Information flow in the auditory cortical network

PubMed Central

Hackett, Troy A.

2011-01-01

Auditory processing in the cerebral cortex is comprised of an interconnected network of auditory and auditory-related areas distributed throughout the forebrain. The nexus of auditory activity is located in temporal cortex among several specialized areas, or fields, that receive dense inputs from the medial geniculate complex. These areas are collectively referred to as auditory cortex. Auditory activity is extended beyond auditory cortex via connections with auditory-related areas elsewhere in the cortex. Within this network, information flows between areas to and from countless targets, but in a manner that is characterized by orderly regional, areal and laminar patterns. These patterns reflect some of the structural constraints that passively govern the flow of information at all levels of the network. In addition, the exchange of information within these circuits is dynamically regulated by intrinsic neurochemical properties of projecting neurons and their targets. This article begins with an overview of the principal circuits and how each is related to information flow along major axes of the network. The discussion then turns to a description of neurochemical gradients along these axes, highlighting recent work on glutamate transporters in the thalamocortical projections to auditory cortex. The article concludes with a brief discussion of relevant neurophysiological findings as they relate to structural gradients in the network. PMID:20116421

Guidelines on CV networking information flow optimization for Texas.

DOT National Transportation Integrated Search

2017-03-01

Recognizing the fundamental role of information flow in future transportation applications, the research team investigated the quality and security of information flow in the connected vehicle (CV) environment. The research team identified key challe...

Effectiveness of the New Hampshire stream-gaging network in providing regional streamflow information

USGS Publications Warehouse

Olson, Scott A.

2003-01-01

The stream-gaging network in New Hampshire was analyzed for its effectiveness in providing regional information on peak-flood flow, mean-flow, and low-flow frequency. The data available for analysis were from stream-gaging stations in New Hampshire and selected stations in adjacent States. The principles of generalized-least-squares regression analysis were applied to develop regional regression equations that relate streamflow-frequency characteristics to watershed characteristics. Regression equations were developed for (1) the instantaneous peak flow with a 100-year recurrence interval, (2) the mean-annual flow, and (3) the 7-day, 10-year low flow. Active and discontinued stream-gaging stations with 10 or more years of flow data were used to develop the regression equations. Each stream-gaging station in the network was evaluated and ranked on the basis of how much the data from that station contributed to the cost-weighted sampling-error component of the regression equation. The potential effect of data from proposed and new stream-gaging stations on the sampling error also was evaluated. The stream-gaging network was evaluated for conditions in water year 2000 and for estimated conditions under various network strategies if an additional 5 years and 20 years of streamflow data were collected. The effectiveness of the stream-gaging network in providing regional streamflow information could be improved for all three flow characteristics with the collection of additional flow data, both temporally and spatially. With additional years of data collection, the greatest reduction in the average sampling error of the regional regression equations was found for the peak- and low-flow characteristics. In general, additional data collection at stream-gaging stations with unregulated flow, relatively short-term record (less than 20 years), and drainage areas smaller than 45 square miles contributed the largest cost-weighted reduction to the average sampling error of the regional estimating equations. The results of the network analyses can be used to prioritize the continued operation of active stations, the reactivation of discontinued stations, or the activation of new stations to maximize the regional information content provided by the stream-gaging network. Final decisions regarding altering the New Hampshire stream-gaging network would require the consideration of the many uses of the streamflow data serving local, State, and Federal interests.

Smooth information flow in temperature climate network reflects mass transport

NASA Astrophysics Data System (ADS)

Hlinka, Jaroslav; Jajcay, Nikola; Hartman, David; Paluš, Milan

2017-03-01

A directed climate network is constructed by Granger causality analysis of air temperature time series from a regular grid covering the whole Earth. Using winner-takes-all network thresholding approach, a structure of a smooth information flow is revealed, hidden to previous studies. The relevance of this observation is confirmed by comparison with the air mass transfer defined by the wind field. Their close relation illustrates that although the information transferred due to the causal influence is not a physical quantity, the information transfer is tied to the transfer of mass and energy.

Analysis of the streamflow-gaging station network in Ohio for effectiveness in providing regional streamflow information

USGS Publications Warehouse

Straub, D.E.

1998-01-01

The streamflow-gaging station network in Ohio was evaluated for its effectiveness in providing regional streamflow information. The analysis involved application of the principles of generalized least squares regression between streamflow and climatic and basin characteristics. Regression equations were developed for three flow characteristics: (1) the instantaneous peak flow with a 100-year recurrence interval (P100), (2) the mean annual flow (Qa), and (3) the 7-day, 10-year low flow (7Q10). All active and discontinued gaging stations with 5 or more years of unregulated-streamflow data with respect to each flow characteristic were used to develop the regression equations. The gaging-station network was evaluated for the current (1996) condition of the network and estimated conditions of various network strategies if an additional 5 and 20 years of streamflow data were collected. Any active or discontinued gaging station with (1) less than 5 years of unregulated-streamflow record, (2) previously defined basin and climatic characteristics, and (3) the potential for collection of more unregulated-streamflow record were included in the network strategies involving the additional 5 and 20 years of data. The network analysis involved use of the regression equations, in combination with location, period of record, and cost of operation, to determine the contribution of the data for each gaging station to regional streamflow information. The contribution of each gaging station was based on a cost-weighted reduction of the mean square error (average sampling-error variance) associated with each regional estimating equation. All gaging stations included in the network analysis were then ranked according to their contribution to the regional information for each flow characteristic. The predictive ability of the regression equations developed from the gaging station network could be improved for all three flow characteristics with the collection of additional streamflow data. The addition of new gaging stations to the network would result in an even greater improvement of the accuracy of the regional regression equations. Typically, continued data collection at stations with unregulated streamflow for all flow conditions that had less than 11 years of record with drainage areas smaller than 200 square miles contributed the largest cost-weighted reduction to the average sampling-error variance of the regional estimating equations. The results of the network analyses can be used to prioritize the continued operation of active gaging stations or the reactivation of discontinued gaging stations if the objective is to maximize the regional information content in the streamflow-gaging station network.

Stochastic cycle selection in active flow networks.

PubMed

Woodhouse, Francis G; Forrow, Aden; Fawcett, Joanna B; Dunkel, Jörn

2016-07-19

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models.

Stochastic cycle selection in active flow networks

NASA Astrophysics Data System (ADS)

Woodhouse, Francis; Forrow, Aden; Fawcett, Joanna; Dunkel, Jorn

2016-11-01

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such non-equilibrium networks. By connecting concepts from lattice field theory, graph theory and transition rate theory, we show how topology controls dynamics in a generic model for actively driven flow on a network. Through theoretical and numerical analysis we identify symmetry-based rules to classify and predict the selection statistics of complex flow cycles from the network topology. Our conceptual framework is applicable to a broad class of biological and non-biological far-from-equilibrium networks, including actively controlled information flows, and establishes a new correspondence between active flow networks and generalized ice-type models.

Stochastic cycle selection in active flow networks

PubMed Central

Woodhouse, Francis G.; Forrow, Aden; Fawcett, Joanna B.; Dunkel, Jörn

2016-01-01

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models. PMID:27382186

NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities.

PubMed

da Rocha, Edroaldo Lummertz; Ung, Choong Yong; McGehee, Cordelia D; Correia, Cristina; Li, Hu

2016-06-02

The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Information Flow in Interaction Networks II: Channels, Path Lengths, and Potentials

PubMed Central

Stojmirović, Aleksandar

2012-01-01

Abstract In our previous publication, a framework for information flow in interaction networks based on random walks with damping was formulated with two fundamental modes: emitting and absorbing. While many other network analysis methods based on random walks or equivalent notions have been developed before and after our earlier work, one can show that they can all be mapped to one of the two modes. In addition to these two fundamental modes, a major strength of our earlier formalism was its accommodation of context-specific directed information flow that yielded plausible and meaningful biological interpretation of protein functions and pathways. However, the directed flow from origins to destinations was induced via a potential function that was heuristic. Here, with a theoretically sound approach called the channel mode, we extend our earlier work for directed information flow. This is achieved by constructing a potential function facilitating a purely probabilistic interpretation of the channel mode. For each network node, the channel mode combines the solutions of emitting and absorbing modes in the same context, producing what we call a channel tensor. The entries of the channel tensor at each node can be interpreted as the amount of flow passing through that node from an origin to a destination. Similarly to our earlier model, the channel mode encompasses damping as a free parameter that controls the locality of information flow. Through examples involving the yeast pheromone response pathway, we illustrate the versatility and stability of our new framework. PMID:22409812

Information and material flows in complex networks

NASA Astrophysics Data System (ADS)

Helbing, Dirk; Armbruster, Dieter; Mikhailov, Alexander S.; Lefeber, Erjen

2006-04-01

In this special issue, an overview of the Thematic Institute (TI) on Information and Material Flows in Complex Systems is given. The TI was carried out within EXYSTENCE, the first EU Network of Excellence in the area of complex systems. Its motivation, research approach and subjects are presented here. Among the various methods used are many-particle and statistical physics, nonlinear dynamics, as well as complex systems, network and control theory. The contributions are relevant for complex systems as diverse as vehicle and data traffic in networks, logistics, production, and material flows in biological systems. The key disciplines involved are socio-, econo-, traffic- and bio-physics, and a new research area that could be called “biologistics”.

A FRAMEWORK FOR ATTRIBUTE-BASED COMMUNITY DETECTION WITH APPLICATIONS TO INTEGRATED FUNCTIONAL GENOMICS.

PubMed

Yu, Han; Hageman Blair, Rachael

2016-01-01

Understanding community structure in networks has received considerable attention in recent years. Detecting and leveraging community structure holds promise for understanding and potentially intervening with the spread of influence. Network features of this type have important implications in a number of research areas, including, marketing, social networks, and biology. However, an overwhelming majority of traditional approaches to community detection cannot readily incorporate information of node attributes. Integrating structural and attribute information is a major challenge. We propose a exible iterative method; inverse regularized Markov Clustering (irMCL), to network clustering via the manipulation of the transition probability matrix (aka stochastic flow) corresponding to a graph. Similar to traditional Markov Clustering, irMCL iterates between "expand" and "inflate" operations, which aim to strengthen the intra-cluster flow, while weakening the inter-cluster flow. Attribute information is directly incorporated into the iterative method through a sigmoid (logistic function) that naturally dampens attribute influence that is contradictory to the stochastic flow through the network. We demonstrate advantages and the exibility of our approach using simulations and real data. We highlight an application that integrates breast cancer gene expression data set and a functional network defined via KEGG pathways reveal significant modules for survival.

Quantitative angle-insensitive flow measurement using relative standard deviation OCT.

PubMed

Zhu, Jiang; Zhang, Buyun; Qi, Li; Wang, Ling; Yang, Qiang; Zhu, Zhuqing; Huo, Tiancheng; Chen, Zhongping

2017-10-30

Incorporating different data processing methods, optical coherence tomography (OCT) has the ability for high-resolution angiography and quantitative flow velocity measurements. However, OCT angiography cannot provide quantitative information of flow velocities, and the velocity measurement based on Doppler OCT requires the determination of Doppler angles, which is a challenge in a complex vascular network. In this study, we report on a relative standard deviation OCT (RSD-OCT) method which provides both vascular network mapping and quantitative information for flow velocities within a wide range of Doppler angles. The RSD values are angle-insensitive within a wide range of angles, and a nearly linear relationship was found between the RSD values and the flow velocities. The RSD-OCT measurement in a rat cortex shows that it can quantify the blood flow velocities as well as map the vascular network in vivo .

Quantitative angle-insensitive flow measurement using relative standard deviation OCT

NASA Astrophysics Data System (ADS)

Zhu, Jiang; Zhang, Buyun; Qi, Li; Wang, Ling; Yang, Qiang; Zhu, Zhuqing; Huo, Tiancheng; Chen, Zhongping

2017-10-01

Incorporating different data processing methods, optical coherence tomography (OCT) has the ability for high-resolution angiography and quantitative flow velocity measurements. However, OCT angiography cannot provide quantitative information of flow velocities, and the velocity measurement based on Doppler OCT requires the determination of Doppler angles, which is a challenge in a complex vascular network. In this study, we report on a relative standard deviation OCT (RSD-OCT) method which provides both vascular network mapping and quantitative information for flow velocities within a wide range of Doppler angles. The RSD values are angle-insensitive within a wide range of angles, and a nearly linear relationship was found between the RSD values and the flow velocities. The RSD-OCT measurement in a rat cortex shows that it can quantify the blood flow velocities as well as map the vascular network in vivo.

Cross-Layer Scheme to Control Contention Window for Per-Flow in Asymmetric Multi-Hop Networks

NASA Astrophysics Data System (ADS)

Giang, Pham Thanh; Nakagawa, Kenji

The IEEE 802.11 MAC standard for wireless ad hoc networks adopts Binary Exponential Back-off (BEB) mechanism to resolve bandwidth contention between stations. BEB mechanism controls the bandwidth allocation for each station by choosing a back-off value from one to CW according to the uniform random distribution, where CW is the contention window size. However, in asymmetric multi-hop networks, some stations are disadvantaged in opportunity of access to the shared channel and may suffer severe throughput degradation when the traffic load is large. Then, the network performance is degraded in terms of throughput and fairness. In this paper, we propose a new cross-layer scheme aiming to solve the per-flow unfairness problem and achieve good throughput performance in IEEE 802.11 multi-hop ad hoc networks. Our cross-layer scheme collects useful information from the physical, MAC and link layers of own station. This information is used to determine the optimal Contention Window (CW) size for per-station fairness. We also use this information to adjust CW size for each flow in the station in order to achieve per-flow fairness. Performance of our cross-layer scheme is examined on various asymmetric multi-hop network topologies by using Network Simulator (NS-2).

Improving management decision processes through centralized communication linkages

NASA Technical Reports Server (NTRS)

Simanton, D. F.; Garman, J. R.

1985-01-01

Information flow is a critical element to intelligent and timely decision-making. At NASA's Johnson Space Center the flow of information is being automated through the use of a centralized backbone network. The theoretical basis of this network, its implications to the horizontal and vertical flow of information, and the technical challenges involved in its implementation are the focus of this paper. The importance of the use of common tools among programs and some future concerns related to file transfer, graphics transfer, and merging of voice and data are also discussed.

How Did the Information Flow in the #AlphaGo Hashtag Network? A Social Network Analysis of the Large-Scale Information Network on Twitter.

PubMed

Kim, Jinyoung

2017-12-01

As it becomes common for Internet users to use hashtags when posting and searching information on social media, it is important to understand who builds a hashtag network and how information is circulated within the network. This article focused on unlocking the potential of the #AlphaGo hashtag network by addressing the following questions. First, the current study examined whether traditional opinion leadership (i.e., the influentials hypothesis) or grassroot participation by the public (i.e., the interpersonal hypothesis) drove dissemination of information in the hashtag network. Second, several unique patterns of information distribution by key users were identified. Finally, the association between attributes of key users who exerted great influence on information distribution (i.e., the number of followers and follows) and their central status in the network was tested. To answer the proffered research questions, a social network analysis was conducted using a large-scale hashtag network data set from Twitter (n = 21,870). The results showed that the leading actors in the network were actively receiving information from their followers rather than serving as intermediaries between the original information sources and the public. Moreover, the leading actors played several roles (i.e., conversation starters, influencers, and active engagers) in the network. Furthermore, the number of their follows and followers were significantly associated with their central status in the hashtag network. Based on the results, the current research explained how the information was exchanged in the hashtag network by proposing the reciprocal model of information flow.

A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.

PubMed

Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong

2015-01-01

This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.

Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks.

PubMed

Gao, Zhongke; Jin, Ningde

2009-06-01

The identification of flow pattern is a basic and important issue in multiphase systems. Because of the complexity of phase interaction in gas-liquid two-phase flow, it is difficult to discern its flow pattern objectively. In this paper, we make a systematic study on the vertical upward gas-liquid two-phase flow using complex network. Three unique network construction methods are proposed to build three types of networks, i.e., flow pattern complex network (FPCN), fluid dynamic complex network (FDCN), and fluid structure complex network (FSCN). Through detecting the community structure of FPCN by the community-detection algorithm based on K -mean clustering, useful and interesting results are found which can be used for identifying five vertical upward gas-liquid two-phase flow patterns. To investigate the dynamic characteristics of gas-liquid two-phase flow, we construct 50 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of gas-liquid two-phase flow. Furthermore, we construct FSCN and demonstrate how network statistic can be used to reveal the fluid structure of gas-liquid two-phase flow. In this paper, from a different perspective, we not only introduce complex network theory to the study of gas-liquid two-phase flow but also indicate that complex network may be a powerful tool for exploring nonlinear time series in practice.

Survey of Human Systems Integration (HSI) Tools for USCG Acquisitions

DTIC Science & Technology

2009-04-01

an IMPRINT HPM. IMPRINT uses task network modeling to represent human performance. As the name implies, task networks use a flowchart type format...tools; and built-in tutoring support for beginners . A perceptual/motor layer extending ACT-R’s theory of cognition to perception and action is also...chisystems.com B.8 Information and Functional Flow Analysis Description In information flow analysis, a flowchart of the information and decisions

Design and Evaluation of a Proxy-Based Monitoring System for OpenFlow Networks.

PubMed

Taniguchi, Yoshiaki; Tsutsumi, Hiroaki; Iguchi, Nobukazu; Watanabe, Kenzi

2016-01-01

Software-Defined Networking (SDN) has attracted attention along with the popularization of cloud environment and server virtualization. In SDN, the control plane and the data plane are decoupled so that the logical topology and routing control can be configured dynamically depending on network conditions. To obtain network conditions precisely, a network monitoring mechanism is necessary. In this paper, we focus on OpenFlow which is a core technology to realize SDN. We propose, design, implement, and evaluate a network monitoring system for OpenFlow networks. Our proposed system acts as a proxy between an OpenFlow controller and OpenFlow switches. Through experimental evaluations, we confirm that our proposed system can capture packets and monitor traffic information depending on administrator's configuration. In addition, we show that our proposed system does not influence significant performance degradation to overall network performance.

Design and Evaluation of a Proxy-Based Monitoring System for OpenFlow Networks

PubMed Central

Taniguchi, Yoshiaki; Tsutsumi, Hiroaki; Iguchi, Nobukazu; Watanabe, Kenzi

2016-01-01

Software-Defined Networking (SDN) has attracted attention along with the popularization of cloud environment and server virtualization. In SDN, the control plane and the data plane are decoupled so that the logical topology and routing control can be configured dynamically depending on network conditions. To obtain network conditions precisely, a network monitoring mechanism is necessary. In this paper, we focus on OpenFlow which is a core technology to realize SDN. We propose, design, implement, and evaluate a network monitoring system for OpenFlow networks. Our proposed system acts as a proxy between an OpenFlow controller and OpenFlow switches. Through experimental evaluations, we confirm that our proposed system can capture packets and monitor traffic information depending on administrator's configuration. In addition, we show that our proposed system does not influence significant performance degradation to overall network performance. PMID:27006977

Connectomics-based analysis of information flow in the Drosophila brain.

PubMed

Shih, Chi-Tin; Sporns, Olaf; Yuan, Shou-Li; Su, Ta-Shun; Lin, Yen-Jen; Chuang, Chao-Chun; Wang, Ting-Yuan; Lo, Chung-Chuang; Greenspan, Ralph J; Chiang, Ann-Shyn

2015-05-18

Understanding the overall patterns of information flow within the brain has become a major goal of neuroscience. In the current study, we produced a first draft of the Drosophila connectome at the mesoscopic scale, reconstructed from 12,995 images of neuron projections collected in FlyCircuit (version 1.1). Neuron polarities were predicted according to morphological criteria, with nodes of the network corresponding to brain regions designated as local processing units (LPUs). The weight of each directed edge linking a pair of LPUs was determined by the number of neuron terminals that connected one LPU to the other. The resulting network showed hierarchical structure and small-world characteristics and consisted of five functional modules that corresponded to sensory modalities (olfactory, mechanoauditory, and two visual) and the pre-motor center. Rich-club organization was present in this network and involved LPUs in all sensory centers, and rich-club members formed a putative motor center of the brain. Major intra- and inter-modular loops were also identified that could play important roles for recurrent and reverberant information flow. The present analysis revealed whole-brain patterns of network structure and information flow. Additionally, we propose that the overall organizational scheme showed fundamental similarities to the network structure of the mammalian brain. Copyright © 2015 Elsevier Ltd. All rights reserved.

"Time-dependent flow-networks"

NASA Astrophysics Data System (ADS)

Tupikina, Liubov; Molkentin, Nora; Lopez, Cristobal; Hernandez-Garcia, Emilio; Marwan, Norbert; Kurths, Jürgen

2015-04-01

Complex networks have been successfully applied to various systems such as society, technology, and recently climate. Links in a climate network are defined between two geographical locations if the correlation between the time series of some climate variable is higher than a threshold. Therefore, network links are considered to imply information or heat exchange. However, the relationship between the oceanic and atmospheric flows and the climate network's structure is still unclear. Recently, a theoretical approach verifying the correlation between ocean currents and surface air temperature networks has been introduced, where the Pearson correlation networks were constructed from advection-diffusion dynamics on an underlying flow. Since the continuous approach has its limitations, i.e. high computational complexity and fixed variety of the flows in the underlying system, we introduce a new, method of flow-networks for changing in time velocity fields including external forcing in the system, noise and temperature-decay. Method of the flow-network construction can be divided into several steps: first we obtain the linear recursive equation for the temperature time-series. Then we compute the correlation matrix for time-series averaging the tensor product over all realizations of the noise, which we interpret as a weighted adjacency matrix of the flow-network and analyze using network measures. We apply the method to different types of moving flows with geographical relevance such as meandering flow. Analyzing the flow-networks using network measures we find that our approach can highlight zones of high velocity by degree and transition zones by betweenness, while the combination of these network measures can uncover how the flow propagates within time. Flow-networks can be powerful tool to understand the connection between system's dynamics and network's topology analyzed using network measures in order to shed light on different climatic phenomena.

Communication efficiency and congestion of signal traffic in large-scale brain networks.

PubMed

Mišić, Bratislav; Sporns, Olaf; McIntosh, Anthony R

2014-01-01

The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a "rich club" of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication.

Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks

PubMed Central

Mišić, Bratislav; Sporns, Olaf; McIntosh, Anthony R.

2014-01-01

The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a “rich club” of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication. PMID:24415931

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

NASA Astrophysics Data System (ADS)

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 Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks

PubMed Central

Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong

2015-01-01

This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme. PMID:26690571

Social networks and trade of services: modelling interregional flows with spatial and network autocorrelation effects

NASA Astrophysics Data System (ADS)

de la Mata, Tamara; Llano, Carlos

2013-07-01

Recent literature on border effect has fostered research on informal barriers to trade and the role played by network dependencies. In relation to social networks, it has been shown that intensity of trade in goods is positively correlated with migration flows between pairs of countries/regions. In this article, we investigate whether such a relation also holds for interregional trade of services. We also consider whether interregional trade flows in services linked with tourism exhibit spatial and/or social network dependence. Conventional empirical gravity models assume the magnitude of bilateral flows between regions is independent of flows to/from regions located nearby in space, or flows to/from regions related through social/cultural/ethic network connections. With this aim, we provide estimates from a set of gravity models showing evidence of statistically significant spatial and network (demographic) dependence in the bilateral flows of the trade of services considered. The analysis has been applied to the Spanish intra- and interregional monetary flows of services from the accommodation, restaurants and travel agencies for the period 2000-2009, using alternative datasets for the migration stocks and definitions of network effects.

Extended shortest path selection for package routing of complex networks

NASA Astrophysics Data System (ADS)

Ye, Fan; Zhang, Lei; Wang, Bing-Hong; Liu, Lu; Zhang, Xing-Yi

The routing strategy plays a very important role in complex networks such as Internet system and Peer-to-Peer networks. However, most of the previous work concentrates only on the path selection, e.g. Flooding and Random Walk, or finding the shortest path (SP) and rarely considering the local load information such as SP and Distance Vector Routing. Flow-based Routing mainly considers load balance and still cannot achieve best optimization. Thus, in this paper, we propose a novel dynamic routing strategy on complex network by incorporating the local load information into SP algorithm to enhance the traffic flow routing optimization. It was found that the flow in a network is greatly affected by the waiting time of the network, so we should not consider only choosing optimized path for package transformation but also consider node congestion. As a result, the packages should be transmitted with a global optimized path with smaller congestion and relatively short distance. Analysis work and simulation experiments show that the proposed algorithm can largely enhance the network flow with the maximum throughput within an acceptable calculating time. The detailed analysis of the algorithm will also be provided for explaining the efficiency.

Eavesdropping-aware routing and spectrum allocation based on multi-flow virtual concatenation for confidential information service in elastic optical networks

NASA Astrophysics Data System (ADS)

Bai, Wei; Yang, Hui; Yu, Ao; Xiao, Hongyun; He, Linkuan; Feng, Lei; Zhang, Jie

2018-01-01

The leakage of confidential information is one of important issues in the network security area. Elastic Optical Networks (EON) as a promising technology in the optical transport network is under threat from eavesdropping attacks. It is a great demand to support confidential information service (CIS) and design efficient security strategy against the eavesdropping attacks. In this paper, we propose a solution to cope with the eavesdropping attacks in routing and spectrum allocation. Firstly, we introduce probability theory to describe eavesdropping issue and achieve awareness of eavesdropping attacks. Then we propose an eavesdropping-aware routing and spectrum allocation (ES-RSA) algorithm to guarantee information security. For further improving security and network performance, we employ multi-flow virtual concatenation (MFVC) and propose an eavesdropping-aware MFVC-based secure routing and spectrum allocation (MES-RSA) algorithm. The presented simulation results show that the proposed two RSA algorithms can both achieve greater security against the eavesdropping attacks and MES-RSA can also improve the network performance efficiently.

Spike Code Flow in Cultured Neuronal Networks.

PubMed

Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime; Kamimura, Takuya; Yagi, Yasushi; Mizuno-Matsumoto, Yuko; Chen, Yen-Wei

2016-01-01

We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of "1101" and "1011," which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the "maximum cross-correlations" among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network.

Information flows in hierarchical networks and the capability of organizations to successfully respond to failures, crises, and disasters

NASA Astrophysics Data System (ADS)

Helbing, Dirk; Ammoser, Hendrik; Kühnert, Christian

2006-04-01

In this paper we discuss the problem of information losses in organizations and how they depend on the organization network structure. Hierarchical networks are an optimal organization structure only when the failure rate of nodes or links is negligible. Otherwise, redundant information links are important to reduce the risk of information losses and the related costs. However, as redundant information links are expensive, the optimal organization structure is not a fully connected one. It rather depends on the failure rate. We suggest that sidelinks and temporary, adaptive shortcuts can improve the information flows considerably by generating small-world effects. This calls for modified organization structures to cope with today's challenges of businesses and administrations, in particular, to successfully respond to crises or disasters.

Information flow in layered networks of non-monotonic units

NASA Astrophysics Data System (ADS)

Schittler Neves, Fabio; Martim Schubert, Benno; Erichsen, Rubem, Jr.

2015-07-01

Layered neural networks are feedforward structures that yield robust parallel and distributed pattern recognition. Even though much attention has been paid to pattern retrieval properties in such systems, many aspects of their dynamics are not yet well characterized or understood. In this work we study, at different temperatures, the memory activity and information flows through layered networks in which the elements are the simplest binary odd non-monotonic function. Our results show that, considering a standard Hebbian learning approach, the network information content has its maximum always at the monotonic limit, even though the maximum memory capacity can be found at non-monotonic values for small enough temperatures. Furthermore, we show that such systems exhibit rich macroscopic dynamics, including not only fixed point solutions of its iterative map, but also cyclic and chaotic attractors that also carry information.

An information theory framework for dynamic functional domain connectivity.

PubMed

Vergara, Victor M; Miller, Robyn; Calhoun, Vince

2017-06-01

Dynamic functional network connectivity (dFNC) analyzes time evolution of coherent activity in the brain. In this technique dynamic changes are considered for the whole brain. This paper proposes an information theory framework to measure information flowing among subsets of functional networks call functional domains. Our method aims at estimating bits of information contained and shared among domains. The succession of dynamic functional states is estimated at the domain level. Information quantity is based on the probabilities of observing each dynamic state. Mutual information measurement is then obtained from probabilities across domains. Thus, we named this value the cross domain mutual information (CDMI). Strong CDMIs were observed in relation to the subcortical domain. Domains related to sensorial input, motor control and cerebellum form another CDMI cluster. Information flow among other domains was seldom found. Other methods of dynamic connectivity focus on whole brain dFNC matrices. In the current framework, information theory is applied to states estimated from pairs of multi-network functional domains. In this context, we apply information theory to measure information flow across functional domains. Identified CDMI clusters point to known information pathways in the basal ganglia and also among areas of sensorial input, patterns found in static functional connectivity. In contrast, CDMI across brain areas of higher level cognitive processing follow a different pattern that indicates scarce information sharing. These findings show that employing information theory to formally measured information flow through brain domains reveals additional features of functional connectivity. Copyright © 2017 Elsevier B.V. All rights reserved.

An Integrated Bibliographic Information System: Concept and Application for Resource Sharing in Special Libraries.

ERIC Educational Resources Information Center

Cotter, Gladys A.; And Others

The Defense Department Scientific and Technical Information (STI) network is composed of over 200 technical libraries and information centers tied together by the Defense Technical Information Center (DTIC), an organization which seeks to improve the flow of information throughout the STI network by promoting shared cataloging and integrated…

Solving the influence maximization problem reveals regulatory organization of the yeast cell cycle.

PubMed

Gibbs, David L; Shmulevich, Ilya

2017-06-01

The Influence Maximization Problem (IMP) aims to discover the set of nodes with the greatest influence on network dynamics. The problem has previously been applied in epidemiology and social network analysis. Here, we demonstrate the application to cell cycle regulatory network analysis for Saccharomyces cerevisiae. Fundamentally, gene regulation is linked to the flow of information. Therefore, our implementation of the IMP was framed as an information theoretic problem using network diffusion. Utilizing more than 26,000 regulatory edges from YeastMine, gene expression dynamics were encoded as edge weights using time lagged transfer entropy, a method for quantifying information transfer between variables. By picking a set of source nodes, a diffusion process covers a portion of the network. The size of the network cover relates to the influence of the source nodes. The set of nodes that maximizes influence is the solution to the IMP. By solving the IMP over different numbers of source nodes, an influence ranking on genes was produced. The influence ranking was compared to other metrics of network centrality. Although the top genes from each centrality ranking contained well-known cell cycle regulators, there was little agreement and no clear winner. However, it was found that influential genes tend to directly regulate or sit upstream of genes ranked by other centrality measures. The influential nodes act as critical sources of information flow, potentially having a large impact on the state of the network. Biological events that affect influential nodes and thereby affect information flow could have a strong effect on network dynamics, potentially leading to disease. Code and data can be found at: https://github.com/gibbsdavidl/miergolf.

Compression of Flow Can Reveal Overlapping-Module Organization in Networks

NASA Astrophysics Data System (ADS)

Viamontes Esquivel, Alcides; Rosvall, Martin

2011-10-01

To better understand the organization of overlapping modules in large networks with respect to flow, we introduce the map equation for overlapping modules. In this information-theoretic framework, we use the correspondence between compression and regularity detection. The generalized map equation measures how well we can compress a description of flow in the network when we partition it into modules with possible overlaps. When we minimize the generalized map equation over overlapping network partitions, we detect modules that capture flow and determine which nodes at the boundaries between modules should be classified in multiple modules and to what degree. With a novel greedy-search algorithm, we find that some networks, for example, the neural network of the nematode Caenorhabditis elegans, are best described by modules dominated by hard boundaries, but that others, for example, the sparse European-roads network, have an organization of highly overlapping modules.

Communication Dynamics in Finite Capacity Social Networks

NASA Astrophysics Data System (ADS)

Haerter, Jan O.; Jamtveit, Bjørn; Mathiesen, Joachim

2012-10-01

In communication networks, structure and dynamics are tightly coupled. The structure controls the flow of information and is itself shaped by the dynamical process of information exchanged between nodes. In order to reconcile structure and dynamics, a generic model, based on the local interaction between nodes, is considered for the communication in large social networks. In agreement with data from a large human organization, we show that the flow is non-Markovian and controlled by the temporal limitations of individuals. We confirm the versatility of our model by predicting simultaneously the degree-dependent node activity, the balance between information input and output of nodes, and the degree distribution. Finally, we quantify the limitations to network analysis when it is based on data sampled over a finite period of time.

Attacks and Countermeasures in Communications and Power Networks

DTIC Science & Technology

2014-01-01

the victim. This strategy is often used to confuse the intrusion detection system about the adversary’s location. If the adversary compromises a pair...1.2 Detection of Information Flows Detection of information flows between a pair of nodes has been studied in the context of network intrusion ...Theo- rem 3.3.4 were derived purely based on the condition for undetectability. Hence, the same optimality statements hold for the noisy measurement

Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks.

PubMed

Teng, Xian; Pei, Sen; Morone, Flaviano; Makse, Hernán A

2016-10-26

Identifying the most influential spreaders that maximize information flow is a central question in network theory. Recently, a scalable method called "Collective Influence (CI)" has been put forward through collective influence maximization. In contrast to heuristic methods evaluating nodes' significance separately, CI method inspects the collective influence of multiple spreaders. Despite that CI applies to the influence maximization problem in percolation model, it is still important to examine its efficacy in realistic information spreading. Here, we examine real-world information flow in various social and scientific platforms including American Physical Society, Facebook, Twitter and LiveJournal. Since empirical data cannot be directly mapped to ideal multi-source spreading, we leverage the behavioral patterns of users extracted from data to construct "virtual" information spreading processes. Our results demonstrate that the set of spreaders selected by CI can induce larger scale of information propagation. Moreover, local measures as the number of connections or citations are not necessarily the deterministic factors of nodes' importance in realistic information spreading. This result has significance for rankings scientists in scientific networks like the APS, where the commonly used number of citations can be a poor indicator of the collective influence of authors in the community.

Characterizing the Networks of Digital Information that Support Collaborative Adaptive Forest Management in Sierra Nevada Forests.

PubMed

Lei, Shufei; Iles, Alastair; Kelly, Maggi

2015-07-01

Some of the factors that can contribute to the success of collaborative adaptive management--such as social learning, open communication, and trust--are built upon a foundation of the open exchange of information about science and management between participants and the public. Despite the importance of information transparency, the use and flow of information in collaborative adaptive management has not been characterized in detail in the literature, and currently there exist opportunities to develop strategies for increasing the exchange of information, as well as to track information flow in such contexts. As digital information channels and networks have been increased over the last decade, powerful new information monitoring tools have also been evolved allowing for the complete characterization of information products through their production, transport, use, and monitoring. This study uses these tools to investigate the use of various science and management information products in a case study--the Sierra Nevada Adaptive Management Project--using a mixed method (citation analysis, web analytics, and content analysis) research approach borrowed from the information processing and management field. The results from our case study show that information technologies greatly facilitate the flow and use of digital information, leading to multiparty collaborations such as knowledge transfer and public participation in science research. We conclude with recommendations for expanding information exchange in collaborative adaptive management by taking advantage of available information technologies and networks.

Characterizing the Networks of Digital Information that Support Collaborative Adaptive Forest Management in Sierra Nevada Forests

NASA Astrophysics Data System (ADS)

Lei, Shufei; Iles, Alastair; Kelly, Maggi

2015-07-01

Some of the factors that can contribute to the success of collaborative adaptive management—such as social learning, open communication, and trust—are built upon a foundation of the open exchange of information about science and management between participants and the public. Despite the importance of information transparency, the use and flow of information in collaborative adaptive management has not been characterized in detail in the literature, and currently there exist opportunities to develop strategies for increasing the exchange of information, as well as to track information flow in such contexts. As digital information channels and networks have been increased over the last decade, powerful new information monitoring tools have also been evolved allowing for the complete characterization of information products through their production, transport, use, and monitoring. This study uses these tools to investigate the use of various science and management information products in a case study—the Sierra Nevada Adaptive Management Project—using a mixed method (citation analysis, web analytics, and content analysis) research approach borrowed from the information processing and management field. The results from our case study show that information technologies greatly facilitate the flow and use of digital information, leading to multiparty collaborations such as knowledge transfer and public participation in science research. We conclude with recommendations for expanding information exchange in collaborative adaptive management by taking advantage of available information technologies and networks.

PageRank versatility analysis of multilayer modality-based network for exploring the evolution of oil-water slug flow.

PubMed

Gao, Zhong-Ke; Dang, Wei-Dong; Li, Shan; Yang, Yu-Xuan; Wang, Hong-Tao; Sheng, Jing-Ran; Wang, Xiao-Fan

2017-07-14

Numerous irregular flow structures exist in the complicated multiphase flow and result in lots of disparate spatial dynamical flow behaviors. The vertical oil-water slug flow continually attracts plenty of research interests on account of its significant importance. Based on the spatial transient flow information acquired through our designed double-layer distributed-sector conductance sensor, we construct multilayer modality-based network to encode the intricate spatial flow behavior. Particularly, we calculate the PageRank versatility and multilayer weighted clustering coefficient to quantitatively explore the inferred multilayer modality-based networks. Our analysis allows characterizing the complicated evolution of oil-water slug flow, from the opening formation of oil slugs, to the succedent inter-collision and coalescence among oil slugs, and then to the dispersed oil bubbles. These properties render our developed method particularly powerful for mining the essential flow features from the multilayer sensor measurements.

Operationalizing an "Information Ecosystem" to support information management work of the Long-Term Agroecosystem Research (LTAR) network

USDA-ARS?s Scientific Manuscript database

As data collection ramps up across the LTAR Network, the “information ecosystem” (Kaplan et al. 2016 and Nardi and O’Day, 1999) must support three domains of sustainable agriculture, and data flow needs to be operationalized. Approaches to prioritizing, designing, and developing shared cyberinfrastr...

An Open-Access Modeled Passenger Flow Matrix for the Global Air Network in 2010

PubMed Central

Huang, Zhuojie; Wu, Xiao; Garcia, Andres J.; Fik, Timothy J.; Tatem, Andrew J.

2013-01-01

The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air) project at: www.vbd-air.com/data. PMID:23691194

An open-access modeled passenger flow matrix for the global air network in 2010.

PubMed

Huang, Zhuojie; Wu, Xiao; Garcia, Andres J; Fik, Timothy J; Tatem, Andrew J

2013-01-01

The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air) project at: www.vbd-air.com/data.

A Dynamic Three-Dimensional Network Visualization Program for Integration into CyberCIEGE and Other Network Visualization Scenarios

DTIC Science & Technology

2007-06-01

information flow involved in network attacks. This kind of information can be invaluable in learning how to best setup and defend computer networks...administrators, and those interested in learning about securing networks a way to conceptualize this complex system of computing. NTAV3D will provide a three...teaching with visual and other components can make learning more effective” (Baxley et al, 2006). A hyperbox (Alpern and Carter, 1991) is

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach.

PubMed

Xu, Nan; Spreng, R Nathan; Doerschuk, Peter C

2017-01-01

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the "common driver" problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain.

A model for simulating adaptive, dynamic flows on networks: Application to petroleum infrastructure

DOE PAGES

Corbet, Thomas F.; Beyeler, Walt; Wilson, Michael L.; ...

2017-10-03

Simulation models can greatly improve decisions meant to control the consequences of disruptions to critical infrastructures. We describe a dynamic flow model on networks purposed to inform analyses by those concerned about consequences of disruptions to infrastructures and to help policy makers design robust mitigations. We conceptualize the adaptive responses of infrastructure networks to perturbations as market transactions and business decisions of operators. We approximate commodity flows in these networks by a diffusion equation, with nonlinearities introduced to model capacity limits. To illustrate the behavior and scalability of the model, we show its application first on two simple networks, thenmore » on petroleum infrastructure in the United States, where we analyze the effects of a hypothesized earthquake.« less

A model for simulating adaptive, dynamic flows on networks: Application to petroleum infrastructure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Corbet, Thomas F.; Beyeler, Walt; Wilson, Michael L.

