Networks Of Different Sizes Research Articles

Projective synchronization between two delayed networks of different sizes with nonidentical nodes and unknown parameters

This paper investigates the projective synchronization between two delayed networks of different sizes with nonidentical nodes and unknown parameters. The two complex networks are of diverse dynamical nodes, different topological structures and different sizes. Both networks contain unknown parameters and coupling delay. Based on stability theory, the bidirectional coupling control condition is proposed to ensure the realization of projective synchronization between the two delayed networks. During synchronization, the unknown parameters are successful estimated by adaptive updated laws. Furthermore, in numerical simulations, the Small-World network and Scale-Free network based on well-known Lorenz system and Chen system are constructed. The results verified the effectiveness of the proposed synchronization method.

A comparative study of metaheuristic optimization approaches for directional overcurrent relays coordination

In this paper, a comparative study of different meta-heuristic optimization approaches, which had been proposed in the literature for directional overcurrent relay coordination (DOCRs), is presented. Towards this goal, five most effective meta-heuristic optimization approaches such as genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), harmony search (HS) and seeker optimization algorithm (SOA) have been considered. The performances of these optmization methods have been investigated on several power system networks of different sizes. The comparative performances of these methods have been studied by executing each method 100 times with the same initial conditions and based on the obtained results, the best meta-heuristic optimization method for solving the coordination problem of directional overcurrent relays is identified.

The relative efficiency of modular and non-modular networks of different size.

Most biological networks are modular but previous work with small model networks has indicated that modularity does not necessarily lead to increased functional efficiency. Most biological networks are large, however, and here we examine the relative functional efficiency of modular and non-modular neural networks at a range of sizes. We conduct a detailed analysis of efficiency in networks of two size classes: ‘small’ and ‘large’, and a less detailed analysis across a range of network sizes. The former analysis reveals that while the modular network is less efficient than one of the two non-modular networks considered when networks are small, it is usually equally or more efficient than both non-modular networks when networks are large. The latter analysis shows that in networks of small to intermediate size, modular networks are much more efficient that non-modular networks of the same (low) connective density. If connective density must be kept low to reduce energy needs for example, this could promote modularity. We have shown how relative functionality/performance scales with network size, but the precise nature of evolutionary relationship between network size and prevalence of modularity will depend on the costs of connectivity.

Optimal power flow in three-phase islanded microgrids with inverter interfaced units

In this paper, the solution of the optimal power flow (OPF) problem for three phase islanded microgrids is studied, the OPF being one of the core functions of the tertiary regulation level for an AC islanded microgrid with a hierarchical control architecture. The study also aims at evaluating the contextual adjustment of the droop parameters used for primary voltage and frequency regulation of inverter interfaced units. The output of the OPF provides an iso-frequential operating point for all the generation units and a set of droop parameters for primary regulation. In this way, secondary regulation can be neglected in the considered hierarchical control structure. The application section provides the solution of the OPF problem over networks of different sizes and a stability analysis of the microgrid system using the optimized droop parameters, thus giving rise to the optimized management of the system with a new hierarchical control architecture.

The impact of capillary dilation on the distribution of red blood cells in artificial networks.

Recent studies suggest that pericytes around capillaries are contractile and able to alter the diameter of capillaries. To investigate the effects of capillary dilation on network dynamics, we performed simulations in artificial capillary networks of different sizes and complexities. The unequal partition of hematocrit at diverging bifurcations was modeled by assuming that each red blood cell (RBC) enters the branch with the faster instantaneous flow. Network simulations with and without RBCs were performed to investigate the effect of local dilations. The results showed that the increase in flow rate due to capillary dilation was less when the effects of RBCs are included. For bifurcations with sufficient RBCs in the parent vessel and nearly equal flows in the branches, the flow rate in the dilated branch did not increase. Instead, a self-regulation of flow was observed due to accumulation of RBCs in the dilated capillary. A parametric study was performed to examine the dependence on initial capillary diameter, dilation factor, and tube hematocrit. Furthermore, the conditions needed for an efficient self-regulation mechanism are discussed. The results support the hypothesis that RBCs play a significant role for the fluid dynamics in capillary networks and that it is crucial to consider the blood flow rate and the distribution of RBCs to understand the supply of oxygen in the vasculature. Furthermore, our results suggest that capillary dilation/constriction offers the potential of being an efficient mechanism to alter the distribution of RBCs locally and hence could be important for the local regulation of oxygen delivery.

