptive synchronization of general dynamical networks ...
Indian Academy of Sciences (India)
The numerical simulations of the time-delay Lorenz chaotic system as local dynamical node are provided to observe and verify the viability and productivity of the ... The domain part of the email address of all email addresses used by the office of Indian Academy of Sciences, including those of the staff, the journals, various ...
Dynamic concurrent partnership networks incorporating demography
Leung, K.Y.|info:eu-repo/dai/nl/355091208; Kretzschmar, M.E.E.; Diekmann, O.|info:eu-repo/dai/nl/071896856
2012-01-01
We introduce a population model that incorporates •demographic turnover •individuals that are involved in a dynamically varying number of simultaneous partnerships From a mathematical point of view we deal with continuous-time Markov chains at the individual level, with the interaction between
Transport efficiency and dynamics of hydraulic fracture networks
Sachau, Till; Bons, Paul; Gomez-Rivas, Enrique
2015-08-01
Intermittent fluid pulses in the Earth's crust can explain a variety of geological phenomena, for instance the occurrence of hydraulic breccia. Fluid transport in the crust is usually modeled as continuous darcian flow, ignoring that sufficient fluid overpressure can cause hydraulic fractures as fluid pathways with very dynamic behavior. Resulting hydraulic fracture networks are largely self-organized: opening and healing of hydraulic fractures depends on local fluid pressure, which is, in turn, largely controlled by the fracture network. We develop a crustal-scale 2D computer model designed to simulate this process. To focus on the dynamics of the process we chose a setup as simple as possible. Control factors are constant overpressure at a basal fluid source and a constant 'viscous' parameter controlling fracture-healing. Our results indicate that at large healing rates hydraulic fractures are mobile, transporting fluid in intermittent pulses to the surface and displaying a 1/fα behavior. Low healing rates result in stable networks and constant flow. The efficiency of the fluid transport is independent from the closure dynamics of veins or fractures. More important than preexisting fracture networks is the distribution of fluid pressure. A key requirement for dynamic fracture networks is the presence of a fluid pressure gradient.
Transport efficiency and dynamics of hydraulic fracture networks
Directory of Open Access Journals (Sweden)
Till eSachau
2015-08-01
Full Text Available Intermittent fluid pulses in the Earth's crust can explain a variety of geological phenomena, for instance the occurrence of hydraulic breccia. Fluid transport in the crust is usually modeled as continuous darcian flow, ignoring that sufficient fluid overpressure can cause hydraulic fractures as fluid pathways with very dynamic behavior. Resulting hydraulic fracture networks are largely self-organized: opening and healing of hydraulic fractures depends on local fluid pressure, which is, in turn, largely controlled by the fracture network. We develop a crustal-scale 2D computer model designed to simulate this process. To focus on the dynamics of the process we chose a setup as simple as possible. Control factors are constant overpressure at a basal fluid source and a constant 'viscous' parameter controlling fracture-healing. Our results indicate that at large healing rates hydraulic fractures are mobile, transporting fluid in intermittent pulses to the surface and displaying a 1/fα behavior. Low healing rates result in stable networks and constant flow. The efficiency of the fluid transport is independent from the closure dynamics of veins or fractures. More important than preexisting fracture networks is the distribution of fluid pressure. A key requirement for dynamic fracture networks is the presence of a fluid pressure gradient.
Hydration dynamics of a lipid membrane: Hydrogen bond networks and lipid-lipid associations
Srivastava, Abhinav; Debnath, Ananya
2018-03-01
Dynamics of hydration layers of a dimyristoylphosphatidylcholine (DMPC) bilayer are investigated using an all atom molecular dynamics simulation. Based upon the geometric criteria, continuously residing interface water molecules which form hydrogen bonds solely among themselves and then concertedly hydrogen bonded to carbonyl, phosphate, and glycerol head groups of DMPC are identified. The interface water hydrogen bonded to lipids shows slower relaxation rates for translational and rotational dynamics compared to that of the bulk water and is found to follow sub-diffusive and non-diffusive behaviors, respectively. The mean square displacements and the reorientational auto-correlation functions are slowest for the interfacial waters hydrogen bonded to the carbonyl oxygen since these are buried deep in the hydrophobic core among all interfacial water studied. The intermittent hydrogen bond auto-correlation functions are calculated, which allows breaking and reformations of the hydrogen bonds. The auto-correlation functions for interfacial hydrogen bonded networks develop humps during a transition from cage-like motion to eventual power law behavior of t-3/2. The asymptotic t-3/2 behavior indicates translational diffusion dictated dynamics during hydrogen bond breaking and formation irrespective of the nature of the chemical confinement. Employing reactive flux correlation analysis, the forward rate constant of hydrogen bond breaking and formation is calculated which is used to obtain Gibbs energy of activation of the hydrogen bond breaking. The relaxation rates of the networks buried in the hydrophobic core are slower than the networks near the lipid-water interface which is again slower than bulk due to the higher Gibbs energy of activation. Since hydrogen bond breakage follows a translational diffusion dictated mechanism, chemically confined hydrogen bond networks need an activation energy to diffuse through water depleted hydrophobic environments. Our calculations
Deciphering the imprint of topology on nonlinear dynamical network stability
International Nuclear Information System (INIS)
Nitzbon, J; Schultz, P; Heitzig, J; Kurths, J; Hellmann, F
2017-01-01
Coupled oscillator networks show complex interrelations between topological characteristics of the network and the nonlinear stability of single nodes with respect to large but realistic perturbations. We extend previous results on these relations by incorporating sampling-based measures of the transient behaviour of the system, its survivability, as well as its asymptotic behaviour, its basin stability. By combining basin stability and survivability we uncover novel, previously unknown asymptotic states with solitary, desynchronized oscillators which are rotating with a frequency different from their natural one. They occur almost exclusively after perturbations at nodes with specific topological properties. More generally we confirm and significantly refine the results on the distinguished role tree-shaped appendices play for nonlinear stability. We find a topological classification scheme for nodes located in such appendices, that exactly separates them according to their stability properties, thus establishing a strong link between topology and dynamics. Hence, the results can be used for the identification of vulnerable nodes in power grids or other coupled oscillator networks. From this classification we can derive general design principles for resilient power grids. We find that striving for homogeneous network topologies facilitates a better performance in terms of nonlinear dynamical network stability. While the employed second-order Kuramoto-like model is parametrised to be representative for power grids, we expect these insights to transfer to other critical infrastructure systems or complex network dynamics appearing in various other fields. (paper)
Microscale Mechanics of Actin Networks During Dynamic Assembly and Dissociation
Gurmessa, Bekele; Robertson-Anderson, Rae; Ross, Jennifer; Nguyen, Dan; Saleh, Omar
Actin is one of the key components of the cytoskeleton, enabling cells to move and divide while maintaining shape by dynamic polymerization, dissociation and crosslinking. Actin polymerization and network formation is driven by ATP hydrolysis and varies depending on the concentrations of actin monomers and crosslinking proteins. The viscoelastic properties of steady-state actin networks have been well-characterized, yet the mechanical properties of these non-equilibrium systems during dynamic assembly and disassembly remain to be understood. We use semipermeable microfluidic devices to induce in situ dissolution and re-polymerization of entangled and crosslinked actin networks, by varying ATP concentrations in real-time, while measuring the mechanical properties during disassembly and re-assembly. We use optical tweezers to sinusoidally oscillate embedded microspheres and measure the resulting force at set time-intervals and in different regions of the network during cyclic assembly/disassembly. We determine the time-dependent viscoelastic properties of non-equilibrium network intermediates and the reproducibility and homogeneity of network formation and dissolution. Results inform the role that cytoskeleton reorganization plays in the dynamic multifunctional mechanics of cells. NSF CAREER Award (DMR-1255446) and a Scialog Collaborative Innovation Award funded by Research Corporation for Scientific Advancement (Grant No. 24192).
International Nuclear Information System (INIS)
Xiao-Zheng, Jin; Guang-Hong, Yang
2010-01-01
This paper presents a new robust adaptive synchronization method for a class of uncertain dynamical complex networks with network failures and coupling time-varying delays. Adaptive schemes are proposed to adjust controller parameters for the faulty network compensations, as well as to estimate the upper and lower bounds of delayed state errors and perturbations to compensate the effects of delay and perturbation on-line without assuming symmetry or irreducibility of networks. It is shown that, through Lyapunov stability theory, distributed adaptive controllers constructed by the adaptive schemes are successful in ensuring the achievement of asymptotic synchronization of networks in the present of faulty and delayed networks, and perturbation inputs. A Chua's circuit network example is finally given to show the effectiveness of the proposed synchronization criteria. (general)
Applications of flow-networks to opinion-dynamics
Tupikina, Liubov; Kurths, Jürgen
2015-04-01
Networks were successfully applied to describe complex systems, such as brain, climate, processes in society. Recently a socio-physical problem of opinion-dynamics was studied using network techniques. We present the toy-model of opinion-formation based on the physical model of advection-diffusion. We consider spreading of the opinion on the fixed subject, assuming that opinion on society is binary: if person has opinion then the state of the node in the society-network equals 1, if the person doesn't have opinion state of the node equals 0. Opinion can be spread from one person to another if they know each other, or in the network-terminology, if the nodes are connected. We include into the system governed by advection-diffusion equation the external field to model such effects as for instance influence from media. The assumptions for our model can be formulated as the following: 1.the node-states are influenced by the network structure in such a way, that opinion can be spread only between adjacent nodes (the advective term of the opinion-dynamics), 2.the network evolution can have two scenarios: -network topology is not changing with time; -additional links can appear or disappear each time-step with fixed probability which requires adaptive networks properties. Considering these assumptions for our system we obtain the system of equations describing our model-dynamics which corresponds well to other socio-physics models, for instance, the model of the social cohesion and the famous voter-model. We investigate the behavior of the suggested model studying "waiting time" of the system, time to get to the stable state, stability of the model regimes for different values of model parameters and network topology.
Inferring connectivity in networked dynamical systems: Challenges using Granger causality
Lusch, Bethany; Maia, Pedro D.; Kutz, J. Nathan
2016-09-01
Determining the interactions and causal relationships between nodes in an unknown networked dynamical system from measurement data alone is a challenging, contemporary task across the physical, biological, and engineering sciences. Statistical methods, such as the increasingly popular Granger causality, are being broadly applied for data-driven discovery of connectivity in fields from economics to neuroscience. A common version of the algorithm is called pairwise-conditional Granger causality, which we systematically test on data generated from a nonlinear model with known causal network structure. Specifically, we simulate networked systems of Kuramoto oscillators and use the Multivariate Granger Causality Toolbox to discover the underlying coupling structure of the system. We compare the inferred results to the original connectivity for a wide range of parameters such as initial conditions, connection strengths, community structures, and natural frequencies. Our results show a significant systematic disparity between the original and inferred network, unless the true structure is extremely sparse or dense. Specifically, the inferred networks have significant discrepancies in the number of edges and the eigenvalues of the connectivity matrix, demonstrating that they typically generate dynamics which are inconsistent with the ground truth. We provide a detailed account of the dynamics for the Erdős-Rényi network model due to its importance in random graph theory and network science. We conclude that Granger causal methods for inferring network structure are highly suspect and should always be checked against a ground truth model. The results also advocate the need to perform such comparisons with any network inference method since the inferred connectivity results appear to have very little to do with the ground truth system.
Coupled disease-behavior dynamics on complex networks: A review
Wang, Zhen; Andrews, Michael A.; Wu, Zhi-Xi; Wang, Lin; Bauch, Chris T.
2015-12-01
It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.
Coupled disease-behavior dynamics on complex networks: A review.
Wang, Zhen; Andrews, Michael A; Wu, Zhi-Xi; Wang, Lin; Bauch, Chris T
2015-12-01
It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years. Copyright © 2015 Elsevier B.V. All rights reserved.
Danube Delta Biosphere Reserve hydrographic network morphological dynamics
Directory of Open Access Journals (Sweden)
CIOACĂ Eugenia
2009-09-01
Full Text Available The paper presents the Danube Delta Biosphere Reserve (DDBR hydrographic network morphological changes investigated and presented as geospatial data as they resulted from fieldmeasurements. These data are part of a complex project started in 2007 (with the acronym MORFDD. As a preliminary stage of this project, they contribute to the DDBR hydrographic network mathematical / hydraulic model construction related to hydro-morphology and water quality dynamics. Geospatial data, related tomorphological parameters, aim to create a scientific knowledge on hydro-morphologic changes by emphasizing the DDBR hydrographic network zones where fluvial processes, erosion and alluvial sedimentation, are active.
Synchronization in Complex Networks of Nonlinear Dynamical Systems
Wu, Chai Wah
2007-01-01
This book brings together two emerging research areas: synchronization in coupled nonlinear systems and complex networks, and study conditions under which a complex network of dynamical systems synchronizes. While there are many texts that study synchronization in chaotic systems or properties of complex networks, there are few texts that consider the intersection of these two very active and interdisciplinary research areas. The main theme of this book is that synchronization conditions can be related to graph theoretical properties of the underlying coupling topology. The book introduces ide
Glucans monomer-exchange dynamics as an open chemical network
Energy Technology Data Exchange (ETDEWEB)
Rao, Riccardo, E-mail: riccardo.rao@uni.lu; Esposito, Massimiliano, E-mail: massimiliano.esposito@uni.lu [Complex Systems and Statistical Mechanics, Physics and Materials Science Research Unit, University of Luxembourg, L-1511 Luxembourg (Luxembourg); Lacoste, David [Laboratoire de Physico-Chimie Théorique, UMR CNRS Gulliver 7083, ESPCI - 10 rue Vauquelin, F-75231 Paris (France)
2015-12-28
We describe the oligosaccharides-exchange dynamics performed by the so-called D-enzymes on polysaccharides. To mimic physiological conditions, we treat this process as an open chemical network by assuming some of the polymer concentrations fixed (chemostatting). We show that three different long-time behaviors may ensue: equilibrium states, nonequilibrium steady states, and continuous growth states. We dynamically and thermodynamically characterize these states and emphasize the crucial role of conservation laws in identifying the chemostatting conditions inducing them.
An Improved Dynamic Programming Decomposition Approach for Network Revenue Management
Dan Zhang
2011-01-01
We consider a nonlinear nonseparable functional approximation to the value function of a dynamic programming formulation for the network revenue management (RM) problem with customer choice. We propose a simultaneous dynamic programming approach to solve the resulting problem, which is a nonlinear optimization problem with nonlinear constraints. We show that our approximation leads to a tighter upper bound on optimal expected revenue than some known bounds in the literature. Our approach can ...
Dynamics of the mitochondrial network during mitosis.
Kanfer, Gil; Kornmann, Benoît
2016-04-15
During mitosis, cells undergo massive deformation and reorganization, impacting on all cellular structures. Mitochondria, in particular, are highly dynamic organelles, which constantly undergo events of fission, fusion and cytoskeleton-based transport. This plasticity ensures the proper distribution of the metabolism, and the proper inheritance of functional organelles. During cell cycle, mitochondria undergo dramatic changes in distribution. In this review, we focus on the dynamic events that target mitochondria during mitosis. We describe how the cell-cycle-dependent microtubule-associated protein centromeric protein F (Cenp-F) is recruited to mitochondria by the mitochondrial Rho GTPase (Miro) to promote mitochondrial transport and re-distribution following cell division. © 2016 Authors; published by Portland Press Limited.
Controllability of Weighted and Directed Networks with Nonidentical Node Dynamics
Directory of Open Access Journals (Sweden)
Linying Xiang
2013-01-01
Full Text Available The concept of controllability from control theory is applied to weighted and directed networks with heterogenous linear or linearized node dynamics subject to exogenous inputs, where the nodes are grouped into leaders and followers. Under this framework, the controllability of the controlled network can be decomposed into two independent problems: the controllability of the isolated leader subsystem and the controllability of the extended follower subsystem. Some necessary and/or sufficient conditions for the controllability of the leader-follower network are derived based on matrix theory and graph theory. In particular, it is shown that a single-leader network is controllable if it is a directed path or cycle, but it is uncontrollable for a complete digraph or a star digraph in general. Furthermore, some approaches to improving the controllability of a heterogenous network are presented. Some simulation examples are given for illustration and verification.
Sending policies in dynamic wireless mesh using network coding
DEFF Research Database (Denmark)
Pandi, Sreekrishna; Fitzek, Frank; Pihl, Jeppe
2015-01-01
This paper demonstrates the quick prototyping capabilities of the Python-Kodo library for network coding based performance evaluation and investigates the problem of data redundancy in a network coded wireless mesh with opportunistic overhearing. By means of several wireless meshed architectures...... of appropriate relays. Finally, various sending policies that can be employed by the nodes in order to improve the overall transmission efficiency in a dynamic wireless mesh network are discussed and their performance is analysed on the constructed simulation setup....... simulated on the constructed test-bed, the advantage of network coding over state of the art routing schemes and the challenges of this new technology are shown. By providing maximum control of the network coding parameters and the simulation environment to the user, the test-bed facilitates quick...