Simulation models can greatly improve decisions meant to control the consequences of disruptions to critical infrastructures. We describe a dynamic flow model on networks purposed to inform analyses by those concerned about consequences of disruptions to infrastructures and to help policy makers design robust mitigations. We conceptualize the adaptive responses of infrastructure networks to perturbations as market transactions and business decisions of operators. We approximate commodity flows in these networks by a diffusion equation, with nonlinearities introduced to model capacity limits. To illustrate the behavior and scalability of the model, we show its application first on two simple networks, thenmore » on petroleum infrastructure in the United States, where we analyze the effects of a hypothesized earthquake.« less

A Network Scheduling Model for Distributed Control Simulation

NASA Technical Reports Server (NTRS)

Culley, Dennis; Thomas, George; Aretskin-Hariton, Eliot

2016-01-01

Distributed engine control is a hardware technology that radically alters the architecture for aircraft engine control systems. Of its own accord, it does not change the function of control, rather it seeks to address the implementation issues for weight-constrained vehicles that can limit overall system performance and increase life-cycle cost. However, an inherent feature of this technology, digital communication networks, alters the flow of information between critical elements of the closed-loop control. Whereas control information has been available continuously in conventional centralized control architectures through virtue of analog signaling, moving forward, it will be transmitted digitally in serial fashion over the network(s) in distributed control architectures. An underlying effect is that all of the control information arrives asynchronously and may not be available every loop interval of the controller, therefore it must be scheduled. This paper proposes a methodology for modeling the nominal data flow over these networks and examines the resulting impact for an aero turbine engine system simulation.

How has climate change altered network connectivity in a mountain stream network?

NASA Astrophysics Data System (ADS)

Ward, A. S.; Schmadel, N.; Wondzell, S. M.; Johnson, S.

2017-12-01

Connectivity along river networks is broadly recognized as dynamic, with seasonal and event-based expansion and contraction of the network extent. Intermittently flowing streams are particularly important as they define a crucial threshold for continuously connected waters that enable migration by aquatic species. In the Pacific northwestern U.S., changes in atmospheric circulation have been found to alter rainfall patterns and result in decreased summer low-flows in the region. However, the impact of this climate dynamic on network connectivity is heretofore unstudied. Thus, we ask: How has connectivity in the riparian corridor changed in response to observed changes in climate? In this study we take the well-studied H.J. Andrews Experimental Forest as representative of mountain river networks in the Pacific northwestern U.S. First, we analyze 63 years of stream gauge information from a network of 11 gauges to document observed changes in timing and magnitude of stream discharge. We found declining magnitudes of seasonal low-flows and shifting seasonality of water export from the catchment, both of which we attribute to changes in precipitation timing and storage as snow vs. rainfall. Next, we use these discharge data to drive a reduced-complexity model of the river network to simulate network connectivity over 63 years. Model results show that network contraction (i.e., minimum network extent) has decreased over the past 63 years. Unexpectedly, the increasing winter peak flows did not correspond with increasing network expansion, suggesting a geologic control on maximum flowing network extent. We find dynamic expansion and contraction of the network primarily occurs during period of catchment discharge less than about 1 m3/s at the outlet, whereas the network extent is generally constant for discharges from 1 to 300 m3/s. Results of our study are of interest to scientists focused on connectivity as a control on ecological processes both directly (e.g., fish migration) and indirectly (e.g., stream temperature modeling). Additionally, our results inform management and regulatory needs such as estimating connectivity for entire river networks as a basis for regulation, and identifying the complexity of a shifting baseline in identifying a regulatory basis.

Information Sharing Among Military Headquarters: The Effects of Decisionmaking

DTIC Science & Technology

2004-01-01

adopted Murray Gell-Mann’s more neutral term plec - ticity to describe the effects of the network infrastructure on military operations. This...benefits of network plec - ticity for a cluster within the network, associated with the mission at hand. The term ‘costs’ suggests a simple cost-benefit...network is logically connected to support a given mission. Plec - ticity for a cluster is then associated with the flow of information associated with

A Noachian/Hesperian Hiatus and Erosive Reactivation of Martian Valley Networks

NASA Technical Reports Server (NTRS)

Irwin, R. P., III.; Maxwell, T. A.; Howard, A. D.; Craddock, R. A.; Moore, J. M.

2005-01-01

Despite new evidence for persistent flow and sedimentation on early Mars, it remains unclear whether valley networks were active over long geologic timescales (10(exp 5)-10(exp 8) yr), or if flows were persistent only during multiple discrete episodes of moderate (approx. 10(exp 4) yr) to short (<10 yr) duration. Understanding the long-term stability/variability of valley network hydrology would provide an important control on paleoclimate and groundwater models. Here we describe geologic evidence for a hiatus in highland valley network activity while the fretted terrain formed, followed by a discrete reactivation of persistent (but possibly variable) erosive flows. Additional information is included in the original extended abstract.

Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks

NASA Astrophysics Data System (ADS)

Teng, Xian; Pei, Sen; Morone, Flaviano; Makse, Hernán A.

2016-10-01

Identifying the most influential spreaders that maximize information flow is a central question in network theory. Recently, a scalable method called “Collective Influence (CI)” has been put forward through collective influence maximization. In contrast to heuristic methods evaluating nodes’ significance separately, CI method inspects the collective influence of multiple spreaders. Despite that CI applies to the influence maximization problem in percolation model, it is still important to examine its efficacy in realistic information spreading. Here, we examine real-world information flow in various social and scientific platforms including American Physical Society, Facebook, Twitter and LiveJournal. Since empirical data cannot be directly mapped to ideal multi-source spreading, we leverage the behavioral patterns of users extracted from data to construct “virtual” information spreading processes. Our results demonstrate that the set of spreaders selected by CI can induce larger scale of information propagation. Moreover, local measures as the number of connections or citations are not necessarily the deterministic factors of nodes’ importance in realistic information spreading. This result has significance for rankings scientists in scientific networks like the APS, where the commonly used number of citations can be a poor indicator of the collective influence of authors in the community.

Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks

PubMed Central

Teng, Xian; Pei, Sen; Morone, Flaviano; Makse, Hernán A.

2016-01-01

Identifying the most influential spreaders that maximize information flow is a central question in network theory. Recently, a scalable method called “Collective Influence (CI)” has been put forward through collective influence maximization. In contrast to heuristic methods evaluating nodes’ significance separately, CI method inspects the collective influence of multiple spreaders. Despite that CI applies to the influence maximization problem in percolation model, it is still important to examine its efficacy in realistic information spreading. Here, we examine real-world information flow in various social and scientific platforms including American Physical Society, Facebook, Twitter and LiveJournal. Since empirical data cannot be directly mapped to ideal multi-source spreading, we leverage the behavioral patterns of users extracted from data to construct “virtual” information spreading processes. Our results demonstrate that the set of spreaders selected by CI can induce larger scale of information propagation. Moreover, local measures as the number of connections or citations are not necessarily the deterministic factors of nodes’ importance in realistic information spreading. This result has significance for rankings scientists in scientific networks like the APS, where the commonly used number of citations can be a poor indicator of the collective influence of authors in the community. PMID:27782207

Constraints of nonresponding flows based on cross layers in the networks

NASA Astrophysics Data System (ADS)

Zhou, Zhi-Chao; Xiao, Yang; Wang, Dong

2016-02-01

In the active queue management (AQM) scheme, core routers cannot manage and constrain user datagram protocol (UDP) data flows by the sliding window control mechanism in the transport layer due to the nonresponsive nature of such traffic flows. However, the UDP traffics occupy a large part of the network service nowadays which brings a great challenge to the stability of the more and more complex networks. To solve the uncontrollable problem, this paper proposes a cross layers random early detection (CLRED) scheme, which can control the nonresponding UDP-like flows rate effectively when congestion occurs in the access point (AP). The CLRED makes use of the MAC frame acknowledgement (ACK) transmitting congestion information to the sources nodes and utilizes the back-off windows of the MAC layer throttling data rate. Consequently, the UDP-like flows data rate can be restrained timely by the sources nodes in order to alleviate congestion in the complex networks. The proposed CLRED can constrain the nonresponsive flows availably and make the communication expedite, so that the network can sustain stable. The simulation results of network simulator-2 (NS2) verify the proposed CLRED scheme.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chinthavali, Supriya

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) andmore » the criticality index is found to be effective for one test network to identify the vulnerable nodes.« less

Building the Material Flow Networks of Aluminum in the 2007 U.S. Economy.

PubMed

Chen, Wei-Qiang; Graedel, T E; Nuss, Philip; Ohno, Hajime

2016-04-05

Based on the combination of the U.S. economic input-output table and the stocks and flows framework for characterizing anthropogenic metal cycles, this study presents a methodology for building material flow networks of bulk metals in the U.S. economy and applies it to aluminum. The results, which we term the Input-Output Material Flow Networks (IO-MFNs), achieve a complete picture of aluminum flow in the entire U.S. economy and for any chosen industrial sector (illustrated for the Automobile Manufacturing sector). The results are compared with information from our former study on U.S. aluminum stocks and flows to demonstrate the robustness and value of this new methodology. We find that the IO-MFN approach has the following advantages: (1) it helps to uncover the network of material flows in the manufacturing stage in the life cycle of metals; (2) it provides a method that may be less time-consuming but more complete and accurate in estimating new scrap generation, process loss, domestic final demand, and trade of final products of metals, than existing material flow analysis approaches; and, most importantly, (3) it enables the analysis of the material flows of metals in the U.S. economy from a network perspective, rather than merely that of a life cycle chain.

ASIST 2001: Information in a Networked World--Harnessing the Flow. Proceedings of the ASIST Annual Meeting (64th, Washington, DC, November 3-8, 2001).

ERIC Educational Resources Information Center

Aversa, Elizabeth, Ed.; Manley, Cynthia, Ed.

The theme of the 2001 "ASIST" (American Society for Information Science and Technology) annual conference is "Information in a Networked World," which covers a broad range of theory and practice in information science. The program includes 52 refereed papers, 46 SIG and panel sessions, and 33 poster presentations. Topics include: digital…

A security architecture for health information networks.

PubMed

Kailar, Rajashekar; Muralidhar, Vinod

2007-10-11

Health information network security needs to balance exacting security controls with practicality, and ease of implementation in today's healthcare enterprise. Recent work on 'nationwide health information network' architectures has sought to share highly confidential data over insecure networks such as the Internet. Using basic patterns of health network data flow and trust models to support secure communication between network nodes, we abstract network security requirements to a core set to enable secure inter-network data sharing. We propose a minimum set of security controls that can be implemented without needing major new technologies, but yet realize network security and privacy goals of confidentiality, integrity and availability. This framework combines a set of technology mechanisms with environmental controls, and is shown to be sufficient to counter commonly encountered network security threats adequately.

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach

PubMed Central

Xu, Nan; Spreng, R. Nathan; Doerschuk, Peter C.

2017-01-01

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the “common driver” problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain. PMID:28559793

Information Systems Should Be Both Useful and Used: The Benetton Experience.

ERIC Educational Resources Information Center

Zuccaro, Bruno

1990-01-01

Describes the information systems strategy and network development of the Benetton clothing business. Applications in the areas of manufacturing, scheduling, centralized distribution, and centralized cash flow are discussed; the GEIS managed network service is described; and internal and external electronic data interchange (EDI) is explained.…

MYSEA: The Monterey Security Architecture

DTIC Science & Technology

2009-01-01

Security and Protection, Organization and Design General Terms: Design; Security Keywords: access controls, authentication, information flow controls...Applicable environments include: mil- itary coalitions, agencies and organizations responding to security emergencies, and mandated sharing in business ...network architecture affords users the abil- ity to securely access information across networks at dif- ferent classifications using standardized

Smart social adaptation prevents catastrophic ecological regime shifts in networks of myopic harvesters

NASA Astrophysics Data System (ADS)

Donges, Jonathan; Lucht, Wolfgang; Wiedermann, Marc; Heitzig, Jobst; Kurths, Jürgen

2015-04-01

In the anthropocene, the rise of global social and economic networks with ever increasing connectivity and speed of interactions, e.g., the internet or global financial markets, is a key challenge for sustainable development. The spread of opinions, values or technologies on these networks, in conjunction with the coevolution of the network structures themselves, underlies nexuses of current concern such as anthropogenic climate change, biodiversity loss or global land use change. To isolate and quantitatively study the effects and implications of network dynamics for sustainable development, we propose an agent-based model of information flow on adaptive networks between myopic harvesters that exploit private renewable resources. In this conceptual model of a network of socio-ecological systems, information on management practices flows between agents via boundedly rational imitation depending on the state of the resource stocks involved in an interaction. Agents can also adapt the structure of their social network locally by preferentially connecting to culturally similar agents with identical management practices and, at the same time, disconnecting from culturally dissimilar agents. Investigating in detail the statistical mechanics of this model, we find that an increasing rate of information flow through faster imitation dynamics or growing density of network connectivity leads to a marked increase in the likelihood of environmental resource collapse. However, we show that an optimal rate of social network adaptation can mitigate this negative effect without loss of social cohesion through network fragmentation. Our results highlight that seemingly immaterial network dynamics of spreading opinions or values can be of large relevance for the sustainable management of socio-ecological systems and suggest smartly conservative network adaptation as a strategy for mitigating environmental collapse. Hence, facing the great acceleration, these network dynamics should be more routinely incorporated in standard models of economic development or integrated assessment models used for evaluating anthropogenic climate change.

OpenFlow arbitrated programmable network channels for managing quantum metadata

DOE PAGES

Dasari, Venkat R.; Humble, Travis S.

2016-10-10

Quantum networks must classically exchange complex metadata between devices in order to carry out information for protocols such as teleportation, super-dense coding, and quantum key distribution. Demonstrating the integration of these new communication methods with existing network protocols, channels, and data forwarding mechanisms remains an open challenge. Software-defined networking (SDN) offers robust and flexible strategies for managing diverse network devices and uses. We adapt the principles of SDN to the deployment of quantum networks, which are composed from unique devices that operate according to the laws of quantum mechanics. We show how quantum metadata can be managed within a software-definedmore » network using the OpenFlow protocol, and we describe how OpenFlow management of classical optical channels is compatible with emerging quantum communication protocols. We next give an example specification of the metadata needed to manage and control quantum physical layer (QPHY) behavior and we extend the OpenFlow interface to accommodate this quantum metadata. Here, we conclude by discussing near-term experimental efforts that can realize SDN’s principles for quantum communication.« less

OpenFlow arbitrated programmable network channels for managing quantum metadata

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dasari, Venkat R.; Humble, Travis S.

Quantum networks must classically exchange complex metadata between devices in order to carry out information for protocols such as teleportation, super-dense coding, and quantum key distribution. Demonstrating the integration of these new communication methods with existing network protocols, channels, and data forwarding mechanisms remains an open challenge. Software-defined networking (SDN) offers robust and flexible strategies for managing diverse network devices and uses. We adapt the principles of SDN to the deployment of quantum networks, which are composed from unique devices that operate according to the laws of quantum mechanics. We show how quantum metadata can be managed within a software-definedmore » network using the OpenFlow protocol, and we describe how OpenFlow management of classical optical channels is compatible with emerging quantum communication protocols. We next give an example specification of the metadata needed to manage and control quantum physical layer (QPHY) behavior and we extend the OpenFlow interface to accommodate this quantum metadata. Here, we conclude by discussing near-term experimental efforts that can realize SDN’s principles for quantum communication.« less

Random network peristalsis in Physarum polycephalum organizes fluid flows across an individual

PubMed Central

Alim, Karen; Amselem, Gabriel; Peaudecerf, François; Brenner, Michael P.; Pringle, Anne

2013-01-01

Individuals can function as integrated organisms only when information and resources are shared across a body. Signals and substrates are commonly moved using fluids, often channeled through a network of tubes. Peristalsis is one mechanism for fluid transport and is caused by a wave of cross-sectional contractions along a tube. We extend the concept of peristalsis from the canonical case of one tube to a random network. Transport is maximized within the network when the wavelength of the peristaltic wave is of the order of the size of the network. The slime mold Physarum polycephalum grows as a random network of tubes, and our experiments confirm peristalsis is used by the slime mold to drive internal cytoplasmic flows. Comparisons of theoretically generated contraction patterns with the patterns exhibited by individuals of P. polycephalum demonstrate that individuals maximize internal flows by adapting patterns of contraction to size, thus optimizing transport throughout an organism. This control of fluid flow may be the key to coordinating growth and behavior, including the dynamic changes in network architecture seen over time in an individual. PMID:23898203

Random network peristalsis in Physarum polycephalum organizes fluid flows across an individual.

PubMed

Alim, Karen; Amselem, Gabriel; Peaudecerf, François; Brenner, Michael P; Pringle, Anne

2013-08-13

Individuals can function as integrated organisms only when information and resources are shared across a body. Signals and substrates are commonly moved using fluids, often channeled through a network of tubes. Peristalsis is one mechanism for fluid transport and is caused by a wave of cross-sectional contractions along a tube. We extend the concept of peristalsis from the canonical case of one tube to a random network. Transport is maximized within the network when the wavelength of the peristaltic wave is of the order of the size of the network. The slime mold Physarum polycephalum grows as a random network of tubes, and our experiments confirm peristalsis is used by the slime mold to drive internal cytoplasmic flows. Comparisons of theoretically generated contraction patterns with the patterns exhibited by individuals of P. polycephalum demonstrate that individuals maximize internal flows by adapting patterns of contraction to size, thus optimizing transport throughout an organism. This control of fluid flow may be the key to coordinating growth and behavior, including the dynamic changes in network architecture seen over time in an individual.

Relative Influence of Professional Counseling Journals

ERIC Educational Resources Information Center

Fernando, Delini M.; Barrio Minton, Casey A.

2011-01-01

The authors used social network analysis of citation data to study the flow of information and relative influence of 17 professional counseling journals. Although the "Journal of Counseling & Development" ranked very highly in all measures of journal influence, several division journals emerged as key players in the flow of information within the…

Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management

EPA Science Inventory

Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing i...

Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management.

EPA Science Inventory

Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing in...

Impact of trucking network flow on preferred biorefinery locations in the southern United States

Treesearch

Timothy M. Young; Lee D. Han; James H. Perdue; Stephanie R. Hargrove; Frank M. Guess; Xia Huang; Chung-Hao Chen

2017-01-01

The impact of the trucking transportation network flow was modeled for the southern United States. The study addresses a gap in existing research by applying a Bayesian logistic regression and Geographic Information System (GIS) geospatial analysis to predict biorefinery site locations. A one-way trucking cost assuming a 128.8 km (80-mile) haul distance was estimated...

A Security Architecture for Health Information Networks

PubMed Central

Kailar, Rajashekar

2007-01-01

Health information network security needs to balance exacting security controls with practicality, and ease of implementation in today’s healthcare enterprise. Recent work on ‘nationwide health information network’ architectures has sought to share highly confidential data over insecure networks such as the Internet. Using basic patterns of health network data flow and trust models to support secure communication between network nodes, we abstract network security requirements to a core set to enable secure inter-network data sharing. We propose a minimum set of security controls that can be implemented without needing major new technologies, but yet realize network security and privacy goals of confidentiality, integrity and availability. This framework combines a set of technology mechanisms with environmental controls, and is shown to be sufficient to counter commonly encountered network security threats adequately. PMID:18693862

Reinforced communication and social navigation: Remember your friends and remember yourself

NASA Astrophysics Data System (ADS)

Mirshahvalad, A.; Rosvall, M.

2011-09-01

In social systems, people communicate with each other and form groups based on their interests. The pattern of interactions, the network, and the ideas that flow on the network naturally evolve together. Researchers use simple models to capture the feedback between changing network patterns and ideas on the network, but little is understood about the role of past events in the feedback process. Here, we introduce a simple agent-based model to study the coupling between peoples’ ideas and social networks, and better understand the role of history in dynamic social networks. We measure how information about ideas can be recovered from information about network structure and, the other way around, how information about network structure can be recovered from information about ideas. We find that it is, in general, easier to recover ideas from the network structure than vice versa.

Patient Participation at Health Care Conferences: Engaged Patients Increase Information Flow, Expand Propagation, and Deepen Engagement in the Conversation of Tweets Compared to Physicians or Researchers

PubMed Central

2017-01-01

Background Health care conferences present a unique opportunity to network, spark innovation, and disseminate novel information to a large audience, but the dissemination of information typically stays within very specific networks. Social network analysis can be adopted to understand the flow of information between virtual social communities and the role of patients within the network. Objective The purpose of this study is to examine the impact engaged patients bring to health care conference social media information flow and how they expand dissemination and distribution of tweets compared to other health care conference stakeholders such as physicians and researchers. Methods From January 2014 through December 2016, 7,644,549 tweets were analyzed from 1672 health care conferences with at least 1000 tweets who had registered in Symplur’s Health Care Hashtag Project from 2014 to 2016. The tweet content was analyzed to create a list of the top 100 influencers by mention from each conference, who were then subsequently categorized by stakeholder group. Multivariate linear regression models were created using stepwise function building to identify factors explaining variability as predictor variables for the model in which conference tweets were taken as the dependent variable. Results Inclusion of engaged patients in health care conference social media was low compared to that of physicians and has not significantly changed over the last 3 years. When engaged patient voices are included in health care conferences, they greatly increase information flow as measured by total tweet volume (beta=301.6) compared to physicians (beta=137.3, P<.001), expand propagation of information tweeted during a conference as measured by social media impressions created (beta=1,700,000) compared to physicians (beta=270,000, P<.001), and deepen engagement in the tweet conversation as measured by replies to their tweets (beta=24.4) compared to physicians (beta=5.5, P<.001). Social network analysis of hubs and authorities revealed that patients had statistically significant higher hub scores (mean 8.26×10-4, SD 2.96×10-4) compared to other stakeholder groups’ Twitter accounts (mean 7.19×10-4, SD 3.81×10-4; t273.84=4.302, P<.001). Conclusions Although engaged patients are powerful accelerators of information flow, expanders of tweet propagation, and greatly deepen engagement in conversation of tweets on social media of health care conferences compared to physicians, they represent only 1.4% of the stakeholder mix of the top 100 influencers in the conversation. Health care conferences that fail to engage patients in their proceedings may risk limiting their engagement with the public, disseminating scientific information to a narrow community and slowing flow of information across social media channels. PMID:28818821

Real-time determination of fringe pattern frequencies: An application to pressure measurement

NASA Astrophysics Data System (ADS)

Sciammarella, Cesar A.; Piroozan, Parham

2007-05-01

Retrieving information in real time from fringe patterns is a topic of a great deal of interest in scientific and engineering applications of optical methods. This paper presents a method for fringe frequency determination based on the capability of neural networks to recognize signals that are similar but not identical to signals used to train the neural network. Sampled patterns are generated by calibration and stored in memory. Incoming patterns are analyzed by a back-propagation neural network at the speed of the recording device, a CCD camera. This method of information retrieval is utilized to measure pressures on a boundary layer flow. The sensor combines optics and electronics to analyze dynamic pressure distributions and to feed information to a control system that is capable to preserve the stability of the flow.

Fracture Network Characteristics Informed by Detailed Studies of Chlorinated Solvent Plumes in Sedimentary Rock Aquifers

NASA Astrophysics Data System (ADS)

Parker, B. L.; Chapman, S.

2015-12-01

Various numerical approaches have been used to simulate contaminant plumes in fractured porous rock, but the one that allows field and laboratory measurements to be most directly used as inputs to these models is the Discrete Fracture Network (DFN) Approach. To effectively account for fracture-matrix interactions, emphasis must be placed on identifying and parameterizing all of the fractures that participate substantially in groundwater flow and contaminated transport. High resolution plume studies at four primary research sites, where chlorinated solvent plumes serve as long-term (several decades) tracer tests, provide insight concerning the density of the fracture network unattainable by conventional methods. Datasets include contaminant profiles from detailed VOC subsampling informed by continuous core logs, hydraulic head and transmissivity profiles, packer testing and sensitive temperature logging methods in FLUTe™ lined holes. These show presence of many more transmissive fractures, contrasting observations of only a few flow zones per borehole obtained from conventional hydraulic tests including flow metering in open boreholes. Incorporating many more fractures with a wider range of transmissivities is key to predicting contaminant migration. This new understanding of dense fracture networks combined with matrix property measurements have informed 2-D DFN flow and transport modelling using Fractran and HydroGeosphere to simulate plume characteristics ground-truthed by detailed field site plume characterization. These process-based simulations corroborate field findings that plumes in sedimentary rock after decades of transport show limited plume front distances and strong internal plume attenuation by diffusion, transverse dispersion and slow degradation. This successful application of DFN modeling informed by field-derived parameters demonstrates how the DFN Approach can be applied to other sites to inform plume migration rates and remedial efficacy.

Cell shape and negative links in regulatory motifs together control spatial information flow in signaling networks.

PubMed

Neves, Susana R; Tsokas, Panayiotis; Sarkar, Anamika; Grace, Elizabeth A; Rangamani, Padmini; Taubenfeld, Stephen M; Alberini, Cristina M; Schaff, James C; Blitzer, Robert D; Moraru, Ion I; Iyengar, Ravi

2008-05-16

The role of cell size and shape in controlling local intracellular signaling reactions, and how this spatial information originates and is propagated, is not well understood. We have used partial differential equations to model the flow of spatial information from the beta-adrenergic receptor to MAPK1,2 through the cAMP/PKA/B-Raf/MAPK1,2 network in neurons using real geometries. The numerical simulations indicated that cell shape controls the dynamics of local biochemical activity of signal-modulated negative regulators, such as phosphodiesterases and protein phosphatases within regulatory loops to determine the size of microdomains of activated signaling components. The model prediction that negative regulators control the flow of spatial information to downstream components was verified experimentally in rat hippocampal slices. These results suggest a mechanism by which cellular geometry, the presence of regulatory loops with negative regulators, and key reaction rates all together control spatial information transfer and microdomain characteristics within cells.

Social insect colony as a biological regulatory system: modelling information flow in dominance networks.

PubMed

Nandi, Anjan K; Sumana, Annagiri; Bhattacharya, Kunal

2014-12-06

Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the 'feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

The use of nodes attributes in social network analysis with an application to an international trade network

NASA Astrophysics Data System (ADS)

de Andrade, Ricardo Lopes; Rêgo, Leandro Chaves

2018-02-01

The social network analysis (SNA) studies the interactions among actors in a network formed through some relationship (friendship, cooperation, trade, among others). The SNA is constantly approached from a binary point of view, i.e., it is only observed if a link between two actors is present or not regardless of the strength of this link. It is known that different information can be obtained in weighted and unweighted networks and that the information extracted from weighted networks is more accurate and detailed. Another rarely discussed approach in the SNA is related to the individual attributes of the actors (nodes), because such analysis is usually focused on the topological structure of networks. Features of the nodes are not incorporated in the SNA what implies that there is some loss or misperception of information in those analyze. This paper aims at exploring more precisely the complexities of a social network, initially developing a method that inserts the individual attributes in the topological structure of the network and then analyzing the network in four different ways: unweighted, edge-weighted and two methods for using both edge-weights and nodes' attributes. The international trade network was chosen in the application of this approach, where the nodes represent the countries, the links represent the cash flow in the trade transactions and countries' GDP were chosen as nodes' attributes. As a result, it is possible to observe which countries are most connected in the world economy and with higher cash flows, to point out the countries that are central to the intermediation of the wealth flow and those that are most benefited from being included in this network. We also made a principal component analysis to study which metrics are more influential in describing the data variability, which turn out to be mostly the weighted metrics which include the nodes' attributes.

Uncovering the spatial structure of mobility networks

NASA Astrophysics Data System (ADS)

Louail, Thomas; Lenormand, Maxime; Picornell, Miguel; García Cantú, Oliva; Herranz, Ricardo; Frias-Martinez, Enrique; Ramasco, José J.; Barthelemy, Marc

2015-01-01

The extraction of a clear and simple footprint of the structure of large, weighted and directed networks is a general problem that has relevance for many applications. An important example is seen in origin-destination matrices, which contain the complete information on commuting flows, but are difficult to analyze and compare. We propose here a versatile method, which extracts a coarse-grained signature of mobility networks, under the form of a 2 × 2 matrix that separates the flows into four categories. We apply this method to origin-destination matrices extracted from mobile phone data recorded in 31 Spanish cities. We show that these cities essentially differ by their proportion of two types of flows: integrated (between residential and employment hotspots) and random flows, whose importance increases with city size. Finally, the method allows the determination of categories of networks, and in the mobility case, the classification of cities according to their commuting structure.

Optimal Information Processing in Biochemical Networks

NASA Astrophysics Data System (ADS)

Wiggins, Chris

2012-02-01

A variety of experimental results over the past decades provide examples of near-optimal information processing in biological networks, including in biochemical and transcriptional regulatory networks. Computing information-theoretic quantities requires first choosing or computing the joint probability distribution describing multiple nodes in such a network --- for example, representing the probability distribution of finding an integer copy number of each of two interacting reactants or gene products while respecting the `intrinsic' small copy number noise constraining information transmission at the scale of the cell. I'll given an overview of some recent analytic and numerical work facilitating calculation of such joint distributions and the associated information, which in turn makes possible numerical optimization of information flow in models of noisy regulatory and biochemical networks. Illustrating cases include quantification of form-function relations, ideal design of regulatory cascades, and response to oscillatory driving.

Spatial reasoning to determine stream network from LANDSAT imagery

NASA Technical Reports Server (NTRS)

Haralick, R. M.; Wang, S.; Elliott, D. B.

1983-01-01

In LANDSAT imagery, spectral and spatial information can be used to detect the drainage network as well as the relative elevation model in mountainous terrain. To do this, mixed information of material reflectance in the original LANDSAT imagery must be separated. From the material reflectance information, big visible rivers can be detected. From the topographic modulation information, ridges and valleys can be detected and assigned relative elevations. A complete elevation model can be generated by interpolating values for nonridge and non-valley pixels. The small streams not detectable from material reflectance information can be located in the valleys with flow direction known from the elevation model. Finally, the flow directions of big visible rivers can be inferred by solving a consistent labeling problem based on a set of spatial reasoning constraints.

Information Transfer in the Brain: Insights from a Unified Approach

NASA Astrophysics Data System (ADS)

Marinazzo, Daniele; Wu, Guorong; Pellicoro, Mario; Stramaglia, Sebastiano

Measuring directed interactions in the brain in terms of information flow is a promising approach, mathematically treatable and amenable to encompass several methods. In this chapter we propose some approaches rooted in this framework for the analysis of neuroimaging data. First we will explore how the transfer of information depends on the network structure, showing how for hierarchical networks the information flow pattern is characterized by exponential distribution of the incoming information and a fat-tailed distribution of the outgoing information, as a signature of the law of diminishing marginal returns. This was reported to be true also for effective connectivity networks from human EEG data. Then we address the problem of partial conditioning to a limited subset of variables, chosen as the most informative ones for the driver node.We will then propose a formal expansion of the transfer entropy to put in evidence irreducible sets of variables which provide information for the future state of each assigned target. Multiplets characterized by a large contribution to the expansion are associated to informational circuits present in the system, with an informational character (synergetic or redundant) which can be associated to the sign of the contribution. Applications are reported for EEG and fMRI data.

Examining Food Risk in the Large using a Complex, Networked System-of-sytems Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ambrosiano, John; Newkirk, Ryan; Mc Donald, Mark P

2010-12-03

The food production infrastructure is a highly complex system of systems. Characterizing the risks of intentional contamination in multi-ingredient manufactured foods is extremely challenging because the risks depend on the vulnerabilities of food processing facilities and on the intricacies of the supply-distribution networks that link them. A pure engineering approach to modeling the system is impractical because of the overall system complexity and paucity of data. A methodology is needed to assess food contamination risk 'in the large', based on current, high-level information about manufacturing facilities, corrunodities and markets, that will indicate which food categories are most at risk ofmore » intentional contamination and warrant deeper analysis. The approach begins by decomposing the system for producing a multi-ingredient food into instances of two subsystem archetypes: (1) the relevant manufacturing and processing facilities, and (2) the networked corrunodity flows that link them to each other and consumers. Ingredient manufacturing subsystems are modeled as generic systems dynamics models with distributions of key parameters that span the configurations of real facilities. Networks representing the distribution systems are synthesized from general information about food corrunodities. This is done in a series of steps. First, probability networks representing the aggregated flows of food from manufacturers to wholesalers, retailers, other manufacturers, and direct consumers are inferred from high-level approximate information. This is followed by disaggregation of the general flows into flows connecting 'large' and 'small' categories of manufacturers, wholesalers, retailers, and consumers. Optimization methods are then used to determine the most likely network flows consistent with given data. Vulnerability can be assessed for a potential contamination point using a modified CARVER + Shock model. Once the facility and corrunodity flow models are instantiated, a risk consequence analysis can be performed by injecting contaminant at chosen points in the system and propagating the event through the overarching system to arrive at morbidity and mortality figures. A generic chocolate snack cake model, consisting of fluid milk, liquid eggs, and cocoa, is described as an intended proof of concept for multi-ingredient food systems. We aim for an eventual tool that can be used directly by policy makers and planners.« less

Restructuring the Future Classroom--A Global Perspective

ERIC Educational Resources Information Center

Shivakumar, G. S.; Manichander, T.

2013-01-01

The students are the consumers as well as co-creators of knowledge. Information does not flow top-down any more. Networks, peers and students inquisitiveness teach students. Teachers act as filters. Collaboration is the key. In today's world for the netgen, knowingly or unknowingly technology and the free flow of information via internet has made…

Description of a method to support public health information management: organizational network analysis

PubMed Central

Merrill, Jacqueline; Bakken, Suzanne; Rockoff, Maxine; Gebbie, Kristine; Carley, Kathleen

2007-01-01

In this case study we describe a method that has potential to provide systematic support for public health information management. Public health agencies depend on specialized information that travels throughout an organization via communication networks among employees. Interactions that occur within these networks are poorly understood and are generally unmanaged. We applied organizational network analysis, a method for studying communication networks, to assess the method’s utility to support decision making for public health managers, and to determine what links existed between information use and agency processes. Data on communication links among a health department’s staff was obtained via survey with a 93% response rate, and analyzed using Organizational Risk Analyzer (ORA) software. The findings described the structure of information flow in the department’s communication networks. The analysis succeeded in providing insights into organizational processes which informed public health managers’ strategies to address problems and to take advantage of network strengths. PMID:17098480

Robust optimal control of material flows in demand-driven supply networks

NASA Astrophysics Data System (ADS)

Laumanns, Marco; Lefeber, Erjen

2006-04-01

We develop a model based on stochastic discrete-time controlled dynamical systems in order to derive optimal policies for controlling the material flow in supply networks. Each node in the network is described as a transducer such that the dynamics of the material and information flows within the entire network can be expressed by a system of first-order difference equations, where some inputs to the system act as external disturbances. We apply methods from constrained robust optimal control to compute the explicit control law as a function of the current state. For the numerical examples considered, these control laws correspond to certain classes of optimal ordering policies from inventory management while avoiding, however, any a priori assumptions about the general form of the policy.

Collective network routing

DOEpatents

Hoenicke, Dirk

2014-12-02

Disclosed are a unified method and apparatus to classify, route, and process injected data packets into a network so as to belong to a plurality of logical networks, each implementing a specific flow of data on top of a common physical network. The method allows to locally identify collectives of packets for local processing, such as the computation of the sum, difference, maximum, minimum, or other logical operations among the identified packet collective. Packets are injected together with a class-attribute and an opcode attribute. Network routers, employing the described method, use the packet attributes to look-up the class-specific route information from a local route table, which contains the local incoming and outgoing directions as part of the specifically implemented global data flow of the particular virtual network.

From Geo-Social to Geo-Local: The Flows and Biases of Volunteered Geographic Information

ERIC Educational Resources Information Center

Stephens, Monica

2012-01-01

This dissertation analyzes the geography of information in the 21st century where BigData, social networks, user generated production of content and geography combine to create new and complex patterns of space, context and sociability. Both online and offline, social networks are creating a space that simultaneously unifies individuals and…

Availability Improvement of Layer 2 Seamless Networks Using OpenFlow

PubMed Central

Molina, Elias; Jacob, Eduardo; Matias, Jon; Moreira, Naiara; Astarloa, Armando

2015-01-01

The network robustness and reliability are strongly influenced by the implementation of redundancy and its ability of reacting to changes. In situations where packet loss or maximum latency requirements are critical, replication of resources and information may become the optimal technique. To this end, the IEC 62439-3 Parallel Redundancy Protocol (PRP) provides seamless recovery in layer 2 networks by delegating the redundancy management to the end-nodes. In this paper, we present a combination of the Software-Defined Networking (SDN) approach and PRP topologies to establish a higher level of redundancy and thereby, through several active paths provisioned via the OpenFlow protocol, the global reliability is increased, as well as data flows are managed efficiently. Hence, the experiments with multiple failure scenarios, which have been run over the Mininet network emulator, show the improvement in the availability and responsiveness over other traditional technologies based on a single active path. PMID:25759861

Availability improvement of layer 2 seamless networks using OpenFlow.

PubMed

Molina, Elias; Jacob, Eduardo; Matias, Jon; Moreira, Naiara; Astarloa, Armando

2015-01-01

The network robustness and reliability are strongly influenced by the implementation of redundancy and its ability of reacting to changes. In situations where packet loss or maximum latency requirements are critical, replication of resources and information may become the optimal technique. To this end, the IEC 62439-3 Parallel Redundancy Protocol (PRP) provides seamless recovery in layer 2 networks by delegating the redundancy management to the end-nodes. In this paper, we present a combination of the Software-Defined Networking (SDN) approach and PRP topologies to establish a higher level of redundancy and thereby, through several active paths provisioned via the OpenFlow protocol, the global reliability is increased, as well as data flows are managed efficiently. Hence, the experiments with multiple failure scenarios, which have been run over the Mininet network emulator, show the improvement in the availability and responsiveness over other traditional technologies based on a single active path.

Advanced Information Processing System (AIPS) proof-of-concept system functional design I/O network system services

NASA Technical Reports Server (NTRS)

1985-01-01

The function design of the Input/Output (I/O) services for the Advanced Information Processing System (AIPS) proof of concept system is described. The data flow diagrams, which show the functional processes in I/O services and the data that flows among them, are contained. A complete list of the data identified on the data flow diagrams and in the process descriptions are provided.

Network Exploration and Vulnerability Assessment Using a Combined Blackbox and Whitebox Analysis Approach

DTIC Science & Technology

2010-03-01

Employ NetFlow on Edge Router ......................................... 45 E. IMPLEMENT AN INTEGRATED VULNERABILITY ASSESSMENT. 48 1. Conduct...45 Figure 18. Netflow Information on Unauthorized Connections ............................ 46 Figure 19. Algorithm for Detecting...indicating that an attack has being initiated from this port. Figure 17. Information on Traffic Generated by Suspicious Host 3. Employ NetFlow

Overland flow erosion inferred from Martian channel network geometry

NASA Astrophysics Data System (ADS)

Seybold, Hansjörg; Kirchner, James

2016-04-01

The controversy about the origin of Mars' channel networks is almost as old as their discovery 150 years ago. Over the last few decades, new Mars probes have revealed more detailed structures in Martian The controversy about the origin of Mars' channel networks is almost as old as their discovery 150 years ago. Over the last few decades, new Mars probes have revealed more detailed structures in Martian drainage networks, and new studies suggest that Mars once had large volumes of surface water. But how this water flowed, and how it could have carved the channels, remains unclear. Simple scaling arguments show that networks formed by similar mechanisms should have similar branching angles on Earth and Mars, suggesting that Earth analogues can be informative here. A recent analysis of high-resolution data for the continental United States shows that climate leaves a characteristic imprint in the branching geometry of stream networks. Networks growing in humid regions have an average branching angle of α = 2π/5 = 72° [1], which is characteristic of network growth by groundwater sapping [2]. Networks in arid regions, where overland flow erosion is more dominant, show much smaller branching angles. Here we show that the channel networks on Mars have branching angles that resemble those created by surficial flows on Earth. This result implies that the growth of Martian channel networks was dominated by near-surface flow, and suggests that deeper infiltration was inhibited, potentially by permafrost or by impermeable weathered soils. [1] Climate's Watermark in the Geometry of River Networks, Seybold et al.; under review [2] Ramification of stream networks, Devauchelle et al.; PNAS (2012)

Predicting commuter flows in spatial networks using a radiation model based on temporal ranges

NASA Astrophysics Data System (ADS)

Ren, Yihui; Ercsey-Ravasz, Mária; Wang, Pu; González, Marta C.; Toroczkai, Zoltán

2014-11-01

Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and human mobility. Here we show a first-principles based method for traffic prediction using a cost-based generalization of the radiation 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. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events.

Bicriteria Network Optimization Problem using Priority-based Genetic Algorithm

NASA Astrophysics Data System (ADS)

Gen, Mitsuo; Lin, Lin; Cheng, Runwei

Network optimization is being an increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. In many applications, however, there are several criteria associated with traversing each edge of a network. For example, cost and flow measures are both important in the networks. As a result, there has been recent interest in solving Bicriteria Network Optimization Problem. The Bicriteria Network Optimization Problem is known a NP-hard. The efficient set of paths may be very large, possibly exponential in size. Thus the computational effort required to solve it can increase exponentially with the problem size in the worst case. In this paper, we propose a genetic algorithm (GA) approach used a priority-based chromosome for solving the bicriteria network optimization problem including maximum flow (MXF) model and minimum cost flow (MCF) model. The objective is to find the set of Pareto optimal solutions that give possible maximum flow with minimum cost. This paper also combines Adaptive Weight Approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. Computer simulations show the several numerical experiments by using some difficult-to-solve network design problems, and show the effectiveness of the proposed method.