Modeling Stochastic Route Choice Behaviors with Equivalent Impedance

A Logit-based route choice model is proposed to address the overlapping and scaling problems in the traditional multinomial Logit model. The nonoverlapping links are defined as a subnetwork, and its equivalent impedance is explicitly calculated in order to simply network analyzing. The overlapping links are repeatedly merged into subnetworks with Logit-based equivalent travel costs. The choice set at each intersection comprises only the virtual equivalent route without overlapping. In order to capture heterogeneity in perception errors of different sizes of networks, different scale parameters are assigned to subnetworks and they are linked to the topological relationships to avoid estimation burden. The proposed model provides an alternative method to model the stochastic route choice behaviors without the overlapping and scaling problems, and it still maintains the simple and closed-form expression from the MNL model. A link-based loading algorithm based on Dial’s algorithm is proposed to obviate route enumeration and it is suitable to be applied on large-scale networks. Finally a comparison between the proposed model and other route choice models is given by numerical examples.

Improving gene regulatory network inference using network topology information.

Inferring the gene regulatory network (GRN) structure from data is an important problem in computational biology. However, it is a computationally complex problem and approximate methods such as heuristic search techniques, restriction of the maximum-number-of-parents (maxP) for a gene, or an optimal search under special conditions are required. The limitations of a heuristic search are well known but literature on the detailed analysis of the widely used maxP technique is lacking. The optimal search methods require large computational time. We report the theoretical analysis and experimental results of the strengths and limitations of the maxP technique. Further, using an optimal search method, we combine the strengths of the maxP technique and the known GRN topology to propose two novel algorithms. These algorithms are implemented in a Bayesian network framework and tested on biological, realistic, and in silico networks of different sizes and topologies. They overcome the limitations of the maxP technique and show superior computational speed when compared to the current optimal search algorithms.

Multi-frequency sweeping method for periodic steady-state computations on the graphics processor unit

This paper presents the parallelization of the multi-frequency hybrid backward/forward sweeping (BFS) technique on a graphics processor unit (GPU). Primarily, the intrinsic layer structure of a radial network, typical topology of distribution systems, and its multi-frequency behavior are exploited for parallelization of the hybrid BFS method on the GPU. The less computational demanding tasks, e.g., error computation and simple vectorized operations, are assigned to the CPU. The network solution is performed in the Matlab® environment using compute unified device architecture (CUDA). The computational time required by the GPU/CPU BFS implementation is compared with a CPU-only program by solving four networks of different sizes. Validation of the multi-frequency BFS results is made through a CPU implementation of a Newton-type solution scheme. The significant reduction in the computational time of the parallelized GPU implementation of the hybrid BFS method combined with its ability to include a wide range of frequencies and to handle nonlinear components makes it suitable for real-time online applications.

Entropy weighted average method for the determination of a single representative path flow solution for the static user equilibrium traffic assignment problem

The formulation of the static user equilibrium traffic assignment problem (UETAP) under some simplifying assumptions has a unique solution in terms of link flows but not in terms of path flows. Large variations are possible in the path flows obtained using different UETAP solution algorithms. Many transportation planning and management applications entail the need for path flows. This raises the issue of generating a meaningful path flow solution in practice. Past studies have sought to determine a single path flow solution using the maximum entropy concept. This study proposes an alternate approach to determine a single path flow solution that represents the entropy weighted average of the UETAP path flow solution space. It has the minimum expected Euclidean distance from all other path flow solution vectors of the UETAP. The mathematical model of the proposed entropy weighted average method is derived and its solution stability is proved. The model is easy to interpret and generalizes the proportionality condition of Bar-Gera and Boyce (1999). Results of numerical experiments using networks of different sizes suggest that the path flow solutions for the UETAP using the proposed method are about identical to those obtained using the maximum entropy approach. The entropy weighted average method requires low computational effort and is easier to implement, and can therefore serve as a potential alternative to the maximum entropy approach in practice.