Dynamical interplay between awareness and epidemic spreading in multiplex networks.
Granell, Clara; Gómez, Sergio; Arenas, Alex
2013-09-20
We present the analysis of the interrelation between two processes accounting for the spreading of an epidemic, and the information awareness to prevent its infection, on top of multiplex networks. This scenario is representative of an epidemic process spreading on a network of persistent real contacts, and a cyclic information awareness process diffusing in the network of virtual social contacts between the same individuals. The topology corresponds to a multiplex network where two diffusive processes are interacting affecting each other. The analysis using a microscopic Markov chain approach reveals the phase diagram of the incidence of the epidemics and allows us to capture the evolution of the epidemic threshold depending on the topological structure of the multiplex and the interrelation with the awareness process. Interestingly, the critical point for the onset of the epidemics has a critical value (metacritical point) defined by the awareness dynamics and the topology of the virtual network, from which the onset increases and the epidemics incidence decreases.
A network-based dynamical ranking system for competitive sports
Motegi, Shun; Masuda, Naoki
2012-12-01
From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.
2003-01-01
Network Physics, provider of business-level, traffic flow-based network management solutions, today announced the introduction of the Network Physics NP/BizFlow-1000. With the NP/BizFlow-1000, Fortune 1000 companies with complex and dynamic networks can analyze the flows that link business groups, critical applications, and network software and hardware (1 page).
Network Unfolding Map by Vertex-Edge Dynamics Modeling.
Verri, Filipe Alves Neto; Urio, Paulo Roberto; Zhao, Liang
2018-02-01
The emergence of collective dynamics in neural networks is a mechanism of the animal and human brain for information processing. In this paper, we develop a computational technique using distributed processing elements in a complex network, which are called particles, to solve semisupervised learning problems. Three actions govern the particles' dynamics: generation, walking, and absorption. Labeled vertices generate new particles that compete against rival particles for edge domination. Active particles randomly walk in the network until they are absorbed by either a rival vertex or an edge currently dominated by rival particles. The result from the model evolution consists of sets of edges arranged by the label dominance. Each set tends to form a connected subnetwork to represent a data class. Although the intrinsic dynamics of the model is a stochastic one, we prove that there exists a deterministic version with largely reduced computational complexity; specifically, with linear growth. Furthermore, the edge domination process corresponds to an unfolding map in such way that edges "stretch" and "shrink" according to the vertex-edge dynamics. Consequently, the unfolding effect summarizes the relevant relationships between vertices and the uncovered data classes. The proposed model captures important details of connectivity patterns over the vertex-edge dynamics evolution, in contrast to the previous approaches, which focused on only vertex or only edge dynamics. Computer simulations reveal that the new model can identify nonlinear features in both real and artificial data, including boundaries between distinct classes and overlapping structures of data.
Complex human mobility dynamics on a network
International Nuclear Information System (INIS)
Szell, M.
2010-01-01
Massive multiplayer online games provide a fascinating new way of observing hundreds of thousands of simultaneously interacting individuals engaged in virtual socio-economic activities. We have compiled a data set consisting of practically all actions of all players over a period of four years from an online game played by over 350,000 people. The universe of this online world is a lattice-like network on which players move in order to interact with other players. We focus on the mobility of human players on this network over a time-period of 500 days. We take a number of mobility measurements and compare them with measures of simulated random walkers on the same topology. Mobility of players is sub-diffusive - the mean squared displacement follows a power law with exponent 0.4 - and significantly deviates from mobility patterns of random walkers. Mean first passage times and transition counts relate via a power-law with slope -1/3. We compare our results with studies where human mobility was measured via mobile phone data and find striking similarities. (author)
Popularity and Novelty Dynamics in Evolving Networks.
Abbas, Khushnood; Shang, Mingsheng; Abbasi, Alireza; Luo, Xin; Xu, Jian Jun; Zhang, Yu-Xia
2018-04-20
Network science plays a big role in the representation of real-world phenomena such as user-item bipartite networks presented in e-commerce or social media platforms. It provides researchers with tools and techniques to solve complex real-world problems. Identifying and predicting future popularity and importance of items in e-commerce or social media platform is a challenging task. Some items gain popularity repeatedly over time while some become popular and novel only once. This work aims to identify the key-factors: popularity and novelty. To do so, we consider two types of novelty predictions: items appearing in the popular ranking list for the first time; and items which were not in the popular list in the past time window, but might have been popular before the recent past time window. In order to identify the popular items, a careful consideration of macro-level analysis is needed. In this work we propose a model, which exploits item level information over a span of time to rank the importance of the item. We considered ageing or decay effect along with the recent link-gain of the items. We test our proposed model on four various real-world datasets using four information retrieval based metrics.
Brovelli, M. A.; Minghini, M.; Molinari, M. E.
2016-06-01
OpenStreetMap (OSM) is the largest spatial database of the world. One of the most frequently occurring geospatial elements within this database is the road network, whose quality is crucial for applications such as routing and navigation. Several methods have been proposed for the assessment of OSM road network quality, however they are often tightly coupled to the characteristics of the authoritative dataset involved in the comparison. This makes it hard to replicate and extend these methods. This study relies on an automated procedure which was recently developed for comparing OSM with any road network dataset. It is based on three Python modules for the open source GRASS GIS software and provides measures of OSM road network spatial accuracy and completeness. Provided that the user is familiar with the authoritative dataset used, he can adjust the values of the parameters involved thanks to the flexibility of the procedure. The method is applied to assess the quality of the Paris OSM road network dataset through a comparison against the French official dataset provided by the French National Institute of Geographic and Forest Information (IGN). The results show that the Paris OSM road network has both a high completeness and spatial accuracy. It has a greater length than the IGN road network, and is found to be suitable for applications requiring spatial accuracies up to 5-6 m. Also, the results confirm the flexibility of the procedure for supporting users in carrying out their own comparisons between OSM and reference road datasets.
Complex quantum network geometries: Evolution and phase transitions
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.
Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity.
Ly, Cheng
2015-12-01
Heterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a crucial feature in neural processing. Despite its importance, this physiological feature has traditionally been neglected in theoretical studies of cortical neural networks. Thus, there is still a lot unknown about the consequences of cellular and circuit heterogeneity in spiking neural networks. In particular, combining network or synaptic heterogeneity and intrinsic heterogeneity has yet to be considered systematically despite the fact that both are known to exist and likely have significant roles in neural network dynamics. In a canonical recurrent spiking neural network model, we study how these two forms of heterogeneity lead to different distributions of excitatory firing rates. To analytically characterize how these types of heterogeneities affect the network, we employ a dimension reduction method that relies on a combination of Monte Carlo simulations and probability density function equations. We find that the relationship between intrinsic and network heterogeneity has a strong effect on the overall level of heterogeneity of the firing rates. Specifically, this relationship can lead to amplification or attenuation of firing rate heterogeneity, and these effects depend on whether the recurrent network is firing asynchronously or rhythmically firing. These observations are captured with the aforementioned reduction method, and furthermore simpler analytic descriptions based on this dimension reduction method are developed. The final analytic descriptions provide compact and descriptive formulas for how the relationship between intrinsic and network heterogeneity determines the firing rate heterogeneity dynamics in various settings.
Directory of Open Access Journals (Sweden)
Gustavo Fernández-Torres
2015-01-01
Full Text Available A geometric modification to the Newton-Secant method to obtain the root of a nonlinear equation is described and analyzed. With the same number of evaluations, the modified method converges faster than Newton’s method and the convergence order of the new method is 1+2≈2.4142. The numerical examples and the dynamical analysis show that the new method is robust and converges to the root in many cases where Newton’s method and other recently published methods fail.
Network dynamics of human face perception.
Directory of Open Access Journals (Sweden)
Cihan Mehmet Kadipasaoglu
Full Text Available Prevailing theories suggests that cortical regions responsible for face perception operate in a serial, feed-forward fashion. Here, we utilize invasive human electrophysiology to evaluate serial models of face-processing via measurements of cortical activation, functional connectivity, and cortico-cortical evoked potentials. We find that task-dependent changes in functional connectivity between face-selective regions in the inferior occipital (f-IOG and fusiform gyrus (f-FG are bidirectional, not feed-forward, and emerge following feed-forward input from early visual cortex (EVC to both of these regions. Cortico-cortical evoked potentials similarly reveal independent signal propagations between EVC and both f-IOG and f-FG. These findings are incompatible with serial models, and support a parallel, distributed network underpinning face perception in humans.
Network dynamics of human face perception
Baboyan, Vatche George; Rollo, Matthew; Pieters, Thomas Allyn
2017-01-01
Prevailing theories suggests that cortical regions responsible for face perception operate in a serial, feed-forward fashion. Here, we utilize invasive human electrophysiology to evaluate serial models of face-processing via measurements of cortical activation, functional connectivity, and cortico-cortical evoked potentials. We find that task-dependent changes in functional connectivity between face-selective regions in the inferior occipital (f-IOG) and fusiform gyrus (f-FG) are bidirectional, not feed-forward, and emerge following feed-forward input from early visual cortex (EVC) to both of these regions. Cortico-cortical evoked potentials similarly reveal independent signal propagations between EVC and both f-IOG and f-FG. These findings are incompatible with serial models, and support a parallel, distributed network underpinning face perception in humans. PMID:29190811
Gossips and prejudices: ergodic randomized dynamics in social networks
Frasca, Paolo; Ravazzi, Chiara; Tempo, Roberto; Ishii, Hideaki
In this paper we study a new model of opinion dynamics in social networks, which has two main features. First, agents asynchronously interact in pairs, and these pairs are chosen according to a random process: following recent literature, we refer to this communication model as “gossiping‿. Second,
A Neural Network Model for Dynamics Simulation | Bholoa ...
African Journals Online (AJOL)
University of Mauritius Research Journal. Journal Home · ABOUT THIS JOURNAL · Advanced Search · Current Issue · Archives · Journal Home > Vol 15, No 1 (2009) >. Log in or Register to get access to full text downloads. Username, Password, Remember me, or Register. A Neural Network Model for Dynamics Simulation.
Exploring Classroom Interaction with Dynamic Social Network Analysis
Bokhove, Christian
2018-01-01
This article reports on an exploratory project in which technology and dynamic social network analysis (SNA) are used for modelling classroom interaction. SNA focuses on the links between social actors, draws on graphic imagery to reveal and display the patterning of those links, and develops mathematical and computational models to describe and…
Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism
DEFF Research Database (Denmark)
Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu
2012-01-01
Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and...
Global Network Reorganization During Dynamic Adaptations of Bacillus subtilis Metabolism
Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu; Uhr, Markus; Muntel, Jan; Botella, Eric; Hessling, Bernd; Kleijn, Roelco Jacobus; Le Chat, Ludovic; Lecointe, Francois; Maeder, Ulrike; Nicolas, Pierre; Piersma, Sjouke; Ruegheimer, Frank; Becher, Doerte; Bessieres, Philippe; Bidnenko, Elena; Denham, Emma L.; Dervyn, Etienne; Devine, Kevin M.; Doherty, Geoff; Drulhe, Samuel; Felicori, Liza; Fogg, Mark J.; Goelzer, Anne; Hansen, Annette; Harwood, Colin R.; Hecker, Michael; Hubner, Sebastian; Hultschig, Claus; Jarmer, Hanne; Klipp, Edda; Leduc, Aurelie; Lewis, Peter; Molina, Frank; Noirot, Philippe; Peres, Sabine; Pigeonneau, Nathalie; Pohl, Susanne; Rasmussen, Simon; Rinn, Bernd; Schaffer, Marc; Schnidder, Julian; Schwikowski, Benno; Van Dijl, Jan Maarten; Veiga, Patrick; Walsh, Sean; Wilkinson, Anthony J.; Stelling, Joerg; Aymerich, Stephane; Sauer, Uwe
2012-01-01
Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and
Network evolution induced by the dynamical rules of two populations
International Nuclear Information System (INIS)
Platini, Thierry; Zia, R K P
2010-01-01
We study the dynamical properties of a finite dynamical network composed of two interacting populations, namely extrovert (a) and introvert (b). In our model, each group is characterized by its size (N a and N b ) and preferred degree (κ a and κ b a ). The network dynamics is governed by the competing microscopic rules of each population that consist of the creation and destruction of links. Starting from an unconnected network, we give a detailed analysis of the mean field approach which is compared to Monte Carlo simulation data. The time evolution of the restricted degrees (k bb ) and (k ab ) presents three time regimes and a non-monotonic behavior well captured by our theory. Surprisingly, when the population sizes are equal N a = N b , the ratio of the restricted degree θ 0 = (k ab )/(k bb ) appears to be an integer in the asymptotic limits of the three time regimes. For early times (defined by t 1 = κ b ) the total number of links presents a linear evolution, where the two populations are indistinguishable and where θ 0 = 1. Interestingly, in the intermediate time regime (defined for t 1 2 ∝κ a and for which θ 0 = 5), the system reaches a transient stationary state, where the number of contacts among introverts remains constant while the number of connections increases linearly in the extrovert population. Finally, due to the competing dynamics, the network presents a frustrated stationary state characterized by a ratio θ 0 = 3
Network evolution induced by the dynamical rules of two populations
Platini, Thierry; Zia, R. K. P.
2010-10-01
We study the dynamical properties of a finite dynamical network composed of two interacting populations, namely extrovert (a) and introvert (b). In our model, each group is characterized by its size (Na and Nb) and preferred degree (κa and \\kappa_b\\ll \\kappa_a ). The network dynamics is governed by the competing microscopic rules of each population that consist of the creation and destruction of links. Starting from an unconnected network, we give a detailed analysis of the mean field approach which is compared to Monte Carlo simulation data. The time evolution of the restricted degrees langkbbrang and langkabrang presents three time regimes and a non-monotonic behavior well captured by our theory. Surprisingly, when the population sizes are equal Na = Nb, the ratio of the restricted degree θ0 = langkabrang/langkbbrang appears to be an integer in the asymptotic limits of the three time regimes. For early times (defined by t introverts remains constant while the number of connections increases linearly in the extrovert population. Finally, due to the competing dynamics, the network presents a frustrated stationary state characterized by a ratio θ0 = 3.
A proposed concept for a crustal dynamics information management network
Lohman, G. M.; Renfrow, J. T.
1980-01-01
The findings of a requirements and feasibility analysis of the present and potential producers, users, and repositories of space-derived geodetic information are summarized. A proposed concept is presented for a crustal dynamics information management network that would apply state of the art concepts of information management technology to meet the expanding needs of the producers, users, and archivists of this geodetic information.
Dynamic Adaptive Neural Network Arrays: A Neuromorphic Architecture
Energy Technology Data Exchange (ETDEWEB)
Disney, Adam [University of Tennessee (UT); Reynolds, John [University of Tennessee (UT)
2015-01-01
Dynamic Adaptive Neural Network Array (DANNA) is a neuromorphic hardware implementation. It differs from most other neuromorphic projects in that it allows for programmability of structure, and it is trained or designed using evolutionary optimization. This paper describes the DANNA structure, how DANNA is trained using evolutionary optimization, and an application of DANNA to a very simple classification task.
Dynamics of user networks in on-line electronic auctions
Czech Academy of Sciences Publication Activity Database
Slanina, František
2014-01-01
Roč. 17, č. 1 (2014), "1450002-1"-"1450002-14" ISSN 0219-5259 R&D Projects: GA MŠk OC09078 Institutional support: RVO:68378271 Keywords : networks * random graphs * dynamics Subject RIV: BE - Theoretical Physics Impact factor: 0.968, year: 2014
Dynamic Session-Key Generation for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Cheng-Ta Li
2008-09-01
Full Text Available Recently, wireless sensor networks have been used extensively in different domains. For example, if the wireless sensor node of a wireless sensor network is distributed in an insecure area, a secret key must be used to protect the transmission between the sensor nodes. Most of the existing methods consist of preselecting m keys from a key pool and forming a key chain. Then, the sensor nodes make use of the key chain to encrypt the data. However, while the secret key is being transmitted, it can easily be exposed during transmission. We propose a dynamic key management protocol, which can improve the security of the key juxtaposed to existing methods. Additionally, the dynamic update of the key can lower the probability of the key to being guessed correctly. In addition, with the new protocol, attacks on the wireless sensor network can be avoided.