Modular, Hierarchical Learning By Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Baldi, Pierre F.; Toomarian, Nikzad

1996-01-01

Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

Distributed Coordination for Optimal Energy Generation and Distribution in Cyber-Physical Energy Networks.

PubMed

Ahn, Hyo-Sung; Kim, Byeong-Yeon; Lim, Young-Hun; Lee, Byung-Hun; Oh, Kwang-Kyo

2018-03-01

This paper proposes three coordination laws for optimal energy generation and distribution in energy network, which is composed of physical flow layer and cyber communication layer. The physical energy flows through the physical layer; but all the energies are coordinated to generate and flow by distributed coordination algorithms on the basis of communication information. First, distributed energy generation and energy distribution laws are proposed in a decoupled manner without considering the interactive characteristics between the energy generation and energy distribution. Second, a joint coordination law to treat the energy generation and energy distribution in a coupled manner taking account of the interactive characteristics is designed. Third, to handle over- or less-energy generation cases, an energy distribution law for networks with batteries is designed. The coordination laws proposed in this paper are fully distributed in the sense that they are decided optimally only using relative information among neighboring nodes. Through numerical simulations, the validity of the proposed distributed coordination laws is illustrated.

ASIST 2001. Information in a Networked World: Harnessing the Flow. Part III: Poster Presentations.

ERIC Educational Resources Information Center

Proceedings of the ASIST Annual Meeting, 2001

2001-01-01

Topics of Poster Presentations include: electronic preprints; intranets; poster session abstracts; metadata; information retrieval; watermark images; video games; distributed information retrieval; subject domain knowledge; data mining; information theory; course development; historians' use of pictorial images; information retrieval software;…

Network-induced oscillatory behavior in material flow networks and irregular business cycles

NASA Astrophysics Data System (ADS)

Helbing, Dirk; Lämmer, Stefen; Witt, Ulrich; Brenner, Thomas

2004-11-01

Network theory is rapidly changing our understanding of complex systems, but the relevance of topological features for the dynamic behavior of metabolic networks, food webs, production systems, information networks, or cascade failures of power grids remains to be explored. Based on a simple model of supply networks, we offer an interpretation of instabilities and oscillations observed in biological, ecological, economic, and engineering systems. We find that most supply networks display damped oscillations, even when their units—and linear chains of these units—behave in a nonoscillatory way. Moreover, networks of damped oscillators tend to produce growing oscillations. This surprising behavior offers, for example, a different interpretation of business cycles and of oscillating or pulsating processes. The network structure of material flows itself turns out to be a source of instability, and cyclical variations are an inherent feature of decentralized adjustments.

Diffusion Dynamics of Energy Saving Practices in Large Heterogeneous Online Networks

PubMed Central

Mohammadi, Neda; Wang, Qi; Taylor, John E.

2016-01-01

Online social networks are today’s fastest growing communications channel and a popular source of information for many, so understanding their contribution to building awareness and shaping public perceptions of climate change is of utmost importance. Today’s online social networks are composed of complex combinations of entities and communication channels and it is not clear which communicators are the most influential, what the patterns of communication flow are, or even whether the widely accepted two-step flow of communication model applies in this new arena. This study examines the diffusion of energy saving practices in a large online social network across organizations, opinion leaders, and the public by tracking 108,771 communications on energy saving practices among 1,084 communicators, then analyzing the flow of information and influence over a 28 day period. Our findings suggest that diffusion networks of messages advocating energy saving practices are predominantly led by the activities of dedicated organizations but their attempts do not result in substantial public awareness, as most of these communications are effectively trapped in organizational loops in which messages are simply shared between organizations. Despite their comparably significant influential values, opinion leaders played a weak role in diffusing energy saving practices to a wider audience. Thus, the two-step flow of communication model does not appear to describe the sharing of energy conservation practices in large online heterogeneous networks. These results shed new light on the underlying mechanisms driving the diffusion of important societal issues such as energy efficiency, particularly in the context of large online social media outlets. PMID:27736912

Diffusion Dynamics of Energy Saving Practices in Large Heterogeneous Online Networks.

PubMed

Mohammadi, Neda; Wang, Qi; Taylor, John E

2016-01-01

Online social networks are today's fastest growing communications channel and a popular source of information for many, so understanding their contribution to building awareness and shaping public perceptions of climate change is of utmost importance. Today's online social networks are composed of complex combinations of entities and communication channels and it is not clear which communicators are the most influential, what the patterns of communication flow are, or even whether the widely accepted two-step flow of communication model applies in this new arena. This study examines the diffusion of energy saving practices in a large online social network across organizations, opinion leaders, and the public by tracking 108,771 communications on energy saving practices among 1,084 communicators, then analyzing the flow of information and influence over a 28 day period. Our findings suggest that diffusion networks of messages advocating energy saving practices are predominantly led by the activities of dedicated organizations but their attempts do not result in substantial public awareness, as most of these communications are effectively trapped in organizational loops in which messages are simply shared between organizations. Despite their comparably significant influential values, opinion leaders played a weak role in diffusing energy saving practices to a wider audience. Thus, the two-step flow of communication model does not appear to describe the sharing of energy conservation practices in large online heterogeneous networks. These results shed new light on the underlying mechanisms driving the diffusion of important societal issues such as energy efficiency, particularly in the context of large online social media outlets.

Proceedings of the Organization of 1990 Meeting of International Neural Network Society Jointed with IEEE Held in Washington, DC on January 15 - 19, 1990. Volume 1. Theory Track Neural and cognitive Sciences Track

DTIC Science & Technology

1990-11-30

signaI flow , xi. The learning" of such statistics could result from synaptic modification rules similar to those known to exist in the brain 7 " 1 0,1 1...in figure 1 had been established. If the series are appro\\imat.ed by Gaussian process. the information flow from X to Y can be expressed by the...Based on this model. the information flow in different direction were calculated by using eq.(1). RESULTS Figures 2 illustrates the information flow

Single-phase and two-phase flow properties of mesaverde tight sandstone formation; random-network modeling approach

NASA Astrophysics Data System (ADS)

Bashtani, Farzad; Maini, Brij; Kantzas, Apostolos

2016-08-01

3D random networks are constructed in order to represent the tight Mesaverde formation which is located in north Wyoming, USA. The porous-space is represented by pore bodies of different shapes and sizes which are connected to each other by pore throats of varying length and diameter. Pore bodies are randomly distributed in space and their connectivity varies based on the connectivity number distribution which is used in order to generate the network. Network representations are then validated using publicly available mercury porosimetry experiments. The network modeling software solves the fundamental equations of two-phase immiscible flow incorporating wettability and contact angle variability. Quasi-static displacement is assumed. Single phase macroscopic properties (porosity, permeability) are calculated and whenever possible are compared to experimental data. Using this information drainage and imbibition capillary pressure, and relative permeability curves are predicted and (whenever possible) compared to experimental data. The calculated information is grouped and compared to available literature information on typical behavior of tight formations. Capillary pressure curve for primary drainage process is predicted and compared to experimental mercury porosimetry in order to validate the virtual porous media by history matching. Relative permeability curves are also calculated and presented.

Clustering and Flow Conservation Monitoring Tool for Software Defined Networks.

PubMed

Puente Fernández, Jesús Antonio; García Villalba, Luis Javier; Kim, Tai-Hoon

2018-04-03

Prediction systems present some challenges on two fronts: the relation between video quality and observed session features and on the other hand, dynamics changes on the video quality. Software Defined Networks (SDN) is a new concept of network architecture that provides the separation of control plane (controller) and data plane (switches) in network devices. Due to the existence of the southbound interface, it is possible to deploy monitoring tools to obtain the network status and retrieve a statistics collection. Therefore, achieving the most accurate statistics depends on a strategy of monitoring and information requests of network devices. In this paper, we propose an enhanced algorithm for requesting statistics to measure the traffic flow in SDN networks. Such an algorithm is based on grouping network switches in clusters focusing on their number of ports to apply different monitoring techniques. Such grouping occurs by avoiding monitoring queries in network switches with common characteristics and then, by omitting redundant information. In this way, the present proposal decreases the number of monitoring queries to switches, improving the network traffic and preventing the switching overload. We have tested our optimization in a video streaming simulation using different types of videos. The experiments and comparison with traditional monitoring techniques demonstrate the feasibility of our proposal maintaining similar values decreasing the number of queries to the switches.

Extracting spatial information from networks with low-order eigenvectors

NASA Astrophysics Data System (ADS)

Cucuringu, Mihai; Blondel, Vincent D.; Van Dooren, Paul

2013-03-01

We consider the problem of inferring meaningful spatial information in networks from incomplete information on the connection intensity between the nodes of the network. We consider two spatially distributed networks: a population migration flow network within the US, and a network of mobile phone calls between cities in Belgium. For both networks we use the eigenvectors of the Laplacian matrix constructed from the link intensities to obtain informative visualizations and capture natural geographical subdivisions. We observe that some low-order eigenvectors localize very well and seem to reveal small geographically cohesive regions that match remarkably well with political and administrative boundaries. We discuss possible explanations for this observation by describing diffusion maps and localized eigenfunctions. In addition, we discuss a possible connection with the weighted graph cut problem, and provide numerical evidence supporting the idea that lower-order eigenvectors point out local cuts in the network. However, we do not provide a formal and rigorous justification for our observations.

Characterizing the correlations between local phase fractions of gas-liquid two-phase flow with wire-mesh sensor.

PubMed

Tan, C; Liu, W L; Dong, F

2016-06-28

Understanding of flow patterns and their transitions is significant to uncover the flow mechanics of two-phase flow. The local phase distribution and its fluctuations contain rich information regarding the flow structures. A wire-mesh sensor (WMS) was used to study the local phase fluctuations of horizontal gas-liquid two-phase flow, which was verified through comparing the reconstructed three-dimensional flow structure with photographs taken during the experiments. Each crossing point of the WMS is treated as a node, so the measurement on each node is the phase fraction in this local area. An undirected and unweighted flow pattern network was established based on connections that are formed by cross-correlating the time series of each node under different flow patterns. The structure of the flow pattern network reveals the relationship of the phase fluctuations at each node during flow pattern transition, which is then quantified by introducing the topological index of the complex network. The proposed analysis method using the WMS not only provides three-dimensional visualizations of the gas-liquid two-phase flow, but is also a thorough analysis for the structure of flow patterns and the characteristics of flow pattern transition. This article is part of the themed issue 'Supersensing through industrial process tomography'. © 2016 The Author(s).

Characterizing the correlations between local phase fractions of gas–liquid two-phase flow with wire-mesh sensor

PubMed Central

Liu, W. L.; Dong, F.

2016-01-01

Understanding of flow patterns and their transitions is significant to uncover the flow mechanics of two-phase flow. The local phase distribution and its fluctuations contain rich information regarding the flow structures. A wire-mesh sensor (WMS) was used to study the local phase fluctuations of horizontal gas–liquid two-phase flow, which was verified through comparing the reconstructed three-dimensional flow structure with photographs taken during the experiments. Each crossing point of the WMS is treated as a node, so the measurement on each node is the phase fraction in this local area. An undirected and unweighted flow pattern network was established based on connections that are formed by cross-correlating the time series of each node under different flow patterns. The structure of the flow pattern network reveals the relationship of the phase fluctuations at each node during flow pattern transition, which is then quantified by introducing the topological index of the complex network. The proposed analysis method using the WMS not only provides three-dimensional visualizations of the gas–liquid two-phase flow, but is also a thorough analysis for the structure of flow patterns and the characteristics of flow pattern transition. This article is part of the themed issue ‘Supersensing through industrial process tomography’. PMID:27185959

MaxEnt analysis of a water distribution network in Canberra, ACT, Australia

NASA Astrophysics Data System (ADS)

Waldrip, Steven H.; Niven, Robert K.; Abel, Markus; Schlegel, Michael; Noack, Bernd R.

2015-01-01

A maximum entropy (MaxEnt) method is developed to infer the state of a pipe flow network, for situations in which there is insufficient information to form a closed equation set. This approach substantially extends existing deterministic methods for the analysis of engineered flow networks (e.g. Newton's method or the Hardy Cross scheme). The network is represented as an undirected graph structure, in which the uncertainty is represented by a continuous relative entropy on the space of internal and external flow rates. The head losses (potential differences) on the network are treated as dependent variables, using specified pipe-flow resistance functions. The entropy is maximised subject to "observable" constraints on the mean values of certain flow rates and/or potential differences, and also "physical" constraints arising from the frictional properties of each pipe and from Kirchhoff's nodal and loop laws. A numerical method is developed in Matlab for solution of the integral equation system, based on multidimensional quadrature. Several nonlinear resistance functions (e.g. power-law and Colebrook) are investigated, necessitating numerical solution of the implicit Lagrangian by a double iteration scheme. The method is applied to a 1123-node, 1140-pipe water distribution network for the suburb of Torrens in the Australian Capital Territory, Australia, using network data supplied by water authority ACTEW Corporation Limited. A number of different assumptions are explored, including various network geometric representations, prior probabilities and constraint settings, yielding useful predictions of network demand and performance. We also propose this methodology be used in conjunction with in-flow monitoring systems, to obtain better inferences of user consumption without large investments in monitoring equipment and maintenance.

Integrated Information Technology Policy Analysis Research, CSUSB

DTIC Science & Technology

2010-10-01

cience  fields in order to combine efforts to better understand multiple network s systems, including technical, biological and social networks...Flowing Valued Information (FVI) project has been discussed at the Network  cience  Workshops linked form the Center website and the FVI reports and

Flow motifs reveal limitations of the static framework to represent human interactions

NASA Astrophysics Data System (ADS)

Rocha, Luis E. C.; Blondel, Vincent D.

2013-04-01

Networks are commonly used to define underlying interaction structures where infections, information, or other quantities may spread. Although the standard approach has been to aggregate all links into a static structure, some studies have shown that the time order in which the links are established may alter the dynamics of spreading. In this paper, we study the impact of the time ordering in the limits of flow on various empirical temporal networks. By using a random walk dynamics, we estimate the flow on links and convert the original undirected network (temporal and static) into a directed flow network. We then introduce the concept of flow motifs and quantify the divergence in the representativity of motifs when using the temporal and static frameworks. We find that the regularity of contacts and persistence of vertices (common in email communication and face-to-face interactions) result on little differences in the limits of flow for both frameworks. On the other hand, in the case of communication within a dating site and of a sexual network, the flow between vertices changes significantly in the temporal framework such that the static approximation poorly represents the structure of contacts. We have also observed that cliques with 3 and 4 vertices containing only low-flow links are more represented than the same cliques with all high-flow links. The representativity of these low-flow cliques is higher in the temporal framework. Our results suggest that the flow between vertices connected in cliques depend on the topological context in which they are placed and in the time sequence in which the links are established. The structure of the clique alone does not completely characterize the potential of flow between the vertices.

Competition-Driven Network Dynamics: Emergence of a Scale-Free Leadership Structure and Collective Efficiency

NASA Astrophysics Data System (ADS)

Anghel, M.; Toroczkai, Zoltán; Bassler, Kevin E.; Korniss, G.

2004-02-01

Using the minority game as a model for competition dynamics, we investigate the effects of interagent communications across a network on the global evolution of the game. Agent communication across this network leads to the formation of an influence network, which is dynamically coupled to the evolution of the game, and it is responsible for the information flow driving the agents' actions. We show that the influence network spontaneously develops hubs with a broad distribution of in-degrees, defining a scale-free robust leadership structure. Furthermore, in realistic parameter ranges, facilitated by information exchange on the network, agents can generate a high degree of cooperation making the collective almost maximally efficient.

Quantifying Neural Oscillatory Synchronization: A Comparison between Spectral Coherence and Phase-Locking Value Approaches

PubMed Central

Lowet, Eric; Roberts, Mark J.; Bonizzi, Pietro; Karel, Joël; De Weerd, Peter

2016-01-01

Synchronization or phase-locking between oscillating neuronal groups is considered to be important for coordination of information among cortical networks. Spectral coherence is a commonly used approach to quantify phase locking between neural signals. We systematically explored the validity of spectral coherence measures for quantifying synchronization among neural oscillators. To that aim, we simulated coupled oscillatory signals that exhibited synchronization dynamics using an abstract phase-oscillator model as well as interacting gamma-generating spiking neural networks. We found that, within a large parameter range, the spectral coherence measure deviated substantially from the expected phase-locking. Moreover, spectral coherence did not converge to the expected value with increasing signal-to-noise ratio. We found that spectral coherence particularly failed when oscillators were in the partially (intermittent) synchronized state, which we expect to be the most likely state for neural synchronization. The failure was due to the fast frequency and amplitude changes induced by synchronization forces. We then investigated whether spectral coherence reflected the information flow among networks measured by transfer entropy (TE) of spike trains. We found that spectral coherence failed to robustly reflect changes in synchrony-mediated information flow between neural networks in many instances. As an alternative approach we explored a phase-locking value (PLV) method based on the reconstruction of the instantaneous phase. As one approach for reconstructing instantaneous phase, we used the Hilbert Transform (HT) preceded by Singular Spectrum Decomposition (SSD) of the signal. PLV estimates have broad applicability as they do not rely on stationarity, and, unlike spectral coherence, they enable more accurate estimations of oscillatory synchronization across a wide range of different synchronization regimes, and better tracking of synchronization-mediated information flow among networks. PMID:26745498

Traffic flow collection wireless sensor network node for intersection light control

NASA Astrophysics Data System (ADS)

Li, Xu; Li, Xue

2011-10-01

Wireless sensor network (WSN) is expected to be deployed in intersection to monitor the traffic flow continuously, and the monitoring datum can be used as the foundation of traffic light control. In this paper, a WSN based on ZigBee protocol for monitoring traffic flow is proposed. Structure, hardware and work flow of WSN nodes are designed. CC2431 from Texas Instrument is chosen as the main computational and transmission unit, and CC2591 as the amplification unit. The stability experiment and the actual environment experiment are carried out in the last of the paper. The results of experiments show that WSN has the ability to collect traffic flow information quickly and transmit the datum to the processing center in real time.

Improving the effectiveness of traffic monitoring based on wireless location technology.

DOT National Transportation Integrated Search

2004-01-01

A fundamental requirement for effectively monitoring and operating transportation facilities is reliable, accurate data on traffic flow. The current state of the practice is to use networks of point detectors to gather information on traffic flow at ...

An investigation of the relationships between junior high school students' (8th and 9th grades) background variables and structure of knowledge recall of biological content

NASA Astrophysics Data System (ADS)

Demetrius, Olive Joyce

The purpose of this study was to examine the relationships between Junior High School students' (8th and 9th grades) background variables (e.g. cognitive factors, prior knowledge, preference for science versus non-science activities, formal and informal activities) and structure of information recall of biological content. In addition, this study will illustrate how flow maps, a graphic display, designed to represent the sequential flow and cross linkage of ideas in information recalled by the learner can be used as a tool for analyzing science learning data. The participants (46 junior high school students) were taught a lesson on the human digestive system during which they were shown a model of the human torso. Their pattern of information recall was determined by using an interview technique to elicit their understanding of the functional anatomy of the human digestive system. The taped responses were later transcribed for construction of the flow map. The interview was also used to assess knowledge recall of biological content. The flow map, science interest questionnaire and the cognitive operations (based on content analysis of student's narrative) were used to analyze data from each respondent. This is a case study using individual subjects and interview techniques. The findings of this study are: (1) Based on flow map data higher academic ability students have more networking of ideas than low ability students. (2) A large percentage of 9th grade low ability students intend to pursue science/applied science course work after leaving school but they lack well organized ways of representing science knowledge in memory. (3) Content analysis of the narratives shows that students with more complex ideational networks use higher order cognitive thought processes compared to those with less networking of ideas. If students are to make a successful transition from low academic performance to high academic performance it seems that more emphasis should be placed on information networking skills. This is specifically likely to be productive for student currently performing on low academic ability levels and yet have high aspirations for pursuing science as a career.

Development of an optical fiber flow velocity sensor.

PubMed

Harada, Toshio; Kamoto, Kenji; Abe, Kyutaro; Izumo, Masaki

2009-01-01

A new optical fiber flow velocity sensor was developed by using an optical fiber information network system in sewer drainage pipes. The optical fiber flow velocity sensor operates without electric power, and the signals from the sensor can be transmitted over a long distance through the telecommunication system in the optical fiber network. Field tests were conducted to check the performance of the sensor in conduits in the pumping station and sewage pond managed by the Tokyo Metropolitan Government. Test results confirmed that the velocity sensor can be used for more than six months without any trouble even in sewer drainage pipes.

Bit Threads and Holographic Entanglement

NASA Astrophysics Data System (ADS)

Freedman, Michael; Headrick, Matthew

2017-05-01

The Ryu-Takayanagi (RT) formula relates the entanglement entropy of a region in a holographic theory to the area of a corresponding bulk minimal surface. Using the max flow-min cut principle, a theorem from network theory, we rewrite the RT formula in a way that does not make reference to the minimal surface. Instead, we invoke the notion of a "flow", defined as a divergenceless norm-bounded vector field, or equivalently a set of Planck-thickness "bit threads". The entanglement entropy of a boundary region is given by the maximum flux out of it of any flow, or equivalently the maximum number of bit threads that can emanate from it. The threads thus represent entanglement between points on the boundary, and naturally implement the holographic principle. As we explain, this new picture clarifies several conceptual puzzles surrounding the RT formula. We give flow-based proofs of strong subadditivity and related properties; unlike the ones based on minimal surfaces, these proofs correspond in a transparent manner to the properties' information-theoretic meanings. We also briefly discuss certain technical advantages that the flows offer over minimal surfaces. In a mathematical appendix, we review the max flow-min cut theorem on networks and on Riemannian manifolds, and prove in the network case that the set of max flows varies Lipshitz continuously in the network parameters.

Embeddedness and New Idea Discussion in Professional Networks: The Mediating Role of Affect-Based Trust

ERIC Educational Resources Information Center

Chua, Roy Y. J.; Morris, Michael W.; Ingram, Paul

2010-01-01

This article examines how managers' tendency to discuss new ideas with others in their professional networks depends on the density of shared ties surrounding a given relationship. Consistent with prior research which found that embeddedness enhances information flow, an egocentric network survey of mid-level executives shows that managers tend to…

Large-scale Granger causality analysis on resting-state functional MRI

NASA Astrophysics Data System (ADS)

D'Souza, Adora M.; Abidin, Anas Zainul; Leistritz, Lutz; Wismüller, Axel

2016-03-01

We demonstrate an approach to measure the information flow between each pair of time series in resting-state functional MRI (fMRI) data of the human brain and subsequently recover its underlying network structure. By integrating dimensionality reduction into predictive time series modeling, large-scale Granger Causality (lsGC) analysis method can reveal directed information flow suggestive of causal influence at an individual voxel level, unlike other multivariate approaches. This method quantifies the influence each voxel time series has on every other voxel time series in a multivariate sense and hence contains information about the underlying dynamics of the whole system, which can be used to reveal functionally connected networks within the brain. To identify such networks, we perform non-metric network clustering, such as accomplished by the Louvain method. We demonstrate the effectiveness of our approach to recover the motor and visual cortex from resting state human brain fMRI data and compare it with the network recovered from a visuomotor stimulation experiment, where the similarity is measured by the Dice Coefficient (DC). The best DC obtained was 0.59 implying a strong agreement between the two networks. In addition, we thoroughly study the effect of dimensionality reduction in lsGC analysis on network recovery. We conclude that our approach is capable of detecting causal influence between time series in a multivariate sense, which can be used to segment functionally connected networks in the resting-state fMRI.

Peeling Away Timing Error in NetFlow Data

NASA Astrophysics Data System (ADS)

Trammell, Brian; Tellenbach, Bernhard; Schatzmann, Dominik; Burkhart, Martin

In this paper, we characterize, quantify, and correct timing errors introduced into network flow data by collection and export via Cisco NetFlow version 9. We find that while some of these sources of error (clock skew, export delay) are generally implementation-dependent and known in the literature, there is an additional cyclic error of up to one second that is inherent to the design of the export protocol. We present a method for correcting this cyclic error in the presence of clock skew and export delay. In an evaluation using traffic with known timing collected from a national-scale network, we show that this method can successfully correct the cyclic error. However, there can also be other implementation-specific errors for which insufficient information remains for correction. On the routers we have deployed in our network, this limits the accuracy to about 70ms, reinforcing the point that implementation matters when conducting research on network measurement data.

Variable speed limit strategies analysis with mesoscopic traffic flow model based on complex networks

NASA Astrophysics Data System (ADS)

Li, Shu-Bin; Cao, Dan-Ni; Dang, Wen-Xiu; Zhang, Lin

As a new cross-discipline, the complexity science has penetrated into every field of economy and society. With the arrival of big data, the research of the complexity science has reached its summit again. In recent years, it offers a new perspective for traffic control by using complex networks theory. The interaction course of various kinds of information in traffic system forms a huge complex system. A new mesoscopic traffic flow model is improved with variable speed limit (VSL), and the simulation process is designed, which is based on the complex networks theory combined with the proposed model. This paper studies effect of VSL on the dynamic traffic flow, and then analyzes the optimal control strategy of VSL in different network topologies. The conclusion of this research is meaningful to put forward some reasonable transportation plan and develop effective traffic management and control measures to help the department of traffic management.

Application guide for AFINCH (Analysis of Flows in Networks of Channels) described by NHDPlus

USGS Publications Warehouse

Holtschlag, David J.

2009-01-01

AFINCH (Analysis of Flows in Networks of CHannels) is a computer application that can be used to generate a time series of monthly flows at stream segments (flowlines) and water yields for catchments defined in the National Hydrography Dataset Plus (NHDPlus) value-added attribute system. AFINCH provides a basis for integrating monthly flow data from streamgages, water-use data, monthly climatic data, and land-cover characteristics to estimate natural monthly water yields from catchments by user-defined regression equations. Images of monthly water yields for active streamgages are generated in AFINCH and provide a basis for detecting anomalies in water yields, which may be associated with undocumented flow diversions or augmentations. Water yields are multiplied by the drainage areas of the corresponding catchments to estimate monthly flows. Flows from catchments are accumulated downstream through the streamflow network described by the stream segments. For stream segments where streamgages are active, ratios of measured to accumulated flows are computed. These ratios are applied to upstream water yields to proportionally adjust estimated flows to match measured flows. Flow is conserved through the NHDPlus network. A time series of monthly flows can be generated for stream segments that average about 1-mile long, or monthly water yields from catchments that average about 1 square mile. Estimated monthly flows can be displayed within AFINCH, examined for nonstationarity, and tested for monotonic trends. Monthly flows also can be used to estimate flow-duration characteristics at stream segments. AFINCH generates output files of monthly flows and water yields that are compatible with ArcMap, a geographical information system analysis and display environment. Chloropleth maps of monthly water yield and flow can be generated and analyzed within ArcMap by joining NHDPlus data structures with AFINCH output. Matlab code for the AFINCH application is presented.

The International Postal Network and Other Global Flows as Proxies for National Wellbeing.

PubMed

Hristova, Desislava; Rutherford, Alex; Anson, Jose; Luengo-Oroz, Miguel; Mascolo, Cecilia

2016-01-01

The digital exhaust left by flows of physical and digital commodities provides a rich measure of the nature, strength and significance of relationships between countries in the global network. With this work, we examine how these traces and the network structure can reveal the socioeconomic profile of different countries. We take into account multiple international networks of physical and digital flows, including the previously unexplored international postal network. By measuring the position of each country in the Trade, Postal, Migration, International Flights, IP and Digital Communications networks, we are able to build proxies for a number of crucial socioeconomic indicators such as GDP per capita and the Human Development Index ranking along with twelve other indicators used as benchmarks of national well-being by the United Nations and other international organisations. In this context, we have also proposed and evaluated a global connectivity degree measure applying multiplex theory across the six networks that accounts for the strength of relationships between countries. We conclude by showing how countries with shared community membership over multiple networks have similar socioeconomic profiles. Combining multiple flow data sources can help understand the forces which drive economic activity on a global level. Such an ability to infer proxy indicators in a context of incomplete information is extremely timely in light of recent discussions on measurement of indicators relevant to the Sustainable Development Goals.

The International Postal Network and Other Global Flows as Proxies for National Wellbeing

PubMed Central

Rutherford, Alex; Anson, Jose; Luengo-Oroz, Miguel; Mascolo, Cecilia

2016-01-01

The digital exhaust left by flows of physical and digital commodities provides a rich measure of the nature, strength and significance of relationships between countries in the global network. With this work, we examine how these traces and the network structure can reveal the socioeconomic profile of different countries. We take into account multiple international networks of physical and digital flows, including the previously unexplored international postal network. By measuring the position of each country in the Trade, Postal, Migration, International Flights, IP and Digital Communications networks, we are able to build proxies for a number of crucial socioeconomic indicators such as GDP per capita and the Human Development Index ranking along with twelve other indicators used as benchmarks of national well-being by the United Nations and other international organisations. In this context, we have also proposed and evaluated a global connectivity degree measure applying multiplex theory across the six networks that accounts for the strength of relationships between countries. We conclude by showing how countries with shared community membership over multiple networks have similar socioeconomic profiles. Combining multiple flow data sources can help understand the forces which drive economic activity on a global level. Such an ability to infer proxy indicators in a context of incomplete information is extremely timely in light of recent discussions on measurement of indicators relevant to the Sustainable Development Goals. PMID:27248142

The Role of Small Impoundments on Flow Alteration Within River Networks

NASA Astrophysics Data System (ADS)

Brogan, C. O.; Keys, T.; Scott, D.; Burgholzer, R.; Kleiner, J.

2017-12-01

Numerous water quality and quantity models have been established to illustrate the ecologic and hydrologic effects of large reservoirs. Smaller, unregulated ponds are often assumed to have a negligible impact on watershed flow regimes even though they overwhelmingly outnumber larger waterbodies. Individually, these small impoundments impart merely a fraction of the flow alteration larger reservoirs do; however, a network of ponds may act cumulatively to alter the flow regime. Many models have attempted to study smaller impoundments but rely on selectively available rating curves or bathymetry surveys. This study created a generalized process to model impoundments of varying size across a 58 square mile watershed exclusively using satellite imagery and publicly available information as inputs. With information drawn from public Army Corps of Engineers databases and LiDAR surveys, it was found that impoundment surface and drainage area served as useful explanatory variables, capable of predicting both pond bathymetry and outlet structure area across the 37 waterbodies modeled within the study area. Working within a flow routing model with inputs from the Chesapeake Bay HSPF model and verified with USGS gauge data, flow simulations were conducted with increasing number of impoundments to quantify how small ponds affect the overall flow regime. As the total impounded volume increased, simulations showed a notable reduction in both low and peak flows. Medium-sized floods increased as the network of ponds and reservoirs stabilized the catchment's streamflow. The results of this study illustrate the importance of including ponded waters into river corridor models to improve downstream management of both water quantity and quality.

Patient Participation at Health Care Conferences: Engaged Patients Increase Information Flow, Expand Propagation, and Deepen Engagement in the Conversation of Tweets Compared to Physicians or Researchers.

PubMed

Utengen, Audun; Rouholiman, Dara; Gamble, Jamison G; Grajales, Francisco Jose; Pradhan, Nisha; Staley, Alicia C; Bernstein, Liza; Young, Sean D; Clauson, Kevin A; Chu, Larry F

2017-08-17

Health care conferences present a unique opportunity to network, spark innovation, and disseminate novel information to a large audience, but the dissemination of information typically stays within very specific networks. Social network analysis can be adopted to understand the flow of information between virtual social communities and the role of patients within the network. The purpose of this study is to examine the impact engaged patients bring to health care conference social media information flow and how they expand dissemination and distribution of tweets compared to other health care conference stakeholders such as physicians and researchers. From January 2014 through December 2016, 7,644,549 tweets were analyzed from 1672 health care conferences with at least 1000 tweets who had registered in Symplur's Health Care Hashtag Project from 2014 to 2016. The tweet content was analyzed to create a list of the top 100 influencers by mention from each conference, who were then subsequently categorized by stakeholder group. Multivariate linear regression models were created using stepwise function building to identify factors explaining variability as predictor variables for the model in which conference tweets were taken as the dependent variable. Inclusion of engaged patients in health care conference social media was low compared to that of physicians and has not significantly changed over the last 3 years. When engaged patient voices are included in health care conferences, they greatly increase information flow as measured by total tweet volume (beta=301.6) compared to physicians (beta=137.3, P<.001), expand propagation of information tweeted during a conference as measured by social media impressions created (beta=1,700,000) compared to physicians (beta=270,000, P<.001), and deepen engagement in the tweet conversation as measured by replies to their tweets (beta=24.4) compared to physicians (beta=5.5, P<.001). Social network analysis of hubs and authorities revealed that patients had statistically significant higher hub scores (mean 8.26×10-4, SD 2.96×10-4) compared to other stakeholder groups' Twitter accounts (mean 7.19×10-4, SD 3.81×10-4; t273.84=4.302, P<.001). Although engaged patients are powerful accelerators of information flow, expanders of tweet propagation, and greatly deepen engagement in conversation of tweets on social media of health care conferences compared to physicians, they represent only 1.4% of the stakeholder mix of the top 100 influencers in the conversation. Health care conferences that fail to engage patients in their proceedings may risk limiting their engagement with the public, disseminating scientific information to a narrow community and slowing flow of information across social media channels. ©Audun Utengen, Dara Rouholiman, Jamison G. Gamble, Francisco Jose Grajales III, Nisha Pradhan, Alicia C Staley, Liza Bernstein, Sean D Young, Kevin A Clauson, Larry F Chu. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 17.08.2017.

Realistic Data-Driven Traffic Flow Animation Using Texture Synthesis.

PubMed

Chao, Qianwen; Deng, Zhigang; Ren, Jiaping; Ye, Qianqian; Jin, Xiaogang

2018-02-01

We present a novel data-driven approach to populate virtual road networks with realistic traffic flows. Specifically, given a limited set of vehicle trajectories as the input samples, our approach first synthesizes a large set of vehicle trajectories. By taking the spatio-temporal information of traffic flows as a 2D texture, the generation of new traffic flows can be formulated as a texture synthesis process, which is solved by minimizing a newly developed traffic texture energy. The synthesized output captures the spatio-temporal dynamics of the input traffic flows, and the vehicle interactions in it strictly follow traffic rules. After that, we position the synthesized vehicle trajectory data to virtual road networks using a cage-based registration scheme, where a few traffic-specific constraints are enforced to maintain each vehicle's original spatial location and synchronize its motion in concert with its neighboring vehicles. Our approach is intuitive to control and scalable to the complexity of virtual road networks. We validated our approach through many experiments and paired comparison user studies.

Process connectivity in a naturally prograding river delta

NASA Astrophysics Data System (ADS)

Sendrowski, Alicia; Passalacqua, Paola

2017-03-01

River deltas are lowland systems that can display high hydrological connectivity. This connectivity can be structural (morphological connections), functional (control of fluxes), and process connectivity (information flow from system drivers to sinks). In this work, we quantify hydrological process connectivity in Wax Lake Delta, coastal Louisiana, by analyzing couplings among external drivers (discharge, tides, and wind) and water levels recorded at five islands and one channel over summer 2014. We quantify process connections with information theory, a branch of mathematics concerned with the communication of information. We represent process connections as a network; variables serve as network nodes and couplings as network links describing the strength, direction, and time scale of information flow. Comparing process connections at long (105 days) and short (10 days) time scales, we show that tides exhibit daily synchronization with water level, with decreasing strength from downstream to upstream, and that tides transfer information as tides transition from spring to neap. Discharge synchronizes with water level and the time scale of its information transfer compares well to physical travel times through the system, computed with a hydrodynamic model. Information transfer and physical transport show similar spatial patterns, although information transfer time scales are larger than physical travel times. Wind events associated with water level setup lead to increased process connectivity with highly variable information transfer time scales. We discuss the information theory results in the context of the hydrologic behavior of the delta, the role of vegetation as a connector/disconnector on islands, and the applicability of process networks as tools for delta modeling results.

The study on variation of influential regions in China from a perspective of asymmetry economic information flow

NASA Astrophysics Data System (ADS)

Yang, Chunxia; Tang, Minxuan; Cao, Yongjian; Chen, Yanhua; Deng, Qiangqiang

2015-10-01

Based on the annual GDP (Gross Domestic Product) in 27 Chinese provinces and autonomous regions, the asymmetric economic information flows between different regions are calculated by the symbolic transfer entropy method and corresponding economic information flow networks are built over two periods, one is before the reform and opening up policy, the other is after that. By analyzing such networks, the obtained results are as follows. First, before the policy, balanced development strategy weakens or cuts off the ties between adjacent areas, resulting in a slow regional economic development, does not conform to the law of scientific development. Second, with introducing market mechanisms and promoting the reform and opening up policy, increasing economic activities have gradually shifted from coast to inland of China over Period II. Last but not least, there has a dramatic alternation of the influential centers that Jilin, Beijing and Jiangsu become new influential centers. Especially, at Beijing-Tianjin-Hebei metropolis circle Beijing becomes an influential center after the policy.

Clustering and Flow Conservation Monitoring Tool for Software Defined Networks

PubMed Central

Puente Fernández, Jesús Antonio

2018-01-01

Prediction systems present some challenges on two fronts: the relation between video quality and observed session features and on the other hand, dynamics changes on the video quality. Software Defined Networks (SDN) is a new concept of network architecture that provides the separation of control plane (controller) and data plane (switches) in network devices. Due to the existence of the southbound interface, it is possible to deploy monitoring tools to obtain the network status and retrieve a statistics collection. Therefore, achieving the most accurate statistics depends on a strategy of monitoring and information requests of network devices. In this paper, we propose an enhanced algorithm for requesting statistics to measure the traffic flow in SDN networks. Such an algorithm is based on grouping network switches in clusters focusing on their number of ports to apply different monitoring techniques. Such grouping occurs by avoiding monitoring queries in network switches with common characteristics and then, by omitting redundant information. In this way, the present proposal decreases the number of monitoring queries to switches, improving the network traffic and preventing the switching overload. We have tested our optimization in a video streaming simulation using different types of videos. The experiments and comparison with traditional monitoring techniques demonstrate the feasibility of our proposal maintaining similar values decreasing the number of queries to the switches. PMID:29614049

Dynamics of traffic flow with real-time traffic information

NASA Astrophysics Data System (ADS)

Yokoya, Yasushi

2004-01-01

We studied dynamics of traffic flow with real-time information provided. Provision of the real-time traffic information based on advancements in telecommunication technology is expected to facilitate the efficient utilization of available road capacity. This system has a potentiality of not only engineering for road usage but also the science of complexity series. In the system, the information plays a role of feedback connecting microscopic and macroscopic phenomena beyond the hierarchical structure of statistical physics. In this paper, we tried to clarify how the information works in a network of traffic flow from the perspective of statistical physics. The dynamical feature of the traffic flow is abstracted by a contrastive study between the nonequilibrium statistical physics and a computer simulation based on cellular automaton. We found that the information disrupts the local equilibrium of traffic flow by a characteristic dissipation process due to interaction between the information and individual vehicles. The dissipative structure was observed in the time evolution of traffic flow driven far from equilibrium as a consequence of the breakdown of the local-equilibrium hypothesis.

Social networks in primates: smart and tolerant species have more efficient networks.

PubMed

Pasquaretta, Cristian; Levé, Marine; Claidière, Nicolas; van de Waal, Erica; Whiten, Andrew; MacIntosh, Andrew J J; Pelé, Marie; Bergstrom, Mackenzie L; Borgeaud, Christèle; Brosnan, Sarah F; Crofoot, Margaret C; Fedigan, Linda M; Fichtel, Claudia; Hopper, Lydia M; Mareno, Mary Catherine; Petit, Odile; Schnoell, Anna Viktoria; di Sorrentino, Eugenia Polizzi; Thierry, Bernard; Tiddi, Barbara; Sueur, Cédric

2014-12-23

Network optimality has been described in genes, proteins and human communicative networks. In the latter, optimality leads to the efficient transmission of information with a minimum number of connections. Whilst studies show that differences in centrality exist in animal networks with central individuals having higher fitness, network efficiency has never been studied in animal groups. Here we studied 78 groups of primates (24 species). We found that group size and neocortex ratio were correlated with network efficiency. Centralisation (whether several individuals are central in the group) and modularity (how a group is clustered) had opposing effects on network efficiency, showing that tolerant species have more efficient networks. Such network properties affecting individual fitness could be shaped by natural selection. Our results are in accordance with the social brain and cultural intelligence hypotheses, which suggest that the importance of network efficiency and information flow through social learning relates to cognitive abilities.

