Analyzing complex networks through correlations in centrality measurements

International Nuclear Information System (INIS)

Ricardo Furlan Ronqui, José; Travieso, Gonzalo

2015-01-01

Many real world systems can be expressed as complex networks of interconnected nodes. It is frequently important to be able to quantify the relative importance of the various nodes in the network, a task accomplished by defining some centrality measures, with different centrality definitions stressing different aspects of the network. It is interesting to know to what extent these different centrality definitions are related for different networks. In this work, we study the correlation between pairs of a set of centrality measures for different real world networks and two network models. We show that the centralities are in general correlated, but with stronger correlations for network models than for real networks. We also show that the strength of the correlation of each pair of centralities varies from network to network. Taking this fact into account, we propose the use of a centrality correlation profile, consisting of the values of the correlation coefficients between all pairs of centralities of interest, as a way to characterize networks. Using the yeast protein interaction network as an example we show also that the centrality correlation profile can be used to assess the adequacy of a network model as a representation of a given real network. (paper)

Collaborative Multi-Layer Network Coding in Hybrid Cellular Cognitive Radio Networks

KAUST Repository

Moubayed, Abdallah J.; Sorour, Sameh; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

2015-01-01

In this paper, as an extension to [1], we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in hybrid (interweave and underlay) cellular cognitive radio networks. This scheme allows the uncoordinated

Collaborative Multi-Layer Network Coding For Hybrid Cellular Cognitive Radio Networks

KAUST Repository

Moubayed, Abdallah J.

2014-01-01

In this thesis, as an extension to [1], we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in hybrid (interweave and underlay) cellular cognitive radio networks. This scheme allows the uncoordinated

On hybrid cooperation in underlay cognitive radio networks

KAUST Repository

Mahmood, Nurul Huda

2012-11-01

In wireless systems where transmitters are subject to a strict received power constraint, such as in underlay cognitive radio networks, cooperative communication is a promising strategy to enhance network performance, as it helps to improve the coverage area and outage performance of a network. However, this comes at the expense of increased resource utilization. To balance the performance gain against the possible over-utilization of resources, we propose a hybrid-cooperation technique for underlay cognitive radio networks, where secondary users cooperate only when required. Various performance measures of the proposed hybrid-cooperation technique are analyzed in this paper, and are also further validated numerically. © 2012 IEEE.

Efficient Hybrid Detection of Node Replication Attacks in Mobile Sensor Networks

Directory of Open Access Journals (Sweden)

Ze Wang

2017-01-01

Full Text Available The node replication attack is one of the notorious attacks that can be easily launched by adversaries in wireless sensor networks. A lot of literatures have studied mitigating the node replication attack in static wireless sensor networks. However, it is more difficult to detect the replicas in mobile sensor networks because of their node mobility. Considering the limitations of centralized detection schemes for static wireless sensor networks, a few distributed solutions have been recently proposed. Some existing schemes identified replicated attacks by sensing mobile nodes with identical ID but different locations. To facilitate the discovery of contradictory conflicts, we propose a hybrid local and global detection method. The local detection is performed in a local area smaller than the whole deployed area to improve the meeting probability of contradictory nodes, while the distant replicated nodes in larger area can also be efficiently detected by the global detection. The complementary two levels of detection achieve quick discovery by searching of the replicas with reasonable overhead.

On Hybrid Cooperation in Underlay Cognitive Radio Networks

DEFF Research Database (Denmark)

Mahmood, Nurul Huda; Yilmaz, Ferkan; Øien, Geir E.

2013-01-01

of opportunistic wireless systems such as cognitive radio networks. In order to balance the performance gains from cooperative communication against the possible over-utilization of resources, we propose and analyze an adaptive-cooperation technique for underlay cognitive radio networks, termed as hybrid......Cooperative communication is a promising strategy to enhance the performance of a communication network as it helps to improve the coverage area and the outage performance. However, such enhancement comes at the expense of increased resource utilization, which is undesirable; more so in the case......-cooperation. Under the proposed cooperation scheme, secondary users in a cognitive radio network cooperate adaptively to enhance the spectral efficiency and the error performance of the network. The bit error rate, the spectral efficiency and the outage performance of the network under the proposed hybrid...

Fast network centrality analysis using GPUs

Directory of Open Access Journals (Sweden)

Shi Zhiao

2011-05-01

Full Text Available Abstract Background With the exploding volume of data generated by continuously evolving high-throughput technologies, biological network analysis problems are growing larger in scale and craving for more computational power. General Purpose computation on Graphics Processing Units (GPGPU provides a cost-effective technology for the study of large-scale biological networks. Designing algorithms that maximize data parallelism is the key in leveraging the power of GPUs. Results We proposed an efficient data parallel formulation of the All-Pairs Shortest Path problem, which is the key component for shortest path-based centrality computation. A betweenness centrality algorithm built upon this formulation was developed and benchmarked against the most recent GPU-based algorithm. Speedup between 11 to 19% was observed in various simulated scale-free networks. We further designed three algorithms based on this core component to compute closeness centrality, eccentricity centrality and stress centrality. To make all these algorithms available to the research community, we developed a software package gpu-fan (GPU-based Fast Analysis of Networks for CUDA enabled GPUs. Speedup of 10-50× compared with CPU implementations was observed for simulated scale-free networks and real world biological networks. Conclusions gpu-fan provides a significant performance improvement for centrality computation in large-scale networks. Source code is available under the GNU Public License (GPL at http://bioinfo.vanderbilt.edu/gpu-fan/.

Computing all hybridization networks for multiple binary phylogenetic input trees.

Science.gov (United States)

Albrecht, Benjamin

2015-07-30

The computation of phylogenetic trees on the same set of species that are based on different orthologous genes can lead to incongruent trees. One possible explanation for this behavior are interspecific hybridization events recombining genes of different species. An important approach to analyze such events is the computation of hybridization networks. This work presents the first algorithm computing the hybridization number as well as a set of representative hybridization networks for multiple binary phylogenetic input trees on the same set of taxa. To improve its practical runtime, we show how this algorithm can be parallelized. Moreover, we demonstrate the efficiency of the software Hybroscale, containing an implementation of our algorithm, by comparing it to PIRNv2.0, which is so far the best available software computing the exact hybridization number for multiple binary phylogenetic trees on the same set of taxa. The algorithm is part of the software Hybroscale, which was developed specifically for the investigation of hybridization networks including their computation and visualization. Hybroscale is freely available(1) and runs on all three major operating systems. Our simulation study indicates that our approach is on average 100 times faster than PIRNv2.0. Moreover, we show how Hybroscale improves the interpretation of the reported hybridization networks by adding certain features to its graphical representation.

EIGENVECTOR-BASED CENTRALITY MEASURES FOR TEMPORAL NETWORKS*

Science.gov (United States)

TAYLOR, DANE; MYERS, SEAN A.; CLAUSET, AARON; PORTER, MASON A.; MUCHA, PETER J.

2017-01-01

Numerous centrality measures have been developed to quantify the importances of nodes in time-independent networks, and many of them can be expressed as the leading eigenvector of some matrix. With the increasing availability of network data that changes in time, it is important to extend such eigenvector-based centrality measures to time-dependent networks. In this paper, we introduce a principled generalization of network centrality measures that is valid for any eigenvector-based centrality. We consider a temporal network with N nodes as a sequence of T layers that describe the network during different time windows, and we couple centrality matrices for the layers into a supra-centrality matrix of size NT × NT whose dominant eigenvector gives the centrality of each node i at each time t. We refer to this eigenvector and its components as a joint centrality, as it reflects the importances of both the node i and the time layer t. We also introduce the concepts of marginal and conditional centralities, which facilitate the study of centrality trajectories over time. We find that the strength of coupling between layers is important for determining multiscale properties of centrality, such as localization phenomena and the time scale of centrality changes. In the strong-coupling regime, we derive expressions for time-averaged centralities, which are given by the zeroth-order terms of a singular perturbation expansion. We also study first-order terms to obtain first-order-mover scores, which concisely describe the magnitude of nodes’ centrality changes over time. As examples, we apply our method to three empirical temporal networks: the United States Ph.D. exchange in mathematics, costarring relationships among top-billed actors during the Golden Age of Hollywood, and citations of decisions from the United States Supreme Court. PMID:29046619

Hybrid Mobile Communication Networks for Planetary Exploration

Science.gov (United States)

Alena, Richard; Lee, Charles; Walker, Edward; Osenfort, John; Stone, Thom

2007-01-01

A paper discusses the continuing work of the Mobile Exploration System Project, which has been performing studies toward the design of hybrid communication networks for future exploratory missions to remote planets. A typical network could include stationary radio transceivers on a remote planet, mobile radio transceivers carried by humans and robots on the planet, terrestrial units connected via the Internet to an interplanetary communication system, and radio relay transceivers aboard spacecraft in orbit about the planet. Prior studies have included tests on prototypes of these networks deployed in Arctic and desert regions chosen to approximate environmental conditions on Mars. Starting from the findings of the prior studies, the paper discusses methods of analysis, design, and testing of the hybrid communication networks. It identifies key radio-frequency (RF) and network engineering issues. Notable among these issues is the study of wireless LAN throughput loss due to repeater use, RF signal strength, and network latency variations. Another major issue is that of using RF-link analysis to ensure adequate link margin in the face of statistical variations in signal strengths.

Hybrid Polymer-Network Hydrogels with Tunable Mechanical Response

Directory of Open Access Journals (Sweden)

Sebastian Czarnecki

2016-03-01

Full Text Available Hybrid polymer-network gels built by both physical and covalent polymer crosslinking combine the advantages of both these crosslinking types: they exhibit high mechanical strength along with excellent fracture toughness and extensibility. If these materials are extensively deformed, their physical crosslinks can break such that strain energy is dissipated and irreversible fracturing is restricted to high strain only. This mechanism of energy dissipation is determined by the kinetics and thermodynamics of the physical crosslinking contribution. In this paper, we present a poly(ethylene glycol (PEG based material toolkit to control these contributions in a rational and custom fashion. We form well-defined covalent polymer-network gels with regularly distributed additional supramolecular mechanical fuse links, whose strength of connectivity can be tuned without affecting the primary polymer-network composition. This is possible because the supramolecular fuse links are based on terpyridine–metal complexation, such that the mere choice of the fuse-linking metal ion adjusts their kinetics and thermodynamics of complexation–decomplexation, which directly affects the mechanical properties of the hybrid gels. We use oscillatory shear rheology to demonstrate this rational control and enhancement of the mechanical properties of the hybrid gels. In addition, static light scattering reveals their highly regular and well-defined polymer-network structures. As a result of both, the present approach provides an easy and reliable concept for preparing hybrid polymer-network gels with rationally designed properties.

Centrality in earthquake multiplex networks

Science.gov (United States)

Lotfi, Nastaran; Darooneh, Amir Hossein; Rodrigues, Francisco A.

2018-06-01

Seismic time series has been mapped as a complex network, where a geographical region is divided into square cells that represent the nodes and connections are defined according to the sequence of earthquakes. In this paper, we map a seismic time series to a temporal network, described by a multiplex network, and characterize the evolution of the network structure in terms of the eigenvector centrality measure. We generalize previous works that considered the single layer representation of earthquake networks. Our results suggest that the multiplex representation captures better earthquake activity than methods based on single layer networks. We also verify that the regions with highest seismological activities in Iran and California can be identified from the network centrality analysis. The temporal modeling of seismic data provided here may open new possibilities for a better comprehension of the physics of earthquakes.

Research on centrality of urban transport network nodes

Science.gov (United States)

Wang, Kui; Fu, Xiufen

2017-05-01

Based on the actual data of urban transport in Guangzhou, 19,150 bus stations in Guangzhou (as of 2014) are selected as nodes. Based on the theory of complex network, the network model of Guangzhou urban transport is constructed. By analyzing the degree centrality index, betweenness centrality index and closeness centrality index of nodes in the network, the level of centrality of each node in the network is studied. From a different point of view to determine the hub node of Guangzhou urban transport network, corresponding to the city's key sites and major transfer sites. The reliability of the network is determined by the stability of some key nodes (transport hub station). The research of network node centralization can provide a theoretical basis for the rational allocation of urban transport network sites and public transport system planning.

Centrality measures for immunization of weighted networks

Directory of Open Access Journals (Sweden)

Mohammad Khansari

2016-03-01

Full Text Available Effective immunization of individual communities with minimal cost in vaccination has made great discussion surrounding the realm of complex networks. Meanwhile, proper realization of relationship among people in society and applying it to social networks brings about substantial improvements in immunization. Accordingly, weighted graph in which link weights represent the intensity and intimacy of relationships is an acceptable approach. In this work we employ weighted graphs and a wide variety of weighted centrality measures to distinguish important individuals in contagion of diseases. Furthermore, we propose new centrality measures for weighted networks. Our experimental results show that Radiality-Degree centrality is satisfying for weighted BA networks. Additionally, PageRank-Degree and Radiality-Degree centralities showmoreacceptable performance in targeted immunization of weighted networks.

Temporal node centrality in complex networks

Science.gov (United States)

Kim, Hyoungshick; Anderson, Ross

2012-02-01

Many networks are dynamic in that their topology changes rapidly—on the same time scale as the communications of interest between network nodes. Examples are the human contact networks involved in the transmission of disease, ad hoc radio networks between moving vehicles, and the transactions between principals in a market. While we have good models of static networks, so far these have been lacking for the dynamic case. In this paper we present a simple but powerful model, the time-ordered graph, which reduces a dynamic network to a static network with directed flows. This enables us to extend network properties such as vertex degree, closeness, and betweenness centrality metrics in a very natural way to the dynamic case. We then demonstrate how our model applies to a number of interesting edge cases, such as where the network connectivity depends on a small number of highly mobile vertices or edges, and show that our centrality definition allows us to track the evolution of connectivity. Finally we apply our model and techniques to two real-world dynamic graphs of human contact networks and then discuss the implication of temporal centrality metrics in the real world.

Centrality metrics and localization in core-periphery networks

International Nuclear Information System (INIS)

Barucca, Paolo; Lillo, Fabrizio; Tantari, Daniele

2016-01-01

Two concepts of centrality have been defined in complex networks. The first considers the centrality of a node and many different metrics for it have been defined (e.g. eigenvector centrality, PageRank, non-backtracking centrality, etc). The second is related to large scale organization of the network, the core-periphery structure, composed by a dense core plus an outlying and loosely-connected periphery. In this paper we investigate the relation between these two concepts. We consider networks generated via the stochastic block model, or its degree corrected version, with a core-periphery structure and we investigate the centrality properties of the core nodes and the ability of several centrality metrics to identify them. We find that the three measures with the best performance are marginals obtained with belief propagation, PageRank, and degree centrality, while non-backtracking and eigenvector centrality (or MINRES [10], showed to be equivalent to the latter in the large network limit) perform worse in the investigated networks. (paper: interdisciplinary statistical mechanics )

Energy efficiency in hybrid mobile and wireless networks

Energy Technology Data Exchange (ETDEWEB)

Abbas, Ziaul Haq

2012-07-01

Wireless Internet access is almost pervasive nowadays, and many types of wireless networks can be used to access the Internet. However, along with this growth, there is an even greater concern about the energy consumption and efficiency of mobile devices as well as of the supporting networks, triggering the appearance of the concept of green communication. While some efforts have been made towards this direction, challenges still exist and need to be tackled from diverse perspectives. Cellular networks, WLANs, and ad hoc networks in the form of wireless mesh networks are the most popular technologies for wireless Internet access. The availability of such a variety of access networks has also paved the way to explore synergistic approaches for Internet access, leading to the concept of hybrid networks and relay communications. In addition, many mobile devices are being equipped with multiple interfaces, enabling them to operate in hybrid networks. In contrast, the improvements in the battery technology itself have not matched the pace of the emerging mobile applications. The situation becomes more sophisticated when a mobile device functions also as a relay node to forward other station's data. In the literature, energy efficiency of mobile devices has been addressed from various perspectives such as protocol-level efforts, battery management efforts, etc. However, there is little work on energy efficiency in hybrid mobile and wireless networks and devices with heterogeneous connections. For example, when there are multiple networks available to a mobile device, how to achieve optimum long-term energy consumption of such a device is an open question. Furthermore, in today's cellular networks, micro-, pico-, and femto-cells are the most popular network topologies in order to support high data rate services and high user density. With the growth of such small-cell solutions, the energy consumption of these networks is also becoming an important concern for operators

Design of Hybrid Mobile Communication Networks for Planetary Exploration

Science.gov (United States)

Alena, Richard L.; Ossenfort, John; Lee, Charles; Walker, Edward; Stone, Thom

2004-01-01

The Mobile Exploration System Project (MEX) at NASA Ames Research Center has been conducting studies into hybrid communication networks for future planetary missions. These networks consist of space-based communication assets connected to ground-based Internets and planetary surface-based mobile wireless networks. These hybrid mobile networks have been deployed in rugged field locations in the American desert and the Canadian arctic for support of science and simulation activities on at least six occasions. This work has been conducted over the past five years resulting in evolving architectural complexity, improved component characteristics and better analysis and test methods. A rich set of data and techniques have resulted from the development and field testing of the communication network during field expeditions such as the Haughton Mars Project and NASA Mobile Agents Project.

Exponential Synchronization of Networked Chaotic Delayed Neural Network by a Hybrid Event Trigger Scheme.

Science.gov (United States)

Fei, Zhongyang; Guan, Chaoxu; Gao, Huijun; Zhongyang Fei; Chaoxu Guan; Huijun Gao; Fei, Zhongyang; Guan, Chaoxu; Gao, Huijun

2018-06-01

This paper is concerned with the exponential synchronization for master-slave chaotic delayed neural network with event trigger control scheme. The model is established on a network control framework, where both external disturbance and network-induced delay are taken into consideration. The desired aim is to synchronize the master and slave systems with limited communication capacity and network bandwidth. In order to save the network resource, we adopt a hybrid event trigger approach, which not only reduces the data package sending out, but also gets rid of the Zeno phenomenon. By using an appropriate Lyapunov functional, a sufficient criterion for the stability is proposed for the error system with extended ( , , )-dissipativity performance index. Moreover, hybrid event trigger scheme and controller are codesigned for network-based delayed neural network to guarantee the exponential synchronization between the master and slave systems. The effectiveness and potential of the proposed results are demonstrated through a numerical example.

Existing PON Infrastructure Supported Hybrid Fiber-Wireless Sensor Networks

DEFF Research Database (Denmark)

Yu, Xianbin; Zhao, Ying; Deng, Lei

2012-01-01

We propose a hybrid fiber wireless sensor network based on the existing PON infrastructure. The feasibility of remote sensing and PON convergence is experimentally proven by transmitting direct-sequence spread-spectrum wireless sensing and 2.5Gbps GPON signals.......We propose a hybrid fiber wireless sensor network based on the existing PON infrastructure. The feasibility of remote sensing and PON convergence is experimentally proven by transmitting direct-sequence spread-spectrum wireless sensing and 2.5Gbps GPON signals....

Hybrid Distributed Iterative Capacity Allocation over Bluetooth Network

DEFF Research Database (Denmark)

Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun

2002-01-01

of service requirements and constraints in Bluetooth network, such as limited capacity, decentralized, frequent changes of topology and of capacities assigned to nodes in the network. The simulation shows that the performance of Bluetooth could be improved by applying the hybrid distributed iterative...

Hybrid Distributed Iterative Capacity Allocation over Bluetooth Network

DEFF Research Database (Denmark)

Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun

of service requirements and constraints in Bluetooth network, such as limited capacity, decentralized, frequent changes of topology and of capacities assigned to nodes in the network. The simulation shows that the performance of Bluetooth could be improved by applying the hybrid distributed iterative...

Fast Construction of Near Parsimonious Hybridization Networks for Multiple Phylogenetic Trees.

Science.gov (United States)

Mirzaei, Sajad; Wu, Yufeng

2016-01-01

Hybridization networks represent plausible evolutionary histories of species that are affected by reticulate evolutionary processes. An established computational problem on hybridization networks is constructing the most parsimonious hybridization network such that each of the given phylogenetic trees (called gene trees) is "displayed" in the network. There have been several previous approaches, including an exact method and several heuristics, for this NP-hard problem. However, the exact method is only applicable to a limited range of data, and heuristic methods can be less accurate and also slow sometimes. In this paper, we develop a new algorithm for constructing near parsimonious networks for multiple binary gene trees. This method is more efficient for large numbers of gene trees than previous heuristics. This new method also produces more parsimonious results on many simulated datasets as well as a real biological dataset than a previous method. We also show that our method produces topologically more accurate networks for many datasets.

Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Rechargeable Sensor Networks.

Science.gov (United States)

Wei, Yunkai; Ma, Xiaohui; Yang, Ning; Chen, Yijin

2017-09-15

Software Defined Wireless Rechargeable Sensor Networks (SDWRSNs) are an inexorable trend for Wireless Sensor Networks (WSNs), including Wireless Rechargeable Sensor Network (WRSNs). However, the traditional network devices cannot be completely substituted in the short term. Hybrid SDWRSNs, where software defined devices and traditional devices coexist, will last for a long time. Hybrid SDWRSNs bring new challenges as well as opportunities for energy saving issues, which is still a key problem considering that the wireless chargers are also exhaustible, especially in some rigid environment out of the main supply. Numerous energy saving schemes for WSNs, or even some works for WRSNs, are no longer suitable for the new features of hybrid SDWRSNs. To solve this problem, this paper puts forward an Energy-saving Traffic Scheduling (ETS) algorithm. The ETS algorithm adequately considers the new characters in hybrid SDWRSNs, and takes advantage of the Software Defined Networking (SDN) controller's direct control ability on SDN nodes and indirect control ability on normal nodes. The simulation results show that, comparing with traditional Minimum Transmission Energy (MTE) protocol, ETS can substantially improve the energy efficiency in hybrid SDWRSNs for up to 20-40% while ensuring feasible data delay.

Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Rechargeable Sensor Networks

Directory of Open Access Journals (Sweden)

Yunkai Wei

2017-09-01

Full Text Available Software Defined Wireless Rechargeable Sensor Networks (SDWRSNs are an inexorable trend for Wireless Sensor Networks (WSNs, including Wireless Rechargeable Sensor Network (WRSNs. However, the traditional network devices cannot be completely substituted in the short term. Hybrid SDWRSNs, where software defined devices and traditional devices coexist, will last for a long time. Hybrid SDWRSNs bring new challenges as well as opportunities for energy saving issues, which is still a key problem considering that the wireless chargers are also exhaustible, especially in some rigid environment out of the main supply. Numerous energy saving schemes for WSNs, or even some works for WRSNs, are no longer suitable for the new features of hybrid SDWRSNs. To solve this problem, this paper puts forward an Energy-saving Traffic Scheduling (ETS algorithm. The ETS algorithm adequately considers the new characters in hybrid SDWRSNs, and takes advantage of the Software Defined Networking (SDN controller’s direct control ability on SDN nodes and indirect control ability on normal nodes. The simulation results show that, comparing with traditional Minimum Transmission Energy (MTE protocol, ETS can substantially improve the energy efficiency in hybrid SDWRSNs for up to 20–40% while ensuring feasible data delay.

Small-Firm Networks: hybrid arrangement or organizational form?

OpenAIRE

Verschoore, Jorge Renato; Balestrin, Alsones; Perucia, Alexandre

2014-01-01

In the field of organizations, one relevant question is whether or not to consider networks as organizational forms. On the one hand, Williamson (1985) says that networks are hybrid arrangements. On the other, authors like Powell (1990) argue that networks constitute themselves as organizational forms. Given this dilemma, the present article proposes the analysis of organizational characteristics of small-firm networks (SFN). To reach such objective, twelve SFNs in distinct stages of developm...

On the Performance of Grooming Strategies for Offloading IP Flows onto Lightpaths in Hybrid Networks

NARCIS (Netherlands)

Biesbroek, Rudolf; Fioreze, Tiago; Granville, L.; Pras, Aiko

Hybrid networks take data forwarding decisions at multiple network levels. In order to make an efficient use of hybrid networks, traffic engineering solutions (e.g., routing and data grooming techniques) are commonly employed. Within the specific context of a self-managed hybrid optical and packet

PV-Diesel Hybrid SCADA Experiment Network Design

Science.gov (United States)

Kalu, Alex; Durand, S.; Emrich, Carol; Ventre, G.; Wilson, W.; Acosta, R.

1999-01-01

The essential features of an experimental network for renewable power system satellite based supervisory, control and data acquisition (SCADA) are communication links, controllers, diagnostic equipment and a hybrid power system. Required components for implementing the network consist of two satellite ground stations, to satellite modems, two 486 PCs, two telephone receivers, two telephone modems, two analog telephone lines, one digital telephone line, a hybrid-power system equipped with controller and a satellite spacecraft. In the technology verification experiment (TVE) conducted by Savannah State University and Florida Solar Energy Center, the renewable energy hybrid system is the Apex-1000 Mini-Hybrid which is equipped with NGC3188 for user interface and remote control and the NGC2010 for monitoring and basic control tasks. This power system is connected to a satellite modem via a smart interface, RS232. Commands are sent to the power system control unit through a control PC designed as PC1. PC1 is thus connected to a satellite model through RS232. A second PC, designated PC2, the diagnostic PC is connected to both satellite modems via separate analog telephone lines for checking modems'health. PC2 is also connected to PC1 via a telephone line. Due to the unavailability of a second ground station for the ACTS, one ground station is used to serve both the sending and receiving functions in this experiment. Signal is sent from the control PC to the Hybrid system at a frequency f(sub 1), different from f(sub 2), the signal from the hybrid system to the control PC. f(sub l) and f(sub 2) are sufficiently separated to avoid interference.

Stochastic resonance in small-world neuronal networks with hybrid electrical–chemical synapses

International Nuclear Information System (INIS)

Wang, Jiang; Guo, Xinmeng; Yu, Haitao; Liu, Chen; Deng, Bin; Wei, Xile; Chen, Yingyuan

2014-01-01

Highlights: •We study stochastic resonance in small-world neural networks with hybrid synapses. •The resonance effect depends largely on the probability of chemical synapse. •An optimal chemical synapse probability exists to evoke network resonance. •Network topology affects the stochastic resonance in hybrid neuronal networks. - Abstract: The dependence of stochastic resonance in small-world neuronal networks with hybrid electrical–chemical synapses on the probability of chemical synapse and the rewiring probability is investigated. A subthreshold periodic signal is imposed on one single neuron within the neuronal network as a pacemaker. It is shown that, irrespective of the probability of chemical synapse, there exists a moderate intensity of external noise optimizing the response of neuronal networks to the pacemaker. Moreover, the effect of pacemaker driven stochastic resonance of the system depends largely on the probability of chemical synapse. A high probability of chemical synapse will need lower noise intensity to evoke the phenomenon of stochastic resonance in the networked neuronal systems. In addition, for fixed noise intensity, there is an optimal chemical synapse probability, which can promote the propagation of the localized subthreshold pacemaker across neural networks. And the optimal chemical synapses probability turns even larger as the coupling strength decreases. Furthermore, the small-world topology has a significant impact on the stochastic resonance in hybrid neuronal networks. It is found that increasing the rewiring probability can always enhance the stochastic resonance until it approaches the random network limit

Analysis of Network Parameters Influencing Performance of Hybrid Multimedia Networks

Directory of Open Access Journals (Sweden)

Dominik Kovac

2013-10-01

Full Text Available Multimedia networks is an emerging subject that currently attracts the attention of research and industrial communities. This environment provides new entertainment services and business opportunities merged with all well-known network services like VoIP calls or file transfers. Such a heterogeneous system has to be able satisfy all network and end-user requirements which are increasing constantly. Therefore the simulation tools enabling deep analysis in order to find the key performance indicators and factors which influence the overall quality for specific network service the most are highly needed. This paper provides a study on the network parameters like communication technology, routing protocol, QoS mechanism, etc. and their effect on the performance of hybrid multimedia network. The analysis was performed in OPNET Modeler environment and the most interesting results are discussed at the end of this paper

Evaluation and Control of Thiol-ene/Thiol-epoxy Hybrid Networks.

Science.gov (United States)

Carioscia, Jacquelyn A; Stansbury, Jeffrey W; Bowman, Christopher N

2007-03-08

The development of thiol-ene/thiol-epoxy hybrid networks offers the advantage of tailorable polymerization kinetics while producing a highly crosslinked, high T(g) polymer that has significantly reduced shrinkage stress. Stoichiometric mixtures of pentaerythritol tetra(3-mercaptopropionate) (PETMP)/triallyl-1,3,5-triazine-2,4,6-trione (TATATO) (thiol-ene, mixture 1) and PETMP/bisphenol a diglycidyl ether (BADGE) (thiol-epoxy, mixture 2) were prepared and hybrid mixtures of 75/25, 50/50, 25/75, and 10/90 w/w of mixtures 1 and 2 were polymerized using a combination of both radical and anionic initiation. The light exposure timing and the relative initiation conditions of the two types were used to control the order and relative rates of the radical and anionic polymerizations. The 50/50 w/w thiol-ene/thiol-epoxy hybrid material exhibited a final stress of only 0.2 MPa, which is 90 % lower than the stress developed in a control dimethacrylate resin. Kinetic analysis indicates composition affects network development in thiol-ene/thiol-epoxy hybrid networks and produces materials with robust mechanical properties.

Joint Hybrid Backhaul and Access Links Design in Cloud-Radio Access Networks

KAUST Repository

Dhifallah, Oussama Najeeb

2015-09-06

The cloud-radio access network (CRAN) is expected to be the core network architecture for next generation mobile radio systems. In this paper, we consider the downlink of a CRAN formed of one central processor (the cloud) and several base station (BS), where each BS is connected to the cloud via either a wireless or capacity-limited wireline backhaul link. The paper addresses the joint design of the hybrid backhaul links (i.e., designing the wireline and wireless backhaul connections from the cloud to the BSs) and the access links (i.e., determining the sparse beamforming solution from the BSs to the users). The paper formulates the hybrid backhaul and access link design problem by minimizing the total network power consumption. The paper solves the problem using a two-stage heuristic algorithm. At one stage, the sparse beamforming solution is found using a weighted mixed 11/12 norm minimization approach; the correlation matrix of the quantization noise of the wireline backhaul links is computed using the classical rate-distortion theory. At the second stage, the transmit powers of the wireless backhaul links are found by solving a power minimization problem subject to quality-of-service constraints, based on the principle of conservation of rate by utilizing the rates found in the first stage. Simulation results suggest that the performance of the proposed algorithm approaches the global optimum solution, especially at high signal-to-interference-plus-noise ratio (SINR).

Compressive sensing of high betweenness centrality nodes in networks

Science.gov (United States)

Mahyar, Hamidreza; Hasheminezhad, Rouzbeh; Ghalebi K., Elahe; Nazemian, Ali; Grosu, Radu; Movaghar, Ali; Rabiee, Hamid R.

2018-05-01

Betweenness centrality is a prominent centrality measure expressing importance of a node within a network, in terms of the fraction of shortest paths passing through that node. Nodes with high betweenness centrality have significant impacts on the spread of influence and idea in social networks, the user activity in mobile phone networks, the contagion process in biological networks, and the bottlenecks in communication networks. Thus, identifying k-highest betweenness centrality nodes in networks will be of great interest in many applications. In this paper, we introduce CS-HiBet, a new method to efficiently detect top- k betweenness centrality nodes in networks, using compressive sensing. CS-HiBet can perform as a distributed algorithm by using only the local information at each node. Hence, it is applicable to large real-world and unknown networks in which the global approaches are usually unrealizable. The performance of the proposed method is evaluated by extensive simulations on several synthetic and real-world networks. The experimental results demonstrate that CS-HiBet outperforms the best existing methods with notable improvements.

Association and Centrality in Criminal Networks

DEFF Research Database (Denmark)

Petersen, Rasmus Rosenqvist

Network-based techniques are widely used in criminal investigations because patterns of association are actionable and understandable. Existing network models with nodes as first class entities and their related measures (e.g., social networks and centrality measures) are unable to capture...

Integrated resource management for Hybrid Optical Wireless (HOW) networks

DEFF Research Database (Denmark)

Yan, Ying; Yu, Hao; Wessing, Henrik

2009-01-01

Efficient utilization of available bandwidth over hybrid optical wireless networks is a critical issue, especially for multimedia applications with high data rates and stringent Quality of Service (QoS) requirements. In this paper, we propose an integrated resource management including an enhanced...... resource sharing scheme and an integrated admission control scheme for the hybrid optical wireless networks. It provides QoS guarantees for connections through both optical and wireless domain. Simulation results show that our proposed scheme improves QoS performances in terms of high throughput and low...

GFT centrality: A new node importance measure for complex networks

Science.gov (United States)

Singh, Rahul; Chakraborty, Abhishek; Manoj, B. S.

2017-12-01

Identifying central nodes is very crucial to design efficient communication networks or to recognize key individuals of a social network. In this paper, we introduce Graph Fourier Transform Centrality (GFT-C), a metric that incorporates local as well as global characteristics of a node, to quantify the importance of a node in a complex network. GFT-C of a reference node in a network is estimated from the GFT coefficients derived from the importance signal of the reference node. Our study reveals the superiority of GFT-C over traditional centralities such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and Google PageRank centrality, in the context of various arbitrary and real-world networks with different degree-degree correlations.

Delivering low-bandwidth telemedicine services over hybrid networks in developing countries.

Science.gov (United States)

Amble, R; Comparini, A; Kumar, K R; Dahlgren, R; Lurie, Y M

2004-01-01

The results of medical specialist consultations sampled from several rural clinics located throughout India indicate that remote expert opinions can improve the speed and accuracy of diagnosis. Central to this presentation is a description of how real-time and store & forward telemedicine services can be provided to rural populations over hybrid networks made up of ISDN, POTS, VSAT, cellular, and Cable Internet connections. A model for meeting the specialized medical needs of developing countries will be highlighted. Descriptions, examples, and benefits of how Browser-based client-server architectures are being used in over 20 locations in India and Mexico for triaging real-time vital signs, DICOM images, audio & video, and clinical text information will be highlighted.

Nafion–clay hybrids with a network structure

KAUST Repository

Burgaz, Engin; Lian, Huiqin; Alonso, Rafael Herrera; Estevez, Luis; Kelarakis, Antonios; Giannelis, Emmanuel P.

2009-01-01

Nafion-clay hybrid membranes with a unique microstructure were synthesized using a fundamentally new approach. The new approach is based on depletion aggregation of suspended particles - a well-known phenomenon in colloids. For certain concentrations of clay and polymer, addition of Nafion solution to clay suspensions in water leads to a gel. Using Cryo-TEM we show that the clay particles in the hybrid gels form a network structure with an average cell size in the order of 500 nm. The hybrid gels are subsequently cast to produce hybrid Nafion-clay membranes. Compared to pure Nafion the swelling of the hybrid membranes in water and methanol is dramatically reduced while their selectivity (ratio of conductivity over permeability) increases. The small decrease of ionic conductivity for the hybrid membranes is more than compensated by the large decrease in methanol permeability. Lastly the hybrid membranes are much stiffer and can withstand higher temperatures compared to pure Nafion. Both of these characteristics are highly desirable for use in fuel cell applications, since a) they will allow the use of a thinner membrane circumventing problems associated with the membrane resistance and b) enable high temperature applications. © 2009 Elsevier Ltd. All rights reserved.

Nafion–clay hybrids with a network structure

KAUST Repository

Burgaz, Engin

2009-05-01

Nafion-clay hybrid membranes with a unique microstructure were synthesized using a fundamentally new approach. The new approach is based on depletion aggregation of suspended particles - a well-known phenomenon in colloids. For certain concentrations of clay and polymer, addition of Nafion solution to clay suspensions in water leads to a gel. Using Cryo-TEM we show that the clay particles in the hybrid gels form a network structure with an average cell size in the order of 500 nm. The hybrid gels are subsequently cast to produce hybrid Nafion-clay membranes. Compared to pure Nafion the swelling of the hybrid membranes in water and methanol is dramatically reduced while their selectivity (ratio of conductivity over permeability) increases. The small decrease of ionic conductivity for the hybrid membranes is more than compensated by the large decrease in methanol permeability. Lastly the hybrid membranes are much stiffer and can withstand higher temperatures compared to pure Nafion. Both of these characteristics are highly desirable for use in fuel cell applications, since a) they will allow the use of a thinner membrane circumventing problems associated with the membrane resistance and b) enable high temperature applications. © 2009 Elsevier Ltd. All rights reserved.

Structural controllability and controlling centrality of temporal networks.

Science.gov (United States)

Pan, Yujian; Li, Xiang

2014-01-01

Temporal networks are such networks where nodes and interactions may appear and disappear at various time scales. With the evidence of ubiquity of temporal networks in our economy, nature and society, it's urgent and significant to focus on its structural controllability as well as the corresponding characteristics, which nowadays is still an untouched topic. We develop graphic tools to study the structural controllability as well as its characteristics, identifying the intrinsic mechanism of the ability of individuals in controlling a dynamic and large-scale temporal network. Classifying temporal trees of a temporal network into different types, we give (both upper and lower) analytical bounds of the controlling centrality, which are verified by numerical simulations of both artificial and empirical temporal networks. We find that the positive relationship between aggregated degree and controlling centrality as well as the scale-free distribution of node's controlling centrality are virtually independent of the time scale and types of datasets, meaning the inherent robustness and heterogeneity of the controlling centrality of nodes within temporal networks.

Link reliability based hybrid routing for tactical mobile ad hoc network

Institute of Scientific and Technical Information of China (English)

Xie Xiaochuan; Wei Gang; Wu Keping; Wang Gang; Jia Shilou

2008-01-01

Tactical mobile ad hoc network (MANET) is a collection of mobile nodes forming a temporary network,without the aid of pre-established network infrastructure. The routing protocol has a crucial impact on the networkperformance in battlefields. Link reliability based hybrid routing (LRHR) is proposed, which is a novel hybrid routing protocol, for tactical MANET. Contrary to the traditional single path routing strategy, multiple paths are established between a pair of source-destination nodes. In the hybrid routing strategy, the rate of topological change provides a natural mechanism for switching dynamically between table-driven and on-demand routing. The simulation results indicate that the performances of the protocol in packet delivery ratio, routing overhead, and average end-to-end delay are better than the conventional routing protocol.

Self-Management of Hybrid Optical and Packet Switching Networks

NARCIS (Netherlands)

Fioreze, Tiago

2010-01-01

Hybrid optical and packet switching networks are composed of multi-service hybrid devices that enable forwarding of data at multiple levels. Large IP flows at the IP level may be therefore moved to the optical level bypassing therefore the per hop routing decisions of the IP level. Such move could

Probabilistic Wind Power Forecasting with Hybrid Artificial Neural Networks

DEFF Research Database (Denmark)

Wan, Can; Song, Yonghua; Xu, Zhao

2016-01-01

probabilities of prediction errors provide an alternative yet effective solution. This article proposes a hybrid artificial neural network approach to generate prediction intervals of wind power. An extreme learning machine is applied to conduct point prediction of wind power and estimate model uncertainties...... via a bootstrap technique. Subsequently, the maximum likelihood estimation method is employed to construct a distinct neural network to estimate the noise variance of forecasting results. The proposed approach has been tested on multi-step forecasting of high-resolution (10-min) wind power using...... actual wind power data from Denmark. The numerical results demonstrate that the proposed hybrid artificial neural network approach is effective and efficient for probabilistic forecasting of wind power and has high potential in practical applications....

ABOUT HYBRID BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH DISCRETE DELAYS

Institute of Scientific and Technical Information of China (English)

无

2010-01-01

In this paper, hybrid bidirectional associative memory neural networks with discrete delays is considered. By ingeniously importing real parameters di > 0(i = 1,2,···,n) which can be adjusted, we establish some new sufficient conditions for the dynamical characteristics of hybrid bidirectional associative memory neural networks with discrete delays by the method of variation of parameters and some analysis techniques. Our results generalize and improve the related results in [10,11]. Our work is significant...

On hybrid cooperation in underlay cognitive radio networks

KAUST Repository

Mahmood, Nurul Huda

2013-09-01

Cooperative communication is a promising strategy to enhance the performance of a communication network as it helps to improve the coverage area and the outage performance. However, such enhancement comes at the expense of increased resource utilization, which is undesirable; more so in the case of opportunistic wireless systems such as cognitive radio networks. In order to balance the performance gains from cooperative communication against the possible over-utilization of resources, we propose and analyze an adaptive-cooperation technique for underlay cognitive radio networks, termed as hybrid-cooperation. Under the proposed cooperation scheme, secondary users in a cognitive radio network cooperate adaptively to enhance the spectral efficiency and the error performance of the network. The bit error rate, the spectral efficiency and the outage performance of the network under the proposed hybrid cooperation scheme with amplify-and-forward relaying are analyzed in this paper, and compared against conventional cooperation technique. Findings of the analytical performance analyses are further validated numerically through selected computer-based Monte-Carlo simulations. The proposed scheme is found to achieve significantly better performance in terms of the spectral efficiency and the bit error rate, compared to the conventional amplify-and-forward cooperation scheme. © 2013 IEEE.

Transmission network expansion planning based on hybridization model of neural networks and harmony search algorithm

Directory of Open Access Journals (Sweden)

Mohammad Taghi Ameli

2012-01-01

Full Text Available Transmission Network Expansion Planning (TNEP is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI tools such as Genetic Algorithm (GA, Simulated Annealing (SA, Tabu Search (TS and Artificial Neural Networks (ANNs are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs and Harmony Search Algorithm (HSA was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network.