Dynamic Business Networks: A Headache for Sustainable Systems Interoperability
Agostinho, Carlos; Jardim-Goncalves, Ricardo
Collaborative networked environments emerged with the spread of the internet, contributing to overcome past communication barriers, and identifying interoperability as an essential property. When achieved seamlessly, efficiency is increased in the entire product life cycle. Nowadays, most organizations try to attain interoperability by establishing peer-to-peer mappings with the different partners, or in optimized networks, by using international standard models as the core for information exchange. In current industrial practice, mappings are only defined once, and the morphisms that represent them, are hardcoded in the enterprise systems. This solution has been effective for static environments, where enterprise and product models are valid for decades. However, with an increasingly complex and dynamic global market, models change frequently to answer new customer requirements. This paper draws concepts from the complex systems science and proposes a framework for sustainable systems interoperability in dynamic networks, enabling different organizations to evolve at their own rate.
Dynamic Enhanced Inter-Cell Interference Coordination for Realistic Networks
DEFF Research Database (Denmark)
Pedersen, Klaus I.; Alvarez, Beatriz Soret; Barcos, Sonia
2016-01-01
ICIC configuration leads to modest gains, whereas the set of proposed fast dynamic eICIC algorithms result in capacity gains on the order of 35-120% depending on the local environment characteristics. These attractive gains together with the simplicity of the proposed solutions underline the practical relevance...... area. Rather than the classical semi-static and network-wise configuration, the importance of having highly dynamic and distributed mechanisms that are able to adapt to local environment conditions is revealed. We propose two promising cell association algorithms: one aiming at pure load balancing...... and an opportunistic approach exploiting the varying cell conditions. Moreover, an autonomous fast distributed muting algorithm is presented, which is simple, robust, and well suited for irregular network deployments. Performance results for realistic network deployments show that the traditional semi-static e...
Dynamic Session-Key Generation for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Chen Chin-Ling
2008-01-01
Full Text Available Abstract Recently, wireless sensor networks have been used extensively in different domains. For example, if the wireless sensor node of a wireless sensor network is distributed in an insecure area, a secret key must be used to protect the transmission between the sensor nodes. Most of the existing methods consist of preselecting keys from a key pool and forming a key chain. Then, the sensor nodes make use of the key chain to encrypt the data. However, while the secret key is being transmitted, it can easily be exposed during transmission. We propose a dynamic key management protocol, which can improve the security of the key juxtaposed to existing methods. Additionally, the dynamic update of the key can lower the probability of the key to being guessed correctly. In addition, with the new protocol, attacks on the wireless sensor network can be avoided.
Nonlinear identification of process dynamics using neural networks
International Nuclear Information System (INIS)
Parlos, A.G.; Atiya, A.F.; Chong, K.T.
1992-01-01
In this paper the nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input-output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios
Dynamic network-based epistasis analysis: Boolean examples
Directory of Open Access Journals (Sweden)
Eugenio eAzpeitia
2011-12-01
Full Text Available In this review we focus on how the hierarchical and single-path assumptions of epistasis analysis can bias the topologies of gene interactions infered. This has been acknowledged in several previous papers and reviews, but here we emphasize the critical importance of dynamic analyses, and specifically illustrate the use of Boolean network models. Epistasis in a broad sense refers to gene interactions, however, as originally proposed by Bateson (herein, classical epistasis, defined as the blocking of a particular allelic effect due to the effect of another allele at a different locus. Classical epistasis analysis has proven powerful and useful, allowing researchers to infer and assign directionality to gene interactions. As larger data sets are becoming available, the analysis of classical epistasis is being complemented with computer science tools and system biology approaches. We show that when the hierarchical and single-path assumptions are not met in classical epistasis analysis, the access to relevant information and the correct gene interaction topologies are hindered, and it becomes necessary to consider the temporal dynamics of gene interactions. The use of dynamical networks can overcome these limitations. We particularly focus on the use of Boolean networks that, like classical epistasis analysis, relies on logical formalisms, and hence can complement classical epistasis analysis and relax its assumptions. We develop a couple of theoretical examples and analyze them from a dynamic Boolean network model perspective. Boolean networks could help to guide additional experiments and discern among alternative regulatory schemes that would be impossible or difficult to infer without the elimination of these assumption from the classical epistasis analysis. We also use examples from the literature to show how a Boolean network-based approach has resolved ambiguities and guided epistasis analysis. Our review complements previous accounts, not
Design of multi-phase dynamic chemical networks
Chen, Chenrui; Tan, Junjun; Hsieh, Ming-Chien; Pan, Ting; Goodwin, Jay T.; Mehta, Anil K.; Grover, Martha A.; Lynn, David G.
2017-08-01
Template-directed polymerization reactions enable the accurate storage and processing of nature's biopolymer information. This mutualistic relationship of nucleic acids and proteins, a network known as life's central dogma, is now marvellously complex, and the progressive steps necessary for creating the initial sequence and chain-length-specific polymer templates are lost to time. Here we design and construct dynamic polymerization networks that exploit metastable prion cross-β phases. Mixed-phase environments have been used for constructing synthetic polymers, but these dynamic phases emerge naturally from the growing peptide oligomers and create environments suitable both to nucleate assembly and select for ordered templates. The resulting templates direct the amplification of a phase containing only chain-length-specific peptide-like oligomers. Such multi-phase biopolymer dynamics reveal pathways for the emergence, self-selection and amplification of chain-length- and possibly sequence-specific biopolymers.
Geometric inequalities for black holes
Energy Technology Data Exchange (ETDEWEB)
Dain, Sergio [Universidad Nacional de Cordoba (Argentina)
2013-07-01
Full text: A geometric inequality in General Relativity relates quantities that have both a physical interpretation and a geometrical definition. It is well known that the parameters that characterize the Kerr-Newman black hole satisfy several important geometric inequalities. Remarkably enough, some of these inequalities also hold for dynamical black holes. This kind of inequalities, which are valid in the dynamical and strong field regime, play an important role in the characterization of the gravitational collapse. They are closed related with the cosmic censorship conjecture. In this talk I will review recent results in this subject. (author)
Geometric inequalities for black holes
International Nuclear Information System (INIS)
Dain, Sergio
2013-01-01
Full text: A geometric inequality in General Relativity relates quantities that have both a physical interpretation and a geometrical definition. It is well known that the parameters that characterize the Kerr-Newman black hole satisfy several important geometric inequalities. Remarkably enough, some of these inequalities also hold for dynamical black holes. This kind of inequalities, which are valid in the dynamical and strong field regime, play an important role in the characterization of the gravitational collapse. They are closed related with the cosmic censorship conjecture. In this talk I will review recent results in this subject. (author)
Threshold Learning Dynamics in Social Networks
González-Avella, Juan Carlos; Eguíluz, Victor M.; Marsili, Matteo; Vega-Redondo, Fernado; San Miguel, Maxi
2011-01-01
Social learning is defined as the ability of a population to aggregate information, a process which must crucially depend on the mechanisms of social interaction. Consumers choosing which product to buy, or voters deciding which option to take with respect to an important issue, typically confront external signals to the information gathered from their contacts. Economic models typically predict that correct social learning occurs in large populations unless some individuals display unbounded influence. We challenge this conclusion by showing that an intuitive threshold process of individual adjustment does not always lead to such social learning. We find, specifically, that three generic regimes exist separated by sharp discontinuous transitions. And only in one of them, where the threshold is within a suitable intermediate range, the population learns the correct information. In the other two, where the threshold is either too high or too low, the system either freezes or enters into persistent flux, respectively. These regimes are generally observed in different social networks (both complex or regular), but limited interaction is found to promote correct learning by enlarging the parameter region where it occurs. PMID:21637714
Limited urban growth: London's street network dynamics since the 18th century.
Masucci, A Paolo; Stanilov, Kiril; Batty, Michael
2013-01-01
We investigate the growth dynamics of Greater London defined by the administrative boundary of the Greater London Authority, based on the evolution of its street network during the last two centuries. This is done by employing a unique dataset, consisting of the planar graph representation of nine time slices of Greater London's road network spanning 224 years, from 1786 to 2010. Within this time-frame, we address the concept of the metropolitan area or city in physical terms, in that urban evolution reveals observable transitions in the distribution of relevant geometrical properties. Given that London has a hard boundary enforced by its long standing green belt, we show that its street network dynamics can be described as a fractal space-filling phenomena up to a capacitated limit, whence its growth can be predicted with a striking level of accuracy. This observation is confirmed by the analytical calculation of key topological properties of the planar graph, such as the topological growth of the network and its average connectivity. This study thus represents an example of a strong violation of Gibrat's law. In particular, we are able to show analytically how London evolves from a more loop-like structure, typical of planned cities, toward a more tree-like structure, typical of self-organized cities. These observations are relevant to the discourse on sustainable urban planning with respect to the control of urban sprawl in many large cities which have developed under the conditions of spatial constraints imposed by green belts and hard urban boundaries.
Limited urban growth: London's street network dynamics since the 18th century.
Directory of Open Access Journals (Sweden)
A Paolo Masucci
Full Text Available We investigate the growth dynamics of Greater London defined by the administrative boundary of the Greater London Authority, based on the evolution of its street network during the last two centuries. This is done by employing a unique dataset, consisting of the planar graph representation of nine time slices of Greater London's road network spanning 224 years, from 1786 to 2010. Within this time-frame, we address the concept of the metropolitan area or city in physical terms, in that urban evolution reveals observable transitions in the distribution of relevant geometrical properties. Given that London has a hard boundary enforced by its long standing green belt, we show that its street network dynamics can be described as a fractal space-filling phenomena up to a capacitated limit, whence its growth can be predicted with a striking level of accuracy. This observation is confirmed by the analytical calculation of key topological properties of the planar graph, such as the topological growth of the network and its average connectivity. This study thus represents an example of a strong violation of Gibrat's law. In particular, we are able to show analytically how London evolves from a more loop-like structure, typical of planned cities, toward a more tree-like structure, typical of self-organized cities. These observations are relevant to the discourse on sustainable urban planning with respect to the control of urban sprawl in many large cities which have developed under the conditions of spatial constraints imposed by green belts and hard urban boundaries.
Network Signaling Channel for Improving ZigBee Performance in Dynamic Cluster-Tree Networks
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D. Hämäläinen
2008-03-01
Full Text Available ZigBee is one of the most potential standardized technologies for wireless sensor networks (WSNs. Yet, sufficient energy-efficiency for the lowest power WSNs is achieved only in rather static networks. This severely limits the applicability of ZigBee in outdoor and mobile applications, where operation environment is harsh and link failures are common. This paper proposes a network channel beaconing (NCB algorithm for improving ZigBee performance in dynamic cluster-tree networks. NCB reduces the energy consumption of passive scans by dedicating one frequency channel for network beacon transmissions and by energy optimizing their transmission rate. According to an energy analysis, the power consumption of network maintenance operations reduces by 70%Ã¢Â€Â“76% in dynamic networks. In static networks, energy overhead is negligible. Moreover, the service time for data routing increases up to 37%. The performance of NCB is validated by ns-2 simulations. NCB can be implemented as an extension on MAC and NWK layers and it is fully compatible with ZigBee.
Submodularity in dynamics and control of networked systems
Clark, Andrew; Bushnell, Linda; Poovendran, Radha
2016-01-01
This book presents a framework for the control of networked systems utilizing submodular optimization techniques. The main focus is on selecting input nodes for the control of networked systems, an inherently discrete optimization problem with applications in power system stability, social influence dynamics, and the control of vehicle formations. The first part of the book is devoted to background information on submodular functions, matroids, and submodular optimization, and presents algorithms for distributed submodular optimization that are scalable to large networked systems. In turn, the second part develops a unifying submodular optimization approach to controlling networked systems based on multiple performance and controllability criteria. Techniques are introduced for selecting input nodes to ensure smooth convergence, synchronization, and robustness to environmental and adversarial noise. Submodular optimization is the first unifying approach towards guaranteeing both performance and controllabilit...
Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks
Jorgensen, Charles C.
1997-01-01
A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.
Weighted Networks Model Based on Traffic Dynamics with Local Perturbation
International Nuclear Information System (INIS)
Zhao Hui; Gao Ziyou
2007-01-01
In the study of weighted complex networks, the interplay between traffic and topology have been paid much attention. However, the variation of topology and weight brought by new added vertices or edges should also be considered. In this paper, an evolution model of weighted networks driven by traffic dynamics with local perturbation is proposed. The model gives power-law distribution of degree, weight and strength, as confirmed by empirical measurements. By choosing appropriate parameters W and δ, the exponents of various power law distributions can be adjusted to meet real world networks. Nontrivial clustering coefficient C, degree assortativity coefficient r, and strength-degree correlation are also considered. What should be emphasized is that, with the consideration of local perturbation, one can adjust the exponent of strength-degree correlation more effectively. It makes our model more general than previous ones and may help reproducing real world networks more appropriately.
Perception of similarity: a model for social network dynamics
International Nuclear Information System (INIS)
Javarone, Marco Alberto; Armano, Giuliano
2013-01-01
Some properties of social networks (e.g., the mixing patterns and the community structure) appear deeply influenced by the individual perception of people. In this work we map behaviors by considering similarity and popularity of people, also assuming that each person has his/her proper perception and interpretation of similarity. Although investigated in different ways (depending on the specific scientific framework), from a computational perspective similarity is typically calculated as a distance measure. In accordance with this view, to represent social network dynamics we developed an agent-based model on top of a hyperbolic space on which individual distance measures are calculated. Simulations, performed in accordance with the proposed model, generate small-world networks that exhibit a community structure. We deem this model to be valuable for analyzing the relevant properties of real social networks. (paper)
Dynamic Network Connectivity: A New Form of Neuroplasticity
Arnsten, Amy F.T.; Paspalas, Constantinos D.; Gamo, Nao J.; Yang, Yang; Wang, Min
2010-01-01
Prefrontal cortical (PFC) working memory functions depend on pyramidal cell networks that interconnect on dendritic spines. Recent research has revealed that the strength of PFC network connections can be rapidly and reversibly increased or decreased by molecular signaling events within slender, elongated spines, a process we term Dynamic Network Connectivity (DNC). This newly discovered form of neuroplasticity provides great flexibility in mental state, but also confers vulnerability and limits mental capacity. A remarkable number of genetic and/or environmental insults to DNC signaling cascades are associated with cognitive disorders such as schizophrenia and age-related cognitive decline. These insults may dysregulate network connections and erode higher cognitive abilities, leading to symptoms such as forgetfulness, susceptibility to interference, and disorganized thought and behavior. PMID:20554470
Changes in dynamic resting state network connectivity following aphasia therapy.
Duncan, E Susan; Small, Steven L
2017-10-24
Resting state magnetic resonance imaging (rsfMRI) permits observation of intrinsic neural networks produced by task-independent correlations in low frequency brain activity. Various resting state networks have been described, with each thought to reflect common engagement in some shared function. There has been limited investigation of the plasticity in these network relationships after stroke or induced by therapy. Twelve individuals with language disorders after stroke (aphasia) were imaged at multiple time points before (baseline) and after an imitation-based aphasia therapy. Language assessment using a narrative production task was performed at the same time points. Group independent component analysis (ICA) was performed on the rsfMRI data to identify resting state networks. A sliding window approach was then applied to assess the dynamic nature of the correlations among these networks. Network correlations during each 30-second window were used to cluster the data into ten states for each window at each time point for each subject. Correlation was performed between changes in time spent in each state and therapeutic gains on the narrative task. The amount of time spent in a single one of the (ten overall) dynamic states was positively associated with behavioral improvement on the narrative task at the 6-week post-therapy maintenance interval, when compared with either baseline or assessment immediately following therapy. This particular state was characterized by minimal correlation among the task-independent resting state networks. Increased functional independence and segregation of resting state networks underlies improvement on a narrative production task following imitation-based aphasia treatment. This has important clinical implications for the targeting of noninvasive brain stimulation in post-stroke remediation.
Directory of Open Access Journals (Sweden)
Helmut Schmidt
2014-11-01
Full Text Available Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz and low-alpha (6-9 Hz bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic
Dynamic reorganization of intrinsic functional networks in the mouse brain.
Grandjean, Joanes; Preti, Maria Giulia; Bolton, Thomas A W; Buerge, Michaela; Seifritz, Erich; Pryce, Christopher R; Van De Ville, Dimitri; Rudin, Markus
2017-05-15
Functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) allows for the integrative study of neuronal processes at a macroscopic level. The majority of studies to date have assumed stationary interactions between brain regions, without considering the dynamic aspects of network organization. Only recently has the latter received increased attention, predominantly in human studies. Applying dynamic FC (dFC) analysis to mice is attractive given the relative simplicity of the mouse brain and the possibility to explore mechanisms underlying network dynamics using pharmacological, environmental or genetic interventions. Therefore, we have evaluated the feasibility and research potential of mouse dFC using the interventions of social stress or anesthesia duration as two case-study examples. By combining a sliding-window correlation approach with dictionary learning, several dynamic functional states (dFS) with a complex organization were identified, exhibiting highly dynamic inter- and intra-modular interactions. Each dFS displayed a high degree of reproducibility upon changes in analytical parameters and across datasets. They fluctuated at different degrees as a function of anesthetic depth, and were sensitive indicators of pathology as shown for the chronic psychosocial stress mouse model of depression. Dynamic functional states are proposed to make a major contribution to information integration and processing in the healthy and diseased brain. Copyright © 2017 Elsevier Inc. All rights reserved.