Social networks in primates: smart and tolerant species have more efficient networks

PubMed Central

Pasquaretta, Cristian; Levé, Marine; Claidière, Nicolas; van de Waal, Erica; Whiten, Andrew; MacIntosh, Andrew J. J.; Pelé, Marie; Bergstrom, Mackenzie L.; Borgeaud, Christèle; Brosnan, Sarah F.; Crofoot, Margaret C.; Fedigan, Linda M.; Fichtel, Claudia; Hopper, Lydia M.; Mareno, Mary Catherine; Petit, Odile; Schnoell, Anna Viktoria; di Sorrentino, Eugenia Polizzi; Thierry, Bernard; Tiddi, Barbara; Sueur, Cédric

2014-01-01

Network optimality has been described in genes, proteins and human communicative networks. In the latter, optimality leads to the efficient transmission of information with a minimum number of connections. Whilst studies show that differences in centrality exist in animal networks with central individuals having higher fitness, network efficiency has never been studied in animal groups. Here we studied 78 groups of primates (24 species). We found that group size and neocortex ratio were correlated with network efficiency. Centralisation (whether several individuals are central in the group) and modularity (how a group is clustered) had opposing effects on network efficiency, showing that tolerant species have more efficient networks. Such network properties affecting individual fitness could be shaped by natural selection. Our results are in accordance with the social brain and cultural intelligence hypotheses, which suggest that the importance of network efficiency and information flow through social learning relates to cognitive abilities. PMID:25534964

Quantification of network structural dissimilarities.

PubMed

Schieber, Tiago A; Carpi, Laura; Díaz-Guilera, Albert; Pardalos, Panos M; Masoller, Cristina; Ravetti, Martín G

2017-01-09

Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and real-world networks show that this measure returns non-zero values only when the graphs are non-isomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components.

Quantification of groundwater infiltration and surface water inflows in urban sewer networks based on a multiple model approach.

PubMed

Karpf, Christian; Krebs, Peter

2011-05-01

The management of sewer systems requires information about discharge and variability of typical wastewater sources in urban catchments. Especially the infiltration of groundwater and the inflow of surface water (I/I) are important for making decisions about the rehabilitation and operation of sewer networks. This paper presents a methodology to identify I/I and estimate its quantity. For each flow fraction in sewer networks, an individual model approach is formulated whose parameters are optimised by the method of least squares. This method was applied to estimate the contributions to the wastewater flow in the sewer system of the City of Dresden (Germany), where data availability is good. Absolute flows of I/I and their temporal variations are estimated. Further information on the characteristics of infiltration is gained by clustering and grouping sewer pipes according to the attributes construction year and groundwater influence and relating these resulting classes to infiltration behaviour. Further, it is shown that condition classes based on CCTV-data can be used to estimate the infiltration potential of sewer pipes. Copyright © 2011 Elsevier Ltd. All rights reserved.

OpenFlow Extensions for Programmable Quantum Networks

DTIC Science & Technology

2017-06-19

Extensions for Programmable Quantum Networks by Venkat Dasari, Nikolai Snow, and Billy Geerhart Computational and Information Sciences Directorate...distribution is unlimited. 1 1. Introduction Quantum networks and quantum computing have been receiving a surge of interest recently.1–3 However, there has...communicate using entangled particles and perform calculations using quantum logic gates. Additionally, quantum computing uses a quantum bit (qubit

Networking for the Region and beyond--Role of the Southeast Asian Geography Association (SEAGA)

ERIC Educational Resources Information Center

Ooi, Giok Ling; Goh, Kim Chuan

2008-01-01

Networking among geographers in the Southeast Asian region is a challenge because of language and other barriers such as the costs of travel as well as keeping up communications and information flows. This networking effort however, is crucial to research and scholarship on issues that are relevant to the region. Many of these concern sustainable…

Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks

PubMed Central

Valdivieso Caraguay, Ángel Leonardo; García Villalba, Luis Javier

2017-01-01

This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on gathering and storing the information (low-level metrics) related to physical and virtual devices, cloud environments, flow metrics, SDN traffic and sensors. Similarly, it provides the monitoring data as a generic information source in order to allow the correlation and aggregation tasks. Our design enables the collection and storing of information provided by all the underlying SELFNET sublayers, including the dynamically onboarded and instantiated SDN/NFV Apps, also known as SELFNET sensors. PMID:28362346

Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks.

PubMed

Caraguay, Ángel Leonardo Valdivieso; Villalba, Luis Javier García

2017-03-31

This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on gathering and storing the information (low-level metrics) related to physical and virtual devices, cloud environments, flow metrics, SDN traffic and sensors. Similarly, it provides the monitoring data as a generic information source in order to allow the correlation and aggregation tasks. Our design enables the collection and storing of information provided by all the underlying SELFNET sublayers, including the dynamically onboarded and instantiated SDN/NFV Apps, also known as SELFNET sensors.

Microbubble transport through a bifurcating vessel network with pulsatile flow.

PubMed

Valassis, Doug T; Dodde, Robert E; Esphuniyani, Brijesh; Fowlkes, J Brian; Bull, Joseph L

2012-02-01

Motivated by two-phase microfluidics and by the clinical applications of air embolism and a developmental gas embolotherapy technique, experimental and theoretical models of microbubble transport in pulsatile flow are presented. The one-dimensional time-dependent theoretical model is developed from an unsteady Bernoulli equation that has been modified to include viscous and unsteady effects. Results of both experiments and theory show that roll angle (the angle the plane of the bifurcating network makes with the horizontal) is an important contributor to bubble splitting ratio at each bifurcation within the bifurcating network. When compared to corresponding constant flow, pulsatile flow was shown to produce insignificant changes to the overall splitting ratio of the bubble despite the order one Womersley numbers, suggesting that bubble splitting through the vasculature could be modeled adequately with a more modest constant flow model. However, bubble lodging was affected by the flow pulsatility, and the effects of pulsatile flow were evident in the dependence of splitting ratio of bubble length. The ability of bubbles to remain lodged after reaching a steady state in the bifurcations is promising for the effectiveness of gas embolotherapy to occlude blood flow to tumors, and indicates the importance of understanding where lodging will occur in air embolism. The ability to accurately predict the bubble dynamics in unsteady flow within a bifurcating network is demonstrated and suggests the potential for bubbles in microfluidics devices to encode information in both steady and unsteady aspects of their dynamics.

Scaling of Average Weighted Receiving Time on Double-Weighted Koch Networks

NASA Astrophysics Data System (ADS)

Dai, Meifeng; Ye, Dandan; Hou, Jie; Li, Xingyi

2015-03-01

In this paper, we introduce a model of the double-weighted Koch networks based on actual road networks depending on the two weight factors w,r ∈ (0, 1]. The double weights represent the capacity-flowing weight and the cost-traveling weight, respectively. Denote by wFij the capacity-flowing weight connecting the nodes i and j, and denote by wCij the cost-traveling weight connecting the nodes i and j. Let wFij be related to the weight factor w, and let wCij be related to the weight factor r. This paper assumes that the walker, at each step, starting from its current node, moves to any of its neighbors with probability proportional to the capacity-flowing weight of edge linking them. The weighted time for two adjacency nodes is the cost-traveling weight connecting the two nodes. We define the average weighted receiving time (AWRT) on the double-weighted Koch networks. The obtained result displays that in the large network, the AWRT grows as power-law function of the network order with the exponent, represented by θ(w,r) = ½ log2(1 + 3wr). We show that the AWRT exhibits a sublinear or linear dependence on network order. Thus, the double-weighted Koch networks are more efficient than classic Koch networks in receiving information.

Tracing information flow on a global scale using Internet chain-letter data

PubMed Central

Liben-Nowell, David; Kleinberg, Jon

2008-01-01

Although information, news, and opinions continuously circulate in the worldwide social network, the actual mechanics of how any single piece of information spreads on a global scale have been a mystery. Here, we trace such information-spreading processes at a person-by-person level using methods to reconstruct the propagation of massively circulated Internet chain letters. We find that rather than fanning out widely, reaching many people in very few steps according to “small-world” principles, the progress of these chain letters proceeds in a narrow but very deep tree-like pattern, continuing for several hundred steps. This suggests a new and more complex picture for the spread of information through a social network. We describe a probabilistic model based on network clustering and asynchronous response times that produces trees with this characteristic structure on social-network data. PMID:18353985

A low cost strategy to monitor the expansion and contraction of the flowing stream network in mountainous headwater catchments

NASA Astrophysics Data System (ADS)

Assendelft, Rick; van Meerveld, Ilja; Seibert, Jan

2017-04-01

Streams are dynamic features in the landscape. The flowing stream network expands and contracts, connects and disconnects in response to rainfall events and seasonal changes in catchment wetness. Sections of the river system that experience these wet and dry cycles are often referred to as temporary streams. Temporary streams are abundant and widely distributed freshwater ecosystems. They account for more than half of the total length of the global stream network, are unique habitats and form important hydrological and ecological links between the uplands and perennial streams. However, temporary streams have been largely unstudied, especially in mountainous headwater catchments. The dynamic character of these systems makes it difficult to monitor them. We describe a low-cost, do-it-yourself strategy to monitor the occurrence of water and flow in temporary streams. We evaluate this strategy in two headwater catchments in Switzerland. The low cost sensor network consists of electrical resistivity sensors, water level switches, temperature sensors and flow sensors. These sensors are connected to Arduino microcontrollers and data loggers, which log the data every 5 minutes. The data from the measurement network are compared with observations (mapping of the temporary stream network) as well as time lapse camera data to evaluate the performance of the sensors. We look at how frequently the output of the sensors (presence and absence of water from the ER and water level data, and flow or no-flow from the flow sensors) corresponds to the observed channel state. This is done for each sensor, per sub-catchment, per precipitation event and per sensor location to determine the best sensor combination to monitor temporary streams in mountainous catchments and in which situation which sensor combination works best. The preliminary results show that the sensors and monitoring network work well. The data from the sensors corresponds with the observations and provides information on the expansion of the stream network pattern.

Computer simulation of airflow through a multi-generation tracheobronchial conducting airway

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fan, B.; Cheng, Yung-Sung; Yeh, Hsu-Chi

1995-12-01

Knowledge of airflow patterns in the human lung is important for an analysis of lung diseases and drug delivery of aerosolized medicine for medical treatment. However, very little systematic information is available on the pattern of airflow in the lung and on how this pattern affects the deposition of toxicants in the lung, and the efficacy of aerosol drug therapy. Most previous studies have only considered the airflow through a single bifurcating airway. However, the flow in a network of more than one bifurcation is more complicated due to the effect of interrelated lung generations. Because of the variation ofmore » airway geometry and flow condition from generation to generation, a single bifurcating airway cannot be taken as a representative for the others in different generations. The flow in the network varies significantly with airway generations because of a redistribution of axial momentum by the secondary flow motions. The influence of the redistribution of flow is expected in every generation. Therefore, a systematic information of the airflow through a multi-generation tracheobronchial conducting airway is needed, and it becomes the purpose of this study. This study has provided information on airflow in a lung model which is necessary to the study of the deposition of toxicants and therapeutic aerosols.« less

Interconnecting network for switching data packets and method for switching data packets

DOEpatents

Benner, Alan Frederic; Minkenberg, Cyriel Johan Agnes; Stunkel, Craig Brian

2010-05-25

The interconnecting network for switching data packets, having data and flow control information, comprises a local packet switch element (S1) with local input buffers (I(1,1) . . . I(1,y)) for buffering the incoming data packets, a remote packet switch element (S2) with remote input buffers (I(2,1) . . . I(2,y)) for buffering the incoming data packets, and data lines (L) for interconnecting the local and the remote packet switch elements (S1, S2). The interconnecting network further comprises a local and a remote arbiter (A1, A2) which are connected via control lines (CL) to the input buffers (I(1,1) . . . I(1,y), I(2,1) . . . I(2,y)), and which are formed such that they can provide that the flow control information is transmitted via the data lines (L) and the control lines (CL).

Distinguishing anticipation from causality: anticipatory bias in the estimation of information flow.

PubMed

Hahs, Daniel W; Pethel, Shawn D

2011-09-16

We report that transfer entropy estimates obtained from low-resolution and/or small data sets show net information flow away from a purely anticipatory element whereas transfer entropy calculated using exact distributions show the flow towards it. This means that for real-world data sets anticipatory elements can appear to be strongly driving the network dynamics even when there is no possibility of such an influence. Furthermore, we show that in the low-resolution limit there is no statistic that can distinguish anticipatory elements from causal ones.

Information Processing in Living Systems

NASA Astrophysics Data System (ADS)

Tkačik, Gašper; Bialek, William

2016-03-01

Life depends as much on the flow of information as on the flow of energy. Here we review the many efforts to make this intuition precise. Starting with the building blocks of information theory, we explore examples where it has been possible to measure, directly, the flow of information in biological networks, or more generally where information-theoretic ideas have been used to guide the analysis of experiments. Systems of interest range from single molecules (the sequence diversity in families of proteins) to groups of organisms (the distribution of velocities in flocks of birds), and all scales in between. Many of these analyses are motivated by the idea that biological systems may have evolved to optimize the gathering and representation of information, and we review the experimental evidence for this optimization, again across a wide range of scales.

Evaluation of the streamflow-gaging network of Alaska in providing regional streamflow information

USGS Publications Warehouse

Brabets, Timothy P.

1996-01-01

In 1906, the U.S. Geological Survey (USGS) began operating a network of streamflow-gaging stations in Alaska. The primary purpose of the streamflow- gaging network has been to provide peak flow, average flow, and low-flow characteristics to a variety of users. In 1993, the USGS began a study to evaluate the current network of 78 stations. The objectives of this study were to determine the adequacy of the existing network in predicting selected regional flow characteristics and to determine if providing additional streamflow-gaging stations could improve the network's ability to predict these characteristics. Alaska was divided into six distinct hydrologic regions: Arctic, Northwest, Southcentral, Southeast, Southwest, and Yukon. For each region, historical and current streamflow data were compiled. In Arctic, Northwest, and Southwest Alaska, insufficient data were available to develop regional regression equations. In these areas, proposed locations of streamflow-gaging stations were selected by using clustering techniques to define similar areas within a region and by spatial visual analysis using the precipitation, physiographic, and hydrologic unit maps of Alaska. Sufficient data existed in Southcentral and Southeast Alaska to use generalized least squares (GLS) procedures to develop regional regression equations to estimate the 50-year peak flow, annual average flow, and a low-flow statistic. GLS procedures were also used for Yukon Alaska but the results should be used with caution because the data do not have an adequate spatial distribution. Network analysis procedures were used for the Southcentral, Southeast, and Yukon regions. Network analysis indicates the reduction in the sampling error of the regional regression equation that can be obtained given different scenarios. For Alaska, a 10-year planning period was used. One scenario showed the results of continuing the current network with no additional gaging stations and another scenario showed the results of adding gaging stations to the network. With the exception of the annual average discharge equation for Southeast Alaska, by adding gaging stations in all three regions, the sampling error was reduced to a greater extent than by not adding gaging stations. The proposed streamflow-gaging network for Alaska consists of 308 gaging stations, of which 32 are designated as index stations. If the proposed network can not be implemented in its entirety, then a lesser cost alternative would be to establish the index stations and to implement the network for a particular region.

Influence of geomorphological properties and stage on in-stream travel time

NASA Astrophysics Data System (ADS)

Åkesson, Anna; Wörman, Anders

2014-05-01

The travel time distribution within stream channels is known to vary non-linearly with stage (discharge), depending on the combined effects of geomorphologic, hydrodynamic and kinematic dispersions. This non-linearity, implying that stream network travel time generally decreases with increasing discharge is a factor that is important to account for in hydrological modelling - especially when making peak flow predictions where uncertainty is often high and large values can be at risk. Through hydraulic analysis of several stream networks, we analyse how travel time distributions varies with discharge. The principal focus is the coupling to the geomorphologic properties of stream networks with the final goal being to use this physically based information as a parameterisation tool of the streamflow component of hydrologic models. For each of the studied stream networks, a 1D, steady-state, distributed routing model was set up to determine the velocities in each reach during different flow conditions. Although the model (based in the Manning friction formula) is built on the presence of uniform conditions within sub-reaches, the model can in the stream network scale be considered to include effects of non-uniformity as supercritical conditions in sections of the stream network give rise to backwater effects that reduce the flow velocities in upstream reaches in the stream. By coupling the routing model to a particle tracking routine tracing water "parcels" through the stream network, the average travel time within the stream network can be determined quantitatively for different flow conditions. The data used to drive the model is digitised stream network maps, topographical data (DEMs). The model is not calibrated in any way, but is run for with different sets of parameters representing a span of possible friction coefficients and cross-sectional geometries as this information is not generally known. The routing model is implemented in several different stream networks (representing catchments of the spatial scale of a few hundred km2) in different geographic regions in Sweden displaying different geomorphological properties. Results show that the geomorphological properties (data that is often available in the form of maps and/or DEMs) of individual stream networks have major influence on the stream network travel times. By coupling the geomorphological information to general expressions for stage dependency, catchment-specific relationships of how the travel times within stream networks can be determined. Basing the parameterisation procedure of a hydrological model in physical catchment properties and process understanding rather than statistical parameterisation (based in how a catchment has responded in the past) - is believed to lead to more reliable hydrological predictions - during extreme conditions as well as during changing conditions such as climate change and landscape modifications, and/or when making predictions in ungauged basins.

Globalization and International Student Mobility: A Network Analysis

ERIC Educational Resources Information Center

Shields, Robin

2013-01-01

This article analyzes changes to the network of international student mobility in higher education over a 10-year period (1999-2008). International student flows have increased rapidly, exceeding 3 million in 2009, and extensive data on mobility provide unique insight into global educational processes. The analysis is informed by three theoretical…

Social Network Analysis of the Farabi Exchange Program: Student Mobility

ERIC Educational Resources Information Center

Ugurlu, Zeynep

2016-01-01

Problem Statement: Exchange programs offer communication channels created through student and instructor exchanges; a flow of information takes place through these channels. The Farabi Exchange Program (FEP) is a student and instructor exchange program between institutions of higher education. Through the use of social network analysis and…

Computational Models for Belief Revision, Group Decision-Making and Cultural Shifts

DTIC Science & Technology

2010-10-25

34social" networks; the green numbers are pseudo-trees or artificial (non-social) constructions. The dashed blue line indicates the range of Erdos- Renyi ...non-social networks such as Erdos- Renyi random graphs or the more passive non-cognitive spreading of disease or information flow, As mentioned

Managing Information Sharing within an Organizational Setting: A Social Network Perspective

ERIC Educational Resources Information Center

Hatala, John-Paul; Lutta, Joseph George

2009-01-01

Information sharing is critical to an organization's competitiveness and requires a free flow of information among members if the organization is to remain competitive. A review of the literature on organizational structure and information sharing was conducted to examine the research in this area. A case example illustrates how a social network…

Sonification of network traffic flow for monitoring and situational awareness

PubMed Central

2018-01-01

Maintaining situational awareness of what is happening within a computer network is challenging, not only because the behaviour happens within machines, but also because data traffic speeds and volumes are beyond human ability to process. Visualisation techniques are widely used to present information about network traffic dynamics. Although they provide operators with an overall view and specific information about particular traffic or attacks on the network, they often still fail to represent the events in an understandable way. Also, because they require visual attention they are not well suited to continuous monitoring scenarios in which network administrators must carry out other tasks. Here we present SoNSTAR (Sonification of Networks for SiTuational AwaReness), a real-time sonification system for monitoring computer networks to support network administrators’ situational awareness. SoNSTAR provides an auditory representation of all the TCP/IP traffic within a network based on the different traffic flows between between network hosts. A user study showed that SoNSTAR raises situational awareness levels by enabling operators to understand network behaviour and with the benefit of lower workload demands (as measured by the NASA TLX method) than visual techniques. SoNSTAR identifies network traffic features by inspecting the status flags of TCP/IP packet headers. Combinations of these features define particular traffic events which are mapped to recorded sounds to generate a soundscape that represents the real-time status of the network traffic environment. The sequence, timing, and loudness of the different sounds allow the network to be monitored and anomalous behaviour to be detected without the need to continuously watch a monitor screen. PMID:29672543

Sonification of network traffic flow for monitoring and situational awareness.

PubMed

Debashi, Mohamed; Vickers, Paul

2018-01-01

Maintaining situational awareness of what is happening within a computer network is challenging, not only because the behaviour happens within machines, but also because data traffic speeds and volumes are beyond human ability to process. Visualisation techniques are widely used to present information about network traffic dynamics. Although they provide operators with an overall view and specific information about particular traffic or attacks on the network, they often still fail to represent the events in an understandable way. Also, because they require visual attention they are not well suited to continuous monitoring scenarios in which network administrators must carry out other tasks. Here we present SoNSTAR (Sonification of Networks for SiTuational AwaReness), a real-time sonification system for monitoring computer networks to support network administrators' situational awareness. SoNSTAR provides an auditory representation of all the TCP/IP traffic within a network based on the different traffic flows between between network hosts. A user study showed that SoNSTAR raises situational awareness levels by enabling operators to understand network behaviour and with the benefit of lower workload demands (as measured by the NASA TLX method) than visual techniques. SoNSTAR identifies network traffic features by inspecting the status flags of TCP/IP packet headers. Combinations of these features define particular traffic events which are mapped to recorded sounds to generate a soundscape that represents the real-time status of the network traffic environment. The sequence, timing, and loudness of the different sounds allow the network to be monitored and anomalous behaviour to be detected without the need to continuously watch a monitor screen.

Spatial Associations and Network Dynamics Between the Vaccine Exemption Dicsussion in Twitter and the Corresponding Geographic Space

NASA Astrophysics Data System (ADS)

Coronado, Alejandra

Recent outbreaks of vaccine-preventable diseases in the United States have drawn attention to the phenomena of vaccine hesitancy and refusal. Hesitancy is seen through the increasing use of exemptions from state vaccine mandates and the recent use of social media for expressing opinions and perspectives related to vaccination. This research places the vaccination narrative into a geographic context and seeks to understand the relationship between vaccine refusal in physical space and the vaccine discussion in cyberspace. Vaccines have long been considered an effective means of eradicating diseases. Recently, however, California has experienced a decline in vaccination rates and an increase in vaccine exemptions. Until the passing of Senate Bill 277 (SB277) in 2015, children were allowed by California law to skip immunizations if a parent submitted a personal beliefs exemption (PBEs). Under SB277, children who are not vaccinated cannot attend school. Some children are still allowed to skip immunizations by submitting a medical exemption (PMEs) at enrollment. Other children are conditionally admitted to school on the 'condition' that they complete any remaining vaccinations when due. This research analyzed the spatial distribution of vaccine exemptions in kindergarten schools in California using the 2015-2016 school immunization data. The two methods used for analysis included Kernel Density Estimation (KDE) and choropleth maps using data aggregated by county. The results from the choropleth maps show that personal belief exemptions for public, private, and charter kindergarten schools are highly concentrated in northern and rural counties. Aggregating vaccine exemptions at the county level and normalizing by school enrollment showed that counties with high ratios of vaccine exemptions vary across public, private, and charter schools. This research also explored the diffusion networks of the vaccine exemption topic in Twitter. Twitter messages related to the California vaccine exemption topic were collected for the whole United States. However, this research only focused on analyzing tweets in California. Two types of information diffusion networks, retweet network and mention network, were examined. This research quantified the influence of users in the networks by applying two network metrics--degree centrality and betweenness centrality. Degree centrality measures the number of connections of a node and is useful to asses which nodes are central for spreading information and influencing others in their immediate neighborhood. Betweenness centrality identifies brokers of information or nodes that connect disparate clusters. Nodes with high betweenness centrality have control over the flow of information in the network. The results suggest that influential users are ranked differently by degree centrality and betweenness centrality for both networks. The results showed that ordinary users may also have strong impacts in the diffusion of information as seen by their high betweenness values despite their low degree centrality. Retweets were found to be more prominent in the diffusion of the vaccine exemption topic compared to mentions. Social network analysis does not capture diffusion processes from a spatial perspective. This research included the spatial context of the mention and retweet networks by using the location information embedded in each node. Nodes were aggregated at the county level and social networks were transformed into visual maps with spatial context. In addition to spatial networks, this research also created chord diagrams to represent the outbound flow and interactions between counties. The findings suggest that county population plays a role in the diffusion of information by social media. Highly populated counties, such as Los Angeles and Sacramento provided a large amount of mention and retweet activity. Additionally, the mention and retweet spatial networks showed counties to have higher in-degree value than out-degree values which indicates more in-flow hubs than out-flow hubs in the network. Unlike the results from the inter-personal social networks, the mention and retweet networks showed that the counties with the highest degree centralities also resulted being the counties with the highest betweenness centrality. Highly populated counties, such as Los Angeles and Sacramento, had very high betweenness centralities in both retweet and mention activity, which means that they served as the bridge and information broker for spreading information related to the vaccine exemption topic. This research is important because most vaccine literature is written from an epidemiological perspective and lacks a geographical component. This research presented an example of applying the spatial social network concept for studying the interaction dynamics between geographic areas. This research expanded studying inter-personal diffusion networks by adding a spatial component. The objective of this research was to study vaccine exemption use and information diffusion across a cyber-physical space in means of better understanding the dynamics of public opinions, views, and responses to the vaccine exemption topic. (Abstract shortened by ProQuest.).

Some Probable Technological Trends and Their Impact on an Information Network System. LINCS Project Document Series.

ERIC Educational Resources Information Center

Ebersole, Joseph L.

Improvements in the technology associated with the information sciences will have their primary potential impact on the distribution of costs, information flow level, information availability, and use among information channels. This improvement implied not only a capability to perform a given function, but a lower cost. For example, the trend…

Review: The state-of-art of sparse channel models and their applicability to performance assessment of radioactive waste repositories in fractured crystalline formations

NASA Astrophysics Data System (ADS)

Figueiredo, Bruno; Tsang, Chin-Fu; Niemi, Auli; Lindgren, Georg

2016-11-01

Laboratory and field experiments done on fractured rock show that flow and solute transport often occur along flow channels. `Sparse channels' refers to the case where these channels are characterised by flow in long flow paths separated from each other by large spacings relative to the size of flow domain. A literature study is presented that brings together information useful to assess whether a sparse-channel network concept is an appropriate representation of the flow system in tight fractured rock of low transmissivity, such as that around a nuclear waste repository in deep crystalline rocks. A number of observations are made in this review. First, conventional fracture network models may lead to inaccurate results for flow and solute transport in tight fractured rocks. Secondly, a flow dimension of 1, as determined by the analysis of pressure data in well testing, may be indicative of channelised flow, but such interpretation is not unique or definitive. Thirdly, in sparse channels, the percolation may be more influenced by the fracture shape than the fracture size and orientation but further studies are needed. Fourthly, the migration of radionuclides from a waste canister in a repository to the biosphere may be strongly influenced by the type of model used (e.g. discrete fracture network, channel model). Fifthly, the determination of appropriateness of representing an in situ flow system by a sparse-channel network model needs parameters usually neglected in site characterisation, such as the density of channels or fracture intersections.

Roads at risk - traffic detours from debris flows in southern Norway

NASA Astrophysics Data System (ADS)

Meyer, N. K.; Schwanghart, W.; Korup, O.; Nadim, F.

2014-10-01

Globalization and interregional exchange of people, goods, and services has boosted the importance of and reliance on all kinds of transport networks. The linear structure of road networks is especially sensitive to natural hazards. In southern Norway, steep topography and extreme weather events promote frequent traffic disruption caused by debris flows. Topographic susceptibility and trigger frequency maps serve as input into a hazard appraisal at the scale of first-order catchments to quantify the impact of debris flows on the road network in terms of a failure likelihood of each link connecting two network vertices, e.g., road junctions. We compute total additional traffic loads as a function of traffic volume and excess distance, i.e. the extra length of an alternative path connecting two previously disrupted network vertices using a shortest-path algorithm. Our risk metric of link failure is the total additional annual traffic load expressed as vehicle kilometers because of debris-flow related road closures. We present two scenarios demonstrating the impact of debris flows on the road network, and quantify the associated path failure likelihood between major cities in southern Norway. The scenarios indicate that major routes crossing the central and northwestern part of the study area are associated with high link failure risk. Yet options for detours on major routes are manifold, and incur only little additional costs provided that drivers are sufficiently well informed about road closures. Our risk estimates may be of importance to road network managers and transport companies relying of speedy delivery of services and goods.

Roads at risk: traffic detours from debris flows in southern Norway

NASA Astrophysics Data System (ADS)

Meyer, N. K.; Schwanghart, W.; Korup, O.; Nadim, F.

2015-05-01

Globalisation and interregional exchange of people, goods, and services has boosted the importance of and reliance on all kinds of transport networks. The linear structure of road networks is especially sensitive to natural hazards. In southern Norway, steep topography and extreme weather events promote frequent traffic disruption caused by debris flows. Topographic susceptibility and trigger frequency maps serve as input into a hazard appraisal at the scale of first-order catchments to quantify the impact of debris flows on the road network in terms of a failure likelihood of each link connecting two network vertices, e.g. road junctions. We compute total additional traffic loads as a function of traffic volume and excess distance, i.e. the extra length of an alternative path connecting two previously disrupted network vertices using a shortest-path algorithm. Our risk metric of link failure is the total additional annual traffic load, expressed as vehicle kilometres, because of debris-flow-related road closures. We present two scenarios demonstrating the impact of debris flows on the road network and quantify the associated path-failure likelihood between major cities in southern Norway. The scenarios indicate that major routes crossing the central and north-western part of the study area are associated with high link-failure risk. Yet options for detours on major routes are manifold and incur only little additional costs provided that drivers are sufficiently well informed about road closures. Our risk estimates may be of importance to road network managers and transport companies relying on speedy delivery of services and goods.

Inverse modeling of the hydraulic properties of fractured media : development of a flow tomography approach

NASA Astrophysics Data System (ADS)

Bour, O.; Klepikova, M.; Le Borgne, T.; De Dreuzy, J.

2013-12-01

Inverse modeling of hydraulic and geometrical properties of fractured media is a very challenging objective due to the spatial heterogeneity of the medium and the scarcity of data. Here we present a flow tomography approach that permits to characterize the location, the connectivity and the hydraulic properties of main flow paths in fractured media. The accurate characterization of the location, hydraulic properties and connectivity of major fracture zones is essential to model flow and solute transport in fractured media. Cross-borehole flowmeter tests, which consist of measuring changes in vertical borehole flows when pumping a neighboring borehole, were shown to be an efficient technique to provide information on the properties of the flow zones that connect borehole pairs [Paillet, 1998; Le Borgne et al., 2006]. The interpretation of such experiments may however be quite uncertain when multiple connections exist. In this study, we explore the potential of flow tomography (i.e., sequential cross-borehole flowmeter tests) for characterizing aquifer heterogeneity. We first propose a framework for inverting flow and drawdown data to infer fracture connectivity and transmissivities. Here we use a simplified discrete fracture network approach that highlights main connectivity structures. This conceptual model attempts to reproduce fracture network connectivity without taking fracture geometry (length, orientation, dip) into account. We then explore the potential of the method for simplified synthetic fracture network models and quantify the sensitivity of drawdown and borehole flow velocities to the transmissivity of the connecting flowpaths. Flow tomography is expected to be most effective if cross-borehole pumping induces large changes in vertical borehole velocities. The uncertainty of the transmissivity estimates increases for small borehole flow velocities. The uncertainty about the transmissivity of fractures that connect the main flowpath but not the boreholes is generally higher. We demonstrate that successively changing pumping and observation boreholes improves the quality of available information and reduces the indetermination of the problem. The inverse method is validated for different synthetic flow scenarios. It is shown to provide a good estimation of connectivity patterns and transmissivities of main flowpaths. Although the chosen fracture network geometry has been simplified, flow tomography appears to be a promising approach for characterizing connectivity patterns and transmissivities of fractured media.

A quantitative approach to measure road network information based on edge diversity

NASA Astrophysics Data System (ADS)

Wu, Xun; Zhang, Hong; Lan, Tian; Cao, Weiwei; He, Jing

2015-12-01

The measure of map information has been one of the key issues in assessing cartographic quality and map generalization algorithms. It is also important for developing efficient approaches to transfer geospatial information. Road network is the most common linear object in real world. Approximately describe road network information will benefit road map generalization, navigation map production and urban planning. Most of current approaches focused on node diversities and supposed that all the edges are the same, which is inconsistent to real-life condition, and thus show limitations in measuring network information. As real-life traffic flow are directed and of different quantities, the original undirected vector road map was first converted to a directed topographic connectivity map. Then in consideration of preferential attachment in complex network study and rich-club phenomenon in social network, the from and to weights of each edge are assigned. The from weight of a given edge is defined as the connectivity of its end node to the sum of the connectivities of all the neighbors of the from nodes of the edge. After getting the from and to weights of each edge, edge information, node information and the whole network structure information entropies could be obtained based on information theory. The approach has been applied to several 1 square mile road network samples. Results show that information entropies based on edge diversities could successfully describe the structural differences of road networks. This approach is a complementarity to current map information measurements, and can be extended to measure other kinds of geographical objects.

Capital and Knowledge Flows: Global Higher Education Markets

ERIC Educational Resources Information Center

Luke, Carmen

2005-01-01

Accelerating global flows of people and information have formed new communities and networks across social and political borders. Higher education is one such globalised knowledge community in which new patterns of knowledge, accreditation, research alliances, and social and professional relationships are emerging. In this paper I outline the…

Phenotypic assortment in wild primate networks: implications for the dissemination of information.

PubMed

Carter, Alecia J; Lee, Alexander E G; Marshall, Harry H; Ticó, Miquel Torrents; Cowlishaw, Guy

2015-05-01

Individuals' access to social information can depend on their social network. Homophily-a preference to associate with similar phenotypes-may cause assortment within social networks that could preclude information transfer from individuals who generate information to those who would benefit from acquiring it. Thus, understanding phenotypic assortment may lead to a greater understanding of the factors that could limit the transfer of information between individuals. We tested whether there was assortment in wild baboon (Papio ursinus) networks, using data collected from two troops over 6 years for six phenotypic traits-boldness, age, dominance rank, sex and the propensity to generate/exploit information-using two methods for defining a connection between individuals-time spent in proximity and grooming. Our analysis indicated that assortment was more common in grooming than proximity networks. In general, there was homophily for boldness, age, rank and the propensity to both generate and exploit information, but heterophily for sex. However, there was considerable variability both between troops and years. The patterns of homophily we observed for these phenotypes may impede information transfer between them. However, the inconsistency in the strength of assortment between troops and years suggests that the limitations to information flow may be quite variable.

Loan and nonloan flows in the Australian interbank network

NASA Astrophysics Data System (ADS)

Sokolov, Andrey; Webster, Rachel; Melatos, Andrew; Kieu, Tien

2012-05-01

High-value transactions between banks in Australia are settled in the Reserve Bank Information and Transfer System (RITS) administered by the Reserve Bank of Australia. RITS operates on a real-time gross settlement (RTGS) basis and settles payments and transfers sourced from the SWIFT payment delivery system, the Austraclear securities settlement system, and the interbank transactions entered directly into RITS. In this paper, we analyse a dataset received from the Reserve Bank of Australia that includes all interbank transactions settled in RITS on an RTGS basis during five consecutive weekdays from 19 February 2007 inclusive, a week of relatively quiescent market conditions. The source, destination, and value of each transaction are known, which allows us to separate overnight loans from other transactions (nonloans) and reconstruct monetary flows between banks for every day in our sample. We conduct a novel analysis of the flow stability and examine the connection between loan and nonloan flows. Our aim is to understand the underlying causal mechanism connecting loan and nonloan flows. We find that the imbalances in the banks' exchange settlement funds resulting from the daily flows of nonloan transactions are almost exactly counterbalanced by the flows of overnight loans. The correlation coefficient between loan and nonloan imbalances is about -0.9 on most days. Some flows that persist over two consecutive days can be highly variable, but overall the flows are moderately stable in value. The nonloan network is characterised by a large fraction of persistent flows, whereas only half of the flows persist over any two consecutive days in the loan network. Moreover, we observe an unusual degree of coherence between persistent loan flow values on Tuesday and Wednesday. We probe static topological properties of the Australian interbank network and find them consistent with those observed in other countries.

A Real-Time Method to Estimate Speed of Object Based on Object Detection and Optical Flow Calculation

NASA Astrophysics Data System (ADS)

Liu, Kaizhan; Ye, Yunming; Li, Xutao; Li, Yan

2018-04-01

In recent years Convolutional Neural Network (CNN) has been widely used in computer vision field and makes great progress in lots of contents like object detection and classification. Even so, combining Convolutional Neural Network, which means making multiple CNN frameworks working synchronously and sharing their output information, could figure out useful message that each of them cannot provide singly. Here we introduce a method to real-time estimate speed of object by combining two CNN: YOLOv2 and FlowNet. In every frame, YOLOv2 provides object size; object location and object type while FlowNet providing the optical flow of whole image. On one hand, object size and object location help to select out the object part of optical flow image thus calculating out the average optical flow of every object. On the other hand, object type and object size help to figure out the relationship between optical flow and true speed by means of optics theory and priori knowledge. Therefore, with these two key information, speed of object can be estimated. This method manages to estimate multiple objects at real-time speed by only using a normal camera even in moving status, whose error is acceptable in most application fields like manless driving or robot vision.

Information Flows? A Critique of Transfer Entropies

NASA Astrophysics Data System (ADS)

James, Ryan G.; Barnett, Nix; Crutchfield, James P.

2016-06-01

A central task in analyzing complex dynamics is to determine the loci of information storage and the communication topology of information flows within a system. Over the last decade and a half, diagnostics for the latter have come to be dominated by the transfer entropy. Via straightforward examples, we show that it and a derivative quantity, the causation entropy, do not, in fact, quantify the flow of information. At one and the same time they can overestimate flow or underestimate influence. We isolate why this is the case and propose several avenues to alternate measures for information flow. We also address an auxiliary consequence: The proliferation of networks as a now-common theoretical model for large-scale systems, in concert with the use of transferlike entropies, has shoehorned dyadic relationships into our structural interpretation of the organization and behavior of complex systems. This interpretation thus fails to include the effects of polyadic dependencies. The net result is that much of the sophisticated organization of complex systems may go undetected.

Multi-agent coordination in directed moving neighbourhood random networks

NASA Astrophysics Data System (ADS)

Shang, Yi-Lun

2010-07-01

This paper considers the consensus problem of dynamical multiple agents that communicate via a directed moving neighbourhood random network. Each agent performs random walk on a weighted directed network. Agents interact with each other through random unidirectional information flow when they coincide in the underlying network at a given instant. For such a framework, we present sufficient conditions for almost sure asymptotic consensus. Numerical examples are taken to show the effectiveness of the obtained results.

A semi-automatic method for extracting thin line structures in images as rooted tree network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brazzini, Jacopo; Dillard, Scott; Soille, Pierre

2010-01-01

This paper addresses the problem of semi-automatic extraction of line networks in digital images - e.g., road or hydrographic networks in satellite images, blood vessels in medical images, robust. For that purpose, we improve a generic method derived from morphological and hydrological concepts and consisting in minimum cost path estimation and flow simulation. While this approach fully exploits the local contrast and shape of the network, as well as its arborescent nature, we further incorporate local directional information about the structures in the image. Namely, an appropriate anisotropic metric is designed by using both the characteristic features of the targetmore » network and the eigen-decomposition of the gradient structure tensor of the image. Following, the geodesic propagation from a given seed with this metric is combined with hydrological operators for overland flow simulation to extract the line network. The algorithm is demonstrated for the extraction of blood vessels in a retina image and of a river network in a satellite image.« less

Age-dependent modulation of the somatosensory network upon eye closure.

PubMed

Brodoehl, Stefan; Klingner, Carsten; Witte, Otto W

2016-02-01

Eye closure even in complete darkness can improve somatosensory perception by switching the brain to a uni-sensory processing mode. This causes an increased information flow between the thalamus and the somatosensory cortex while decreasing modulation by the visual cortex. Previous work suggests that these modulations are age-dependent and that the benefit in somatosensory performance due to eye closing diminishes with age. The cause of this age-dependency and to what extent somatosensory processing is involved remains unclear. Therefore, we intended to characterize the underlying age-dependent modifications in the interaction and connectivity of different sensory networks caused by eye closure. We performed functional MR-imaging with tactile stimulation of the right hand under the conditions of opened and closed eyes in healthy young and elderly participants. Conditional Granger causality analysis was performed to assess the somatosensory and visual networks, including the thalamus. Independent of age, eye closure improved the information transfer from the thalamus to and within the somatosensory cortex. However, beyond that, we found an age-dependent recruitment strategy. Whereas young participants were characterized by an optimized information flow within the relays of the somatosensory network, elderly participants revealed a stronger modulatory influence of the visual network upon the somatosensory cortex. Our results demonstrate that the modulation of the somatosensory and visual networks by eye closure diminishes with age and that the dominance of the visual system is more pronounced in the aging brain. Copyright © 2015 Elsevier B.V. All rights reserved.