Systematic identification of transcriptional and post-transcriptional regulations in human respiratory epithelial cells during influenza A virus infection.

BackgroundRespiratory epithelial cells are the primary target of influenza virus infection in human. However, the molecular mechanisms of airway epithelial cell responses to viral infection are not fully understood. Revealing genome-wide transcriptional and post-transcriptional regulatory relationships can further advance our understanding of this problem, which motivates the development of novel and more efficient computational methods to simultaneously infer the transcriptional and post-transcriptional regulatory networks.ResultsHere we propose a novel framework named SITPR to investigate the interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes. Briefly, a background regulatory network on a genome-wide scale (~23,000 nodes and ~370,000 potential interactions) is constructed from curated knowledge and algorithm predictions, to which the identification of transcriptional and post-transcriptional regulatory relationships is anchored. To reduce the dimension of the associated computing problem down to an affordable size, several topological and data-based approaches are used. Furthermore, we propose the constrained LASSO formulation and combine it with the dynamic Bayesian network (DBN) model to identify the activated regulatory relationships from time-course expression data. Our simulation studies on networks of different sizes suggest that the proposed framework can effectively determine the genuine regulations among TFs, miRNAs and target genes; also, we compare SITPR with several selected state-of-the-art algorithms to further evaluate its performance. By applying the SITPR framework to mRNA and miRNA expression data generated from human lung epithelial A549 cells in response to A/Mexico/InDRE4487/2009 (H1N1) virus infection, we are able to detect the activated transcriptional and post-transcriptional regulatory relationships as well as the significant regulatory motifs.ConclusionCompared with other representative state-of-the-art algorithms, the proposed SITPR framework can more effectively identify the activated transcriptional and post-transcriptional regulations simultaneously from a given background network. The idea of SITPR is generally applicable to the analysis of gene regulatory networks in human cells. The results obtained for human respiratory epithelial cells suggest the importance of the transcriptional, post-transcriptional regulations as well as their synergies in the innate immune responses against IAV infection.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-336) contains supplementary material, which is available to authorized users.

Warm-starting dynamic traffic assignment with static solutions

Dynamic traffic assignment (DTA) convergence can be accelerated by providing an initial feasible solution from static traffic assignment (STA). While this strategy seems natural, it has received little attention in the literature despite a number of choices which must be made, including STA stopping criteria, determining STA demand from dynamic demand, how to create a path-based DTA solution from the STA solution and the choice of STA algorithm itself. This paper studies these variations in the context of a cell-transmission based DTA algorithm. Experimental results on two networks of different size indicate that the STA solution can provide significant improvements in convergence despite the time-varying and flow model differences. A sensitivity analysis provides evidence that these benefits to convergence are likely to persist over varying inputs. The analysis further suggests that although the optimal parameter choice depends on the network, practitioners will observe significant improvements in convergence for a fairly wide range of reasonable parameters.

Distributed Consensus-Based Economic Dispatch With Transmission Losses

A distributed algorithm is presented to solve the economic power dispatch with transmission line losses and generator constraints. The proposed approach is based on two consensus algorithms running in parallel. The first algorithm is a first-order consensus protocol modified by a correction term which uses a local estimation of the system power mismatch to ensure the generation-demand equality. The second algorithm performs the estimation of the power mismatch in the system using a consensus strategy called consensus on the most up-to-date information. The proposed approach can handle networks of different size and topology using the information about the number of nodes which is also evaluated in a distributed fashion. Simulations performed on standard test cases demonstrate the effectiveness of the proposed approach for both small and large systems.