Yin, Youbing; Hoffman, Eric A; Ding, Kai; Reinhardt, Joseph M; Lin, Ching-Long
2011-01-07
The goal of this study is to develop a matching algorithm that can handle large geometric changes in x-ray computed tomography (CT)-derived lung geometry occurring during deep breath maneuvers. These geometric relationships are further utilized to build a dynamic lung airway model for computational fluid dynamics (CFD) studies of pulmonary air flow. The proposed algorithm is based on a cubic B-spline-based hybrid registration framework that incorporates anatomic landmark information with intensity patterns. A sequence of invertible B-splines is composed in a multiresolution framework to ensure local invertibility of the large deformation transformation and a physiologically meaningful similarity measure is adopted to compensate for changes in voxel intensity due to inflation. Registrations are performed using the proposed approach to match six pairs of 3D CT human lung datasets. Results show that the proposed approach has the ability to match the intensity pattern and the anatomical landmarks, and ensure local invertibility for large deformation transformations. Statistical results also show that the proposed hybrid approach yields significantly improved results as compared with approaches using either landmarks or intensity alone.
Dynamics and steady-state properties of adaptive networks
Wieland, Stefan
Collective phenomena often arise through structured interactions among a system's constituents. In the subclass of adaptive networks, the interaction structure coevolves with the dynamics it supports, yielding a feedback loop that is common in a variety of complex systems. To understand and steer such systems, modeling their asymptotic regimes is an essential prerequisite. In the particular case of a dynamic equilibrium, each node in the adaptive network experiences a perpetual change in connections and state, while a comprehensive set of measures characterizing the node ensemble are stationary. Furthermore, the dynamic equilibria of a wide class of adaptive networks appear to be unique, as their characteristic measures are insensitive to initial conditions in both state and topology. This work focuses on dynamic equilibria in adaptive networks, and while it does so in the context of two paradigmatic coevolutionary processes, obtained results easily generalize to other dynamics. In the first part, a low-dimensional framework is elaborated on using the adaptive contact process. A tentative description of the phase diagram and the steady state is obtained, and a parameter region identified where asymmetric microscopic dynamics yield a symmetry between node subensembles. This symmetry is accounted for by novel recurrence relations, which predict it for a wide range of adaptive networks. Furthermore, stationary nodeensemble distributions are analytically generated by these relations from one free parameter. Secondly, another analytic framework is put forward that detects and describes dynamic equilibria, while assigning to them general properties that must hold for a variety of adaptive networks. Modeling a single node's evolution in state and connections as a random walk, the ergodic properties of the network process are used to extract node-ensemble statistics from the node's long-term behavior. These statistical measures are composed of a variety of stationary
Competitive dynamics of lexical innovations in multi-layer networks
Javarone, Marco Alberto
2014-04-01
We study the introduction of lexical innovations into a community of language users. Lexical innovations, i.e. new term added to people's vocabulary, plays an important role in the process of language evolution. Nowadays, information is spread through a variety of networks, including, among others, online and offline social networks and the World Wide Web. The entire system, comprising networks of different nature, can be represented as a multi-layer network. In this context, lexical innovations diffusion occurs in a peculiar fashion. In particular, a lexical innovation can undergo three different processes: its original meaning is accepted; its meaning can be changed or misunderstood (e.g. when not properly explained), hence more than one meaning can emerge in the population. Lastly, in the case of a loan word, it can be translated into the population language (i.e. defining a new lexical innovation or using a synonym) or into a dialect spoken by part of the population. Therefore, lexical innovations cannot be considered simply as information. We develop a model for analyzing this scenario using a multi-layer network comprising a social network and a media network. The latter represents the set of all information systems of a society, e.g. television, the World Wide Web and radio. Furthermore, we identify temporal directed edges between the nodes of these two networks. In particular, at each time-step, nodes of the media network can be connected to randomly chosen nodes of the social network and vice versa. In doing so, information spreads through the whole system and people can share a lexical innovation with their neighbors or, in the event they work as reporters, by using media nodes. Lastly, we use the concept of "linguistic sign" to model lexical innovations, showing its fundamental role in the study of these dynamics. Many numerical simulations have been performed to analyze the proposed model and its outcomes.
Dynamic Modeling of Systemic Risk in Financial Networks
Avakian, Adam
Modern financial networks are complicated structures that can contain multiple types of nodes and connections between those nodes. Banks, governments and even individual people weave into an intricate network of debt, risk correlations and many other forms of interconnectedness. We explore multiple types of financial network models with a focus on understanding the dynamics and causes of cascading failures in such systems. In particular, we apply real-world data from multiple sources to these models to better understand real-world financial networks. We use the results of the Federal Reserve "Banking Organization Systemic Risk Report" (FR Y-15), which surveys the largest US banks on their level of interconnectedness, to find relationships between various measures of network connectivity and systemic risk in the US financial sector. This network model is then stress-tested under a number of scenarios to determine systemic risks inherent in the various network structures. We also use detailed historical balance sheet data from the Venezuelan banking system to build a bipartite network model and find relationships between the changing network structure over time and the response of the system to various shocks. We find that the relationship between interconnectedness and systemic risk is highly dependent on the system and model but that it is always a significant one. These models are useful tools that add value to regulators in creating new measurements of systemic risk in financial networks. These models could be used as macroprudential tools for monitoring the health of the entire banking system as a whole rather than only of individual banks.
Strategic tradeoffs in competitor dynamics on adaptive networks.
Hébert-Dufresne, Laurent; Allard, Antoine; Noël, Pierre-André; Young, Jean-Gabriel; Libby, Eric
2017-08-08
Recent empirical work highlights the heterogeneity of social competitions such as political campaigns: proponents of some ideologies seek debate and conversation, others create echo chambers. While symmetric and static network structure is typically used as a substrate to study such competitor dynamics, network structure can instead be interpreted as a signature of the competitor strategies, yielding competition dynamics on adaptive networks. Here we demonstrate that tradeoffs between aggressiveness and defensiveness (i.e., targeting adversaries vs. targeting like-minded individuals) creates paradoxical behaviour such as non-transitive dynamics. And while there is an optimal strategy in a two competitor system, three competitor systems have no such solution; the introduction of extreme strategies can easily affect the outcome of a competition, even if the extreme strategies have no chance of winning. Not only are these results reminiscent of classic paradoxical results from evolutionary game theory, but the structure of social networks created by our model can be mapped to particular forms of payoff matrices. Consequently, social structure can act as a measurable metric for social games which in turn allows us to provide a game theoretical perspective on online political debates.
Multi-Topic Tracking Model for dynamic social network
Li, Yuhua; Liu, Changzheng; Zhao, Ming; Li, Ruixuan; Xiao, Hailing; Wang, Kai; Zhang, Jun
2016-07-01
The topic tracking problem has attracted much attention in the last decades. However, existing approaches rarely consider network structures and textual topics together. In this paper, we propose a novel statistical model based on dynamic bayesian network, namely Multi-Topic Tracking Model for Dynamic Social Network (MTTD). It takes influence phenomenon, selection phenomenon, document generative process and the evolution of textual topics into account. Specifically, in our MTTD model, Gibbs Random Field is defined to model the influence of historical status of users in the network and the interdependency between them in order to consider the influence phenomenon. To address the selection phenomenon, a stochastic block model is used to model the link generation process based on the users' interests to topics. Probabilistic Latent Semantic Analysis (PLSA) is used to describe the document generative process according to the users' interests. Finally, the dependence on the historical topic status is also considered to ensure the continuity of the topic itself in topic evolution model. Expectation Maximization (EM) algorithm is utilized to estimate parameters in the proposed MTTD model. Empirical experiments on real datasets show that the MTTD model performs better than Popular Event Tracking (PET) and Dynamic Topic Model (DTM) in generalization performance, topic interpretability performance, topic content evolution and topic popularity evolution performance.
Analyzing, Modeling, and Simulation for Human Dynamics in Social Network
Directory of Open Access Journals (Sweden)
Yunpeng Xiao
2012-01-01
Full Text Available This paper studies the human behavior in the top-one social network system in China (Sina Microblog system. By analyzing real-life data at a large scale, we find that the message releasing interval (intermessage time obeys power law distribution both at individual level and at group level. Statistical analysis also reveals that human behavior in social network is mainly driven by four basic elements: social pressure, social identity, social participation, and social relation between individuals. Empirical results present the four elements' impact on the human behavior and the relation between these elements. To further understand the mechanism of such dynamic phenomena, a hybrid human dynamic model which combines “interest” of individual and “interaction” among people is introduced, incorporating the four elements simultaneously. To provide a solid evaluation, we simulate both two-agent and multiagent interactions with real-life social network topology. We achieve the consistent results between empirical studies and the simulations. The model can provide a good understanding of human dynamics in social network.
Applying differential dynamic logic to reconfigurable biological networks.
Figueiredo, Daniel; Martins, Manuel A; Chaves, Madalena
2017-09-01
Qualitative and quantitative modeling frameworks are widely used for analysis of biological regulatory networks, the former giving a preliminary overview of the system's global dynamics and the latter providing more detailed solutions. Another approach is to model biological regulatory networks as hybrid systems, i.e., systems which can display both continuous and discrete dynamic behaviors. Actually, the development of synthetic biology has shown that this is a suitable way to think about biological systems, which can often be constructed as networks with discrete controllers, and present hybrid behaviors. In this paper we discuss this approach as a special case of the reconfigurability paradigm, well studied in Computer Science (CS). In CS there are well developed computational tools to reason about hybrid systems. We argue that it is worth applying such tools in a biological context. One interesting tool is differential dynamic logic (dL), which has recently been developed by Platzer and applied to many case-studies. In this paper we discuss some simple examples of biological regulatory networks to illustrate how dL can be used as an alternative, or also as a complement to methods already used. Copyright © 2017 Elsevier Inc. All rights reserved.
Secure Collaborative Key Management for Dynamic Groups in Mobile Networks
Directory of Open Access Journals (Sweden)
Sukin Kang
2014-01-01
Full Text Available Mobile networks are composed of heterogeneous mobile devices with peer-to-peer wireless communication. Their dynamic and self-organizing natures pose security challenge. We consider secure group key management for peer dynamic groups in mobile wireless networks. Many group based applications have achieved remarkable growth along with increasing use of multicast based services. The key sharing among the group members is an important issue for secure group communication because the communication for many participants implies that the likelihood of illegal overhearing increases. We propose a group key sharing scheme and efficient rekeying methods for frequent membership changes from network dynamics. The proposed method enables the group members to simply establish a group key and provide high flexibility for dynamic group changes such as member join or leave and group merging or partition. We conduct mathematical evaluation with other group key management protocols and finally prove its security by demonstrating group key secrecy, backward and forward secrecy, key independence, and implicit key authentication under the decisional Diffie-Hellman (DDH assumption.
Synchronization of networks of chaotic oscillators: Structural and dynamical datasets
Directory of Open Access Journals (Sweden)
Ricardo Sevilla-Escoboza
2016-06-01
Full Text Available We provide the topological structure of a series of N=28 Rössler chaotic oscillators diffusively coupled through one of its variables. The dynamics of the y variable describing the evolution of the individual nodes of the network are given for a wide range of coupling strengths. Datasets capture the transition from the unsynchronized behavior to the synchronized one, as a function of the coupling strength between oscillators. The fact that both the underlying topology of the system and the dynamics of the nodes are given together makes this dataset a suitable candidate to evaluate the interplay between functional and structural networks and serve as a benchmark to quantify the ability of a given algorithm to extract the structural network of connections from the observation of the dynamics of the nodes. At the same time, it is possible to use the dataset to analyze the different dynamical properties (randomness, complexity, reproducibility, etc. of an ensemble of oscillators as a function of the coupling strength.
Core reactivity estimation in space reactors using recurrent dynamic networks
International Nuclear Information System (INIS)
Parlos, A.G.; Tsai, W.K.
1991-01-01
A recurrent Multi Layer Perceptron (MLP) network topology is used in the identification of nonlinear dynamic systems from only the input/output measurements. This effort is part of a research program devoted in developing real-time diagnostics and predictive control techniques for large-scale complex nonlinear dynamic systems. The identification is performed in the discrete time domain, with the learning algorithm being a modified form of the Back Propagation (BP) rule. The Recurrent Dynamic Network (RDN) developed is applied for the total core reactivity prediction of a spacecraft reactor from only neutronic power level measurements. Results indicate that the RDN can reproduce the nonlinear response of the reactor while keeping the number of nodes roughly equal to the relative order of the system. As accuracy requirements are increased, the number of required nodes also increases, however, the order of the RDN necessary to obtain such results is still in the same order of magnitude as the order of the matematical model of the system. There are a number of issues identified regarding the behavior of the RDN, which at this point are unresolved and require further research. Nevertheless, it is believed that use of the recurrent MLP structure with a variety of different learning algorithms may prove useful in utilizing artifical neural networks (ANNs) for recognition, classification and prediction of dynamic systems
Dynamical Response of Networks Under External Perturbations: Exact Results
Chinellato, David D.; Epstein, Irving R.; Braha, Dan; Bar-Yam, Yaneer; de Aguiar, Marcus A. M.
2015-04-01
We give exact statistical distributions for the dynamic response of influence networks subjected to external perturbations. We consider networks whose nodes have two internal states labeled 0 and 1. We let nodes be frozen in state 0, in state 1, and the remaining nodes change by adopting the state of a connected node with a fixed probability per time step. The frozen nodes can be interpreted as external perturbations to the subnetwork of free nodes. Analytically extending and to be smaller than 1 enables modeling the case of weak coupling. We solve the dynamical equations exactly for fully connected networks, obtaining the equilibrium distribution, transition probabilities between any two states and the characteristic time to equilibration. Our exact results are excellent approximations for other topologies, including random, regular lattice, scale-free and small world networks, when the numbers of fixed nodes are adjusted to take account of the effect of topology on coupling to the environment. This model can describe a variety of complex systems, from magnetic spins to social networks to population genetics, and was recently applied as a framework for early warning signals for real-world self-organized economic market crises.
Memory Dynamics in Cross-linked Actin Networks
Scheff, Danielle; Majumdar, Sayantan; Gardel, Margaret
Cells demonstrate the remarkable ability to adapt to mechanical stimuli through rearrangement of the actin cytoskeleton, a cross-linked network of actin filaments. In addition to its importance in cell biology, understanding this mechanical response provides strategies for creation of novel materials. A recent study has demonstrated that applied stress can encode mechanical memory in these networks through changes in network geometry, which gives rise to anisotropic shear response. Under later shear, the network is stiffer in the direction of the previously applied stress. However, the dynamics behind the encoding of this memory are unknown. To address this question, we explore the effect of varying either the rigidity of the cross-linkers or the length of actin filament on the time scales required for both memory encoding and over which it later decays. While previous experiments saw only a long-lived memory, initial results suggest another mechanism where memories relax relatively quickly. Overall, our study is crucial for understanding the process by which an external stress can impact network arrangement and thus the dynamics of memory formation.
An Efficient Route Maintenance Protocol for Dynamic Bluetooth Networks
Directory of Open Access Journals (Sweden)
Sabeen Tahir
2017-10-01
Full Text Available Bluetooth is a widespread technology for small wireless networks that permits Bluetooth devices to construct a multi-hop network called scatternet. Routing in multi-hop dynamic Bluetooth network, where a number of masters and bridges exist creates technical hitches. It is observed that frequent link disconnections and a new route construction consume extra system resources that degrade the whole network performance. Therefore, in this paper an Efficient Route Maintenance Protocol for Dynamic Bluetooth Networks (ERMP is proposed that repairs the weak routing paths based on the prediction of weak links and weak devices. The ERMP predicts the weak links through the signal strength and weak devices through low energy levels. During the main route construction, routing masters and bridges keep the information of the Fall Back Devices (FBDs for route maintenance. On the prediction of a weak link, the ERMP activates an alternate link, on the other hand, for a weak device it activates the FBD. The proposed ERMP is compared with some existing closely related protocols, and the simulation results show that the proposed ERMP successfully recovers the weak paths and improves the system performance.