Visual sensory networks and effective information transfer in animal groups.

PubMed

Strandburg-Peshkin, Ariana; Twomey, Colin R; Bode, Nikolai W F; Kao, Albert B; Katz, Yael; Ioannou, Christos C; Rosenthal, Sara B; Torney, Colin J; Wu, Hai Shan; Levin, Simon A; Couzin, Iain D

2013-09-09

Social transmission of information is vital for many group-living animals, allowing coordination of motion and effective response to complex environments. Revealing the interaction networks underlying information flow within these groups is a central challenge. Previous work has modeled interactions between individuals based directly on their relative spatial positions: each individual is considered to interact with all neighbors within a fixed distance (metric range), a fixed number of nearest neighbors (topological range), a 'shell' of near neighbors (Voronoi range), or some combination (Figure 1A). However, conclusive evidence to support these assumptions is lacking. Here, we employ a novel approach that considers individual movement decisions to be based explicitly on the sensory information available to the organism. In other words, we consider that while spatial relations do inform interactions between individuals, they do so indirectly, through individuals' detection of sensory cues. We reconstruct computationally the visual field of each individual throughout experiments designed to investigate information propagation within fish schools (golden shiners, Notemigonus crysoleucas). Explicitly considering visual sensing allows us to more accurately predict the propagation of behavioral change in these groups during leadership events. Furthermore, we find that structural properties of visual interaction networks differ markedly from those of metric and topological counterparts, suggesting that previous assumptions may not appropriately reflect information flow in animal groups. Copyright © 2013 Elsevier Ltd. All rights reserved.

Estimation of Flux Between Interacting Nodes on Huge Inter-Firm Networks

NASA Astrophysics Data System (ADS)

Tamura, Koutarou; Miura, Wataru; Takayasu, Misako; Takayasu, Hideki; Kitajima, Satoshi; Goto, Hayato

We analyze Japanese inter-firm network data showing scale-free properties as an example of a real complex network. The data contains information on money flow (annual transaction volume) between about 7000 pairs of firms. We focus on this money-flow data and investigate the correlation between various quantities such as sales or link numbers. We find that the flux from a buyer to a supplier is given by the product of the fractional powers of both sales with different exponents. This result indicates that the principle of detailed balance does not hold in the real transport of money; therefore, random walk type transport models such as PageRank are not suitable.

Exact and heuristic algorithms for Space Information Flow.

PubMed

Uwitonze, Alfred; Huang, Jiaqing; Ye, Yuanqing; Cheng, Wenqing; Li, Zongpeng

2018-01-01

Space Information Flow (SIF) is a new promising research area that studies network coding in geometric space, such as Euclidean space. The design of algorithms that compute the optimal SIF solutions remains one of the key open problems in SIF. This work proposes the first exact SIF algorithm and a heuristic SIF algorithm that compute min-cost multicast network coding for N (N ≥ 3) given terminal nodes in 2-D Euclidean space. Furthermore, we find that the Butterfly network in Euclidean space is the second example besides the Pentagram network where SIF is strictly better than Euclidean Steiner minimal tree. The exact algorithm design is based on two key techniques: Delaunay triangulation and linear programming. Delaunay triangulation technique helps to find practically good candidate relay nodes, after which a min-cost multicast linear programming model is solved over the terminal nodes and the candidate relay nodes, to compute the optimal multicast network topology, including the optimal relay nodes selected by linear programming from all the candidate relay nodes and the flow rates on the connection links. The heuristic algorithm design is also based on Delaunay triangulation and linear programming techniques. The exact algorithm can achieve the optimal SIF solution with an exponential computational complexity, while the heuristic algorithm can achieve the sub-optimal SIF solution with a polynomial computational complexity. We prove the correctness of the exact SIF algorithm. The simulation results show the effectiveness of the heuristic SIF algorithm.

Responses to Climate Change: Exploring Organisational Learning across Internationally Networked Organisations for Development

ERIC Educational Resources Information Center

Boyd, Emily; Osbahr, Henny

2010-01-01

Drawing from the organisational learning and governance literature, this paper assesses four internationally networked governmental and non-governmental organisations in the UK addressing climate change. We analyse how those concerned understand the climate change crisis, what mechanisms are put in place to address information flows, and what…

Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis

NASA Astrophysics Data System (ADS)

Li, Yuanyuan; Jin, Suoqin; Lei, Lei; Pan, Zishu; Zou, Xiufen

2015-03-01

The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.

Performance of Social Network Sensors during Hurricane Sandy

PubMed Central

Kryvasheyeu, Yury; Chen, Haohui; Moro, Esteban; Van Hentenryck, Pascal; Cebrian, Manuel

2015-01-01

Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow and derive early-warning sensors, thus improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioral properties derived from the “friendship paradox”, is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in users’ network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays a significant role in determining the scale of such an advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility to implement a simple “sentiment sensing” technique that can detect and locate disasters. PMID:25692690

Performance of social network sensors during Hurricane Sandy.

PubMed

Kryvasheyeu, Yury; Chen, Haohui; Moro, Esteban; Van Hentenryck, Pascal; Cebrian, Manuel

2015-01-01

Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow and derive early-warning sensors, thus improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioral properties derived from the "friendship paradox", is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in users' network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays a significant role in determining the scale of such an advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility to implement a simple "sentiment sensing" technique that can detect and locate disasters.

Application of Geographical Information System Arc/info Grid-Based Surface Hyrologic Modeling to the Eastern Hellas Region, Mars

NASA Astrophysics Data System (ADS)

Mest, S. C.; Harbert, W.; Crown, D. A.

2001-05-01

Geographical Information System GRID-based raster modeling of surface water runoff in the eastern Hellas region of Mars has been completed. We utilized the 0.0625 by 0.0625 degree topographic map of Mars collected by the Mars Global Surveyor Mars Orbiter Laser Altimeter (MOLA) instrument to model watershed and surface runoff drainage systems. Scientific interpretation of these models with respect to ongoing geological mapping is presented in Mest et al., (2001). After importing a region of approximately 77,000,000 square kilometers into Arc/Info 8.0.2 we reprojected this digital elevation model (DEM) from a Mars sphere into a Mars ellipsoid. Using a simple cylindrical geographic projection and horizontal spatial units of decimal degrees and then an Albers projection with horizontal spatial units of meters, we completed basic hydrological modeling. Analysis of the raw DEM to determine slope, aspect, flow direction, watershed and flow accumulation grids demonstrated the need for correction of single pixel sink anomalies. After analysis of zonal elevation statistics associated with single pixel sinks, which identified 0.8 percent of the DEM points as having undefined surface water flow directions, we filled single pixel sink values of 89 meters or less. This correction is comparable with terrestrial DEMs that contain 0.9 percent to 4.7 percent of cells, which are sinks (Tarboton et al., 1991). The fill-corrected DEM was then used to determine slope, aspect, surface water flow direction and surface water flow accumulation. Within the region of interest 8,776 watersheds were identified. Using Arc/Info GRID flow direction and flow accumulation tools, regions of potential surface water flow accumulation were identified. These networks were then converted to a Strahler ordered stream network. Surface modeling produced Strahler orders one through six. As presented in Mest et al., (2001) comparisons of mapped features may prove compatible with drainage networks and watersheds derived using this methodology. Mest, Scott C., Crown, David A., and Harbert, William, 2001, Highland drainage basins and valley networks in the eastern Hellas Region of Mars, Abstract 1419, Lunar and Planetary Science XXXII Meeting Houston (CDROM). Tarboton D. G., Bras, R. L., and Rodriguez-Iturbe, 1991, On the Extraction of Channel Networks from Digital Elevation Data, Hydrological Processes, v. 5, 81-100. http://viking.eps.pitt.edu

Extracting topographic structure from digital elevation data for geographic information-system analysis

USGS Publications Warehouse

Jenson, Susan K.; Domingue, Julia O.

1988-01-01

The first phase of analysis is a conditioning phase that generates three data sets: the original OEM with depressions filled, a data set indicating the flow direction for each cell, and a flow accumulation data set in which each cell receives a value equal to the total number of cells that drain to it. The original OEM and these three derivative data sets can then be processed in a variety of ways to optionally delineate drainage networks, overland paths, watersheds for userspecified locations, sub-watersheds for the major tributaries of a drainage network, or pour point linkages between watersheds. The computer-generated drainage lines and watershed polygons and the pour point linkage information can be transferred to vector-based geographic information systems for futher analysis. Comparisons between these computergenerated features and their manually delineated counterparts generally show close agreement, indicating that these software tools will save analyst time spent in manual interpretation and digitizing.

Spectral properties of Google matrix of Wikipedia and other networks

NASA Astrophysics Data System (ADS)

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

2013-05-01

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

The Loss of Efficiency Caused by Agents’ Uncoordinated Routing in Transport Networks

PubMed Central

Wang, Junjie; Wang, Pu

2014-01-01

Large-scale daily commuting data were combined with detailed geographical information system (GIS) data to analyze the loss of transport efficiency caused by drivers’ uncoordinated routing in urban road networks. We used Price of Anarchy (POA) to quantify the loss of transport efficiency and found that both volume and distribution of human mobility demand determine the POA. In order to reduce POA, a small number of highways require considerable decreases in traffic, and their neighboring arterial roads need to attract more traffic. The magnitude of the adjustment in traffic flow can be estimated using the fundamental measure traffic flow only, which is widely available and easy to collect. Surprisingly, the most congested roads or the roads with largest traffic flow were not those requiring the most reduction of traffic. This study can offer guidance for the optimal control of urban traffic and facilitate improvements in the efficiency of transport networks. PMID:25349995

Social network analysis of Iranian researchers in the field of violence.

PubMed

Salamati, Payman; Soheili, Faramarz

2016-10-01

The social network analysis (SNA) is a paradigm for analyzing structural patterns in social re- lations, testing knowledge sharing process and identifying bottlenecks of information flow. The purpose of this study was to determine the status of research in the fleld of violence in Iran using SNA. Research population included all the papers with at least one Iranian affiliation published in violence fleld indexed in SCIE, PubMed and Scopus databases. The co-word maps, co-authorship network and structural holes were drawn using related software. In the next step, the active authors and some measures of our network including degree centrality (DC), closeness, eigenvector, betweeness, density, diameter, compactness and size of the main component were assessed. Likewise, the trend of the published articles was evaluated based on the number of documents and their citations from 1972 to 2014. Five hundred and seventy one records were obtained. The five main clusters and hot spots were mental health, violence, war, psychiatric disorders and suicide. The co-authorship network was complex, tangled and scale free. The top nine authors with cut point role and top ten active authors were identified. The mean (standard deviation) of normalized DC, closeness, eigenvector and betweeness were 0.449 (0.805), 0.609 (0.214), 2.373 (7.353) and 0.338 (1.122), respectively. The density, diameter and mean compactness of our co-authorship network were 0.0494, 3.955 and 0.125, respectively. The main component consisted of 216 nodes that formed 17% of total size of the network. Both the number of the documents and their citations has increased in the field of violence in the recent years. Although the number of the documents has recently increased in the field of violence, the information flow is slow and there are not many relations among the authors in the network. However, the active authors have ability to influence the flow of knowledge within the network.

Technology Horizons: A Vision for Air Force Science and Technology 2010-30

DTIC Science & Technology

2011-09-01

software, hardware, and networks, it is now recognized as en- compassing the entire system that couples information flow and decision processes across...acceleration, and scramjet cruise. Inward turning inlets and a dual- flow path design allow high volumetric efficiency, and high cruise speed provides...the same time, emerging “third- stream engine architectures” can enable constant-mass- flow engines that can provide further reductions in fuel

Mapping physician Twitter networks: describing how they work as a first step in understanding connectivity, information flow, and message diffusion.

PubMed

Mishori, Ranit; Singh, Lisa Oberoi; Levy, Brendan; Newport, Calvin

2014-04-14

Twitter is becoming an important tool in medicine, but there is little information on Twitter metrics. In order to recommend best practices for information dissemination and diffusion, it is important to first study and analyze the networks. This study describes the characteristics of four medical networks, analyzes their theoretical dissemination potential, their actual dissemination, and the propagation and distribution of tweets. Open Twitter data was used to characterize four networks: the American Medical Association (AMA), the American Academy of Family Physicians (AAFP), the American Academy of Pediatrics (AAP), and the American College of Physicians (ACP). Data were collected between July 2012 and September 2012. Visualization was used to understand the follower overlap between the groups. Actual flow of the tweets for each group was assessed. Tweets were examined using Topsy, a Twitter data aggregator. The theoretical information dissemination potential for the groups is large. A collective community is emerging, where large percentages of individuals are following more than one of the groups. The overlap across groups is small, indicating a limited amount of community cohesion and cross-fertilization. The AMA followers' network is not as active as the other networks. The AMA posted the largest number of tweets while the AAP posted the fewest. The number of retweets for each organization was low indicating dissemination that is far below its potential. To increase the dissemination potential, medical groups should develop a more cohesive community of shared followers. Tweet content must be engaging to provide a hook for retweeting and reaching potential audience. Next steps call for content analysis, assessment of the behavior and actions of the messengers and the recipients, and a larger-scale study that considers other medical groups using Twitter.

Reinforcement Learning of Two-Joint Virtual Arm Reaching in a Computer Model of Sensorimotor Cortex

PubMed Central

Neymotin, Samuel A.; Chadderdon, George L.; Kerr, Cliff C.; Francis, Joseph T.; Lytton, William W.

2014-01-01

Neocortical mechanisms of learning sensorimotor control involve a complex series of interactions at multiple levels, from synaptic mechanisms to cellular dynamics to network connectomics. We developed a model of sensory and motor neocortex consisting of 704 spiking model neurons. Sensory and motor populations included excitatory cells and two types of interneurons. Neurons were interconnected with AMPA/NMDA and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a two-joint virtual arm to reach to a fixed target. For each of 125 trained networks, we used 200 training sessions, each involving 15 s reaches to the target from 16 starting positions. Learning altered network dynamics, with enhancements to neuronal synchrony and behaviorally relevant information flow between neurons. After learning, networks demonstrated retention of behaviorally relevant memories by using proprioceptive information to perform reach-to-target from multiple starting positions. Networks dynamically controlled which joint rotations to use to reach a target, depending on current arm position. Learning-dependent network reorganization was evident in both sensory and motor populations: learned synaptic weights showed target-specific patterning optimized for particular reach movements. Our model embodies an integrative hypothesis of sensorimotor cortical learning that could be used to interpret future electrophysiological data recorded in vivo from sensorimotor learning experiments. We used our model to make the following predictions: learning enhances synchrony in neuronal populations and behaviorally relevant information flow across neuronal populations, enhanced sensory processing aids task-relevant motor performance and the relative ease of a particular movement in vivo depends on the amount of sensory information required to complete the movement. PMID:24047323

Path Flow Estimation Using Time Varying Coefficient State Space Model

NASA Astrophysics Data System (ADS)

Jou, Yow-Jen; Lan, Chien-Lun

2009-08-01

The dynamic path flow information is very crucial in the field of transportation operation and management, i.e., dynamic traffic assignment, scheduling plan, and signal timing. Time-dependent path information, which is important in many aspects, is nearly impossible to be obtained. Consequently, researchers have been seeking estimation methods for deriving valuable path flow information from less expensive traffic data, primarily link traffic counts of surveillance systems. This investigation considers a path flow estimation problem involving the time varying coefficient state space model, Gibbs sampler, and Kalman filter. Numerical examples with part of a real network of the Taipei Mass Rapid Transit with real O-D matrices is demonstrated to address the accuracy of proposed model. Results of this study show that this time-varying coefficient state space model is very effective in the estimation of path flow compared to time-invariant model.

Petri net-based method for the analysis of the dynamics of signal propagation in signaling pathways.

PubMed

Hardy, Simon; Robillard, Pierre N

2008-01-15

Cellular signaling networks are dynamic systems that propagate and process information, and, ultimately, cause phenotypical responses. Understanding the circuitry of the information flow in cells is one of the keys to understanding complex cellular processes. The development of computational quantitative models is a promising avenue for attaining this goal. Not only does the analysis of the simulation data based on the concentration variations of biological compounds yields information about systemic state changes, but it is also very helpful for obtaining information about the dynamics of signal propagation. This article introduces a new method for analyzing the dynamics of signal propagation in signaling pathways using Petri net theory. The method is demonstrated with the Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) regulation network. The results constitute temporal information about signal propagation in the network, a simplified graphical representation of the network and of the signal propagation dynamics and a characterization of some signaling routes as regulation motifs.

Discrepancy between mRNA and protein abundance: Insight from information retrieval process in computers

PubMed Central

Wang, Degeng

2008-01-01

Discrepancy between the abundance of cognate protein and RNA molecules is frequently observed. A theoretical understanding of this discrepancy remains elusive, and it is frequently described as surprises and/or technical difficulties in the literature. Protein and RNA represent different steps of the multi-stepped cellular genetic information flow process, in which they are dynamically produced and degraded. This paper explores a comparison with a similar process in computers - multi-step information flow from storage level to the execution level. Functional similarities can be found in almost every facet of the retrieval process. Firstly, common architecture is shared, as the ribonome (RNA space) and the proteome (protein space) are functionally similar to the computer primary memory and the computer cache memory respectively. Secondly, the retrieval process functions, in both systems, to support the operation of dynamic networks – biochemical regulatory networks in cells and, in computers, the virtual networks (of CPU instructions) that the CPU travels through while executing computer programs. Moreover, many regulatory techniques are implemented in computers at each step of the information retrieval process, with a goal of optimizing system performance. Cellular counterparts can be easily identified for these regulatory techniques. In other words, this comparative study attempted to utilize theoretical insight from computer system design principles as catalysis to sketch an integrative view of the gene expression process, that is, how it functions to ensure efficient operation of the overall cellular regulatory network. In context of this bird’s-eye view, discrepancy between protein and RNA abundance became a logical observation one would expect. It was suggested that this discrepancy, when interpreted in the context of system operation, serves as a potential source of information to decipher regulatory logics underneath biochemical network operation. PMID:18757239

Discrepancy between mRNA and protein abundance: insight from information retrieval process in computers.

PubMed

Wang, Degeng

2008-12-01

Discrepancy between the abundance of cognate protein and RNA molecules is frequently observed. A theoretical understanding of this discrepancy remains elusive, and it is frequently described as surprises and/or technical difficulties in the literature. Protein and RNA represent different steps of the multi-stepped cellular genetic information flow process, in which they are dynamically produced and degraded. This paper explores a comparison with a similar process in computers-multi-step information flow from storage level to the execution level. Functional similarities can be found in almost every facet of the retrieval process. Firstly, common architecture is shared, as the ribonome (RNA space) and the proteome (protein space) are functionally similar to the computer primary memory and the computer cache memory, respectively. Secondly, the retrieval process functions, in both systems, to support the operation of dynamic networks-biochemical regulatory networks in cells and, in computers, the virtual networks (of CPU instructions) that the CPU travels through while executing computer programs. Moreover, many regulatory techniques are implemented in computers at each step of the information retrieval process, with a goal of optimizing system performance. Cellular counterparts can be easily identified for these regulatory techniques. In other words, this comparative study attempted to utilize theoretical insight from computer system design principles as catalysis to sketch an integrative view of the gene expression process, that is, how it functions to ensure efficient operation of the overall cellular regulatory network. In context of this bird's-eye view, discrepancy between protein and RNA abundance became a logical observation one would expect. It was suggested that this discrepancy, when interpreted in the context of system operation, serves as a potential source of information to decipher regulatory logics underneath biochemical network operation.

Branching pattern in natural drainage network

NASA Astrophysics Data System (ADS)

Hooshyar, M.; Singh, A.; Wang, D.

2017-12-01

The formation and growth of river channels and their network evolution are governed by the erosional and depositional processes operating on the landscape due to movement of water. The branching structure of drainage network is an important feature related to the network topology and contain valuable information about the forming mechanisms of the landscape. We studied the branching patterns in natural drainage networks, extracted from 1 m Digital Elevation Models (DEMs) of 120 catchments with minimal human impacts across the United States. We showed that the junction angles have two distinct modes an the observed modes are physically explained as the optimal angles that result in minimum energy dissipation and are linked to the exponent characterizing slope-area curve. Our findings suggest that the flow regimes, debris-flow dominated or fluvial, have distinct characteristic angles which are functions of the scaling exponent of the slope-area curve. These findings enable us to understand the geomorphological signature of hydrological processes on drainage networks and develop more refined landscape evolution models.

Quantifying spatial and temporal patterns of flow intermittency using spatially contiguous runoff data

NASA Astrophysics Data System (ADS)

Yu (于松延), Songyan; Bond, Nick R.; Bunn, Stuart E.; Xu, Zongxue; Kennard, Mark J.

2018-04-01

River channel drying caused by intermittent stream flow is a widely-recognized factor shaping stream ecosystems. There is a strong need to quantify the distribution of intermittent streams across catchments to inform management. However, observational gauge networks provide only point estimates of streamflow variation. Increasingly, this limitation is being overcome through the use of spatially contiguous estimates of the terrestrial water-balance, which can also assist in estimating runoff and streamflow at large-spatial scales. Here we proposed an approach to quantifying spatial and temporal variation in monthly flow intermittency throughout river networks in eastern Australia. We aggregated gridded (5 × 5 km) monthly water-balance data with a hierarchically nested catchment dataset to simulate catchment runoff accumulation throughout river networks from 1900 to 2016. We also predicted zero flow duration for the entire river network by developing a robust predictive model relating measured zero flow duration (% months) to environmental predictor variables (based on 43 stream gauges). We then combined these datasets by using the predicted zero flow duration from the regression model to determine appropriate 'zero' flow thresholds for the modelled discharge data, which varied spatially across the catchments examined. Finally, based on modelled discharge data and identified actual zero flow thresholds, we derived summary metrics describing flow intermittency across the catchment (mean flow duration and coefficient-of-variation in flow permanence from 1900 to 2016). We also classified the relative degree of flow intermittency annually to characterise temporal variation in flow intermittency. Results showed that the degree of flow intermittency varied substantially across streams in eastern Australia, ranging from perennial streams flowing permanently (11-12 months) to strongly intermittent streams flowing 4 months or less of year. Results also showed that the temporal extent of flow intermittency varied dramatically inter-annually from 1900 to 2016, with the proportion of intermittent (weakly and strongly intermittent) streams ranging in length from 3% to nearly 100% of the river network, but there was no evidence of an increasing trend towards flow intermittency over this period. Our approach to generating spatially explicit and catchment-wide estimates of streamflow intermittency can facilitate improved ecological understanding and management of intermittent streams in Australia and around the world.

Network-based study of Lagrangian transport and mixing

NASA Astrophysics Data System (ADS)

Padberg-Gehle, Kathrin; Schneide, Christiane

2017-10-01

Transport and mixing processes in fluid flows are crucially influenced by coherent structures and the characterization of these Lagrangian objects is a topic of intense current research. While established mathematical approaches such as variational methods or transfer-operator-based schemes require full knowledge of the flow field or at least high-resolution trajectory data, this information may not be available in applications. Recently, different computational methods have been proposed to identify coherent behavior in flows directly from Lagrangian trajectory data, that is, numerical or measured time series of particle positions in a fluid flow. In this context, spatio-temporal clustering algorithms have been proven to be very effective for the extraction of coherent sets from sparse and possibly incomplete trajectory data. Inspired by these recent approaches, we consider an unweighted, undirected network, where Lagrangian particle trajectories serve as network nodes. A link is established between two nodes if the respective trajectories come close to each other at least once in the course of time. Classical graph concepts are then employed to analyze the resulting network. In particular, local network measures such as the node degree, the average degree of neighboring nodes, and the clustering coefficient serve as indicators of highly mixing regions, whereas spectral graph partitioning schemes allow us to extract coherent sets. The proposed methodology is very fast to run and we demonstrate its applicability in two geophysical flows - the Bickley jet as well as the Antarctic stratospheric polar vortex.

Roles of Course Facilitators, Learners, and Technology in the Flow of Information of a cMOOC

ERIC Educational Resources Information Center

Skrypnyk, Oleksandra; Joksimovic, Srec´ko; Kovanovic, Vitomir; Gas?evic, Dragan; Dawson, Shane

2015-01-01

Distributed Massive Open Online Courses (MOOCs) are based on the premise that online learning occurs through a network of interconnected learners. The teachers' role in distributed courses extends to forming such a network by facilitating communication that connects learners and their separate personal learning environments scattered around the…

Urban occupational structures as information networks: The effect on network density of increasing number of occupations.

PubMed

Shutters, Shade T; Lobo, José; Muneepeerakul, Rachata; Strumsky, Deborah; Mellander, Charlotta; Brachert, Matthias; Farinha, Teresa; Bettencourt, Luis M A

2018-01-01

Urban economies are composed of diverse activities, embodied in labor occupations, which depend on one another to produce goods and services. Yet little is known about how the nature and intensity of these interdependences change as cities increase in population size and economic complexity. Understanding the relationship between occupational interdependencies and the number of occupations defining an urban economy is relevant because interdependence within a networked system has implications for system resilience and for how easily can the structure of the network be modified. Here, we represent the interdependencies among occupations in a city as a non-spatial information network, where the strengths of interdependence between pairs of occupations determine the strengths of the links in the network. Using those quantified link strengths we calculate a single metric of interdependence-or connectedness-which is equivalent to the density of a city's weighted occupational network. We then examine urban systems in six industrialized countries, analyzing how the density of urban occupational networks changes with network size, measured as the number of unique occupations present in an urban workforce. We find that in all six countries, density, or economic interdependence, increases superlinearly with the number of distinct occupations. Because connections among occupations represent flows of information, we provide evidence that connectivity scales superlinearly with network size in information networks.

Urban occupational structures as information networks: The effect on network density of increasing number of occupations

PubMed Central

Lobo, José; Muneepeerakul, Rachata; Strumsky, Deborah; Mellander, Charlotta; Brachert, Matthias; Farinha, Teresa; Bettencourt, Luis M. A.

2018-01-01

Urban economies are composed of diverse activities, embodied in labor occupations, which depend on one another to produce goods and services. Yet little is known about how the nature and intensity of these interdependences change as cities increase in population size and economic complexity. Understanding the relationship between occupational interdependencies and the number of occupations defining an urban economy is relevant because interdependence within a networked system has implications for system resilience and for how easily can the structure of the network be modified. Here, we represent the interdependencies among occupations in a city as a non-spatial information network, where the strengths of interdependence between pairs of occupations determine the strengths of the links in the network. Using those quantified link strengths we calculate a single metric of interdependence–or connectedness–which is equivalent to the density of a city’s weighted occupational network. We then examine urban systems in six industrialized countries, analyzing how the density of urban occupational networks changes with network size, measured as the number of unique occupations present in an urban workforce. We find that in all six countries, density, or economic interdependence, increases superlinearly with the number of distinct occupations. Because connections among occupations represent flows of information, we provide evidence that connectivity scales superlinearly with network size in information networks. PMID:29734354

How important is exact knowledge of preferential flowpath locations and orientations for understanding spatiotemporally integrated spring hydrologic and transport response?

NASA Astrophysics Data System (ADS)

Henson, W.; De Rooij, R.; Graham, W. D.

2016-12-01

The Upper Floridian Aquifer is hydrogeologically complex; limestone dissolution has led to vertical and horizontal preferential flow paths. Locations of karst conduits are unknown and conduit properties are poorly constrained. Uncertainty in effects of conduit location, size, and density, network geometry and connectivity on hydrologic and transport responses is not well quantified, leading to limited use of discrete-continuum models that incorporate conduit networks for regional-scale hydrologic regulatory models. However, conduit networks typically dominate flow and contaminant transport in karst aquifers. We evaluated sensitivity of simulated water and nitrate fluxes and flow paths to karst conduit geometry in a springshed representative of Silver Springs, Florida, using a novel calcite dissolution conduit-generation algorithm coupled with a discrete-continuum flow and transport model (DisCo). Monte Carlo simulations of conduit generation, groundwater flow, and conservative solute transport indicate that, if a first magnitude spring system conduit network developed (i.e., spring flow >2.8 m3/s), the uncertainty in hydraulic and solute pulse response metrics at the spring vent was minimally related to locational uncertainty of network elements. Across the ensemble of realizations for various distributions of conduits, first magnitude spring hydraulic pulse metrics (e.g., steady-flow, peak flow, and recession coefficients) had < 0.01 coefficient of variation (CV). Similarly, spring solute breakthrough curve moments had low CV (<0.08); peak arrival had CV=0.06, mean travel time had CV=0.05, and travel time standard deviation had CV=0.08. Nevertheless, hydraulic and solute pulse response metrics were significantly different than those predicted by an equivalent porous-media model. These findings indicate that regional-scale decision models that incorporate karst preferential flow paths within an uncertainty framework can be used to better constrain aquifer-vulnerability estimates, despite lacking information about actual conduit locations.

Towards understanding the behavior of physical systems using information theory

NASA Astrophysics Data System (ADS)

Quax, Rick; Apolloni, Andrea; Sloot, Peter M. A.

2013-09-01

One of the goals of complex network analysis is to identify the most influential nodes, i.e., the nodes that dictate the dynamics of other nodes. In the case of autonomous systems or transportation networks, highly connected hubs play a preeminent role in diffusing the flow of information and viruses; in contrast, in language evolution most linguistic norms come from the peripheral nodes who have only few contacts. Clearly a topological analysis of the interactions alone is not sufficient to identify the nodes that drive the state of the network. Here we show how information theory can be used to quantify how the dynamics of individual nodes propagate through a system. We interpret the state of a node as a storage of information about the state of other nodes, which is quantified in terms of Shannon information. This information is transferred through interactions and lost due to noise, and we calculate how far it can travel through a network. We apply this concept to a model of opinion formation in a complex social network to calculate the impact of each node by measuring how long its opinion is remembered by the network. Counter-intuitively we find that the dynamics of opinions are not determined by the hubs or peripheral nodes, but rather by nodes with an intermediate connectivity.

Information propagation and nutrient flow in Physarum polycephalum

NASA Astrophysics Data System (ADS)

Amselem, Gabriel; Peaudecerf, Francois; Alim, Karen; Dumais, Jacques; Pringle, Anne; Brenner, Michael

2012-02-01

Basal organisms such as slime mold and fungi grow as extended networks that can reach several square meters in size. Despite lacking a central coordination center, these organisms are able to globally reshape their morphology in response to local cues, such as the presence of a patch of nutrient. How are local signals integrated in these organisms, and how do they lead to an overall response? To answer this question, we focus on the flow of nutrients in the slime mold Physarum polycephalum. This slime mold exhibits internal flow oscillations, as well as periodic contractions of its veins. Using plastic masks, we constrain network growth to simple geometries. This allows for an experimental characterization of the relationship between the contractions and the flow. We next describe the change in the overall oscillation pattern when a food source is presented locally to the slime mold, and its implication on the internal flow. Internal flows are both inferred from the contraction pattern and experimentally measured using fluorescent markers.

40-Gbps optical backbone network deep packet inspection based on FPGA

NASA Astrophysics Data System (ADS)

Zuo, Yuan; Huang, Zhiping; Su, Shaojing

2014-11-01

In the era of information, the big data, which contains huge information, brings about some problems, such as high speed transmission, storage and real-time analysis and process. As the important media for data transmission, the Internet is the significant part for big data processing research. With the large-scale usage of the Internet, the data streaming of network is increasing rapidly. The speed level in the main fiber optic communication of the present has reached 40Gbps, even 100Gbps, therefore data on the optical backbone network shows some features of massive data. Generally, data services are provided via IP packets on the optical backbone network, which is constituted with SDH (Synchronous Digital Hierarchy). Hence this method that IP packets are directly mapped into SDH payload is named POS (Packet over SDH) technology. Aiming at the problems of real time process of high speed massive data, this paper designs a process system platform based on ATCA for 40Gbps POS signal data stream recognition and packet content capture, which employs the FPGA as the CPU. This platform offers pre-processing of clustering algorithms, service traffic identification and data mining for the following big data storage and analysis with high efficiency. Also, the operational procedure is proposed in this paper. Four channels of 10Gbps POS signal decomposed by the analysis module, which chooses FPGA as the kernel, are inputted to the flow classification module and the pattern matching component based on TCAM. Based on the properties of the length of payload and net flows, buffer management is added to the platform to keep the key flow information. According to data stream analysis, DPI (deep packet inspection) and flow balance distribute, the signal is transmitted to the backend machine through the giga Ethernet ports on back board. Practice shows that the proposed platform is superior to the traditional applications based on ASIC and NP.

Network-wide BGP route prediction for traffic engineering

NASA Astrophysics Data System (ADS)

Feamster, Nick; Rexford, Jennifer

2002-07-01

The Internet consists of about 13,000 Autonomous Systems (AS's) that exchange routing information using the Border Gateway Protocol (BGP). The operators of each AS must have control over the flow of traffic through their network and between neighboring AS's. However, BGP is a complicated, policy-based protocol that does not include any direct support for traffic engineering. In previous work, we have demonstrated that network operators can adapt the flow of traffic in an efficient and predictable fashion through careful adjustments to the BGP policies running on their edge routers. Nevertheless, many details of the BGP protocol and decision process make predicting the effects of these policy changes difficult. In this paper, we describe a tool that predicts traffic flow at network exit points based on the network topology, the import policy associated with each BGP session, and the routing advertisements received from neighboring AS's. We present a linear-time algorithm that computes a network-wide view of the best BGP routes for each destination prefix given a static snapshot of the network state, without simulating the complex details of BGP message passing. We describe how to construct this snapshot using the BGP routing tables and router configuration files available from operational routers. We verify the accuracy of our algorithm by applying our tool to routing and configuration data from AT&T's commercial IP network. Our route prediction techniques help support the operation of large IP backbone networks, where interdomain routing is an important aspect of traffic engineering.

Analysis of the U.S. geological survey streamgaging network

USGS Publications Warehouse

Scott, A.G.

1987-01-01

This paper summarizes the results from the first 3 years of a 5-year cost-effectiveness study of the U.S. Geological Survey streamgaging network. The objective of the study is to define and document the most cost-effective means of furnishing streamflow information. In the first step of this study, data uses were identified for 3,493 continuous-record stations currently being operated in 32 States. In the second step, evaluation of alternative methods of providing streamflow information, flow-routing models, and regression models were developed for estimating daily flows at 251 stations of the 3,493 stations analyzed. In the third step of the analysis, relationships were developed between the accuracy of the streamflow records and the operating budget. The weighted standard error for all stations, with current operating procedures, was 19.9 percent. By altering field activities, as determined by the analyses, this could be reduced to 17.8 percent. The existing streamgaging networks in four Districts were further analyzed to determine the impacts that satellite telemetry would have on the cost effectiveness. Satellite telemetry was not found to be cost effective on the basis of hydrologic data collection alone, given present cost of equipment and operation.This paper summarizes the results from the first 3 years of a 5-year cost-effectiveness study of the U. S. Geological Survey streamgaging network. The objective of the study is to define and document the most cost-effective means of furnishing streamflow information. In the first step of this study, data uses were identified for 3,493 continuous-record stations currently being operated in 32 States. In the second step, evaluation of alternative methods of providing streamflow information, flow-routing models, and regression models were developed for estimating daily flows at 251 stations of the 3, 493 stations analyzed. In the third step of the analysis, relationships were developed between the accuracy of the streamflow records and the operating budget. The weighted standard error for all stations, with current operating procedures, was 19. 9 percent. By altering field activities, as determined by the analyses, this could be reduced to 17. 8 percent. Additional study results are discussed.

The Homeland Security Ecosystem: An Analysis of Hierarchical and Ecosystem Models and Their Influence on Decision Makers

DTIC Science & Technology

2012-12-01

flows, diversity, emergence, networks, fusion, strategic planning, information sharing, ecosystem, hierarchy, NJ Regional Operations Intelligence ...Related Information...........................................................................79 viii 3. Production of Disaster Intelligence for... Intelligence for Field Personnel .................80 5. Focused Collection Efforts to Support FEMA and NJ OEM Operations

A source-controlled data center network model.

PubMed

Yu, Yang; Liang, Mangui; Wang, Zhe

2017-01-01

The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS.

A source-controlled data center network model

PubMed Central

Yu, Yang; Liang, Mangui; Wang, Zhe

2017-01-01

The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS. PMID:28328925

High-efficient Extraction of Drainage Networks from Digital Elevation Model Data Constrained by Enhanced Flow Enforcement from Known River Map

NASA Astrophysics Data System (ADS)

Wu, T.; Li, T.; Li, J.; Wang, G.

2017-12-01

Improved drainage network extraction can be achieved by flow enforcement whereby information of known river maps is imposed to the flow-path modeling process. However, the common elevation-based stream burning method can sometimes cause unintended topological errors and misinterpret the overall drainage pattern. We presented an enhanced flow enforcement method to facilitate accurate and efficient process of drainage network extraction. Both the topology of the mapped hydrography and the initial landscape of the DEM are well preserved and fully utilized in the proposed method. An improved stream rasterization is achieved here, yielding continuous, unambiguous and stream-collision-free raster equivalent of stream vectors for flow enforcement. By imposing priority-based enforcement with a complementary flow direction enhancement procedure, the drainage patterns of the mapped hydrography are fully represented in the derived results. The proposed method was tested over the Rogue River Basin, using DEMs with various resolutions. As indicated by the visual and statistical analyses, the proposed method has three major advantages: (1) it significantly reduces the occurrences of topological errors, yielding very accurate watershed partition and channel delineation, (2) it ensures scale-consistent performance at DEMs of various resolutions, and (3) the entire extraction process is well-designed to achieve great computational efficiency.

Cortical network architecture for context processing in primate brain

PubMed Central

Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka

2015-01-01

Context is information linked to a situation that can guide behavior. In the brain, context is encoded by sensory processing and can later be retrieved from memory. How context is communicated within the cortical network in sensory and mnemonic forms is unknown due to the lack of methods for high-resolution, brain-wide neuronal recording and analysis. Here, we report the comprehensive architecture of a cortical network for context processing. Using hemisphere-wide, high-density electrocorticography, we measured large-scale neuronal activity from monkeys observing videos of agents interacting in situations with different contexts. We extracted five context-related network structures including a bottom-up network during encoding and, seconds later, cue-dependent retrieval of the same network with the opposite top-down connectivity. These findings show that context is represented in the cortical network as distributed communication structures with dynamic information flows. This study provides a general methodology for recording and analyzing cortical network neuronal communication during cognition. DOI: http://dx.doi.org/10.7554/eLife.06121.001 PMID:26416139

Dynamic baseline detection method for power data network service

NASA Astrophysics Data System (ADS)

Chen, Wei

2017-08-01

This paper proposes a dynamic baseline Traffic detection Method which is based on the historical traffic data for the Power data network. The method uses Cisco's NetFlow acquisition tool to collect the original historical traffic data from network element at fixed intervals. This method uses three dimensions information including the communication port, time, traffic (number of bytes or number of packets) t. By filtering, removing the deviation value, calculating the dynamic baseline value, comparing the actual value with the baseline value, the method can detect whether the current network traffic is abnormal.

Google matrix of Twitter

NASA Astrophysics Data System (ADS)

Frahm, K. M.; Shepelyansky, D. L.

2012-10-01

We construct the Google matrix of the entire Twitter network, dated by July 2009, and analyze its spectrum and eigenstate properties including the PageRank and CheiRank vectors and 2DRanking of all nodes. Our studies show much stronger inter-connectivity between top PageRank nodes for the Twitter network compared to the networks of Wikipedia and British Universities studied previously. Our analysis allows to locate the top Twitter users which control the information flow on the network. We argue that this small fraction of the whole number of users, which can be viewed as the social network elite, plays the dominant role in the process of opinion formation on the network.

Network structure impacts global commodity trade growth and resilience.