Hybrid neural network bushing model for vehicle dynamics simulation

International Nuclear Information System (INIS)

Sohn, Jeong Hyun; Lee, Seung Kyu; Yoo, Wan Suk

2008-01-01

Although the linear model was widely used for the bushing model in vehicle suspension systems, it could not express the nonlinear characteristics of bushing in terms of the amplitude and the frequency. An artificial neural network model was suggested to consider the hysteretic responses of bushings. This model, however, often diverges due to the uncertainties of the neural network under the unexpected excitation inputs. In this paper, a hybrid neural network bushing model combining linear and neural network is suggested. A linear model was employed to represent linear stiffness and damping effects, and the artificial neural network algorithm was adopted to take into account the hysteretic responses. A rubber test was performed to capture bushing characteristics, where sine excitation with different frequencies and amplitudes is applied. Random test results were used to update the weighting factors of the neural network model. It is proven that the proposed model has more robust characteristics than a simple neural network model under step excitation input. A full car simulation was carried out to verify the proposed bushing models. It was shown that the hybrid model results are almost identical to the linear model under several maneuvers

Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

Science.gov (United States)

Wagstaff, Kiri L.; Sosnowski, Scott; Lane, Terran

2012-01-01

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers

Hybrid Wavelength Routed and Optical Packet Switched Ring Networks for the Metropolitan Area Network

DEFF Research Database (Denmark)

Nord, Martin

2005-01-01

Increased data traffic in the metropolitan area network calls for new network architectures. This paper evaluates optical ring architectures based on optical packet switching, wavelength routing, and hybrid combinations of the two concepts. The evaluation includes overall throughput and fairness...... attractive when traffic is unbalanced....

Modified-hybrid optical neural network filter for multiple object recognition within cluttered scenes

Science.gov (United States)

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.

2009-08-01

Motivated by the non-linear interpolation and generalization abilities of the hybrid optical neural network filter between the reference and non-reference images of the true-class object we designed the modifiedhybrid optical neural network filter. We applied an optical mask to the hybrid optical neural network's filter input. The mask was built with the constant weight connections of a randomly chosen image included in the training set. The resulted design of the modified-hybrid optical neural network filter is optimized for performing best in cluttered scenes of the true-class object. Due to the shift invariance properties inherited by its correlator unit the filter can accommodate multiple objects of the same class to be detected within an input cluttered image. Additionally, the architecture of the neural network unit of the general hybrid optical neural network filter allows the recognition of multiple objects of different classes within the input cluttered image by modifying the output layer of the unit. We test the modified-hybrid optical neural network filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. The filter is shown to exhibit with a single pass over the input data simultaneously out-of-plane rotation, shift invariance and good clutter tolerance. It is able to successfully detect and classify correctly the true-class objects within background clutter for which there has been no previous training.

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

Science.gov (United States)

Hagen, Espen; Dahmen, David; Stavrinou, Maria L; Lindén, Henrik; Tetzlaff, Tom; van Albada, Sacha J; Grün, Sonja; Diesmann, Markus; Einevoll, Gaute T

2016-12-01

With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm 2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail. © The Author 2016. Published by Oxford University Press.

Sizing and Optimization for Hybrid Central in South Algeria Based on Three Different Generators

Directory of Open Access Journals (Sweden)

Chouaib Ammari

2017-11-01

Full Text Available In this paper, we will size an optimum hybrid central content three different generators, two on renewable energy (solar photovoltaic and wind power and two nonrenewable (diesel generator and storage system because the new central generator has started to consider the green power technology in order for best future to the world, this central will use all the green power resource available and distributes energy to a small isolated village in southwest of Algeria named “Timiaouine”. The consumption of this village estimated with detailed in two season; season low consumption (winter and high consumption (summer, the hybrid central will be optimized by program Hybrid Optimization Model for Electric Renewable (HOMER PRO, this program will simulate in two configuration, the first with storage system, the second without storage system and in the end the program HOMER PRO will choose the best configuration which is the mixture of both economic and ecologic configurations, this central warrants the energetic continuity of village. Article History: Received May 18th 2017; Received in revised form July 17th 2017; Accepted Sept 3rd 2017; Available online How to Cite This Article: Ammari, C., Hamouda,M., and Makhloufi,S. (2017 Sizing and Optimization for Hybrid Central in South Algeria Based on Three Different Generators. International Journal of Renewable Energy Development, 6(3, 263-272. http://doi.org/10.14710/ijred.6.3.263-272

The organisational structure of protein networks: revisiting the centrality-lethality hypothesis.

Science.gov (United States)

Raman, Karthik; Damaraju, Nandita; Joshi, Govind Krishna

2014-03-01

Protein networks, describing physical interactions as well as functional associations between proteins, have been unravelled for many organisms in the recent past. Databases such as the STRING provide excellent resources for the analysis of such networks. In this contribution, we revisit the organisation of protein networks, particularly the centrality-lethality hypothesis, which hypothesises that nodes with higher centrality in a network are more likely to produce lethal phenotypes on removal, compared to nodes with lower centrality. We consider the protein networks of a diverse set of 20 organisms, with essentiality information available in the Database of Essential Genes and assess the relationship between centrality measures and lethality. For each of these organisms, we obtained networks of high-confidence interactions from the STRING database, and computed network parameters such as degree, betweenness centrality, closeness centrality and pairwise disconnectivity indices. We observe that the networks considered here are predominantly disassortative. Further, we observe that essential nodes in a network have a significantly higher average degree and betweenness centrality, compared to the network average. Most previous studies have evaluated the centrality-lethality hypothesis for Saccharomyces cerevisiae and Escherichia coli; we here observe that the centrality-lethality hypothesis hold goods for a large number of organisms, with certain limitations. Betweenness centrality may also be a useful measure to identify essential nodes, but measures like closeness centrality and pairwise disconnectivity are not significantly higher for essential nodes.

Predicting Hybrid Performances for Quality Traits through Genomic-Assisted Approaches in Central European Wheat

KAUST Repository

Liu, Guozheng

2016-07-06

Bread-making quality traits are central targets for wheat breeding. The objectives of our study were to (1) examine the presence of major effect QTLs for quality traits in a Central European elite wheat population, (2) explore the optimal strategy for predicting the hybrid performance for wheat quality traits, and (3) investigate the effects of marker density and the composition and size of the training population on the accuracy of prediction of hybrid performance. In total 135 inbred lines of Central European bread wheat (Triticum aestivum L.) and 1,604 hybrids derived from them were evaluated for seven quality traits in up to six environments. The 135 parental lines were genotyped using a 90k single-nucleotide polymorphism array. Genome-wide association mapping initially suggested presence of several quantitative trait loci (QTLs), but cross-validation rather indicated the absence of major effect QTLs for all quality traits except of 1000-kernel weight. Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance. A resampling study revealed that increasing the effective population size in the estimation set of hybrids is relevant to boost the accuracy of prediction for an unrelated test population.

Predicting Hybrid Performances for Quality Traits through Genomic-Assisted Approaches in Central European Wheat.

Directory of Open Access Journals (Sweden)

Guozheng Liu

Full Text Available Bread-making quality traits are central targets for wheat breeding. The objectives of our study were to (1 examine the presence of major effect QTLs for quality traits in a Central European elite wheat population, (2 explore the optimal strategy for predicting the hybrid performance for wheat quality traits, and (3 investigate the effects of marker density and the composition and size of the training population on the accuracy of prediction of hybrid performance. In total 135 inbred lines of Central European bread wheat (Triticum aestivum L. and 1,604 hybrids derived from them were evaluated for seven quality traits in up to six environments. The 135 parental lines were genotyped using a 90k single-nucleotide polymorphism array. Genome-wide association mapping initially suggested presence of several quantitative trait loci (QTLs, but cross-validation rather indicated the absence of major effect QTLs for all quality traits except of 1000-kernel weight. Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance. A resampling study revealed that increasing the effective population size in the estimation set of hybrids is relevant to boost the accuracy of prediction for an unrelated test population.

Predicting Hybrid Performances for Quality Traits through Genomic-Assisted Approaches in Central European Wheat

KAUST Repository

Liu, Guozheng; Zhao, Yusheng; Gowda, Manje; Longin, C. Friedrich H.; Reif, Jochen C.; Mette, Michael F.

2016-01-01

Bread-making quality traits are central targets for wheat breeding. The objectives of our study were to (1) examine the presence of major effect QTLs for quality traits in a Central European elite wheat population, (2) explore the optimal strategy for predicting the hybrid performance for wheat quality traits, and (3) investigate the effects of marker density and the composition and size of the training population on the accuracy of prediction of hybrid performance. In total 135 inbred lines of Central European bread wheat (Triticum aestivum L.) and 1,604 hybrids derived from them were evaluated for seven quality traits in up to six environments. The 135 parental lines were genotyped using a 90k single-nucleotide polymorphism array. Genome-wide association mapping initially suggested presence of several quantitative trait loci (QTLs), but cross-validation rather indicated the absence of major effect QTLs for all quality traits except of 1000-kernel weight. Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance. A resampling study revealed that increasing the effective population size in the estimation set of hybrids is relevant to boost the accuracy of prediction for an unrelated test population.

Predicting Hybrid Performances for Quality Traits through Genomic-Assisted Approaches in Central European Wheat

Science.gov (United States)

Liu, Guozheng; Zhao, Yusheng; Gowda, Manje; Longin, C. Friedrich H.; Reif, Jochen C.; Mette, Michael F.

2016-01-01

Bread-making quality traits are central targets for wheat breeding. The objectives of our study were to (1) examine the presence of major effect QTLs for quality traits in a Central European elite wheat population, (2) explore the optimal strategy for predicting the hybrid performance for wheat quality traits, and (3) investigate the effects of marker density and the composition and size of the training population on the accuracy of prediction of hybrid performance. In total 135 inbred lines of Central European bread wheat (Triticum aestivum L.) and 1,604 hybrids derived from them were evaluated for seven quality traits in up to six environments. The 135 parental lines were genotyped using a 90k single-nucleotide polymorphism array. Genome-wide association mapping initially suggested presence of several quantitative trait loci (QTLs), but cross-validation rather indicated the absence of major effect QTLs for all quality traits except of 1000-kernel weight. Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance. A resampling study revealed that increasing the effective population size in the estimation set of hybrids is relevant to boost the accuracy of prediction for an unrelated test population. PMID:27383841

A HYBRID HOPFIELD NEURAL NETWORK AND TABU SEARCH ALGORITHM TO SOLVE ROUTING PROBLEM IN COMMUNICATION NETWORK

Directory of Open Access Journals (Sweden)

MANAR Y. KASHMOLA

2012-06-01

Full Text Available The development of hybrid algorithms for solving complex optimization problems focuses on enhancing the strengths and compensating for the weakness of two or more complementary approaches. The goal is to intelligently combine the key elements of these approaches to find superior solutions to solve optimization problems. Optimal routing in communication network is considering a complex optimization problem. In this paper we propose a hybrid Hopfield Neural Network (HNN and Tabu Search (TS algorithm, this algorithm called hybrid HNN-TS algorithm. The paradigm of this hybridization is embedded. We embed the short-term memory and tabu restriction features from TS algorithm in the HNN model. The short-term memory and tabu restriction control the neuron selection process in the HNN model in order to get around the local minima problem and find an optimal solution using the HNN model to solve complex optimization problem. The proposed algorithm is intended to find the optimal path for packet transmission in the network which is fills in the field of routing problem. The optimal path that will be selected is depending on 4-tuples (delay, cost, reliability and capacity. Test results show that the propose algorithm can find path with optimal cost and a reasonable number of iterations. It also shows that the complexity of the network model won’t be a problem since the neuron selection is done heuristically.

Solar central receiver hybrid power system, Phase I. Volume 3. Appendices. Final technical report, October 1978-August 1979

Energy Technology Data Exchange (ETDEWEB)

None

1979-09-01

A design study for a central receiver/fossil fuel hybrid power system using molten salts for heat transfer and heat storage is presented. This volume contains the appendices: (A) parametric salt piping data; (B) sample heat exchanger calculations; (C) salt chemistry and salt/materials compatibility evaluation; (D) heliostat field coordinates; (E) data lists; (F) STEAEC program input data; (G) hybrid receiver design drawings; (H) hybrid receiver absorber tube thermal math model; (I) piping stress analysis; (J) 100-MWe 18-hour storage solar central receiver hybrid power system capital cost worksheets; and (K) 500-MWe 18-hour solar central receiver hybrid power system cost breakdown. (WHK)

Genomic networks of hybrid sterility.

Science.gov (United States)

Turner, Leslie M; White, Michael A; Tautz, Diethard; Payseur, Bret A

2014-02-01

Hybrid dysfunction, a common feature of reproductive barriers between species, is often caused by negative epistasis between loci ("Dobzhansky-Muller incompatibilities"). The nature and complexity of hybrid incompatibilities remain poorly understood because identifying interacting loci that affect complex phenotypes is difficult. With subspecies in the early stages of speciation, an array of genetic tools, and detailed knowledge of reproductive biology, house mice (Mus musculus) provide a model system for dissecting hybrid incompatibilities. Male hybrids between M. musculus subspecies often show reduced fertility. Previous studies identified loci and several X chromosome-autosome interactions that contribute to sterility. To characterize the genetic basis of hybrid sterility in detail, we used a systems genetics approach, integrating mapping of gene expression traits with sterility phenotypes and QTL. We measured genome-wide testis expression in 305 male F2s from a cross between wild-derived inbred strains of M. musculus musculus and M. m. domesticus. We identified several thousand cis- and trans-acting QTL contributing to expression variation (eQTL). Many trans eQTL cluster into eleven 'hotspots,' seven of which co-localize with QTL for sterility phenotypes identified in the cross. The number and clustering of trans eQTL-but not cis eQTL-were substantially lower when mapping was restricted to a 'fertile' subset of mice, providing evidence that trans eQTL hotspots are related to sterility. Functional annotation of transcripts with eQTL provides insights into the biological processes disrupted by sterility loci and guides prioritization of candidate genes. Using a conditional mapping approach, we identified eQTL dependent on interactions between loci, revealing a complex system of epistasis. Our results illuminate established patterns, including the role of the X chromosome in hybrid sterility. The integrated mapping approach we employed is applicable in a broad

A Two-Phase Coverage-Enhancing Algorithm for Hybrid Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Qingguo Zhang

2017-01-01

Full Text Available Providing field coverage is a key task in many sensor network applications. In certain scenarios, the sensor field may have coverage holes due to random initial deployment of sensors; thus, the desired level of coverage cannot be achieved. A hybrid wireless sensor network is a cost-effective solution to this problem, which is achieved by repositioning a portion of the mobile sensors in the network to meet the network coverage requirement. This paper investigates how to redeploy mobile sensor nodes to improve network coverage in hybrid wireless sensor networks. We propose a two-phase coverage-enhancing algorithm for hybrid wireless sensor networks. In phase one, we use a differential evolution algorithm to compute the candidate’s target positions in the mobile sensor nodes that could potentially improve coverage. In the second phase, we use an optimization scheme on the candidate’s target positions calculated from phase one to reduce the accumulated potential moving distance of mobile sensors, such that the exact mobile sensor nodes that need to be moved as well as their final target positions can be determined. Experimental results show that the proposed algorithm provided significant improvement in terms of area coverage rate, average moving distance, area coverage–distance rate and the number of moved mobile sensors, when compare with other approaches.

Self-Management of Hybrid Optical and Packet Switching Networks

NARCIS (Netherlands)

Fioreze, Tiago; Pras, Aiko

Hybrid optical and packet switching networks enable data to be forwarded at multiple levels. Large IP flows at the IP level may be therefore moved to the optical level bypassing the per hop routing decisions of the IP level. Such move could be beneficial since congested IP networks could be

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

Science.gov (United States)

Kobayashi, Takahisa; Simon, Donald L.

2001-01-01

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

Hybrid energy system evaluation in water supply system energy production: neural network approach

Energy Technology Data Exchange (ETDEWEB)

Goncalves, Fabio V.; Ramos, Helena M. [Civil Engineering Department, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal); Reis, Luisa Fernanda R. [Universidade de Sao Paulo, EESC/USP, Departamento de Hidraulica e Saneamento., Avenida do Trabalhador Saocarlense, 400, Sao Carlos-SP (Brazil)

2010-07-01

Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator - CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator - HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.

Robustness Analysis of Hybrid Stochastic Neural Networks with Neutral Terms and Time-Varying Delays

Directory of Open Access Journals (Sweden)

Chunmei Wu

2015-01-01

Full Text Available We analyze the robustness of global exponential stability of hybrid stochastic neural networks subject to neutral terms and time-varying delays simultaneously. Given globally exponentially stable hybrid stochastic neural networks, we characterize the upper bounds of contraction coefficients of neutral terms and time-varying delays by using the transcendental equation. Moreover, we prove theoretically that, for any globally exponentially stable hybrid stochastic neural networks, if additive neutral terms and time-varying delays are smaller than the upper bounds arrived, then the perturbed neural networks are guaranteed to also be globally exponentially stable. Finally, a numerical simulation example is given to illustrate the presented criteria.

Hybrid Organic/Inorganic Thiol-ene-Based Photopolymerized Networks

OpenAIRE

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

2011-01-01

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

Qualitative exploration of centralities in municipal science education networks

DEFF Research Database (Denmark)

von der Fehr, Ane; Sølberg, Jan

2016-01-01

This article examines the social nature of educational change by conducting a social network analysis of social networks involving stakeholders of science education from teachers to political stakeholders. Social networks that comprise supportive structures for development of science education ar...... of science education, especially if they are aware of their own centrality and are able to use their position intentionally for the benefit of science education.......This article examines the social nature of educational change by conducting a social network analysis of social networks involving stakeholders of science education from teachers to political stakeholders. Social networks that comprise supportive structures for development of science education...... are diverse and in order to understand how municipal stakeholders may support such development, we explored four different municipal science education networks (MSE networks) using three different measures of centrality. The centrality measures differed in terms of what kind of stakeholder functions...

Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

Science.gov (United States)

Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao

2015-02-01

This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.

The resilient hybrid fiber sensor network with self-healing function

Energy Technology Data Exchange (ETDEWEB)

Xu, Shibo, E-mail: Shibo-Xu@tju.edu.cn; Liu, Tiegen; Ge, Chunfeng; Chen, Qinnan; Zhang, Hongxia [College of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072 (China); Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin 300072 (China)

2015-03-15

This paper presents a novel resilient fiber sensor network (FSN) with multi-ring architecture, which could interconnect various kinds of fiber sensors responsible for more than one measurands. We explain how the intelligent control system provides sensors with self-healing function meanwhile sensors are working properly, besides each fiber in FSN is under real-time monitoring. We explain the software process and emergency mechanism to respond failures or other circumstances. To improve the efficiency in the use of limited spectrum resources in some situations, we have two different structures to distribute the light sources rationally. Then, we propose a hybrid sensor working in FSN which is a combination of a distributed sensor and a FBG (Fiber Bragg Grating) array fused in a common fiber sensing temperature and vibrations simultaneously with neglectable crosstalk to each other. By making a failure to a working fiber in experiment, the feasibility and effectiveness of the network with a hybrid sensor has been demonstrated, hybrid sensors could not only work as designed but also survive from destructive failures with the help of resilient network and smart and quick self-healing actions. The network has improved the viability of the fiber sensors and diversity of measurands.

The resilient hybrid fiber sensor network with self-healing function

Science.gov (United States)

Xu, Shibo; Liu, Tiegen; Ge, Chunfeng; Chen, Qinnan; Zhang, Hongxia

2015-03-01

This paper presents a novel resilient fiber sensor network (FSN) with multi-ring architecture, which could interconnect various kinds of fiber sensors responsible for more than one measurands. We explain how the intelligent control system provides sensors with self-healing function meanwhile sensors are working properly, besides each fiber in FSN is under real-time monitoring. We explain the software process and emergency mechanism to respond failures or other circumstances. To improve the efficiency in the use of limited spectrum resources in some situations, we have two different structures to distribute the light sources rationally. Then, we propose a hybrid sensor working in FSN which is a combination of a distributed sensor and a FBG (Fiber Bragg Grating) array fused in a common fiber sensing temperature and vibrations simultaneously with neglectable crosstalk to each other. By making a failure to a working fiber in experiment, the feasibility and effectiveness of the network with a hybrid sensor has been demonstrated, hybrid sensors could not only work as designed but also survive from destructive failures with the help of resilient network and smart and quick self-healing actions. The network has improved the viability of the fiber sensors and diversity of measurands.

The resilient hybrid fiber sensor network with self-healing function

International Nuclear Information System (INIS)

Xu, Shibo; Liu, Tiegen; Ge, Chunfeng; Chen, Qinnan; Zhang, Hongxia

2015-01-01

This paper presents a novel resilient fiber sensor network (FSN) with multi-ring architecture, which could interconnect various kinds of fiber sensors responsible for more than one measurands. We explain how the intelligent control system provides sensors with self-healing function meanwhile sensors are working properly, besides each fiber in FSN is under real-time monitoring. We explain the software process and emergency mechanism to respond failures or other circumstances. To improve the efficiency in the use of limited spectrum resources in some situations, we have two different structures to distribute the light sources rationally. Then, we propose a hybrid sensor working in FSN which is a combination of a distributed sensor and a FBG (Fiber Bragg Grating) array fused in a common fiber sensing temperature and vibrations simultaneously with neglectable crosstalk to each other. By making a failure to a working fiber in experiment, the feasibility and effectiveness of the network with a hybrid sensor has been demonstrated, hybrid sensors could not only work as designed but also survive from destructive failures with the help of resilient network and smart and quick self-healing actions. The network has improved the viability of the fiber sensors and diversity of measurands

Hybrid-source impedance network and its generalized cascading concepts

DEFF Research Database (Denmark)

Li, Ding; Gao, Feng; Loh, Poh Chiang

2009-01-01

Hybrid-source impedance networks have attracted attention among researchers because of their flexibility in performing buck-boost energy conversion. To date, three distinct types of impedance networks can be summarized for implementing voltage-type inverters, with another three types summarized...... for current-type inverters. These impedance networks can in principle be combined into a single generic network entity, before generalized cascading concepts are proposed for connecting multiple of them together to form energy converters with a higher output voltage gain and other unique advantages...

Hybrid optical CDMA-FSO communications network under spatially correlated gamma-gamma scintillation

DEFF Research Database (Denmark)

Jurado-Navas, Antonio; Raddo, Thiago R.; Garrido-Balsells, José María

2016-01-01

In this paper, we propose a new hybrid network solution based on asynchronous optical code-division multiple-access (OCDMA) and free-space optical (FSO) technologies for last-mile access networks, where fiber deployment is impractical. The architecture of the proposed hybrid OCDMA-FSO network...... is thoroughly described. The users access the network in a fully asynchronous manner by means of assigned fast frequency hopping (FFH)-based codes. In the FSO receiver, an equal gain-combining technique is employed along with intensity modulation and direct detection. New analytical formalisms for evaluating...... can successfully achieve error-free ABER levels for the three scenarios considered as long as forward error correction (FEC) algorithms are employed. Therefore, OCDMA-FSO networks can be a prospective alternative to deliver high-speed communication services to access networks with deficient fiber...

High capacity fiber optic sensor networks using hybrid multiplexing techniques and their applications

Science.gov (United States)

Sun, Qizhen; Li, Xiaolei; Zhang, Manliang; Liu, Qi; Liu, Hai; Liu, Deming

2013-12-01

Fiber optic sensor network is the development trend of fiber senor technologies and industries. In this paper, I will discuss recent research progress on high capacity fiber sensor networks with hybrid multiplexing techniques and their applications in the fields of security monitoring, environment monitoring, Smart eHome, etc. Firstly, I will present the architecture of hybrid multiplexing sensor passive optical network (HSPON), and the key technologies for integrated access and intelligent management of massive fiber sensor units. Two typical hybrid WDM/TDM fiber sensor networks for perimeter intrusion monitor and cultural relics security are introduced. Secondly, we propose the concept of "Microstructure－Optical X Domin Refecltor (M-OXDR)" for fiber sensor network expansion. By fabricating smart micro-structures with the ability of multidimensional encoded and low insertion loss along the fiber, the fiber sensor network of simple structure and huge capacity more than one thousand could be achieved. Assisted by the WDM/TDM and WDM/FDM decoding methods respectively, we built the verification systems for long-haul and real-time temperature sensing. Finally, I will show the high capacity and flexible fiber sensor network with IPv6 protocol based hybrid fiber/wireless access. By developing the fiber optic sensor with embedded IPv6 protocol conversion module and IPv6 router, huge amounts of fiber optic sensor nodes can be uniquely addressed. Meanwhile, various sensing information could be integrated and accessed to the Next Generation Internet.

Collaborative Multi-Layer Network Coding in Hybrid Cellular Cognitive Radio Networks

KAUST Repository

Moubayed, Abdallah J.

2015-05-01

In this paper, as an extension to [1], we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in hybrid (interweave and underlay) cellular cognitive radio networks. This scheme allows the uncoordinated collaboration between the collocated primary and cognitive radio base-stations in order to minimize their own as well as each other\\'s packet recovery overheads, thus by improving their throughput. The proposed scheme ensures that each network\\'s performance is not degraded by its help to the other network. Moreover, it guarantees that the primary network\\'s interference threshold is not violated in the same and adjacent cells. Yet, the scheme allows the reduction of the recovery overhead in the collocated primary and cognitive radio networks. The reduction in the cognitive radio network is further amplified due to the perfect detection of spectrum holes which allows the cognitive radio base station to transmit at higher power without fear of violating the interference threshold of the primary network. For the secondary network, simulation results show reductions of 20% and 34% in the packet recovery overhead, compared to the non-collaborative scheme, for low and high probabilities of primary packet arrivals, respectively. For the primary network, this reduction was found to be 12%. © 2015 IEEE.

Genomic networks of hybrid sterility.

Directory of Open Access Journals (Sweden)

Leslie M Turner

2014-02-01

Full Text Available Hybrid dysfunction, a common feature of reproductive barriers between species, is often caused by negative epistasis between loci ("Dobzhansky-Muller incompatibilities". The nature and complexity of hybrid incompatibilities remain poorly understood because identifying interacting loci that affect complex phenotypes is difficult. With subspecies in the early stages of speciation, an array of genetic tools, and detailed knowledge of reproductive biology, house mice (Mus musculus provide a model system for dissecting hybrid incompatibilities. Male hybrids between M. musculus subspecies often show reduced fertility. Previous studies identified loci and several X chromosome-autosome interactions that contribute to sterility. To characterize the genetic basis of hybrid sterility in detail, we used a systems genetics approach, integrating mapping of gene expression traits with sterility phenotypes and QTL. We measured genome-wide testis expression in 305 male F2s from a cross between wild-derived inbred strains of M. musculus musculus and M. m. domesticus. We identified several thousand cis- and trans-acting QTL contributing to expression variation (eQTL. Many trans eQTL cluster into eleven 'hotspots,' seven of which co-localize with QTL for sterility phenotypes identified in the cross. The number and clustering of trans eQTL-but not cis eQTL-were substantially lower when mapping was restricted to a 'fertile' subset of mice, providing evidence that trans eQTL hotspots are related to sterility. Functional annotation of transcripts with eQTL provides insights into the biological processes disrupted by sterility loci and guides prioritization of candidate genes. Using a conditional mapping approach, we identified eQTL dependent on interactions between loci, revealing a complex system of epistasis. Our results illuminate established patterns, including the role of the X chromosome in hybrid sterility. The integrated mapping approach we employed is

Effectiveness evaluation of double-layered satellite network with laser and microwave hybrid links based on fuzzy analytic hierarchy process

Science.gov (United States)

Zhang, Wei; Rao, Qiaomeng

2018-01-01

In order to solve the problem of high speed, large capacity and limited spectrum resources of satellite communication network, a double-layered satellite network with global seamless coverage based on laser and microwave hybrid links is proposed in this paper. By analyzing the characteristics of the double-layered satellite network with laser and microwave hybrid links, an effectiveness evaluation index system for the network is established. And then, the fuzzy analytic hierarchy process, which combines the analytic hierarchy process and the fuzzy comprehensive evaluation theory, is used to evaluate the effectiveness of the double-layered satellite network with laser and microwave hybrid links. Furthermore, the evaluation result of the proposed hybrid link network is obtained by simulation. The effectiveness evaluation process of the proposed double-layered satellite network with laser and microwave hybrid links can help to optimize the design of hybrid link double-layered satellite network and improve the operating efficiency of the satellite system.

The importance of centralities in dark network value chains

Science.gov (United States)

Toth, Noemi; Gulyás, László; Legendi, Richard O.; Duijn, Paul; Sloot, Peter M. A.; Kampis, George

2013-09-01

This paper introduces three novel centrality measures based on the nodes' role in the operation of a joint task, i.e., their position in a criminal network value chain. For this, we consider networks where nodes have attributes describing their "capabilities" or "colors", i.e., the possible roles they may play in a value chain. A value chain here is understood as a series of tasks to be performed in a specific order, each requiring a specific capability. The first centrality notion measures how many value chain instances a given node participates in. The other two assess the costs of replacing a node in the value chain in case the given node is no longer available to perform the task. The first of them considers the direct distance (shortest path length) between the node in question and its nearest replacement, while the second evaluates the actual replacement process, assuming that preceding and following nodes in the network should each be able to find and contact the replacement. In this report, we demonstrate the properties of the new centrality measures using a few toy examples and compare them to classic centralities, such as betweenness, closeness and degree centrality. We also apply the new measures to randomly colored empirical networks. We find that the newly introduced centralities differ sufficiently from the classic measures, pointing towards different aspects of the network. Our results also pinpoint the difference between having a replacement node in the network and being able to find one. This is the reason why "introduction distance" often has a noticeable correlation with betweenness. Our studies show that projecting value chains over networks may significantly alter the nodes' perceived importance. These insights might have important implications for the way law enforcement or intelligence agencies look at the effectiveness of dark network disruption strategies over time.

Software architecture for hybrid electrical/optical data center network

DEFF Research Database (Denmark)

Mehmeri, Victor; Vegas Olmos, Juan José; Tafur Monroy, Idelfonso

2016-01-01

This paper presents hardware and software architecture based on Software-Defined Networking (SDN) paradigm and OpenFlow/NETCONF protocols for enabling topology management of hybrid electrical/optical switching data center networks. In particular, a development on top of SDN open-source controller...... OpenDaylight is presented to control an optical switching matrix based on Micro-Electro-Mechanical System (MEMS) technology....

Centrality Robustness and Link Prediction in Complex Social Networks

DEFF Research Database (Denmark)

Davidsen, Søren Atmakuri; Ortiz-Arroyo, Daniel

2012-01-01

. Secondly, we present a method to predict edges in dynamic social networks. Our experimental results indicate that the robustness of the centrality measures applied to more realistic social networks follows a predictable pattern and that the use of temporal statistics could improve the accuracy achieved......This chapter addresses two important issues in social network analysis that involve uncertainty. Firstly, we present am analysis on the robustness of centrality measures that extend the work presented in Borgati et al. using three types of complex network structures and one real social network...

A Hybrid Satellite-Terrestrial Approach to Aeronautical Communication Networks

Science.gov (United States)

Kerczewski, Robert J.; Chomos, Gerald J.; Griner, James H.; Mainger, Steven W.; Martzaklis, Konstantinos S.; Kachmar, Brian A.

2000-01-01

Rapid growth in air travel has been projected to continue for the foreseeable future. To maintain a safe and efficient national and global aviation system, significant advances in communications systems supporting aviation are required. Satellites will increasingly play a critical role in the aeronautical communications network. At the same time, current ground-based communications links, primarily very high frequency (VHF), will continue to be employed due to cost advantages and legacy issues. Hence a hybrid satellite-terrestrial network, or group of networks, will emerge. The increased complexity of future aeronautical communications networks dictates that system-level modeling be employed to obtain an optimal system fulfilling a majority of user needs. The NASA Glenn Research Center is investigating the current and potential future state of aeronautical communications, and is developing a simulation and modeling program to research future communications architectures for national and global aeronautical needs. This paper describes the primary requirements, the current infrastructure, and emerging trends of aeronautical communications, including a growing role for satellite communications. The need for a hybrid communications system architecture approach including both satellite and ground-based communications links is explained. Future aeronautical communication network topologies and key issues in simulation and modeling of future aeronautical communications systems are described.

Hybrid Network Simulation for the ATLAS Trigger and Data Acquisition (TDAQ) System

CERN Document Server

Bonaventura, Matias Alejandro; The ATLAS collaboration; Castro, Rodrigo Daniel; Foguelman, Daniel Jacob

2015-01-01

The poster shows the ongoing research in the ATLAS TDAQ group in collaboration with the University of Buenos Aires in the area of hybrid data network simulations. he Data Network and Processing Cluster filters data in real-time, achieving a rejection factor in the order of 40000x and has real-time latency constrains. The dataflow between the processing units (TPUs) and Readout System (ROS) presents a “TCP Incast”-type network pathology which TCP cannot handle it efficiently. A credits system is in place which limits rate of queries and reduces latency. This large computer network, and the complex dataflow has been modelled and simulated using a PowerDEVS, a DEVS-based simulator. The simulation has been validated and used to produce what-if scenarios in the real network. Network Simulation with Hybrid Flows: Speedups and accuracy, combined • For intensive network traffic, Discrete Event simulation models (packet-level granularity) soon becomes prohibitive: Too high computing demands. • Fluid Flow simul...

Receding horizon control of hybrid linear delayed systems: Application to sewer networks

OpenAIRE

Joseph-Duran, Bernat; Ocampo-Martinez, Carlos; Cembrano, Gabriela

2013-01-01

A control-oriented hybrid linear model for water transport in sewer networks is proposed as a suitable framework for the computation of real-time controllers for the minimization of flooding in presence of heavy-rain events. The model is based on individual network elements (sewers, gates, weirs and tanks) and does not rely on topological simplifications, thus providing a better description of the hydrological and hydraulic phenomena than in similar works. Using a generic form of a hybrid lin...

Computational modeling of electrically conductive networks formed by graphene nanoplatelet-carbon nanotube hybrid particles

KAUST Repository

Mora Cordova, Angel

2018-01-30

One strategy to ensure that nanofiller networks in a polymer composite percolate at low volume fractions is to promote segregation. In a segregated structure, the concentration of nanofillers is kept low in some regions of the sample. In turn, the concentration in remaining regions is much higher than the average concentration of the sample. This selective placement of the nanofillers ensures percolation at low average concentration. One original strategy to promote segregation is by tuning the shape of the nanofillers. We use a computational approach to study the conductive networks formed by hybrid particles obtained by growing carbon nanotubes (CNTs) on graphene nanoplatelets (GNPs). The objective of this study is (1) to show that the higher electrical conductivity of these composites is due to the hybrid particles forming a segregated structure and (2) to understand which parameters defining the hybrid particles determine the efficiency of the segregation. We construct a microstructure to observe the conducting paths and determine whether a segregated structure has indeed been formed inside the composite. A measure of efficiency is presented based on the fraction of nanofillers that contribute to the conductive network. Then, the efficiency of the hybrid-particle networks is compared to those of three other networks of carbon-based nanofillers in which no hybrid particles are used: only CNTs, only GNPs, and a mix of CNTs and GNPs. Finally, some parameters of the hybrid particle are studied: the CNT density on the GNPs, and the CNT and GNP geometries. We also present recommendations for the further improvement of a composite\\'s conductivity based on these parameters.

Computational modeling of electrically conductive networks formed by graphene nanoplatelet-carbon nanotube hybrid particles

KAUST Repository

Mora Cordova, Angel; Han, Fei; Lubineau, Gilles

2018-01-01

One strategy to ensure that nanofiller networks in a polymer composite percolate at low volume fractions is to promote segregation. In a segregated structure, the concentration of nanofillers is kept low in some regions of the sample. In turn, the concentration in remaining regions is much higher than the average concentration of the sample. This selective placement of the nanofillers ensures percolation at low average concentration. One original strategy to promote segregation is by tuning the shape of the nanofillers. We use a computational approach to study the conductive networks formed by hybrid particles obtained by growing carbon nanotubes (CNTs) on graphene nanoplatelets (GNPs). The objective of this study is (1) to show that the higher electrical conductivity of these composites is due to the hybrid particles forming a segregated structure and (2) to understand which parameters defining the hybrid particles determine the efficiency of the segregation. We construct a microstructure to observe the conducting paths and determine whether a segregated structure has indeed been formed inside the composite. A measure of efficiency is presented based on the fraction of nanofillers that contribute to the conductive network. Then, the efficiency of the hybrid-particle networks is compared to those of three other networks of carbon-based nanofillers in which no hybrid particles are used: only CNTs, only GNPs, and a mix of CNTs and GNPs. Finally, some parameters of the hybrid particle are studied: the CNT density on the GNPs, and the CNT and GNP geometries. We also present recommendations for the further improvement of a composite's conductivity based on these parameters.

Genetic algorithm and neural network hybrid approach for job-shop scheduling

OpenAIRE

Zhao, Kai; Yang, Shengxiang; Wang, Dingwei

1998-01-01

Copyright @ 1998 ACTA Press This paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate for searching optimal solutions, CSANN is used to obtain feasible solutions during the iteration of genetic algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop scheduling with respect to the quality of solutions and ...

Research on key technology of planning and design for AC/DC hybrid distribution network

Science.gov (United States)

Shen, Yu; Wu, Guilian; Zheng, Huan; Deng, Junpeng; Shi, Pengjia

2018-04-01

With the increasing demand of DC generation and DC load, the development of DC technology, AC and DC distribution network integrating will become an important form of future distribution network. In this paper, the key technology of planning and design for AC/DC hybrid distribution network is proposed, including the selection of AC and DC voltage series, the design of typical grid structure and the comprehensive evaluation method of planning scheme. The research results provide some ideas and directions for the future development of AC/DC hybrid distribution network.

Hybrid architecture for building secure sensor networks

Science.gov (United States)

Owens, Ken R., Jr.; Watkins, Steve E.

2012-04-01

Sensor networks have various communication and security architectural concerns. Three approaches are defined to address these concerns for sensor networks. The first area is the utilization of new computing architectures that leverage embedded virtualization software on the sensor. Deploying a small, embedded virtualization operating system on the sensor nodes that is designed to communicate to low-cost cloud computing infrastructure in the network is the foundation to delivering low-cost, secure sensor networks. The second area focuses on securing the sensor. Sensor security components include developing an identification scheme, and leveraging authentication algorithms and protocols that address security assurance within the physical, communication network, and application layers. This function will primarily be accomplished through encrypting the communication channel and integrating sensor network firewall and intrusion detection/prevention components to the sensor network architecture. Hence, sensor networks will be able to maintain high levels of security. The third area addresses the real-time and high priority nature of the data that sensor networks collect. This function requires that a quality-of-service (QoS) definition and algorithm be developed for delivering the right data at the right time. A hybrid architecture is proposed that combines software and hardware features to handle network traffic with diverse QoS requirements.

Identifying influential spreaders in complex networks based on kshell hybrid method

Science.gov (United States)

Namtirtha, Amrita; Dutta, Animesh; Dutta, Biswanath

2018-06-01

Influential spreaders are the key players in maximizing or controlling the spreading in a complex network. Identifying the influential spreaders using kshell decomposition method has become very popular in the recent time. In the literature, the core nodes i.e. with the largest kshell index of a network are considered as the most influential spreaders. We have studied the kshell method and spreading dynamics of nodes using Susceptible-Infected-Recovered (SIR) epidemic model to understand the behavior of influential spreaders in terms of its topological location in the network. From the study, we have found that every node in the core area is not the most influential spreader. Even a strategically placed lower shell node can also be a most influential spreader. Moreover, the core area can also be situated at the periphery of the network. The existing indexing methods are only designed to identify the most influential spreaders from core nodes and not from lower shells. In this work, we propose a kshell hybrid method to identify highly influential spreaders not only from the core but also from lower shells. The proposed method comprises the parameters such as kshell power, node's degree, contact distance, and many levels of neighbors' influence potential. The proposed method is evaluated using nine real world network datasets. In terms of the spreading dynamics, the experimental results show the superiority of the proposed method over the other existing indexing methods such as the kshell method, the neighborhood coreness centrality, the mixed degree decomposition, etc. Furthermore, the proposed method can also be applied to large-scale networks by considering the three levels of neighbors' influence potential.

Investigation of Hybrid Pseudo Bipolar HVDC Performances Supply Power to Passive AC Network

Directory of Open Access Journals (Sweden)

Kuan Li

2014-07-01

Full Text Available The traditional HVDC plays an important role in the development of power grid. But the traditional HVDC cannot supply power either to entirely passive AC network or to weak AC system. In fact, an entirely passive AC network can be effectively powered through VSC-HVDC. However, the cost of investment in VSC-HVDC is amazingly high due to the limitation of power electronics technology. Based on CSC and VSC, this paper proposes a method to build Hybrid HVDC, which makes the power supply to the passive AC network come true and, at the same time, lowers the investment cost. The effect of topology, steady mathematical model, startup characteristic, steady and transient characteristics in Hybrid HVDC system are systematically studied in this paper. The simulation result shows that Hybrid HVDC can supply power to the passive AC network with high stability. This study provides a theoretical basis for the further development of HVDC.