SCOUT: simultaneous time segmentation and community detection in dynamic networks
Hulovatyy, Yuriy; Milenković, Tijana
2016-11-01
Many evolving complex real-world systems can be modeled via dynamic networks. An important problem in dynamic network research is community detection, which finds groups of topologically related nodes. Typically, this problem is approached by assuming either that each time point has a distinct community organization or that all time points share a single community organization. The reality likely lies between these two extremes. To find the compromise, we consider community detection in the context of the problem of segment detection, which identifies contiguous time periods with consistent network structure. Consequently, we formulate a combined problem of segment community detection (SCD), which simultaneously partitions the network into contiguous time segments with consistent community organization and finds this community organization for each segment. To solve SCD, we introduce SCOUT, an optimization framework that explicitly considers both segmentation quality and partition quality. SCOUT addresses limitations of existing methods that can be adapted to solve SCD, which consider only one of segmentation quality or partition quality. In a thorough evaluation, SCOUT outperforms the existing methods in terms of both accuracy and computational complexity. We apply SCOUT to biological network data to study human aging.
Dynamic Resource Allocation in Hybrid Access Femtocell Network
Directory of Open Access Journals (Sweden)
Afaz Uddin Ahmed
2014-01-01
Full Text Available Intercell interference is one of the most challenging issues in femtocell deployment under the coverage of existing macrocell. Allocation of resources between femtocell and macrocell is essential to counter the effects of interference in dense femtocell networks. Advances in resource management strategies have improved the control mechanism for interference reduction at lower node density, but most of them are ineffective at higher node density. In this paper, a dynamic resource allocation management algorithm (DRAMA for spectrum shared hybrid access OFDMA femtocell network is proposed. To reduce the macro-femto-tier interference and to improve the quality of service, the proposed algorithm features a dynamic resource allocation scheme by controlling them both centrally and locally. The proposed scheme focuses on Femtocell Access Point (FAP owners’ satisfaction and allows maximum utilization of available resources based on congestion in the network. A simulation environment is developed to study the quantitative performance of DRAMA in hybrid access-control femtocell network and compare it to closed and open access mechanisms. The performance analysis shows that higher number of random users gets connected to the FAP without compromising FAP owners’ satisfaction allowing the macrocell to offload a large number of users in a dense heterogeneous network.
Fractional Dynamics of Network Growth Constrained by Aging Node Interactions.
Directory of Open Access Journals (Sweden)
Hadiseh Safdari
Full Text Available In many social complex systems, in which agents are linked by non-linear interactions, the history of events strongly influences the whole network dynamics. However, a class of "commonly accepted beliefs" seems rarely studied. In this paper, we examine how the growth process of a (social network is influenced by past circumstances. In order to tackle this cause, we simply modify the well known preferential attachment mechanism by imposing a time dependent kernel function in the network evolution equation. This approach leads to a fractional order Barabási-Albert (BA differential equation, generalizing the BA model. Our results show that, with passing time, an aging process is observed for the network dynamics. The aging process leads to a decay for the node degree values, thereby creating an opposing process to the preferential attachment mechanism. On one hand, based on the preferential attachment mechanism, nodes with a high degree are more likely to absorb links; but, on the other hand, a node's age has a reduced chance for new connections. This competitive scenario allows an increased chance for younger members to become a hub. Simulations of such a network growth with aging constraint confirm the results found from solving the fractional BA equation. We also report, as an exemplary application, an investigation of the collaboration network between Hollywood movie actors. It is undubiously shown that a decay in the dynamics of their collaboration rate is found, even including a sex difference. Such findings suggest a widely universal application of the so generalized BA model.
Fast Distributed Dynamics of Semantic Networks via Social Media.
Carrillo, Facundo; Cecchi, Guillermo A; Sigman, Mariano; Slezak, Diego Fernández
2015-01-01
We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS), based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network.
RD2: Resilient Dynamic Desynchronization for TDMA over Lossy Networks
DEFF Research Database (Denmark)
Hinterhofer, Thomas; Schwefel, Hans-Peter; Tomic, Slobodanka
2012-01-01
We present a distributed TDMA negotiation approach for single-hop ad-hoc network communication. It is distributed, resilient to arbitrary transient packet loss and defines a non-overlapping TDMA schedule without the need of global time synchronization. A participating node can dynamically request...... a fraction of the static TDMA period 푇. It will receive its fraction if enough time resources are available. In any case, every node can request and will receive at least a fair fraction of size 1 푁 . Due to its resilience to arbitrary transient packet loss, the algorithm is well suited for lossy networks...
Dynamic Pricing in Electronic Commerce Using Neural Network
Ghose, Tapu Kumar; Tran, Thomas T.
In this paper, we propose an approach where feed-forward neural network is used for dynamically calculating a competitive price of a product in order to maximize sellers’ revenue. In the approach we considered that along with product price other attributes such as product quality, delivery time, after sales service and seller’s reputation contribute in consumers purchase decision. We showed that once the sellers, by using their limited prior knowledge, set an initial price of a product our model adjusts the price automatically with the help of neural network so that sellers’ revenue is maximized.
Fast Distributed Dynamics of Semantic Networks via Social Media
Directory of Open Access Journals (Sweden)
Facundo Carrillo
2015-01-01
Full Text Available We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS, based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network.
Shrirang Ambaji KULKARNI; Raghavendra G . RAO
2017-01-01
Routing data packets in a dynamic network is a difficult and important problem in computer networks. As the network is dynamic, it is subject to frequent topology changes and is subject to variable link costs due to congestion and bandwidth. Existing shortest path algorithms fail to converge to better solutions under dynamic network conditions. Reinforcement learning algorithms posses better adaptation techniques in dynamic environments. In this paper we apply model based Q-Routing technique ...
Tejedor, Alejandro; Longjas, Anthony; Zaliapin, Ilya; Ambroj, Samuel; Foufoula-Georgiou, Efi
2017-08-17
Network robustness against attacks has been widely studied in fields as diverse as the Internet, power grids and human societies. But current definition of robustness is only accounting for half of the story: the connectivity of the nodes unaffected by the attack. Here we propose a new framework to assess network robustness, wherein the connectivity of the affected nodes is also taken into consideration, acknowledging that it plays a crucial role in properly evaluating the overall network robustness in terms of its future recovery from the attack. Specifically, we propose a dual perspective approach wherein at any instant in the network evolution under attack, two distinct networks are defined: (i) the Active Network (AN) composed of the unaffected nodes and (ii) the Idle Network (IN) composed of the affected nodes. The proposed robustness metric considers both the efficiency of destroying the AN and that of building-up the IN. We show, via analysis of well-known prototype networks and real world data, that trade-offs between the efficiency of Active and Idle Network dynamics give rise to surprising robustness crossovers and re-rankings, which can have significant implications for decision making.
DEFF Research Database (Denmark)
Andersen, Jørgen Ellegaard; Borot, Gaëtan; Orantin, Nicolas
We propose a general theory whose main component are functorial assignments ∑→Ω∑ ∈ E (∑), for a large class of functors E from a certain category of bordered surfaces (∑'s) to a suitable a target category of topological vector spaces. The construction is done by summing appropriate compositions...... of the initial data over all homotopy classes of successive excisions of embedded pair of pants. We provide sufficient conditions to guarantee these infinite sums converge and as a result, we can generate mapping class group invariant vectors Ω∑ which we call amplitudes. The initial data encode the amplitude...... for pair of pants and tori with one boundary, as well as the "recursion kernels" used for glueing. We give this construction the name of "geometric recursion", abbreviated GR. As an illustration, we show how to apply our formalism to various spaces of continuous functions over Teichmueller spaces, as well...
Directory of Open Access Journals (Sweden)
Ingrid Paine
2016-04-01
Full Text Available Mathematics is often used to model biological systems. In mammary gland development, mathematical modeling has been limited to acinar and branching morphogenesis and breast cancer, without reference to normal duct formation. We present a model of ductal elongation that exploits the geometrically-constrained shape of the terminal end bud (TEB, the growing tip of the duct, and incorporates morphometrics, region-specific proliferation and apoptosis rates. Iterative model refinement and behavior analysis, compared with biological data, indicated that the traditional metric of nipple to the ductal front distance, or percent fat pad filled to evaluate ductal elongation rate can be misleading, as it disregards branching events that can reduce its magnitude. Further, model driven investigations of the fates of specific TEB cell types confirmed migration of cap cells into the body cell layer, but showed their subsequent preferential elimination by apoptosis, thus minimizing their contribution to the luminal lineage and the mature duct.
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.
International Nuclear Information System (INIS)
Trostel, R.
1982-01-01
A mathematical objective of this paper is to provide geometrical formulation of the integrability conditions for the existence of an action functional, that is, to provide a geometrical counterpart (similar to that by Abraham, Marsden, and Hughes) of the variational and functional approach to self-adjointness. This objective is achieved via the exterior variational calculus, an exterior differential calculus on the vector space of functions depending on time or space time, using from the outset extensively the concept of functional differentiation as its foundation. Variational self-adjointness equals the variational closure of the physical 1-form, the vanishing of a generalized curl-operation applied to the equations of motion. The convenience of this more formal approach is demonstrated, not only when deriving the conditions of variational self-adjointness for materials of differential type of arbitrary order (particles or fields), using roughly no more than Dirac's delta-distributions, but also when treating materials of a broader class (including causal and acausal constitutive functionals, materials of rate type, integral type, etc.). A physical objective of this paper is achieved by pointing out that, as physics is primarily concerned with the solutions of the evolution equations, i.e., with the set of the zero points of the physical 1-form, an equivalence relation among the physical 1-forms on the infinite dimensional vector space of functions is constructed by leaving the set of their zero points unchanged. Using this result, a direct Lagrangian universality is indicated and an almost one presented. Moreover, all physical 1-forms connected by invertible supermatrices (thus mixing the evolution law of different times or space-time) are equivalent. Choosing these supermatrices to be diagonal in time or space-time yields the indirect analytical representation factors
Architecture and dynamics of overlapped RNA regulatory networks.
Lapointe, Christopher P; Preston, Melanie A; Wilinski, Daniel; Saunders, Harriet A J; Campbell, Zachary T; Wickens, Marvin
2017-11-01
A single protein can bind and regulate many mRNAs. Multiple proteins with similar specificities often bind and control overlapping sets of mRNAs. Yet little is known about the architecture or dynamics of overlapped networks. We focused on three proteins with similar structures and related RNA-binding specificities-Puf3p, Puf4p, and Puf5p of S. cerevisiae Using RNA Tagging, we identified a "super-network" comprised of four subnetworks: Puf3p, Puf4p, and Puf5p subnetworks, and one controlled by both Puf4p and Puf5p. The architecture of individual subnetworks, and thus the super-network, is determined by competition among particular PUF proteins to bind mRNAs, their affinities for binding elements, and the abundances of the proteins. The super-network responds dramatically: The remaining network can either expand or contract. These strikingly opposite outcomes are determined by an interplay between the relative abundance of the RNAs and proteins, and their affinities for one another. The diverse interplay between overlapping RNA-protein networks provides versatile opportunities for regulation and evolution. © 2017 Lapointe et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.
Neural network modeling of chaotic dynamics in nuclear reactor flows
International Nuclear Information System (INIS)
Welstead, S.T.
1992-01-01
Neural networks have many scientific applications in areas such as pattern classification and time series prediction. The universal approximation property of these networks, however, can also be exploited to provide researchers with tool for modeling observed nonlinear phenomena. It has been shown that multilayer feed forward networks can capture important global nonlinear properties, such as chaotic dynamics, merely by training the network on a finite set of observed data. The network itself then provides a model of the process that generated the data. Characterizations such as the existence and general shape of a strange attractor and the sign of the largest Lyapunov exponent can then be extracted from the neural network model. In this paper, the author applies this idea to data generated from a nonlinear process that is representative of convective flows that can arise in nuclear reactor applications. Such flows play a role in forced convection heat removal from pressurized water reactors and boiling water reactors, and decay heat removal from liquid-metal-cooled reactors, either by natural convection or by thermosyphons
Global value chains: Building blocks and network dynamics
Tsekeris, Theodore
2017-12-01
The paper employs measures and tools from complex network analysis to enhance the understanding and interpretation of structural characteristics pertaining to the Global Value Chains (GVCs) during the period 1995-2011. The analysis involves the country, sector and country-sector value chain networks to identify main drivers of structural change. The results indicate significant intertemporal changes, mirroring the increased globalization in terms of network size, strength and connectivity. They also demonstrate higher clustering and increased concentration of the most influential countries and country-sectors relative to all others in the GVC network, with the geographical dimension to prevail over the sectoral dimension in the formation of value chains. The regionalization and less hierarchical organization drive country-sector production sharing, while the sectoral value chain network has become more integrated and more competitive over time. The findings suggest that the impact of country-sector policies and/or shocks may vary with the own-group and network-wide influence of each country, take place in multiple geographical scales, as GVCs have a block structure, and involve time dynamics.
A Dynamic Reputation Management System for Mobile Ad Hoc Networks
Directory of Open Access Journals (Sweden)
Eric Chiejina
2015-04-01
Full Text Available Nodes in mobile ad hoc networks (MANETs are mandated to utilize their limited energy resources in forwarding routing control and data packets for other nodes. Since a MANET lacks a centralized administration and control, a node may decide to act selfishly, either by refusing to respond to route requests from other nodes or deceitfully by responding to some route requests, but dropping the corresponding data packets that are presented for forwarding. A significant increase in the presence of these misbehaving nodes in a MANET can subsequently degrade network performance. In this paper, we propose a dynamic reputation management system for detecting and isolating misbehaving nodes in MANETs. Our model employs a novel direct monitoring technique to evaluate the reputation of a node in the network, which ensures that nodes that expend their energy in transmitting data and routing control packets for others are allowed to carry out their network activities while the misbehaving nodes are detected and isolated from the network. Simulation results show that our model is effective at curbing and mitigating the effects of misbehaving nodes in the network.
International Nuclear Information System (INIS)
Vedam, S.; Docef, A.; Fix, M.; Murphy, M.; Keall, P.
2005-01-01
The synchronization of dynamic multileaf collimator (DMLC) response with respiratory motion is critical to ensure the accuracy of DMLC-based four dimensional (4D) radiation delivery. In practice, however, a finite time delay (response time) between the acquisition of tumor position and multileaf collimator response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces geometric errors, which have been reported in several publications. However, the dosimetric effect of such errors on 4D radiation delivery has not yet been investigated. Thus, our aim in this work was to quantify the dosimetric effects of geometric error due to prediction under several different conditions. Conformal and intensity modulated radiation therapy (IMRT) plans for a lung patient were generated for anterior-posterior/posterior-anterior (AP/PA) beam arrangements at 6 and 18 MV energies to provide planned dose distributions. Respiratory motion data was obtained from 60 diaphragm-motion fluoroscopy recordings from five patients. A linear adaptive filter was employed to predict the tumor position. The geometric error of prediction was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of geometric error of prediction were obtained for all of the respiratory motion data. Planned dose distributions were then convolved with distributions for the geometric error of prediction to obtain convolved dose distributions. The dosimetric effect of such geometric errors was determined as a function of several variables: response time (0-0.6 s), beam energy (6/18 MV), treatment delivery (3D/4D), treatment type (conformal/IMRT), beam direction (AP/PA), and breathing training type (free breathing/audio instruction/visual feedback). Dose difference and distance-to-agreement analysis was employed to quantify results. Based on our data, the
Adaptive Control Using a Neural Network Estimator and Dynamic Inversion
Ninomiya, Tetsujiro; Miyazawa, Yoshikazu
More and more UAVs are developed for various purposes and their flight controllers are required to have sufficient robustness and performance to realize their versatile missions. To design these sophisticated controller is pretty much time-consuming task by traditional design method. Neural network based adaptive control with dynamic inversion is applied to solve this problem. This method has two advantages. One is that the gain scheduling is not necessary because nonlinear dynamic inversion is applied to control nonlinear systems. The other is that neural network improves the controller performance by estimating parameters of the actual plant. Numerical examples show its effectiveness and its ability to adapt to modeling errors. This paper concludes that proposed method reduces the workload of controller design task and it has ability to adapt various errors of nonlinear systems.
Dynamic Power Tariff for Congestion Management in Distribution Networks
DEFF Research Database (Denmark)
Huang, Shaojun; Wu, Qiuwei; Shahidehpour, Mohammad
2018-01-01
This paper proposes dynamic power tariff (DPT), a new concept for congestion management in distribution networks with high penetration of electric vehicles (EVs), and heat pumps (HPs). The DPT concept is proposed to overcome a drawback of the dynamic tariff (DT) method, i.e., DPT can replace...... the price sensitivity parameter in the DT method, which is relatively unrealistic in practice. Based on the control theory, a control model with two control loops, i.e., the power flow control and voltage control, is established to analyze the congestion management process by the DPT method. Furthermore......, an iterative method based on distributed optimization is proposed to determine the DPT rates, which enables active participation of aggregators in the congestion management. The case studies demonstrate the efficacy of the DPT method for congestion management in distribution networks, and show its ability...