PubMed

Kharrazi, Ali; Rovenskaya, Elena; Fath, Brian D

2017-01-01

Global commodity trade networks are critical to our collective sustainable development. Their increasing interconnectedness pose two practical questions: (i) Do the current network configurations support their further growth? (ii) How resilient are these networks to economic shocks? We analyze the data of global commodity trade flows from 1996 to 2012 to evaluate the relationship between structural properties of the global commodity trade networks and (a) their dynamic growth, as well as (b) the resilience of their growth with respect to the 2009 global economic shock. Specifically, we explore the role of network efficiency and redundancy using the information theory-based network flow analysis. We find that, while network efficiency is positively correlated with growth, highly efficient systems appear to be less resilient, losing more and gaining less growth following an economic shock. While all examined networks are rather redundant, we find that network redundancy does not hinder their growth. Moreover, systems exhibiting higher levels of redundancy lose less and gain more growth following an economic shock. We suggest that a strategy to support making global trade networks more efficient via, e.g., preferential trade agreements and higher specialization, can promote their further growth; while a strategy to increase the global trade networks' redundancy via e.g., more abundant free-trade agreements, can improve their resilience to global economic shocks.

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

NASA Astrophysics Data System (ADS)

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 network and we demonstrate that the changes can propagate globally, affecting traffic several hundreds of miles away. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events. In the second part of the thesis we focus on network deconstruction and community detection problems, both intensely studied topics in network science, using a weighted betweenness centrality approach. We present an algorithm that solves both problems efficiently and accurately and demonstrate that on both benchmark networks and data networks.

Information seeking for making evidence-informed decisions: a social network analysis on the staff of a public health department in Canada.

PubMed

Yousefi-Nooraie, Reza; Dobbins, Maureen; Brouwers, Melissa; Wakefield, Patricia

2012-05-16

Social network analysis is an approach to study the interactions and exchange of resources among people. It can help understanding the underlying structural and behavioral complexities that influence the process of capacity building towards evidence-informed decision making. A social network analysis was conducted to understand if and how the staff of a public health department in Ontario turn to peers to get help incorporating research evidence into practice. The staff were invited to respond to an online questionnaire inquiring about information seeking behavior, identification of colleague expertise, and friendship status. Three networks were developed based on the 170 participants. Overall shape, key indices, the most central people and brokers, and their characteristics were identified. The network analysis showed a low density and localized information-seeking network. Inter-personal connections were mainly clustered by organizational divisions; and people tended to limit information-seeking connections to a handful of peers in their division. However, recognition of expertise and friendship networks showed more cross-divisional connections. Members of the office of the Medical Officer of Health were located at the heart of the department, bridging across divisions. A small group of professional consultants and middle managers were the most-central staff in the network, also connecting their divisions to the center of the information-seeking network. In each division, there were some locally central staff, mainly practitioners, who connected their neighboring peers; but they were not necessarily connected to other experts or managers. The methods of social network analysis were useful in providing a systems approach to understand how knowledge might flow in an organization. The findings of this study can be used to identify early adopters of knowledge translation interventions, forming Communities of Practice, and potential internal knowledge brokers.

Artificial neural networks applied to flow prediction scenarios in Tomebamba River - Paute watershed, for flood and water quality control and management at City of Cuenca Ecuador

NASA Astrophysics Data System (ADS)

Cisneros, Felipe; Veintimilla, Jaime

2013-04-01

The main aim of this research is to create a model of Artificial Neural Networks (ANN) that allows predicting the flow in Tomebamba River both, at real time and in a certain day of year. As inputs we are using information of rainfall and flow of the stations along of the river. This information is organized in scenarios and each scenario is prepared to a specific area. The information is acquired from the hydrological stations placed in the watershed using an electronic system developed at real time and it supports any kind or brands of this type of sensors. The prediction works very good three days in advance This research includes two ANN models: Back propagation and a hybrid model between back propagation and OWO-HWO. These last two models have been tested in a preliminary research. To validate the results we are using some error indicators such as: MSE, RMSE, EF, CD and BIAS. The results of this research reached high levels of reliability and the level of error are minimal. These predictions are useful for flood and water quality control and management at City of Cuenca Ecuador

Scaling theory for information networks.

PubMed

Moses, Melanie E; Forrest, Stephanie; Davis, Alan L; Lodder, Mike A; Brown, James H

2008-12-06

Networks distribute energy, materials and information to the components of a variety of natural and human-engineered systems, including organisms, brains, the Internet and microprocessors. Distribution networks enable the integrated and coordinated functioning of these systems, and they also constrain their design. The similar hierarchical branching networks observed in organisms and microprocessors are striking, given that the structure of organisms has evolved via natural selection, while microprocessors are designed by engineers. Metabolic scaling theory (MST) shows that the rate at which networks deliver energy to an organism is proportional to its mass raised to the 3/4 power. We show that computational systems are also characterized by nonlinear network scaling and use MST principles to characterize how information networks scale, focusing on how MST predicts properties of clock distribution networks in microprocessors. The MST equations are modified to account for variation in the size and density of transistors and terminal wires in microprocessors. Based on the scaling of the clock distribution network, we predict a set of trade-offs and performance properties that scale with chip size and the number of transistors. However, there are systematic deviations between power requirements on microprocessors and predictions derived directly from MST. These deviations are addressed by augmenting the model to account for decentralized flow in some microprocessor networks (e.g. in logic networks). More generally, we hypothesize a set of constraints between the size, power and performance of networked information systems including transistors on chips, hosts on the Internet and neurons in the brain.

A Quantitative Experimental Study of the Effectiveness of Systems to Identify Network Attackers

ERIC Educational Resources Information Center

Handorf, C. Russell

2016-01-01

This study analyzed the meta-data collected from a honeypot that was run by the Federal Bureau of Investigation for a period of 5 years. This analysis compared the use of existing industry methods and tools, such as Intrusion Detection System alerts, network traffic flow and system log traffic, within the Open Source Security Information Manager…

What Is a "Good" Social Network for a System?: The Flow of Know-How for Organizational Change. Working Paper #48

ERIC Educational Resources Information Center

Frank, Kenneth

2014-01-01

This study concerns how intra-organizational networks affect the implementation of policies and practices in organizations. In particular, we attend to the role of the informal subgroup or clique in cultivating and distributing locally adapted and integrated knowledge, or know-how. We develop two hypotheses based on the importance of…

Reentrant Information Flow in Electrophysiological Rat Default Mode Network.

PubMed

Jing, Wei; Guo, Daqing; Zhang, Yunxiang; Guo, Fengru; Valdés-Sosa, Pedro A; Xia, Yang; Yao, Dezhong

2017-01-01

Functional MRI (fMRI) studies have demonstrated that the rodent brain shows a default mode network (DMN) activity similar to that in humans, offering a potential preclinical model both for physiological and pathophysiological studies. However, the neuronal mechanism underlying rodent DMN remains poorly understood. Here, we used electrophysiological data to analyze the power spectrum and estimate the directed phase transfer entropy (dPTE) within rat DMN across three vigilance states: wakeful rest (WR), slow-wave sleep (SWS), and rapid-eye-movement sleep (REMS). We observed decreased gamma powers during SWS compared with WR in most of the DMN regions. Increased gamma powers were found in prelimbic cortex, cingulate cortex, and hippocampus during REMS compared with WR, whereas retrosplenial cortex showed a reverse trend. These changed gamma powers are in line with the local metabolic variation of homologous brain regions in humans. In the analysis of directional interactions, we observed well-organized anterior-to-posterior patterns of information flow in the delta band, while opposite patterns of posterior-to-anterior flow were found in the theta band. These frequency-specific opposite patterns were only observed in WR and REMS. Additionally, most of the information senders in the delta band were also the receivers in the theta band, and vice versa. Our results provide electrophysiological evidence that rat DMN is similar to its human counterpart, and there is a frequency-dependent reentry loop of anterior-posterior information flow within rat DMN, which may offer a mechanism for functional integration, supporting conscious awareness.

Mapping cortical hubs in tinnitus

PubMed Central

2009-01-01

Background Subjective tinnitus is the perception of a sound in the absence of any physical source. It has been shown that tinnitus is associated with hyperactivity of the auditory cortices. Accompanying this hyperactivity, changes in non-auditory brain structures have also been reported. However, there have been no studies on the long-range information flow between these regions. Results Using Magnetoencephalography, we investigated the long-range cortical networks of chronic tinnitus sufferers (n = 23) and healthy controls (n = 24) in the resting state. A beamforming technique was applied to reconstruct the brain activity at source level and the directed functional coupling between all voxels was analyzed by means of Partial Directed Coherence. Within a cortical network, hubs are brain structures that either influence a great number of other brain regions or that are influenced by a great number of other brain regions. By mapping the cortical hubs in tinnitus and controls we report fundamental group differences in the global networks, mainly in the gamma frequency range. The prefrontal cortex, the orbitofrontal cortex and the parieto-occipital region were core structures in this network. The information flow from the global network to the temporal cortex correlated positively with the strength of tinnitus distress. Conclusion With the present study we suggest that the hyperactivity of the temporal cortices in tinnitus is integrated in a global network of long-range cortical connectivity. Top-down influence from the global network on the temporal areas relates to the subjective strength of the tinnitus distress. PMID:19930625

Average weighted receiving time on the non-homogeneous double-weighted fractal networks

NASA Astrophysics Data System (ADS)

Ye, Dandan; Dai, Meifeng; Sun, Yu; Su, Weiyi

2017-05-01

In this paper, based on actual road networks, a model of the non-homogeneous double-weighted fractal networks is introduced depending on the number of copies s and two kinds of weight factors wi ,ri(i = 1 , 2 , … , s) . The double-weights represent the capacity-flowing weights and the cost-traveling weights, respectively. Denote by wijF the capacity-flowing weight connecting the nodes i and j, and denote by wijC the cost-traveling weight connecting the nodes i and j. Let wijF be related to the weight factors w1 ,w2 , … ,ws, and let wijC be related to the weight factors r1 ,r2 , … ,rs. Assuming that the walker, at each step, starting from its current node, moves to any of its neighbors with probability proportional to the capacity-flowing weight of edge linking them. The weighted time for two adjacency nodes is the cost-traveling weight connecting the two nodes. The average weighted receiving time (AWRT) is defined on the non-homogeneous double-weighted fractal networks. AWRT depends on the relationships of the number of copies s and two kinds of weight factors wi ,ri(i = 1 , 2 , … , s) . The obtained remarkable results display that in the large network, the AWRT grows as a power-law function of the network size Ng with the exponent, represented by θ =logs(w1r1 +w2r2 + ⋯ +wsrs) < 1 when w1r1 +w2r2 + ⋯ +wsrs ≠ 1, which means that the smaller the value of w1r1 +w2r2 + ⋯ +wsrs is, the more efficient the process of receiving information is. Especially when w1r1 +w2r2 + ⋯ +wsrs = 1, AWRT grows with increasing order Ng as logNg or (logNg) 2 . In the classic fractal networks, the average receiving time (ART) grows with linearly with the network size Ng. Thus, the non-homogeneous double-weighted fractal networks are more efficient than classic fractal networks in term of receiving information.

Effective connectivity of facial expression network by using Granger causality analysis

NASA Astrophysics Data System (ADS)

Zhang, Hui; Li, Xiaoting

2013-10-01

Functional magnetic resonance imaging (fMRI) is an advanced non-invasive data acquisition technique to investigate the neural activity in human brain. In addition to localize the functional brain regions that is activated by specific cognitive task, fMRI can also be utilized to measure the task-related functional interactions among the active regions of interest (ROI) in the brain. Among the variety of analysis tools proposed for modeling the connectivity of brain regions, Granger causality analysis (GCA) measure the directions of information interactions by looking for the lagged effect among the brain regions. In this study, we use fMRI and Granger Causality analysis to investigate the effective connectivity of brain network induced by viewing several kinds of expressional faces. We focus on four kinds of facial expression stimuli: fearful, angry, happy and neutral faces. Five face selective regions of interest are localized and the effective connectivity within these regions is measured for the expressional faces. Our result based on 8 subjects showed that there is significant effective connectivity from STS to amygdala, from amygdala to OFA, aFFA and pFFA, from STS to aFFA and from pFFA to aFFA. This result suggested that there is an information flow from the STS to the amygdala when perusing expressional faces. This emotional expressional information flow that is conveyed by STS and amygdala, flow back to the face selective regions in occipital-temporal lobes, which constructed a emotional face processing network.

The dynamics of socio-connective trust within support networks accessed by informal caregivers.

PubMed

Ray, Robin A; Street, Annette F

2011-03-01

This article introduces the concept of socio-connective trust, the synapse between the social structures and processes that underpin relationships in supportive care networks. Data from an ethnographic case study of 18 informal caregivers providing in-home care for people with life-limiting illness were analysed drawing on theoretical concepts from the work of Giddens and writings on social capital, as well as the construction of trust in the caregiving literature. While conceptions of trust were found to contribute to understanding supportive care relationships, they did not account for the dynamic nature of the availability and use of support networks. Instead, informal caregivers undertook ongoing reflexive negotiation of relationship boundaries in response to their own conception of the current situation and their perception of trust in their relationships with the various members of the support network. The concept of socio-connective trust describes the movement and flow of the flexible bonds that influence relationships among care networks and determine the type and range of support accessed by informal caregivers. Understanding the complexities of socio-connective trust in caregiving relationships will assist health and social care workers to mobilize relevant resources to support informal caregivers.

A mathematical method for verifying the validity of measured information about the flows of energy resources based on the state estimation theory

NASA Astrophysics Data System (ADS)

Pazderin, A. V.; Sof'in, V. V.; Samoylenko, V. O.

2015-11-01

Efforts aimed at improving energy efficiency in all branches of the fuel and energy complex shall be commenced with setting up a high-tech automated system for monitoring and accounting energy resources. Malfunctions and failures in the measurement and information parts of this system may distort commercial measurements of energy resources and lead to financial risks for power supplying organizations. In addition, measurement errors may be connected with intentional distortion of measurements for reducing payment for using energy resources on the consumer's side, which leads to commercial loss of energy resource. The article presents a universal mathematical method for verifying the validity of measurement information in networks for transporting energy resources, such as electricity and heat, petroleum, gas, etc., based on the state estimation theory. The energy resource transportation network is represented by a graph the nodes of which correspond to producers and consumers, and its branches stand for transportation mains (power lines, pipelines, and heat network elements). The main idea of state estimation is connected with obtaining the calculated analogs of energy resources for all available measurements. Unlike "raw" measurements, which contain inaccuracies, the calculated flows of energy resources, called estimates, will fully satisfy the suitability condition for all state equations describing the energy resource transportation network. The state equations written in terms of calculated estimates will be already free from residuals. The difference between a measurement and its calculated analog (estimate) is called in the estimation theory an estimation remainder. The obtained large values of estimation remainders are an indicator of high errors of particular energy resource measurements. By using the presented method it is possible to improve the validity of energy resource measurements, to estimate the transportation network observability, to eliminate the energy resource flows measurement imbalances, and to filter invalid measurements at the data acquisition and processing stage in performing monitoring of an automated energy resource monitoring and accounting system.

NetFlow Dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Corbet Jr., Thomas F; Beyeler, Walter E; Vanwestrienen, Dirk

NetFlow Dynamics is a web-accessible analysis environment for simulating dynamic flows of materials on model networks. Performing a simulation requires both the NetFlow Dynamics application and a network model which is a description of the structure of the nodes and edges of a network including the flow capacity of each edge and the storage capacity of each node, and the sources and sinks of the material flowing on the network. NetFlow Dynamics consists of databases for storing network models, algorithms to calculate flows on networks, and a GIS-based graphical interface for performing simulations and viewing simulation results. Simulated flows aremore » dynamic in the sense that flows on each edge of the network and inventories at each node change with time and can be out of equilibrium with boundary conditions. Any number of network models could be simulated using Net Flow Dynamics. To date, the models simulated have been models of petroleum infrastructure. The main model has been the National Transportation Fuels Model (NTFM), a network of U.S. oil fields, transmission pipelines, rail lines, refineries, tank farms, and distribution terminals. NetFlow Dynamics supports two different flow algorithms, the Gradient Flow algorithm and the Inventory Control algorithm, that were developed specifically for the NetFlow Dynamics application. The intent is to add additional algorithms in the future as needed. The ability to select from multiple algorithms is desirable because a single algorithm never covers all analysis needs. The current algorithms use a demand-driven capacity-constrained formulation which means that the algorithms strive to use all available capacity and stored inventory to meet desired flows to sinks, subject to the capacity constraints of each network component. The current flow algorithms are best suited for problems in which a material flows on a capacity-constrained network representing a supply chain in which the material supplied can be stored at each node of the network. In the petroleum models, the flowing materials are crude oil and refined products that can be stored at tank farms, refineries, or terminals (i.e. the nodes of the network). Examples of other network models that could be simulated are currency flowing in a financial network, agricultural products moving to market, or natural gas flowing on a pipeline network.« less

Social Insects: A Model System for Network Dynamics

NASA Astrophysics Data System (ADS)

Charbonneau, Daniel; Blonder, Benjamin; Dornhaus, Anna

Social insect colonies (ants, bees, wasps, and termites) show sophisticated collective problem-solving in the face of variable constraints. Individuals exchange information and materials such as food. The resulting network structure and dynamics can inform us about the mechanisms by which the insects achieve particular collective behaviors and these can be transposed to man-made and social networks. We discuss how network analysis can answer important questions about social insects, such as how effective task allocation or information flow is realized. We put forward the idea that network analysis methods are under-utilized in social insect research, and that they can provide novel ways to view the complexity of collective behavior, particularly if network dynamics are taken into account. To illustrate this, we present an example of network tasks performed by ant workers, linked by instances of workers switching from one task to another. We show how temporal network analysis can propose and test new hypotheses on mechanisms of task allocation, and how adding temporal elements to static networks can drastically change results. We discuss the benefits of using social insects as models for complex systems in general. There are multiple opportunities emergent technologies and analysis methods in facilitating research on social insect network. The potential for interdisciplinary work could significantly advance diverse fields such as behavioral ecology, computer sciences, and engineering.

Neural network computer simulation of medical aerosols.

PubMed

Richardson, C J; Barlow, D J

1996-06-01

Preliminary investigations have been conducted to assess the potential for using artificial neural networks to simulate aerosol behaviour, with a view to employing this type of methodology in the evaluation and design of pulmonary drug-delivery systems. Details are presented of the general purpose software developed for these tasks; it implements a feed-forward back-propagation algorithm with weight decay and connection pruning, the user having complete run-time control of the network architecture and mode of training. A series of exploratory investigations is then reported in which different network structures and training strategies are assessed in terms of their ability to simulate known patterns of fluid flow in simple model systems. The first of these involves simulations of cellular automata-generated data for fluid flow through a partially obstructed two-dimensional pipe. The artificial neural networks are shown to be highly successful in simulating the behaviour of this simple linear system, but with important provisos relating to the information content of the training data and the criteria used to judge when the network is properly trained. A second set of investigations is then reported in which similar networks are used to simulate patterns of fluid flow through aerosol generation devices, using training data furnished through rigorous computational fluid dynamics modelling. These more complex three-dimensional systems are modelled with equal success. It is concluded that carefully tailored, well trained networks could provide valuable tools not just for predicting but also for analysing the spatial dynamics of pharmaceutical aerosols.

Serial Network Flow Monitor

NASA Technical Reports Server (NTRS)

Robinson, Julie A.; Tate-Brown, Judy M.

2009-01-01

Using a commercial software CD and minimal up-mass, SNFM monitors the Payload local area network (LAN) to analyze and troubleshoot LAN data traffic. Validating LAN traffic models may allow for faster and more reliable computer networks to sustain systems and science on future space missions. Research Summary: This experiment studies the function of the computer network onboard the ISS. On-orbit packet statistics are captured and used to validate ground based medium rate data link models and enhance the way that the local area network (LAN) is monitored. This information will allow monitoring and improvement in the data transfer capabilities of on-orbit computer networks. The Serial Network Flow Monitor (SNFM) experiment attempts to characterize the network equivalent of traffic jams on board ISS. The SNFM team is able to specifically target historical problem areas including the SAMS (Space Acceleration Measurement System) communication issues, data transmissions from the ISS to the ground teams, and multiple users on the network at the same time. By looking at how various users interact with each other on the network, conflicts can be identified and work can begin on solutions. SNFM is comprised of a commercial off the shelf software package that monitors packet traffic through the payload Ethernet LANs (local area networks) on board ISS.

Network structure impacts global commodity trade growth and resilience

PubMed Central

Rovenskaya, Elena; Fath, Brian D.

2017-01-01

Global commodity trade networks are critical to our collective sustainable development. Their increasing interconnectedness pose two practical questions: (i) Do the current network configurations support their further growth? (ii) How resilient are these networks to economic shocks? We analyze the data of global commodity trade flows from 1996 to 2012 to evaluate the relationship between structural properties of the global commodity trade networks and (a) their dynamic growth, as well as (b) the resilience of their growth with respect to the 2009 global economic shock. Specifically, we explore the role of network efficiency and redundancy using the information theory-based network flow analysis. We find that, while network efficiency is positively correlated with growth, highly efficient systems appear to be less resilient, losing more and gaining less growth following an economic shock. While all examined networks are rather redundant, we find that network redundancy does not hinder their growth. Moreover, systems exhibiting higher levels of redundancy lose less and gain more growth following an economic shock. We suggest that a strategy to support making global trade networks more efficient via, e.g., preferential trade agreements and higher specialization, can promote their further growth; while a strategy to increase the global trade networks’ redundancy via e.g., more abundant free-trade agreements, can improve their resilience to global economic shocks. PMID:28207790

A new algorithm for grid-based hydrologic analysis by incorporating stormwater infrastructure

NASA Astrophysics Data System (ADS)

Choi, Yosoon; Yi, Huiuk; Park, Hyeong-Dong

2011-08-01

We developed a new algorithm, the Adaptive Stormwater Infrastructure (ASI) algorithm, to incorporate ancillary data sets related to stormwater infrastructure into the grid-based hydrologic analysis. The algorithm simultaneously considers the effects of the surface stormwater collector network (e.g., diversions, roadside ditches, and canals) and underground stormwater conveyance systems (e.g., waterway tunnels, collector pipes, and culverts). The surface drainage flows controlled by the surface runoff collector network are superimposed onto the flow directions derived from a DEM. After examining the connections between inlets and outfalls in the underground stormwater conveyance system, the flow accumulation and delineation of watersheds are calculated based on recursive computations. Application of the algorithm to the Sangdong tailings dam in Korea revealed superior performance to that of a conventional D8 single-flow algorithm in terms of providing reasonable hydrologic information on watersheds with stormwater infrastructure.

Practical lessons in remote connectivity.

PubMed Central

Kouroubali, A.; Starren, J.; Barrows, R. C.; Clayton, P. D.

1997-01-01

Community Health Information Networks (CHINs) require the ability to provide computer network connections to many remote sites. During the implementation of the Washington Heights and Inwood Community Health Management Information System (WHICHIS) at the Columbia-Presbyterian Medical Center (CPMC), a number of remote connectivity issues have been encountered. Both technical and non-technical issues were significant during the installation. We developed a work-flow model for this process which may be helpful to any health care institution attempting to provide seamless remote connectivity. This model is presented and implementation lessons are discussed. PMID:9357643

Flows, scaling, and the control of moment hierarchies for stochastic chemical reaction networks

NASA Astrophysics Data System (ADS)

Smith, Eric; Krishnamurthy, Supriya

2017-12-01

Stochastic chemical reaction networks (CRNs) are complex systems that combine the features of concurrent transformation of multiple variables in each elementary reaction event and nonlinear relations between states and their rates of change. Most general results concerning CRNs are limited to restricted cases where a topological characteristic known as deficiency takes a value 0 or 1, implying uniqueness and positivity of steady states and surprising, low-information forms for their associated probability distributions. Here we derive equations of motion for fluctuation moments at all orders for stochastic CRNs at general deficiency. We show, for the standard base case of proportional sampling without replacement (which underlies the mass-action rate law), that the generator of the stochastic process acts on the hierarchy of factorial moments with a finite representation. Whereas simulation of high-order moments for many-particle systems is costly, this representation reduces the solution of moment hierarchies to a complexity comparable to solving a heat equation. At steady states, moment hierarchies for finite CRNs interpolate between low-order and high-order scaling regimes, which may be approximated separately by distributions similar to those for deficiency-zero networks and connected through matched asymptotic expansions. In CRNs with multiple stable or metastable steady states, boundedness of high-order moments provides the starting condition for recursive solution downward to low-order moments, reversing the order usually used to solve moment hierarchies. A basis for a subset of network flows defined by having the same mean-regressing property as the flows in deficiency-zero networks gives the leading contribution to low-order moments in CRNs at general deficiency, in a 1 /n expansion in large particle numbers. Our results give a physical picture of the different informational roles of mean-regressing and non-mean-regressing flows and clarify the dynamical meaning of deficiency not only for first-moment conditions but for all orders in fluctuations.

A neural network approach for the blind deconvolution of turbulent flows

NASA Astrophysics Data System (ADS)

Maulik, R.; San, O.

2017-11-01

We present a single-layer feedforward artificial neural network architecture trained through a supervised learning approach for the deconvolution of flow variables from their coarse grained computations such as those encountered in large eddy simulations. We stress that the deconvolution procedure proposed in this investigation is blind, i.e. the deconvolved field is computed without any pre-existing information about the filtering procedure or kernel. This may be conceptually contrasted to the celebrated approximate deconvolution approaches where a filter shape is predefined for an iterative deconvolution process. We demonstrate that the proposed blind deconvolution network performs exceptionally well in the a-priori testing of both two-dimensional Kraichnan and three-dimensional Kolmogorov turbulence and shows promise in forming the backbone of a physics-augmented data-driven closure for the Navier-Stokes equations.

A statistical method to predict flow permanence in dryland streams from time series of stream temperature

USGS Publications Warehouse

Arismendi, Ivan; Dunham, Jason B.; Heck, Michael; Schultz, Luke; Hockman-Wert, David

2017-01-01

Intermittent and ephemeral streams represent more than half of the length of the global river network. Dryland freshwater ecosystems are especially vulnerable to changes in human-related water uses as well as shifts in terrestrial climates. Yet, the description and quantification of patterns of flow permanence in these systems is challenging mostly due to difficulties in instrumentation. Here, we took advantage of existing stream temperature datasets in dryland streams in the northwest Great Basin desert, USA, to extract critical information on climate-sensitive patterns of flow permanence. We used a signal detection technique, Hidden Markov Models (HMMs), to extract information from daily time series of stream temperature to diagnose patterns of stream drying. Specifically, we applied HMMs to time series of daily standard deviation (SD) of stream temperature (i.e., dry stream channels typically display highly variable daily temperature records compared to wet stream channels) between April and August (2015–2016). We used information from paired stream and air temperature data loggers as well as co-located stream temperature data loggers with electrical resistors as confirmatory sources of the timing of stream drying. We expanded our approach to an entire stream network to illustrate the utility of the method to detect patterns of flow permanence over a broader spatial extent. We successfully identified and separated signals characteristic of wet and dry stream conditions and their shifts over time. Most of our study sites within the entire stream network exhibited a single state over the entire season (80%), but a portion of them showed one or more shifts among states (17%). We provide recommendations to use this approach based on a series of simple steps. Our findings illustrate a successful method that can be used to rigorously quantify flow permanence regimes in streams using existing records of stream temperature.

Surface-water data and statistics from U.S. Geological Survey data-collection networks in New Jersey on the World Wide Web

USGS Publications Warehouse

Reiser, Robert G.; Watson, Kara M.; Chang, Ming; Nieswand, Steven P.

2002-01-01

The U.S. Geological Survey (USGS), in cooperation with other Federal, State, and local agencies, operates and maintains a variety of surface-water data-collection networks throughout the State of New Jersey. The networks include streamflow-gaging stations, low-flow sites, crest-stage gages, tide gages, tidal creststage gages, and water-quality sampling sites. Both real-time and historical surface-water data for many of the sites in these networks are available at the USGS, New Jersey District, web site (http://nj.usgs.gov/), and water-quality data are available at the USGS National Water Information System (NWIS) web site (http://waterdata.usgs.gov/nwis/). These data are an important source of information for water managers, engineers, environmentalists, and private citizens.

The challenges of informatics in synthetic biology: from biomolecular networks to artificial organisms

PubMed Central

Ramoni, Marco F.

2010-01-01

The field of synthetic biology holds an inspiring vision for the future; it integrates computational analysis, biological data and the systems engineering paradigm in the design of new biological machines and systems. These biological machines are built from basic biomolecular components analogous to electrical devices, and the information flow among these components requires the augmentation of biological insight with the power of a formal approach to information management. Here we review the informatics challenges in synthetic biology along three dimensions: in silico, in vitro and in vivo. First, we describe state of the art of the in silico support of synthetic biology, from the specific data exchange formats, to the most popular software platforms and algorithms. Next, we cast in vitro synthetic biology in terms of information flow, and discuss genetic fidelity in DNA manipulation, development strategies of biological parts and the regulation of biomolecular networks. Finally, we explore how the engineering chassis can manipulate biological circuitries in vivo to give rise to future artificial organisms. PMID:19906839

Fundamental trade-offs between information flow in single cells and cellular populations.

PubMed

Suderman, Ryan; Bachman, John A; Smith, Adam; Sorger, Peter K; Deeds, Eric J

2017-05-30

Signal transduction networks allow eukaryotic cells to make decisions based on information about intracellular state and the environment. Biochemical noise significantly diminishes the fidelity of signaling: networks examined to date seem to transmit less than 1 bit of information. It is unclear how networks that control critical cell-fate decisions (e.g., cell division and apoptosis) can function with such low levels of information transfer. Here, we use theory, experiments, and numerical analysis to demonstrate an inherent trade-off between the information transferred in individual cells and the information available to control population-level responses. Noise in receptor-mediated apoptosis reduces information transfer to approximately 1 bit at the single-cell level but allows 3-4 bits of information to be transmitted at the population level. For processes such as eukaryotic chemotaxis, in which single cells are the functional unit, we find high levels of information transmission at a single-cell level. Thus, low levels of information transfer are unlikely to represent a physical limit. Instead, we propose that signaling networks exploit noise at the single-cell level to increase population-level information transfer, allowing extracellular ligands, whose levels are also subject to noise, to incrementally regulate phenotypic changes. This is particularly critical for discrete changes in fate (e.g., life vs. death) for which the key variable is the fraction of cells engaged. Our findings provide a framework for rationalizing the high levels of noise in metazoan signaling networks and have implications for the development of drugs that target these networks in the treatment of cancer and other diseases.

Bipartite consensus for multi-agent systems with antagonistic interactions and communication delays

NASA Astrophysics Data System (ADS)

Guo, Xing; Lu, Jianquan; Alsaedi, Ahmed; Alsaadi, Fuad E.

2018-04-01

This paper studies the consensus problems over signed digraphs with arbitrary finite communication delays. For the considered system, the information flow is directed and only locally delayed information can be used for each node. We derive that bipartite consensus of this system can be realized when the associated signed digraph is strongly connected. Furthermore, for structurally balanced networks, this paper studies the pinning partite consensus for the considered system. we design a pinning scheme to pin any one agent in the signed network, and obtain that the network achieves pinning bipartite consensus with any initial conditions. Finally, two examples are provided to demonstrate the effectiveness of our main results.

Teaching New Dogs Old Tricks.

ERIC Educational Resources Information Center

Goldberg, Judy Hiller; Laurin, Mark Alfred

For the new two-year college instructor, gaining information that specifically addresses his/her problems and concerns, and establishing the network to generate the flow of quality information can be a problem. How the instructor deals with this problem is a function of his/her stage of growth. In the neophyte stage, the new faculty member is…

Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent.

PubMed

Moioli, Renan C; Vargas, Patricia A; Husbands, Phil

2012-09-01

Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. In addition, cognitive systems cannot be properly appreciated without taking into account brain-body- environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly, it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour.

Innovation flow through social networks: productivity distribution in France and Italy

NASA Astrophysics Data System (ADS)

di Matteo, T.; Aste, T.; Gallegati, M.

2005-10-01

From a detailed empirical analysis of the productivity of non financial firms across several countries and years we show that productivity follows a non-Gaussian distribution with `fat tails' in the large productivity region which are well mimicked by power law behaviors. We discuss how these empirical findings can be linked to a mechanism of exchanges in a social network where firms improve their productivity by direct innovation and/or by imitation of other firm's technological and organizational solutions. The type of network-connectivity determines how fast and how efficiently information can diffuse and how quickly innovation will permeate or behaviors will be imitated. From a model for innovation flow through a complex network we show that the expectation values of the productivity of each firm are proportional to its connectivity in the network of links between firms. The comparison with the empirical distributions in France and Italy reveals that in this model, such a network must be of a scale-free type with a power-law degree distribution in the large connectivity range.

Reconstructing multi-mode networks from multivariate time series

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Yang, Yu-Xuan; Dang, Wei-Dong; Cai, Qing; Wang, Zhen; Marwan, Norbert; Boccaletti, Stefano; Kurths, Jürgen

2017-09-01

Unveiling the dynamics hidden in multivariate time series is a task of the utmost importance in a broad variety of areas in physics. We here propose a method that leads to the construction of a novel functional network, a multi-mode weighted graph combined with an empirical mode decomposition, and to the realization of multi-information fusion of multivariate time series. The method is illustrated in a couple of successful applications (a multi-phase flow and an epileptic electro-encephalogram), which demonstrate its powerfulness in revealing the dynamical behaviors underlying the transitions of different flow patterns, and enabling to differentiate brain states of seizure and non-seizure.

Sediment and Vegetation Controls on Delta Channel Networks

NASA Astrophysics Data System (ADS)

Lauzon, R.; Murray, A. B.; Piliouras, A.; Kim, W.

2016-12-01

Numerous factors control the patterns of distributary channels formed on a delta, including water and sediment discharge, grain size, sea level rise rates, and vegetation type. In turn, these channel networks influence the shape and evolution of a delta, including what types of plant and animal life - such as humans - it can support. Previous fluvial modeling and flume experiments, outside of the delta context, have addressed how interactions between sediment and vegetation, through their influence on lateral transport of sediment, determine what type of channel networks develops. Similar interactions likely also shape delta flow patterns. Vegetation introduces cohesion, tending to reduce channel migration rates and strengthen existing channel banks, reinforcing existing channels and resulting in localized, relatively stable flow patterns. On the other hand, sediment transport processes can result in lateral migration and frequent switching of active channels, resulting in flow resembling that of a braided stream. While previous studies of deltas have indirectly explored the effects of vegetation through the introduction of cohesive sediment, we directly incorporate key effects of vegetation on flow and sediment transport into the delta-building model DeltaRCM to explore how these effects influence delta channel network formation. Model development is informed by laboratory flume experiments at UT Austin. Here we present initial results of experiments exploring the effects of sea level rise rate, sediment grain size, vegetation type, and vegetation growth rate on delta channel network morphology. These results support the hypothesis that the ability for lateral transport of sediment to occur plays a key role in determining the evolution of delta channel networks and delta morphology.

Inferring the effective TOR-dependent network: a computational study in yeast

PubMed Central

2013-01-01

Background Calorie restriction (CR) is one of the most conserved non-genetic interventions that extends healthspan in evolutionarily distant species, ranging from yeast to mammals. The target of rapamycin (TOR) has been shown to play a key role in mediating healthspan extension in response to CR by integrating different signals that monitor nutrient-availability and orchestrating various components of cellular machinery in response. Both genetic and pharmacological interventions that inhibit the TOR pathway exhibit a similar phenotype, which is not further amplified by CR. Results In this paper, we present the first comprehensive, computationally derived map of TOR downstream effectors, with the objective of discovering key lifespan mediators, their crosstalk, and high-level organization. We adopt a systematic approach for tracing information flow from the TOR complex and use it to identify relevant signaling elements. By constructing a high-level functional map of TOR downstream effectors, we show that our approach is not only capable of recapturing previously known pathways, but also suggests potential targets for future studies. Information flow scores provide an aggregate ranking of relevance of proteins with respect to the TOR signaling pathway. These rankings must be normalized for degree bias, appropriately interpreted, and mapped to associated roles in pathways. We propose a novel statistical framework for integrating information flow scores, the set of differentially expressed genes in response to rapamycin treatment, and the transcriptional regulatory network. We use this framework to identify the most relevant transcription factors in mediating the observed transcriptional response, and to construct the effective response network of the TOR pathway. This network is hypothesized to mediate life-span extension in response to TOR inhibition. Conclusions Our approach, unlike experimental methods, is not limited to specific aspects of cellular response. Rather, it predicts transcriptional changes and post-translational modifications in response to TOR inhibition. The constructed effective response network greatly enhances understanding of the mechanisms underlying the aging process and helps in identifying new targets for further investigation of anti-aging regimes. It also allows us to identify potential network biomarkers for diagnosis and prognosis of age-related pathologies. PMID:24005029

Information Graph Flow: A Geometric Approximation of Quantum and Statistical Systems

NASA Astrophysics Data System (ADS)

Vanchurin, Vitaly

2018-05-01

Given a quantum (or statistical) system with a very large number of degrees of freedom and a preferred tensor product factorization of the Hilbert space (or of a space of distributions) we describe how it can be approximated with a very low-dimensional field theory with geometric degrees of freedom. The geometric approximation procedure consists of three steps. The first step is to construct weighted graphs (we call information graphs) with vertices representing subsystems (e.g., qubits or random variables) and edges representing mutual information (or the flow of information) between subsystems. The second step is to deform the adjacency matrices of the information graphs to that of a (locally) low-dimensional lattice using the graph flow equations introduced in the paper. (Note that the graph flow produces very sparse adjacency matrices and thus might also be used, for example, in machine learning or network science where the task of graph sparsification is of a central importance.) The third step is to define an emergent metric and to derive an effective description of the metric and possibly other degrees of freedom. To illustrate the procedure we analyze (numerically and analytically) two information graph flows with geometric attractors (towards locally one- and two-dimensional lattices) and metric perturbations obeying a geometric flow equation. Our analysis also suggests a possible approach to (a non-perturbative) quantum gravity in which the geometry (a secondary object) emerges directly from a quantum state (a primary object) due to the flow of the information graphs.

Information Graph Flow: A Geometric Approximation of Quantum and Statistical Systems

NASA Astrophysics Data System (ADS)

Vanchurin, Vitaly

2018-06-01

Given a quantum (or statistical) system with a very large number of degrees of freedom and a preferred tensor product factorization of the Hilbert space (or of a space of distributions) we describe how it can be approximated with a very low-dimensional field theory with geometric degrees of freedom. The geometric approximation procedure consists of three steps. The first step is to construct weighted graphs (we call information graphs) with vertices representing subsystems (e.g., qubits or random variables) and edges representing mutual information (or the flow of information) between subsystems. The second step is to deform the adjacency matrices of the information graphs to that of a (locally) low-dimensional lattice using the graph flow equations introduced in the paper. (Note that the graph flow produces very sparse adjacency matrices and thus might also be used, for example, in machine learning or network science where the task of graph sparsification is of a central importance.) The third step is to define an emergent metric and to derive an effective description of the metric and possibly other degrees of freedom. To illustrate the procedure we analyze (numerically and analytically) two information graph flows with geometric attractors (towards locally one- and two-dimensional lattices) and metric perturbations obeying a geometric flow equation. Our analysis also suggests a possible approach to (a non-perturbative) quantum gravity in which the geometry (a secondary object) emerges directly from a quantum state (a primary object) due to the flow of the information graphs.

Current-flow efficiency of networks

NASA Astrophysics Data System (ADS)

Liu, Kai; Yan, Xiaoyong

2018-02-01

Many real-world networks, from infrastructure networks to social and communication networks, can be formulated as flow networks. How to realistically measure the transport efficiency of these networks is of fundamental importance. The shortest-path-based efficiency measurement has limitations, as it assumes that flow travels only along those shortest paths. Here, we propose a new metric named current-flow efficiency, in which we calculate the average reciprocal effective resistance between all pairs of nodes in the network. This metric takes the multipath effect into consideration and is more suitable for measuring the efficiency of many real-world flow equilibrium networks. Moreover, this metric can handle a disconnected graph and can thus be used to identify critical nodes and edges from the efficiency-loss perspective. We further analyze how the topological structure affects the current-flow efficiency of networks based on some model and real-world networks. Our results enable a better understanding of flow networks and shed light on the design and improvement of such networks with higher transport efficiency.

A two-stage flow-based intrusion detection model for next-generation networks.

PubMed

Umer, Muhammad Fahad; Sher, Muhammad; Bi, Yaxin

2018-01-01

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results.