The impact of orbital errors on the estimation of satellite clock errors and PPP

Precise satellite orbit and clocks are essential for providing high accuracy real-time PPP (Precise Point Positioning) service. However, by treating the predicted orbits as fixed, the orbital errors may be partially assimilated by the estimated satellite clock and hence impact the positioning solutions. This paper presents the impact analysis of errors in radial and tangential orbital components on the estimation of satellite clocks and PPP through theoretical study and experimental evaluation. The relationship between the compensation of the orbital errors by the satellite clocks and the satellite-station geometry is discussed in details. Based on the satellite clocks estimated with regional station networks of different sizes (∼100, ∼300, ∼500 and ∼700km in radius), results indicated that the orbital errors compensated by the satellite clock estimates reduce as the size of the network increases. An interesting regional PPP mode based on the broadcast ephemeris and the corresponding estimated satellite clocks is proposed and evaluated through the numerical study. The impact of orbital errors in the broadcast ephemeris has shown to be negligible for PPP users in a regional network of a radius of ∼300km, with positioning RMS of about 1.4, 1.4 and 3.7cm for east, north and up component in the post-mission kinematic mode, comparable with 1.3, 1.3 and 3.6cm using the precise orbits and the corresponding estimated clocks. Compared with the DGPS and RTK positioning, only the estimated satellite clocks are needed to be disseminated to PPP users for this approach. It can significantly alleviate the communication burdens and therefore can be beneficial to the real time applications.

Toward networked foresight? Exploring the use of futures research in innovation networks

Along with the rise of the now popular ‘open’ paradigm in innovation management, networks have become a common approach to practicing innovation. Foresight could potentially greatly benefit from resources that become available when the knowledge base increases through networks. This article seeks to investigate how innovation networks and foresight are related, to what extent networked foresight activities exist and how they are practiced. For the former the Cyclic Innovation Model (CIM) is utilized as analytical framework and applied to three cases. The foresight activities are analyzed in terms of type, scope and role.The cases are a collaboration between government agencies and a research organization and two inter-organizational networks of different size. ‘Networked foresight’ is clearly observable in all three cases. Indeed, a networked approach to foresight seems to strengthen the various roles of foresight. However, the rooting and openness of foresight activities in the three networks varies significantly. The advantages that ‘networked foresight’ entails could be exploited to a much higher degree for the networks themselves, e.g., the broad resource base and the large pool of people with diverse backgrounds that are available. Furthermore, effective instruments for the re-integration of knowledge into the networks’ partner organizations are needed.

Community Discovery for Knowledge Collaborations in Collective intelligence Systems

Knowledge collaborative communities play an important role in collective intelligence systems. To discover a knowledge collaborative community, we need to consider not only the structure of a network but also the performance of knowledge collaboration among members within the community. Traditional community discovery approaches are not suitable to discover knowledge collaborative communities since most of them focus too much on the network topologies, and ignore some other important factors. In this paper, we propose two community discovery approaches, which can be used in different sizes of networks, and take more knowledge collaboration factors into account. Compared with some other existing approaches, the proposed approach can perform better in forming knowledge collaborative communities for multi-domain problem solving.

Morphisms of reaction networks that couple structure to function.

BackgroundThe mechanisms underlying complex biological systems are routinely represented as networks. Network kinetics is widely studied, and so is the connection between network structure and behavior. However, similarity of mechanism is better revealed by relationships between network structures.ResultsWe define morphisms (mappings) between reaction networks that establish structural connections between them. Some morphisms imply kinetic similarity, and yet their properties can be checked statically on the structure of the networks. In particular we can determine statically that a complex network will emulate a simpler network: it will reproduce its kinetics for all corresponding choices of reaction rates and initial conditions. We use this property to relate the kinetics of many common biological networks of different sizes, also relating them to a fundamental population algorithm.ConclusionsStructural similarity between reaction networks can be revealed by network morphisms, elucidating mechanistic and functional aspects of complex networks in terms of simpler networks.