Simulating dynamic plastic continuous neural networks by finite elements.
Joghataie, Abdolreza; Torghabehi, Omid Oliyan
2014-08-01
We introduce dynamic plastic continuous neural network (DPCNN), which is comprised of neurons distributed in a nonlinear plastic medium where wire-like connections of neural networks are replaced with the continuous medium. We use finite element method to model the dynamic phenomenon of information processing within the DPCNNs. During the training, instead of weights, the properties of the continuous material at its different locations and some properties of neurons are modified. Input and output can be vectors and/or continuous functions over lines and/or areas. Delay and feedback from neurons to themselves and from outputs occur in the DPCNNs. We model a simple form of the DPCNN where the medium is a rectangular plate of bilinear material, and the neurons continuously fire a signal, which is a function of the horizontal displacement.
Successive lag synchronization on dynamical networks with communication delay
Xin-Jian, Zhang; Ai-Ju, Wei; Ke-Zan, Li
2016-03-01
In this paper, successive lag synchronization (SLS) on a dynamical network with communication delay is investigated. In order to achieve SLS on the dynamical network with communication delay, we design linear feedback control and adaptive control, respectively. By using the Lyapunov function method, we obtain some sufficient conditions for global stability of SLS. To verify these results, some numerical examples are further presented. This work may find potential applications in consensus of multi-agent systems. Project supported by the National Natural Science Foundation of China (Grant No. 61004101), the Natural Science Foundation Program of Guangxi Province, China (Grant No. 2015GXNSFBB139002), the Graduate Innovation Project of Guilin University of Electronic Technology, China (Grant No. GDYCSZ201472), and the Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, China.
Sign Inference for Dynamic Signed Networks via Dictionary Learning
Directory of Open Access Journals (Sweden)
Yi Cen
2013-01-01
Full Text Available Mobile online social network (mOSN is a burgeoning research area. However, most existing works referring to mOSNs deal with static network structures and simply encode whether relationships among entities exist or not. In contrast, relationships in signed mOSNs can be positive or negative and may be changed with time and locations. Applying certain global characteristics of social balance, in this paper, we aim to infer the unknown relationships in dynamic signed mOSNs and formulate this sign inference problem as a low-rank matrix estimation problem. Specifically, motivated by the Singular Value Thresholding (SVT algorithm, a compact dictionary is selected from the observed dataset. Based on this compact dictionary, the relationships in the dynamic signed mOSNs are estimated via solving the formulated problem. Furthermore, the estimation accuracy is improved by employing a dictionary self-updating mechanism.
Fiber-Optic Transmission Networks Efficient Design and Dynamic Operation
Pachnicke, Stephan
2012-01-01
Next generation optical communication systems will have to transport a significantly increased data volume at a reduced cost per transmitted bit. To achieve these ambitious goals optimum design is crucial in combination with dynamic adaptation to actual traffic demands and improved energy efficiency. In the first part of the book the author elaborates on the design of optical transmission systems. Several methods for efficient numerical simulation are presented ranging from meta-model based optimization to parallelization techniques for solving the nonlinear Schrödinger equation. Furthermore, fast analytical and semi-analytical models are described to estimate the various degradation effects occurring on the transmission line. In the second part of the book operational aspects of optical networks are investigated. Physical layer impairment-aware routing and regenerator placement are studied. Finally, it is analyzed how the energy efficiency of a multi-layer optical core network can be increased by dynamic ad...
A dynamic allocation mechanism of delivering capacity in coupled networks
International Nuclear Information System (INIS)
Du, Wen-Bo; Zhou, Xing-Lian; Zhu, Yan-Bo; Zheng, Zheng
2015-01-01
Traffic process is ubiquitous in many critical infrastructures. In this paper, we introduce a mechanism to dynamically allocate the delivering capacity into the data-packet traffic model on the coupled Internet autonomous-system-level network of South Korea and Japan, and focus on its effect on the transport efficiency. In this mechanism, the total delivering capacity is constant and the lowest-load node will give one unit delivering capacity to the highest-load node at each time step. It is found that the delivering capacity of busy nodes and non-busy nodes can be well balanced and the effective betweenness of busy nodes with interconnections is significantly reduced. Consequently, the transport efficiency such as average traveling time and packet arrival rate is remarkably improved. Our work may shed some light on the traffic dynamics in coupled networks.
Distance learning, problem based learning and dynamic knowledge networks.
Giani, U; Martone, P
1998-06-01
This paper is an attempt to develop a distance learning model grounded upon a strict integration of problem based learning (PBL), dynamic knowledge networks (DKN) and web tools, such as hypermedia documents, synchronous and asynchronous communication facilities, etc. The main objective is to develop a theory of distance learning based upon the idea that learning is a highly dynamic cognitive process aimed at connecting different concepts in a network of mutually supporting concepts. Moreover, this process is supposed to be the result of a social interaction that has to be facilitated by the web. The model was tested by creating a virtual classroom of medical and nursing students and activating a learning session on the concept of knowledge representation in health sciences.
Empirical Modeling of the Plasmasphere Dynamics Using Neural Networks
Zhelavskaya, Irina S.; Shprits, Yuri Y.; Spasojević, Maria
2017-11-01
We present the PINE (Plasma density in the Inner magnetosphere Neural network-based Empirical) model - a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural-network-based Upper hybrid Resonance Determination) algorithm for the period of 1 October 2012 to 1 July 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distribution for 2≤L≤6 and all local times. We validate and test the model by measuring its performance on independent data sets withheld from the training set and by comparing the model-predicted global evolution with global images of He+ distribution in the Earth's plasmasphere from the IMAGE Extreme UltraViolet (EUV) instrument. We identify the parameters that best quantify the plasmasphere dynamics by training and comparing multiple neural networks with different combinations of input parameters (geomagnetic indices, solar wind data, and different durations of their time history). The optimal model is based on the 96 h time history of Kp, AE, SYM-H, and F10.7 indices. The model successfully reproduces erosion of the plasmasphere on the nightside and plume formation and evolution. We demonstrate results of both local and global plasma density reconstruction. This study illustrates how global dynamics can be reconstructed from local in situ observations by using machine learning techniques.
On the dynamic analysis of piecewise-linear networks
Heemels, W.P.M.H.; Camlibel, M.K.; Schumacher, J.M.
2002-01-01
Piecewise-linear (PL) modeling is often used to approximate the behavior of nonlinear circuits. One of the possible PL modeling methodologies is based on the linear complementarity problem, and this approach has already been used extensively in the circuits and systems community for static networks. In this paper, the object of study will be dynamic electrical circuits that can be recast as linear complementarity systems, i.e., as interconnections of linear time-invariant differential equatio...
Automatic dialogue act recognition using a dynamic Bayesian network
Dielmann, Alfred; Renals, Steve
2007-01-01
We propose a joint segmentation and classification approach for the dialogue act recognition task on natural multi-party meetings (ICSI Meeting Corpus). Five broad DA categories are automatically recognised using a generative Dynamic Bayesian Network based infrastructure. Prosodic features and a switching graphical model are used to estimate DA boundaries, in conjunction with a factored language model which is used to relate words and DA categories. This easily generalizable and extensible sy...
Amplitude chimeras and chimera death in dynamical networks
International Nuclear Information System (INIS)
Zakharova, Anna; Kapeller, Marie; Schöll, Eckehard
2016-01-01
We find chimera states with respect to amplitude dynamics in a network of Stuart- Landau oscillators. These partially coherent and partially incoherent spatio-temporal patterns appear due to the interplay of nonlocal network topology and symmetry-breaking coupling. As the coupling range is increased, the oscillations are quenched, amplitude chimeras disappear and the network enters a symmetry-breaking stationary state. This particular regime is a novel pattern which we call chimera death. It is characterized by the coexistence of spatially coherent and incoherent inhomogeneous steady states and therefore combines the features of chimera state and oscillation death. Additionally, we show two different transition scenarios from amplitude chimera to chimera death. Moreover, for amplitude chimeras we uncover the mechanism of transition towards in-phase synchronized regime and discuss the role of initial conditions. (paper)
Dynamic Subsidy Method for Congestion Management in Distribution Networks
DEFF Research Database (Denmark)
Huang, Shaojun; Wu, Qiuwei
2016-01-01
Dynamic subsidy (DS) is a locational price paid by the distribution system operator (DSO) to its customers in order to shift energy consumption to designated hours and nodes. It is promising for demand side management and congestion management. This paper proposes a new DS method for congestion...... management in distribution networks, including the market mechanism, the mathematical formulation through a two-level optimization, and the method solving the optimization by tightening the constraints and linearization. Case studies were conducted with a one node system and the Bus 4 distribution network...... of the Roy Billinton Test System (RBTS) with high penetration of electric vehicles (EVs) and heat pumps (HPs). The case studies demonstrate the efficacy of the DS method for congestion management in distribution networks. Studies in this paper show that the DS method offers the customers a fair opportunity...
Information processing in neural networks with dynamic synapses
Torres, J. J.; Mejias, J. F.; Marro, J.; Kappen, H. J.
2009-01-01
We review here our recent work over the effect of activity-dependent synaptic processes, such as short-time depression and facilitation, on the emergent behaviour of different neural networks. We have studied, for instance, how synaptic depression affects the dynamic properties of Hopfield-type recurrent networks. We demonstrated that synaptic depression induces a novel phase in which the neural activity jumps among the memory attractors [1], which can be related with the appearance of oscillations between up and down states in the brain [2]. If one also considers the possibility of facilitation, and depending on the balance of depression, facilitation, and the underlying noise, the network displays different behaviors, including associative memory and switching of activity between different attractors. We concluded that synaptic facilitation enhances the attractor instability in a way that (1) enhances the system adaptability to external stimuli, and (2) favors the retrieval of information with less error during short time intervals [3].
Stimulation-Based Control of Dynamic Brain Networks.
Directory of Open Access Journals (Sweden)
Sarah Feldt Muldoon
2016-09-01
Full Text Available The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing interest in brain stimulation as a form of neuromodulation, much remains unknown about the network-level impact of these focal perturbations. To study the system wide impact of regional stimulation, we employ a data-driven computational model of nonlinear brain dynamics to systematically explore the effects of targeted stimulation. Validating predictions from network control theory, we uncover the relationship between regional controllability and the focal versus global impact of stimulation, and we relate these findings to differences in the underlying network architecture. Finally, by mapping brain regions to cognitive systems, we observe that the default mode system imparts large global change despite being highly constrained by structural connectivity. This work forms an important step towards the development of personalized stimulation protocols for medical treatment or performance enhancement.
A High-Resolution Sensor Network for Monitoring Glacier Dynamics
Edwards, S.; Murray, T.; O'Farrell, T.; Rutt, I. C.; Loskot, P.; Martin, I.; Selmes, N.; Aspey, R.; James, T.; Bevan, S. L.; Baugé, T.
2013-12-01
Changes in Greenland and Antarctic ice sheets due to ice flow/ice-berg calving are a major uncertainty affecting sea-level rise forecasts. Latterly GNSS (Global Navigation Satellite Systems) have been employed extensively to monitor such glacier dynamics. Until recently however, the favoured methodology has been to deploy sensors onto the glacier surface, collect data for a period of time, then retrieve and download the sensors. This approach works well in less dynamic environments where the risk of sensor loss is low. In more extreme environments e.g. approaching the glacial calving front, the risk of sensor loss and hence data loss increases dramatically. In order to provide glaciologists with new insights into flow dynamics and calving processes we have developed a novel sensor network to increase the robustness of data capture. We present details of the technological requirements for an in-situ Zigbee wireless streaming network infrastructure supporting instantaneous data acquisition from high resolution GNSS sensors thereby increasing data capture robustness. The data obtained offers new opportunities to investigate the interdependence of mass flow, uplift, velocity and geometry and the network architecture has been specifically designed for deployment by helicopter close to the calving front to yield unprecedented detailed information. Following successful field trials of a pilot three node network during 2012, a larger 20 node network was deployed on the fast-flowing Helheim glacier, south-east Greenland over the summer months of 2013. The utilisation of dual wireless transceivers in each glacier node, multiple frequencies and four ';collector' stations located on the valley sides creates overlapping networks providing enhanced capacity, diversity and redundancy of data 'back-haul', even close to ';floor' RSSI (Received Signal Strength Indication) levels around -100 dBm. Data loss through radio packet collisions within sub-networks are avoided through the
Evolution properties of the community members for dynamic networks
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.
Core reactivity estimation in space reactors using recurrent dynamic networks
Parlos, Alexander G.; Tsai, Wei K.
1991-01-01
A recurrent multilayer perceptron network topology is used in the identification of nonlinear dynamic systems from only the input/output measurements. The identification is performed in the discrete time domain, with the learning algorithm being a modified form of the back propagation (BP) rule. The recurrent dynamic network (RDN) developed is applied for the total core reactivity prediction of a spacecraft reactor from only neutronic power level measurements. Results indicate that the RDN can reproduce the nonlinear response of the reactor while keeping the number of nodes roughly equal to the relative order of the system. As accuracy requirements are increased, the number of required nodes also increases, however, the order of the RDN necessary to obtain such results is still in the same order of magnitude as the order of the mathematical model of the system. It is believed that use of the recurrent MLP structure with a variety of different learning algorithms may prove useful in utilizing artificial neural networks for recognition, classification, and prediction of dynamic systems.
Nonequilibrium dynamics of probe filaments in actin-myosin networks
Gladrow, J.; Broedersz, C. P.; Schmidt, C. F.
2017-08-01
Active dynamic processes of cells are largely driven by the cytoskeleton, a complex and adaptable semiflexible polymer network, motorized by mechanoenzymes. Small dimensions, confined geometries, and hierarchical structures make it challenging to probe dynamics and mechanical response of such networks. Embedded semiflexible probe polymers can serve as nonperturbing multiscale probes to detect force distributions in active polymer networks. We show here that motor-induced forces transmitted to the probe polymers are reflected in nonequilibrium bending dynamics, which we analyze in terms of spatial eigenmodes of an elastic beam under steady-state conditions. We demonstrate how these active forces induce correlations among the mode amplitudes, which furthermore break time-reversal symmetry. This leads to a breaking of detailed balance in this mode space. We derive analytical predictions for the magnitude of resulting probability currents in mode space in the white-noise limit of motor activity. We relate the structure of these currents to the spatial profile of motor-induced forces along the probe polymers and provide a general relation for observable currents on two-dimensional hyperplanes.
Information dynamics of brain–heart physiological networks during sleep
International Nuclear Information System (INIS)
Faes, L; Nollo, G; Jurysta, F; Marinazzo, D
2014-01-01
This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain–heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain–brain and brain–heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep. (paper)
Dynamic finite size effects in spiking neural networks.
Directory of Open Access Journals (Sweden)
Michael A Buice
Full Text Available We investigate the dynamics of a deterministic finite-sized network of synaptically coupled spiking neurons and present a formalism for computing the network statistics in a perturbative expansion. The small parameter for the expansion is the inverse number of neurons in the network. The network dynamics are fully characterized by a neuron population density that obeys a conservation law analogous to the Klimontovich equation in the kinetic theory of plasmas. The Klimontovich equation does not possess well-behaved solutions but can be recast in terms of a coupled system of well-behaved moment equations, known as a moment hierarchy. The moment hierarchy is impossible to solve but in the mean field limit of an infinite number of neurons, it reduces to a single well-behaved conservation law for the mean neuron density. For a large but finite system, the moment hierarchy can be truncated perturbatively with the inverse system size as a small parameter but the resulting set of reduced moment equations that are still very difficult to solve. However, the entire moment hierarchy can also be re-expressed in terms of a functional probability distribution of the neuron density. The moments can then be computed perturbatively using methods from statistical field theory. Here we derive the complete mean field theory and the lowest order second moment corrections for physiologically relevant quantities. Although we focus on finite-size corrections, our method can be used to compute perturbative expansions in any parameter.
Emergence of structural and dynamical properties of ecological mutualistic networks.