A two-stage flow-based intrusion detection model for next-generation networks

PubMed Central

2018-01-01

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results. PMID:29329294

Inverse modeling of flow tomography experiments in fractured media

NASA Astrophysics Data System (ADS)

Klepikova, Maria; Le Borgne, Tanguy; Bour, Olivier; de Dreuzy, Jean-Raynald

2014-05-01

Inverse modeling of fracture hydraulic properties and connectivity is a very challenging objective due to the strong heterogeneity of the medium at multiple scales and the scarcity of data. Cross-borehole flowmeter tests, which consist of measuring changes in vertical borehole flows when pumping a neighboring borehole, were shown to be an efficient technique to provide information on the properties of the flow zones that connect borehole pairs (Paillet, 1998, Le Borgne et al., 2007). The interpretation of such experiments may, however, be quite uncertain when multiple connections exist. We propose the flow tomography approach (i.e., sequential cross-borehole flowmeter tests) to characterize the connectivity and transmissivity of preferential permeable flow paths in fractured aquifers (Klepikova et al., 2013). An inverse model approach is developed to estimate log-transformed transmissivity values of hydraulically active fractures between the pumping and observation wells by inverting cross-borehole flow and water level data. Here a simplified discrete fracture network approach that highlights main connectivity structures is used. This conceptual model attempts to reproduce fracture network connectivity without taking fracture geometry (length, orientation, dip) into account. We demonstrate that successively exchanging the roles of pumping and observation boreholes improves the quality of available information and reduces the under-determination of the problem. The inverse method is validated for several synthetic flow scenarios. It is shown to provide a good estimation of connectivity patterns and transmissivities of main flow paths. It also allows the estimation of the transmissivity of fractures that connect the flow paths but do not cross the boreholes, although the associated uncertainty may be high for some geometries. The results of this investigation encourage the application of flow tomography to natural fractured aquifers.

Altered Connectivity of the Balance Processing Network After Tongue Stimulation in Balance-Impaired Individuals

PubMed Central

Tyler, Mitchell E.; Danilov, Yuri P.; Kaczmarek, Kurt A.; Meyerand, Mary E.

2013-01-01

Abstract Some individuals with balance impairment have hypersensitivity of the motion-sensitive visual cortices (hMT+) compared to healthy controls. Previous work showed that electrical tongue stimulation can reduce the exaggerated postural sway induced by optic flow in this subject population and decrease the hypersensitive response of hMT+. Additionally, a region within the brainstem (BS), likely containing the vestibular and trigeminal nuclei, showed increased optic flow-induced activity after tongue stimulation. The aim of this study was to understand how the modulation induced by tongue stimulation affects the balance-processing network as a whole and how modulation of BS structures can influence cortical activity. Four volumes of interest, discovered in a general linear model analysis, constitute major contributors to the balance-processing network. These regions were entered into a dynamic causal modeling analysis to map the network and measure any connection or topology changes due to the stimulation. Balance-impaired individuals had downregulated response of the primary visual cortex (V1) to visual stimuli but upregulated modulation of the connection between V1 and hMT+ by visual motion compared to healthy controls (p≤1E–5). This upregulation was decreased to near-normal levels after stimulation. Additionally, the region within the BS showed increased response to visual motion after stimulation compared to both prestimulation and controls. Stimulation to the tongue enters the central nervous system at the BS but likely propagates to the cortex through supramodal information transfer. We present a model to explain these brain responses that utilizes an anatomically present, but functionally dormant pathway of information flow within the processing network. PMID:23216162

Decoding Network Structure in On-Chip Integrated Flow Cells with Synchronization of Electrochemical Oscillators

NASA Astrophysics Data System (ADS)

Jia, Yanxin; Kiss, István Z.

2017-04-01

The analysis of network interactions among dynamical units and the impact of the coupling on self-organized structures is a challenging task with implications in many biological and engineered systems. We explore the coupling topology that arises through the potential drops in a flow channel in a lab-on-chip device that accommodates chemical reactions on electrode arrays. The networks are revealed by analysis of the synchronization patterns with the use of an oscillatory chemical reaction (nickel electrodissolution) and are further confirmed by direct decoding using phase model analysis. In dual electrode configuration, a variety coupling schemes, (uni- or bidirectional positive or negative) were identified depending on the relative placement of the reference and counter electrodes (e.g., placed at the same or the opposite ends of the flow channel). With three electrodes, the network consists of a superposition of a localized (upstream) and global (all-to-all) coupling. With six electrodes, the unique, position dependent coupling topology resulted spatially organized partial synchronization such that there was a synchrony gradient along the quasi-one-dimensional spatial coordinate. The networked, electrode potential (current) spike generating electrochemical reactions hold potential for construction of an in-situ information processing unit to be used in electrochemical devices in sensors and batteries.

Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks

PubMed Central

Artuñedo, Antonio; del Toro, Raúl M.; Haber, Rodolfo E.

2017-01-01

Nowadays many studies are being conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and how to exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is proposed to provide a solution for mobility problems in cities. The interconnected traffic lights controller (TLC) network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease the pollution level. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach is applied to design a consensus-based control algorithm. The designed control strategy contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Discrete event systems specification is applied for modelling and simulation. The proposed solution is assessed by simulation studies with very promising results to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks. PMID:28445398

Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks.

PubMed

Artuñedo, Antonio; Del Toro, Raúl M; Haber, Rodolfo E

2017-04-26

Nowadays many studies are being conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and how to exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is proposed to provide a solution for mobility problems in cities. The interconnected traffic lights controller ( TLC ) network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease the pollution level. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach is applied to design a consensus-based control algorithm. The designed control strategy contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Discrete event systems specification is applied for modelling and simulation. The proposed solution is assessed by simulation studies with very promising results to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks.

Synaptic activity induces input-specific rearrangements in a targeted synaptic protein interaction network.

PubMed

Lautz, Jonathan D; Brown, Emily A; VanSchoiack, Alison A Williams; Smith, Stephen E P

2018-05-27

Cells utilize dynamic, network level rearrangements in highly interconnected protein interaction networks to transmit and integrate information from distinct signaling inputs. Despite the importance of protein interaction network dynamics, the organizational logic underlying information flow through these networks is not well understood. Previously, we developed the quantitative multiplex co-immunoprecipitation platform, which allows for the simultaneous and quantitative measurement of the amount of co-association between large numbers of proteins in shared complexes. Here, we adapt quantitative multiplex co-immunoprecipitation to define the activity dependent dynamics of an 18-member protein interaction network in order to better understand the underlying principles governing glutamatergic signal transduction. We first establish that immunoprecipitation detected by flow cytometry can detect activity dependent changes in two known protein-protein interactions (Homer1-mGluR5 and PSD-95-SynGAP). We next demonstrate that neuronal stimulation elicits a coordinated change in our targeted protein interaction network, characterized by the initial dissociation of Homer1 and SynGAP-containing complexes followed by increased associations among glutamate receptors and PSD-95. Finally, we show that stimulation of distinct glutamate receptor types results in different modular sets of protein interaction network rearrangements, and that cells activate both modules in order to integrate complex inputs. This analysis demonstrates that cells respond to distinct types of glutamatergic input by modulating different combinations of protein co-associations among a targeted network of proteins. Our data support a model of synaptic plasticity in which synaptic stimulation elicits dissociation of preexisting multiprotein complexes, opening binding slots in scaffold proteins and allowing for the recruitment of additional glutamatergic receptors. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Explosive percolation on directed networks due to monotonic flow of activity

NASA Astrophysics Data System (ADS)

Waagen, Alex; D'Souza, Raissa M.; Lu, Tsai-Ching

2017-07-01

An important class of real-world networks has directed edges, and in addition, some rank ordering on the nodes, for instance the popularity of users in online social networks. Yet, nearly all research related to explosive percolation has been restricted to undirected networks. Furthermore, information on such rank-ordered networks typically flows from higher-ranked to lower-ranked individuals, such as follower relations, replies, and retweets on Twitter. Here we introduce a simple percolation process on an ordered, directed network where edges are added monotonically with respect to the rank ordering. We show with a numerical approach that the emergence of a dominant strongly connected component appears to be discontinuous. Large-scale connectivity occurs at very high density compared with most percolation processes, and this holds not just for the strongly connected component structure but for the weakly connected component structure as well. We present analysis with branching processes, which explains this unusual behavior and gives basic intuition for the underlying mechanisms. We also show that before the emergence of a dominant strongly connected component, multiple giant strongly connected components may exist simultaneously. By adding a competitive percolation rule with a small bias to link uses of similar rank, we show this leads to formation of two distinct components, one of high-ranked users, and one of low-ranked users, with little flow between the two components.

Power laws and fragility in flow networks.

PubMed

Shore, Jesse; Chu, Catherine J; Bianchi, Matt T

2013-01-01

What makes economic and ecological networks so unlike other highly skewed networks in their tendency toward turbulence and collapse? Here, we explore the consequences of a defining feature of these networks: their nodes are tied together by flow. We show that flow networks tend to the power law degree distribution (PLDD) due to a self-reinforcing process involving position within the global network structure, and thus present the first random graph model for PLDDs that does not depend on a rich-get-richer function of nodal degree. We also show that in contrast to non-flow networks, PLDD flow networks are dramatically more vulnerable to catastrophic failure than non-PLDD flow networks, a finding with potential explanatory power in our age of resource- and financial-interdependence and turbulence.

An Approach and Instrumentation for Management System Analysis

DTIC Science & Technology

1974-10-01

Benefit Analysis Systems Analysis Manpower Planning Resource Planning Information Theory 20. ABSTRACT (Conlliwa on ravaraa alda It nacaaaary...participants the data necessary to trace both formal and informal information flows and make cost- benefit judgments about specific communications. The...network within a manage- ment structure and to provide a basis tor preliminary cost- benefit evaluations. This objective was in response to Phase I of the

An Agent-Based Model of Private Woodland Owner Management Behavior Using Social Interactions, Information Flow, and Peer-To-Peer Networks

PubMed Central

Huff, Emily Silver; Leahy, Jessica E.; Hiebeler, David; Weiskittel, Aaron R.; Noblet, Caroline L.

2015-01-01

Privately owned woodlands are an important source of timber and ecosystem services in North America and worldwide. Impacts of management on these ecosystems and timber supply from these woodlands are difficult to estimate because complex behavioral theory informs the owner’s management decisions. The decision-making environment consists of exogenous market factors, internal cognitive processes, and social interactions with fellow landowners, foresters, and other rural community members. This study seeks to understand how social interactions, information flow, and peer-to-peer networks influence timber harvesting behavior using an agent-based model. This theoretical model includes forested polygons in various states of ‘harvest readiness’ and three types of agents: forest landowners, foresters, and peer leaders (individuals trained in conservation who use peer-to-peer networking). Agent rules, interactions, and characteristics were parameterized with values from existing literature and an empirical survey of forest landowner attitudes, intentions, and demographics. The model demonstrates that as trust in foresters and peer leaders increases, the percentage of the forest that is harvested sustainably increases. Furthermore, peer leaders can serve to increase landowner trust in foresters. Model output and equations will inform forest policy and extension/outreach efforts. The model also serves as an important testing ground for new theories of landowner decision making and behavior. PMID:26562429

Design and Implementation of an Architectural Framework for Web Portals in a Ubiquitous Pervasive Environment

PubMed Central

Raza, Muhammad Taqi; Yoo, Seung-Wha; Kim, Ki-Hyung; Joo, Seong-Soon; Jeong, Wun-Cheol

2009-01-01

Web Portals function as a single point of access to information on the World Wide Web (WWW). The web portal always contacts the portal’s gateway for the information flow that causes network traffic over the Internet. Moreover, it provides real time/dynamic access to the stored information, but not access to the real time information. This inherent functionality of web portals limits their role for resource constrained digital devices in the Ubiquitous era (U-era). This paper presents a framework for the web portal in the U-era. We have introduced the concept of Local Regions in the proposed framework, so that the local queries could be solved locally rather than having to route them over the Internet. Moreover, our framework enables one-to-one device communication for real time information flow. To provide an in-depth analysis, firstly, we provide an analytical model for query processing at the servers for our framework-oriented web portal. At the end, we have deployed a testbed, as one of the world’s largest IP based wireless sensor networks testbed, and real time measurements are observed that prove the efficacy and workability of the proposed framework. PMID:22346693

Design and implementation of an architectural framework for web portals in a ubiquitous pervasive environment.

PubMed

Raza, Muhammad Taqi; Yoo, Seung-Wha; Kim, Ki-Hyung; Joo, Seong-Soon; Jeong, Wun-Cheol

2009-01-01

Web Portals function as a single point of access to information on the World Wide Web (WWW). The web portal always contacts the portal's gateway for the information flow that causes network traffic over the Internet. Moreover, it provides real time/dynamic access to the stored information, but not access to the real time information. This inherent functionality of web portals limits their role for resource constrained digital devices in the Ubiquitous era (U-era). This paper presents a framework for the web portal in the U-era. We have introduced the concept of Local Regions in the proposed framework, so that the local queries could be solved locally rather than having to route them over the Internet. Moreover, our framework enables one-to-one device communication for real time information flow. To provide an in-depth analysis, firstly, we provide an analytical model for query processing at the servers for our framework-oriented web portal. At the end, we have deployed a testbed, as one of the world's largest IP based wireless sensor networks testbed, and real time measurements are observed that prove the efficacy and workability of the proposed framework.

Report on dynamic speed harmonization and queue warning algorithm design.

DOT National Transportation Integrated Search

2014-02-01

This report provides a detailed description of the algorithms that will be used to generate harmonized recommended speeds and queue warning information in the proposed Intelligent Network Flow Optimization (INFLO) prototype. This document describes t...

Modeling regional freight flow assignment through intermodal terminals

DOT National Transportation Integrated Search

2005-03-01

An analytical model is developed to assign regional freight across a multimodal highway and railway network using geographic information systems. As part of the regional planning process, the model is an iterative procedure that assigns multimodal fr...

Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI.

PubMed

Stramaglia, Sebastiano; Angelini, Leonardo; Wu, Guorong; Cortes, Jesus M; Faes, Luca; Marinazzo, Daniele

2016-12-01

We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits.

Computer Simulations of the Tumor Vasculature: Applications to Interstitial Fluid Flow, Drug Delivery, and Oxygen Supply.

PubMed

Welter, Michael; Rieger, Heiko

2016-01-01

Tumor vasculature, the blood vessel network supplying a growing tumor with nutrients such as oxygen or glucose, is in many respects different from the hierarchically organized arterio-venous blood vessel network in normal tissues. Angiogenesis (the formation of new blood vessels), vessel cooption (the integration of existing blood vessels into the tumor vasculature), and vessel regression remodel the healthy vascular network into a tumor-specific vasculature. Integrative models, based on detailed experimental data and physical laws, implement, in silico, the complex interplay of molecular pathways, cell proliferation, migration, and death, tissue microenvironment, mechanical and hydrodynamic forces, and the fine structure of the host tissue vasculature. With the help of computer simulations high-precision information about blood flow patterns, interstitial fluid flow, drug distribution, oxygen and nutrient distribution can be obtained and a plethora of therapeutic protocols can be tested before clinical trials. This chapter provides an overview over the current status of computer simulations of vascular remodeling during tumor growth including interstitial fluid flow, drug delivery, and oxygen supply within the tumor. The model predictions are compared with experimental and clinical data and a number of longstanding physiological paradigms about tumor vasculature and intratumoral solute transport are critically scrutinized.

Locating Sensors for Detecting Source-to-Target Patterns of Special Nuclear Material Smuggling: A Spatial Information Theoretic Approach

PubMed Central

Przybyla, Jay; Taylor, Jeffrey; Zhou, Xuesong

2010-01-01

In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM) smuggling. In order to ship the nuclear materials from a source location with SNM production to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy. PMID:22163641

Sender–receiver systems and applying information theory for quantitative synthetic biology

PubMed Central

Barcena Menendez, Diego; Senthivel, Vivek Raj; Isalan, Mark

2015-01-01

Sender–receiver (S–R) systems abound in biology, with communication systems sending information in various forms. Information theory provides a quantitative basis for analysing these processes and is being applied to study natural genetic, enzymatic and neural networks. Recent advances in synthetic biology are providing us with a wealth of artificial S–R systems, giving us quantitative control over networks with a finite number of well-characterised components. Combining the two approaches can help to predict how to maximise signalling robustness, and will allow us to make increasingly complex biological computers. Ultimately, pushing the boundaries of synthetic biology will require moving beyond engineering the flow of information and towards building more sophisticated circuits that interpret biological meaning. PMID:25282688

Locating sensors for detecting source-to-target patterns of special nuclear material smuggling: a spatial information theoretic approach.

PubMed

Przybyla, Jay; Taylor, Jeffrey; Zhou, Xuesong

2010-01-01

In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM) smuggling. In order to ship the nuclear materials from a source location with SNM production to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy.

Architecture of the human regulatory network derived from ENCODE data.

PubMed

Gerstein, Mark B; Kundaje, Anshul; Hariharan, Manoj; Landt, Stephen G; Yan, Koon-Kiu; Cheng, Chao; Mu, Xinmeng Jasmine; Khurana, Ekta; Rozowsky, Joel; Alexander, Roger; Min, Renqiang; Alves, Pedro; Abyzov, Alexej; Addleman, Nick; Bhardwaj, Nitin; Boyle, Alan P; Cayting, Philip; Charos, Alexandra; Chen, David Z; Cheng, Yong; Clarke, Declan; Eastman, Catharine; Euskirchen, Ghia; Frietze, Seth; Fu, Yao; Gertz, Jason; Grubert, Fabian; Harmanci, Arif; Jain, Preti; Kasowski, Maya; Lacroute, Phil; Leng, Jing Jane; Lian, Jin; Monahan, Hannah; O'Geen, Henriette; Ouyang, Zhengqing; Partridge, E Christopher; Patacsil, Dorrelyn; Pauli, Florencia; Raha, Debasish; Ramirez, Lucia; Reddy, Timothy E; Reed, Brian; Shi, Minyi; Slifer, Teri; Wang, Jing; Wu, Linfeng; Yang, Xinqiong; Yip, Kevin Y; Zilberman-Schapira, Gili; Batzoglou, Serafim; Sidow, Arend; Farnham, Peggy J; Myers, Richard M; Weissman, Sherman M; Snyder, Michael

2012-09-06

Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.

Driving and driven architectures of directed small-world human brain functional networks.

PubMed

Yan, Chaogan; He, Yong

2011-01-01

Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA) and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86) to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus) and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule). Further split-half analyses indicated that our results were highly reproducible between two independent subgroups. The current study demonstrated the directions of spontaneous information flow and causal influences in the directed brain networks, thus providing new insights into our understanding of human brain functional connectome.

Hierarchicality of trade flow networks reveals complexity of products.

PubMed

Shi, Peiteng; Zhang, Jiang; Yang, Bo; Luo, Jingfei

2014-01-01

With globalization, countries are more connected than before by trading flows, which amounts to at least 36 trillion dollars today. Interestingly, around 30-60 percents of exports consist of intermediate products in global. Therefore, the trade flow network of particular product with high added values can be regarded as value chains. The problem is weather we can discriminate between these products from their unique flow network structure? This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent η can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, it is pointed out that the flow networks of products with higher added values and complexity like machinary, transport equipment etc. have larger exponents, meaning that their trade flow networks are more hierarchical. As a result, without the extra data like global input-output table, we can identify the product categories with higher complexity, and the relative importance of a country in the global value chain by the trading network solely.

Hierarchicality of Trade Flow Networks Reveals Complexity of Products

PubMed Central

Shi, Peiteng; Zhang, Jiang; Yang, Bo; Luo, Jingfei

2014-01-01

With globalization, countries are more connected than before by trading flows, which amounts to at least trillion dollars today. Interestingly, around percents of exports consist of intermediate products in global. Therefore, the trade flow network of particular product with high added values can be regarded as value chains. The problem is weather we can discriminate between these products from their unique flow network structure? This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, it is pointed out that the flow networks of products with higher added values and complexity like machinary, transport equipment etc. have larger exponents, meaning that their trade flow networks are more hierarchical. As a result, without the extra data like global input-output table, we can identify the product categories with higher complexity, and the relative importance of a country in the global value chain by the trading network solely. PMID:24905753

Vector Symbolic Spiking Neural Network Model of Hippocampal Subarea CA1 Novelty Detection Functionality.

PubMed

Agerskov, Claus

2016-04-01

A neural network model is presented of novelty detection in the CA1 subdomain of the hippocampal formation from the perspective of information flow. This computational model is restricted on several levels by both anatomical information about hippocampal circuitry and behavioral data from studies done in rats. Several studies report that the CA1 area broadcasts a generalized novelty signal in response to changes in the environment. Using the neural engineering framework developed by Eliasmith et al., a spiking neural network architecture is created that is able to compare high-dimensional vectors, symbolizing semantic information, according to the semantic pointer hypothesis. This model then computes the similarity between the vectors, as both direct inputs and a recalled memory from a long-term memory network by performing the dot-product operation in a novelty neural network architecture. The developed CA1 model agrees with available neuroanatomical data, as well as the presented behavioral data, and so it is a biologically realistic model of novelty detection in the hippocampus, which can provide a feasible explanation for experimentally observed dynamics.

Active Control of Complex Systems via Dynamic (Recurrent) Neural Networks

DTIC Science & Technology

1992-05-30

course, to on-going changes brought about by learning processes. As research in neurodynamics proceeded, the concept of reverberatory information flows...Microstructure of Cognition . Vol. 1: Foundations, M.I.T. Press, Cambridge, Massachusetts, pp. 354-361, 1986. 100 I Schwarz, G., "Estimating the dimension of a...Continually Running Fully Recurrent Neural Networks, ICS Report 8805, Institute of Cognitive Science, University of California at San Diego, 1988. 10 II

Influence of anesthesia on cerebral blood flow, cerebral metabolic rate, and brain functional connectivity.

PubMed

Bonhomme, Vincent; Boveroux, Pierre; Hans, Pol; Brichant, Jean François; Vanhaudenhuyse, Audrey; Boly, Melanie; Laureys, Steven

2011-10-01

To describe recent studies exploring brain function under the influence of hypnotic anesthetic agents, and their implications on the understanding of consciousness physiology and anesthesia-induced alteration of consciousness. Cerebral cortex is the primary target of the hypnotic effect of anesthetic agents, and higher-order association areas are more sensitive to this effect than lower-order processing regions. Increasing concentration of anesthetic agents progressively attenuates connectivity in the consciousness networks, while connectivity in lower-order sensory and motor networks is preserved. Alteration of thalamic sub-cortical regulation could compromise the cortical integration of information despite preserved thalamic activation by external stimuli. At concentrations producing unresponsiveness, the activity of consciousness networks becomes anticorrelated with thalamic activity, while connectivity in lower-order sensory networks persists, although with cross-modal interaction alterations. Accumulating evidence suggests that hypnotic anesthetic agents disrupt large-scale cerebral connectivity. This would result in an inability of the brain to generate and integrate information, while external sensory information is still processed at a lower order of complexity.

Modern Workflows for Fracture Rock Hydrogeology

NASA Astrophysics Data System (ADS)

Doe, T.

2015-12-01

Discrete Fracture Network (DFN) is a numerical simulation approach that represents a conducting fracture network using geologically realistic geometries and single-conductor hydraulic and transport properties. In terms of diffusion analogues, equivalent porous media derive from heat conduction in continuous media, while DFN simulation is more similar to electrical flow and diffusion in circuits with discrete pathways. DFN modeling grew out of pioneering work of David Snow in the late 1960s with additional impetus in the 1970's from the development of the development of stochastic approaches for describing of fracture geometric and hydrologic properties. Research in underground test facilities for radioactive waste disposal developed the necessary linkages between characterization technologies and simulation as well as bringing about a hybrid deterministic stochastic approach. Over the past 40 years DFN simulation and characterization methods have moved from the research environment into practical, commercial application. The key geologic, geophysical and hydrologic tools provide the required DFN inputs of conductive fracture intensity, orientation, and transmissivity. Flow logging either using downhole tool or by detailed packer testing identifies the locations of conducting features in boreholes, and image logging provides information on the geology and geometry of the conducting features. Multi-zone monitoring systems isolate the individual conductors, and with subsequent drilling and characterization perturbations help to recognize connectivity and compartmentalization in the fracture network. Tracer tests and core analysis provide critical information on the transport properties especially matrix diffusion unidentified conducting pathways. Well test analyses incorporating flow dimension boundary effects provide further constraint on the conducting geometry of the fracture network.

Improving Statewide Freight Routing Capabilities for Sub-National Commodity Flows

DOT National Transportation Integrated Search

2012-10-01

The ability to fully understand and accurately characterize freight vehicle route choices is important in helping to inform regional and state decisions. This project recommends improvements to WSDOTs Statewide Freight GIS Network Model to more ac...

Characterization of fractures and flow zones in a contaminated shale at the Watervliet Arsenal, Albany County, New York

USGS Publications Warehouse

Williams, John H.; Paillet, Frederick L.

2002-01-01

Flow zones in a fractured shale in and near a plume of volatile organic compounds at the Watervliet Arsenal in Albany County, N. Y. were characterized through the integrated analysis of geophysical logs and single- and cross-hole flow tests. Information on the fracture-flow network at the site was needed to design an effective groundwater monitoring system, estimate offsite contaminant migration, and evaluate potential containment and remedial actions.Four newly drilled coreholes and four older monitoring wells were logged and tested to define the distribution and orientation of fractures that intersected a combined total of 500 feet of open hole. Analysis of borehole-wall image logs obtained with acoustic and optical televiewers indicated 79 subhorizontal to steeply dipping fractures with a wide range of dip directions. Analysis of fluid resistivity, temperature, and heat-pulse and electromagnetic flowmeter logs obtained under ambient and short-term stressed conditions identified 14 flow zones, which consist of one to several fractures and whose estimated transmissivity values range from 0.1 to more than 250 feet squared per day.Cross-hole flow tests, which were used to characterize the hydraulic connection between fracture-flow zones intersected by the boreholes, entailed (1) injection into or extraction from boreholes that penetrated a single fracture-flow zone or whose zones were isolated by an inflatable packer, and (2) measurement of the transient response of water levels and flow in surrounding boreholes. Results indicate a wellconnected fracture network with an estimated transmissivity of 80 to 250 feet squared per day that extends for at least 200 feet across the site. This interconnected fracture-flow network greatly affects the hydrology of the site and has important implications for contaminant monitoring and remedial actions.

Spatiotemporal coding in the cortex: information flow-based learning in spiking neural networks.

PubMed

Deco, G; Schürmann, B

1999-05-15

We introduce a learning paradigm for networks of integrate-and-fire spiking neurons that is based on an information-theoretic criterion. This criterion can be viewed as a first principle that demonstrates the experimentally observed fact that cortical neurons display synchronous firing for some stimuli and not for others. The principle can be regarded as the postulation of a nonparametric reconstruction method as optimization criteria for learning the required functional connectivity that justifies and explains synchronous firing for binding of features as a mechanism for spatiotemporal coding. This can be expressed in an information-theoretic way by maximizing the discrimination ability between different sensory inputs in minimal time.

Flow interaction based propagation model and bursty influence behavior analysis of Internet flows

NASA Astrophysics Data System (ADS)

Wu, Xiao-Yu; Gu, Ren-Tao; Ji, Yue-Feng

2016-11-01

QoS (quality of service) fluctuations caused by Internet bursty flows influence the user experience in the Internet, such as the increment of packet loss and transmission time. In this paper, we establish a mathematical model to study the influence propagation behavior of the bursty flow, which is helpful for developing a deep understanding of the network dynamics in the Internet complex system. To intuitively reflect the propagation process, a data flow interaction network with a hierarchical structure is constructed, where the neighbor order is proposed to indicate the neighborhood relationship between the bursty flow and other flows. The influence spreads from the bursty flow to each order of neighbors through flow interactions. As the influence spreads, the bursty flow has negative effects on the odd order neighbors and positive effects on the even order neighbors. The influence intensity of bursty flow decreases sharply between two adjacent orders and the decreasing degree can reach up to dozens of times in the experimental simulation. Moreover, the influence intensity increases significantly when network congestion situation becomes serious, especially for the 1st order neighbors. Network structural factors are considered to make a further study. Simulation results show that the physical network scale expansion can reduce the influence intensity of bursty flow by decreasing the flow distribution density. Furthermore, with the same network scale, the influence intensity in WS small-world networks is 38.18% and 18.40% lower than that in ER random networks and BA scale-free networks, respectively, due to a lower interaction probability between flows. These results indicate that the macro-structural changes such as network scales and styles will affect the inner propagation behaviors of the bursty flow.

A Multi-Technology Communication Platform for Urban Mobile Sensing.

PubMed

Almeida, Rodrigo; Oliveira, Rui; Luís, Miguel; Senna, Carlos; Sargento, Susana

2018-04-12

A common concern in smart cities is the focus on sensing procedures to provide city-wide information to city managers and citizens. To meet the growing demands of smart cities, the network must provide the ability to handle a large number of mobile sensors/devices, with high heterogeneity and unpredictable mobility, by collecting and delivering the sensed information for future treatment. This work proposes a multi-wireless technology communication platform for opportunistic data gathering and data exchange with respect to smart cities. Through the implementation of a proprietary long-range (LoRa) network and an urban sensor network, our platform addresses the heterogeneity of Internet of Things (IoT) devices while conferring communications in an opportunistic manner, increasing the interoperability of our platform. It implements and evaluates a medium access communication (MAC) protocol for LoRa networks with multiple gateways. It also implements mobile Opportunistic VEhicular (mOVE), a delay-tolerant network (DTN)-based architecture to address the mobility dimension. The platform provides vehicle-to-everything (V2X) communication with support for highly reliable and actionable information flows. Moreover, taking into account the high mobility pattern that a smart city scenario presents, we propose and evaluate two forwarding strategies for the opportunistic sensor network.

The growth of partnerships to support patient safety practice adoption.

PubMed

Mendel, Peter; Damberg, Cheryl L; Sorbero, Melony E S; Varda, Danielle M; Farley, Donna O

2009-04-01

To document the numbers and types of interorganizational partnerships within the national patient safety domain, changes over time in these networks, and their potential for disseminating patient safety knowledge and practices. Self-reported information gathered from representatives of national-level organizations active in promoting patient safety. Social network analysis was used to examine the structure and composition of partnership networks and changes between 2004 and 2006. Two rounds of structured telephone interviews (n=35 organizations in 2004 and 55 in 2006). Patient safety partnerships expanded between 2004 and 2006. The average number of partnerships per interviewed organization increased 40 percent and activities per reported partnership increased over 50 percent. Partnerships increased in all activity domains, particularly dissemination and tools development. Fragmentation of the overall partnership network decreased and potential for information flow increased. Yet network centralization increased, suggesting vulnerability to partnership failure if key participants disengage. Growth in partnerships signifies growing strength in the capacity to disseminate and implement patient safety advancements in the U.S. health care system. The centrality of AHRQ in these networks of partnerships bodes well for its leadership role in disseminating information, tools, and practices generated by patient safety research projects.

Information seeking for making evidence-informed decisions: a social network analysis on the staff of a public health department in Canada

PubMed Central

2012-01-01

Background Social network analysis is an approach to study the interactions and exchange of resources among people. It can help understanding the underlying structural and behavioral complexities that influence the process of capacity building towards evidence-informed decision making. A social network analysis was conducted to understand if and how the staff of a public health department in Ontario turn to peers to get help incorporating research evidence into practice. Methods The staff were invited to respond to an online questionnaire inquiring about information seeking behavior, identification of colleague expertise, and friendship status. Three networks were developed based on the 170 participants. Overall shape, key indices, the most central people and brokers, and their characteristics were identified. Results The network analysis showed a low density and localized information-seeking network. Inter-personal connections were mainly clustered by organizational divisions; and people tended to limit information-seeking connections to a handful of peers in their division. However, recognition of expertise and friendship networks showed more cross-divisional connections. Members of the office of the Medical Officer of Health were located at the heart of the department, bridging across divisions. A small group of professional consultants and middle managers were the most-central staff in the network, also connecting their divisions to the center of the information-seeking network. In each division, there were some locally central staff, mainly practitioners, who connected their neighboring peers; but they were not necessarily connected to other experts or managers. Conclusions The methods of social network analysis were useful in providing a systems approach to understand how knowledge might flow in an organization. The findings of this study can be used to identify early adopters of knowledge translation interventions, forming Communities of Practice, and potential internal knowledge brokers. PMID:22591757

Evaluation of multilayer perceptron algorithms for an analysis of network flow data

NASA Astrophysics Data System (ADS)

Bieniasz, Jedrzej; Rawski, Mariusz; Skowron, Krzysztof; Trzepiński, Mateusz

2016-09-01

The volume of exchanged information through IP networks is larger than ever and still growing. It creates a space for both benign and malicious activities. The second one raises awareness on security network devices, as well as network infrastructure and a system as a whole. One of the basic tools to prevent cyber attacks is Network Instrusion Detection System (NIDS). NIDS could be realized as a signature-based detector or an anomaly-based one. In the last few years the emphasis has been placed on the latter type, because of the possibility of applying smart and intelligent solutions. An ideal NIDS of next generation should be composed of self-learning algorithms that could react on known and unknown malicious network activities respectively. In this paper we evaluated a machine learning approach for detection of anomalies in IP network data represented as NetFlow records. We considered Multilayer Perceptron (MLP) as the classifier and we used two types of learning algorithms - Backpropagation (BP) and Particle Swarm Optimization (PSO). This paper includes a comprehensive survey on determining the most optimal MLP learning algorithm for the classification problem in application to network flow data. The performance, training time and convergence of BP and PSO methods were compared. The results show that PSO algorithm implemented by the authors outperformed other solutions if accuracy of classifications is considered. The major disadvantage of PSO is training time, which could be not acceptable for larger data sets or in real network applications. At the end we compared some key findings with the results from the other papers to show that in all cases results from this study outperformed them.

Relationship between microscopic dynamics in traffic flow and complexity in networks.

PubMed

Li, Xin-Gang; Gao, Zi-You; Li, Ke-Ping; Zhao, Xiao-Mei

2007-07-01

Complex networks are constructed in the evolution process of traffic flow, and the states of traffic flow are represented by nodes in the network. The traffic dynamics can then be studied by investigating the statistical properties of those networks. According to Kerner's three-phase theory, there are two different phases in congested traffic, synchronized flow and wide moving jam. In the framework of this theory, we study different properties of synchronized flow and moving jam in relation to complex network. Scale-free network is constructed in stop-and-go traffic, i.e., a sequence of moving jams [Chin. Phys. Lett. 10, 2711 (2005)]. In this work, the networks generated in synchronized flow are investigated in detail. Simulation results show that the degree distribution of the networks constructed in synchronized flow has two power law regions, so the distinction in topological structure can really reflect the different dynamics in traffic flow. Furthermore, the real traffic data are investigated by this method, and the results are consistent with the simulations.

Creating Turbulent Flow Realizations with Generative Adversarial Networks

NASA Astrophysics Data System (ADS)

King, Ryan; Graf, Peter; Chertkov, Michael

2017-11-01

Generating valid inflow conditions is a crucial, yet computationally expensive, step in unsteady turbulent flow simulations. We demonstrate a new technique for rapid generation of turbulent inflow realizations that leverages recent advances in machine learning for image generation using a deep convolutional generative adversarial network (DCGAN). The DCGAN is an unsupervised machine learning technique consisting of two competing neural networks that are trained against each other using backpropagation. One network, the generator, tries to produce samples from the true distribution of states, while the discriminator tries to distinguish between true and synthetic samples. We present results from a fully-trained DCGAN that is able to rapidly draw random samples from the full distribution of possible inflow states without needing to solve the Navier-Stokes equations, eliminating the costly process of spinning up inflow turbulence. This suggests a new paradigm in physics informed machine learning where the turbulence physics can be encoded in either the discriminator or generator. Finally, we also propose additional applications such as feature identification and subgrid scale modeling.

Identification of informative subgraphs in brain networks

NASA Astrophysics Data System (ADS)

Marinazzo, D.; Wu, G.; Pellicoro, M.; Stramaglia, S.

2013-01-01

Measuring directed interactions in the brain in terms of information flow is a promising approach, mathematically treatable and amenable to encompass several methods. Here we present a formal expansion of the transfer entropy to put in evidence irreducible sets of variables which provide information for the future state of each assigned target. Multiplets characterized by a large contribution to the expansion are associated to informational circuits present in the system, with an informational character (synergetic or redundant) which can be inferred from the sign of the contribution.

An experimental study of the Online Information Paradox: Does en-route information improve road network performance?

PubMed

Wijayaratna, Kasun P; Dixit, Vinayak V; Denant-Boemont, Laurent; Waller, S Travis

2017-01-01

This study investigates the empirical presence of a theoretical transportation paradox, defined as the "Online Information Paradox" (OIP). The paradox suggests that, for certain road networks, the provision of online information deteriorate travel conditions for all users of that network relative to the situation where no online information is provided to users. The analytical presence of the paradox was derived for a specific network structure by using two equilibrium models, the first being the Expected User Equilibrium (EUE) solution (no information scenario) and the other being the User Equilibrium with Recourse (UER) solution (with information scenario). An incentivised computerised route choice game was designed using the concepts of experimental economics and administered in a controlled laboratory environment to investigate the physical presence of the paradox. Aggregate statistics of path flows and Total System Travel Costs (TSTC) were used to compare the experimental results with the theoretical findings. A total of 12 groups of 12 participants completed the experiment and the OIP and the occurrence of the OIP being significant was observed in 11 of the 12 cases. Though information increased travel costs for users on average, it reduced the volatility of travel costs experienced in the no information scenario indicating that information can achieve a more reliable system. Further replications of similar experiments and more importantly field based identification of the phenomena will force transport professionals to be aware of the emergence of the paradox. In addition, studies such as this emphasise the need for the adoption of adaptive traffic assignment techniques to appropriately model the acquisition of information on a road network.

Information dynamics of brain-heart physiological networks during sleep

NASA Astrophysics Data System (ADS)

Faes, L.; Nollo, G.; Jurysta, F.; Marinazzo, D.

2014-10-01

This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain-brain and brain-heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep.

Innovation in monitoring: The U.S. Geological Survey Sacramento–San Joaquin River Delta, California, flow-station network

USGS Publications Warehouse

Burau, Jon; Ruhl, Cathy; Work, Paul A.

2016-01-29

The U.S. Geological Survey (USGS) installed the first gage to measure the flow of water into California’s Sacramento–San Joaquin River Delta from the Sacramento River in the late 1800s. Today, a network of 35 hydro-acoustic meters measure flow throughout the delta. This region is a critical part of California’s freshwater supply and conveyance system. With the data provided by this flow-station network—sampled every 15 minutes and updated to the web every hour—state and federal water managers make daily decisions about how much freshwater can be pumped for human use, at which locations, and when. Fish and wildlife scientists, working with water managers, also use this information to protect fish species affected by pumping and loss of habitat. The data are also used to help determine the success or failure of efforts to restore ecosystem processes in what has been called the “most managed and highly altered” watershed in the country.

Symbolic phase transfer entropy method and its application

NASA Astrophysics Data System (ADS)

Zhang, Ningning; Lin, Aijing; Shang, Pengjian

2017-10-01

In this paper, we introduce symbolic phase transfer entropy (SPTE) to infer the direction and strength of information flow among systems. The advantages of the proposed method are investigated by simulations on synthetic signals and real-world data. We demonstrate that symbolic phase transfer entropy is a robust and efficient tool to infer the information flow between complex systems. Based on the study of the synthetic data, we find a significant advantage of SPTE is its reduced sensitivity to noise. In addition, SPTE requires less amount of data than symbolic transfer entropy(STE). We analyze the direction and strength of information flow between six stock markets during the period from 2006 to 2016. The results indicate that the information flow among stocks varies over different periods. We also find that the interaction network pattern among stocks undergoes hierarchial reorganization with transition from one period to another. It is shown that the clusters are mainly classified according to period, and then by region. The stocks during the same time period are shown to drop into the same cluster.

Inference of topology and the nature of synapses, and the flow of information in neuronal networks

NASA Astrophysics Data System (ADS)

Borges, F. S.; Lameu, E. L.; Iarosz, K. C.; Protachevicz, P. R.; Caldas, I. L.; Viana, R. L.; Macau, E. E. N.; Batista, A. M.; Baptista, M. S.

2018-02-01

The characterization of neuronal connectivity is one of the most important matters in neuroscience. In this work, we show that a recently proposed informational quantity, the causal mutual information, employed with an appropriate methodology, can be used not only to correctly infer the direction of the underlying physical synapses, but also to identify their excitatory or inhibitory nature, considering easy to handle and measure bivariate time series. The success of our approach relies on a surprising property found in neuronal networks by which nonadjacent neurons do "understand" each other (positive mutual information), however, this exchange of information is not capable of causing effect (zero transfer entropy). Remarkably, inhibitory connections, responsible for enhancing synchronization, transfer more information than excitatory connections, known to enhance entropy in the network. We also demonstrate that our methodology can be used to correctly infer directionality of synapses even in the presence of dynamic and observational Gaussian noise, and is also successful in providing the effective directionality of intermodular connectivity, when only mean fields can be measured.