Modelling dependable systems using hybrid Bayesian networks

International Nuclear Information System (INIS)

Neil, Martin; Tailor, Manesh; Marquez, David; Fenton, Norman; Hearty, Peter

2008-01-01

A hybrid Bayesian network (BN) is one that incorporates both discrete and continuous nodes. In our extensive applications of BNs for system dependability assessment, the models are invariably hybrid and the need for efficient and accurate computation is paramount. We apply a new iterative algorithm that efficiently combines dynamic discretisation with robust propagation algorithms on junction tree structures to perform inference in hybrid BNs. We illustrate its use in the field of dependability with two example of reliability estimation. Firstly we estimate the reliability of a simple single system and next we implement a hierarchical Bayesian model. In the hierarchical model we compute the reliability of two unknown subsystems from data collected on historically similar subsystems and then input the result into a reliability block model to compute system level reliability. We conclude that dynamic discretisation can be used as an alternative to analytical or Monte Carlo methods with high precision and can be applied to a wide range of dependability problems

Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network

NARCIS (Netherlands)

Tran, Van Tung; Thobiani, Faisal Al; Tinga, Tiedo; Ball, Andrew David; Niu, Gang

2017-01-01

In this paper, a hybrid deep belief network is proposed to diagnose single and combined faults of suction and discharge valves in a reciprocating compressor. This hybrid integrates the deep belief network structured by multiple stacked restricted Boltzmann machines for pre-training and simplified

Distributed Fault-Tolerant Quality Of Service Routing in Hybrid Directional Wireless Networks

National Research Council Canada - National Science Library

Llewellyn, II, Larry C

2007-01-01

This thesis presents a distributed fault-tolerant routing protocol (EFDCB) for QoS supporting hybrid mobile ad hoc networks with the aim of mitigating QoS disruption time when network failures occur...

Throughput analysis of point-to-multi-point hybric FSO/RF network

KAUST Repository

Rakia, Tamer

2017-07-31

This paper presents and analyzes a point-to-multi-point (P2MP) network that uses a number of free-space optical (FSO) links for data transmission from the central node to the different remote nodes. A common backup radio-frequency (RF) link is used by the central node for data transmission to any remote node in case of the failure of any one of FSO links. We develop a cross-layer Markov chain model to study the throughput from central node to a tagged remote node. Numerical examples are presented to compare the performance of the proposed P2MP hybrid FSO/RF network with that of a P2MP FSO-only network and show that the P2MP Hybrid FSO/RF network achieves considerable performance improvement over the P2MP FSO-only network.

Analysis methodology for flow-level evaluation of a hybrid mobile-sensor network

NARCIS (Netherlands)

Dimitrova, D.C.; Heijenk, Geert; Braun, T.

2012-01-01

Our society uses a large diversity of co-existing wired and wireless networks in order to satisfy its communication needs. A cooper- ation between these networks can benefit performance, service availabil- ity and deployment ease, and leads to the emergence of hybrid networks. This position paper

Solar central receiver hybrid power system. Phase I study

Energy Technology Data Exchange (ETDEWEB)

None

1978-11-01

A management plan is presented for implementation during the Solar Central Receiver Hybrid Power System - Phase I study project. The project plan and the management controls that will be used to assure technically adequate, timely and cost effective performance of the work required to prepare the designated end products are described. Bechtel in-house controls and those to be used in directing the subcontractors are described. Phase I of the project consists of tradeoff studies, parametric analyses, and engineering studies leading to conceptual definition and evaluation of a commercial hybrid power system that has the potential for supplying economically competitive electric power to a utility grid in the 1985-1990 time frame. The scope also includes the preparation of a development plan for the resolution of technical uncertainties and the preparation of plans and a proposal for Phase II of the program. The technical approach will be based on a central receiver solar energy collection scheme which supplies thermal energy to a combined cycle, generating system, consisting of a gas turbine cycle combined with a steam bottoming cycle by means of a heat recovery steam generator.

Empathy and well-being correlate with centrality in different social networks.

Science.gov (United States)

Morelli, Sylvia A; Ong, Desmond C; Makati, Rucha; Jackson, Matthew O; Zaki, Jamil

2017-09-12

Individuals benefit from occupying central roles in social networks, but little is known about the psychological traits that predict centrality. Across four college freshman dorms ( n = 193), we characterized individuals with a battery of personality questionnaires and also asked them to nominate dorm members with whom they had different types of relationships. This revealed several social networks within dorm communities with differing characteristics. In particular, additional data showed that networks varied in the degree to which nominations depend on ( i ) trust and ( ii ) shared fun and excitement. Networks more dependent upon trust were further defined by fewer connections than those more dependent on fun. Crucially, network and personality features interacted to predict individuals' centrality: people high in well-being (i.e., life satisfaction and positive emotion) were central to networks characterized by fun, whereas people high in empathy were central to networks characterized by trust. Together, these findings provide network-based corroboration of psychological evidence that well-being is socially attractive, whereas empathy supports close relationships. More broadly, these data highlight how an individual's personality relates to the roles that they play in sustaining their community.

Computational modeling of electrically conductive networks formed by graphene nanoplatelet-carbon nanotube hybrid particles

Science.gov (United States)

Mora, A.; Han, F.; Lubineau, G.

2018-04-01

One strategy to ensure that nanofiller networks in a polymer composite percolate at low volume fractions is to promote segregation. In a segregated structure, the concentration of nanofillers is kept low in some regions of the sample. In turn, the concentration in the remaining regions is much higher than the average concentration of the sample. This selective placement of the nanofillers ensures percolation at low average concentration. One original strategy to promote segregation is by tuning the shape of the nanofillers. We use a computational approach to study the conductive networks formed by hybrid particles obtained by growing carbon nanotubes (CNTs) on graphene nanoplatelets (GNPs). The objective of this study is (1) to show that the higher electrical conductivity of these composites is due to the hybrid particles forming a segregated structure and (2) to understand which parameters defining the hybrid particles determine the efficiency of the segregation. We construct a microstructure to observe the conducting paths and determine whether a segregated structure has indeed been formed inside the composite. A measure of efficiency is presented based on the fraction of nanofillers that contribute to the conductive network. Then, the efficiency of the hybrid-particle networks is compared to those of three other networks of carbon-based nanofillers in which no hybrid particles are used: only CNTs, only GNPs, and a mix of CNTs and GNPs. Finally, some parameters of the hybrid particle are studied: the CNT density on the GNPs, and the CNT and GNP geometries. We also present recommendations for the further improvement of a composite’s conductivity based on these parameters.

Hidden Neural Networks: A Framework for HMM/NN Hybrids

DEFF Research Database (Denmark)

Riis, Søren Kamaric; Krogh, Anders Stærmose

1997-01-01

This paper presents a general framework for hybrids of hidden Markov models (HMM) and neural networks (NN). In the new framework called hidden neural networks (HNN) the usual HMM probability parameters are replaced by neural network outputs. To ensure a probabilistic interpretation the HNN is nor...... HMMs on TIMIT continuous speech recognition benchmarks. On the task of recognizing five broad phoneme classes an accuracy of 84% is obtained compared to 76% for a standard HMM. Additionally, we report a preliminary result of 69% accuracy on the TIMIT 39 phoneme task...

Mobility-aware Hybrid Synchronization for Wireless Sensor Network

DEFF Research Database (Denmark)

Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

2015-01-01

Random mobility of node causes the frequent changes in the network dynamics causing the increased cost in terms of energy and bandwidth. It needs the additional efforts to synchronize the activities of nodes during data collection and transmission in Wireless Sensor Networks (WSNs). A key challenge...... in maintaining the effective data collection and transmission is to schedule and synchronize the activities of the nodes with the global clock. This paper proposes the Mobility-aware Hybrid Synchronization Algorithm (MHS) which works on the formation of cluster based on spanning tree mechanism (SPT). Nodes used...... for formation of the network have random mobility and heterogeneous in terms of energy with static sink. The nodes in the cluster and cluster heads in the network are synchronized with the notion of global time scale. In the initial stage, the algorithm establishes the hierarchical structure of the network...

Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization

Directory of Open Access Journals (Sweden)

Nasir Saeed

2017-12-01

Full Text Available Underwater wireless technologies demand to transmit at higher data rate for ocean exploration. Currently, large coverage is achieved by acoustic sensor networks with low data rate, high cost, high latency, high power consumption, and negative impact on marine mammals. Meanwhile, optical communication for underwater networks has the advantage of the higher data rate albeit for limited communication distances. Moreover, energy consumption is another major problem for underwater sensor networks, due to limited battery power and difficulty in replacing or recharging the battery of a sensor node. The ultimate solution to this problem is to add energy harvesting capability to the acoustic-optical sensor nodes. Localization of underwater sensor networks is of utmost importance because the data collected from underwater sensor nodes is useful only if the location of the nodes is known. Therefore, a novel localization technique for energy harvesting hybrid acoustic-optical underwater wireless sensor networks (AO-UWSNs is proposed. AO-UWSN employs optical communication for higher data rate at a short transmission distance and employs acoustic communication for low data rate and long transmission distance. A hybrid received signal strength (RSS based localization technique is proposed to localize the nodes in AO-UWSNs. The proposed technique combines the noisy RSS based measurements from acoustic communication and optical communication and estimates the final locations of acoustic-optical sensor nodes. A weighted multiple observations paradigm is proposed for hybrid estimated distances to suppress the noisy observations and give more importance to the accurate observations. Furthermore, the closed form solution for Cramer-Rao lower bound (CRLB is derived for localization accuracy of the proposed technique.

Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization.

Science.gov (United States)

Saeed, Nasir; Celik, Abdulkadir; Al-Naffouri, Tareq Y; Alouini, Mohamed-Slim

2017-12-26

Underwater wireless technologies demand to transmit at higher data rate for ocean exploration. Currently, large coverage is achieved by acoustic sensor networks with low data rate, high cost, high latency, high power consumption, and negative impact on marine mammals. Meanwhile, optical communication for underwater networks has the advantage of the higher data rate albeit for limited communication distances. Moreover, energy consumption is another major problem for underwater sensor networks, due to limited battery power and difficulty in replacing or recharging the battery of a sensor node. The ultimate solution to this problem is to add energy harvesting capability to the acoustic-optical sensor nodes. Localization of underwater sensor networks is of utmost importance because the data collected from underwater sensor nodes is useful only if the location of the nodes is known. Therefore, a novel localization technique for energy harvesting hybrid acoustic-optical underwater wireless sensor networks (AO-UWSNs) is proposed. AO-UWSN employs optical communication for higher data rate at a short transmission distance and employs acoustic communication for low data rate and long transmission distance. A hybrid received signal strength (RSS) based localization technique is proposed to localize the nodes in AO-UWSNs. The proposed technique combines the noisy RSS based measurements from acoustic communication and optical communication and estimates the final locations of acoustic-optical sensor nodes. A weighted multiple observations paradigm is proposed for hybrid estimated distances to suppress the noisy observations and give more importance to the accurate observations. Furthermore, the closed form solution for Cramer-Rao lower bound (CRLB) is derived for localization accuracy of the proposed technique.

Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization

KAUST Repository

Saeed, Nasir; Celik, Abdulkadir; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

2017-01-01

Underwater wireless technologies demand to transmit at higher data rate for ocean exploration. Currently, large coverage is achieved by acoustic sensor networks with low data rate, high cost, high latency, high power consumption, and negative impact on marine mammals. Meanwhile, optical communication for underwater networks has the advantage of the higher data rate albeit for limited communication distances. Moreover, energy consumption is another major problem for underwater sensor networks, due to limited battery power and difficulty in replacing or recharging the battery of a sensor node. The ultimate solution to this problem is to add energy harvesting capability to the acoustic-optical sensor nodes. Localization of underwater sensor networks is of utmost importance because the data collected from underwater sensor nodes is useful only if the location of the nodes is known. Therefore, a novel localization technique for energy harvesting hybrid acoustic-optical underwater wireless sensor networks (AO-UWSNs) is proposed. AO-UWSN employs optical communication for higher data rate at a short transmission distance and employs acoustic communication for low data rate and long transmission distance. A hybrid received signal strength (RSS) based localization technique is proposed to localize the nodes in AO-UWSNs. The proposed technique combines the noisy RSS based measurements from acoustic communication and optical communication and estimates the final locations of acoustic-optical sensor nodes. A weighted multiple observations paradigm is proposed for hybrid estimated distances to suppress the noisy observations and give more importance to the accurate observations. Furthermore, the closed form solution for Cramer-Rao lower bound (CRLB) is derived for localization accuracy of the proposed technique.

Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization

KAUST Repository

Saeed, Nasir

2017-12-26

Underwater wireless technologies demand to transmit at higher data rate for ocean exploration. Currently, large coverage is achieved by acoustic sensor networks with low data rate, high cost, high latency, high power consumption, and negative impact on marine mammals. Meanwhile, optical communication for underwater networks has the advantage of the higher data rate albeit for limited communication distances. Moreover, energy consumption is another major problem for underwater sensor networks, due to limited battery power and difficulty in replacing or recharging the battery of a sensor node. The ultimate solution to this problem is to add energy harvesting capability to the acoustic-optical sensor nodes. Localization of underwater sensor networks is of utmost importance because the data collected from underwater sensor nodes is useful only if the location of the nodes is known. Therefore, a novel localization technique for energy harvesting hybrid acoustic-optical underwater wireless sensor networks (AO-UWSNs) is proposed. AO-UWSN employs optical communication for higher data rate at a short transmission distance and employs acoustic communication for low data rate and long transmission distance. A hybrid received signal strength (RSS) based localization technique is proposed to localize the nodes in AO-UWSNs. The proposed technique combines the noisy RSS based measurements from acoustic communication and optical communication and estimates the final locations of acoustic-optical sensor nodes. A weighted multiple observations paradigm is proposed for hybrid estimated distances to suppress the noisy observations and give more importance to the accurate observations. Furthermore, the closed form solution for Cramer-Rao lower bound (CRLB) is derived for localization accuracy of the proposed technique.

Multi-Agent-Based Controller for Voltage Enhancement in AC/DC Hybrid Microgrid Using Energy Storages

Directory of Open Access Journals (Sweden)

Ahmadali Khatibzadeh

2017-02-01

Full Text Available Development of renewable energies and DC loads have led microgrids toward the creation of DC networks. The predictions show that the hybrid microgrids will be used widely in the future. This article has studied the voltage stability in the presence of sources of energy storage in AC/DC hybrid networks. However, because the different dynamics of hybrid networks applying centralized and distributed controllers will be faced with different problems, in this study, a multi-agent control for the microgrid has been used. A new structure referred to here as an event-driven microgrid control management (EDMCM has been developed to control the microgrid. This method increases response speed and accuracy of decision making. Hybrid Network Simulation results confirm the validity of the developed model.

Filtering in Hybrid Dynamic Bayesian Networks

Science.gov (United States)

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).

Electroencephalography epilepsy classifications using hybrid cuckoo search and neural network

Science.gov (United States)

Pratiwi, A. B.; Damayanti, A.; Miswanto

2017-07-01

Epilepsy is a condition that affects the brain and causes repeated seizures. This seizure is episodes that can vary and nearly undetectable to long periods of vigorous shaking or brain contractions. Epilepsy often can be confirmed with an electrocephalography (EEG). Neural Networks has been used in biomedic signal analysis, it has successfully classified the biomedic signal, such as EEG signal. In this paper, a hybrid cuckoo search and neural network are used to recognize EEG signal for epilepsy classifications. The weight of the multilayer perceptron is optimized by the cuckoo search algorithm based on its error. The aim of this methods is making the network faster to obtained the local or global optimal then the process of classification become more accurate. Based on the comparison results with the traditional multilayer perceptron, the hybrid cuckoo search and multilayer perceptron provides better performance in term of error convergence and accuracy. The purpose methods give MSE 0.001 and accuracy 90.0 %.

Control of a hybrid compensator in a power network by an artificial neural network

Directory of Open Access Journals (Sweden)

I. S. Shaw

1998-07-01

Full Text Available Increased interest in the elimination of distortion in electrical power networks has led to the development of various compensator topologies. The increasing cost of electrical energy necessitates the cost-effective operation of any of these topologies. This paper considers the development of an artificial neural network based controller, trained by means of the backpropagation method, that ensures the cost-effective operation of the hybrid compensator consisting of various converters and filters.

Manifold absolute pressure estimation using neural network with hybrid training algorithm.

Directory of Open Access Journals (Sweden)

Mohd Taufiq Muslim

Full Text Available In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM algorithm, Bayesian Regularization (BR algorithm and Particle Swarm Optimization (PSO algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS. The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value.

Manifold absolute pressure estimation using neural network with hybrid training algorithm.

Science.gov (United States)

Muslim, Mohd Taufiq; Selamat, Hazlina; Alimin, Ahmad Jais; Haniff, Mohamad Fadzli

2017-01-01

In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP) sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS). The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value.

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

Science.gov (United States)

Pesavento, Michael J; Pinto, David J

2012-11-01

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

Novel blends of acrylonitrile butadiene rubber and polyurethane-silica hybrid networks

Directory of Open Access Journals (Sweden)

X. P. Wang

2012-07-01

Full Text Available Novel blends of acrylonitrile butadiene rubber (NBR and polyurethane-silica (PU-SiO2 hybrid networks have been prepared by melt blending. The PU-SiO2 hybrid networks were formed via the reaction of NCO groups of NCO-terminated PU prepolymer and OH groups of SiO2 in the absence of an external crosslinking agent (i.e. alcohols and amines during the curing process of NBR. Both in the neat PU-SiO2 system and the NBR/(PU-SiO2 system, the NCO-terminated PU prepolymer could be crosslinked by SiO2 to form PU-SiO2 hybrid networks. The effects of PU-SiO2 introduction into the NBR, on the properties of the resulting blends were studied. It was found that the vulcanization was activated by the incorporation of PU-SiO2. Transmission electronic microscopy (TEM studies indicated that the interpenetration and entanglement structures between NBR and PU-SiO2 increased with increasing PU-SiO2 content and the quasi-interpenetrating polymer networks (quasi-IPN structures were formed when the PU-SiO2 was 50 wt% in the NBR/(PU-SiO2 systems. The microstructures formed in the blends led to good compatibility between NBR and PU-SiO2 and significantly improved the mechanical properties, abrasion resistance and flex-fatigue life of the blends.

Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach

Directory of Open Access Journals (Sweden)

Oliveira Rui

2010-09-01

Full Text Available Abstract Background This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics. Results The proposed hybrid modelling methodology was evaluated using two case studies. The first of these deals with dynamic modelling of transcriptional factor A in mammalian cells. The protein transport from the cytosol to the nucleus introduced a delay that was accounted for by discrete time series formulation. The second case study focused on a simple network with distributed time delays that demonstrated that the discrete time delay formalism has broad applicability to both discrete and distributed delay problems. Conclusions Significantly better prediction qualities of the novel hybrid model were obtained when compared to dynamical structures without time delays, being the more distinctive the more significant the underlying system delay is. The identification of the system delays by studies of different discrete modelling delays was enabled by the proposed structure. Further, it was shown that the hybrid discrete delay methodology is not limited to discrete delay systems. The proposed method is a powerful tool to identify time delays in ill-defined biochemical networks.

Hybrid network defense model based on fuzzy evaluation.

Science.gov (United States)

Cho, Ying-Chiang; Pan, Jen-Yi

2014-01-01

With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network's existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter's inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture.

Hybrid Control of Long-Endurance Aerial Robotic Vehicles for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Deok-Jin Lee

2011-06-01

Full Text Available This paper presents an effective hybrid control approach for building stable wireless sensor networks between heterogeneous unmanned vehicles using long‐ endurance aerial robotic vehicles. For optimal deployment of the aerial vehicles in communication networks, a gradient climbing based self‐estimating control algorithm is utilized to locate the aerial platforms to maintain maximum communication throughputs between distributed multiple nodes. The autonomous aerial robots, which function as communication relay nodes, extract and harvest thermal energy from the atmospheric environment to improve their flight endurance within specified communication coverage areas. The rapidly‐deployable sensor networks with the high‐endurance aerial vehicles can be used for various application areas including environment monitoring, surveillance, tracking, and decision‐making support. Flight test and simulation studies are conducted to evaluate the effectiveness of the proposed hybrid control technique for robust communication networks.

Evaluation and Control of Thiol-ene/Thiol-epoxy Hybrid Networks

OpenAIRE

Carioscia, Jacquelyn A.; Stansbury, Jeffrey W.; Bowman, Christopher N.

2007-01-01

The development of thiol-ene/thiol-epoxy hybrid networks offers the advantage of tailorable polymerization kinetics while producing a highly crosslinked, high Tg polymer that has significantly reduced shrinkage stress. Stoichiometric mixtures of pentaerythritol tetra(3-mercaptopropionate) (PETMP)/triallyl-1,3,5-triazine-2,4,6-trione (TATATO) (thiol-ene, mixture 1) and PETMP/bisphenol a diglycidyl ether (BADGE) (thiol-epoxy, mixture 2) were prepared and hybrid mixtures of 75/25, 50/50, 25/75, ...

Collaborative Multi-Layer Network Coding For Hybrid Cellular Cognitive Radio Networks

KAUST Repository

Moubayed, Abdallah J.

2014-05-01

In this thesis, as an extension to [1], we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in hybrid (interweave and underlay) cellular cognitive radio networks. This scheme allows the uncoordinated collaboration between the collocated primary and cognitive radio base-stations in order to minimize their own as well as each other’s packet recovery overheads, thus by improving their throughput. The proposed scheme ensures that each network’s performance is not degraded by its help to the other network. Moreover, it guarantees that the primary network’s interference threshold is not violated in the same and adjacent cells. Yet, the scheme allows the reduction of the recovery overhead in the collocated primary and cognitive radio networks. The reduction in the cognitive radio network is further amplified due to the perfect detection of spectrum holes which allows the cognitive radio base station to transmit at higher power without fear of violating the interference threshold of the primary network. For the secondary network, simulation results show reductions of 20% and 34% in the packet recovery overhead, compared to the non-collaborative scheme, for low and high probabilities of primary packet arrivals, respectively. For the primary network, this reduction was found to be 12%. Furthermore, with the use of fractional cooperation, the average recovery overhead is further reduced by around 5% for the primary network and around 10% for the secondary network when a high fractional cooperation probability is used.

Hybrid optical CDMA-FSO communications network under spatially correlated gamma-gamma scintillation.

Science.gov (United States)

Jurado-Navas, Antonio; Raddo, Thiago R; Garrido-Balsells, José María; Borges, Ben-Hur V; Olmos, Juan José Vegas; Monroy, Idelfonso Tafur

2016-07-25

In this paper, we propose a new hybrid network solution based on asynchronous optical code-division multiple-access (OCDMA) and free-space optical (FSO) technologies for last-mile access networks, where fiber deployment is impractical. The architecture of the proposed hybrid OCDMA-FSO network is thoroughly described. The users access the network in a fully asynchronous manner by means of assigned fast frequency hopping (FFH)-based codes. In the FSO receiver, an equal gain-combining technique is employed along with intensity modulation and direct detection. New analytical formalisms for evaluating the average bit error rate (ABER) performance are also proposed. These formalisms, based on the spatially correlated gamma-gamma statistical model, are derived considering three distinct scenarios, namely, uncorrelated, totally correlated, and partially correlated channels. Numerical results show that users can successfully achieve error-free ABER levels for the three scenarios considered as long as forward error correction (FEC) algorithms are employed. Therefore, OCDMA-FSO networks can be a prospective alternative to deliver high-speed communication services to access networks with deficient fiber infrastructure.

Heating networks and domestic central heating systems

Energy Technology Data Exchange (ETDEWEB)

Kamler, W; Wasilewski, W

1976-08-01

This is a comprehensive survey of the 26 contributions from 8 European countries submitted to the 3rd International District Heating Conference in Warsaw held on the subject 'Heating Networks and Domestic Central Heating Systems'. The contributions are grouped according to 8 groups of subjects: (1) heat carriers and their parameters; (2) system of heating networks; (3) calculation and optimization of heating networks; (4) construction of heating networks; (5) operation control and automation; (6) operational problems; (7) corrosion problems; and (8) methods of heat accounting.

On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

DEFF Research Database (Denmark)

Alizadeh, Tohid

2008-01-01

This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP......-RBF neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system....

Centrality measures in temporal networks with time series analysis

Science.gov (United States)

Huang, Qiangjuan; Zhao, Chengli; Zhang, Xue; Wang, Xiaojie; Yi, Dongyun

2017-05-01

The study of identifying important nodes in networks has a wide application in different fields. However, the current researches are mostly based on static or aggregated networks. Recently, the increasing attention to networks with time-varying structure promotes the study of node centrality in temporal networks. In this paper, we define a supra-evolution matrix to depict the temporal network structure. With using of the time series analysis, the relationships between different time layers can be learned automatically. Based on the special form of the supra-evolution matrix, the eigenvector centrality calculating problem is turned into the calculation of eigenvectors of several low-dimensional matrices through iteration, which effectively reduces the computational complexity. Experiments are carried out on two real-world temporal networks, Enron email communication network and DBLP co-authorship network, the results of which show that our method is more efficient at discovering the important nodes than the common aggregating method.

Hybrid solar central receiver for combined cycle power plant

Science.gov (United States)

Bharathan, Desikan; Bohn, Mark S.; Williams, Thomas A.

1995-01-01

A hybrid combined cycle power plant including a solar central receiver for receiving solar radiation and converting it to thermal energy. The power plant includes a molten salt heat transfer medium for transferring the thermal energy to an air heater. The air heater uses the thermal energy to preheat the air from the compressor of the gas cycle. The exhaust gases from the gas cycle are directed to a steam turbine for additional energy production.

Development of a space-systems network testbed

Science.gov (United States)

Lala, Jaynarayan; Alger, Linda; Adams, Stuart; Burkhardt, Laura; Nagle, Gail; Murray, Nicholas

1988-01-01

This paper describes a communications network testbed which has been designed to allow the development of architectures and algorithms that meet the functional requirements of future NASA communication systems. The central hardware components of the Network Testbed are programmable circuit switching communication nodes which can be adapted by software or firmware changes to customize the testbed to particular architectures and algorithms. Fault detection, isolation, and reconfiguration has been implemented in the Network with a hybrid approach which utilizes features of both centralized and distributed techniques to provide efficient handling of faults within the Network.

Efficient MAC Protocol for Hybrid Wireless Network with Heterogeneous Sensor Nodes

Directory of Open Access Journals (Sweden)

Md. Nasre Alam

2016-01-01

Full Text Available Although several Directional Medium Access Control (DMAC protocols have been designed for use with homogeneous networks, it can take a substantial amount of time to change sensor nodes that are equipped with an omnidirectional antenna for sensor nodes with a directional antenna. Thus, we require a novel MAC protocol for use with an intermediate wireless network that consists of heterogeneous sensor nodes equipped with either an omnidirectional antenna or a directional antenna. The MAC protocols that have been designed for use in homogeneous networks are not suitable for use in a hybrid network due to deaf, hidden, and exposed nodes. Therefore, we propose a MAC protocol that exploits the characteristics of a directional antenna and can also work efficiently with omnidirectional nodes in a hybrid network. In order to address the deaf, hidden, and exposed node problems, we define RTS/CTS for the neighbor (RTSN/CTSN and Neighbor Information (NIP packets. The performance of the proposed MAC protocol is evaluated through a numerical analysis using a Markov model. In addition, the analytical results of the MAC protocol are verified through an OPNET simulation.

Bandwidth Efficient Hybrid Synchronization for Wireless Sensor Network

DEFF Research Database (Denmark)

Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

2015-01-01

Data collection and transmission are the fundamental operations of Wireless Sensor Networks (WSNs). A key challenge in effective data collection and transmission is to schedule and synchronize the activities of the nodes with the global clock. This paper proposes the Bandwidth Efficient Hybrid...... in the network and then perform the pair-wise synchronization. With the mobility of node, the structure frequently changes causing an increase in energy consumption. To mitigate the problem BESDA aggregate data with the notion of a global timescale throughout the network and schedule based time-division multiple...... accesses (TDMA) techniques as MAC layer protocol. It reduces the collision of packets. Simulation results show that BESDA is energy efficient, with increased throughput, and has less delay as compared with state-of-the-art....

Model-based design of RNA hybridization networks implemented in living cells.

Science.gov (United States)

Rodrigo, Guillermo; Prakash, Satya; Shen, Shensi; Majer, Eszter; Daròs, José-Antonio; Jaramillo, Alfonso

2017-09-19

Synthetic gene circuits allow the behavior of living cells to be reprogrammed, and non-coding small RNAs (sRNAs) are increasingly being used as programmable regulators of gene expression. However, sRNAs (natural or synthetic) are generally used to regulate single target genes, while complex dynamic behaviors would require networks of sRNAs regulating each other. Here, we report a strategy for implementing such networks that exploits hybridization reactions carried out exclusively by multifaceted sRNAs that are both targets of and triggers for other sRNAs. These networks are ultimately coupled to the control of gene expression. We relied on a thermodynamic model of the different stable conformational states underlying this system at the nucleotide level. To test our model, we designed five different RNA hybridization networks with a linear architecture, and we implemented them in Escherichia coli. We validated the network architecture at the molecular level by native polyacrylamide gel electrophoresis, as well as the network function at the bacterial population and single-cell levels with a fluorescent reporter. Our results suggest that it is possible to engineer complex cellular programs based on RNA from first principles. Because these networks are mainly based on physical interactions, our designs could be expanded to other organisms as portable regulatory resources or to implement biological computations. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Centrality measures and thermodynamic formalism for complex networks.

Science.gov (United States)

Delvenne, Jean-Charles; Libert, Anne-Sophie

2011-04-01

In the study of small and large networks it is customary to perform a simple random walk where the random walker jumps from one node to one of its neighbors with uniform probability. The properties of this random walk are intimately related to the combinatorial properties of the network. In this paper we propose to use the Ruelle-Bowens random walk instead, whose probability transitions are chosen in order to maximize the entropy rate of the walk on an unweighted graph. If the graph is weighted, then a free energy is optimized instead of the entropy rate. Specifically, we introduce a centrality measure for large networks, which is the stationary distribution attained by the Ruelle-Bowens random walk; we name it entropy rank. We introduce a more general version, which is able to deal with disconnected networks, under the name of free-energy rank. We compare the properties of those centrality measures with the classic PageRank and hyperlink-induced topic search (HITS) on both toy and real-life examples, in particular their robustness to small modifications of the network. We show that our centrality measures are more discriminating than PageRank, since they are able to distinguish clearly pages that PageRank regards as almost equally interesting, and are more sensitive to the medium-scale details of the graph.

Identification of chaotic systems by neural network with hybrid learning algorithm

International Nuclear Information System (INIS)

Pan, S.-T.; Lai, C.-C.

2008-01-01

Based on the genetic algorithm (GA) and steepest descent method (SDM), this paper proposes a hybrid algorithm for the learning of neural networks to identify chaotic systems. The systems in question are the logistic map and the Duffing equation. Different identification schemes are used to identify both the logistic map and the Duffing equation, respectively. Simulation results show that our hybrid algorithm is more efficient than that of other methods

Energy Harvesting in Heterogeneous Networks with Hybrid Powered Communication Systems

KAUST Repository

Alsharoa, Ahmad; Celik, Abdulkadir; Kamal, Ahmed E.

2018-01-01

In this paper, we investigate an energy efficient and energy harvesting (EH) system model in heterogeneous networks (HetNets) where all base stations (BSS) are equipped to harvest energy from renewable energy sources. We consider a hybrid power

SIMULATION OF WIRELESS SENSOR NETWORK WITH HYBRID TOPOLOGY

Directory of Open Access Journals (Sweden)

J. Jaslin Deva Gifty

2016-03-01

Full Text Available The design of low rate Wireless Personal Area Network (WPAN by IEEE 802.15.4 standard has been developed to support lower data rates and low power consuming application. Zigbee Wireless Sensor Network (WSN works on the network and application layer in IEEE 802.15.4. Zigbee network can be configured in star, tree or mesh topology. The performance varies from topology to topology. The performance parameters such as network lifetime, energy consumption, throughput, delay in data delivery and sensor field coverage area varies depending on the network topology. In this paper, designing of hybrid topology by using two possible combinations such as star-tree and star-mesh is simulated to verify the communication reliability. This approach is to combine all the benefits of two network model. The parameters such as jitter, delay and throughput are measured for these scenarios. Further, MAC parameters impact such as beacon order (BO and super frame order (SO for low power consumption and high channel utilization, has been analysed for star, tree and mesh topology in beacon disable mode and beacon enable mode by varying CBR traffic loads.

Why a Central Network Position Isn't Enough

DEFF Research Database (Denmark)

Reinholt, Mia; Pedersen, Torben; Foss, Nicolai Juul

2011-01-01

in such networks. This problem may, however, be resolved by including motivation and ability to share knowledge as moderators of the association between network position and knowledge sharing. Our analysis of 705 employees in a consultancy shows that employees’ knowledge acquisition and provision are highest when...... network centrality, autonomous motivation, and ability are all high, thus supporting the proposed three-way interaction....

Autumn Algorithm-Computation of Hybridization Networks for Realistic Phylogenetic Trees.

Science.gov (United States)

Huson, Daniel H; Linz, Simone

2018-01-01

A minimum hybridization network is a rooted phylogenetic network that displays two given rooted phylogenetic trees using a minimum number of reticulations. Previous mathematical work on their calculation has usually assumed the input trees to be bifurcating, correctly rooted, or that they both contain the same taxa. These assumptions do not hold in biological studies and "realistic" trees have multifurcations, are difficult to root, and rarely contain the same taxa. We present a new algorithm for computing minimum hybridization networks for a given pair of "realistic" rooted phylogenetic trees. We also describe how the algorithm might be used to improve the rooting of the input trees. We introduce the concept of "autumn trees", a nice framework for the formulation of algorithms based on the mathematics of "maximum acyclic agreement forests". While the main computational problem is hard, the run-time depends mainly on how different the given input trees are. In biological studies, where the trees are reasonably similar, our parallel implementation performs well in practice. The algorithm is available in our open source program Dendroscope 3, providing a platform for biologists to explore rooted phylogenetic networks. We demonstrate the utility of the algorithm using several previously studied data sets.

Distributed Hybrid Scheduling in Multi-Cloud Networks using Conflict Graphs

KAUST Repository

Douik, Ahmed; Dahrouj, Hayssam; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

2017-01-01

Recent studies on cloud-radio access networks assume either signal-level or scheduling-level coordination. This paper considers a hybrid coordinated scheme as a means to benefit from both policies. Consider the downlink of a multi-cloud radio access

A study on the control of a hybrid MTDC system supplying a passive network

DEFF Research Database (Denmark)

Kotb, Omar; Ghandhari, Mehrdad; Eriksson, Robert

2014-01-01

A hybrid Multi-Terminal DC (MTDC) system can combine the benefits of both Line Commutated Converter (LCC) and Voltage Source Converter (VSC) technologies in the form of reduced losses and flexibility to connect to weak and passive grids. In this paper, an analysis of control strategies used...... in a hybrid MTDC system is presented. A case study of a four terminal hybrid MTDC system supplying a passive AC network was considered for simulation study. A control scheme based on voltage margin was developed to cope with the condition of main DC voltage controlling station tripping. Two various control...... scenarios for controlling the VSCs connected to the passive network were presented and compared. The system performance was studied through EMTP-RV simulations under different disturbances. The results show the ability of selected converter control modes and proposed control schemes to operate the hybrid...

Educational Research Network for West and Central Africa ...

International Development Research Centre (IDRC) Digital Library (Canada)

This grant will assist the Educational Research Network for West and Central Africa (ERNWACA) by providing funding for succession planning, recruiting a regional coordinator (to be based in Mali) and strengthening the Network's capacity to mobilize resources with a view to long-term sustainability.

HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks

Directory of Open Access Journals (Sweden)

Luca Marchetti

2017-01-01

Full Text Available HSimulator is a multithread simulator for mass-action biochemical reaction systems placed in a well-mixed environment. HSimulator provides optimized implementation of a set of widespread state-of-the-art stochastic, deterministic, and hybrid simulation strategies including the first publicly available implementation of the Hybrid Rejection-based Stochastic Simulation Algorithm (HRSSA. HRSSA, the fastest hybrid algorithm to date, allows for an efficient simulation of the models while ensuring the exact simulation of a subset of the reaction network modeling slow reactions. Benchmarks show that HSimulator is often considerably faster than the other considered simulators. The software, running on Java v6.0 or higher, offers a simulation GUI for modeling and visually exploring biological processes and a Javadoc-documented Java library to support the development of custom applications. HSimulator is released under the COSBI Shared Source license agreement (COSBI-SSLA.

Improved Image Encryption for Real-Time Application over Wireless Communication Networks using Hybrid Cryptography Technique

Directory of Open Access Journals (Sweden)

Kazeem B. Adedeji

2016-12-01

Full Text Available Advances in communication networks have enabled organization to send confidential data such as digital images over wireless networks. However, the broadcast nature of wireless communication channel has made it vulnerable to attack from eavesdroppers. We have developed a hybrid cryptography technique, and we present its application to digital images as a means of improving the security of digital image for transmission over wireless communication networks. The hybrid technique uses a combination of a symmetric (Data Encryption Standard and asymmetric (Rivest Shamir Adleman cryptographic algorithms to secure data to be transmitted between different nodes of a wireless network. Three different image samples of type jpeg, png and jpg were tested using this technique. The results obtained showed that the hybrid system encrypt the images with minimal simulation time, and high throughput. More importantly, there is no relation or information between the original images and their encrypted form, according to Shannon’s definition of perfect security, thereby making the system much more secure.

The hybridized front end electronics of the Central Drift Chamber in the Stanford Linear Collider Detector

International Nuclear Information System (INIS)

Lo, C.C.; Kirsten, F.A.; Nakamura, M.

1987-10-01

In order to accommodate the high packaging density requirements for the front end electronics of the Central Drift Chamber (CDC) in the SLAC Linear Collider Detector (SLD), the CDC front end electronics has been hybridized. The hybrid package contains eight channels of amplifiers together with all the associated circuits for calibration, event recognition and power economy switching functions. A total of 1280 such hybrids are used in the CDC

Hybrid Electric Vehicle Experimental Model with CAN Network Real Time Control

Directory of Open Access Journals (Sweden)

RATOI, M.

2010-05-01

Full Text Available In this paper an experimental model with a distributed control system of a hybrid electrical vehicle is presented. A communication CAN network of high speed (1 Mbps assures a distributed control of the all components. The modeling and the control of different operating regimes are realized on an experimental test-bench of a hybrid electrical vehicle. The experimental results concerning the variations of the mains variables (currents, torques, speeds are presented.

Hybrid SDN Architecture for Resource Consolidation in MPLS Networks

OpenAIRE

Katov, Anton Nikolaev; Mihovska, Albena D.; Prasad, Neeli R.

2015-01-01

This paper proposes a methodology for resourceconsolidation towards minimizing the power consumption in alarge network, with a substantial resource overprovisioning. Thefocus is on the operation of the core MPLS networks. Theproposed approach is based on a software defined networking(SDN) scheme with a reconfigurable centralized controller, whichturns off certain network elements. The methodology comprisesthe process of identifying time periods with lower traffic demand;the ranking of the net...

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

Science.gov (United States)

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

2011-09-15

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

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

International Nuclear Information System (INIS)

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

2014-01-01

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

Collaborative-Hybrid Multi-Layer Network Control for Emerging Cyber-Infrastructures

Energy Technology Data Exchange (ETDEWEB)

Lehman, Tom [USC; Ghani, Nasir [UNM; Boyd, Eric [UCAID

2010-08-31

At a high level, there were four basic task areas identified for the Hybrid-MLN project. They are: o Multi-Layer, Multi-Domain, Control Plane Architecture and Implementation, including OSCARS layer2 and InterDomain Adaptation, Integration of LambdaStation and Terapaths with Layer2 dynamic provisioning, Control plane software release, Scheduling, AAA, security architecture, Network Virtualization architecture, Multi-Layer Network Architecture Framework Definition; o Heterogeneous DataPlane Testing; o Simulation; o Project Publications, Reports, and Presentations.

An agent-based model of centralized institutions, social network technology, and revolution.

Science.gov (United States)

Makowsky, Michael D; Rubin, Jared

2013-01-01

This paper sheds light on the general mechanisms underlying large-scale social and institutional change. We employ an agent-based model to test the impact of authority centralization and social network technology on preference falsification and institutional change. We find that preference falsification is increasing with centralization and decreasing with social network range. This leads to greater cascades of preference revelation and thus more institutional change in highly centralized societies and this effect is exacerbated at greater social network ranges. An empirical analysis confirms the connections that we find between institutional centralization, social radius, preference falsification, and institutional change.

MicroRNA expression in the adult mouse central nervous system

DEFF Research Database (Denmark)

Bak, Mads; Silahtaroglu, Asli; Møller, Morten

2008-01-01

distinct areas of the adult mouse central nervous system (CNS). Microarray profiling in combination with real-time RT-PCR and LNA (locked nucleic acid)-based in situ hybridization uncovered 44 miRNAs displaying more than threefold enrichment in the spinal cord, cerebellum, medulla oblongata, pons......RNA-related gene regulatory networks in the mammalian central nervous system. Udgivelsesdato: 2008-Mar...

On Hybrid Energy Utilization in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Mohammad Tala’t

2017-11-01

Full Text Available In a wireless sensor network (WSN, many applications have limited energy resources for data transmission. In order to accomplish a better green communication for WSN, a hybrid energy scheme can supply a more reliable energy source. In this article, hybrid energy utilization—which consists of constant energy source and solar harvested energy—is considered for WSN. To minimize constant energy usage from the hybrid source, a Markov decision process (MDP is designed to find the optimal transmission policy. With a finite packet buffer and a finite battery size, an MDP model is presented to define the states, actions, state transition probabilities, and the cost function including the cost values for all actions. A weighted sum of constant energy source consumption and a packet dropping probability (PDP are adopted as the cost value, enabling us to find the optimal solution for balancing the minimization of the constant energy source utilization and the PDP using a value iteration algorithm. As shown in the simulation results, the performance of optimal solution using MDP achieves a significant improvement compared to solution without its use.