Suweis, Samir; Simini, Filippo; Banavar, Jayanth R; Maritan, Amos
2013-08-22
Mutualistic networks are formed when the interactions between two classes of species are mutually beneficial. They are important examples of cooperation shaped by evolution. Mutualism between animals and plants has a key role in the organization of ecological communities. Such networks in ecology have generally evolved a nested architecture independent of species composition and latitude; specialist species, with only few mutualistic links, tend to interact with a proper subset of the many mutualistic partners of any of the generalist species. Despite sustained efforts to explain observed network structure on the basis of community-level stability or persistence, such correlative studies have reached minimal consensus. Here we show that nested interaction networks could emerge as a consequence of an optimization principle aimed at maximizing the species abundance in mutualistic communities. Using analytical and numerical approaches, we show that because of the mutualistic interactions, an increase in abundance of a given species results in a corresponding increase in the total number of individuals in the community, and also an increase in the nestedness of the interaction matrix. Indeed, the species abundances and the nestedness of the interaction matrix are correlated by a factor that depends on the strength of the mutualistic interactions. Nestedness and the observed spontaneous emergence of generalist and specialist species occur for several dynamical implementations of the variational principle under stationary conditions. Optimized networks, although remaining stable, tend to be less resilient than their counterparts with randomly assigned interactions. In particular, we show analytically that the abundance of the rarest species is linked directly to the resilience of the community. Our work provides a unifying framework for studying the emergent structural and dynamical properties of ecological mutualistic networks.
Dynamical networks of influence in small group discussions.
Moussaïd, Mehdi; Noriega Campero, Alejandro; Almaatouq, Abdullah
2018-01-01
In many domains of life, business and management, numerous problems are addressed by small groups of individuals engaged in face-to-face discussions. While research in social psychology has a long history of studying the determinants of small group performances, the internal dynamics that govern a group discussion are not yet well understood. Here, we rely on computational methods based on network analyses and opinion dynamics to describe how individuals influence each other during a group discussion. We consider the situation in which a small group of three individuals engages in a discussion to solve an estimation task. We propose a model describing how group members gradually influence each other and revise their judgments over the course of the discussion. The main component of the model is an influence network-a weighted, directed graph that determines the extent to which individuals influence each other during the discussion. In simulations, we first study the optimal structure of the influence network that yields the best group performances. Then, we implement a social learning process by which individuals adapt to the past performance of their peers, thereby affecting the structure of the influence network in the long run. We explore the mechanisms underlying the emergence of efficient or maladaptive networks and show that the influence network can converge towards the optimal one, but only when individuals exhibit a social discounting bias by downgrading the relative performances of their peers. Finally, we find a late-speaker effect, whereby individuals who speak later in the discussion are perceived more positively in the long run and are thus more influential. The numerous predictions of the model can serve as a basis for future experiments, and this work opens research on small group discussion to computational social sciences.
Multiplex visibility graphs to investigate recurrent neural network dynamics
Bianchi, Filippo Maria; Livi, Lorenzo; Alippi, Cesare; Jenssen, Robert
2017-03-01
A recurrent neural network (RNN) is a universal approximator of dynamical systems, whose performance often depends on sensitive hyperparameters. Tuning them properly may be difficult and, typically, based on a trial-and-error approach. In this work, we adopt a graph-based framework to interpret and characterize internal dynamics of a class of RNNs called echo state networks (ESNs). We design principled unsupervised methods to derive hyperparameters configurations yielding maximal ESN performance, expressed in terms of prediction error and memory capacity. In particular, we propose to model time series generated by each neuron activations with a horizontal visibility graph, whose topological properties have been shown to be related to the underlying system dynamics. Successively, horizontal visibility graphs associated with all neurons become layers of a larger structure called a multiplex. We show that topological properties of such a multiplex reflect important features of ESN dynamics that can be used to guide the tuning of its hyperparamers. Results obtained on several benchmarks and a real-world dataset of telephone call data records show the effectiveness of the proposed methods.
Information geometric methods for complexity
Felice, Domenico; Cafaro, Carlo; Mancini, Stefano
2018-03-01
Research on the use of information geometry (IG) in modern physics has witnessed significant advances recently. In this review article, we report on the utilization of IG methods to define measures of complexity in both classical and, whenever available, quantum physical settings. A paradigmatic example of a dramatic change in complexity is given by phase transitions (PTs). Hence, we review both global and local aspects of PTs described in terms of the scalar curvature of the parameter manifold and the components of the metric tensor, respectively. We also report on the behavior of geodesic paths on the parameter manifold used to gain insight into the dynamics of PTs. Going further, we survey measures of complexity arising in the geometric framework. In particular, we quantify complexity of networks in terms of the Riemannian volume of the parameter space of a statistical manifold associated with a given network. We are also concerned with complexity measures that account for the interactions of a given number of parts of a system that cannot be described in terms of a smaller number of parts of the system. Finally, we investigate complexity measures of entropic motion on curved statistical manifolds that arise from a probabilistic description of physical systems in the presence of limited information. The Kullback-Leibler divergence, the distance to an exponential family and volumes of curved parameter manifolds, are examples of essential IG notions exploited in our discussion of complexity. We conclude by discussing strengths, limits, and possible future applications of IG methods to the physics of complexity.
Active and dynamic information fusion for multisensor systems with dynamic Bayesian networks.
Zhang, Yongmian; Ji, Qiang
2006-04-01
Many information fusion applications are often characterized by a high degree of complexity because: (1) data are often acquired from sensors of different modalities and with different degrees of uncertainty; (2) decisions must be made efficiently; and (3) the world situation evolves over time. To address these issues, we propose an information fusion framework based on dynamic Bayesian networks to provide active, dynamic, purposive and sufficing information fusion in order to arrive at a reliable conclusion with reasonable time and limited resources. The proposed framework is suited to applications where the decision must be made efficiently from dynamically available information of diverse and disparate sources.
Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models
DEFF Research Database (Denmark)
Johansen, Simon Vestergaard; Bendtsen, Jan Dimon; Riisgaard-Jensen, Martin
2017-01-01
In this article, the dynamic influence of environmental broiler house conditions and broiler growth is investigated. Dynamic neural network forecasting models have been trained on farm-scale broiler batch production data from 12 batches from the same house. The model forecasts future broiler weight...... and uses environmental conditions such as heating, ventilation, and temperature along with broiler behavior such as feed and water consumption. Training data and forecasting data is analyzed to explain when the model might fail at generalizing. We present ensemble broiler weight forecasts to day 7, 14, 21......, 28 and 34 from all preceding days and provide our interpretation of the results. Results indicate that the dynamic interconnection between environmental conditions and broiler growth can be captured by the model. Furthermore, we found that a comparable forecast can be obtained by using input data...
Spatio-Temporal Dynamics in Cellular Neural Networks
Directory of Open Access Journals (Sweden)
Liviu GORAS
2009-07-01
Full Text Available Analog Parallel Architectures like Cellular Neural Networks (CNN’s have been thoroughly studied not only for their potential in high-speed image processing applications but also for their rich and exciting spatio-temporal dynamics. An interesting behavior such architectures can exhibit is spatio-temporal filtering and pattern formation, aspects that will be discussed in this work for a general structure consisting of linear cells locally and homogeneously connected within a specified neighborhood. The results are generalizations of those regarding Turing pattern formation in CNN’s. Using linear cells (or piecewise linear cells working in the central linear part of their characteristic allows the use of the decoupling technique – a powerful technique that gives significant insight into the dynamics of the CNN. The roles of the cell structure as well as that of the connection template are discussed and models for the spatial modes dynamics are made as well.
Introduction to modern dynamics chaos, networks, space and time
Nolte, David D
2015-01-01
The best parts of physics are the last topics that our students ever see. These are the exciting new frontiers of nonlinear and complex systems that are at the forefront of university research and are the basis of many high-tech businesses. Topics such as traffic on the World Wide Web, the spread of epidemics through globally-mobile populations, or the synchronization of global economies are governed by universal principles just as profound as Newton's laws. Nonetheless, the conventional university physics curriculum reserves most of these topics for advanced graduate study. Two justifications are given for this situation: first, that the mathematical tools needed to understand these topics are beyond the skill set of undergraduate students, and second, that these are speciality topics with no common theme and little overlap. Introduction to Modern Dynamics dispels these myths. The structure of this book combines the three main topics of modern dynamics - chaos theory, dynamics on complex networks, and gener...
International Nuclear Information System (INIS)
Arnaud, Nicolas; Barsuglia, Matteo; Bizouard, Marie-Anne; Canitrot, Philippe; Cavalier, Fabien; Davier, Michel; Hello, Patrice; Pradier, Thierry
2002-01-01
Detecting gravitational wave bursts (characterized by short durations and poorly modeled waveforms) requires coincidences between several interferometric detectors in order to reject nonstationary noise events. As the wave amplitude seen in a detector depends on its location with respect to the source direction and as the signal to noise ratio of these bursts is expected to be low, coincidences between antennas may not be very likely. This paper investigates this question from a statistical point of view by using a simple model of a network of detectors; it also estimates the timing precision of a detection in an interferometer, which is an important issue for the reconstruction of the source location based on time delays
Discrete opinion dynamics on networks based on social influence
International Nuclear Information System (INIS)
Hu Haibo; Wang Xiaofan
2009-01-01
A model of opinion dynamics based on social influence on networks was studied. The opinion of each agent can have integer values i = 1, 2, ..., I and opinion exchanges are restricted to connected agents. It was found that for any I ≥ 2 and self-confidence parameter 0 ≤ u i ) of the population that hold a given opinion i is a martingale, and the fraction q i of opinion i will gradually converge to (q i ). The tendency can slow down with the increase of degree assortativity of networks. When u is degree dependent, (q i ) does not possess the martingale property, however q i still converges to it. In both cases for a finite network the states of all agents will finally reach consensus. Further if there exist stubborn persons in the population whose opinions do not change over time, it was found that for degree-independent constant u, both q i and (q i ) will converge to fixed proportions which only depend on the distribution of initial obstinate persons, and naturally the final equilibrium state will be the coexistence of diverse opinions held by the stubborn people. The analytical results were verified by numerical simulations on Barabasi-Albert (BA) networks. The model highlights the influence of high-degree agents on the final consensus or coexistence state and captures some realistic features of the diffusion of opinions in social networks
Dynamical and geometric aspects of Hamilton-Jacobi and linearized Monge-Ampère equations VIASM 2016
Tran, Hung
2017-01-01
Consisting of two parts, the first part of this volume is an essentially self-contained exposition of the geometric aspects of local and global regularity theory for the Monge–Ampère and linearized Monge–Ampère equations. As an application, we solve the second boundary value problem of the prescribed affine mean curvature equation, which can be viewed as a coupling of the latter two equations. Of interest in its own right, the linearized Monge–Ampère equation also has deep connections and applications in analysis, fluid mechanics and geometry, including the semi-geostrophic equations in atmospheric flows, the affine maximal surface equation in affine geometry and the problem of finding Kahler metrics of constant scalar curvature in complex geometry. Among other topics, the second part provides a thorough exposition of the large time behavior and discounted approximation of Hamilton–Jacobi equations, which have received much attention in the last two decades, and a new approach to the subject, the n...
Bestvina, Mladen; Vogtmann, Karen
2014-01-01
Geometric group theory refers to the study of discrete groups using tools from topology, geometry, dynamics and analysis. The field is evolving very rapidly and the present volume provides an introduction to and overview of various topics which have played critical roles in this evolution. The book contains lecture notes from courses given at the Park City Math Institute on Geometric Group Theory. The institute consists of a set of intensive short courses offered by leaders in the field, designed to introduce students to exciting, current research in mathematics. These lectures do not duplicate standard courses available elsewhere. The courses begin at an introductory level suitable for graduate students and lead up to currently active topics of research. The articles in this volume include introductions to CAT(0) cube complexes and groups, to modern small cancellation theory, to isometry groups of general CAT(0) spaces, and a discussion of nilpotent genus in the context of mapping class groups and CAT(0) gro...
Geometric interpretation of the geometric discord
International Nuclear Information System (INIS)
Yao, Yao; Li, Hong-Wei; Yin, Zhen-Qiang; Han, Zheng-Fu
2012-01-01
We investigate the level surfaces of geometric measure of quantum discord, and provide a pictorial interpretation of geometric discord for Bell-diagonal states. We have observed its nonanalytic behavior under decoherence employing this approach and interestingly found if we expect geometric discord to remain constant under phase-flip channel for a finite period, the initial state must be separable. Besides, this geometric understanding can be applied to verify the hierarchical relationships between geometric discord and the original one. The present work makes us conjecture that the incompatibility of these two definitions may originate from the discrepancy of the geometric structures of them. -- Highlights: ► We investigate geometry structure of geometric measure of quantum discord. ► If geometric discord is assumed to remain constant, the initial state must be separable. ► Geometry interpretation can be applied to verify hierarchical relationships between geometric discord and the original one.
Discrete dynamic modeling of T cell survival signaling networks
Zhang, Ranran
2009-03-01
Biochemistry-based frameworks are often not applicable for the modeling of heterogeneous regulatory systems that are sparsely documented in terms of quantitative information. As an alternative, qualitative models assuming a small set of discrete states are gaining acceptance. This talk will present a discrete dynamic model of the signaling network responsible for the survival and long-term competence of cytotoxic T cells in the blood cancer T-LGL leukemia. We integrated the signaling pathways involved in normal T cell activation and the known deregulations of survival signaling in leukemic T-LGL, and formulated the regulation of each network element as a Boolean (logic) rule. Our model suggests that the persistence of two signals is sufficient to reproduce all known deregulations in leukemic T-LGL. It also indicates the nodes whose inactivity is necessary and sufficient for the reversal of the T-LGL state. We have experimentally validated several model predictions, including: (i) Inhibiting PDGF signaling induces apoptosis in leukemic T-LGL. (ii) Sphingosine kinase 1 and NFκB are essential for the long-term survival of T cells in T-LGL leukemia. (iii) T box expressed in T cells (T-bet) is constitutively activated in the T-LGL state. The model has identified potential therapeutic targets for T-LGL leukemia and can be used for generating long-term competent CTL necessary for tumor and cancer vaccine development. The success of this model, and of other discrete dynamic models, suggests that the organization of signaling networks has an determining role in their dynamics. Reference: R. Zhang, M. V. Shah, J. Yang, S. B. Nyland, X. Liu, J. K. Yun, R. Albert, T. P. Loughran, Jr., Network Model of Survival Signaling in LGL Leukemia, PNAS 105, 16308-16313 (2008).
Kachalo, Sëma
2015-05-14
Geometric and mechanical properties of individual cells and interactions among neighboring cells are the basis of formation of tissue patterns. Understanding the complex interplay of cells is essential for gaining insight into embryogenesis, tissue development, and other emerging behavior. Here we describe a cell model and an efficient geometric algorithm for studying the dynamic process of tissue formation in 2D (e.g. epithelial tissues). Our approach improves upon previous methods by incorporating properties of individual cells as well as detailed description of the dynamic growth process, with all topological changes accounted for. Cell size, shape, and division plane orientation are modeled realistically. In addition, cell birth, cell growth, cell shrinkage, cell death, cell division, cell collision, and cell rearrangements are now fully accounted for. Different models of cell-cell interactions, such as lateral inhibition during the process of growth, can be studied in detail. Cellular pattern formation for monolayered tissues from arbitrary initial conditions, including that of a single cell, can also be studied in detail. Computational efficiency is achieved through the employment of a special data structure that ensures access to neighboring cells in constant time, without additional space requirement. We have successfully generated tissues consisting of more than 20,000 cells starting from 2 cells within 1 hour. We show that our model can be used to study embryogenesis, tissue fusion, and cell apoptosis. We give detailed study of the classical developmental process of bristle formation on the epidermis of D. melanogaster and the fundamental problem of homeostatic size control in epithelial tissues. Simulation results reveal significant roles of solubility of secreted factors in both the bristle formation and the homeostatic control of tissue size. Our method can be used to study broad problems in monolayered tissue formation. Our software is publicly
Directory of Open Access Journals (Sweden)
Sëma Kachalo
Full Text Available Geometric and mechanical properties of individual cells and interactions among neighboring cells are the basis of formation of tissue patterns. Understanding the complex interplay of cells is essential for gaining insight into embryogenesis, tissue development, and other emerging behavior. Here we describe a cell model and an efficient geometric algorithm for studying the dynamic process of tissue formation in 2D (e.g. epithelial tissues. Our approach improves upon previous methods by incorporating properties of individual cells as well as detailed description of the dynamic growth process, with all topological changes accounted for. Cell size, shape, and division plane orientation are modeled realistically. In addition, cell birth, cell growth, cell shrinkage, cell death, cell division, cell collision, and cell rearrangements are now fully accounted for. Different models of cell-cell interactions, such as lateral inhibition during the process of growth, can be studied in detail. Cellular pattern formation for monolayered tissues from arbitrary initial conditions, including that of a single cell, can also be studied in detail. Computational efficiency is achieved through the employment of a special data structure that ensures access to neighboring cells in constant time, without additional space requirement. We have successfully generated tissues consisting of more than 20,000 cells starting from 2 cells within 1 hour. We show that our model can be used to study embryogenesis, tissue fusion, and cell apoptosis. We give detailed study of the classical developmental process of bristle formation on the epidermis of D. melanogaster and the fundamental problem of homeostatic size control in epithelial tissues. Simulation results reveal significant roles of solubility of secreted factors in both the bristle formation and the homeostatic control of tissue size. Our method can be used to study broad problems in monolayered tissue formation. Our software
Gao, Zilin; Wang, Yinhe; Zhang, Lili
2018-02-01
In the existing research results of the complex dynamical networks controlled, the controllers are mainly used to guarantee the synchronization or stabilization of the nodes’ state, and the terms coupled with connection relationships may affect the behaviors of nodes, this obviously ignores the dynamic common behavior of the connection relationships between the nodes. In fact, from the point of view of large-scale system, a complex dynamical network can be regarded to be composed of two time-varying dynamic subsystems, which can be called the nodes subsystem and the connection relationships subsystem, respectively. Similar to the synchronization or stabilization of the nodes subsystem, some characteristic phenomena can be also emerged in the connection relationships subsystem. For example, the structural balance in the social networks and the synaptic facilitation in the biological neural networks. This paper focuses on the structural balance in dynamic complex networks. Generally speaking, the state of the connection relationships subsystem is difficult to be measured accurately in practical applications, and thus it is not easy to implant the controller directly into the connection relationships subsystem. It is noted that the nodes subsystem and the relationships subsystem are mutually coupled, which implies that the state of the connection relationships subsystem can be affected by the controllable state of nodes subsystem. Inspired by this observation, by using the structural balance theory of triad, the controller with the parameter adaptive law is proposed for the nodes subsystem in this paper, which may ensure the connection relationship matrix to approximate a given structural balance matrix in the sense of the uniformly ultimately bounded (UUB). That is, the structural balance may be obtained by employing the controlling state of the nodes subsystem. Finally, the simulations are used to show the validity of the method in this paper.