Algebraic connectivity maximization of an air transportation network: The flight routes’ addition/deletion problem

A common metric to measure the robustness of a network is its algebraic connectivity. This paper introduces the flight routes addition/deletion problem and compares three different methods to analyze and optimize the algebraic connectivity of the air transportation network. The Modified Greedy Perturbation algorithm (MGP) provides a local optimum in an efficient iterative manner. The Weighted Tabu Search (WTS) is developed for the flight routes addition/deletion problem to offer a better optimal solution with longer computation time. The relaxed semidefinite programming (SDP) is used to set a performance upper bound and then three rounding techniques are applied to obtain feasible solutions. The simulation results show the trade-off among the Modified Greedy Perturbation, Weighted Tabu Search and relaxed SDP, with which we can decide the appropriate algorithm to adopt for maximizing the algebraic connectivity of the air transportation networks of different sizes. Finally a real air transportation network of Virgin America is investigated.

EVALUATING PERFORMANCE OF HYBRID NETWORKS BY USING LATENCY AND PDV

Hybrid networks are widely used in networking sector. They combine the finest features of both Wired and Wireless networks to give optimum results. Using different types of routing protocols, the capabilities of a hybrid network will be demonstrated using certain performance metrics. In this paper, we will be simulating real-time scenarios of three networks of different sizes. Each of these networks will be implemented with single routing protocol i.e. Enhanced Interior Gateway Routing Protocol (EIGRP). The networks will be simulated using Cisco Packet Tracer simulation tool. Furthermore, we have evaluated the performance of the networks by considering performance metrics like network latency and packet delay variation.

Representation of multiscale heterogeneity via multiscale pore networks

[1] Developing a better understanding of single-/multiphase flow through reservoir rocks largely relies on characterizing and modeling the pore system. For simple homogeneous rock materials, a complete description of the real pore structure can be obtained from the pore network extracted from a rock image at a single resolution, and then an accurate prediction of fluid flow properties can be achieved by using network model. However, for complex rocks (e.g., carbonates, heterogeneous sandstones, deformed rocks), a comprehensive description of the real pore structure may involve several decades of length scales (e.g., from submicron to centimeters), which cannot be captured by a single-resolution image due to the restriction of image size and resolution. Hence, the reconstruction of a single 3-D multiple-scale model of a porous medium is an important step in quantitatively characterizing such heterogeneous rocks and predicting their multiphase flow properties. In this paper, we present a novel methodology for the numerical construction of the multiscale pore structure of a complex rock from a number of CT images/models of a carbonate sample at several length scales. The success of this reconstruction relies heavily on image segmentation, pore network extraction and stochastic network generation, which are provided by our existing software system, referred to as Pore Analysis Tools (PAT). Specifically, the statistical description of pore networks of 3-D rock images at multiple resolutions makes it possible for us to: (a) construct an arbitrary sized network which is equivalent in a specified domain, and (b) integrate multiple networks of different sizes into a single network incorporating all scales. Using multiscale networks of carbonate rocks generated in this manner, two-phase network modeling results are presented to show how the resulting flow properties are dependent on inclusion of information from multiple scales. These outcomes reinforce the importance of capturing both geometry and topology in the hierarchical pore structure for such complex pore systems. The example presented reveals that isolated large-scale (e.g., macro-) pores are mainly connected by small-scale (e.g., micro-) pores, which in turn determines the combined effective petrophysical properties (capillary pressure, absolute and relative permeability). It is also demonstrated that multi- (three) scale networks reveal the effects of the interacting multiscale pore systems (e.g., micropores, macropores, and vugs) on bulk flow properties in terms of two-phase flow properties.

Generalized projective synchronization of two coupled complex networks of different sizes

We investigate a new generalized projective synchronization between two complex dynamical networks of different sizes. To the best of our knowledge, most of the current studies on projective synchronization have dealt with coupled networks of the same size. By generalized projective synchronization, we mean that the states of the nodes in each network can realize complete synchronization, and the states of a pair of nodes from both networks can achieve projective synchronization. Using the stability theory of the dynamical system, several sufficient conditions for guaranteeing the existence of the generalized projective synchronization under feedback control and adaptive control are obtained. As an example, we use Chua's circuits to demonstrate the effectiveness of our proposed approach.