Neural-network hybrid control for antilock braking systems.

Science.gov (United States)

Lin, Chih-Min; Hsu, C F

2003-01-01

The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this paper, a hybrid control system with a recurrent neural network (RNN) observer is developed for antilock braking systems. This hybrid control system is comprised of an ideal controller and a compensation controller. The ideal controller, containing an RNN uncertainty observer, is the principal controller; and the compensation controller is a compensator for the difference between the system uncertainty and the estimated uncertainty. Since for dynamic response the RNN has capabilities superior to the feedforward NN, it is utilized for the uncertainty observer. The Taylor linearization technique is employed to increase the learning ability of the RNN. In addition, the on-line parameter adaptation laws are derived based on a Lyapunov function, so the stability of the system can be guaranteed. Simulations are performed to demonstrate the effectiveness of the proposed NN hybrid control system for antilock braking control under various road conditions.

Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

Directory of Open Access Journals (Sweden)

Antonino Laudani

2015-01-01

Full Text Available A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database.

Final Technical Report for Terabit-scale hybrid networking project.

Energy Technology Data Exchange (ETDEWEB)

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

2015-12-12

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

Unified synchronization criteria in an array of coupled neural networks with hybrid impulses.

Science.gov (United States)

Wang, Nan; Li, Xuechen; Lu, Jianquan; Alsaadi, Fuad E

2018-05-01

This paper investigates the problem of globally exponential synchronization of coupled neural networks with hybrid impulses. Two new concepts on average impulsive interval and average impulsive gain are proposed to deal with the difficulties coming from hybrid impulses. By employing the Lyapunov method combined with some mathematical analysis, some efficient unified criteria are obtained to guarantee the globally exponential synchronization of impulsive networks. Our method and criteria are proved to be effective for impulsively coupled neural networks simultaneously with synchronizing impulses and desynchronizing impulses, and we do not need to discuss these two kinds of impulses separately. Moreover, by using our average impulsive interval method, we can obtain an interesting and valuable result for the case of average impulsive interval T a =∞. For some sparse impulsive sequences with T a =∞, the impulses can happen for infinite number of times, but they do not have essential influence on the synchronization property of networks. Finally, numerical examples including scale-free networks are exploited to illustrate our theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

Convergence analysis of stochastic hybrid bidirectional associative memory neural networks with delays

International Nuclear Information System (INIS)

Wan Li; Zhou Qinghua

2007-01-01

The stability property of stochastic hybrid bidirectional associate memory (BAM) neural networks with discrete delays is considered. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the delay-independent sufficient conditions to guarantee the exponential stability of the equilibrium solution for such networks are given by using the nonnegative semimartingale convergence theorem

Convergence analysis of stochastic hybrid bidirectional associative memory neural networks with delays

Science.gov (United States)

Wan, Li; Zhou, Qinghua

2007-10-01

The stability property of stochastic hybrid bidirectional associate memory (BAM) neural networks with discrete delays is considered. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the delay-independent sufficient conditions to guarantee the exponential stability of the equilibrium solution for such networks are given by using the nonnegative semimartingale convergence theorem.

Upgrade of hybrid fibre coax networks towards bi-directional access

NARCIS (Netherlands)

Khoe, G.D.; Wolters, R.P.C.; Boom, van den H.P.A.; Prati, G.

1997-01-01

In this paper we describe an upgrade scenario for Hybrid Fibre Coax (HFC) CATV Networks towards hi-directional access. The communication system described has been newly designed, and is based on the use of Direct Sequence- Code Division Multiple-Access (DS-CDMA). Due to its spread-spectrum

Konsep Dan Kinerja Dari Sistem Hybrid OCDMA/WDM Untuk Local Area Network

OpenAIRE

Nasaruddin, Nasaruddin

2011-01-01

Peningkatan kapasitas, distribusi bandwidth dan daya merupakan beberapa isu penting untuk aplikasi local area network (LAN). Saat ini, teknologi fiber optik sudah dapat mendukung jaringan akses dengan kecepatan tinggi untuk layanan multimedia diantaranya teknologi OCDMA dan WDM. Penambahan kapasitas transmisi LAN bisa dilakukan dengan penggabungan sistem transmisi OCDMA dengan WDM. Untuk itu, paper ini mengusulkan konsep dan kinerja dari sistem hybrid OCDMA/WDM. Sistem hybrid OCDMA/WDM ini be...

Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction

Directory of Open Access Journals (Sweden)

Chengdong Li

2018-01-01

Full Text Available To enhance the prediction performance for building energy consumption, this paper presents a modified deep belief network (DBN based hybrid model. The proposed hybrid model combines the outputs from the DBN model with the energy-consuming pattern to yield the final prediction results. The energy-consuming pattern in this study represents the periodicity property of building energy consumption and can be extracted from the observed historical energy consumption data. The residual data generated by removing the energy-consuming pattern from the original data are utilized to train the modified DBN model. The training of the modified DBN includes two steps, the first one of which adopts the contrastive divergence (CD algorithm to optimize the hidden parameters in a pre-train way, while the second one determines the output weighting vector by the least squares method. The proposed hybrid model is applied to two kinds of building energy consumption data sets that have different energy-consuming patterns (daily-periodicity and weekly-periodicity. In order to examine the advantages of the proposed model, four popular artificial intelligence methods—the backward propagation neural network (BPNN, the generalized radial basis function neural network (GRBFNN, the extreme learning machine (ELM, and the support vector regressor (SVR are chosen as the comparative approaches. Experimental results demonstrate that the proposed DBN based hybrid model has the best performance compared with the comparative techniques. Another thing to be mentioned is that all the predictors constructed by utilizing the energy-consuming patterns perform better than those designed only by the original data. This verifies the usefulness of the incorporation of the energy-consuming patterns. The proposed approach can also be extended and applied to some other similar prediction problems that have periodicity patterns, e.g., the traffic flow forecasting and the electricity consumption

A biplex approach to PageRank centrality: From classic to multiplex networks.

Science.gov (United States)

Pedroche, Francisco; Romance, Miguel; Criado, Regino

2016-06-01

In this paper, we present a new view of the PageRank algorithm inspired by multiplex networks. This new approach allows to introduce a new centrality measure for classic complex networks and a new proposal to extend the usual PageRank algorithm to multiplex networks. We give some analytical relations between these new approaches and the classic PageRank centrality measure, and we illustrate the new parameters presented by computing them on real underground networks.

A biplex approach to PageRank centrality: From classic to multiplex networks

Science.gov (United States)

Pedroche, Francisco; Romance, Miguel; Criado, Regino

2016-06-01

In this paper, we present a new view of the PageRank algorithm inspired by multiplex networks. This new approach allows to introduce a new centrality measure for classic complex networks and a new proposal to extend the usual PageRank algorithm to multiplex networks. We give some analytical relations between these new approaches and the classic PageRank centrality measure, and we illustrate the new parameters presented by computing them on real underground networks.

From Spinal Central Pattern Generators to Cortical Network: Integrated BCI for Walking Rehabilitation

Directory of Open Access Journals (Sweden)

G. Cheron

2012-01-01

Full Text Available Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs. Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG, upper limb electromyogram (EMG, or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs or dynamic recurrent neural networks (DRNNs. Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy.

Analysis of Adoption Spell of Hybrid Maize in the Central Rift Valley ...

African Journals Online (AJOL)

This paper estimates farm household level determinants of the speed of adoption of hybrid maize in the central rift valley of Ethiopia in the framework of the dynamic time on cross-sectional data. Descriptive statistics and duration model were used to study the objectives of the study. The results from descriptive analysis ...

A Novel Entropy-Based Centrality Approach for Identifying Vital Nodes in Weighted Networks

Directory of Open Access Journals (Sweden)

Tong Qiao

2018-04-01

Full Text Available Measuring centrality has recently attracted increasing attention, with algorithms ranging from those that simply calculate the number of immediate neighbors and the shortest paths to those that are complicated iterative refinement processes and objective dynamical approaches. Indeed, vital nodes identification allows us to understand the roles that different nodes play in the structure of a network. However, quantifying centrality in complex networks with various topological structures is not an easy task. In this paper, we introduce a novel definition of entropy-based centrality, which can be applicable to weighted directed networks. By design, the total power of a node is divided into two parts, including its local power and its indirect power. The local power can be obtained by integrating the structural entropy, which reveals the communication activity and popularity of each node, and the interaction frequency entropy, which indicates its accessibility. In addition, the process of influence propagation can be captured by the two-hop subnetworks, resulting in the indirect power. In order to evaluate the performance of the entropy-based centrality, we use four weighted real-world networks with various instance sizes, degree distributions, and densities. Correspondingly, these networks are adolescent health, Bible, United States (US airports, and Hep-th, respectively. Extensive analytical results demonstrate that the entropy-based centrality outperforms degree centrality, betweenness centrality, closeness centrality, and the Eigenvector centrality.

An agent-based QoS provisioning mechanism for WDM optical networks

Science.gov (United States)

Ouyang, Yong; Zeng, Qingji; Yue, Ling

2004-04-01

This paper addresses QoS provisioning mechanisms in the WDM optical networks. With the appearance of metropolitan optical network, a hierarchical metro and wide area optical network will be envisioned in the near future. This hierarchical optical transport network is often divided into optical domains by geography, administration and technology, which usually employ different QoS routing algorithms and policies. To provide end-to-end optical QoS is becoming a new challenge for the optical network design. In this paper, we first give an overview of issues on the QoS provisioning in data, control and management planes of the WDM optical network. And then three provisioning approaches are analyzed and compared. Finally, we propose an agent-based hybrid centralized/distributed QoS provisioning mechanism based on the concept of domain agent. This agent-based hybrid mechanism employs centralized approach in the domain and distributed approach between domains. It offers scalability and intra-domain optimal QoS routing. It also keeps independence and interoperability between domains.

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks

DEFF Research Database (Denmark)

Hagen, Espen; Dahmen, David; Stavrinou, Maria L

2016-01-01

on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network......With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical...... and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely...

Development of a hybrid system of artificial neural networks and ...

African Journals Online (AJOL)

Development of a hybrid system of artificial neural networks and artificial bee colony algorithm for prediction and modeling of customer choice in the market. ... attempted to present a new method for the modeling and prediction of customer choice in the market using the combination of artificial intelligence and data mining.

Finite-time hybrid projective synchronization of the drive-response complex networks with distributed-delay via adaptive intermittent control

Science.gov (United States)

Cheng, Lin; Yang, Yongqing; Li, Li; Sui, Xin

2018-06-01

This paper studies the finite-time hybrid projective synchronization of the drive-response complex networks. In the model, general transmission delays and distributed delays are also considered. By designing the adaptive intermittent controllers, the response network can achieve hybrid projective synchronization with the drive system in finite time. Based on finite-time stability theory and several differential inequalities, some simple finite-time hybrid projective synchronization criteria are derived. Two numerical examples are given to illustrate the effectiveness of the proposed method.

Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System

Directory of Open Access Journals (Sweden)

Y. D. Song

2013-01-01

Full Text Available This paper deals with an energy management of a hybrid power generation system. The proposed control strategy for the energy management is based on the combination of wavelet transform and neural network arithmetic. The hybrid system in this paper consists of an emulated wind turbine generator, PV panels, DC and AC loads, lithium ion battery, and super capacitor, which are all connected on a DC bus with unified DC voltage. The control strategy is responsible for compensating the difference between the generated power from the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into smoothed component and fast fluctuated component. In consideration of battery protection, the neural network is introduced to calculate the reference power of battery. Super capacitor (SC is controlled to regulate the DC bus voltage. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.

Fuzzy-Based Adaptive Hybrid Burst Assembly Technique for Optical Burst Switched Networks

Directory of Open Access Journals (Sweden)

Abubakar Muhammad Umaru

2014-01-01

Full Text Available The optical burst switching (OBS paradigm is perceived as an intermediate switching technology for future all-optical networks. Burst assembly that is the first process in OBS is the focus of this paper. In this paper, an intelligent hybrid burst assembly algorithm that is based on fuzzy logic is proposed. The new algorithm is evaluated against the traditional hybrid burst assembly algorithm and the fuzzy adaptive threshold (FAT burst assembly algorithm via simulation. Simulation results show that the proposed algorithm outperforms the hybrid and the FAT algorithms in terms of burst end-to-end delay, packet end-to-end delay, and packet loss ratio.

Network centrality measures and systemic risk: An application to the Turkish financial crisis

Science.gov (United States)

Kuzubaş, Tolga Umut; Ömercikoğlu, Inci; Saltoğlu, Burak

2014-07-01

In this paper, we analyze the performance of several network centrality measures in detecting systemically important financial institutions (SIFI) using data from the Turkish Interbank market during the financial crisis in 2000. We employ various network investigation tools such as volume, transactions, links, connectivity and reciprocity to gain a clearer picture of the network topology of the interbank market. We study the main borrower role of Demirbank in the crash of the banking system with network centrality measures which are extensively used in the network theory. This ex-post analysis of the crisis shows that centrality measures perform well in identifying and monitoring systemically important financial institutions which provide useful insights for financial regulations.

Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

Science.gov (United States)

Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057

OMNI: An optoelectronic multichannel network interface based on hybrid CMOS-SEED technology

Science.gov (United States)

Pinkston, Timothy M.

1996-11-01

This paper presents a hybrid CMOS-SEED multiprocessor network interface smart pixel design that implements a reservation-based channel control protocol for collisionless concurrent access to multiple optical interprocessor communication channels. An asynchronous optical token is used as the arbitration mechanism for reservation control instead of slotted access. This work demonstrates that complex network protocol functions can be implemented using optoelectronic smart pixel technology.

Multi-Radio Mobile Device in Role of Hybrid Node Between WiFi and LTE networks

Directory of Open Access Journals (Sweden)

Pavel Masek

2015-05-01

Full Text Available With the ubiquitous wireless network coverage, Machine-Type Communications (MTC is emerging to enable data transfers using devices/sensors without need for human interaction. In this paper we, we introduce a comprehensive simulation scenario for modeling and analysis for heterogeneous MTC. We demonstrate the most expected scenario of MTC communication using the IEEE 802.11 standard for direct communication between sensors and for transmitting data between individual sensor and Machine-Type Communication Gateway (MTCG. The MTCG represents the hybrid node serving as bridge between two heterogeneous networks (WiFi and LTE. Following the idea of hybrid node, two active interfaces must be implemented on this node together with mechanism for handling the incoming traffic (from WiFi network to LTE network. As a simulation tool, the Network Simulator 3 (NS-3 with implemented LTE/EPC Network Simulator (LENA framework was used. The major contribution of this paper therefore lies in the implementation of logic for interconnection of two heterogeneous networks in simulation environment NS-3.

Attack robustness and centrality of complex networks.

Directory of Open Access Journals (Sweden)

Swami Iyer

Full Text Available Many complex systems can be described by networks, in which the constituent components are represented by vertices and the connections between the components are represented by edges between the corresponding vertices. A fundamental issue concerning complex networked systems is the robustness of the overall system to the failure of its constituent parts. Since the degree to which a networked system continues to function, as its component parts are degraded, typically depends on the integrity of the underlying network, the question of system robustness can be addressed by analyzing how the network structure changes as vertices are removed. Previous work has considered how the structure of complex networks change as vertices are removed uniformly at random, in decreasing order of their degree, or in decreasing order of their betweenness centrality. Here we extend these studies by investigating the effect on network structure of targeting vertices for removal based on a wider range of non-local measures of potential importance than simply degree or betweenness. We consider the effect of such targeted vertex removal on model networks with different degree distributions, clustering coefficients and assortativity coefficients, and for a variety of empirical networks.

Role of centrality for the identification of influential spreaders in complex networks.

Science.gov (United States)

de Arruda, Guilherme Ferraz; Barbieri, André Luiz; Rodríguez, Pablo Martín; Rodrigues, Francisco A; Moreno, Yamir; Costa, Luciano da Fontoura

2014-09-01

The identification of the most influential spreaders in networks is important to control and understand the spreading capabilities of the system as well as to ensure an efficient information diffusion such as in rumorlike dynamics. Recent works have suggested that the identification of influential spreaders is not independent of the dynamics being studied. For instance, the key disease spreaders might not necessarily be so important when it comes to analyzing social contagion or rumor propagation. Additionally, it has been shown that different metrics (degree, coreness, etc.) might identify different influential nodes even for the same dynamical processes with diverse degrees of accuracy. In this paper, we investigate how nine centrality measures correlate with the disease and rumor spreading capabilities of the nodes in different synthetic and real-world (both spatial and nonspatial) networks. We also propose a generalization of the random walk accessibility as a new centrality measure and derive analytical expressions for the latter measure for simple network configurations. Our results show that for nonspatial networks, the k-core and degree centralities are the most correlated to epidemic spreading, whereas the average neighborhood degree, the closeness centrality, and accessibility are the most related to rumor dynamics. On the contrary, for spatial networks, the accessibility measure outperforms the rest of the centrality metrics in almost all cases regardless of the kind of dynamics considered. Therefore, an important consequence of our analysis is that previous studies performed in synthetic random networks cannot be generalized to the case of spatial networks.

Least-cost network evaluation of centralized and decentralized contributions to global electrification

International Nuclear Information System (INIS)

Levin, Todd; Thomas, Valerie M.

2012-01-01

The choice between centralized and decentralized electricity generation is examined for 150 countries as a function of population distribution, electricity consumption, transmission cost, and the cost difference between decentralized and centralized electricity generation. A network algorithm is developed to find the shortest centralized transmission network that spans a given fraction of the population in a country. The least-cost combination of centralized and decentralized electricity that serves the country is determined. Case studies of Botswana, Uganda, and Bangladesh illustrate situations that are more and less suited for decentralized electrification. Specific maps for centralized and decentralized generation are presented to show how the least-cost option varies with the relative costs of centralized and decentralized generation and transmission cost. Centralized and decentralized fractions are calculated for 150 countries. For most of the world's population, centralized electricity is the least-cost option. For a number of countries, particularly in Africa, substantial populations and regions may be most cost-effectively served by decentralized electricity. - Highlights: ► Centralized and decentralized electrification are compared for 150 countries. ► A cost-optimized network algorithm finds the least-cost electrification system. ► Least-cost infrastructures combine centralized and decentralized portions. ► For most people, centralized electricity is cheapest option. ► In much of Africa, decentralized electricity may be cheaper than centralized.

Random walk centrality for temporal networks

International Nuclear Information System (INIS)

Rocha, Luis E C; Masuda, Naoki

2014-01-01

Nodes can be ranked according to their relative importance within a network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks under periodic boundary conditions that we call TempoRank. It is known that, in static networks, the stationary density of the random walk is proportional to the degree or the strength of a node. In contrast, we find that, in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network, a weighted and directed network explicitly constructed from the original sequence of transition matrices. The stationary density also depends on the sojourn probability q, which regulates the tendency of the walker to stay in the node, and on the temporal resolution of the data. We apply our method to human interaction networks and show that although it is important for a node to be connected to another node with many random walkers (one of the principles of the PageRank) at the right moment, this effect is negligible in practice when the time order of link activation is included. (paper)

Random walk centrality for temporal networks

Science.gov (United States)

Rocha, Luis E. C.; Masuda, Naoki

2014-06-01

Nodes can be ranked according to their relative importance within a network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks under periodic boundary conditions that we call TempoRank. It is known that, in static networks, the stationary density of the random walk is proportional to the degree or the strength of a node. In contrast, we find that, in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network, a weighted and directed network explicitly constructed from the original sequence of transition matrices. The stationary density also depends on the sojourn probability q, which regulates the tendency of the walker to stay in the node, and on the temporal resolution of the data. We apply our method to human interaction networks and show that although it is important for a node to be connected to another node with many random walkers (one of the principles of the PageRank) at the right moment, this effect is negligible in practice when the time order of link activation is included.

Impact of delays on the synchronization transitions of modular neuronal networks with hybrid synapses

Science.gov (United States)

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

2013-09-01

The combined effects of the information transmission delay and the ratio of the electrical and chemical synapses on the synchronization transitions in the hybrid modular neuronal network are investigated in this paper. Numerical results show that the synchronization of neuron activities can be either promoted or destroyed as the information transmission delay increases, irrespective of the probability of electrical synapses in the hybrid-synaptic network. Interestingly, when the number of the electrical synapses exceeds a certain level, further increasing its proportion can obviously enhance the spatiotemporal synchronization transitions. Moreover, the coupling strength has a significant effect on the synchronization transition. The dominated type of the synapse always has a more profound effect on the emergency of the synchronous behaviors. Furthermore, the results of the modular neuronal network structures demonstrate that excessive partitioning of the modular network may result in the dramatic detriment of neuronal synchronization. Considering that information transmission delays are inevitable in intra- and inter-neuronal networks communication, the obtained results may have important implications for the exploration of the synchronization mechanism underlying several neural system diseases such as Parkinson's Disease.

Combined cycle solar central receiver hybrid power system study. Final technical report. Volume II

Energy Technology Data Exchange (ETDEWEB)

None

1979-11-01

This study develops the conceptual design for a commercial-scale (nominal 100 MWe) central receiver solar/fossil fuel hybrid power system with combined cycle energy conversion. A near-term, metallic heat pipe receiver and an advanced ceramic tube receiver hybrid system are defined through parametric and market potential analyses. Comparative evaluations of the cost of power generation, the fuel displacement potential, and the technological readiness of these two systems indicate that the near-term hybrid system has better potential for commercialization by 1990. Based on the assessment of the conceptual design, major cost and performance improvements are projected for the near-term system. Constraints preventing wide-spread use were not identified. Energy storage is not required for this system and analyses show no economic advantages with energy storage provisions. It is concluded that the solar hybrid system is a cost effective alternative to conventional gas turbines and combined cycle generating plants, and has potential for intermediate-load market penetration at 15% annual fuel escalation rate. Due to their flexibility, simple solar/nonsolar interfacing, and short startup cycles, these hybrid plants have significant operating advantages. Utility company comments suggest that hybrid power systems will precede stand-alone solar plants.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

Science.gov (United States)

Falat, Lukas; Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

Recurrent neural network based hybrid model for reconstructing gene regulatory network.

Science.gov (United States)

Raza, Khalid; Alam, Mansaf

2016-10-01

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Impacts of hybrid synapses on the noise-delayed decay in scale-free neural networks

International Nuclear Information System (INIS)

Yilmaz, Ergin

2014-01-01

Highlights: • We investigate the NDD phenomenon in a hybrid scale-free network. • Electrical synapses are more impressive on the emergence of NDD. • Electrical synapses are more efficient in suppressing of the NDD. • Average degree has two opposite effects on the appearance time of the first spike. - Abstract: We study the phenomenon of noise-delayed decay in a scale-free neural network consisting of excitable FitzHugh–Nagumo neurons. In contrast to earlier works, where only electrical synapses are considered among neurons, we primarily examine the effects of hybrid synapses on the noise-delayed decay in this study. We show that the electrical synaptic coupling is more impressive than the chemical coupling in determining the appearance time of the first-spike and more efficient on the mitigation of the delay time in the detection of a suprathreshold input signal. We obtain that hybrid networks including inhibitory chemical synapses have higher signal detection capabilities than those of including excitatory ones. We also find that average degree exhibits two different effects, which are strengthening and weakening the noise-delayed decay effect depending on the noise intensity

Modeling infection transmission in primate networks to predict centrality-based risk.

Science.gov (United States)

Romano, Valéria; Duboscq, Julie; Sarabian, Cécile; Thomas, Elodie; Sueur, Cédric; MacIntosh, Andrew J J

2016-07-01

Social structure can theoretically regulate disease risk by mediating exposure to pathogens via social proximity and contact. Investigating the role of central individuals within a network may help predict infectious agent transmission as well as implement disease control strategies, but little is known about such dynamics in real primate networks. We combined social network analysis and a modeling approach to better understand transmission of a theoretical infectious agent in wild Japanese macaques, highly social animals which form extended but highly differentiated social networks. We collected focal data from adult females living on the islands of Koshima and Yakushima, Japan. Individual identities as well as grooming networks were included in a Markov graph-based simulation. In this model, the probability that an individual will transmit an infectious agent depends on the strength of its relationships with other group members. Similarly, its probability of being infected depends on its relationships with already infected group members. We correlated: (i) the percentage of subjects infected during a latency-constrained epidemic; (ii) the mean latency to complete transmission; (iii) the probability that an individual is infected first among all group members; and (iv) each individual's mean rank in the chain of transmission with different individual network centralities (eigenvector, strength, betweenness). Our results support the hypothesis that more central individuals transmit infections in a shorter amount of time and to more subjects but also become infected more quickly than less central individuals. However, we also observed that the spread of infectious agents on the Yakushima network did not always differ from expectations of spread on random networks. Generalizations about the importance of observed social networks in pathogen flow should thus be made with caution, since individual characteristics in some real world networks appear less relevant than

HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

International Nuclear Information System (INIS)

Marchetti, Luca; Priami, Corrado; Thanh, Vo Hong

2016-01-01

This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.

HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

Energy Technology Data Exchange (ETDEWEB)

Marchetti, Luca, E-mail: marchetti@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy); Priami, Corrado, E-mail: priami@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy); University of Trento, Department of Mathematics (Italy); Thanh, Vo Hong, E-mail: vo@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy)

2016-07-15

This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.

Hybrid centralized-distributed power conditioning system for thermoelectric generator with high energy efficiency

DEFF Research Database (Denmark)

Wu, Hongfei; Sun, Kai; Chen, Min

2013-01-01

the proposed system, which benefits for implementing high MPPT efficiency and high conversion efficiency simultaneously. A hybrid MPPT control strategy is proposed for this HCD power conditioning system. The characteristics, circuit implementation and operation principles of the proposed system are presented......-distributed (HCD) power conditioning system for TEG and its control strategy are proposed in this paper. The HCD power conditioning system is composed by a centralized power conversion stage and multiple distributed power conversion stages. Most of the power is processed by the centralized power conversion stage...

A centralized informatics infrastructure for the National Institute on Drug Abuse Clinical Trials Network

Science.gov (United States)

Pan, Jeng-Jong; Nahm, Meredith; Wakim, Paul; Cushing, Carol; Poole, Lori; Tai, Betty; Pieper, Carl F.

2009-01-01

Background Clinical trial networks were created to provide a sustaining infrastructure for the conduct of multisite clinical trials. As such, they must withstand changes in membership. Centralization of infrastructure including knowledge management, portfolio management, information management, process automation, work policies, and procedures in clinical research networks facilitates consistency and ultimately research. Purpose In 2005, the National Institute on Drug Abuse (NIDA) Clinical Trials Network (CTN) transitioned from a distributed data management model to a centralized informatics infrastructure to support the network’s trial activities and administration. We describe the centralized informatics infrastructure and discuss our challenges to inform others considering such an endeavor. Methods During the migration of a clinical trial network from a decentralized to a centralized data center model, descriptive data were captured and are presented here to assess the impact of centralization. Results We present the framework for the informatics infrastructure and evaluative metrics. The network has decreased the time from last patient-last visit to database lock from an average of 7.6 months to 2.8 months. The average database error rate decreased from 0.8% to 0.2%, with a corresponding decrease in the interquartile range from 0.04%–1.0% before centralization to 0.01%–0.27% after centralization. Centralization has provided the CTN with integrated trial status reporting and the first standards-based public data share. A preliminary cost-benefit analysis showed a 50% reduction in data management cost per study participant over the life of a trial. Limitations A single clinical trial network comprising addiction researchers and community treatment programs was assessed. The findings may not be applicable to other research settings. Conclusions The identified informatics components provide the information and infrastructure needed for our clinical trial

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

Directory of Open Access Journals (Sweden)

Asmau M. Ahmed

2017-07-01

Full Text Available Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1 The mixing process should occur at macroscopic level and (2 Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model. Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms.

Benefiting from networks by occupying central positions: an empirical study of the Taiwan health care industry.

Science.gov (United States)

Peng, Tzu-Ju Ann; Lo, Fang-Yi; Lin, Chin-Shien; Yu, Chwo-Ming Joseph

2006-01-01

At issue is whether network resources imply some resources available to all members in networks or available only to those occupying structurally central positions in networks. In this article, two conceptual models, the additive and interaction models of the firm, are empirically tested regarding the impact of hospital resources, network resources, and centrality on hospital performance in the Taiwan health care industry. The results demonstrate that: (1) in the additive model, hospital resources and centrality independently affect performance, whereas network resources do not; and (2) no evidence supports the interaction effect of centrality and resources on performance. Based on our findings in Taiwanese practices, the extent to which the resources are acquired externally from networks, we suggest that while adopting interorganizational strategies, hospitals should clearly identify those important resources that reside in-house and those transferred from network partners. How hospitals access resources from central positions is more important than what network resources can hospitals acquire from networks. Hospitals should improve performance by exploiting its in-house resources rather than obtaining network resources externally. In addition, hospitals should not only invest in hospital resources for better performance but should also move to central positions in networks to benefit from collaborations.

Distributed Hybrid Scheduling in Multi-Cloud Networks using Conflict Graphs

KAUST Repository

Douik, Ahmed

2017-09-07

Recent studies on cloud-radio access networks assume either signal-level or scheduling-level coordination. This paper considers a hybrid coordinated scheme as a means to benefit from both policies. Consider the downlink of a multi-cloud radio access network, where each cloud is connected to several base-stations (BSs) via high capacity links, and, therefore, allows for joint signal processing within the cloud transmission. Across the multiple clouds, however, only scheduling-level coordination is permitted, as low levels of backhaul communication are feasible. The frame structure of every BS is composed of various time/frequency blocks, called power-zones (PZs), which are maintained at a fixed power level. The paper addresses the problem of maximizing a network-wide utility by associating users to clouds and scheduling them to the PZs, under the practical constraints that each user is scheduled to a single cloud at most, but possibly to many BSs within the cloud, and can be served by one or more distinct PZs within the BSs’ frame. The paper solves the problem using graph theory techniques by constructing the conflict graph. The considered scheduling problem is, then, shown to be equivalent to a maximum-weight independent set problem in the constructed graph, which can be solved using efficient techniques. The paper then proposes solving the problem using both optimal and heuristic algorithms that can be implemented in a distributed fashion across the network. The proposed distributed algorithms rely on the well-chosen structure of the constructed conflict graph utilized to solve the maximum-weight independent set problem. Simulation results suggest that the proposed optimal and heuristic hybrid scheduling strategies provide appreciable gain as compared to the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.

What makes you more central? : antecedents of changes in betweenness-centrality in technology-based alliance networks

NARCIS (Netherlands)

Gilsing, V.A.; Cloodt, M.M.A.H.; Bertrand-Cloodt, D.A.M.

2016-01-01

Although central network positions have been associated with above average performance effects, an important void that still remains is how firms come to occupy a more central position in the first place. Whereas recently made exogenous explanations have shed some more light on aggregate changes in

Identifying Central Bank Liquidity Super-Spreaders in Interbank Funds Networks

NARCIS (Netherlands)

Leon Rincon, C.E.; Machado, C.; Sarmiento Paipilla, N.M.

2015-01-01

We model the allocation of central bank liquidity among the participants of the interbank market by using network analysis’ metrics. Our analytical framework considers that a super-spreader simultaneously excels at receiving (borrowing) and distributing (lending) central bank’s liquidity for the

Overview of hybrid fiber-coaxial network deployment in the deregulated UK environment

Science.gov (United States)

Cox, Alan L.

1995-11-01

Cable operators in the U.K. enjoy unprecedented license to construct networks and operate cable TV and telecommunications services within their franchise areas. In general, operators have built hybrid-fiber-coax (HFC) networks for cable TV in parallel with fiber-copper-pair networks for telephony. The commonly used network architectures are reviewed, together with their present and future capacities. Despite this dual-technology approach, there is considerable interest in the integration of telephony services onto the HFC network and the development of new interactive services for which HFC may be more suitable than copper pairs. Certain technological and commercial developments may have considerable significance for HFC networks and their operators. These include the digitalization of TV distribution and the rising demand for high-rate digital access lines. Possible scenarios are discussed.

Spatio-temporal networks: reachability, centrality and robustness.

Science.gov (United States)

Williams, Matthew J; Musolesi, Mirco

2016-06-01

Recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks.

Logarithmic r-θ mapping for hybrid optical neural network filter for multiple objects recognition within cluttered scenes

Science.gov (United States)

Kypraios, Ioannis; Young, Rupert C. D.; Chatwin, Chris R.; Birch, Phil M.

2009-04-01

θThe window unit in the design of the complex logarithmic r-θ mapping for hybrid optical neural network filter can allow multiple objects of the same class to be detected within the input image. Additionally, the architecture of the neural network unit of the complex logarithmic r-θ mapping for hybrid optical neural network filter becomes attractive for accommodating the recognition of multiple objects of different classes within the input image by modifying the output layer of the unit. We test the overall filter for multiple objects of the same and of different classes' recognition within cluttered input images and video sequences of cluttered scenes. Logarithmic r-θ mapping for hybrid optical neural network filter is shown to exhibit with a single pass over the input data simultaneously in-plane rotation, out-of-plane rotation, scale, log r-θ map translation and shift invariance, and good clutter tolerance by recognizing correctly the different objects within the cluttered scenes. We record in our results additional extracted information from the cluttered scenes about the objects' relative position, scale and in-plane rotation.

A hybrid model based on neural networks for biomedical relation extraction.

Science.gov (United States)

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Zhang, Shaowu; Sun, Yuanyuan; Yang, Liang

2018-05-01

Biomedical relation extraction can automatically extract high-quality biomedical relations from biomedical texts, which is a vital step for the mining of biomedical knowledge hidden in the literature. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two major neural network models for biomedical relation extraction. Neural network-based methods for biomedical relation extraction typically focus on the sentence sequence and employ RNNs or CNNs to learn the latent features from sentence sequences separately. However, RNNs and CNNs have their own advantages for biomedical relation extraction. Combining RNNs and CNNs may improve biomedical relation extraction. In this paper, we present a hybrid model for the extraction of biomedical relations that combines RNNs and CNNs. First, the shortest dependency path (SDP) is generated based on the dependency graph of the candidate sentence. To make full use of the SDP, we divide the SDP into a dependency word sequence and a relation sequence. Then, RNNs and CNNs are employed to automatically learn the features from the sentence sequence and the dependency sequences, respectively. Finally, the output features of the RNNs and CNNs are combined to detect and extract biomedical relations. We evaluate our hybrid model using five public (protein-protein interaction) PPI corpora and a (drug-drug interaction) DDI corpus. The experimental results suggest that the advantages of RNNs and CNNs in biomedical relation extraction are complementary. Combining RNNs and CNNs can effectively boost biomedical relation extraction performance. Copyright © 2018 Elsevier Inc. All rights reserved.

Resilient backhaul network design using hybrid radio/free-space optical technology

KAUST Repository

Douik, Ahmed

2016-07-26

The radio-frequency (RF) technology is a scalable solution for the backhaul planning. However, its performance is limited in terms of data rate and latency. Free Space Optical (FSO) backhaul, on the other hand, offers a higher data rate but is sensitive to weather conditions. To combine the advantages of RF and FSO backhauls, this paper proposes a cost-efficient backhaul network using the hybrid RF/FSO technology. To ensure a resilient backhaul, the paper imposes a given degree of redundancy by connecting each node through K link-disjoint paths so as to cope with potential link failures. Hence, the network planning problem considered in this paper is the one of minimizing the total deployment cost by choosing the appropriate link type, i.e., either hybrid RF/FSO or optical fiber (OF), between each couple of base-stations while guaranteeing K link-disjoint connections, a data rate target, and a reliability threshold. The paper solves the problem using graph theory techniques. It reformulates the problem as a maximum weight clique problem in the planning graph, under a specified realistic assumption about the cost of OF and hybrid RF/FSO links. Simulation results show the cost of the different planning and suggest that the proposed heuristic solution has a close-to-optimal performance for a significant gain in computation complexity. © 2016 IEEE.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

Directory of Open Access Journals (Sweden)

Lukas Falat

2016-01-01

Full Text Available This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

Science.gov (United States)

Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450

DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.

Science.gov (United States)

Mistry, Divya; Wise, Roger P; Dickerson, Julie A

2017-01-01

Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be

Hybrid Scheduling/Signal-Level Coordination in the Downlink of Multi-Cloud Radio-Access Networks

KAUST Repository

Douik, Ahmed; Dahrouj, Hayssam; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

2016-01-01

results suggest that the proposed hybrid scheduling strategy provides appreciable gain as compared to the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.

Agent-based modeling of the energy network for hybrid cars

International Nuclear Information System (INIS)

Gonzalez de Durana, José María; Barambones, Oscar; Kremers, Enrique; Varga, Liz

2015-01-01

Highlights: • An approach to represent and calculate multicarrier energy networks has been developed. • It provides a modeling method based on agents, for multicarrier energy networks. • It allows the system representation on a single sheet. • Energy flows circulating in the system can be observed dynamically during simulation. • The method is technology independent. - Abstract: Studies in complex energy networks devoted to the modeling of electrical power grids, were extended in previous work, where a computational multi-layered ontology, implemented using agent-based methods, was adopted. This structure is compatible with recently introduced Multiplex Networks which using Multi-linear Algebra generalize some of classical results for single-layer networks, to multilayer networks in steady state. Static results do not assist overly in understanding dynamic networks in which the values of the variables in the nodes and edges can change suddenly, driven by events, and even where new nodes or edges may appear or disappear, also because of other events. To address this gap, a computational agent-based model is developed to extend the multi-layer and multiplex approaches. In order to demonstrate the benefits of a dynamical extension, a model of the energy network in a hybrid car is presented as a case study

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

Directory of Open Access Journals (Sweden)

Tong Qiao

2017-11-01

Full Text Available Centrality is one of the most studied concepts in network analysis. Despite an abundance of methods for measuring centrality in social networks has been proposed, each approach exclusively characterizes limited parts of what it implies for an actor to be “vital” to the network. In this paper, a novel mechanism is proposed to quantitatively measure centrality using the re-defined entropy centrality model, which is based on decompositions of a graph into subgraphs and analysis on the entropy of neighbor nodes. By design, the re-defined entropy centrality which describes associations among node pairs and captures the process of influence propagation can be interpreted explained as a measure of actor potential for communication activity. We evaluate the efficiency of the proposed model by using four real-world datasets with varied sizes and densities and three artificial networks constructed by models including Barabasi-Albert, Erdos-Renyi and Watts-Stroggatz. The four datasets are Zachary’s karate club, USAir97, Collaboration network and Email network URV respectively. Extensive experimental results prove the effectiveness of the proposed method.

A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process.

Science.gov (United States)

Choi, D J; Park, H

2001-11-01

For control and automation of biological treatment processes, lack of reliable on-line sensors to measure water quality parameters is one of the most important problems to overcome. Many parameters cannot be measured directly with on-line sensors. The accuracy of existing hardware sensors is also not sufficient and maintenance problems such as electrode fouling often cause trouble. This paper deals with the development of software sensor techniques that estimate the target water quality parameter from other parameters using the correlation between water quality parameters. We focus our attention on the preprocessing of noisy data and the selection of the best model feasible to the situation. Problems of existing approaches are also discussed. We propose a hybrid neural network as a software sensor inferring wastewater quality parameter. Multivariate regression, artificial neural networks (ANN), and a hybrid technique that combines principal component analysis as a preprocessing stage are applied to data from industrial wastewater processes. The hybrid ANN technique shows an enhancement of prediction capability and reduces the overfitting problem of neural networks. The result shows that the hybrid ANN technique can be used to extract information from noisy data and to describe the nonlinearity of complex wastewater treatment processes.

The Effects of Social Network Centrality on Group Satisfaction

National Research Council Canada - National Science Library

Choi, Peter M

2007-01-01

.... To determine the relationship between social network centrality and work group satisfaction, a longitudinal field study was conducted on 440 active duty enlisted military members in a leadership...

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata.

Science.gov (United States)

Chen, Yangzhou; Guo, Yuqi; Wang, Ying

2017-03-29

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research.

Cross Layer Analysis of P2MP Hybrid FSO/RF Network

KAUST Repository

Rakia, Tamer; Gebali, Fayez; Yang, Hong-Chuan; Alouini, Mohamed-Slim

2017-01-01

This paper presents and analyzes a point-tomultipoint (P2MP) network that uses a number of freespace optical (FSO) links for data transmission from the central node to the different remote nodes of the network. A common backup radio frequency (RF

Cognitively Central Actors and Their Personal Networks in an Energy Efficiency Training Program

Science.gov (United States)

Hytönen, Kaisa; Palonen, Tuire; Hakkarainen, Kai

2014-01-01

This article aims to examine cognitively central actors and their personal networks in the emerging field of energy efficiency. Cognitively central actors are frequently sought for professional advice by other actors and, therefore, they are positioned in the middle of a social network. They often are important knowledge resources, especially in…

Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network.

Science.gov (United States)

Wang, K W; Deng, C; Li, J P; Zhang, Y Y; Li, X Y; Wu, M C

2017-04-01

Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

Towards Effective Network Intrusion Detection: A Hybrid Model Integrating Gini Index and GBDT with PSO

Directory of Open Access Journals (Sweden)

Longjie Li

2018-01-01

Full Text Available In order to protect computing systems from malicious attacks, network intrusion detection systems have become an important part in the security infrastructure. Recently, hybrid models that integrating several machine learning techniques have captured more attention of researchers. In this paper, a novel hybrid model was proposed with the purpose of detecting network intrusion effectively. In the proposed model, Gini index is used to select the optimal subset of features, the gradient boosted decision tree (GBDT algorithm is adopted to detect network attacks, and the particle swarm optimization (PSO algorithm is utilized to optimize the parameters of GBDT. The performance of the proposed model is experimentally evaluated in terms of accuracy, detection rate, precision, F1-score, and false alarm rate using the NSL-KDD dataset. Experimental results show that the proposed model is superior to the compared methods.