Dynamic characterisation of the specific surface area for fracture networks
Cvetkovic, V.
2017-12-01
One important application of chemical transport is geological disposal of high-level nuclear waste for which crystalline rock is a prime candidate for instance in Scandinavia. Interconnected heterogeneous fractures of sparsely fractured rock such as granite, act as conduits for transport of dissolved tracers. Fluid flow is known to be highly channelized in such rocks. Channels imply narrow flow paths, adjacent to essentially stagnant water in the fracture and/or the rock matrix. Tracers are transported along channelised flow paths and retained by minerals and/or stagnant water, depending on their sorption properties; this mechanism is critical for rocks to act as a barrier and ultimately provide safety for a geological repository. The sorbing tracers are retained by diffusion and sorption on mineral surfaces, whereas non-sorbing tracers can be retained only by diffusion into stagnant water of fractures. The retention and transport properties of a sparsely fractured rock will primarily depend on the specific surface area (SSA) of the fracture network which is determined by the heterogeneous structure and flow. The main challenge when characterising SSA on the field-scale is its dependence on the flow dynamics. We first define SSA as a physical quantity and clarify its importance for chemical transport. A methodology for dynamic characterisation of SSA in fracture networks is proposed that relies on three sets of data: i) Flow rate data as obtained by a flow logging procedure; ii) transmissivity data as obtained by pumping tests; iii) fracture network data as obtained from outcrop and geophysical observations. The proposed methodology utilises these data directly as well as indirectly through flow and particle tracking simulations in three-dimensional discrete fracture networks. The methodology is exemplified using specific data from the Swedish site Laxemar. The potential impact of uncertainties is of particular significance and is illustrated for radionuclide
Inertial forces affect fluid front displacement dynamics in a pore-throat network model.
Moebius, Franziska; Or, Dani
2014-08-01
The seemingly regular and continuous motion of fluid displacement fronts in porous media at the macroscopic scale is propelled by numerous (largely invisible) pore-scale abrupt interfacial jumps and pressure bursts. Fluid fronts in porous media are characterized by sharp phase discontinuities and by rapid pore-scale dynamics that underlie their motion; both attributes challenge standard continuum theories of these flow processes. Moreover, details of pore-scale dynamics affect front morphology and subsequent phase entrapment behind a front and thereby shape key macroscopic transport properties of the unsaturated zone. The study presents a pore-throat network model that focuses on quantifying interfacial dynamics and interactions along fluid displacement fronts. The porous medium is represented by a lattice of connected pore throats capable of detaining menisci and giving rise to fluid-fluid interfacial jumps (the study focuses on flow rate controlled drainage). For each meniscus along the displacement front we formulate a local inertial, capillary, viscous, and hydrostatic force balance that is then solved simultaneously for the entire front. The model enables systematic evaluation of the role of inertia and boundary conditions. Results show that while displacement patterns are affected by inertial forces mainly by invasion of throats with higher capillary resistance, phase entrapment (residual saturation) is largely unaffected by inertia, limiting inertial effects on hydrological properties behind a front. Interfacial jump velocities are often an order of magnitude larger than mean front velocity, are strongly dependent on geometrical throat dimensions, and become less predictable (more scattered) when inertia is considered. Model simulations of the distributions of capillary pressure fluctuations and waiting times between invasion events follow an exponential distribution and are in good agreement with experimental results. The modeling approach provides insights
Dynamics of Research Team Formation in Complex Networks
Sun, Caihong; Wan, Yuzi; Chen, Yu
Most organizations encourage the formation of teams to accomplish complicated tasks, and vice verse, effective teams could bring lots benefits and profits for organizations. Network structure plays an important role in forming teams. In this paper, we specifically study the dynamics of team formation in large research communities in which knowledge of individuals plays an important role on team performance and individual utility. An agent-based model is proposed, in which heterogeneous agents from research communities are described and empirically tested. Each agent has a knowledge endowment and a preference for both income and leisure. Agents provide a variable input (‘effort’) and their knowledge endowments to production. They could learn from others in their team and those who are not in their team but have private connections in community to adjust their own knowledge endowment. They are allowed to join other teams or work alone when it is welfare maximizing to do so. Various simulation experiments are conducted to examine the impacts of network topology, knowledge diffusion among community network, and team output sharing mechanisms on the dynamics of team formation.
A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses
Directory of Open Access Journals (Sweden)
Karim El-Laithy
2011-01-01
Full Text Available An integration of both the Hebbian-based and reinforcement learning (RL rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.
Dynamical Structure of a Traditional Amazonian Social Network.
Hooper, Paul L; DeDeo, Simon; Caldwell Hooper, Ann E; Gurven, Michael; Kaplan, Hillard S
2013-11-13
Reciprocity is a vital feature of social networks, but relatively little is known about its temporal structure or the mechanisms underlying its persistence in real world behavior. In pursuit of these two questions, we study the stationary and dynamical signals of reciprocity in a network of manioc beer (Spanish: chicha ; Tsimane': shocdye' ) drinking events in a Tsimane' village in lowland Bolivia. At the stationary level, our analysis reveals that social exchange within the community is heterogeneously patterned according to kinship and spatial proximity. A positive relationship between the frequencies at which two families host each other, controlling for kinship and proximity, provides evidence for stationary reciprocity. Our analysis of the dynamical structure of this network presents a novel method for the study of conditional, or non-stationary, reciprocity effects. We find evidence that short-timescale reciprocity (within three days) is present among non- and distant-kin pairs; conversely, we find that levels of cooperation among close kin can be accounted for on the stationary hypothesis alone.
Dynamical Structure of a Traditional Amazonian Social Network
Directory of Open Access Journals (Sweden)
Paul L. Hooper
2013-11-01
Full Text Available Reciprocity is a vital feature of social networks, but relatively little is known about its temporal structure or the mechanisms underlying its persistence in real world behavior. In pursuit of these two questions, we study the stationary and dynamical signals of reciprocity in a network of manioc beer (Spanish: chicha; Tsimane’: shocdye’ drinking events in a Tsimane’ village in lowland Bolivia. At the stationary level, our analysis reveals that social exchange within the community is heterogeneously patterned according to kinship and spatial proximity. A positive relationship between the frequencies at which two families host each other, controlling for kinship and proximity, provides evidence for stationary reciprocity. Our analysis of the dynamical structure of this network presents a novel method for the study of conditional, or non-stationary, reciprocity effects. We find evidence that short-timescale reciprocity (within three days is present among non- and distant-kin pairs; conversely, we find that levels of cooperation among close kin can be accounted for on the stationary hypothesis alone.
International Nuclear Information System (INIS)
Ploeger, Lennert S.; Smitsmans, Monique H.P.; Gilhuijs, Kenneth G.A.; Herk, Marcel van
2002-01-01
In order to guarantee the safe delivery of dynamic intensity modulated radiotherapy (IMRT), verification of the leaf trajectories during the treatment is necessary. Our aim in this study is to develop a method for on-line verification of leaf trajectories using an electronic portal imaging device with scanning read-out, independent of the multileaf collimator. Examples of such scanning imagers are electronic portal imaging devices (EPIDs) based on liquid-filled ionization chambers and those based on amorphous silicon. Portal images were acquired continuously with a liquid-filled ionization chamber EPID during the delivery, together with the signal of treatment progress that is generated by the accelerator. For each portal image, the prescribed leaf and diaphragm positions were computed from the dynamic prescription and the progress information. Motion distortion effects of the leaves are corrected based on the treatment progress that is recorded for each image row. The aperture formed by the prescribed leaves and diaphragms is used as the reference field edge, while the actual field edge is found using a maximum-gradient edge detector. The errors in leaf and diaphragm position are found from the deviations between the reference field edge and the detected field edge. Earlier measurements of the dynamic EPID response show that the accuracy of the detected field edge is better than 1 mm. To ensure that the verification is independent of inaccuracies in the acquired progress signal, the signal was checked with diode measurements beforehand. The method was tested on three different dynamic prescriptions. Using the described method, we correctly reproduced the distorted field edges. Verifying a single portal image took 0.1 s on an 866 MHz personal computer. Two flaws in the control system of our experimental dynamic multileaf collimator were correctly revealed with our method. First, the errors in leaf position increase with leaf speed, indicating a delay of
Energy Efficient Routing Algorithms in Dynamic Optical Core Networks with Dual Energy Sources
DEFF Research Database (Denmark)
Wang, Jiayuan; Fagertun, Anna Manolova; Ruepp, Sarah Renée
2013-01-01
This paper proposes new energy efficient routing algorithms in optical core networks, with the application of solar energy sources and bundled links. A comprehensive solar energy model is described in the proposed network scenarios. Network performance in energy savings, connection blocking...... probability, resource utilization and bundled link usage are evaluated with dynamic network simulations. Results show that algorithms proposed aiming for reducing the dynamic part of the energy consumption of the network may raise the fixed part of the energy consumption meanwhile....
Studies on the population dynamics of a rumor-spreading model in online social networks
Dong, Suyalatu; Fan, Feng-Hua; Huang, Yong-Chang
2018-02-01
This paper sets up a rumor spreading model in online social networks based on the European fox rabies SIR model. The model considers the impact of changing number of online social network users, combines the transmission dynamics to set up a population dynamics of rumor spreading model in online social networks. Simulation is carried out on online social network, and results show that the new rumor spreading model is in accordance with the real propagation characteristics in online social networks.
Runt, James; Iacob, Ciprian
2015-03-01
Segmental and local dynamics as well as charge transport are investigated in a series of poly(ethylene oxide)-based single-ion conductors (ionomers) with varying counterions (Li +, Na +) confined in uni-directional nanoporous silica membranes. The dynamics are explored over a wide frequency and temperature range by broadband dielectric relaxation spectroscopy. Slowing of segmental dynamics and a decrease in dc conductivity (strongly coupled with segmental relaxation) of the confined ionomers are associated with surface effects - resulting from interfacial hydrogen bonding between the host nanoporous silica membrane and the guest ionomers. These effects are significantly reduced or eliminated upon pore surface modification through silanization. The primary transport properties for the confined ionomers decrease by about one decade compared to the bulk ionomer. A model assuming reduced mobility of an adsorbed layer at the pore wall/ionomer interface is shown to provide a quantitative explanation for the decrease in effective transport quantities in non-silanized porous silica membranes. Additionally, the effect of confinement on ion aggregation in ionomers by using X-ray scattering will also be discussed. Supported by the National Science Foundation, Polymers Program.
Network dynamics of social influence in the wisdom of crowds.
Becker, Joshua; Brackbill, Devon; Centola, Damon
2017-06-27
A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton's discovery of the "wisdom of crowds" [Galton F (1907) Nature 75:450-451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals to be either independent, with uncorrelated beliefs, or diverse, with negatively correlated beliefs [Page S (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies ]. Previous experimental studies have supported this view by arguing that social influence undermines the wisdom of crowds. These results showed that individuals' estimates became more similar when subjects observed each other's beliefs, thereby reducing diversity without a corresponding increase in group accuracy [Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) Proc Natl Acad Sci USA 108:9020-9025]. By contrast, we show general network conditions under which social influence improves the accuracy of group estimates, even as individual beliefs become more similar. We present theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange. We further show that the dynamics of group accuracy change with network structure. In centralized networks, where the influence of central individuals dominates the collective estimation process, group estimates become more likely to increase in error.
A network analysis of the dynamics of seizure.
Burns, Samuel P; Sritharan, Duluxan; Jouny, Christophe; Bergey, Gregory; Crone, Nathan; Anderson, William S; Sarma, Sridevi V
2012-01-01
Seizures are events that spread through the brain's network of connections and create pathological activity. To understand what is occurring in the brain during seizure we investigated the time progression of the brain's state from seizure onset to seizure suppression. Knowledge of a seizure's dynamics and the associated spatial structure is important for localizing the seizure foci and determining the optimal location and timing of electrical stimulation to mitigate seizure development. In this study, we analyzed intracranial EEG data recorded in 2 human patients with drug-resistant epilepsy prior to undergoing resection surgery using network analyses. Specifically, we computed a time sequence of connectivity matrices from iEEG (intracranial electroencephalography) recordings that represent network structure over time. For each patient, connectivity between electrodes was measured using the coherence in the band of frequencies with the strongest modulation during seizure. The connectivity matrices' structure was analyzed using an eigen-decomposition. The leading eigenvector was used to estimate each electrode's time dependent centrality (importance to the network's connectivity). The electrode centralities were clustered over the course of each seizure and the cluster centroids were compared across seizures. We found, for each patient, there was a consistent set of centroids that occurred during each seizure. Further, the brain reliably evolved through the same progression of states across multiple seizures including characteristic onset and suppression states.
Fast Entanglement Establishment via Local Dynamics for Quantum Repeater Networks
Gyongyosi, Laszlo; Imre, Sandor
Quantum entanglement is a necessity for future quantum communication networks, quantum internet, and long-distance quantum key distribution. The current approaches of entanglement distribution require high-delay entanglement transmission, entanglement swapping to extend the range of entanglement, high-cost entanglement purification, and long-lived quantum memories. We introduce a fundamental protocol for establishing entanglement in quantum communication networks. The proposed scheme does not require entanglement transmission between the nodes, high-cost entanglement swapping, entanglement purification, or long-lived quantum memories. The protocol reliably establishes a maximally entangled system between the remote nodes via dynamics generated by local Hamiltonians. The method eliminates the main drawbacks of current schemes allowing fast entanglement establishment with a minimized delay. Our solution provides a fundamental method for future long-distance quantum key distribution, quantum repeater networks, quantum internet, and quantum-networking protocols. This work was partially supported by the GOP-1.1.1-11-2012-0092 project sponsored by the EU and European Structural Fund, by the Hungarian Scientific Research Fund - OTKA K-112125, and by the COST Action MP1006.
Directory of Open Access Journals (Sweden)
Kaitlin M Fisher
the protein is geometrically frustrated, requiring the more stable elements to fold first, acting as a scaffold for docking of the functional element to allow productive folding to the native state.
Fisher, Kaitlin M; Haglund, Ellinor; Noel, Jeffrey K; Hailey, Kendra L; Onuchic, José N; Jennings, Patricia A
2015-01-01
geometrically frustrated, requiring the more stable elements to fold first, acting as a scaffold for docking of the functional element to allow productive folding to the native state.
Mathematical model of the Danube Delta Hydrographical Network Morphological Dynamics
Directory of Open Access Journals (Sweden)
CIOACA Eugenia
2010-09-01
Full Text Available This paper presents an innovative technology used to investigate the Danube Delta Biosphere Reserve hydrographical network from the morphologic changes point of view, as result of fluvial processes, erosion and alluvial sedimentation. Field measurements and data processing are performed on water flow, sediment transport andbathymetry. Geospatial databases resulted help for constructing the mathematical /hydraulic model to simulate the hydro-morphological dynamics. There is used as workbench the Delft3D software – a product of DELTARES - Delft Hydraulic Institute, The Netherlands. The model results serve as practical tool for end users to scientifically justify the management decisions made on hydrographic network rehabilitation / reconstruction in order to improve the water flow regime.