A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks

Science.gov (United States)

Figueiredo, Carlos M. S.; Nakamura, Eduardo F.; Loureiro, Antonio A. F.

2009-01-01

Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption. PMID:22423207

Traffic sharing algorithms for hybrid mobile networks

Science.gov (United States)

Arcand, S.; Murthy, K. M. S.; Hafez, R.

1995-01-01

In a hybrid (terrestrial + satellite) mobile personal communications networks environment, a large size satellite footprint (supercell) overlays on a large number of smaller size, contiguous terrestrial cells. We assume that the users have either a terrestrial only single mode terminal (SMT) or a terrestrial/satellite dual mode terminal (DMT) and the ratio of DMT to the total terminals is defined gamma. It is assumed that the call assignments to and handovers between terrestrial cells and satellite supercells take place in a dynamic fashion when necessary. The objectives of this paper are twofold, (1) to propose and define a class of traffic sharing algorithms to manage terrestrial and satellite network resources efficiently by handling call handovers dynamically, and (2) to analyze and evaluate the algorithms by maximizing the traffic load handling capability (defined in erl/cell) over a wide range of terminal ratios (gamma) given an acceptable range of blocking probabilities. Two of the algorithms (G & S) in the proposed class perform extremely well for a wide range of gamma.

A new centrality measure for identifying influential nodes in social networks

Science.gov (United States)

Rhouma, Delel; Ben Romdhane, Lotfi

2018-04-01

The identification of central nodes has been a key problem in the field of social network analysis. In fact, it is a measure that accounts the popularity or the visibility of an actor within a network. In order to capture this concept, various measures, either sample or more elaborate, has been developed. Nevertheless, many of "traditional" measures are not designed to be applicable to huge data. This paper sets out a new node centrality index suitable for large social network. It uses the amount of the neighbors of a node and connections between them to characterize a "pivot" node in the graph. We presented experimental results on real data sets which show the efficiency of our proposal.

From social network (centralized vs. decentralized) to collective decision-making (unshared vs. shared consensus).

Science.gov (United States)

Sueur, Cédric; Deneubourg, Jean-Louis; Petit, Odile

2012-01-01

Relationships we have with our friends, family, or colleagues influence our personal decisions, as well as decisions we make together with others. As in human beings, despotism and egalitarian societies seem to also exist in animals. While studies have shown that social networks constrain many phenomena from amoebae to primates, we still do not know how consensus emerges from the properties of social networks in many biological systems. We created artificial social networks that represent the continuum from centralized to decentralized organization and used an agent-based model to make predictions about the patterns of consensus and collective movements we observed according to the social network. These theoretical results showed that different social networks and especially contrasted ones--star network vs. equal network--led to totally different patterns. Our model showed that, by moving from a centralized network to a decentralized one, the central individual seemed to lose its leadership in the collective movement's decisions. We, therefore, showed a link between the type of social network and the resulting consensus. By comparing our theoretical data with data on five groups of primates, we confirmed that this relationship between social network and consensus also appears to exist in animal societies.

Optimisation of Software-Defined Networks Performance Using a Hybrid Intelligent System

Directory of Open Access Journals (Sweden)

Ann Sabih

2017-06-01

Full Text Available This paper proposes a novel intelligent technique that has been designed to optimise the performance of Software Defined Networks (SDN. The proposed hybrid intelligent system has employed integration of intelligence-based optimisation approaches with the artificial neural network. These heuristic optimisation methods include Genetic Algorithms (GA and Particle Swarm Optimisation (PSO. These methods were utilised separately in order to select the best inputs to maximise SDN performance. In order to identify SDN behaviour, the neural network model is trained and applied. The maximal optimisation approach has been identified using an analytical approach that considered SDN performance and the computational time as objective functions. Initially, the general model of the neural network was tested with unseen data before implementing the model using GA and PSO to determine the optimal performance of SDN. The results showed that the SDN represented by Artificial Neural Network ANN, and optmised by PSO, generated a better configuration with regards to computational efficiency and performance index.

Hybrid neural network model for simulating sorbitol synthesis by glucose-fructose oxidoreductase in Zymomonas mobilis CP4

Directory of Open Access Journals (Sweden)

Bravo S.

2004-01-01

Full Text Available A hybrid neural network model for simulating the process of enzymatic reduction of fructose to sorbitol process catalyzed by glucose-fructose oxidoreductase in Zymomonas mobilis CP4 is presented. Data used to derive and validate the model was obtained from experiments carried out under different conditions of pH, temperature and concentrations of both substrates (glucose and fructose involved in the reaction. Sonicated and lyophilized cells were used as source of the enzyme. The optimal pH for sorbitol synthesis at 30º C is 6.5. For a value of pH of 6, the optimal temperature is 35º C. The neural network in the model computes the value of the kinetic relationship. The hybrid neural network model is able to simulate changes in the substrates and product concentrations during sorbitol synthesis under pH and temperature conditions ranging between 5 and 7.5 and 25 and 40º C, respectively. Under these conditions the rate of sorbitol synthesis shows important differences. Values computed using the hybrid neural network model have an average error of 1.7·10-3 mole.

Cost- and reliability-oriented aggregation point association in long-term evolution and passive optical network hybrid access infrastructure for smart grid neighborhood area network

Science.gov (United States)

Cheng, Xiao; Feng, Lei; Zhou, Fanqin; Wei, Lei; Yu, Peng; Li, Wenjing

2018-02-01

With the rapid development of the smart grid, the data aggregation point (AP) in the neighborhood area network (NAN) is becoming increasingly important for forwarding the information between the home area network and wide area network. Due to limited budget, it is unable to use one-single access technology to meet the ongoing requirements on AP coverage. This paper first introduces the wired and wireless hybrid access network with the integration of long-term evolution (LTE) and passive optical network (PON) system for NAN, which allows a good trade-off among cost, flexibility, and reliability. Then, based on the already existing wireless LTE network, an AP association optimization model is proposed to make the PON serve as many APs as possible, considering both the economic efficiency and network reliability. Moreover, since the features of the constraints and variables of this NP-hard problem, a hybrid intelligent optimization algorithm is proposed, which is achieved by the mixture of the genetic, ant colony and dynamic greedy algorithm. By comparing with other published methods, simulation results verify the performance of the proposed method in improving the AP coverage and the performance of the proposed algorithm in terms of convergence.

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

Science.gov (United States)

Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-10-06

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

Convergence dynamics of hybrid bidirectional associative memory neural networks with distributed delays

International Nuclear Information System (INIS)

Liao Xiaofeng; Wong, K.-W.; Yang Shizhong

2003-01-01

In this Letter, the characteristics of the convergence dynamics of hybrid bidirectional associative memory neural networks with distributed transmission delays are studied. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the Lyapunov functionals are constructed and the generalized Halanay-type inequalities are employed to derive the delay-independent sufficient conditions under which the networks converge exponentially to the equilibria associated with temporally uniform external inputs. Some examples are given to illustrate the correctness of our results

Structural covariance network centrality in maltreated youth with posttraumatic stress disorder.

Science.gov (United States)

Sun, Delin; Peverill, Matthew R; Swanson, Chelsea S; McLaughlin, Katie A; Morey, Rajendra A

2018-03-01

Childhood maltreatment is associated with posttraumatic stress disorder (PTSD) and elevated rates of adolescent and adult psychopathology including major depression, bipolar disorder, substance use disorders, and other medical comorbidities. Gray matter volume changes have been found in maltreated youth with (versus without) PTSD. However, little is known about the alterations of brain structural covariance network topology derived from cortical thickness in maltreated youth with PTSD. High-resolution T1-weighted magnetic resonance imaging scans were from demographically matched maltreated youth with PTSD (N = 24), without PTSD (N = 64), and non-maltreated healthy controls (n = 67). Cortical thickness data from 148 cortical regions was entered into interregional partial correlation analyses across participants. The supra-threshold correlations constituted connections in a structural brain network derived from four types of centrality measures (degree, betweenness, closeness, and eigenvector) estimated network topology and the importance of nodes. Between-group differences were determined by permutation testing. Maltreated youth with PTSD exhibited larger centrality in left anterior cingulate cortex than the other two groups, suggesting cortical network topology specific to maltreated youth with PTSD. Moreover, maltreated youth with versus without PTSD showed smaller centrality in right orbitofrontal cortex, suggesting that this may represent a vulnerability factor to PTSD following maltreatment. Longitudinal follow-up of the present results will help characterize the role that altered centrality plays in vulnerability and resilience to PTSD following childhood maltreatment. Copyright © 2017. Published by Elsevier Ltd.

Travelling in time with networks: Revealing present day hybridization versus ancestral polymorphism between two species of brown algae, Fucus vesiculosus and F. spiralis

Directory of Open Access Journals (Sweden)

Pearson Gareth A

2011-01-01

Full Text Available Abstract Background Hybridization or divergence between sympatric sister species provides a natural laboratory to study speciation processes. The shared polymorphism in sister species may either be ancestral or derive from hybridization, and the accuracy of analytic methods used thus far to derive convincing evidence for the occurrence of present day hybridization is largely debated. Results Here we propose the application of network analysis to test for the occurrence of present day hybridization between the two species of brown algae Fucus spiralis and F. vesiculosus. Individual-centered networks were analyzed on the basis of microsatellite genotypes from North Africa to the Pacific American coast, through the North Atlantic. Two genetic distances integrating different time steps were used, the Rozenfeld (RD; based on alleles divergence and the Shared Allele (SAD; based on alleles identity distances. A diagnostic level of genotype divergence and clustering of individuals from each species was obtained through RD while screening for exchanges through putative hybridization was facilitated using SAD. Intermediate individuals linking both clusters on the RD network were those sampled at the limits of the sympatric zone in Northwest Iberia. Conclusion These results suggesting rare hybridization were confirmed by simulation of hybrids and F2 with directed backcrosses. Comparison with the Bayesian method STRUCTURE confirmed the usefulness of both approaches and emphasized the reliability of network analysis to unravel and study hybridization

An attempt to understand glioma stem cell biology through centrality analysis of a protein interaction network.

Science.gov (United States)

Mallik, Mrinmay Kumar

2018-02-07

Biological networks can be analyzed using "Centrality Analysis" to identify the more influential nodes and interactions in the network. This study was undertaken to create and visualize a biological network comprising of protein-protein interactions (PPIs) amongst proteins which are preferentially over-expressed in glioma cancer stem cell component (GCSC) of glioblastomas as compared to the glioma non-stem cancer cell (GNSC) component and then to analyze this network through centrality analyses (CA) in order to identify the essential proteins in this network and their interactions. In addition, this study proposes a new centrality analysis method pertaining exclusively to transcription factors (TFs) and interactions amongst them. Moreover the relevant molecular functions, biological processes and biochemical pathways amongst these proteins were sought through enrichment analysis. A protein interaction network was created using a list of proteins which have been shown to be preferentially expressed or over-expressed in GCSCs isolated from glioblastomas as compared to the GNSCs. This list comprising of 38 proteins, created using manual literature mining, was submitted to the Reactome FIViz tool, a web based application integrated into Cytoscape, an open source software platform for visualizing and analyzing molecular interaction networks and biological pathways to produce the network. This network was subjected to centrality analyses utilizing ranked lists of six centrality measures using the FIViz application and (for the first time) a dedicated centrality analysis plug-in ; CytoNCA. The interactions exclusively amongst the transcription factors were nalyzed through a newly proposed centrality analysis method called "Gene Expression Associated Degree Centrality Analysis (GEADCA)". Enrichment analysis was performed using the "network function analysis" tool on Reactome. The CA was able to identify a small set of proteins with consistently high centrality ranks that

From social network (centralized vs. decentralized to collective decision-making (unshared vs. shared consensus.

Directory of Open Access Journals (Sweden)

Cédric Sueur

Full Text Available Relationships we have with our friends, family, or colleagues influence our personal decisions, as well as decisions we make together with others. As in human beings, despotism and egalitarian societies seem to also exist in animals. While studies have shown that social networks constrain many phenomena from amoebae to primates, we still do not know how consensus emerges from the properties of social networks in many biological systems. We created artificial social networks that represent the continuum from centralized to decentralized organization and used an agent-based model to make predictions about the patterns of consensus and collective movements we observed according to the social network. These theoretical results showed that different social networks and especially contrasted ones--star network vs. equal network--led to totally different patterns. Our model showed that, by moving from a centralized network to a decentralized one, the central individual seemed to lose its leadership in the collective movement's decisions. We, therefore, showed a link between the type of social network and the resulting consensus. By comparing our theoretical data with data on five groups of primates, we confirmed that this relationship between social network and consensus also appears to exist in animal societies.

Neural network control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle

Science.gov (United States)

Harmon, Frederick G.

2005-11-01

Parallel hybrid-electric propulsion systems would be beneficial for small unmanned aerial vehicles (UAVs) used for military, homeland security, and disaster-monitoring missions. The benefits, due to the hybrid and electric-only modes, include increased time-on-station and greater range as compared to electric-powered UAVs and stealth modes not available with gasoline-powered UAVs. This dissertation contributes to the research fields of small unmanned aerial vehicles, hybrid-electric propulsion system control, and intelligent control. A conceptual design of a small UAV with a parallel hybrid-electric propulsion system is provided. The UAV is intended for intelligence, surveillance, and reconnaissance (ISR) missions. A conceptual design reveals the trade-offs that must be considered to take advantage of the hybrid-electric propulsion system. The resulting hybrid-electric propulsion system is a two-point design that includes an engine primarily sized for cruise speed and an electric motor and battery pack that are primarily sized for a slower endurance speed. The electric motor provides additional power for take-off, climbing, and acceleration and also serves as a generator during charge-sustaining operation or regeneration. The intelligent control of the hybrid-electric propulsion system is based on an instantaneous optimization algorithm that generates a hyper-plane from the nonlinear efficiency maps for the internal combustion engine, electric motor, and lithium-ion battery pack. The hyper-plane incorporates charge-depletion and charge-sustaining strategies. The optimization algorithm is flexible and allows the operator/user to assign relative importance between the use of gasoline, electricity, and recharging depending on the intended mission. A MATLAB/Simulink model was developed to test the control algorithms. The Cerebellar Model Arithmetic Computer (CMAC) associative memory neural network is applied to the control of the UAVs parallel hybrid

High-Capacity Hybrid Optical Fiber-Wireless Communications Links in Access Networks

DEFF Research Database (Denmark)

Pang, Xiaodan

of broadband services access. To realize the seamless convergence between the two network segments, the lower capacity of wireless systems need to be increased to match the continuously increasing bandwidth of fiber-optic systems. The research works included in this thesis are devoted to experimental...... investigations of photonic-wireless links with record high capacities to fulfill the requirements of next generation hybrid optical fiber-wireless access networks. The main contributions of this thesis have expanded the state-of-the-art in two main areas: high speed millimeter-wave (mm-wave) communication links......Integration between fiber-optic and wireless communications systems in the "last mile" access networks is currently considered as a promising solution for both service providers and users, in terms of minimizing deployment cost, shortening upgrading period and increasing mobility and flexibility...

De-optical-line-terminal hybrid access-aggregation optical network for time-sensitive services based on software-defined networking orchestration

Science.gov (United States)

Bai, Wei; Yang, Hui; Xiao, Hongyun; Yu, Ao; He, Linkuan; Zhang, Jie; Li, Zhen; Du, Yi

2017-11-01

With the increase in varieties of services in network, time-sensitive services (TSSs) appear and bring forward an impending need for delay performance. Ultralow-latency communication has become one of the important development goals for many scenarios in the coming 5G era (e.g., robotics and driverless cars). However, the conventional methods, which decrease delay by promoting the available resources and the network transmission speed, have limited effect; a new breakthrough for ultralow-latency communication is necessary. We propose a de-optical-line-terminal (De-OLT) hybrid access-aggregation optical network (DAON) for TSS based on software-defined networking (SDN) orchestration. In this network, low-latency all-optical communication based on optical burst switching can be achieved by removing OLT. For supporting this network and guaranteeing the quality of service for TSSs, we design SDN-driven control method and service provision method. Numerical results demonstrate the proposed DAON promotes network service efficiency and avoids traffic congestion.

Service and multimedia data transmission in IoT networks using hybrid communication devices

Directory of Open Access Journals (Sweden)

Saveliev Anton

2017-01-01

Full Text Available Employment of various protocols and technologies in IoT networks leads to the lack of module unification and increase in incompatible technical solutions. Modern IoT networks are not designed for streaming audio/video data, so their application field is limited. Also, modern IoT networks should have connection areas for devices transferring data to the Internet, and consider hardware and software specific characteristics of these devices. We offer one-size-fits-all solution for organization of IoT network, using hybrid modules. These devices provide flexibility, scalability, energy efficiency and multi-use of network for the transfer of various types of data. This approach takes into account software and hardware features of the devices used for data transmission in IoT networks, which helps to automate connecting the modules chosen by user.

Joint Hybrid Backhaul and Access Links Design in Cloud-Radio Access Networks

KAUST Repository

Dhifallah, Oussama Najeeb; Dahrouj, Hayssam; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

2015-01-01

The cloud-radio access network (CRAN) is expected to be the core network architecture for next generation mobile radio systems. In this paper, we consider the downlink of a CRAN formed of one central processor (the cloud) and several base station

Gap analysis and conservation network for freshwater wetlands in Central Yangtze Ecoregion.

Science.gov (United States)

Xiaowen, Li; Haijin, Zhuge; Li, Mengdi

2013-01-01

The Central Yangtze Ecoregion contains a large area of internationally important freshwater wetlands and supports a huge number of endangered waterbirds; however, these unique wetlands and the biodiversity they support are under the constant threats of human development pressures, and the prevailing conservation strategies generated based on the local scale cannot adequately be used as guidelines for ecoregion-based conservation initiatives for Central Yangtze at the broad scale. This paper aims at establishing and optimizing an ecological network for freshwater wetland conservation in the Central Yangtze Ecoregion based on large-scale gap analysis. A group of focal species and GIS-based extrapolation technique were employed to identify the potential habitats and conservation gaps, and the optimized conservation network was then established by combining existing protective system and identified conservation gaps. Our results show that only 23.49% of the potential habitats of the focal species have been included in the existing nature reserves in the Central Yangtze Ecoregion. To effectively conserve over 80% of the potential habitats for the focal species by optimizing the existing conservation network for the freshwater wetlands in Central Yangtze Ecoregion, it is necessary to establish new wetland nature reserves in 22 county units across Hubei, Anhui, and Jiangxi provinces.

Gas ultracentrifuge separative parameters modeling using hybrid neural networks

International Nuclear Information System (INIS)

Crus, Maria Ursulina de Lima

2005-01-01

A hybrid neural network is developed for the calculation of the separative performance of an ultracentrifuge. A feed forward neural network is trained to estimate the internal flow parameters of a gas ultracentrifuge, and then these parameters are applied in the diffusion equation. For this study, a 573 experimental data set is used to establish the relation between the separative performance and the controlled variables. The process control variables considered are: the feed flow rate F, the cut θ and the product pressure Pp. The mechanical arrangements consider the radial waste scoop dimension, the rotating baffle size D s and the axial feed location Z E . The methodology was validated through the comparison of the calculated separative performance with experimental values. This methodology may be applied to other processes, just by adapting the phenomenological procedures. (author)

Fluid power network for centralized electricity generation in offshore wind farms

NARCIS (Netherlands)

Jarquin-Laguna, A.

2014-01-01

An innovative and completely different wind-energy conversion system is studied where a centralized electricity generation within a wind farm is proposed by means of a hydraulic network. This paper presents the dynamic interaction of two turbines when they are coupled to the same hydraulic network.

Fluid Centrality: A Social Network Analysis of Social-Technical Relations in Computer-Mediated Communication

Science.gov (United States)

Enriquez, Judith Guevarra

2010-01-01

In this article, centrality is explored as a measure of computer-mediated communication (CMC) in networked learning. Centrality measure is quite common in performing social network analysis (SNA) and in analysing social cohesion, strength of ties and influence in CMC, and computer-supported collaborative learning research. It argues that measuring…

Communication for Influence : Building ICTD Networks in Central ...

International Development Research Centre (IDRC) Digital Library (Canada)

This project seeks to help achieve universal affordable access to broadband information and communication technology (ICT) infrastructure in a number of countries on the East and West coasts of Africa. It will do so by building regional ICT for development (ICTD) research and advocacy networks in Central, East and West ...

Emulation-based comparative study of centralized and distributed control schemes for optical networks

Science.gov (United States)

Xin, Chunsheng; Ye, Yinghua; Dixit, Sudhir; Qiao, Chunming

2001-07-01

Recently there are considerable amount of research about the automatic control and provisioning in all optical networks. One of the critical issues is how to provide effective lightpath provisioning to improve network performance, such as blocking probability and decision time. Depending on the network topology, configuration, and administration policy, a distributed or centralized control scheme can be employed to manage the routing and signaling. In a distributed control scheme, each node exchanges information with other nodes, but performs routing and signaling independently from other nodes. On the other hand, in a centralized scheme, each node communicates with a central controller and the controller performs routing and signaling on behalf of all other nodes. Intuitively, the centralized scheme can obtain a lower blocking probability since the controller has the complete resource availability information. We have studied the two schemes through emulations, determined the signaling and processing overheads and quantified the conditions that favor one approach over the other.

Vertex centrality as a measure of information flow in Italian Corporate Board Networks

Science.gov (United States)

Grassi, Rosanna

2010-06-01

The aim of this article is to investigate the governance models of companies listed on the Italian Stock Exchange by using a network approach, which describes the interlinks between boards of directors. Following mainstream literature, I construct a weighted graph representing the listed companies (vertices) and their relationships (weighted edges), the Corporate Board Network; I then apply three different vertex centrality measures: degree, betweenness and flow betweenness. What emerges from the network construction and by applying the degree centrality is a structure with a large number of connections but not particularly dense, where the presence of a small number of highly connected nodes (hubs) is evident. Then I focus on betweenness and flow betweenness; indeed I expect that these centrality measures may give a representation of the intensity of the relationship between companies, capturing the volume of information flowing from one vertex to another. Finally, I investigate the possible scale-free structure of the network.

A Game Theoretic Optimization Method for Energy Efficient Global Connectivity in Hybrid Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

JongHyup Lee

2016-08-01

Full Text Available For practical deployment of wireless sensor networks (WSN, WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections.

A Game Theoretic Optimization Method for Energy Efficient Global Connectivity in Hybrid Wireless Sensor Networks

Science.gov (United States)

Lee, JongHyup; Pak, Dohyun

2016-01-01

For practical deployment of wireless sensor networks (WSN), WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections. PMID:27589743

Hybrid-Lambda: simulation of multiple merger and Kingman gene genealogies in species networks and species trees.

Science.gov (United States)

Zhu, Sha; Degnan, James H; Goldstien, Sharyn J; Eldon, Bjarki

2015-09-15

There has been increasing interest in coalescent models which admit multiple mergers of ancestral lineages; and to model hybridization and coalescence simultaneously. Hybrid-Lambda is a software package that simulates gene genealogies under multiple merger and Kingman's coalescent processes within species networks or species trees. Hybrid-Lambda allows different coalescent processes to be specified for different populations, and allows for time to be converted between generations and coalescent units, by specifying a population size for each population. In addition, Hybrid-Lambda can generate simulated datasets, assuming the infinitely many sites mutation model, and compute the F ST statistic. As an illustration, we apply Hybrid-Lambda to infer the time of subdivision of certain marine invertebrates under different coalescent processes. Hybrid-Lambda makes it possible to investigate biogeographic concordance among high fecundity species exhibiting skewed offspring distribution.

Hybrid Indoor-Based WLAN-WSN Localization Scheme for Improving Accuracy Based on Artificial Neural Network

Directory of Open Access Journals (Sweden)

Zahid Farid

2016-01-01

Full Text Available In indoor environments, WiFi (RSS based localization is sensitive to various indoor fading effects and noise during transmission, which are the main causes of localization errors that affect its accuracy. Keeping in view those fading effects, positioning systems based on a single technology are ineffective in performing accurate localization. For this reason, the trend is toward the use of hybrid positioning systems (combination of two or more wireless technologies in indoor/outdoor localization scenarios for getting better position accuracy. This paper presents a hybrid technique to implement indoor localization that adopts fingerprinting approaches in both WiFi and Wireless Sensor Networks (WSNs. This model exploits machine learning, in particular Artificial Natural Network (ANN techniques, for position calculation. The experimental results show that the proposed hybrid system improved the accuracy, reducing the average distance error to 1.05 m by using ANN. Applying Genetic Algorithm (GA based optimization technique did not incur any further improvement to the accuracy. Compared to the performance of GA optimization, the nonoptimized ANN performed better in terms of accuracy, precision, stability, and computational time. The above results show that the proposed hybrid technique is promising for achieving better accuracy in real-world positioning applications.

Competitive Supply Chain Network Design Considering Marketing Strategies: A Hybrid Metaheuristic Algorithm

Directory of Open Access Journals (Sweden)

Ali Akbar Hasani

2016-11-01

Full Text Available In this paper, a comprehensive model is proposed to design a network for multi-period, multi-echelon, and multi-product inventory controlled the supply chain. Various marketing strategies and guerrilla marketing approaches are considered in the design process under the static competition condition. The goal of the proposed model is to efficiently respond to the customers’ demands in the presence of the pre-existing competitors and the price inelasticity of demands. The proposed optimization model considers multiple objectives that incorporate both market share and total profit of the considered supply chain network, simultaneously. To tackle the proposed multi-objective mixed-integer nonlinear programming model, an efficient hybrid meta-heuristic algorithm is developed that incorporates a Taguchi-based non-dominated sorting genetic algorithm-II and a particle swarm optimization. A variable neighborhood decomposition search is applied to enhance a local search process of the proposed hybrid solution algorithm. Computational results illustrate that the proposed model and solution algorithm are notably efficient in dealing with the competitive pressure by adopting the proper marketing strategies.

Dynamic Load Balancing with Handover in Hybrid Li-Fi and Wi-Fi Networks

OpenAIRE

Haas, Harald; Wang, Yunlu

2015-01-01

In this paper, a hybrid network combining lightfidelity (Li-Fi) with a radio frequency (RF) wireless fidelity(Wi-Fi) network is considered. An additional tier of very smallLi-Fi attocells which utilise the visible light spectrum offers asignificant increase in wireless data throughput in an indoorenvironment while at the same time providing room illumination.Importantly, there is no interference between Li-Fi and Wi-Fi.A Li-Fi attocell covers a significantly smaller area than a Wi-Fi access p...

Robust Stability Analysis of Neutral-Type Hybrid Bidirectional Associative Memory Neural Networks with Time-Varying Delays

OpenAIRE

Wei Feng; Simon X. Yang; Haixia Wu

2014-01-01

The global asymptotic robust stability of equilibrium is considered for neutral-type hybrid bidirectional associative memory neural networks with time-varying delays and parameters uncertainties. The results we obtained in this paper are delay-derivative-dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported ...

Solar Central Receiver Hybrid Power Systems sodium-cooled receiver concept. Final report. Volume II, Book 2. Conceptual design, Sections 5 and 6

Energy Technology Data Exchange (ETDEWEB)

None

1980-01-01

The overall, long-term objective of the Solar Central Receiver Hybrid Power System program is to identify, characterize, and ultimately demonstrate the viability and cost effectiveness of solar/fossil, steam Rankine cycle, hybrid power systems that: (1) consist of a combined solar central receiver energy source and a nonsolar energy source at a single, common site, (2) may operate in the base, intermediate, and peaking capacity modes, (3) produce the rated output independent of variations in solar insolation, (4) provide a significant savings (50% or more) in fuel consumption, and (5) produce power at the minimum possible cost in mills/kWh. It is essential that these hybrid concepts be technically feasible and economically competitive with other systems in the near to mid-term time period (1985-1990) on a commercial scale. The program objective for Phase I is to identify and conceptually characterize solar/fossil steam Rankine cycle, commercial-scale, power plant systems that are economically viable and technically feasible. This volume contains the detailed conceptual design and cost/performance estimates and an assessment of the commercial scale solar central receiver hybrid power system. (WHK)

Betweenness centrality and its applications from modeling traffic flows to network community detection

Science.gov (United States)

Ren, Yihui

As real-world complex networks are heterogeneous structures, not all their components such as nodes, edges and subgraphs carry the same role or importance in the functions performed by the networks: some elements are more critical than others. Understanding the roles of the components of a network is crucial for understanding the behavior of the network as a whole. One the most basic function of networks is transport; transport of vehicles/people, information, materials, forces, etc., and these quantities are transported along edges between source and destination nodes. For this reason, network path-based importance measures, also called centralities, play a crucial role in the understanding of the transport functions of the network and the network's structural and dynamical behavior in general. In this thesis we study the notion of betweenness centrality, which measures the fraction of lowest-cost (or shortest) paths running through a network component, in particular through a node or an edge. High betweenness centrality nodes/edges are those that will be frequently used by the entities transported through the network and thus they play a key role in the overall transport properties of the network. In the first part of the thesis we present a first-principles based method for traffic prediction using a cost-based generalization of the radiation model (emission/absorbtion model) for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data, we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the log-normal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared with real traffic. We then focus on studying the extent of changes in traffic flows in the wake of a localized damage or alteration to the

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

Directory of Open Access Journals (Sweden)

Xike Zhang

2018-05-01

Full Text Available Daily land surface temperature (LST forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD coupled with Machine Learning (ML algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs and a single residue item. Then, the Partial Autocorrelation Function (PACF is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE, Mean Absolute Error (MAE, Mean Absolute Percentage Error (MAPE, Root Mean Square Error (RMSE, Pearson Correlation Coefficient (CC and Nash-Sutcliffe Coefficient of Efficiency (NSCE. To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN, LSTM and Empirical Mode Decomposition (EMD coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.

Science.gov (United States)

Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

2018-05-21

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five

Hybrid SDN Architecture for Resource Consolidation in MPLS Networks

DEFF Research Database (Denmark)

Katov, Anton Nikolaev; Mihovska, Albena D.; Prasad, Neeli R.

2015-01-01

) scheme with a reconfigurable centralized controller, which turns off certain network elements. The methodology comprises the process of identifying time periods with lower traffic demand; the ranking of the network elements, based on their utilization and criticality; the rerouting of the traffic off...... the least utilized elements; and finally, the switching off of the appropriate nodes or links. An algorithm for traffic rerouting, based on the MPLS traffic engineering techniques is proposed and its performance is evaluated in terms of the achieved energy efficiency in accordance with predefined...

Spontaneous centralization of control in a network of company ownerships.

Directory of Open Access Journals (Sweden)

Sebastian M Krause

Full Text Available We introduce a model for the adaptive evolution of a network of company ownerships. In a recent work it has been shown that the empirical global network of corporate control is marked by a central, tightly connected "core" made of a small number of large companies which control a significant part of the global economy. Here we show how a simple, adaptive "rich get richer" dynamics can account for this characteristic, which incorporates the increased buying power of more influential companies, and in turn results in even higher control. We conclude that this kind of centralized structure can emerge without it being an explicit goal of these companies, or as a result of a well-organized strategy.

NEURAL NETWORKS CONTROL OF THE HYBRID POWER UNIT BASED ON THE METHOD OF ADAPTIVE CRITICS

Directory of Open Access Journals (Sweden)

S. Serikov

2012-01-01

Full Text Available The formal statement of the optimization problem of hybrid vehicle power unit control is given. Its solving by neural networks method application on the basis of adaptive critic is considered.

Multihopping Multilevel Clustering Heterogeneity-Sensitive Optimized Routing Protocol for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Muhammad Aslam

2017-01-01

Full Text Available Effective utilization of energy resources in Wireless Sensor Networks (WSNs has become challenging under uncertain distributed cluster-formation and single-hop intercluster communication capabilities. So, sensor nodes are forced to operate at expensive full rate transmission power level continuously during whole network operation. These challenging network environments experience unwanted phenomena of drastic energy consumption and packet drop. In this paper, we propose an adaptive immune Multihopping Multilevel Clustering (MHMLC protocol that executes a Hybrid Clustering Algorithm (HCA to perform optimal centralized selection of Cluster-Heads (CHs within radius of centrally located Base Station (BS and distributed CHs selection in the rest of network area. HCA of MHMLC also produces optimal intermediate CHs for intercluster multihop communications that develop heterogeneity-aware economical links. This hybrid cluster-formation facilitates the sensors to function at short range transmission power level that enhances link quality and avoids packet drop. The simulation environments produce fair comparison among proposed MHMLC and existing state-of-the-art routing protocols. Experimental results give significant evidence of better performance of the proposed model in terms of network lifetime, stability period, and data delivery ratio.

A novel survivable architecture for hybrid WDM/TDM passive optical networks

Science.gov (United States)

Qiu, Yang; Chan, Chun-Kit

2014-02-01

A novel tree-ring survivable architecture, which consists of an organization of a wavelength-division-multiplexing (WDM) tree from optical line terminal (OLT) to remote nodes (RNs) and a time division multiplexing (TDM) ring in each RN, is proposed for hybrid WDM/TDM passive optical networks. By utilizing the cyclic property of arrayed waveguide gratings (AWGs) and the single-ring topology among a group of optical network units (ONUs) in the remote node, not only the feeder and distribution fibers, but also any fiber failures in the RN rings are protected simultaneously. Five-Gbit/s transmissions under both normal working and protection modes were experimentally demonstrated and a traffic restoration time was successfully measured.

Branch-Based Centralized Data Collection for Smart Grids Using Wireless Sensor Networks

OpenAIRE

Kwangsoo Kim; Seong-il Jin

2015-01-01

A smart grid is one of the most important applications in smart cities. In a smart grid, a smart meter acts as a sensor node in a sensor network, and a central device collects power usage from every smart meter. This paper focuses on a centralized data collection problem of how to collect every power usage from every meter without collisions in an environment in which the time synchronization among smart meters is not guaranteed. To solve the problem, we divide a tree that a sensor network co...

Understanding interpenetrating-polymer-network-like porous nitrile butadiene rubber hybrids by their long-period miscibility

International Nuclear Information System (INIS)

Zhang, Jihua; Wang, Lifeng; Zhao, Yunfeng

2013-01-01

Highlights: • Hydrogen bonds are introduced into NBR to develop its IPN-like porous hybrids. • NBR is partly miscible with AO-60. • AO-60 possesses the viscoelastic behavior resembling that of polymers. • Phase separation aggravates and AO-60 crystallizes in the durations. • The porous hybrids may have potential damping applications. - Abstract: In this article, tetrakis [methylene-3-(3, 5-di-tert-butyl-4-hydroxy phenyl) propionyloxy] methane (AO-60) with hydrogen bonds was designed to interpenetrate into the chemical crosslinking bonds of nitrile butadiene rubber (NBR) and then porous materials were prepared. Scanning electron microscopy (SEM), atomic force microscopy (AFM) images and dynamic mechanical analyses (DMA) demonstrate that NBR is partly miscible with AO-60 which induces the micro-pores and interpenetrating-polymer-network (IPN)-like phase morphology in the hybrids. The wide double tan δ peak in DMA curve displays that AO-60 possesses similar viscoelastic behaviors to polymers which come from supramolecular interactions between polar groups of NBR chains and hydroxyl (OH) groups of AO-60. To further understand the supramolecular abilities of AO-60 in the rubber, the long-period observations for their miscibility are conducted. With the increase of durations, the hydrogen bond network from AO-60 is weakened. The phase separation between AO-60 and NBR is aggravated and even extremely few AO-60 crystallizes which develops multi-scale porous morphology in the hybrids. It is believed that these findings can serve as a guide for the designs of the IPN-like hybrids with small molecule substances and their applications of damping materials

Demonstration and field trial of a resilient hybrid NG-PON test-bed

Science.gov (United States)

Prat, Josep; Polo, Victor; Schrenk, Bernhard; Lazaro, Jose A.; Bonada, Francesc; Lopez, Eduardo T.; Omella, Mireia; Saliou, Fabienne; Le, Quang T.; Chanclou, Philippe; Leino, Dmitri; Soila, Risto; Spirou, Spiros; Costa, Liliana; Teixeira, Antonio; Tosi-Beleffi, Giorgio M.; Klonidis, Dimitrios; Tomkos, Ioannis

2014-10-01

A multi-layer next generation PON prototype has been built and tested, to show the feasibility of extended hybrid DWDM/TDM-XGPON FTTH networks with resilient optically-integrated ring-trees architecture, supporting broadband multimedia services. It constitutes a transparent common platform for the coexistence of multiple operators sharing the optical infrastructure of the central metro ring, passively combining the access and the metropolitan network sections. It features 32 wavelength connections at 10 Gbps, up to 1000 users distributed in 16 independent resilient sub-PONs over 100 km. This paper summarizes the network operation, demonstration and field trial results.

Seafloor classification using artificial neural network architecture from central western continental shelf of India

Science.gov (United States)

Mahale, Vasudev; Chakraborty, Bishwajit; Navelkar, Gajanan S.; Prabhu Desai, R. G.

2005-04-01

Seafloor classification studies are carried out at the central western continental shelf of India employing two frequency normal incidence single beam echo-sounder backscatter data. Echo waveform data from different seafloor sediment areas are utilized for present study. Three artificial neural network (ANN) architectures, e.g., Self-Organization Feature Maps (SOFM), Multi-Layer Perceptron (MLP), and Learning Vector Quantization (LVQ) are applied for seafloor classifications. In case of MLP, features are extracted from the received echo signal, on the basis of which, classification is carried out. In the case of the SOFM, a simple moving average echo waveform pre-processing technique is found to yield excellent classification results. Finally, LVQ, which is known as ANN of hybrid architecture is found to be the efficient seafloor classifier especially from the point of view of the real-time application. The simultaneously acquired sediment sample, multi-beam bathymetry and side scan sonar and echo waveform based seafloor classifications results are indicative of the depositional (inner shelf), non-depositional or erosion (outer shelf) environment and combination of both in the transition zone. [Work supported by DIT.

A P2P Query Algorithm for Opportunistic Networks Utilizing betweenness Centrality Forwarding

Directory of Open Access Journals (Sweden)

Jianwei Niu

2013-01-01

Full Text Available With the proliferation of high-end mobile devices that feature wireless interfaces, many promising applications are enabled in opportunistic networks. In contrary to traditional networks, opportunistic networks utilize the mobility of nodes to relay messages in a store-carry-forward paradigm. Thus, the relay process in opportunistic networks faces several practical challenges in terms of delay and delivery rate. In this paper, we propose a novel P2P Query algorithm, namely Betweenness Centrality Forwarding (PQBCF, for opportunistic networking. PQBCF adopts a forwarding metric called Betweenness Centrality (BC, which is borrowed from social network, to quantify the active degree of nodes in the networks. In PQBCF, nodes with a higher BC are preferable to serve as relays, leading to higher query success rate and lower query delay. A comparison with the state-of-the-art algorithms reveals that PQBCF can provide better performance on both the query success Ratio and query delay, and approaches the performance of Epidemic Routing (ER with much less resource consumption.

Interactions of the Salience Network and Its Subsystems with the Default-Mode and the Central-Executive Networks in Normal Aging and Mild Cognitive Impairment.

Science.gov (United States)

Chand, Ganesh B; Wu, Junjie; Hajjar, Ihab; Qiu, Deqiang

2017-09-01

Previous functional magnetic resonance imaging (fMRI) investigations suggest that the intrinsically organized large-scale networks and the interaction between them might be crucial for cognitive activities. A triple network model, which consists of the default-mode network, salience network, and central-executive network, has been recently used to understand the connectivity patterns of the cognitively normal brains versus the brains with disorders. This model suggests that the salience network dynamically controls the default-mode and central-executive networks in healthy young individuals. However, the patterns of interactions have remained largely unknown in healthy aging or those with cognitive decline. In this study, we assess the patterns of interactions between the three networks using dynamical causal modeling in resting state fMRI data and compare them between subjects with normal cognition and mild cognitive impairment (MCI). In healthy elderly subjects, our analysis showed that the salience network, especially its dorsal subnetwork, modulates the interaction between the default-mode network and the central-executive network (Mann-Whitney U test; p control correlated significantly with lower overall cognitive performance measured by Montreal Cognitive Assessment (r = 0.295; p control, especially the dorsal salience network, over other networks provides a neuronal basis for cognitive decline and may be a candidate neuroimaging biomarker of cognitive impairment.

Indicators of opinion leadership in customer networks : self-reports and degree centrality

NARCIS (Netherlands)

Risselada, Hans; Verhoef, Pieter; Bijmolt, Tammo

In this paper, we assess two alternative indicators of opinion leadership, self-reported opinion leadership and degree centrality, on the same dataset. We also investigate the interaction effect of these two indicators and the social network environment on opinion leadership. We use social network

Hyper-cross-linked, hybrid membranes via interfacial polymerization

NARCIS (Netherlands)

Raaijmakers, Michiel

2015-01-01

Hyper-cross-linked, hybrid membranes consist of covalent networks of alternating organic and inorganic, or biological groups. This thesis reports on the preparation of such hybrid networks via interfacial polymerization. The structure-property relationships of the hybrid networks depend strongly on

The central monitoring station of Indian Environmental Radiation Monitoring Network (IERMON): the architecture and functions

International Nuclear Information System (INIS)

Garg, Saurabh; Ratheesh, M.P.; Mukundan, T.; Patel, M.D.; Nair, C.K.G.; Puranik, V.D.

2010-01-01

The Indian Environmental Radiation Monitoring Network (IERMON) is being established across the country by the Bhabha Atomic Research Centre, Mumbai. The network consists of stations with automated systems for environmental radiation monitoring with online data communication facility. Currently about 100 stations are operational and additional 500 stations are expected to be installed by March, 2012. The network is established with different objectives, the main objective being the detection and reporting of any nuclear emergency anywhere in the country. The central monitoring station of the network is established in Mumbai. This paper describes the architecture and functions of IERMON Central Station. The Central Station consists of server room for online data collection from remote stations and maintenance of databases for various applications; central monitoring room for user interaction with database and IERMON website maintenance and development room for the development of new applications. The functions of IERMON Central Station include detection and reporting of nuclear emergency, maintenance of remote stations, enhancement of public awareness on environmental radiation through public display systems and website, etc. The details on system layout and data protocols can be found in the paper. (author)

An Introduction to the Hybrid Approach of Neural Networks and the Linear Regression Model : An Illustration in the Hedonic Pricing Model of Building Costs

OpenAIRE

浅野, 美代子; マーコ, ユー K.W.

2007-01-01

This paper introduces the hybrid approach of neural networks and linear regression model proposed by Asano and Tsubaki (2003). Neural networks are often credited with its superiority in data consistency whereas the linear regression model provides simple interpretation of the data enabling researchers to verify their hypotheses. The hybrid approach aims at combing the strengths of these two well-established statistical methods. A step-by-step procedure for performing the hybrid approach is pr...

Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

Science.gov (United States)

Chiadamrong, N.; Piyathanavong, V.

2017-12-01

Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.

Modeling integrated cellular machinery using hybrid Petri-Boolean networks.

Directory of Open Access Journals (Sweden)

Natalie Berestovsky

Full Text Available The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them

ANOMALY DETECTION IN NETWORKING USING HYBRID ARTIFICIAL IMMUNE ALGORITHM

Directory of Open Access Journals (Sweden)

D. Amutha Guka

2012-01-01

Full Text Available Especially in today’s network scenario, when computers are interconnected through internet, security of an information system is very important issue. Because no system can be absolutely secure, the timely and accurate detection of anomalies is necessary. The main aim of this research paper is to improve the anomaly detection by using Hybrid Artificial Immune Algorithm (HAIA which is based on Artificial Immune Systems (AIS and Genetic Algorithm (GA. In this research work, HAIA approach is used to develop Network Anomaly Detection System (NADS. The detector set is generated by using GA and the anomalies are identified using Negative Selection Algorithm (NSA which is based on AIS. The HAIA algorithm is tested with KDD Cup 99 benchmark dataset. The detection rate is used to measure the effectiveness of the NADS. The results and consistency of the HAIA are compared with earlier approaches and the results are presented. The proposed algorithm gives best results when compared to the earlier approaches.

Centralized light-source optical access network based on polarization multiplexing.

Science.gov (United States)

Grassi, Fulvio; Mora, José; Ortega, Beatriz; Capmany, José

2010-03-01

This paper presents and demonstrates a centralized light source optical access network based on optical polarization multiplexing technique. By using two optical sources emitting light orthogonally polarized in the Central Node for downstream and upstream operations, the Remote Node is kept source-free. EVM values below telecommunication standard requirements have been measured experimentally when bidirectional digital signals have been transmitted over 10 km of SMF employing subcarrier multiplexing technique in the electrical domain.

An energy efficient hybrid interference-resilient frame fragmentation for wireless sensor networks

KAUST Repository

Meer, Ammar M.; Daghistani, Anas; Shihada, Basem

2015-01-01

Frame fragmentation into small blocks with dedicated error detection codes per block can reduce the unnecessary retransmission of the correctly received blocks. However, the optimal block size varies based on the wireless channel conditions. Further, blocks within a single frame may have different optimal sizes based on variations in interference patterns. This paper proposes a hybrid interference-resilient frame fragmentation (Hi-Frag) link-layer scheme for wireless sensor networks. It effectively addresses the challenges associated with dynamic partitioning of blocks while accounting for the observed error patterns. Hi-Frag is the first work to introduce an adaptive frame fragmentation scheme with hybrid block sizing, implemented and evaluated on a real WSN testbed. Hi-Frag shows substantial enhancements over fixed-size partial packet recovery protocols, achieving up to 2.5× improvement in throughput when the channel condition is noisy, while reducing network delays by up to 14% of the observed delay. On average, Hi-Frag shows 35% gain in throughput compared to static fragmentation approaches across all channel conditions used in our experiments. Also, Hi-Frag lowers the energy consumed per useful bit by 66% on average compared to conventional protocols, which increases the energy efficiency.

An energy efficient hybrid interference-resilient frame fragmentation for wireless sensor networks

KAUST Repository

Meer, Ammar M.

2015-08-30

Frame fragmentation into small blocks with dedicated error detection codes per block can reduce the unnecessary retransmission of the correctly received blocks. However, the optimal block size varies based on the wireless channel conditions. Further, blocks within a single frame may have different optimal sizes based on variations in interference patterns. This paper proposes a hybrid interference-resilient frame fragmentation (Hi-Frag) link-layer scheme for wireless sensor networks. It effectively addresses the challenges associated with dynamic partitioning of blocks while accounting for the observed error patterns. Hi-Frag is the first work to introduce an adaptive frame fragmentation scheme with hybrid block sizing, implemented and evaluated on a real WSN testbed. Hi-Frag shows substantial enhancements over fixed-size partial packet recovery protocols, achieving up to 2.5× improvement in throughput when the channel condition is noisy, while reducing network delays by up to 14% of the observed delay. On average, Hi-Frag shows 35% gain in throughput compared to static fragmentation approaches across all channel conditions used in our experiments. Also, Hi-Frag lowers the energy consumed per useful bit by 66% on average compared to conventional protocols, which increases the energy efficiency.

Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

Science.gov (United States)

Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

2015-10-01

Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

The Central and Eastern European Earthquake Research Network - CE3RN

Science.gov (United States)

Bragato, Pier Luigi; Costa, Giovanni; Gallo, Antonella; Gosar, Andrej; Horn, Nikolaus; Lenhardt, Wolfgang; Mucciarelli, Marco; Pesaresi, Damiano; Steiner, Rudolf; Suhadolc, Peter; Tiberi, Lara; Živčić, Mladen; Zoppé, Giuliana

2014-05-01

The region of the Central and Eastern Europe is an area characterised by a relatively high seismicity. The active seismogenic structures and the related potentially destructive events are located in the proximity of the political boundaries between several countries existing in the area. An example is the seismic region between the NE Italy (FVG, Trentino-Alto Adige and Veneto), Austria (Tyrol, Carinthia) and Slovenia. So when a destructive earthquake occurs in the area, all the three countries are involved. In the year 2001 the Agencija Republike Slovenije za Okolje (ARSO) in Slovenia, the Department of Mathematics and Geoscience of the University of Trieste (DMG), the OGS (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale) in Italy and the Zentralanstalt für Meteorologie und Geodynamik (ZAMG) in Austria signed an agreement for the real-time seismological data exchange in the Southeastern Alps region. Soon after the Interreg IIIa Italia-Austria projects "Trans-National Seismological Networks in the South-Eastern Alps" and "FASTLINK" started. The main goal of these projects was the creation of a transfrontier network for the common seismic monitoring of the region for scientific and civil defense purposes. During these years the high quality data recorded by the transfrontier network has been used, by the involved institutions, for their scientific research, for institutional activities and for the civil defense services. Several common international projects have been realized with success. The instrumentation has been continuously upgraded, the installations quality improved as well as the data transmission efficiency. In the 2013 ARSO, DMG, OGS and ZAMG decided to name the cooperative network "Central and Eastern European Earthquake Research Network - CE3RN". The national/regional seismic networks actually involved in the CE3RN network are: • Austrian national BB network (ZAMG - OE) • Friuli Veneto SP network (OGS - FV) • Friuli VG

A hybrid medium access control for convergence of broadband wireless and wireline ATM networks

DEFF Research Database (Denmark)

Liu, Hong; Gliese, Ulrik Bo; Dittmann, Lars

2000-01-01

In this paper, we propose a hybrid medium access control protocol for supporting broadband integrated services in the wireless ATM networks. The integrated services include CBR, VBR and ABR traffic varying from low bit-rate to very high bit-rate. The proposed protocol is an excellent compromise...

Reconstruction of in-plane strain maps using hybrid dense sensor network composed of sensing skin

International Nuclear Information System (INIS)

Downey, Austin; Laflamme, Simon; Ubertini, Filippo

2016-01-01

The authors have recently developed a soft-elastomeric capacitive (SEC)-based thin film sensor for monitoring strain on mesosurfaces. Arranged in a network configuration, the sensing system is analogous to a biological skin, where local strain can be monitored over a global area. Under plane stress conditions, the sensor output contains the additive measurement of the two principal strain components over the monitored surface. In applications where the evaluation of strain maps is useful, in structural health monitoring for instance, such signal must be decomposed into linear strain components along orthogonal directions. Previous work has led to an algorithm that enabled such decomposition by leveraging a dense sensor network configuration with the addition of assumed boundary conditions. Here, we significantly improve the algorithm’s accuracy by leveraging mature off-the-shelf solutions to create a hybrid dense sensor network (HDSN) to improve on the boundary condition assumptions. The system’s boundary conditions are enforced using unidirectional RSGs and assumed virtual sensors. Results from an extensive experimental investigation demonstrate the good performance of the proposed algorithm and its robustness with respect to sensors’ layout. Overall, the proposed algorithm is seen to effectively leverage the advantages of a hybrid dense network for application of the thin film sensor to reconstruct surface strain fields over large surfaces. (paper)

A large interconnecting network within hybrid MEH-PPV/TiO2 nanorod photovoltaic devices

International Nuclear Information System (INIS)

Zeng, T-W; Lin, Y-Y; Lo, H-H; Chen, C-W; Chen, C-H; Liou, S-C; Huang, H-Y; Su, W-F

2006-01-01

This is a study of hybrid photovoltaic devices based on TiO 2 nanorods and poly[2-methoxy-5-(2'-ethyl-hexyloxy)-1,4-phenylene vinylene] (MEH-PPV). We use TiO 2 nanorods as the electron acceptors and conduction pathways. Here we describe how to develop a large interconnecting network within the photovoltaic device fabricated by inserting a layer of TiO 2 nanorods between the MEH-PPV:TiO 2 nanorod hybrid active layer and the aluminium electrode. The formation of a large interconnecting network provides better connectivity to the electrode, leading to a 2.5-fold improvement in external quantum efficiency as compared to the reference device without the TiO 2 nanorod layer. A power conversion efficiency of 2.2% under illumination at 565 nm and a maximum external quantum efficiency of 24% at 430 nm are achieved. A power conversion efficiency of 0.49% is obtained under Air Mass 1.5 illumination

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

Directory of Open Access Journals (Sweden)

Roy Weiss

2014-12-01

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

Field performance of Populus in short-rotation intensive culture plantations in the north-central U.S.

Science.gov (United States)

Edward A. Hansen; Michael E. Ostry; Wendell D. Johnson; David N. Tolsted; Daniel A. Netzer; William E. Berguson; Richard B. Hall

1994-01-01

Describes a network of short-rotation, Populus research and demonstration plantations that has been established across a 5-state region in the north-central U.S. to identify suitable hybrid poplar clones for large-scale biomass plantations in the region. Reports 6-year results.

BridgeRank: A novel fast centrality measure based on local structure of the network

Science.gov (United States)

Salavati, Chiman; Abdollahpouri, Alireza; Manbari, Zhaleh

2018-04-01

Ranking nodes in complex networks have become an important task in many application domains. In a complex network, influential nodes are those that have the most spreading ability. Thus, identifying influential nodes based on their spreading ability is a fundamental task in different applications such as viral marketing. One of the most important centrality measures to ranking nodes is closeness centrality which is efficient but suffers from high computational complexity O(n3) . This paper tries to improve closeness centrality by utilizing the local structure of nodes and presents a new ranking algorithm, called BridgeRank centrality. The proposed method computes local centrality value for each node. For this purpose, at first, communities are detected and the relationship between communities is completely ignored. Then, by applying a centrality in each community, only one best critical node from each community is extracted. Finally, the nodes are ranked based on computing the sum of the shortest path length of nodes to obtained critical nodes. We have also modified the proposed method by weighting the original BridgeRank and selecting several nodes from each community based on the density of that community. Our method can find the best nodes with high spread ability and low time complexity, which make it applicable to large-scale networks. To evaluate the performance of the proposed method, we use the SIR diffusion model. Finally, experiments on real and artificial networks show that our method is able to identify influential nodes so efficiently, and achieves better performance compared to other recent methods.

Performance Analysis of ZigBee Wireless Networks for AAL through Hybrid Ray Launching and Collaborative Filtering

Directory of Open Access Journals (Sweden)

Peio Lopez-Iturri

2016-01-01

Full Text Available This paper presents a novel hybrid simulation method based on the combination of an in-house developed 3D ray launching algorithm and a collaborative filtering (CF technique, which will be used to analyze the performance of ZigBee-based wireless sensor networks (WSNs to enable ambient assisted living (AAL. The combination of Low Definition results obtained by means of a deterministic ray launching method and the application of a CF technique leads to a drastic reduction of the time and computational cost required to obtain accurate simulation results. The paper also reports that this kind of AAL indoor complex scenario with multiple wireless devices needs a thorough and personalized radioplanning analysis as radiopropagation has a strong dependence on the network topology and the specific morphology of the scenario. The wireless channel analysis performed by our hybrid method provides valuable insight into network design phases of complex wireless systems, typical in AAL-oriented environments. Thus, it results in optimizing network deployment, reducing overall interference levels, and increasing the overall system performance in terms of cost reduction, transmission rates, and energy efficiency.

A Hierarchical Optimal Operation Strategy of Hybrid Energy Storage System in Distribution Networks with High Photovoltaic Penetration

Directory of Open Access Journals (Sweden)

Jian Chen

2018-02-01

Full Text Available In this paper, a hierarchical optimal operation strategy for a hybrid energy storage system (HESS is proposed, which is suitable to be utilized in distribution networks (DNs with high photovoltaic (PV penetration to achieve PV power smoothing, voltage regulation and price arbitrage. Firstly, a fuzzy-logic based variable step-size control strategy for an ultracapacitor (UC with the improvement of the lifetime of UC and tracking performance is adopted to smooth PV power fluctuations. The impact of PV forecasting errors is eliminated by adjusting the UC power in real time. Secondly, a coordinated control strategy, which includes centralized and local controls, is proposed for lithium-ion batteries. The centralized control is structured to determine the optimal battery unit for voltage regulation or price arbitrage according to lithium-ion battery performance indices. A modified lithium-ion battery aging model with better accuracy is proposed and the coupling relationship between the lifetime and the effective capacity is also considered. Additionally, the local control of the selected lithium-ion battery unit determines the charging/discharging power. A case study is used to validate the operation strategy and the results show that the lifetime equilibrium among different lithium-ion battery units can be achieved using the proposed strategy.

Hybrid neural network for density limit disruption prediction and avoidance on J-TEXT tokamak

Science.gov (United States)

Zheng, W.; Hu, F. R.; Zhang, M.; Chen, Z. Y.; Zhao, X. Q.; Wang, X. L.; Shi, P.; Zhang, X. L.; Zhang, X. Q.; Zhou, Y. N.; Wei, Y. N.; Pan, Y.; J-TEXT team

2018-05-01

Increasing the plasma density is one of the key methods in achieving an efficient fusion reaction. High-density operation is one of the hot topics in tokamak plasmas. Density limit disruptions remain an important issue for safe operation. An effective density limit disruption prediction and avoidance system is the key to avoid density limit disruptions for long pulse steady state operations. An artificial neural network has been developed for the prediction of density limit disruptions on the J-TEXT tokamak. The neural network has been improved from a simple multi-layer design to a hybrid two-stage structure. The first stage is a custom network which uses time series diagnostics as inputs to predict plasma density, and the second stage is a three-layer feedforward neural network to predict the probability of density limit disruptions. It is found that hybrid neural network structure, combined with radiation profile information as an input can significantly improve the prediction performance, especially the average warning time ({{T}warn} ). In particular, the {{T}warn} is eight times better than that in previous work (Wang et al 2016 Plasma Phys. Control. Fusion 58 055014) (from 5 ms to 40 ms). The success rate for density limit disruptive shots is above 90%, while, the false alarm rate for other shots is below 10%. Based on the density limit disruption prediction system and the real-time density feedback control system, the on-line density limit disruption avoidance system has been implemented on the J-TEXT tokamak.

Designing and assessing a sustainable networked delivery (SND) system: hybrid business-to-consumer book delivery case study.

Science.gov (United States)

Kim, Junbeum; Xu, Ming; Kahhat, Ramzy; Allenby, Braden; Williams, Eric

2009-01-01

We attempted to design and assess an example of a sustainable networked delivery (SND) system: a hybrid business-to-consumer book delivery system. This system is intended to reduce costs, achieve significant reductions in energy consumption, and reduce environmental emissions of critical local pollutants and greenhouse gases. The energy consumption and concomitant emissions of this delivery system compared with existing alternative delivery systems were estimated. We found that regarding energy consumption, an emerging hybrid delivery system which is a sustainable networked delivery system (SND) would consume 47 and 7 times less than the traditional networked delivery system (TND) and e-commerce networked delivery system (END). Regarding concomitant emissions, in the case of CO2, the SND system produced 32 and 7 times fewer emissions than the TND and END systems. Also the SND system offer meaningful economic benefit such as the costs of delivery and packaging, to the online retailer, grocery, and consumer. Our research results show that the SND system has a lot of possibilities to save local transportation energy consumption and delivery costs, and reduce environmental emissions in delivery system.

Diseases of intensively cultured hybrid poplars: a summary of recent research in the north central region

Science.gov (United States)

M. E. Ostry; H. S. McNabb

1983-01-01

Several potentially damaging diseases of hybrid poplars hue been identified in the north-central United States. Among the most serious are leaf and stem diseases caused by Melampsora, Marssonina, and Septoria. Short-term chemical controls are of limited usefulness. The most practical control strategy appears to be the use of resistant clones obtained through local...

Efficient interruption of infection chains by targeted removal of central holdings in an animal trade network.

Science.gov (United States)

Büttner, Kathrin; Krieter, Joachim; Traulsen, Arne; Traulsen, Imke

2013-01-01

Centrality parameters in animal trade networks typically have right-skewed distributions, implying that these networks are highly resistant against the random removal of holdings, but vulnerable to the targeted removal of the most central holdings. In the present study, we analysed the structural changes of an animal trade network topology based on the targeted removal of holdings using specific centrality parameters in comparison to the random removal of holdings. Three different time periods were analysed: the three-year network, the yearly and the monthly networks. The aim of this study was to identify appropriate measures for the targeted removal, which lead to a rapid fragmentation of the network. Furthermore, the optimal combination of the removal of three holdings regardless of their centrality was identified. The results showed that centrality parameters based on ingoing trade contacts, e.g. in-degree, ingoing infection chain and ingoing closeness, were not suitable for a rapid fragmentation in all three time periods. More efficient was the removal based on parameters considering the outgoing trade contacts. In all networks, a maximum percentage of 7.0% (on average 5.2%) of the holdings had to be removed to reduce the size of the largest component by more than 75%. The smallest difference from the optimal combination for all three time periods was obtained by the removal based on out-degree with on average 1.4% removed holdings, followed by outgoing infection chain and outgoing closeness. The targeted removal using the betweenness centrality differed the most from the optimal combination in comparison to the other parameters which consider the outgoing trade contacts. Due to the pyramidal structure and the directed nature of the pork supply chain the most efficient interruption of the infection chain for all three time periods was obtained by using the targeted removal based on out-degree.

Analysis of Road Network Pattern Considering Population Distribution and Central Business District.

Directory of Open Access Journals (Sweden)

Fangxia Zhao

Full Text Available This paper proposes a road network growing model with the consideration of population distribution and central business district (CBD attraction. In the model, the relative neighborhood graph (RNG is introduced as the connection mechanism to capture the characteristics of road network topology. The simulation experiment is set up to illustrate the effects of population distribution and CBD attraction on the characteristics of road network. Moreover, several topological attributes of road network is evaluated by using coverage, circuitness, treeness and total length in the experiment. Finally, the suggested model is verified in the simulation of China and Beijing Highway networks.

Brain structural covariance network centrality in maltreated youth with PTSD and in maltreated youth resilient to PTSD.

Science.gov (United States)

Sun, Delin; Haswell, Courtney C; Morey, Rajendra A; De Bellis, Michael D

2018-04-10

Child maltreatment is a major cause of pediatric posttraumatic stress disorder (PTSD). Previous studies have not investigated potential differences in network architecture in maltreated youth with PTSD and those resilient to PTSD. High-resolution magnetic resonance imaging brain scans at 3 T were completed in maltreated youth with PTSD (n = 31), without PTSD (n = 32), and nonmaltreated controls (n = 57). Structural covariance network architecture was derived from between-subject intraregional correlations in measures of cortical thickness in 148 cortical regions (nodes). Interregional positive partial correlations controlling for demographic variables were assessed, and those correlations that exceeded specified thresholds constituted connections in cortical brain networks. Four measures of network centrality characterized topology, and the importance of cortical regions (nodes) within the network architecture were calculated for each group. Permutation testing and principle component analysis method were employed to calculate between-group differences. Principle component analysis is a methodological improvement to methods used in previous brain structural covariance network studies. Differences in centrality were observed between groups. Larger centrality was found in maltreated youth with PTSD in the right posterior cingulate cortex; smaller centrality was detected in the right inferior frontal cortex compared to youth resilient to PTSD and controls, demonstrating network characteristics unique to pediatric maltreatment-related PTSD. Larger centrality was detected in right frontal pole in maltreated youth resilient to PTSD compared to youth with PTSD and controls, demonstrating structural covariance network differences in youth resilience to PTSD following maltreatment. Smaller centrality was found in the left posterior cingulate cortex and in the right inferior frontal cortex in maltreated youth compared to controls, demonstrating attributes of structural

Robust Stability Analysis of Neutral-Type Hybrid Bidirectional Associative Memory Neural Networks with Time-Varying Delays

Directory of Open Access Journals (Sweden)

Wei Feng

2014-01-01

Full Text Available The global asymptotic robust stability of equilibrium is considered for neutral-type hybrid bidirectional associative memory neural networks with time-varying delays and parameters uncertainties. The results we obtained in this paper are delay-derivative-dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported literature results. Two numerical examples are illustrated to verify our results.

HYBRID ARTIFICIAL NEURAL NETWORK APPLIEDTO MODELING SCFE OF BASIL AND ROSEMARY OILS

Directory of Open Access Journals (Sweden)

Giane STUART

1997-12-01

Full Text Available This work presents the results of a Hybrid Neural Network (HNN technique as applied to modeling SCFE curves obtained from two Brazilian vegetable matrices. A series Hybrid Neural Network was employed to estimate the parameters of the phenomenological model. A small set of SCFE data of each vegetable was used to generate an extended data set, sufficient to train the network. Afterwards, other sets of experimental data, not used in the network training, were used to validate the present approach. The series HNN correlates well the experimental data and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.Neste trabalho são apresentados os resultados obtidos na modelagem da extração supercrítica de óleo essencial de alfavaca e alecrim usando uma rede híbrida neuronal. Utilizou-se uma rede híbrida na configuração em série para estimar os parâmetros do modelo fenomenológico empregado para descrever o processo de extração, o modelo de Sovová. Um pequeno conjunto de dados experimentais, para cada matriz vegetal, foi usado para gerar um conjunto estendido de dados, suficiente para a etapa de treinamento da rede. A validação da presente proposta foi efetuada através da comparação entre os resultados preditos e aqueles obtidos experimentalmente que não constaram do processo de treinamento da rede. Demonstra-se que a rede híbrida neuronal correlaciona e prediz satisfatoriamente os dados experimentais, mostrando-se portanto promissora no campo da modelagem do processo de extração supercrítica.

Centralized cooperative spectrum sensing for ad-hoc disaster relief network clusters

DEFF Research Database (Denmark)

Pratas, Nuno; Marchetti, Nicola; Prasad, Neeli R.

2010-01-01

Disaster relief networks have to be highly adaptable and resilient. Cognitive radio enhanced ad-hoc architecture have been put forward as a candidate to enable such networks. Spectrum sensing is the cornerstone of the cognitive radio paradigm, and it has been the target of intensive research....... The main common conclusion was that the achievable spectrum sensing accuracy can be greatly enhanced through the use of cooperative sensing schemes. When considering applying Cognitive Radio to ad-hoc disaster relief networks, spectrum sensing cooperative schemes are paramount. A centralized cluster...

Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm

Science.gov (United States)

Mundher Yaseen, Zaher; Abdulmohsin Afan, Haitham; Tran, Minh-Tung

2018-04-01

Scientifically evidenced that beam-column joints are a critical point in the reinforced concrete (RC) structure under the fluctuation loads effects. In this novel hybrid data-intelligence model developed to predict the joint shear behavior of exterior beam-column structure frame. The hybrid data-intelligence model is called genetic algorithm integrated with deep learning neural network model (GA-DLNN). The genetic algorithm is used as prior modelling phase for the input approximation whereas the DLNN predictive model is used for the prediction phase. To demonstrate this structural problem, experimental data is collected from the literature that defined the dimensional and specimens’ properties. The attained findings evidenced the efficitveness of the hybrid GA-DLNN in modelling beam-column joint shear problem. In addition, the accurate prediction achived with less input variables owing to the feasibility of the evolutionary phase.

Application of Hybrid Meta-Heuristic Techniques for Optimal Load Shedding Planning and Operation in an Islanded Distribution Network Integrated with Distributed Generation

Directory of Open Access Journals (Sweden)

Jafar Jallad

2018-05-01

Full Text Available In a radial distribution network integrated with distributed generation (DG, frequency and voltage instability could occur due to grid disconnection, which would result in an islanded network. This paper proposes an optimal load shedding scheme to balance the electricity demand and the generated power of DGs. The integration of the Firefly Algorithm and Particle Swarm Optimization (FAPSO is proposed for the application of the planned load shedding and under frequency load shedding (UFLS scheme. In planning mode, the hybrid optimization maximizes the amount of load remaining and improves the voltage profile of load buses within allowable limits. Moreover, the hybrid optimization can be used in UFLS scheme to identify the optimal combination of loads that need to be shed from a network in operation mode. In order to assess the capabilities of the hybrid optimization, the IEEE 33-bus radial distribution system and part of the Malaysian distribution network with different types of DGs were used. The response of the proposed optimization method in planning and operation were compared with other optimization techniques. The simulation results confirmed the effectiveness of the proposed hybrid optimization in planning mode and demonstrated that the proposed UFLS scheme is quick enough to restore the system frequency without overshooting in less execution time.

Hybrid Access Femtocells in Overlaid MIMO Cellular Networks with Transmit Selection under Poisson Field Interference

KAUST Repository

Abdel Nabi, Amr A

2017-09-21

This paper analyzes the performance of hybrid control-access schemes for small cells (such as femtocells) in the context of two-tier overlaid cellular networks. The proposed hybrid access schemes allow for sharing the same downlink resources between the small-cell network and the original macrocell network, and their mode of operations are characterized considering post-processed signal-to-interference-plus-noise ratios (SINRs) or pre-processed interference-aware operation. The work presents a detailed treatment of achieved performance of a desired user that benefits from MIMO arrays configuration through the use of transmit antenna selection (TAS) and maximal ratio combining (MRC) in the presence of Poisson field interference processes on spatial links. Furthermore, based on the interference awareness at the desired user, two TAS approaches are treated, which are the signal-to-noise (SNR)-based selection and SINR-based selection. The analysis is generalized to address the cases of highly-correlated and un-correlated aggregated interference on different transmit channels. In addition, the effect of delayed TAS due to imperfect feedback and the impact of arbitrary TAS processing are investigated. The analytical results are validated by simulations, to clarify some of the main outcomes herein.

Hybrid Access Femtocells in Overlaid MIMO Cellular Networks with Transmit Selection under Poisson Field Interference

KAUST Repository

Abdel Nabi, Amr A; Al-Qahtani, Fawaz S.; Radaydeh, Redha Mahmoud Mesleh; Shaqfeh, Mohammed

2017-01-01

This paper analyzes the performance of hybrid control-access schemes for small cells (such as femtocells) in the context of two-tier overlaid cellular networks. The proposed hybrid access schemes allow for sharing the same downlink resources between the small-cell network and the original macrocell network, and their mode of operations are characterized considering post-processed signal-to-interference-plus-noise ratios (SINRs) or pre-processed interference-aware operation. The work presents a detailed treatment of achieved performance of a desired user that benefits from MIMO arrays configuration through the use of transmit antenna selection (TAS) and maximal ratio combining (MRC) in the presence of Poisson field interference processes on spatial links. Furthermore, based on the interference awareness at the desired user, two TAS approaches are treated, which are the signal-to-noise (SNR)-based selection and SINR-based selection. The analysis is generalized to address the cases of highly-correlated and un-correlated aggregated interference on different transmit channels. In addition, the effect of delayed TAS due to imperfect feedback and the impact of arbitrary TAS processing are investigated. The analytical results are validated by simulations, to clarify some of the main outcomes herein.

CLASSIFICATION OF NEURAL NETWORK FOR TECHNICAL CONDITION OF TURBOFAN ENGINES BASED ON HYBRID ALGORITHM

Directory of Open Access Journals (Sweden)

Valentin Potapov

2016-12-01

Full Text Available Purpose: This work presents a method of diagnosing the technical condition of turbofan engines using hybrid neural network algorithm based on software developed for the analysis of data obtained in the aircraft life. Methods: allows the engine diagnostics with deep recognition to the structural assembly in the presence of single structural damage components of the engine running and the multifaceted damage. Results: of the optimization of neural network structure to solve the problems of evaluating technical state of the bypass turbofan engine, when used with genetic algorithms.

Functional Carbon Nanotube/Mesoporous Carbon/MnO2 Hybrid Network for High-Performance Supercapacitors

Directory of Open Access Journals (Sweden)

Tao Tao

2014-01-01

Full Text Available A functional carbon nanotube/mesoporous carbon/MnO2 hybrid network has been developed successfully through a facile route. The resulting composites exhibited a high specific capacitance of 351 F/g at 1 A g−1, with intriguing charge/discharge rate performance and cycling stability due to a synergistic combination of large surface area and excellent electron-transport capabilities of MnO2 with the good conductivity of the carbon nanotube/mesoporous carbon networks. Such composite shows great potential to be used as electrodes for supercapacitors.

A hybrid Genetic and Simulated Annealing Algorithm for Chordal Ring implementation in large-scale networks

DEFF Research Database (Denmark)

Riaz, M. Tahir; Gutierrez Lopez, Jose Manuel; Pedersen, Jens Myrup

2011-01-01

The paper presents a hybrid Genetic and Simulated Annealing algorithm for implementing Chordal Ring structure in optical backbone network. In recent years, topologies based on regular graph structures gained a lot of interest due to their good communication properties for physical topology of the...

PK20, a new opioid-neurotensin hybrid peptide that exhibits central and peripheral antinociceptive effects

Directory of Open Access Journals (Sweden)

Tsuda Yuko

2010-12-01

Full Text Available Abstract Background The clinical treatment of various types of pain relies upon the use of opioid analgesics. However most of them produce, in addition to the analgesic effect, several side effects such as the development of dependence and addiction as well as sedation, dysphoria, and constipation. One solution to these problems are chimeric compounds in which the opioid pharmacophore is hybridized with another type of compound to incease antinociceptive effects. Neurotensin-induced antinociception is not mediated through the opioid system. Therefore, hybridizing neurotensin with opioid elements may result in a potent synergistic antinociceptor. Results Using the known structure-activity relationships of neurotensin we have synthesized a new chimeric opioid-neurotensin compound PK20 which is characterized by a very strong antinociceptive potency. The observation that the opioid antagonist naltrexone did not completely reverse the antinociceptive effect, indicates the partial involvement of the nonopioid component in PK20 in the produced analgesia. Conclusions The opioid-neurotensin hybrid analogue PK20, in which opioid and neurotensin pharmacophores overlap partially, expresses high antinociceptive tail-flick effects after central as well as peripheral applications.

Hybrid Scheduling/Signal-Level Coordination in the Downlink of Multi-Cloud Radio-Access Networks

KAUST Repository

Douik, Ahmed

2016-03-28

In the context of resource allocation in cloud- radio access networks, recent studies assume either signal-level or scheduling-level coordination. This paper, instead, considers a hybrid level of coordination for the scheduling problem in the downlink of a multi-cloud radio- access network, so as to benefit from both scheduling policies. Consider a multi-cloud radio access network, where each cloud is connected to several base-stations (BSs) via high capacity links, and therefore allows joint signal processing between them. Across the multiple clouds, however, only scheduling-level coordination is permitted, as it requires a lower level of backhaul communication. The frame structure of every BS is composed of various time/frequency blocks, called power- zones (PZs), and kept at fixed power level. The paper addresses the problem of maximizing a network-wide utility by associating users to clouds and scheduling them to the PZs, under the practical constraints that each user is scheduled, at most, to a single cloud, but possibly to many BSs within the cloud, and can be served by one or more distinct PZs within the BSs\\' frame. The paper solves the problem using graph theory techniques by constructing the conflict graph. The scheduling problem is, then, shown to be equivalent to a maximum- weight independent set problem in the constructed graph, in which each vertex symbolizes an association of cloud, user, BS and PZ, with a weight representing the utility of that association. Simulation results suggest that the proposed hybrid scheduling strategy provides appreciable gain as compared to the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.

ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

Institute of Scientific and Technical Information of China (English)

X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

2003-01-01

An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm-Artificial Neural Network.

Science.gov (United States)

Ramadan Suleiman, Ahmed; Nehdi, Moncef L

2017-02-07

This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm-artificial neural network (GA-ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA-ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials.

Bosch automotive electrics and automotive electronics systems and components, networking and hybrid drive

CERN Document Server

2014-01-01

The significance of electrical and electronic systems has increased considerably in the last few years and this trend is set to continue. The characteristics feature of innovative systems is the fact that they can work together in a network. This requires powerful bus systems that the electronic control units can use to exchange information. Networking and the various bus systems used in motor vehicles are the prominent new topic in the 5th edition of the "Automotive Electric, Automotive Electronics" technical manual. The existing chapters have also been updated, so that this new edition brings the reader up to date on the subjects of electrical and electronic systems in the motor vehicle. Content Electrical and electronical systems – Basic principles of networking - Examples of networked vehicles – Bus systems – Architecture of electronic systems – Mechatronics – Elektronics – Electronic control Units – Software – Sensors – Actuators – Hybrid drives – Vehicle electrical system – Start...

At the edge? HIV stigma and centrality in a community's social network in Namibia.

Science.gov (United States)

Smith, Rachel A; Baker, Michelle

2012-04-01

Social network analysis was used to examine the relationship between HIV/AIDS stigmatization, perceived risk, and centrality in the community network (via participation in community groups). The findings from respondents in Keetmanshoop, Namibia (N = 375) showed an interaction between stigma and risk perceptions\\hose who perceived higher HIV risk and stronger HIV stigma participated in fewer community groups and participated in groups with members who participated less widely across the network. In contrast, those who perceived higher HIV risk and weaker HIV stigma participated more, and were in community groups that are located on a greater share of the paths between entities in the network. Taboo, secrecy, resistance, knowing a person living with HIV/AIDS, and desire for diagnosis secrecy were also related to centrality. Findings suggest that the interaction of perceived HIV risk and HIV stigma are related to structural-level features of community networks based on participation in community groups.

Neural networks: Application to medical imaging

Science.gov (United States)

Clarke, Laurence P.

1994-01-01

The research mission is the development of computer assisted diagnostic (CAD) methods for improved diagnosis of medical images including digital x-ray sensors and tomographic imaging modalities. The CAD algorithms include advanced methods for adaptive nonlinear filters for image noise suppression, hybrid wavelet methods for feature segmentation and enhancement, and high convergence neural networks for feature detection and VLSI implementation of neural networks for real time analysis. Other missions include (1) implementation of CAD methods on hospital based picture archiving computer systems (PACS) and information networks for central and remote diagnosis and (2) collaboration with defense and medical industry, NASA, and federal laboratories in the area of dual use technology conversion from defense or aerospace to medicine.

Central executive network in young people with familial risk for psychosis--the Oulu Brain and Mind Study.

Science.gov (United States)

Jukuri, Tuomas; Kiviniemi, Vesa; Nikkinen, Juha; Miettunen, Jouko; Mäki, Pirjo; Mukkala, Sari; Koivukangas, Jenni; Nordström, Tanja; Parkkisenniemi, Juha; Moilanen, Irma; Barnett, Jennifer H; Jones, Peter B; Murray, Graham K; Veijola, Juha

2015-02-01

The central executive network controls and manages high-level cognitive functions. Abnormal activation in the central executive network has been related to psychosis and schizophrenia but it is not established how this applies to people with familial risk for psychosis (FR). We conducted a resting-state functional MRI (R-fMRI) in 72 (29 males) young adults with a history of psychosis in one or both parents (FR) but without psychosis themselves, and 72 (29 males) similarly healthy control subjects without parental psychosis. Both groups in the Oulu Brain and Mind Study were drawn from the Northern Finland Birth Cohort 1986. Participants were 20-25years old. Parental psychosis was established using the Care Register for Health Care. R-fMRI data pre-processing was conducted using independent component analysis with 30 and 70 components. A dual regression technique was used to detect between-group differences in the central executive network with pcontrol subjects in the right inferior frontal gyrus, a key area of central executive network corresponding to Brodmann areas 44 and 45, known as Broca's area. The volume of the lower activation area with 30 components was 896mm(3) and with 70 components was 1151mm(3). The activity of the central executive network differed in the right inferior frontal gyrus between FR and control groups. This suggests that abnormality of the right inferior frontal gyrus may be a central part of vulnerability for psychosis. Copyright © 2014 Elsevier B.V. All rights reserved.

Wireless sensors in complex networks: study and performance evaluation of a new hybrid model

Science.gov (United States)

Curia, Vincenzo; Santamaria, Amilcare Francesco; Sottile, Cesare; Voznak, Miroslav

2014-05-01

Many recent research efforts have confirmed that, given the natural evolution of telecommunication systems, they can be approached by a new modeling technique, not based yet on traditional approach of graphs theory. The branch of complex networking, although young, is able to introduce a new and strong way of networks modeling, nevertheless they are social, telecommunication or friendship networks. In this paper we propose a new modeling technique applied to Wireless Sensor Networks (WSNs). The modeling has the purpose of ensuring an improvement of the distributed communication, quantifying it in terms of clustering coefficient and average diameter of the entire network. The main idea consists in the introduction of hybrid Data Mules, able to enhance the whole connectivity of the entire network. The distribution degree of individual nodes in the network will follow a logarithmic trend, meaning that the most of the nodes are not necessarily adjacent but, for each pair of them, there exists a relatively short path that connects them. The effectiveness of the proposed idea has been validated thorough a deep campaign of simulations, proving also the power of complex and small-world networks.

The Impacts of Network Centrality and Self-Regulation on an E-Learning Environment with the Support of Social Network Awareness

Science.gov (United States)

Lin, Jian-Wei; Huang, Hsieh-Hong; Chuang, Yuh-Shy

2015-01-01

An e-learning environment that supports social network awareness (SNA) is a highly effective means of increasing peer interaction and assisting student learning by raising awareness of social and learning contexts of peers. Network centrality profoundly impacts student learning in an SNA-related e-learning environment. Additionally,…

Active Low Intrusion Hybrid Monitor for Wireless Sensor Networks.

Science.gov (United States)

Navia, Marlon; Campelo, Jose C; Bonastre, Alberto; Ors, Rafael; Capella, Juan V; Serrano, Juan J

2015-09-18

Several systems have been proposed to monitor wireless sensor networks (WSN). These systems may be active (causing a high degree of intrusion) or passive (low observability inside the nodes). This paper presents the implementation of an active hybrid (hardware and software) monitor with low intrusion. It is based on the addition to the sensor node of a monitor node (hardware part) which, through a standard interface, is able to receive the monitoring information sent by a piece of software executed in the sensor node. The intrusion on time, code, and energy caused in the sensor nodes by the monitor is evaluated as a function of data size and the interface used. Then different interfaces, commonly available in sensor nodes, are evaluated: serial transmission (USART), serial peripheral interface (SPI), and parallel. The proposed hybrid monitor provides highly detailed information, barely disturbed by the measurement tool (interference), about the behavior of the WSN that may be used to evaluate many properties such as performance, dependability, security, etc. Monitor nodes are self-powered and may be removed after the monitoring campaign to be reused in other campaigns and/or WSNs. No other hardware-independent monitoring platforms with such low interference have been found in the literature.

A Hybrid Energy Sharing Framework for Green Cellular Networks

KAUST Repository

Farooq, Muhammad Junaid

2016-12-09

Cellular operators are increasingly turning towards renewable energy (RE) as an alternative to using traditional electricity in order to reduce operational expenditure and carbon footprint. Due to the randomness in both RE generation and mobile traffic at each base station (BS), a surplus or shortfall of energy may occur at any given time. To increase energy selfreliance and minimize the network’s energy cost, the operator needs to efficiently exploit the RE generated across all BSs. In this paper, a hybrid energy sharing framework for cellular network is proposed, where a combination of physical power lines and energy trading with other BSs using smart grid is used. Algorithms for physical power lines deployment between BSs, based on average and complete statistics of the net RE available, are developed. Afterwards, an energy management framework is formulated to optimally determine the quantities of electricity and RE to be procured and exchanged among BSs, respectively, while considering battery capacities and real-time energy pricing. Three cases are investigated where RE generation is unknown, perfectly known, and partially known ahead of time. Results investigate the time varying energy management of BSs and demonstrate considerable reduction in average energy cost thanks to the hybrid energy sharing scheme.

61.3-Gbps hybrid fiber-wireless in-home network enabled by optical heterodyne and polarization multiplexing

NARCIS (Netherlands)

Cao, Z.; Li, F.; Liu, Y.; Yu, J.; Wang, Q.; Oh, C.W.; Jiao, Y.; Tran, N.C.; Boom, van den H.P.A.; Tangdiongga, E.; Koonen, A.M.J.

2014-01-01

A hybrid fiber-wireless in-home network is proposed to support high-speed multiple input and multiple output (MIMO) orthogonal frequency division multiplexing systems operating at millimeter wave (mm-wave) band by employing optical heterodyne (OH) and polarization multiplexing (PolMux). OH enables

Diagnoses of hybrid hummingbirds (Aves: Trochilidae). 17. Documentation of the intrageneric hybrid (Archilochus colubris × Archilochus alexandri)

DEFF Research Database (Denmark)

Graves, Gary R.; Dittmann, Donna L.; Cardiff, Steven W.

2016-01-01

Although the breeding ranges of Archilochus alexandri (Black-chinned Hummingbird) and Archilochus colubris (Ruby throated Hummingbird) are narrowly parapatric in central Texas and central and southern Oklahoma, there have been few reports of hybridization in the literature and no well......-documented hybrid specimens. Here we provide a comprehensive assessment of two male hybrids collected, respectively, in Grayson County, Texas, and East Baton Rouge Parish, Louisiana. As has been the pattern in other hummingbird hybrids, both specimens exhibit a blended mosaic of plumage characters of the parental...

Buffer Management and Hybrid Probability Choice Routing for Packet Delivery in Opportunistic Networks

Directory of Open Access Journals (Sweden)

Daru Pan

2012-01-01

Full Text Available Due to the features of long connection delays, frequent network partitions, and topology unsteadiness, the design of opportunistic networks faces the challenge of how to effectively deliver data based only on occasional encountering of nodes, where the conventional routing schemes do not work properly. This paper proposes a hybrid probability choice routing protocol with buffer management for opportunistic networks. A delivery probability function is set up based on continuous encounter duration time, which is used for selecting a better node to relay packets. By combining the buffer management utility and the delivery probability, a total utility is used to decide whether the packet should be kept in the buffer or be directly transmitted to the encountering node. Simulation results show that the proposed routing outperforms the existing one in terms of the delivery rate and the average delay.

An Evolutionary Mobility Aware Multi-Objective Hybrid Routing Algorithm for Heterogeneous Wireless Sensor Networks

DEFF Research Database (Denmark)

Kulkarni, Nandkumar P.; Prasad, Neeli R.; Prasad, Ramjee

deliberation. To tackle these two problems, Mobile Wireless Sensor Networks (MWSNs) is a better choice. In MWSN, Sensor nodes move freely to a target area without the need for any special infrastructure. Due to mobility, the routing process in MWSN has become more complicated as connections in the network can...... such as Average Energy consumption, Control Overhead, Reaction Time, LQI, and HOP Count. The authors study the influence of energy heterogeneity and mobility of sensor nodes on the performance of EMRP. The Performance of EMRP compared with Simple Hybrid Routing Protocol (SHRP) and Dynamic Multi-Objective Routing...

A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China

Science.gov (United States)

Zhao, Shuangming; Zhao, Pengxiang; Cui, Yunfan

2017-07-01

In this paper, we propose an improved network centrality measure framework that takes into account both the topological characteristics and the geometric properties of a road network in order to analyze urban traffic flow in relation to different modes: intersection, road, and community, which correspond to point mode, line mode, and area mode respectively. Degree, betweenness, and PageRank centralities are selected as the analysis measures, and GPS-enabled taxi trajectory data is used to evaluate urban traffic flow. The results show that the mean value of the correlation coefficients between the modified degree, the betweenness, and the PageRank centralities and the traffic flow in all periods are higher than the mean value of the correlation coefficients between the conventional degree, the betweenness, the PageRank centralities and the traffic flow at different modes; this indicates that the modified measurements, for analyzing traffic flow, are superior to conventional centrality measurements. This study helps to shed light into the understanding of urban traffic flow in relation to different modes from the perspective of complex networks.

Hybrid synchronization of two independent chaotic systems on ...

Indian Academy of Sciences (India)

One is how the same network node of the complex network was affected by different information sources. Another is how to achieve hybrid synchronization on the network. In this paper, the theoretical analysis andnumerical simulation on various complex networks are implemented. The results indicate that the hybrid ...

A novel power control strategy of Modular Multi-level Converter in HVDC-AC hybrid transmission systems for passive networks

DEFF Research Database (Denmark)

Hu, Zhenda; Wu, Rui; Yang, Xiaodong

2014-01-01

With the development of High Voltage DC Transmission (HVDC) technology, there will be more and more HVDC-AC hybrid transmission system in the world. A basic challenge in HVDC-AC hybrid transmission systems is to optimize the power sharing between DC and AC lines, which become more severe when sup...... control strategy of Modular Multi-level Converter in VSC-HVDC, which can optimize converter output power according to passive network loading variation. Proposal method is studied with a case study of a VSC-HVDC AC hybrid project by PSCAD/EMTDC simulations....

Fuzzy Based Advanced Hybrid Intrusion Detection System to Detect Malicious Nodes in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Rupinder Singh

2017-01-01

Full Text Available In this paper, an Advanced Hybrid Intrusion Detection System (AHIDS that automatically detects the WSNs attacks is proposed. AHIDS makes use of cluster-based architecture with enhanced LEACH protocol that intends to reduce the level of energy consumption by the sensor nodes. AHIDS uses anomaly detection and misuse detection based on fuzzy rule sets along with the Multilayer Perceptron Neural Network. The Feed Forward Neural Network along with the Backpropagation Neural Network are utilized to integrate the detection results and indicate the different types of attackers (i.e., Sybil attack, wormhole attack, and hello flood attack. For detection of Sybil attack, Advanced Sybil Attack Detection Algorithm is developed while the detection of wormhole attack is done by Wormhole Resistant Hybrid Technique. The detection of hello flood attack is done by using signal strength and distance. An experimental analysis is carried out in a set of nodes; 13.33% of the nodes are determined as misbehaving nodes, which classified attackers along with a detection rate of the true positive rate and false positive rate. Sybil attack is detected at a rate of 99,40%; hello flood attack has a detection rate of 98, 20%; and wormhole attack has a detection rate of 99, 20%.

Saliency U-Net: A regional saliency map-driven hybrid deep learning network for anomaly segmentation

Science.gov (United States)

Karargyros, Alex; Syeda-Mahmood, Tanveer

2018-02-01

Deep learning networks are gaining popularity in many medical image analysis tasks due to their generalized ability to automatically extract relevant features from raw images. However, this can make the learning problem unnecessarily harder requiring network architectures of high complexity. In case of anomaly detection, in particular, there is often sufficient regional difference between the anomaly and the surrounding parenchyma that could be easily highlighted through bottom-up saliency operators. In this paper we propose a new hybrid deep learning network using a combination of raw image and such regional maps to more accurately learn the anomalies using simpler network architectures. Specifically, we modify a deep learning network called U-Net using both the raw and pre-segmented images as input to produce joint encoding (contraction) and expansion paths (decoding) in the U-Net. We present results of successfully delineating subdural and epidural hematomas in brain CT imaging and liver hemangioma in abdominal CT images using such network.

Centrality and get-richer mechanisms in interregional knowledge networks

DEFF Research Database (Denmark)

Mitze, Timo; Strotebeck, Falk

2018-01-01

and relate them to sector-region-specific and overall regional attributes in an explorative regression approach. The results indicate that fit-get-richer mechanisms proxied by regional endowments and policy factors such as biotech research and development funding categories and human capital matter...... for network formation. We find that these correlations differ across centrality measures and that empirical evidence for a richer-get-richer mechanism is limited....

Study of Hybrid Localization Noncooperative Scheme in Wireless Sensor Network

Directory of Open Access Journals (Sweden)

Irfan Dwiguna Sumitra

2017-01-01

Full Text Available In this paper, we evaluated the experiment and analysis measurement accuracy to determine object location based on wireless sensor network (WSN. The algorithm estimates the position of sensor nodes employing received signal strength (RSS from scattered nodes in the environment, in particular for the indoor building. Besides that, we considered another algorithm based on weight centroid localization (WCL. In particular testbed, we combined both RSS and WCL as hybrid localization in case of noncooperative scheme with considering that source nodes directly communicate only with anchor nodes. Our experimental result shows localization accuracy of more than 90% and obtained the estimation error reduction to 4% compared to existing algorithms.

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

Energy Technology Data Exchange (ETDEWEB)

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

2016-04-01

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

Performance evaluations of hybrid modulation with different optical labels over PDQ in high bit-rate OLS network systems.

Science.gov (United States)

Xu, M; Li, Y; Kang, T Z; Zhang, T S; Ji, J H; Yang, S W

2016-11-14

Two orthogonal modulation optical label switching(OLS) schemes, which are based on payload of polarization multiplexing-differential quadrature phase shift keying(POLMUX-DQPSK or PDQ) modulated with identifications of duobinary (DB) label and pulse position modulation(PPM) label, are researched in high bit-rate OLS network. The BER performance of hybrid modulation with payload and label signals are discussed and evaluated in theory and simulation. The theoretical BER expressions of PDQ, PDQ-DB and PDQ-PPM are given with analysis method of hybrid modulation encoding in different the bit-rate ratios of payload and label. Theoretical derivation results are shown that the payload of hybrid modulation has a certain gain of receiver sensitivity than payload without label. The sizes of payload BER gain obtained from hybrid modulation are related to the different types of label. The simulation results are consistent with that of theoretical conclusions. The extinction ratio (ER) conflicting between hybrid encoding of intensity and phase types can be compromised and optimized in OLS system of hybrid modulation. The BER analysis method of hybrid modulation encoding in OLS system can be applied to other n-ary hybrid modulation or combination modulation systems.

Two-phase hybrid cryptography algorithm for wireless sensor networks

Directory of Open Access Journals (Sweden)

Rawya Rizk

2015-12-01

Full Text Available For achieving security in wireless sensor networks (WSNs, cryptography plays an important role. In this paper, a new security algorithm using combination of both symmetric and asymmetric cryptographic techniques is proposed to provide high security with minimized key maintenance. It guarantees three cryptographic primitives, integrity, confidentiality and authentication. Elliptical Curve Cryptography (ECC and Advanced Encryption Standard (AES are combined to provide encryption. XOR-DUAL RSA algorithm is considered for authentication and Message Digest-5 (MD5 for integrity. The results show that the proposed hybrid algorithm gives better performance in terms of computation time, the size of cipher text, and the energy consumption in WSN. It is also robust against different types of attacks in the case of image encryption.

Metropolitian area network services comprised of virtual local area networks running over hybrid fiber-coax and asynchronous transfer mode technologies

Science.gov (United States)

Biedron, William S.

1995-11-01

Since 1990 there has been a rapid increase in the demand for communication services, especially local and wide area network (LAN/WAN) oriented services. With the introduction of the DFB laser transmitter, hybrid-fiber-coax (HFC) cable plant designs, ATM transport technologies and rf modems, new LAN/WAN services can now be defined and marketed to residential and business customers over existing cable TV systems. The term metropolitan area network (MAN) can be used to describe this overall network. This paper discusses the technical components needed to provision these services as well as provides some perspectives on integration issues. Architecture at the headend and in the backbone is discussed, as well as specific service definitions and the technology issues associated with each. The TCP/IP protocol is suggested as a primary protocol to be used throughout the MAN.

Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi-valued logic.

Science.gov (United States)

Khan, Faiz M; Schmitz, Ulf; Nikolov, Svetoslav; Engelmann, David; Pützer, Brigitte M; Wolkenhauer, Olaf; Vera, Julio

2014-01-01

A decade of successful results indicates that systems biology is the appropriate approach to investigate the regulation of complex biochemical networks involving transcriptional and post-transcriptional regulations. It becomes mandatory when dealing with highly interconnected biochemical networks, composed of hundreds of compounds, or when networks are enriched in non-linear motifs like feedback and feedforward loops. An emerging dilemma is to conciliate models of massive networks and the adequate description of non-linear dynamics in a suitable modeling framework. Boolean networks are an ideal representation of massive networks that are humble in terms of computational complexity and data demand. However, they are inappropriate when dealing with nested feedback/feedforward loops, structural motifs common in biochemical networks. On the other hand, models of ordinary differential equations (ODEs) cope well with these loops, but they require enormous amounts of quantitative data for a full characterization of the model. Here we propose hybrid models, composed of ODE and logical sub-modules, as a strategy to handle large scale, non-linear biochemical networks that include transcriptional and post-transcriptional regulations. We illustrate the construction of this kind of models using as example a regulatory network centered on E2F1, a transcription factor involved in cancer. The hybrid modeling approach proposed is a good compromise between quantitative/qualitative accuracy and scalability when considering large biochemical networks with a small highly interconnected core, and module of transcriptionally regulated genes that are not part of critical regulatory loops. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai. Copyright © 2013 Elsevier B.V. All rights reserved.

Decentralized vs. centralized scheduling in wireless sensor networks for data fusion

NARCIS (Netherlands)

Mitici, M.A.; Goseling, Jasper; de Graaf, Maurits; Boucherie, Richardus J.

2014-01-01

We consider the problem of data estimation in a sensor wireless network where sensors transmit their observations according to decentralized and centralized transmission schedules. A data collector is interested in achieving a data estimation using several sensor observations such that the variance

Decentralized vs. centralized scheduling in wireless sensor networks for data fusion

NARCIS (Netherlands)

Mitici, Mihaela; Goseling, Jasper; de Graaf, Maurits; Boucherie, Richardus J.

2013-01-01

We consider the problem of data estimation in a sensor wireless network where sensors transmit their observations according to decentralized and centralized transmission schedules. A data collector is interested in achieving a data estimation using several sensor observations such that the variance

Pinning synchronization of hybrid-coupled directed delayed dynamical network via intermittent control.

Science.gov (United States)

Cai, Shuiming; Zhou, Peipei; Liu, Zengrong

2014-09-01

This paper concerns the problem of exponential synchronization for a class of general delayed dynamical networks with hybrid coupling via pinning periodically intermittent control. Both the internal delay and coupling delay are taken into account in the network model. Meanwhile, the transmission delay and self-feedback delay are involved in the delayed coupling term. By establishing a new differential inequality, several simple and useful exponential synchronization criteria are derived analytically. It is shown that the controlled synchronization state can vary in comparison with the conventional synchronized solution, and the degree of the node and the inner delayed coupling matrix play important roles in the controlled synchronization state. By choosing different inner delayed coupling matrices and the degrees of the node, different controlled synchronization states can be obtained. Furthermore, the detail pinning schemes deciding what nodes should be chosen as pinned candidates and how many nodes are needed to be pinned for a fixed coupling strength are provided. The simple procedures illuminating how to design suitable intermittent controllers in real application are also given. Numerical simulations, including an undirected scale-free network and a directed small-world network, are finally presented to demonstrate the effectiveness of the theoretical results.

Optimal Performance Monitoring of Hybrid Mid-Infrared Wavelength MIMO Free Space Optical and RF Wireless Networks in Fading Channels

Science.gov (United States)

Schmidt, Barnet Michael

An optimal performance monitoring metric for a hybrid free space optical and radio-frequency (RF) wireless network, the Outage Capacity Objective Function, is analytically developed and studied. Current and traditional methods of performance monitoring of both optical and RF wireless networks are centered on measurement of physical layer parameters, the most common being signal-to-noise ratio, error rate, Q factor, and eye diagrams, occasionally combined with link-layer measurements such as data throughput, retransmission rate, and/or lost packet rate. Network management systems frequently attempt to predict or forestall network failures by observing degradations of these parameters and to attempt mitigation (such as offloading traffic, increasing transmitter power, reducing the data rate, or combinations thereof) prior to the failure. These methods are limited by the frequent low sensitivity of the physical layer parameters to the atmospheric optical conditions (measured by optical signal-to-noise ratio) and the radio frequency fading channel conditions (measured by signal-to-interference ratio). As a result of low sensitivity, measurements of this type frequently are unable to predict impending failures sufficiently in advance for the network management system to take corrective action prior to the failure. We derive and apply an optimal measure of hybrid network performance based on the outage capacity of the hybrid optical and RF channel, the outage capacity objective function. The objective function provides high sensitivity and reliable failure prediction, and considers both the effects of atmospheric optical impairments on the performance of the free space optical segment as well as the effect of RF channel impairments on the radio frequency segment. The radio frequency segment analysis considers the three most common RF channel fading statistics: Rayleigh, Ricean, and Nakagami-m. The novel application of information theory to the underlying physics of the

Age-dependent effects of brain stimulation on network centrality.

Science.gov (United States)

Antonenko, Daria; Nierhaus, Till; Meinzer, Marcus; Prehn, Kristin; Thielscher, Axel; Ittermann, Bernd; Flöel, Agnes

2018-04-18

Functional magnetic resonance imaging (fMRI) studies have suggested that advanced age may mediate the effects of transcranial direct current stimulation (tDCS) on brain function. However, studies directly comparing neural tDCS effects between young and older adults are scarce and limited to task-related imaging paradigms. Resting-state (rs-) fMRI, that is independent of age-related differences in performance, is well suited to investigate age-associated differential neural tDCS effects. Three "online" tDCS conditions (anodal, cathodal, sham) were compared in a cross-over, within-subject design, in 30 young and 30 older adults. Active stimulation targeted the left sensorimotor network (active electrode over left sensorimotor cortex with right supraorbital reference electrode). A graph-based rs-fMRI data analysis approach (eigenvector centrality mapping) and complementary seed-based analyses characterized neural tDCS effects. An interaction between anodal tDCS and age group was observed. Specifically, centrality in bilateral paracentral and posterior regions (precuneus, superior parietal cortex) was increased in young, but decreased in older adults. Seed-based analyses revealed that these opposing patterns of tDCS-induced centrality modulation were explained from differential effects of tDCS on functional coupling of the stimulated left paracentral lobule. Cathodal tDCS did not show significant effects. Our study provides first evidence for differential tDCS effects on neural network organization in young and older adults. Anodal stimulation mainly affected coupling of sensorimotor with ventromedial prefrontal areas in young and decoupling with posteromedial areas in older adults. Copyright © 2018. Published by Elsevier Inc.

A Triblock Copolymer Design Leads to Robust Hybrid Hydrogels for High-Performance Flexible Supercapacitors.

Science.gov (United States)

Zhang, Guangzhao; Chen, Yunhua; Deng, Yonghong; Wang, Chaoyang

2017-10-18

We report here an intriguing hybrid conductive hydrogel as electrode for high-performance flexible supercapacitor. The key is using a rationally designed water-soluble ABA triblock copolymer (termed as IAOAI) containing a central poly(ethylene oxide) block (A) and terminal poly(acrylamide) (PAAm) block with aniline moieties randomly incorporated (B), which was synthesized by reversible additional fragment transfer polymerization. The subsequent copolymerization of aniline monomers with the terminated aniline moieties on the IAOAI polymer generates a three-dimensional cross-linking hybrid network. The hybrid hydrogel electrode demonstrates robust mechanical flexibility, remarkable electrochemical capacitance (919 F/g), and cyclic stability (90% capacitance retention after 1000 cycles). Moreover, the flexible supercapacitor based on this hybrid hydrogel electrode presents a large specific capacitance (187 F/g), superior to most reported conductive hydrogel-based supercapacitors. With the demonstrated additional favorable cyclic stability and excellent capacitive and rate performance, this hybrid hydrogel-based supercapacitor holds great promise for flexible energy-storage device.

Biologically Inspired Modular Neural Control for a Leg-Wheel Hybrid Robot

DEFF Research Database (Denmark)

Manoonpong, Poramate; Wörgötter, Florentin; Laksanacharoen, Pudit

2014-01-01

In this article we present modular neural control for a leg-wheel hybrid robot consisting of three legs with omnidirectional wheels. This neural control has four main modules having their functional origin in biological neural systems. A minimal recurrent control (MRC) module is for sensory signal...... processing and state memorization. Its outputs drive two front wheels while the rear wheel is controlled through a velocity regulating network (VRN) module. In parallel, a neural oscillator network module serves as a central pattern generator (CPG) controls leg movements for sidestepping. Stepping directions...... or they can serve as useful modules for other module-based neural control applications....

Separating temporal and topological effects in walk-based network centrality.

Science.gov (United States)

Colman, Ewan R; Charlton, Nathaniel

2016-07-01

The recently introduced concept of dynamic communicability is a valuable tool for ranking the importance of nodes in a temporal network. Two metrics, broadcast score and receive score, were introduced to measure the centrality of a node with respect to a model of contagion based on time-respecting walks. This article examines the temporal and structural factors influencing these metrics by considering a versatile stochastic temporal network model. We analytically derive formulas to accurately predict the expectation of the broadcast and receive scores when one or more columns in a temporal edge-list are shuffled. These methods are then applied to two publicly available data sets and we quantify how much the centrality of each individual depends on structural or temporal influences. From our analysis, we highlight two practical contributions: a way to control for temporal variation when computing dynamic communicability and the conclusion that the broadcast and receive scores can, under a range of circumstances, be replaced by the row and column sums of the matrix exponential of a weighted adjacency matrix given by the data.

An efficient hybrid protection scheme with shared/dedicated backup paths on elastic optical networks

Directory of Open Access Journals (Sweden)

Nogbou G. Anoh

2017-02-01

Full Text Available Fast recovery and minimum utilization of resources are the two main criteria for determining the protection scheme quality. We address the problem of providing a hybrid protection approach on elastic optical networks under contiguity and continuity of available spectrum constraints. Two main hypotheses are used in this paper for backup paths computation. In the first case, it is assumed that backup paths resources are dedicated. In the second case, the assumption is that backup paths resources are available shared resources. The objective of the study is to minimize spectrum utilization to reduce blocking probability on a network. For this purpose, an efficient survivable Hybrid Protection Lightpath (HybPL algorithm is proposed for providing shared or dedicated backup path protection based on the efficient energy calculation and resource availability. Traditional First-Fit and Best-Fit schemes are employed to search and assign the available spectrum resources. The simulation results show that HybPL presents better performance in terms of blocking probability, compared with the Minimum Resources Utilization Dedicated Protection (MRU-DP algorithm which offers better performance than the Dedicated Protection (DP algorithm.

Formation of hybrid gold nanoparticle network aggregates by specific host-guest interactions in a turbulent flow reactor

NARCIS (Netherlands)

Weinhart-Mejia, R.; Huskens, Jurriaan

2014-01-01

A multi-inlet vortex mixer (MIVM) was used to investigate the formation of hybrid gold nanoparticle network aggregates under highly turbulent flow conditions. To form aggregates, gold nanoparticles were functionalized with β-cyclodextrin (CD) and mixed with adamantyl (Ad)-terminated

The Central American Network for Disaster and Health Information.

Science.gov (United States)

Arnesen, Stacey J; Cid, Victor H; Scott, John C; Perez, Ricardo; Zervaas, Dave

2007-07-01

This paper describes an international outreach program to support rebuilding Central America's health information infrastructure after several natural disasters in the region, including Hurricane Mitch in 1998 and two major earthquakes in 2001. The National Library of Medicine joined forces with the Pan American Health Organization/World Health Organization, the United Nations International Strategy for Disaster Reduction, and the Regional Center of Disaster Information for Latin America and the Caribbean (CRID) to strengthen libraries and information centers in Central America and improve the availability of and access to health and disaster information in the region by developing the Central American Network for Disaster and Health Information (CANDHI). Through CRID, the program created ten disaster health information centers in medical libraries and disaster-related organizations in six countries. This project served as a catalyst for the modernization of several medical libraries in Central America. The resulting CANDHI provides much needed electronic access to public health "gray literature" on disasters, as well as access to numerous health information resources. CANDHI members assist their institutions and countries in a variety of disaster preparedness activities through collecting and disseminating information.

Polyaniline nanowires-gold nanoparticles hybrid network based chemiresistive hydrogen sulfide sensor

Science.gov (United States)

Shirsat, Mahendra D.; Bangar, Mangesh A.; Deshusses, Marc A.; Myung, Nosang V.; Mulchandani, Ashok

2009-02-01

We report a sensitive, selective, and fast responding room temperature chemiresistive sensor for hydrogen sulfide detection and quantification using polyaniline nanowires-gold nanoparticles hybrid network. The sensor was fabricated by facile electrochemical technique. Initially, polyaniline nanowires with a diameter of 250-320 nm bridging the gap between a pair of microfabricated gold electrodes were synthesized using templateless electrochemical polymerization using a two step galvanostatic technique. Polyaniline nanowires were then electrochemically functionalized with gold nanoparticles using cyclic voltammetry technique. These chemiresistive sensors show an excellent limit of detection (0.1 ppb), wide dynamic range (0.1-100 ppb), and very good selectivity and reproducibility.

Positive Selection and Centrality in the Yeast and Fly Protein-Protein Interaction Networks

Directory of Open Access Journals (Sweden)

Sandip Chakraborty

2016-01-01

Full Text Available Proteins within a molecular network are expected to be subject to different selective pressures depending on their relative hierarchical positions. However, it is not obvious what genes within a network should be more likely to evolve under positive selection. On one hand, only mutations at genes with a relatively high degree of control over adaptive phenotypes (such as those encoding highly connected proteins are expected to be “seen” by natural selection. On the other hand, a high degree of pleiotropy at these genes is expected to hinder adaptation. Previous analyses of the human protein-protein interaction network have shown that genes under long-term, recurrent positive selection (as inferred from interspecific comparisons tend to act at the periphery of the network. It is unknown, however, whether these trends apply to other organisms. Here, we show that long-term positive selection has preferentially targeted the periphery of the yeast interactome. Conversely, in flies, genes under positive selection encode significantly more connected and central proteins. These observations are not due to covariation of genes’ adaptability and centrality with confounding factors. Therefore, the distribution of proteins encoded by genes under recurrent positive selection across protein-protein interaction networks varies from one species to another.

Hybrid models for chemical reaction networks: Multiscale theory and application to gene regulatory systems

Science.gov (United States)

Winkelmann, Stefanie; Schütte, Christof

2017-09-01

Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations.

Hybrid models for chemical reaction networks: Multiscale theory and application to gene regulatory systems.

Science.gov (United States)

Winkelmann, Stefanie; Schütte, Christof

2017-09-21

Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations.

Label-free detection of DNA hybridization using carbon nanotube network field-effect transistors

Science.gov (United States)

Star, Alexander; Tu, Eugene; Niemann, Joseph; Gabriel, Jean-Christophe P.; Joiner, C. Steve; Valcke, Christian

2006-01-01

We report carbon nanotube network field-effect transistors (NTNFETs) that function as selective detectors of DNA immobilization and hybridization. NTNFETs with immobilized synthetic oligonucleotides have been shown to specifically recognize target DNA sequences, including H63D single-nucleotide polymorphism (SNP) discrimination in the HFE gene, responsible for hereditary hemochromatosis. The electronic responses of NTNFETs upon single-stranded DNA immobilization and subsequent DNA hybridization events were confirmed by using fluorescence-labeled oligonucleotides and then were further explored for label-free DNA detection at picomolar to micromolar concentrations. We have also observed a strong effect of DNA counterions on the electronic response, thus suggesting a charge-based mechanism of DNA detection using NTNFET devices. Implementation of label-free electronic detection assays using NTNFETs constitutes an important step toward low-cost, low-complexity, highly sensitive and accurate molecular diagnostics. hemochromatosis | SNP | biosensor

Luminescent hybrid films obtained by covalent grafting of terbium complex to silica network

International Nuclear Information System (INIS)

Liu Fengyi; Fu Lianshe; Wang Jun; Liu Ze; Li Huanrong; Zhang Hongjie

2002-01-01

Luminescent hybrid thin films consisting of terbium complex covalently bonded to a silica-based network have been obtained in situ via a sol-gel approach. A new monomer, N-(4-benzoic acid-yl), N'-(propyltriethoxysilyl)urea (PABI), has been synthesized by grafting isocyanatopropyltriethoxysilane (ICPTES) to p-aminobenzoic acid and characterized by 1 H NMR, IR and MS. The monomer acts as a ligand for Tb 3+ ion and as a sol-gel precursor. Band emission from Tb 3+ ion due to an efficient ligand-to-metal energy transfer was observed by UV excitation. The decay curves of Tb 3+ in the hybrid films were measured. The energy difference between the triplet state energy of PABI and the 5 D 4 level of Tb 3+ ion falls in the exciting range to sensitize Tb 3+ ion fluorescence

Hybrid E-Learning Tool TransLearning: Video Storytelling to Foster Vicarious Learning within Multi-Stakeholder Collaboration Networks

Science.gov (United States)

van der Meij, Marjoleine G.; Kupper, Frank; Beers, Pieter J.; Broerse, Jacqueline E. W.

2016-01-01

E-learning and storytelling approaches can support informal vicarious learning within geographically widely distributed multi-stakeholder collaboration networks. This case study evaluates hybrid e-learning and video-storytelling approach "TransLearning" by investigation into how its storytelling e-tool supported informal vicarious…

Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications

KAUST Repository

Douik, Ahmed S.

2015-05-01

From its introduction to its quindecennial, network coding have built a strong reputation in enhancing packet recovery process and achieving maximum information flow in both wires and wireless networks. Traditional studies focused on optimizing the throughput of the network by proposing complex schemes that achieve optimal delay. With the shift toward distributed computing at mobile devices, throughput and complexity become both critical factors that affect the efficiency of a coding scheme. Instantly decodable network coding imposed itself as a new paradigm in network coding that trades off this two aspects. This paper presents a survey of instantly decodable network coding schemes that are proposed in the literature. The various schemes are identified, categorized and evaluated. Two categories can be distinguished namely the conventional centralized schemes and the distributed or cooperative schemes. For each scheme, the comparison is carried out in terms of reliability, performance, complexity and packet selection methodology. Although the performance is generally inversely proportional to the computation complexity, numerous successful schemes from both the performance and complexity viewpoint are identified.

Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications

KAUST Repository

Douik, Ahmed S.

2015-01-01

From its introduction to its quindecennial, network coding have built a strong reputation in enhancing packet recovery process and achieving maximum information flow in both wires and wireless networks. Traditional studies focused on optimizing the throughput of the network by proposing complex schemes that achieve optimal delay. With the shift toward distributed computing at mobile devices, throughput and complexity become both critical factors that affect the efficiency of a coding scheme. Instantly decodable network coding imposed itself as a new paradigm in network coding that trades off this two aspects. This paper presents a survey of instantly decodable network coding schemes that are proposed in the literature. The various schemes are identified, categorized and evaluated. Two categories can be distinguished namely the conventional centralized schemes and the distributed or cooperative schemes. For each scheme, the comparison is carried out in terms of reliability, performance, complexity and packet selection methodology. Although the performance is generally inversely proportional to the computation complexity, numerous successful schemes from both the performance and complexity viewpoint are identified.

Solar Central Receiver Hybrid Power Systems sodium-cooled receiver concept. Final report. Volume II, Book 1. Conceptual design, Sections 1 through 4

Energy Technology Data Exchange (ETDEWEB)

None

1980-01-01

The overall, long-term objective of the Solar Central Receiver Hybrid Power System program is to identify, characterize, and ultimately demonstrate the viability and cost effectiveness of solar/fossil, steam Rankine cycle, hybrid power systems that: (1) consist of a combined solar central receiver energy source and a nonsolar energy source at a single, common site, (2) may operate in the base, intermediate, and peaking capacity modes, (3) produce the rated output independent of variations in solar insolation, (4) provide a significant savings (50% or more) in fuel consumption, and (5) produce power at the minimum possible cost in mills/kWh. It is essential that these hybrid concepts be technically feasible and economically competitive with other systems in the near to mid-term time period (1985-1990) on a commercial scale. The program objective for Phase I is to identify and conceptually characterize solar/fossil steam Rankine cycle, commercial-scale, power plant systems that are economically viable and technically feasible. This volume presents in detail the market analysis, parametric analysis, and the selection process for the preferred system. (WHK)

Closeness-Centrality-Based Synchronization Criteria for Complex Dynamical Networks With Interval Time-Varying Coupling Delays.

Science.gov (United States)

Park, Myeongjin; Lee, Seung-Hoon; Kwon, Oh-Min; Seuret, Alexandre

2017-09-06

This paper investigates synchronization in complex dynamical networks (CDNs) with interval time-varying delays. The CDNs are representative of systems composed of a large number of interconnected dynamical units, and for the purpose of the mathematical analysis, the leading work is to model them as graphs whose nodes represent the dynamical units. At this time, we take note of the importance of each node in networks. One way, in this paper, is that the closeness-centrality mentioned in the field of social science is grafted onto the CDNs. By constructing a suitable Lyapunov-Krasovskii functional, and utilizing some mathematical techniques, the sufficient and closeness-centrality-based conditions for synchronization stability of the networks are established in terms of linear matrix inequalities. Ultimately, the use of the closeness-centrality can be weighted with regard to not only the interconnection relation among the nodes, which was utilized in the existing works but also more information about nodes. Here, the centrality will be added as the concerned information. Moreover, to avoid the computational burden causing the nonconvex term including the square of the time-varying delay, how to deal with it is applied by estimating it to the convex term including time-varying delay. Finally, two illustrative examples are given to show the advantage of the closeness-centrality in point of the robustness on time-delay.

A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting

Directory of Open Access Journals (Sweden)

Yuyang Gao

2016-09-01

Full Text Available With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data processing, but the individual back-propagation artificial neural network cannot always satisfy the time series forecasting needs. Thus, a hybrid forecasting approach has been proposed in this study, which consists of data preprocessing, parameter optimization and a neural network for advancing the accuracy of short-term wind speed forecasting. According to the case study, in which the data are collected from Peng Lai, a city located in China, the simulation results indicate that the hybrid forecasting method yields better predictions compared to the individual BP, which indicates that the hybrid method exhibits stronger forecasting ability.

A Study on International Multimodal Transport Networks from Korea to Central Asia: Focus on Secondhand Vehicles

Directory of Open Access Journals (Sweden)

Ying Wang

2016-03-01

Full Text Available Currently, high-income countries use multimodal transportation to export large quantities of secondhand vehicles to low-income countries. Secondhand vehicle export has shown its highest growth in recent years, especially in Korea. The problem of transporting secondhand vehicles from Korea to Central Asia is becoming an important issue, but few researchers are interested in it. The objective of this research is to determine the optimal transport network for exporting secondhand vehicles from Korea to Central Asian countries by combining experts’ opinions and real data from existing transport networks. The fuzzy Delphi method was applied to obtain factors to evaluate alternative multimodal transport networks for moving secondhand vehicles from Korea to Central Asian countries by judgment from experts. The analysis shows that among the five factors (total cost, total time, reliability, security, and transportation capability, total cost is the most considerable factor, followed by reliability, transportation capability, total time, and security. Additionally, in the mainly three multimodal transport networks, the sea+Trans-China Railway route is ranked first, followed by the sea+Trans-Siberian Railway and sea+truck routes.

Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

Directory of Open Access Journals (Sweden)

Aydin Azizi

2017-01-01

Full Text Available Recent advances in modern manufacturing industries have created a great need to track and identify objects and parts by obtaining real-time information. One of the main technologies which has been utilized for this need is the Radio Frequency Identification (RFID system. As a result of adopting this technology to the manufacturing industry environment, RFID Network Planning (RNP has become a challenge. Mainly RNP deals with calculating the number and position of antennas which should be deployed in the RFID network to achieve full coverage of the tags that need to be read. The ultimate goal of this paper is to present and evaluate a way of modelling and optimizing nonlinear RNP problems utilizing artificial intelligence (AI techniques. This effort has led the author to propose a novel AI algorithm, which has been named “hybrid AI optimization technique,” to perform optimization of RNP as a hard learning problem. The proposed algorithm is composed of two different optimization algorithms: Redundant Antenna Elimination (RAE and Ring Probabilistic Logic Neural Networks (RPLNN. The proposed hybrid paradigm has been explored using a flexible manufacturing system (FMS, and results have been compared with Genetic Algorithm (GA that demonstrates the feasibility of the proposed architecture successfully.

The efficacy of centralized flow rate control in 802.11-based wireless mesh networks

KAUST Repository

Jamshaid, K.

2013-06-13

Commodity WiFi-based wireless mesh networks (WMNs) can be used to provide last mile Internet access. These networks exhibit extreme unfairness with backlogged traffic sources. Current solutions propose distributed source-rate control algorithms requiring link-layer or transport-layer changes on all mesh nodes. This is often infeasible in large practical deployments. In wireline networks, router-assisted rate control techniques have been proposed for use alongside end-to-end mechanisms. We wish to evaluate the feasibility of establishing similar centralized control via gateways in WMNs. In this paper, we focus on the efficacy of this control rather than the specifics of the controller design mechanism. We answer the question: Given sources that react predictably to congestion notification, can we enforce a desired rate allocation through a single centralized controller? The answer is not obvious because flows experience varying contention levels, and transmissions are scheduled by a node using imperfect local knowledge. We find that common router-assisted flow control schemes used in wired networks fail in WMNs because they assume that (1) links are independent, and (2) router queue buildups are sufficient for detecting congestion. We show that non-work-conserving, rate-based centralized scheduling can effectively enforce rate allocation. It can achieve results comparable to source rate limiting, without requiring any modifications to mesh routers or client devices. 2013 Jamshaid et al.; licensee Springer.

Rechecking the Centrality-Lethality Rule in the Scope of Protein Subcellular Localization Interaction Networks.

Directory of Open Access Journals (Sweden)

Xiaoqing Peng

Full Text Available Essential proteins are indispensable for living organisms to maintain life activities and play important roles in the studies of pathology, synthetic biology, and drug design. Therefore, besides experiment methods, many computational methods are proposed to identify essential proteins. Based on the centrality-lethality rule, various centrality methods are employed to predict essential proteins in a Protein-protein Interaction Network (PIN. However, neglecting the temporal and spatial features of protein-protein interactions, the centrality scores calculated by centrality methods are not effective enough for measuring the essentiality of proteins in a PIN. Moreover, many methods, which overfit with the features of essential proteins for one species, may perform poor for other species. In this paper, we demonstrate that the centrality-lethality rule also exists in Protein Subcellular Localization Interaction Networks (PSLINs. To do this, a method based on Localization Specificity for Essential protein Detection (LSED, was proposed, which can be combined with any centrality method for calculating the improved centrality scores by taking into consideration PSLINs in which proteins play their roles. In this study, LSED was combined with eight centrality methods separately to calculate Localization-specific Centrality Scores (LCSs for proteins based on the PSLINs of four species (Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster. Compared to the proteins with high centrality scores measured from the global PINs, more proteins with high LCSs measured from PSLINs are essential. It indicates that proteins with high LCSs measured from PSLINs are more likely to be essential and the performance of centrality methods can be improved by LSED. Furthermore, LSED provides a wide applicable prediction model to identify essential proteins for different species.

How Do Neural Networks Enhance the Predictability of Central European Stock Returns?

OpenAIRE

Jozef Baruník

2008-01-01

In this paper, the author applies neural networks as nonparametric and nonlinear methods to Central European (Czech, Polish, Hungarian, and German) stock market returns modeling. In the first part, he presents the intuition of neural networks and also discusses statistical methods for comparing predictive accuracy, as well as economic significance measures. In the empirical tests, he uses data on the daily and weekly returns of the PX-50, BUX, WIG, and DAX stock exchange indices for the 2000–...

Prediction of Currency Volume Issued in Taiwan Using a Hybrid Artificial Neural Network and Multiple Regression Approach

Directory of Open Access Journals (Sweden)

Yuehjen E. Shao

2013-01-01

Full Text Available Because the volume of currency issued by a country always affects its interest rate, price index, income levels, and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. In contrast to the typical single-stage forecast model, this study proposes a hybrid forecasting approach to predict the volume of currency issued in Taiwan. The proposed hybrid models consist of artificial neural network (ANN and multiple regression (MR components. The MR component of the hybrid models is established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN component is then designed to generate forecasts based on those important explanatory variables. Subsequently, the model is used to analyze a real dataset of Taiwan's currency from 1996 to 2011 and twenty associated explanatory variables. The prediction results reveal that the proposed hybrid scheme exhibits superior forecasting performance for predicting the volume of currency issued in Taiwan.

Coordinated control strategy for hybrid wind farms with DFIG-based and PMSG-based wind farms during network unbalance

DEFF Research Database (Denmark)

Yao, Jun; Liu, Ruikuo; Zhou, Te

2017-01-01

This paper investigates the coordinated control strategy for a hybrid wind farm with doubly fed induction generator (DFIG)-based and direct-driven permanent-magnet synchronous generator (PMSG)-based wind farms during network unbalance. The negative-sequence current output capabilities of DFIG...... to the controllable operating regions, a targets selection scheme for each control unit is proposed to improve the stability of the hybrid wind farms containing both DFIG-based and PMSG-based wind farms during network unbalance, especially to avoid DFIG-based wind farm tripping from connected power grid under severe...... grid voltage unbalance conditions. Finally, the proposed coordinated control strategy is validated by the simulation results of a 30-MW-DFIG-based wind farm and a 30-MW-PMSG-based wind farm under different operation conditions and experimental results on a laboratory-scale experimental rig under severe...

The Central and Eastern U.S. Seismic Network: Legacy of USArray

Science.gov (United States)

Eakins, J. A.; Astiz, L.; Benz, H.; Busby, R. W.; Hafner, K.; Reyes, J. C.; Sharer, G.; Vernon, F.; Woodward, R.

2014-12-01

As the USArray Transportable Array entered the central and eastern United States, several Federal agencies (National Science Foundation, U.S. Geological Survey, U.S. Nuclear Regulatory Commission, and Department of Energy) recognized the unique opportunity to retain TA stations beyond the original timeline. The mission of the CEUSN is to produce data that enables researchers and Federal agencies alike to better understand the basic geologic questions, background earthquake rates and distribution, seismic hazard potential, and associated societal risks of this region. The selected long-term sub-array from Transportable Array (TA) stations includes nearly 200 sites, complemented by 100 broadband stations from the existing regional seismic networks to form the Central and Eastern United States Network (CEUSN). Multiple criteria for site selection were weighed by an inter-agency TA Station Selection (TASS) Working Group: seismic noise characteristics, data availability in real time, proximity to nuclear power plants, and homogeneous distribution throughout the region. The Array Network Facility (ANF) started collecting data for CEUSN network stations since late 2013, with all stations collected since May 2014. Regional seismic data streams are collected in real-time from the IRIS Data Management Center (DMC). TA stations selected to be part of CEUSN, retain the broadband sensor to which a 100 sps channel is added, the infrasound and environmental channels, and, at some stations, accelerometers are deployed. The upgraded sites become part of the N4 network for which ANF provides metadata and can issue remote commands to the station equipment. Stations still operated by TA, but planned for CEUSN, are included in the virtual network so all stations are currently available now. By the end of 2015, the remaining TA stations will be upgraded. Data quality control procedures developed for TA stations at ANF and at the DMC are currently performed on N4 data. However

Performance of Overlaid MIMO Cellular Networks with TAS/MRC under Hybrid-Access Small Cells and Poisson Field Interference

KAUST Repository

AbdelNabi, Amr A.

2018-02-12

This paper presents new approaches to characterize the achieved performance of hybrid control-access small cells in the context of two-tier multi-input multi-output (MIMO) cellular networks with random interference distributions. The hybrid scheme at small cells (such as femtocells) allows for sharing radio resources between the two network tiers according to the densities of small cells and their associated users, as well as the observed interference power levels in the two network tiers. The analysis considers MIMO transceivers at all nodes, for which antenna arrays can be utilized to implement transmit antenna selection (TAS) and receive maximal ratio combining (MRC) under MIMO point-to-point channels. Moreover, it tar-gets network-level models of interference sources inside each tier and between the two tiers, which are assumed to follow Poisson field processes. To fully capture the occasions for Poisson field distribution on MIMO spatial domain. Two practical scenarios of interference sources are addressed including highly-correlated or uncorrelated transmit antenna arrays of the serving macrocell base station. The analysis presents new analytical approaches that can characterize the downlink outage probability performance in any tier. Furthermore, the outage performance in high signal-to-noise (SNR) regime is also obtained, which can be useful to deduce diversity and/or coding gains.

Performance of Overlaid MIMO Cellular Networks with TAS/MRC under Hybrid-Access Small Cells and Poisson Field Interference

KAUST Repository

AbdelNabi, Amr A.; Al-Qahtani, Fawaz S.; Radaydeh, Redha Mahmoud Mesleh; Shaqfeh, Mohammad; Manna, Raed F.

2018-01-01

This paper presents new approaches to characterize the achieved performance of hybrid control-access small cells in the context of two-tier multi-input multi-output (MIMO) cellular networks with random interference distributions. The hybrid scheme at small cells (such as femtocells) allows for sharing radio resources between the two network tiers according to the densities of small cells and their associated users, as well as the observed interference power levels in the two network tiers. The analysis considers MIMO transceivers at all nodes, for which antenna arrays can be utilized to implement transmit antenna selection (TAS) and receive maximal ratio combining (MRC) under MIMO point-to-point channels. Moreover, it tar-gets network-level models of interference sources inside each tier and between the two tiers, which are assumed to follow Poisson field processes. To fully capture the occasions for Poisson field distribution on MIMO spatial domain. Two practical scenarios of interference sources are addressed including highly-correlated or uncorrelated transmit antenna arrays of the serving macrocell base station. The analysis presents new analytical approaches that can characterize the downlink outage probability performance in any tier. Furthermore, the outage performance in high signal-to-noise (SNR) regime is also obtained, which can be useful to deduce diversity and/or coding gains.

WebScore: An Effective Page Scoring Approach for Uncertain Web Social Networks

Directory of Open Access Journals (Sweden)

Shaojie Qiao

2011-10-01

Full Text Available To effectively score pages with uncertainty in web social networks, we first proposed a new concept called transition probability matrix and formally defined the uncertainty in web social networks. Second, we proposed a hybrid page scoring algorithm, called WebScore, based on the PageRank algorithm and three centrality measures including degree, betweenness, and closeness. Particularly,WebScore takes into a full consideration of the uncertainty of web social networks by computing the transition probability from one page to another. The basic idea ofWebScore is to: (1 integrate uncertainty into PageRank in order to accurately rank pages, and (2 apply the centrality measures to calculate the importance of pages in web social networks. In order to verify the performance of WebScore, we developed a web social network analysis system which can partition web pages into distinct groups and score them in an effective fashion. Finally, we conducted extensive experiments on real data and the results show that WebScore is effective at scoring uncertain pages with less time deficiency than PageRank and centrality measures based page scoring algorithms.

Demonstration of hybrid orbital angular momentum multiplexing and time-division multiplexing passive optical network.

Science.gov (United States)

Wang, Andong; Zhu, Long; Liu, Jun; Du, Cheng; Mo, Qi; Wang, Jian

2015-11-16

Mode-division multiplexing passive optical network (MDM-PON) is a promising scheme for next-generation access networks to further increase fiber transmission capacity. In this paper, we demonstrate the proof-of-concept experiment of hybrid mode-division multiplexing (MDM) and time-division multiplexing (TDM) PON architecture by exploiting orbital angular momentum (OAM) modes. Bidirectional transmissions with 2.5-Gbaud 4-level pulse amplitude modulation (PAM-4) downstream and 2-Gbaud on-off keying (OOK) upstream are demonstrated in the experiment. The observed optical signal-to-noise ratio (OSNR) penalties for downstream and upstream transmissions at a bit-error rate (BER) of 2 × 10(-3) are less than 2.0 dB and 3.0 dB, respectively.

Measuring Long-Term Impact Based on Network Centrality: Unraveling Cinematic Citations

Science.gov (United States)

Spitz, Andreas; Horvát, Emőke-Ágnes

2014-01-01

Traditional measures of success for film, such as box-office revenue and critical acclaim, lack the ability to quantify long-lasting impact and depend on factors that are largely external to the craft itself. With the growing number of films that are being created and large-scale data becoming available through crowd-sourced online platforms, an endogenous measure of success that is not reliant on manual appraisal is of increasing importance. In this article we propose such a ranking method based on a combination of centrality indices. We apply the method to a network that contains several types of citations between more than 40,000 international feature films. From this network we derive a list of milestone films, which can be considered to constitute the foundations of cinema. In a comparison to various existing lists of ‘greatest’ films, such as personal favourite lists, voting lists, lists of individual experts, and lists deduced from expert polls, the selection of milestone films is more diverse in terms of genres, actors, and main creators. Our results shed light on the potential of a systematic quantitative investigation based on cinematic influences in identifying the most inspiring creations in world cinema. In a broader perspective, we introduce a novel research question to large-scale citation analysis, one of the most intriguing topics that have been at the forefront of scientific enquiries for the past fifty years and have led to the development of various network analytic methods. In doing so, we transfer widely studied approaches from citation analysis to the the newly emerging field of quantification efforts in the arts. The specific contribution of this paper consists in modelling the multidimensional cinematic references as a growing multiplex network and in developing a methodology for the identification of central films in this network. PMID:25295877

Measuring long-term impact based on network centrality: unraveling cinematic citations.

Directory of Open Access Journals (Sweden)

Andreas Spitz

Full Text Available Traditional measures of success for film, such as box-office revenue and critical acclaim, lack the ability to quantify long-lasting impact and depend on factors that are largely external to the craft itself. With the growing number of films that are being created and large-scale data becoming available through crowd-sourced online platforms, an endogenous measure of success that is not reliant on manual appraisal is of increasing importance. In this article we propose such a ranking method based on a combination of centrality indices. We apply the method to a network that contains several types of citations between more than 40,000 international feature films. From this network we derive a list of milestone films, which can be considered to constitute the foundations of cinema. In a comparison to various existing lists of 'greatest' films, such as personal favourite lists, voting lists, lists of individual experts, and lists deduced from expert polls, the selection of milestone films is more diverse in terms of genres, actors, and main creators. Our results shed light on the potential of a systematic quantitative investigation based on cinematic influences in identifying the most inspiring creations in world cinema. In a broader perspective, we introduce a novel research question to large-scale citation analysis, one of the most intriguing topics that have been at the forefront of scientific enquiries for the past fifty years and have led to the development of various network analytic methods. In doing so, we transfer widely studied approaches from citation analysis to the the newly emerging field of quantification efforts in the arts. The specific contribution of this paper consists in modelling the multidimensional cinematic references as a growing multiplex network and in developing a methodology for the identification of central films in this network.

Global network centrality of university rankings

Science.gov (United States)

Guo, Weisi; Del Vecchio, Marco; Pogrebna, Ganna

2017-10-01

Universities and higher education institutions form an integral part of the national infrastructure and prestige. As academic research benefits increasingly from international exchange and cooperation, many universities have increased investment in improving and enabling their global connectivity. Yet, the relationship of university performance and its global physical connectedness has not been explored in detail. We conduct, to our knowledge, the first large-scale data-driven analysis into whether there is a correlation between university relative ranking performance and its global connectivity via the air transport network. The results show that local access to global hubs (as measured by air transport network betweenness) strongly and positively correlates with the ranking growth (statistical significance in different models ranges between 5% and 1% level). We also found that the local airport's aggregate flight paths (degree) and capacity (weighted degree) has no effect on university ranking, further showing that global connectivity distance is more important than the capacity of flight connections. We also examined the effect of local city economic development as a confounding variable and no effect was observed suggesting that access to global transportation hubs outweighs economic performance as a determinant of university ranking. The impact of this research is that we have determined the importance of the centrality of global connectivity and, hence, established initial evidence for further exploring potential connections between university ranking and regional investment policies on improving global connectivity.

Low-cost RAU with Optical Power Supply Used in a Hybrid RoF IEEE 802.11 Network

Science.gov (United States)

Kowalczyk, M.; Siuzdak, J.

2014-09-01

The paper presents design and implementation of a low-cost RAU (Remote Antenna Unit) device. It was designed to work in a hybrid Wi-Fi/optical network based on the IEEE 802.11b/g standard. An unique feature of the device is the possibility of optical power supply.

Robustness analysis of complex networks with power decentralization strategy via flow-sensitive centrality against cascading failures

Science.gov (United States)

Guo, Wenzhang; Wang, Hao; Wu, Zhengping

2018-03-01

Most existing cascading failure mitigation strategy of power grids based on complex network ignores the impact of electrical characteristics on dynamic performance. In this paper, the robustness of the power grid under a power decentralization strategy is analysed through cascading failure simulation based on AC flow theory. The flow-sensitive (FS) centrality is introduced by integrating topological features and electrical properties to help determine the siting of the generation nodes. The simulation results of the IEEE-bus systems show that the flow-sensitive centrality method is a more stable and accurate approach and can enhance the robustness of the network remarkably. Through the study of the optimal flow-sensitive centrality selection for different networks, we find that the robustness of the network with obvious small-world effect depends more on contribution of the generation nodes detected by community structure, otherwise, contribution of the generation nodes with important influence on power flow is more critical. In addition, community structure plays a significant role in balancing the power flow distribution and further slowing the propagation of failures. These results are useful in power grid planning and cascading failure prevention.

Causality in Psychiatry: A Hybrid Symptom Network Construct Model

Directory of Open Access Journals (Sweden)

Gerald eYoung

2015-11-01

Full Text Available Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved that inform approaches to nosology, or classification, such as in the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; American Psychiatric Association, 2013. However, network approaches to symptom interaction (i.e., symptoms are formative of the construct; e.g., McNally, Robinaugh, Wu, Wang, Deserno, & Borsboom, 2014, for PTSD (posttraumatic stress disorder are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth nonlinear dynamical systems theory (NLDST. The article applies the concept of emergent circular causality (Young, 2011 to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning and universal (e.g., causal processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments.

A Novel Model for Stock Price Prediction Using Hybrid Neural Network

Science.gov (United States)

Senapati, Manas Ranjan; Das, Sumanjit; Mishra, Sarojananda

2018-06-01

The foremost challenge for investors is to select stock price by analyzing financial data which is a menial task as of distort associated and massive pattern. Thereby, selecting stock poses one of the greatest difficulties for investors. Nowadays, prediction of financial market like stock market, exchange rate and share value are very challenging field of research. The prediction and scrutinization of stock price is also a potential area of research due to its vital significance in decision making by financial investors. This paper presents an intelligent and an optimal model for prophecy of stock market price using hybridization of Adaline Neural Network (ANN) and modified Particle Swarm Optimization (PSO). The connoted model hybrid of Adaline and PSO uses fluctuations of stock market as a factor and employs PSO to optimize and update weights of Adaline representation to depict open price of Bombay stock exchange. The prediction performance of the proposed model is compared with different representations like interval measurements, CMS-PSO and Bayesian-ANN. The result indicates that proposed scheme has an edge over all the juxtaposed schemes in terms of mean absolute percentage error.

The impact of plug-in hybrid electric vehicles on distribution networks: A review and outlook

International Nuclear Information System (INIS)

Green, Robert C. II.; Wang, Lingfeng; Alam, Mansoor

2011-01-01

Plug-in hybrid electric vehicles (PHEVs) are the next big thing in the electric transportation market. While much work has been done to detail what economic costs and benefits PHEVs will have on consumers and producers alike, it seems that it is also important to understand what impact PHEVs will have on distribution networks nationwide. This paper finds that the impact of PHEVs on the distribution network can be determined using the following aspects of PHEVs: driving patterns, charging characteristics, charge timing, and vehicle penetration. The impacts that these aspects of PHEVs will have on distribution networks have been measured and calculated by multiple authors in different locations using many different tools that range from analytical techniques to simulations and beyond. While much work has already been completed in this area, there is still much to do. Areas left for improvement and future work will include adding more stochasticity into models as well as computing and analyzing reliability indices with respect to distribution networks. (author)

Estimation of Entropy Generation for Ag-MgO/Water Hybrid Nanofluid Flow through Rectangular Minichannel by Using Artificial Neural Network

OpenAIRE

Uysal, Cuneyt; Korkmaz, Mehmet Erdi

2018-01-01

The convective heat transfer andentropy generation characteristics of Ag-MgO/water hybrid nanofluid flowthrough rectangular minichannel were numerically investigated. The Reynoldsnumber was in the range of 200 to 2000 and different nanoparticle volume fractionswere varied between = 0.005 and 0.02. In addition, ArtificialNeural Network was used to create a model for estimating of entropy generationof Ag-MgO/water hybrid nanofluid flow. As a result, it was found th...

Hybrid case-neural network (CNN) diagnostic system

International Nuclear Information System (INIS)

Mohamed, A.H.

2010-01-01

recently, the mobile health care has a great attention for the researcher and people all over the world. Case based reasoning (CBR) systems have proved their performance as world wide web (WWW) medical diagnostic systems. They were preferred rather than different reasoning approaches due to their high performance and results' explanation. But, their operations require a complex knowledge acquisition and management processes. On the other hand, it is found that, artificial neural network (ANN) has a great acceptance as a classifier methodology using a little amount of knowledge. But, ANN lacks of an explanation capability .The present research introduces a new web-based hybrid diagnostic system that can use the ANN inside the CBR , cycle.It can provide higher performance for the web diagnostic systems. Besides, the proposed system can be used as a web diagnostic system. It can be applied for diagnosis different types of systems in several domains. It has been applied in diagnosis of the cancer diseases that has a great spreading in recent years as a case of study . However, the suggested system has proved its acceptance in the manner.

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

Science.gov (United States)

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

2016-09-01

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

Solar central receiver hybrid power system, Phase I. Volume 2. Conceptual design. Final technical report, October 1978-August 1979

Energy Technology Data Exchange (ETDEWEB)

None

1979-09-01

The objectives of this study were to develop a hybrid power system design that (1) produces minimum cost electric power, (2) minimizes the capital investment and operating cost, (3) permits capacity displacement, (4) and achieves utility acceptance for market penetration. We have met the first three of these objectives and therefore believe that the fourth, utility acceptance, will become a reality. These objectives have been met by utilizing the Martin Marietta concept that combines the alternate central receiver power system design and a high-temperature salt primary heat transfer fluid and thermal storage media system with a fossil-fired nonsolar energy source. Task 1 reviewed the requirements definition document and comments and recommendations were provided to DOE/San Francisco. Task 2 consisted of a market analysis to evaluate the potential market of solar hybrid power plants. Twenty-two utilities were selected within nine regions of the country. Both written and verbal correspondence was used to assess solar hybrid power plants with respect to the utilities' future requirements and plans. The parametric analysis of Task 3 evaluated a wide range of subsystem configurations and sizes. These analyses included subsystems from the solar standalone alternate central receiver power system using high-temperature molten salt and from fossil fuel nonsolar subsystems. Task 4, selection of the preferred commerical system configuration, utilized the parametric analyses developed in Task 3 to select system and subsystem configurations for the commercial plant design. Task 5 developed a conceptual design of the selected commercial plant configuration and assessed the related cost and performance. Task 6 assessed the economics and performance of the selected configuration as well as future potential improvements or limitations of the hybrid power plants.

Hybrid ARQ Scheme with Autonomous Retransmission for Multicasting in Wireless Sensor Networks.

Science.gov (United States)

Jung, Young-Ho; Choi, Jihoon

2017-02-25

A new hybrid automatic repeat request (HARQ) scheme for multicast service for wireless sensor networks is proposed in this study. In the proposed algorithm, the HARQ operation is combined with an autonomous retransmission method that ensure a data packet is transmitted irrespective of whether or not the packet is successfully decoded at the receivers. The optimal number of autonomous retransmissions is determined to ensure maximum spectral efficiency, and a practical method that adjusts the number of autonomous retransmissions for realistic conditions is developed. Simulation results show that the proposed method achieves higher spectral efficiency than existing HARQ techniques.

Hybrid ARQ Scheme with Autonomous Retransmission for Multicasting in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Young-Ho Jung

2017-02-01

Full Text Available A new hybrid automatic repeat request (HARQ scheme for multicast service for wireless sensor networks is proposed in this study. In the proposed algorithm, the HARQ operation is combined with an autonomous retransmission method that ensure a data packet is transmitted irrespective of whether or not the packet is successfully decoded at the receivers. The optimal number of autonomous retransmissions is determined to ensure maximum spectral efficiency, and a practical method that adjusts the number of autonomous retransmissions for realistic conditions is developed. Simulation results show that the proposed method achieves higher spectral efficiency than existing HARQ techniques.

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network.

Science.gov (United States)

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins.

Balancing energy consumption with hybrid clustering and routing strategy in wireless sensor networks.

Science.gov (United States)

Xu, Zhezhuang; Chen, Liquan; Liu, Ting; Cao, Lianyang; Chen, Cailian

2015-10-20

Multi-hop data collection in wireless sensor networks (WSNs) is a challenge issue due to the limited energy resource and transmission range of wireless sensors. The hybrid clustering and routing (HCR) strategy has provided an effective solution, which can generate a connected and efficient cluster-based topology for multi-hop data collection in WSNs. However, it suffers from imbalanced energy consumption, which results in the poor performance of the network lifetime. In this paper, we evaluate the energy consumption of HCR and discover an important result: the imbalanced energy consumption generally appears in gradient k = 1, i.e., the nodes that can communicate with the sink directly. Based on this observation, we propose a new protocol called HCR-1, which includes the adaptive relay selection and tunable cost functions to balance the energy consumption. The guideline of setting the parameters in HCR-1 is provided based on simulations. The analytical and numerical results prove that, with minor modification of the topology in Sensors 2015, 15 26584 gradient k = 1, the HCR-1 protocol effectively balances the energy consumption and prolongs the network lifetime.

The centrality of affective instability and identity in Borderline Personality Disorder: Evidence from network analysis

Science.gov (United States)

Costantini, Giulio; De Panfilis, Chiara

2017-01-01

We argue that the series of traits characterizing Borderline Personality Disorder samples do not weigh equally. In this regard, we believe that network approaches employed recently in Personality and Psychopathology research to provide information about the differential relationships among symptoms would be useful to test our claim. To our knowledge, this approach has never been applied to personality disorders. We applied network analysis to the nine Borderline Personality Disorder traits to explore their relationships in two samples drawn from university students and clinical populations (N = 1317 and N = 96, respectively). We used the Fused Graphical Lasso, a technique that allows estimating networks from different populations separately while considering their similarities and differences. Moreover, we examined centrality indices to determine the relative importance of each symptom in each network. The general structure of the two networks was very similar in the two samples, although some differences were detected. Results indicate the centrality of mainly affective instability, identity, and effort to avoid abandonment aspects in Borderline Personality Disorder. Results are consistent with the new DSM Alternative Model for Personality Disorders. We discuss them in terms of implications for therapy. PMID:29040324

Managing hybrid marketing systems.

Science.gov (United States)

Moriarty, R T; Moran, U

1990-01-01

As competition increases and costs become critical, companies that once went to market only one way are adding new channels and using new methods - creating hybrid marketing systems. These hybrid marketing systems hold the promise of greater coverage and reduced costs. But they are also hard to manage; they inevitably raise questions of conflict and control: conflict because marketing units compete for customers; control because new indirect channels are less subject to management authority. Hard as they are to manage, however, hybrid marketing systems promise to become the dominant design, replacing the "purebred" channel strategy in all kinds of businesses. The trick to managing the hybrid is to analyze tasks and channels within and across a marketing system. A map - the hybrid grid - can help managers make sense of their hybrid system. What the chart reveals is that channels are not the basic building blocks of a marketing system; marketing tasks are. The hybrid grid forces managers to consider various combinations of channels and tasks that will optimize both cost and coverage. Managing conflict is also an important element of a successful hybrid system. Managers should first acknowledge the inevitability of conflict. Then they should move to bound it by creating guidelines that spell out which customers to serve through which methods. Finally, a marketing and sales productivity (MSP) system, consisting of a central marketing database, can act as the central nervous system of a hybrid marketing system, helping managers create customized channels and service for specific customer segments.

Comparison of hybrid spectral-decomposition artificial neural network models for understanding climatic forcing of groundwater levels

Science.gov (United States)

Abrokwah, K.; O'Reilly, A. M.

2017-12-01

Groundwater is an important resource that is extracted every day because of its invaluable use for domestic, industrial and agricultural purposes. The need for sustaining groundwater resources is clearly indicated by declining water levels and has led to modeling and forecasting accurate groundwater levels. In this study, spectral decomposition of climatic forcing time series was used to develop hybrid wavelet analysis (WA) and moving window average (MWA) artificial neural network (ANN) models. These techniques are explored by modeling historical groundwater levels in order to provide understanding of potential causes of the observed groundwater-level fluctuations. Selection of the appropriate decomposition level for WA and window size for MWA helps in understanding the important time scales of climatic forcing, such as rainfall, that influence water levels. Discrete wavelet transform (DWT) is used to decompose the input time-series data into various levels of approximate and details wavelet coefficients, whilst MWA acts as a low-pass signal-filtering technique for removing high-frequency signals from the input data. The variables used to develop and validate the models were daily average rainfall measurements from five National Atmospheric and Oceanic Administration (NOAA) weather stations and daily water-level measurements from two wells recorded from 1978 to 2008 in central Florida, USA. Using different decomposition levels and different window sizes, several WA-ANN and MWA-ANN models for simulating the water levels were created and their relative performances compared against each other. The WA-ANN models performed better than the corresponding MWA-ANN models; also higher decomposition levels of the input signal by the DWT gave the best results. The results obtained show the applicability and feasibility of hybrid WA-ANN and MWA-ANN models for simulating daily water levels using only climatic forcing time series as model inputs.

A hybrid MAC protocol design for energy-efficient very-high-throughput millimeter wave, wireless sensor communication networks

Science.gov (United States)

Jian, Wei; Estevez, Claudio; Chowdhury, Arshad; Jia, Zhensheng; Wang, Jianxin; Yu, Jianguo; Chang, Gee-Kung

2010-12-01

This paper presents an energy-efficient Medium Access Control (MAC) protocol for very-high-throughput millimeter-wave (mm-wave) wireless sensor communication networks (VHT-MSCNs) based on hybrid multiple access techniques of frequency division multiplexing access (FDMA) and time division multiplexing access (TDMA). An energy-efficient Superframe for wireless sensor communication network employing directional mm-wave wireless access technologies is proposed for systems that require very high throughput, such as high definition video signals, for sensing, processing, transmitting, and actuating functions. Energy consumption modeling for each network element and comparisons among various multi-access technologies in term of power and MAC layer operations are investigated for evaluating the energy-efficient improvement of proposed MAC protocol.

Hybrid fuzzy charged system search algorithm based state estimation in distribution networks

Directory of Open Access Journals (Sweden)

Sachidananda Prasad

2017-06-01

Full Text Available This paper proposes a new hybrid charged system search (CSS algorithm based state estimation in radial distribution networks in fuzzy framework. The objective of the optimization problem is to minimize the weighted square of the difference between the measured and the estimated quantity. The proposed method of state estimation considers bus voltage magnitude and phase angle as state variable along with some equality and inequality constraints for state estimation in distribution networks. A rule based fuzzy inference system has been designed to control the parameters of the CSS algorithm to achieve better balance between the exploration and exploitation capability of the algorithm. The efficiency of the proposed fuzzy adaptive charged system search (FACSS algorithm has been tested on standard IEEE 33-bus system and Indian 85-bus practical radial distribution system. The obtained results have been compared with the conventional CSS algorithm, weighted least square (WLS algorithm and particle swarm optimization (PSO for feasibility of the algorithm.

Energy Harvesting in Heterogeneous Networks with Hybrid Powered Communication Systems

KAUST Repository

Alsharoa, Ahmad

2018-02-12

In this paper, we investigate an energy efficient and energy harvesting (EH) system model in heterogeneous networks (HetNets) where all base stations (BSS) are equipped to harvest energy from renewable energy sources. We consider a hybrid power supply of green (renewable) and traditional micro-grid, such that traditional micro-grid is not exploited as long as the BSS can meet their power demands from harvested and stored green energy. Therefore, our goal is to minimize the networkwide energy consumption subject to users\\' certain quality of service and BSS\\' power consumption constraints. As a result of binary BS sleeping status and user-cell association variables, proposed is formulated as a binary linear programming (BLP) problem. A green communication algorithm based on binary particle swarm optimization is implemented to solve the problem with low complexity time.

Extending the Performance of Hybrid NoCs beyond the Limitations of Network Heterogeneity

Directory of Open Access Journals (Sweden)

Michael Opoku Agyeman

2017-04-01

Full Text Available To meet the performance and scalability demands of the fast-paced technological growth towards exascale and big data processing with the performance bottleneck of conventional metal-based interconnects (wireline, alternative interconnect fabrics, such as inhomogeneous three-dimensional integrated network-on-chip (3D NoC and hybrid wired-wireless network-on-chip (WiNoC, have emanated as a cost-effective solution for emerging system-on-chip (SoC design. However, these interconnects trade off optimized performance for cost by restricting the number of area and power hungry 3D routers and wireless nodes. Moreover, the non-uniform distributed traffic in a chip multiprocessor (CMP demands an on-chip communication infrastructure that can avoid congestion under high traffic conditions while possessing minimal pipeline delay at low-load conditions. To this end, in this paper, we propose a low-latency adaptive router with a low-complexity single-cycle bypassing mechanism to alleviate the performance degradation due to the slow 2D routers in such emerging hybrid NoCs. The proposed router transmits a flit using dimension-ordered routing (DoR in the bypass datapath at low-loads. When the output port required for intra-dimension bypassing is not available, the packet is routed adaptively to avoid congestion. The router also has a simplified virtual channel allocation (VA scheme that yields a non-speculative low-latency pipeline. By combining the low-complexity bypassing technique with adaptive routing, the proposed router is able to balance the traffic in hybrid NoCs to achieve low-latency communication under various traffic loads. Simulation shows that the proposed router can reduce applications’ execution time by an average of 16.9% compared to low-latency routers, such as SWIFT. By reducing the latency between 2D routers (or wired nodes and 3D routers (or wireless nodes, the proposed router can improve the performance efficiency in terms of average

Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

Science.gov (United States)

Pan, Yongping; Yu, Haoyong

2017-06-01

This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

Cancer-related marketing centrality motifs acting as pivot units in the human signaling network and mediating cross-talk between biological pathways.

Science.gov (United States)

Li, Wan; Chen, Lina; Li, Xia; Jia, Xu; Feng, Chenchen; Zhang, Liangcai; He, Weiming; Lv, Junjie; He, Yuehan; Li, Weiguo; Qu, Xiaoli; Zhou, Yanyan; Shi, Yuchen

2013-12-01

Network motifs in central positions are considered to not only have more in-coming and out-going connections but are also localized in an area where more paths reach the networks. These central motifs have been extensively investigated to determine their consistent functions or associations with specific function categories. However, their functional potentials in the maintenance of cross-talk between different functional communities are unclear. In this paper, we constructed an integrated human signaling network from the Pathway Interaction Database. We identified 39 essential cancer-related motifs in central roles, which we called cancer-related marketing centrality motifs, using combined centrality indices on the system level. Our results demonstrated that these cancer-related marketing centrality motifs were pivotal units in the signaling network, and could mediate cross-talk between 61 biological pathways (25 could be mediated by one motif on average), most of which were cancer-related pathways. Further analysis showed that molecules of most marketing centrality motifs were in the same or adjacent subcellular localizations, such as the motif containing PI3K, PDK1 and AKT1 in the plasma membrane, to mediate signal transduction between 32 cancer-related pathways. Finally, we analyzed the pivotal roles of cancer genes in these marketing centrality motifs in the pathogenesis of cancers, and found that non-cancer genes were potential cancer-related genes.

Second Order Cone Programming (SOCP) Relaxation Based Optimal Power Flow with Hybrid VSC-HVDC Transmission and Active Distribution Networks

DEFF Research Database (Denmark)

Ding, Tao; Li, Cheng; Yang, Yongheng

2017-01-01

The detailed topology of renewable resource bases may have the impact on the optimal power flow of the VSC-HVDC transmission network. To address this issue, this paper develops an optimal power flow with the hybrid VSC-HVDC transmission and active distribution networks to optimally schedule...... the generation output and voltage regulation of both networks, which leads to a non-convex programming model. Furthermore, the non-convex power flow equations are based on the Second Order Cone Programming (SOCP) relaxation approach. Thus, the proposed model can be relaxed to a SOCP that can be tractably solved...

A GPU-based solution for fast calculation of the betweenness centrality in large weighted networks

Directory of Open Access Journals (Sweden)

Rui Fan

2017-12-01

Full Text Available Betweenness, a widely employed centrality measure in network science, is a decent proxy for investigating network loads and rankings. However, its extremely high computational cost greatly hinders its applicability in large networks. Although several parallel algorithms have been presented to reduce its calculation cost for unweighted networks, a fast solution for weighted networks, which are commonly encountered in many realistic applications, is still lacking. In this study, we develop an efficient parallel GPU-based approach to boost the calculation of the betweenness centrality (BC for large weighted networks. We parallelize the traditional Dijkstra algorithm by selecting more than one frontier vertex each time and then inspecting the frontier vertices simultaneously. By combining the parallel SSSP algorithm with the parallel BC framework, our GPU-based betweenness algorithm achieves much better performance than its CPU counterparts. Moreover, to further improve performance, we integrate the work-efficient strategy, and to address the load-imbalance problem, we introduce a warp-centric technique, which assigns many threads rather than one to a single frontier vertex. Experiments on both realistic and synthetic networks demonstrate the efficiency of our solution, which achieves 2.9× to 8.44× speedups over the parallel CPU implementation. Our algorithm is open-source and free to the community; it is publicly available through https://dx.doi.org/10.6084/m9.figshare.4542405. Considering the pervasive deployment and declining price of GPUs in personal computers and servers, our solution will offer unprecedented opportunities for exploring betweenness-related problems and will motivate follow-up efforts in network science.

Analysis of Heat Transfer in Power Split Device for Hybrid Electric Vehicle Using Thermal Network Method

Directory of Open Access Journals (Sweden)

Jixin Wang

2014-06-01

Full Text Available This paper presents a rational prediction of temperature field on the differential hybrid system (DHS based on the thermal network method (TNM. The whole thermal network model is built by considering both the contact thermal resistance between gasket and planet gear and the temperature effect on the physical property parameters of lubricant. The contact thermal resistance is obtained by using the concept of contact branch thermal resistance and G-W elastic model. By building an elaborate thermal network model and computing models for power losses and thermal resistances between components, the whole temperature field of DHS under typical operating condition is predicted. Results show that thermal network method can be effectively used to predict the temperature distribution and the rule of temperature variation, the surface roughness significantly affects contact thermal conduction, and the decrease in the thermal resistance of the natural convection between air and DHS housing can effectively improve the thermal environment of DHS.

Hybrid intelligent engineering systems

CERN Document Server

Jain, L C; Adelaide, Australia University of

1997-01-01

This book on hybrid intelligent engineering systems is unique, in the sense that it presents the integration of expert systems, neural networks, fuzzy systems, genetic algorithms, and chaos engineering. It shows that these new techniques enhance the capabilities of one another. A number of hybrid systems for solving engineering problems are presented.

On Central Branch/Reinsurance Risk Networks: Exact Results and Heuristics

Directory of Open Access Journals (Sweden)

Florin Avram

2018-04-01

Full Text Available Modeling the interactions between a reinsurer and several insurers, or between a central management branch (CB and several subsidiary business branches, or between a coalition and its members, are fascinating problems, which suggest many interesting questions. Beyond two dimensions, one cannot expect exact answers. Occasionally, reductions to one dimension or heuristic simplifications yield explicit approximations, which may be useful for getting qualitative insights. In this paper, we study two such problems: the ruin problem for a two-dimensional CB network under a new mathematical model, and the problem of valuation of two-dimensional CB networks by optimal dividends. A common thread between these two problems is that the one dimensional reduction exploits the concept of invariant cones. Perhaps the most important contribution of the paper is the questions it raises; for that reason, we have found it useful to complement the particular examples solved by providing one possible formalization of the concept of a multi-dimensional risk network, which seems to us an appropriate umbrella for the kind of questions raised here.

A Cyber Physical Model Based on a Hybrid System for Flexible Load Control in an Active Distribution Network

Directory of Open Access Journals (Sweden)

Yun Wang

2017-02-01

Full Text Available To strengthen the integration of the primary and secondary systems, a concept of Cyber Physical Systems (CPS is introduced to construct a CPS in Power Systems (Power CPS. The most basic work of the Power CPS is to build an integration model which combines both a continuous process and a discrete process. The advanced form of smart grid, the Active Distribution Network (ADN is a typical example of Power CPS. After designing the Power CPS model architecture and its application in ADN, a Hybrid System based model and control method of Power CPS is proposed in this paper. As an application example, ADN flexible load is modeled and controlled with ADN feeder power control by a control strategy which includes the normal condition and the underpowered condition. In this model and strategy, some factors like load power consumption and load functional demand are considered and optimized. In order to make up some of the deficiencies of centralized control, a distributed control method is presented to reduce model complexity and improve calculation speed. The effectiveness of all the models and methods are demonstrated in the case study.

A framework using cluster-based hybrid network architecture for collaborative virtual surgery.

Science.gov (United States)

Qin, Jing; Choi, Kup-Sze; Poon, Wai-Sang; Heng, Pheng-Ann

2009-12-01

Research on collaborative virtual environments (CVEs) opens the opportunity for simulating the cooperative work in surgical operations. It is however a challenging task to implement a high performance collaborative surgical simulation system because of the difficulty in maintaining state consistency with minimum network latencies, especially when sophisticated deformable models and haptics are involved. In this paper, an integrated framework using cluster-based hybrid network architecture is proposed to support collaborative virtual surgery. Multicast transmission is employed to transmit updated information among participants in order to reduce network latencies, while system consistency is maintained by an administrative server. Reliable multicast is implemented using distributed message acknowledgment based on cluster cooperation and sliding window technique. The robustness of the framework is guaranteed by the failure detection chain which enables smooth transition when participants join and leave the collaboration, including normal and involuntary leaving. Communication overhead is further reduced by implementing a number of management approaches such as computational policies and collaborative mechanisms. The feasibility of the proposed framework is demonstrated by successfully extending an existing standalone orthopedic surgery trainer into a collaborative simulation system. A series of experiments have been conducted to evaluate the system performance. The results demonstrate that the proposed framework is capable of supporting collaborative surgical simulation.

Hierarchical time series bottom-up approach for forecast the export value in Central Java

Science.gov (United States)

Mahkya, D. A.; Ulama, B. S.; Suhartono

2017-10-01

The purpose of this study is Getting the best modeling and predicting the export value of Central Java using a Hierarchical Time Series. The export value is one variable injection in the economy of a country, meaning that if the export value of the country increases, the country’s economy will increase even more. Therefore, it is necessary appropriate modeling to predict the export value especially in Central Java. Export Value in Central Java are grouped into 21 commodities with each commodity has a different pattern. One approach that can be used time series is a hierarchical approach. Hierarchical Time Series is used Buttom-up. To Forecast the individual series at all levels using Autoregressive Integrated Moving Average (ARIMA), Radial Basis Function Neural Network (RBFNN), and Hybrid ARIMA-RBFNN. For the selection of the best models used Symmetric Mean Absolute Percentage Error (sMAPE). Results of the analysis showed that for the Export Value of Central Java, Bottom-up approach with Hybrid ARIMA-RBFNN modeling can be used for long-term predictions. As for the short and medium-term predictions, it can be used a bottom-up approach RBFNN modeling. Overall bottom-up approach with RBFNN modeling give the best result.

An Energy-Aware Hybrid ARQ Scheme with Multi-ACKs for Data Sensing Wireless Sensor Networks.

Science.gov (United States)

Zhang, Jinhuan; Long, Jun

2017-06-12

Wireless sensor networks (WSNs) are one of the important supporting technologies of edge computing. In WSNs, reliable communications are essential for most applications due to the unreliability of wireless links. In addition, network lifetime is also an important performance metric and needs to be considered in many WSN studies. In the paper, an energy-aware hybrid Automatic Repeat-reQuest protocol (ARQ) scheme is proposed to ensure energy efficiency under the guarantee of network transmission reliability. In the scheme, the source node sends data packets continuously with the correct window size and it does not need to wait for the acknowledgement (ACK) confirmation for each data packet. When the destination receives K data packets, it will return multiple copies of one ACK for confirmation to avoid ACK packet loss. The energy consumption of each node in flat circle network applying the proposed scheme is statistical analyzed and the cases under which it is more energy efficiency than the original scheme is discussed. Moreover, how to select parameters of the scheme is addressed to extend the network lifetime under the constraint of the network reliability. In addition, the energy efficiency of the proposed schemes is evaluated. Simulation results are presented to demonstrate that a node energy consumption reduction could be gained and the network lifetime is prolonged.

Application of nonlinear rheology to assess the effect of secondary nanofiller on network structure of hybrid polymer nanocomposites

Science.gov (United States)

Kamkar, Milad; Aliabadian, Ehsan; Shayesteh Zeraati, Ali; Sundararaj, Uttandaraman

2018-02-01

Carbon nanotube (CNT)/polymer nanocomposites exhibit excellent electrical properties by forming a percolated network. Adding a secondary filler can significantly affect the CNTs' network, resulting in changing the electrical properties. In this work, we investigated the effect of adding manganese dioxide nanowires (MnO2NWs) as a secondary nanofiller on the CNTs' network structure inside a poly(vinylidene fluoride) (PVDF) matrix. Incorporating MnO2NWs to PVDF/CNT samples produced a better state of dispersion of CNTs, as corroborated by light microscopy and transmission electron microscopy. The steady shear and oscillatory shear flows were employed to obtain a better insight into the nanofiller structure and viscoelastic behavior of the nanocomposites. The transient response under steady shear flow revealed that the stress overshoot of hybrid nanocomposites (two-fillers), PVDF/CNT/MnO2NWs, increased dramatically in comparison to binary nanocomposites (single-filler), PVDF/CNT and PVDF/MnO2NWs. This can be attributed to microstructural changes. Large amplitude oscillatory shear characterization was also performed to further investigate the effect of the secondary nanofiller on the nonlinear viscoelastic behavior of the samples. The nonlinear rheological observations were explained using quantitative nonlinear parameters [strain-stiffening ratio (S) and shear-thickening ratio (T)] and Lissajous-Bowditch plots. Results indicated that a more rigid nanofiller network was formed for the hybrid nanocomposites due to the better dispersion state of CNTs and this led to a more nonlinear viscoelastic behavior.

Molecular networks related to the immune system and mitochondria are targets for the pesticide dieldrin in the zebrafish (Danio rerio) central nervous system.