A Survey on Security and Privacy in Emerging Sensor Networks: From Viewpoint of Close-Loop.

PubMed

Zhang, Lifu; Zhang, Heng

2016-03-26

Nowadays, as the next generation sensor networks, Cyber-Physical Systems (CPSs) refer to the complex networked systems that have both physical subsystems and cyber components, and the information flow between different subsystems and components is across a communication network, which forms a closed-loop. New generation sensor networks are found in a growing number of applications and have received increasing attention from many inter-disciplines. Opportunities and challenges in the design, analysis, verification and validation of sensor networks co-exists, among which security and privacy are two important ingredients. This paper presents a survey on some recent results in the security and privacy aspects of emerging sensor networks from the viewpoint of the closed-loop. This paper also discusses several future research directions under these two umbrellas.

Introduction to Social Network Analysis

NASA Astrophysics Data System (ADS)

Zaphiris, Panayiotis; Ang, Chee Siang

Social Network analysis focuses on patterns of relations between and among people, organizations, states, etc. It aims to describe networks of relations as fully as possible, identify prominent patterns in such networks, trace the flow of information through them, and discover what effects these relations and networks have on people and organizations. Social network analysis offers a very promising potential for analyzing human-human interactions in online communities (discussion boards, newsgroups, virtual organizations). This Tutorial provides an overview of this analytic technique and demonstrates how it can be used in Human Computer Interaction (HCI) research and practice, focusing especially on Computer Mediated Communication (CMC). This topic acquires particular importance these days, with the increasing popularity of social networking websites (e.g., youtube, myspace, MMORPGs etc.) and the research interest in studying them.

Flow cytometry in the post fluorescence era.

PubMed

Nolan, Garry P

2011-12-01

While flow cytometry once enabled researchers to examine 10--15 cell surface parameters, new mass flow cytometry technology enables interrogation of up to 45 parameters on a single cell. This new technology has increased understanding of cell expression and how cells differentiate during hematopoiesis. Using this information, knowledge of leukemia cell biology has also increased. Other new technologies, such as SPADE analysis and single cell network profiling (SCNP), are enabling researchers to put different cancers into more biologically similar categories and have the potential to enable more personalized medicine. Copyright © 2011. Published by Elsevier Ltd.

Causal influence in neural systems: Reconciling mechanistic-reductionist and statistical perspectives. Comment on "Foundational perspectives on causality in large-scale brain networks" by M. Mannino & S.L. Bressler

NASA Astrophysics Data System (ADS)

Griffiths, John D.

2015-12-01

The modern understanding of the brain as a large, complex network of interacting elements is a natural consequence of the Neuron Doctrine [1,2] that has been bolstered in recent years by the tools and concepts of connectomics. In this abstracted, network-centric view, the essence of neural and cognitive function derives from the flows between network elements of activity and information - or, more generally, causal influence. The appropriate characterization of causality in neural systems, therefore, is a question at the very heart of systems neuroscience.

Heat transfer prediction in a square porous medium using artificial neural network

NASA Astrophysics Data System (ADS)

Ahamad, N. Ameer; Athani, Abdulgaphur; Badruddin, Irfan Anjum

2018-05-01

Heat transfer in porous media has been investigated extensively because of its applications in various important fields. Neural network approach is applied to analyze steady two dimensional free convection flows through a porous medium fixed in a square cavity. The backpropagation neural network is trained and used to predict the heat transfer. The results are compared with available information in the literature. It is found that the heat transfer increases with increase in Rayleigh number. It is further found that the local Nusselt number decreases along the height of cavity. The neural network is found to predict the heat transfer behavior accurately for given parameters.

Optimal Phase Oscillatory Network

NASA Astrophysics Data System (ADS)

Follmann, Rosangela

2013-03-01

Important topics as preventive detection of epidemics, collective self-organization, information flow and systemic robustness in clusters are typical examples of processes that can be studied in the context of the theory of complex networks. It is an emerging theory in a field, which has recently attracted much interest, involving the synchronization of dynamical systems associated to nodes, or vertices, of the network. Studies have shown that synchronization in oscillatory networks depends not only on the individual dynamics of each element, but also on the combination of the topology of the connections as well as on the properties of the interactions of these elements. Moreover, the response of the network to small damages, caused at strategic points, can enhance the global performance of the whole network. In this presentation we explore an optimal phase oscillatory network altered by an additional term in the coupling function. The application to associative-memory network shows improvement on the correct information retrieval as well as increase of the storage capacity. The inclusion of some small deviations on the nodes, when solutions are attracted to a false state, results in additional enhancement of the performance of the associative-memory network. Supported by FAPESP - Sao Paulo Research Foundation, grant number 2012/12555-4

Investigating accident causation through information network modelling.

PubMed

Griffin, T G C; Young, M S; Stanton, N A

2010-02-01

Management of risk in complex domains such as aviation relies heavily on post-event investigations, requiring complex approaches to fully understand the integration of multi-causal, multi-agent and multi-linear accident sequences. The Event Analysis of Systemic Teamwork methodology (EAST; Stanton et al. 2008) offers such an approach based on network models. In this paper, we apply EAST to a well-known aviation accident case study, highlighting communication between agents as a central theme and investigating the potential for finding agents who were key to the accident. Ultimately, this work aims to develop a new model based on distributed situation awareness (DSA) to demonstrate that the risk inherent in a complex system is dependent on the information flowing within it. By identifying key agents and information elements, we can propose proactive design strategies to optimize the flow of information and help work towards avoiding aviation accidents. Statement of Relevance: This paper introduces a novel application of an holistic methodology for understanding aviation accidents. Furthermore, it introduces an ongoing project developing a nonlinear and prospective method that centralises distributed situation awareness and communication as themes. The relevance of findings are discussed in the context of current ergonomic and aviation issues of design, training and human-system interaction.

Visualization and Hierarchical Analysis of Flow in Discrete Fracture Network Models

NASA Astrophysics Data System (ADS)

Aldrich, G. A.; Gable, C. W.; Painter, S. L.; Makedonska, N.; Hamann, B.; Woodring, J.

2013-12-01

Flow and transport in low permeability fractured rock is primary in interconnected fracture networks. Prediction and characterization of flow and transport in fractured rock has important implications in underground repositories for hazardous materials (eg. nuclear and chemical waste), contaminant migration and remediation, groundwater resource management, and hydrocarbon extraction. We have developed methods to explicitly model flow in discrete fracture networks and track flow paths using passive particle tracking algorithms. Visualization and analysis of particle trajectory through the fracture network is important to understanding fracture connectivity, flow patterns, potential contaminant pathways and fast paths through the network. However, occlusion due to the large number of highly tessellated and intersecting fracture polygons preclude the effective use of traditional visualization methods. We would also like quantitative analysis methods to characterize the trajectory of a large number of particle paths. We have solved these problems by defining a hierarchal flow network representing the topology of particle flow through the fracture network. This approach allows us to analyses the flow and the dynamics of the system as a whole. We are able to easily query the flow network, and use paint-and-link style framework to filter the fracture geometry and particle traces based on the flow analytics. This allows us to greatly reduce occlusion while emphasizing salient features such as the principal transport pathways. Examples are shown that demonstrate the methodology and highlight how use of this new method allows quantitative analysis and characterization of flow and transport in a number of representative fracture networks.

Systematic flood modelling to support flood-proof urban design

NASA Astrophysics Data System (ADS)

Bruwier, Martin; Mustafa, Ahmed; Aliaga, Daniel; Archambeau, Pierre; Erpicum, Sébastien; Nishida, Gen; Zhang, Xiaowei; Pirotton, Michel; Teller, Jacques; Dewals, Benjamin

2017-04-01

Urban flood risk is influenced by many factors such as hydro-meteorological drivers, existing drainage systems as well as vulnerability of population and assets. The urban fabric itself has also a complex influence on inundation flows. In this research, we performed a systematic analysis on how various characteristics of urban patterns control inundation flow within the urban area and upstream of it. An urban generator tool was used to generate over 2,250 synthetic urban networks of 1 km2. This tool is based on the procedural modelling presented by Parish and Müller (2001) which was adapted to generate a broader variety of urban networks. Nine input parameters were used to control the urban geometry. Three of them define the average length, orientation and curvature of the streets. Two orthogonal major roads, for which the width constitutes the fourth input parameter, work as constraints to generate the urban network. The width of secondary streets is given by the fifth input parameter. Each parcel generated by the street network based on a parcel mean area parameter can be either a park or a building parcel depending on the park ratio parameter. Three setback parameters constraint the exact location of the building whithin a building parcel. For each of synthetic urban network, detailed two-dimensional inundation maps were computed with a hydraulic model. The computational efficiency was enhanced by means of a porosity model. This enables the use of a coarser computational grid , while preserving information on the detailed geometry of the urban network (Sanders et al. 2008). These porosity parameters reflect not only the void fraction, which influences the storage capacity of the urban area, but also the influence of buildings on flow conveyance (dynamic effects). A sensitivity analysis was performed based on the inundation maps to highlight the respective impact of each input parameter characteristizing the urban networks. The findings of the study pinpoint which properties of urban networks have a major influence on urban inundation flow, enabling better informed flood-proof urban design. References: Parish, Y. I. H., Muller, P. 2001. Procedural modeling of cities. SIGGRAPH, pp. 301—308. Sanders, B.F., Schubert, J.E., Gallegos, H.A., 2008. Integral formulation of shallow-water equations with anisotropic porosity for urban flood modeling. Journal of Hydrology 362, 19-38. Acknowledgements: The research was funded through the ARC grant for Concerted Research Actions, financed by the Wallonia-Brussels Federation.

Cellular automata model for traffic flow at intersections in internet of vehicles

NASA Astrophysics Data System (ADS)

Zhao, Han-Tao; Liu, Xin-Ru; Chen, Xiao-Xu; Lu, Jian-Cheng

2018-03-01

Considering the effect of the front vehicle's speed, the influence of the brake light and the conflict of the traffic flow, we established a cellular automata model called CE-NS for traffic flow at the intersection in the non-vehicle networking environment. According to the information interaction of Internet of Vehicles (IoV), introducing parameters describing the congestion and the accurate speed of the front vehicle into the CE-NS model, we improved the rules of acceleration, deceleration and conflict, and finally established a cellular automata model for traffic flow at intersections of IoV. The relationship between traffic parameters such as vehicle speed, flow and average travel time is obtained by numerical simulation of two models. Based on this, we compared the traffic situation of the non-vehicle networking environment with conditions of IoV environment, and analyzed the influence of the different degree of IoV on the traffic flow. The results show that the traffic speed is increased, the travel time is reduced, the flux of intersections is increased and the traffic flow is more smoothly under IoV environment. After the vehicle which achieves IoV reaches a certain proportion, the operation effect of the traffic flow begins to improve obviously.

Findings from an Organizational Network Analysis to Support Local Public Health Management

PubMed Central

Caldwell, Michael; Rockoff, Maxine L.; Gebbie, Kristine; Carley, Kathleen M.; Bakken, Suzanne

2008-01-01

We assessed the feasibility of using organizational network analysis in a local public health organization. The research setting was an urban/suburban county health department with 156 employees. The goal of the research was to study communication and information flow in the department and to assess the technique for public health management. Network data were derived from survey questionnaires. Computational analysis was performed with the Organizational Risk Analyzer. Analysis revealed centralized communication, limited interdependencies, potential knowledge loss through retirement, and possible informational silos. The findings suggested opportunities for more cross program coordination but also suggested the presences of potentially efficient communication paths and potentially beneficial social connectedness. Managers found the findings useful to support decision making. Public health organizations must be effective in an increasingly complex environment. Network analysis can help build public health capacity for complex system management. PMID:18481183

Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function.

PubMed

Reimann, Michael W; Nolte, Max; Scolamiero, Martina; Turner, Katharine; Perin, Rodrigo; Chindemi, Giuseppe; Dłotko, Paweł; Levi, Ran; Hess, Kathryn; Markram, Henry

2017-01-01

The lack of a formal link between neural network structure and its emergent function has hampered our understanding of how the brain processes information. We have now come closer to describing such a link by taking the direction of synaptic transmission into account, constructing graphs of a network that reflect the direction of information flow, and analyzing these directed graphs using algebraic topology. Applying this approach to a local network of neurons in the neocortex revealed a remarkably intricate and previously unseen topology of synaptic connectivity. The synaptic network contains an abundance of cliques of neurons bound into cavities that guide the emergence of correlated activity. In response to stimuli, correlated activity binds synaptically connected neurons into functional cliques and cavities that evolve in a stereotypical sequence toward peak complexity. We propose that the brain processes stimuli by forming increasingly complex functional cliques and cavities.

Flow-aggregated traffic-driven label mapping in label-switching networks

NASA Astrophysics Data System (ADS)

Nagami, Kenichi; Katsube, Yasuhiro; Esaki, Hiroshi; Nakamura, Osamu

1998-12-01

Label switching technology enables high performance, flexible, layer-3 packet forwarding based on the fixed length label information mapped to the layer-3 packet stream. A Label Switching Router (LSR) forwards layer-3 packets based on their label information mapped to the layer-3 address information as well as their layer-3 address information. This paper evaluates the required number of labels under traffic-driven label mapping policy using the real backbone traffic traces. The evaluation shows that the label mapping policy requires a large number of labels. In order to reduce the required number of labels, we propose a label mapping policy which is a traffic-driven label mapping for the traffic toward the same destination network. The evaluation shows that the proposed label mapping policy requires only about one tenth as many labels compared with the traffic-driven label mapping for the host-pair packet stream,and the topology-driven label mapping for the destination network packet stream.

Graph Representations of Flow and Transport in Fracture Networks using Machine Learning

NASA Astrophysics Data System (ADS)

Srinivasan, G.; Viswanathan, H. S.; Karra, S.; O'Malley, D.; Godinez, H. C.; Hagberg, A.; Osthus, D.; Mohd-Yusof, J.

2017-12-01

Flow and transport of fluids through fractured systems is governed by the properties and interactions at the micro-scale. Retaining information about the micro-structure such as fracture length, orientation, aperture and connectivity in mesh-based computational models results in solving for millions to billions of degrees of freedom and quickly renders the problem computationally intractable. Our approach depicts fracture networks graphically, by mapping fractures to nodes and intersections to edges, thereby greatly reducing computational burden. Additionally, we use machine learning techniques to build simulators on the graph representation, trained on data from the mesh-based high fidelity simulations to speed up computation by orders of magnitude. We demonstrate our methodology on ensembles of discrete fracture networks, dividing up the data into training and validation sets. Our machine learned graph-based solvers result in over 3 orders of magnitude speedup without any significant sacrifice in accuracy.

Value flow mapping: Using networks to inform stakeholder analysis

NASA Astrophysics Data System (ADS)

Cameron, Bruce G.; Crawley, Edward F.; Loureiro, Geilson; Rebentisch, Eric S.

2008-02-01

Stakeholder theory has garnered significant interest from the corporate community, but has proved difficult to apply to large government programs. A detailed value flow exercise was conducted to identify the value delivery mechanisms among stakeholders for the current Vision for Space Exploration. We propose a method for capturing stakeholder needs that explicitly recognizes the outcomes required of the value creating organization. The captured stakeholder needs are then translated into input-output models for each stakeholder, which are then aggregated into a network model. Analysis of this network suggests that benefits are infrequently linked to the root provider of value. Furthermore, it is noted that requirements should not only be written to influence the organization's outputs, but also to influence the propagation of benefit further along the value chain. A number of future applications of this model to systems architecture and requirement analysis are discussed.

Active matter logic for autonomous microfluidics

NASA Astrophysics Data System (ADS)

Woodhouse, Francis G.; Dunkel, Jörn

2017-04-01

Chemically or optically powered active matter plays an increasingly important role in materials design, but its computational potential has yet to be explored systematically. The competition between energy consumption and dissipation imposes stringent physical constraints on the information transport in active flow networks, facilitating global optimization strategies that are not well understood. Here, we combine insights from recent microbial experiments with concepts from lattice-field theory and non-equilibrium statistical mechanics to introduce a generic theoretical framework for active matter logic. Highlighting conceptual differences with classical and quantum computation, we demonstrate how the inherent non-locality of incompressible active flow networks can be utilized to construct universal logical operations, Fredkin gates and memory storage in set-reset latches through the synchronized self-organization of many individual network components. Our work lays the conceptual foundation for developing autonomous microfluidic transport devices driven by bacterial fluids, active liquid crystals or chemically engineered motile colloids.

Estimating Low-Flow Frequency Statistics and Hydrologic Analysis of Selected Streamflow-Gaging Stations, Nooksack River Basin, Northwestern Washington and Canada

USGS Publications Warehouse

Curran, Christopher A.; Olsen, Theresa D.

2009-01-01

Low-flow frequency statistics were computed at 17 continuous-record streamflow-gaging stations and 8 miscellaneous measurement sites in and near the Nooksack River basin in northwestern Washington and Canada, including the 1, 3, 7, 15, 30, and 60 consecutive-day low flows with recurrence intervals of 2 and 10 years. Using these low-flow statistics, 12 regional regression equations were developed for estimating the same low-flow statistics at ungaged sites in the Nooksack River basin using a weighted-least-squares method. Adjusted R2 (coefficient of determination) values for the equations ranged from 0.79 to 0.93 and the root-mean-squared error (RMSE) expressed as a percentage ranged from 77 to 560 percent. Streamflow records from six gaging stations located in mountain-stream or lowland-stream subbasins of the Nooksack River basin were analyzed to determine if any of the gaging stations could be removed from the network without significant loss of information. Using methods of hydrograph comparison, daily-value correlation, variable space, and flow-duration ratios, and other factors relating to individual subbasins, the six gaging stations were prioritized from most to least important as follows: Skookum Creek (12209490), Anderson Creek (12210900), Warm Creek (12207750), Fishtrap Creek (12212050), Racehorse Creek (12206900), and Clearwater Creek (12207850). The optimum streamflow-gaging station network would contain all gaging stations except Clearwater Creek, and the minimum network would include Skookum Creek and Anderson Creek.

Functional organization and restoration of the brain motor-execution network after stroke and rehabilitation

PubMed Central

Bajaj, Sahil; Butler, Andrew J.; Drake, Daniel; Dhamala, Mukesh

2015-01-01

Multiple cortical areas of the human brain motor system interact coherently in the low frequency range (<0.1 Hz), even in the absence of explicit tasks. Following stroke, cortical interactions are functionally disturbed. How these interactions are affected and how the functional organization is regained from rehabilitative treatments as people begin to recover motor behaviors has not been systematically studied. We recorded the intrinsic functional magnetic resonance imaging (fMRI) signals from 30 participants: 17 young healthy controls and 13 aged stroke survivors. Stroke participants underwent mental practice (MP) or both mental practice and physical therapy (MP+PT) within 14–51 days following stroke. We investigated the network activity of five core areas in the motor-execution network, consisting of the left primary motor area (LM1), the right primary motor area (RM1), the left pre-motor cortex (LPMC), the right pre-motor cortex (RPMC) and the supplementary motor area (SMA). We discovered that (i) the network activity dominated in the frequency range 0.06–0.08 Hz for all the regions, and for both able-bodied and stroke participants (ii) the causal information flow between the regions: LM1 and SMA, RPMC and SMA, RPMC and LM1, SMA and RM1, SMA and LPMC, was reduced significantly for stroke survivors (iii) the flow did not increase significantly after MP alone and (iv) the flow among the regions during MP+PT increased significantly. We also found that sensation and motor scores were significantly higher and correlated with directed functional connectivity measures when the stroke-survivors underwent MP+PT but not MP alone. The findings provide evidence that a combination of mental practice and physical therapy can be an effective means of treatment for stroke survivors to recover or regain the strength of motor behaviors, and that the spectra of causal information flow can be used as a reliable biomarker for evaluating rehabilitation in stroke survivors. PMID:25870557

Weighted complex network analysis of the Beijing subway system: Train and passenger flows

NASA Astrophysics Data System (ADS)

Feng, Jia; Li, Xiamiao; Mao, Baohua; Xu, Qi; Bai, Yun

2017-05-01

In recent years, complex network theory has become an important approach to the study of the structure and dynamics of traffic networks. However, because traffic data is difficult to collect, previous studies have usually focused on the physical topology of subway systems, whereas few studies have considered the characteristics of traffic flows through the network. Therefore, in this paper, we present a multi-layer model to analyze traffic flow patterns in subway networks, based on trip data and an operation timetable obtained from the Beijing Subway System. We characterize the patterns in terms of the spatiotemporal flow size distributions of both the train flow network and the passenger flow network. In addition, we describe the essential interactions between these two networks based on statistical analyses. The results of this study suggest that layered models of transportation systems can elucidate fundamental differences between the coexisting traffic flows and can also clarify the mechanism that causes these differences.

Functional connectivity and information flow of the respiratory neural network in chronic obstructive pulmonary disease.

PubMed

Yu, Lianchun; De Mazancourt, Marine; Hess, Agathe; Ashadi, Fakhrul R; Klein, Isabelle; Mal, Hervé; Courbage, Maurice; Mangin, Laurence

2016-08-01

Breathing involves a complex interplay between the brainstem automatic network and cortical voluntary command. How these brain regions communicate at rest or during inspiratory loading is unknown. This issue is crucial for several reasons: (i) increased respiratory loading is a major feature of several respiratory diseases, (ii) failure of the voluntary motor and cortical sensory processing drives is among the mechanisms that precede acute respiratory failure, (iii) several cerebral structures involved in responding to inspiratory loading participate in the perception of dyspnea, a distressing symptom in many disease. We studied functional connectivity and Granger causality of the respiratory network in controls and patients with chronic obstructive pulmonary disease (COPD), at rest and during inspiratory loading. Compared with those of controls, the motor cortex area of patients exhibited decreased connectivity with their contralateral counterparts and no connectivity with the brainstem. In the patients, the information flow was reversed at rest with the source of the network shifted from the medulla towards the motor cortex. During inspiratory loading, the system was overwhelmed and the motor cortex became the sink of the network. This major finding may help to understand why some patients with COPD are prone to acute respiratory failure. Network connectivity and causality were related to lung function and illness severity. We validated our connectivity and causality results with a mathematical model of neural network. Our findings suggest a new therapeutic strategy involving the modulation of brain activity to increase motor cortex functional connectivity and improve respiratory muscles performance in patients. Hum Brain Mapp 37:2736-2754, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Urban networks among Chinese cities along "the Belt and Road": A case of web search activity in cyberspace.

PubMed

Zhang, Lu; Du, Hongru; Zhao, Yannan; Wu, Rongwei; Zhang, Xiaolei

2017-01-01

"The Belt and Road" initiative has been expected to facilitate interactions among numerous city centers. This initiative would generate a number of centers, both economic and political, which would facilitate greater interaction. To explore how information flows are merged and the specific opportunities that may be offered, Chinese cities along "the Belt and Road" are selected for a case study. Furthermore, urban networks in cyberspace have been characterized by their infrastructure orientation, which implies that there is a relative dearth of studies focusing on the investigation of urban hierarchies by capturing information flows between Chinese cities along "the Belt and Road". This paper employs Baidu, the main web search engine in China, to examine urban hierarchies. The results show that urban networks become more balanced, shifting from a polycentric to a homogenized pattern. Furthermore, cities in networks tend to have both a hierarchical system and a spatial concentration primarily in regions such as Beijing-Tianjin-Hebei, Yangtze River Delta and the Pearl River Delta region. Urban hierarchy based on web search activity does not follow the existing hierarchical system based on geospatial and economic development in all cases. Moreover, urban networks, under the framework of "the Belt and Road", show several significant corridors and more opportunities for more cities, particularly western cities. Furthermore, factors that may influence web search activity are explored. The results show that web search activity is significantly influenced by the economic gap, geographical proximity and administrative rank of the city.

Multi-frequency complex network from time series for uncovering oil-water flow structure.

PubMed

Gao, Zhong-Ke; Yang, Yu-Xuan; Fang, Peng-Cheng; Jin, Ning-De; Xia, Cheng-Yi; Hu, Li-Dan

2015-02-04

Uncovering complex oil-water flow structure represents a challenge in diverse scientific disciplines. This challenge stimulates us to develop a new distributed conductance sensor for measuring local flow signals at different positions and then propose a novel approach based on multi-frequency complex network to uncover the flow structures from experimental multivariate measurements. In particular, based on the Fast Fourier transform, we demonstrate how to derive multi-frequency complex network from multivariate time series. We construct complex networks at different frequencies and then detect community structures. Our results indicate that the community structures faithfully represent the structural features of oil-water flow patterns. Furthermore, we investigate the network statistic at different frequencies for each derived network and find that the frequency clustering coefficient enables to uncover the evolution of flow patterns and yield deep insights into the formation of flow structures. Current results present a first step towards a network visualization of complex flow patterns from a community structure perspective.

Flow networks for Ocean currents

NASA Astrophysics Data System (ADS)

Tupikina, Liubov; Molkenthin, Nora; Marwan, Norbert; Kurths, Jürgen

2014-05-01

Complex networks have been successfully applied to various systems such as society, technology, and recently climate. Links in a climate network are defined between two geographical locations if the correlation between the time series of some climate variable is higher than a threshold. Therefore, network links are considered to imply heat exchange. However, the relationship between the oceanic and atmospheric flows and the climate network's structure is still unclear. Recently, a theoretical approach verifying the correlation between ocean currents and surface air temperature networks has been introduced, where the Pearson correlation networks were constructed from advection-diffusion dynamics on an underlying flow. Since the continuous approach has its limitations, i.e., by its high computational complexity, we here introduce a new, discrete construction of flow-networks, which is then applied to static and dynamic velocity fields. Analyzing the flow-networks of prototypical flows we find that our approach can highlight the zones of high velocity by degree and transition zones by betweenness, while the combination of these network measures can uncover how the flow propagates within time. We also apply the method to time series data of the Equatorial Pacific Ocean Current and the Gulf Stream ocean current for the changing velocity fields, which could not been done before, and analyse the properties of the dynamical system. Flow-networks can be powerful tools to theoretically understand the step from system's dynamics to network's topology that can be analyzed using network measures and is used for shading light on different climatic phenomena.

The Caloosahatchee River Estuary: a monitoring partnership between Federal, State, and local governments, 2007-13

USGS Publications Warehouse

Patino, Eduardo

2014-01-01

From 2007 to 2013, the U.S. Geological Survey (USGS), in cooperation with the Florida Department of Environmental Protection (FDEP) and the South Florida Water Management District (SFWMD), operated a flow and salinity monitoring network at tributaries flowing into and at key locations within the tidal Caloosahatchee River. This network was designed to supplement existing long-term monitoring stations, such as W.P. Franklin Lock, also known as S–79, which are operated by the USGS in cooperation with the U.S. Army Corps of Engineers, Lee County, and the City of Cape Coral. Additionally, a monitoring station was operated on Sanibel Island from 2010 to 2013 as part of the USGS Greater Everglades Priority Ecosystem Science initiative and in partnership with U.S. Fish and Wildlife Service (J.N. Ding Darling National Wildlife Refuge). Moving boat water-quality surveys throughout the tidal Caloosahatchee River and downstream estuaries began in 2011 and are ongoing. Information generated by these monitoring networks has proved valuable to the FDEP for developing total maximum daily load criteria, and to the SFWMD for calibrating and verifying a hydrodynamic model. The information also supports the Caloosahatchee River Watershed Protection Plan.

Dynamic range in the C. elegans brain network

NASA Astrophysics Data System (ADS)

Antonopoulos, Chris G.

2016-01-01

We study external electrical perturbations and their responses in the brain dynamic network of the Caenorhabditis elegans soil worm, given by the connectome of its large somatic nervous system. Our analysis is inspired by a realistic experiment where one stimulates externally specific parts of the brain and studies the persistent neural activity triggered in other cortical regions. In this work, we perturb groups of neurons that form communities, identified by the walktrap community detection method, by trains of stereotypical electrical Poissonian impulses and study the propagation of neural activity to other communities by measuring the corresponding dynamic ranges and Steven law exponents. We show that when one perturbs specific communities, keeping the rest unperturbed, the external stimulations are able to propagate to some of them but not to all. There are also perturbations that do not trigger any response. We found that this depends on the initially perturbed community. Finally, we relate our findings for the former cases with low neural synchronization, self-criticality, and large information flow capacity, and interpret them as the ability of the brain network to respond to external perturbations when it works at criticality and its information flow capacity becomes maximal.

A chronometric functional sub-network in the thalamo-cortical system regulates the flow of neural information necessary for conscious cognitive processes.

PubMed

León-Domínguez, Umberto; Vela-Bueno, Antonio; Froufé-Torres, Manuel; León-Carrión, Jose

2013-06-01

The thalamo-cortical system has been defined as a neural network associated with consciousness. While there seems to be wide agreement that the thalamo-cortical system directly intervenes in vigilance and arousal, a divergence of opinion persists regarding its intervention in the control of other cognitive processes necessary for consciousness. In the present manuscript, we provide a review of recent scientific findings on the thalamo-cortical system and its role in the control and regulation of the flow of neural information necessary for conscious cognitive processes. We suggest that the axis formed by the medial prefrontal cortex and different thalamic nuclei (reticular nucleus, intralaminar nucleus, and midline nucleus), represents a core component for consciousness. This axis regulates different cerebral structures which allow basic cognitive processes like attention, arousal and memory to emerge. In order to produce a synchronized coherent response, neural communication between cerebral structures must have exact timing (chronometry). Thus, a chronometric functional sub-network within the thalamo-cortical system keeps us in an optimal and continuous functional state, allowing high-order cognitive processes, essential to awareness and qualia, to take place. Copyright © 2013 Elsevier Ltd. All rights reserved.

Population Connectivity Measures of Fishery-Targeted Coral Reef Species to Inform Marine Reserve Network Design in Fiji.

PubMed

Eastwood, Erin K; López, Elora H; Drew, Joshua A

2016-01-25

Coral reef fish serve as food sources to coastal communities worldwide, yet are vulnerable to mounting anthropogenic pressures like overfishing and climate change. Marine reserve networks have become important tools for mitigating these pressures, and one of the most critical factors in determining their spatial design is the degree of connectivity among different populations of species prioritized for protection. To help inform the spatial design of an expanded reserve network in Fiji, we used rapidly evolving mitochondrial genes to investigate connectivity patterns of three coral reef species targeted by fisheries in Fiji: Epinephelus merra (Serranidae), Halichoeres trimaculatus (Labridae), and Holothuria atra (Holothuriidae). The two fish species, E. merra and Ha. trimaculatus, exhibited low genetic structuring and high amounts of gene flow, whereas the sea cucumber Ho. atra displayed high genetic partitioning and predominantly westward gene flow. The idiosyncratic patterns observed among these species indicate that patterns of connectivity in Fiji are likely determined by a combination of oceanographic and ecological characteristics. Our data indicate that in the cases of species with high connectivity, other factors such as representation or political availability may dictate where reserves are placed. In low connectivity species, ensuring upstream and downstream connections is critical.

Develop a solution for protecting and securing enterprise networks from malicious attacks

NASA Astrophysics Data System (ADS)

Kamuru, Harshitha; Nijim, Mais

2014-05-01

In the world of computer and network security, there are myriad ways to launch an attack, which, from the perspective of a network, can usually be defined as "traffic that has huge malicious intent." Firewall acts as one of the measure in order to secure the device from incoming unauthorized data. There are infinite number of computer attacks that no firewall can prevent, such as those executed locally on the machine by a malicious user. From the network's perspective, there are numerous types of attack. All the attacks that degrade the effectiveness of data can be grouped into two types: brute force and precision. The Firewall that belongs to Juniper has the capability to protect against both types of attack. Denial of Service (DoS) attacks are one of the most well-known network security threats under brute force attacks, which is largely due to the high-profile way in which they can affect networks. Over the years, some of the largest, most respected Internet sites have been effectively taken offline by Denial of Service (DOS) attacks. A DoS attack typically has a singular focus, namely, to cause the services running on a particular host or network to become unavailable. Some DoS attacks exploit vulnerabilities in an operating system and cause it to crash, such as the infamous Win nuke attack. Others submerge a network or device with traffic so that there are no more resources to handle legitimate traffic. Precision attacks typically involve multiple phases and often involves a bit more thought than brute force attacks, all the way from reconnaissance to machine ownership. Before a precision attack is launched, information about the victim needs to be gathered. This information gathering typically takes the form of various types of scans to determine available hosts, networks, and ports. The hosts available on a network can be determined by ping sweeps. The available ports on a machine can be located by port scans. Screens cover a wide variety of attack traffic as they are configured on a per-zone basis. Depending on the type of screen being configured, there may be additional settings beyond simply blocking the traffic. Attack prevention is also a native function of any firewall. Juniper Firewall handles traffic on a per-flow basis. We can use flows or sessions as a way to determine whether traffic attempting to traverse the firewall is legitimate. We control the state-checking components resident in Juniper Firewall by configuring "flow" settings. These settings allow you to configure state checking for various conditions on the device. You can use flow settings to protect against TCP hijacking, and to generally ensure that the fire-wall is performing full state processing when desired. We take a case study of attack on a network and perform study of the detection of the malicious packets on a Net screen Firewall. A new solution for securing enterprise networks will be developed here.

Aerodynamic parameters from distributed heterogeneous CNT hair sensors with a feedforward neural network.

PubMed

Magar, Kaman Thapa; Reich, Gregory W; Kondash, Corey; Slinker, Keith; Pankonien, Alexander M; Baur, Jeffery W; Smyers, Brian

2016-11-10

Distributed arrays of artificial hair sensors have bio-like sensing capabilities to obtain spatial and temporal surface flow information which is an important aspect of an effective fly-by-feel system. The spatiotemporal surface flow measurement enables further exploration of additional flow features such as flow stagnation, separation, and reattachment points. Due to their inherent robustness and fault tolerant capability, distributed arrays of hair sensors are well equipped to assess the aerodynamic and flow states in adverse conditions. In this paper, a local flow measurement from an array of artificial hair sensors in a wind tunnel experiment is used with a feedforward artificial neural network to predict aerodynamic parameters such as lift coefficient, moment coefficient, free-stream velocity, and angle of attack on an airfoil. We find the prediction error within 6% and 10% for lift and moment coefficients. The error for free-stream velocity and angle of attack were within 0.12 mph and 0.37 degrees. Knowledge of these parameters are key to finding the real time forces and moments which paves the way for effective control design to increase flight agility, stability, and maneuverability.

Passenger flow analysis of Beijing urban rail transit network using fractal approach

NASA Astrophysics Data System (ADS)

Li, Xiaohong; Chen, Peiwen; Chen, Feng; Wang, Zijia

2018-04-01

To quantify the spatiotemporal distribution of passenger flow and the characteristics of an urban rail transit network, we introduce four radius fractal dimensions and two branch fractal dimensions by combining a fractal approach with passenger flow assignment model. These fractal dimensions can numerically describe the complexity of passenger flow in the urban rail transit network and its change characteristics. Based on it, we establish a fractal quantification method to measure the fractal characteristics of passenger follow in the rail transit network. Finally, we validate the reasonability of our proposed method by using the actual data of Beijing subway network. It has been shown that our proposed method can effectively measure the scale-free range of the urban rail transit network, network development and the fractal characteristics of time-varying passenger flow, which further provides a reference for network planning and analysis of passenger flow.

Network structure of subway passenger flows

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

The role of conflict minerals, artisanal mining, and informal trading networks in African intrastate and regional conflicts

USGS Publications Warehouse

Chirico, Peter G.; Malpeli, Katherine C.

2014-01-01

The relationship between natural resources and armed conflict gained public and political attention in the 1990s, when it became evident that the mining and trading of diamonds were connected with brutal rebellions in several African nations. Easily extracted resources such as alluvial diamonds and gold have been and continue to be exploited by rebel groups to fund their activities. Artisanal and small-scale miners operating under a quasi-legal status often mine these mineral deposits. While many African countries have legalized artisanal mining and established flow chains through which production is intended to travel, informal trading networks frequently emerge in which miners seek to evade taxes and fees by selling to unauthorized buyers. These networks have the potential to become international in scope, with actors operating in multiple countries. The lack of government control over the artisanal mining sector and the prominence of informal trade networks can have severe social, political, and economic consequences. In the past, mineral extraction fuelled violent civil wars in Sierra Leone, Liberia, and Angola, and it continues to do so today in several other countries. The significant influence of the informal network that surrounds artisanal mining is therefore an important security concern that can extend across borders and have far-reaching impacts.

A multi-scale network method for two-phase flow in porous media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khayrat, Karim, E-mail: khayratk@ifd.mavt.ethz.ch; Jenny, Patrick

Pore-network models of porous media are useful in the study of pore-scale flow in porous media. In order to extract macroscopic properties from flow simulations in pore-networks, it is crucial the networks are large enough to be considered representative elementary volumes. However, existing two-phase network flow solvers are limited to relatively small domains. For this purpose, a multi-scale pore-network (MSPN) method, which takes into account flow-rate effects and can simulate larger domains compared to existing methods, was developed. In our solution algorithm, a large pore network is partitioned into several smaller sub-networks. The algorithm to advance the fluid interfaces withinmore » each subnetwork consists of three steps. First, a global pressure problem on the network is solved approximately using the multiscale finite volume (MSFV) method. Next, the fluxes across the subnetworks are computed. Lastly, using fluxes as boundary conditions, a dynamic two-phase flow solver is used to advance the solution in time. Simulation results of drainage scenarios at different capillary numbers and unfavourable viscosity ratios are presented and used to validate the MSPN method against solutions obtained by an existing dynamic network flow solver.« less

eQTL networks unveil enriched mRNA master integrators downstream of complex disease-associated SNPs.

PubMed

Li, Haiquan; Pouladi, Nima; Achour, Ikbel; Gardeux, Vincent; Li, Jianrong; Li, Qike; Zhang, Hao Helen; Martinez, Fernando D; 'Skip' Garcia, Joe G N; Lussier, Yves A

2015-12-01

The causal and interplay mechanisms of Single Nucleotide Polymorphisms (SNPs) associated with complex diseases (complex disease SNPs) investigated in genome-wide association studies (GWAS) at the transcriptional level (mRNA) are poorly understood despite recent advancements such as discoveries reported in the Encyclopedia of DNA Elements (ENCODE) and Genotype-Tissue Expression (GTex). Protein interaction network analyses have successfully improved our understanding of both single gene diseases (Mendelian diseases) and complex diseases. Whether the mRNAs downstream of complex disease genes are central or peripheral in the genetic information flow relating DNA to mRNA remains unclear and may be disease-specific. Using expression Quantitative Trait Loci (eQTL) that provide DNA to mRNA associations and network centrality metrics, we hypothesize that we can unveil the systems properties of information flow between SNPs and the transcriptomes of complex diseases. We compare different conditions such as naïve SNP assignments and stringent linkage disequilibrium (LD) free assignments for transcripts to remove confounders from LD. Additionally, we compare the results from eQTL networks between lymphoblastoid cell lines and liver tissue. Empirical permutation resampling (p<0.001) and theoretic Mann-Whitney U test (p<10(-30)) statistics indicate that mRNAs corresponding to complex disease SNPs via eQTL associations are likely to be regulated by a larger number of SNPs than expected. We name this novel property mRNA hubness in eQTL networks, and further term mRNAs with high hubness as master integrators. mRNA master integrators receive and coordinate the perturbation signals from large numbers of polymorphisms and respond to the personal genetic architecture integratively. This genetic signal integration contrasts with the mechanism underlying some Mendelian diseases, where a genetic polymorphism affecting a single protein hub produces a divergent signal that affects a large number of downstream proteins. Indeed, we verify that this property is independent of the hubness in protein networks for which these mRNAs are transcribed. Our findings provide novel insights into the pleiotropy of mRNAs targeted by complex disease polymorphisms and the architecture of the information flow between the genetic polymorphisms and transcriptomes of complex diseases. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Design of real-time voice over internet protocol system under bandwidth network

NASA Astrophysics Data System (ADS)

Zhang, Li; Gong, Lina

2017-04-01

With the increasing bandwidth of the network and network convergence accelerating, VoIP means of communication across the network is becoming increasingly popular phenomenon. The real-time identification and analysis for VOIP flow over backbone network become the urgent needs and research hotspot of network operations management. Based on this, the paper proposes a VoIP business management system over backbone network. The system first filters VoIP data stream over backbone network and further resolves the call signaling information and media voice. The system can also be able to design appropriate rules to complete real-time reduction and presentation of specific categories of calls. Experimental results show that the system can parse and process real-time backbone of the VoIP call, and the results are presented accurately in the management interface, VoIP-based network traffic management and maintenance provide the necessary technical support.

Software defined network architecture based research on load balancing strategy

NASA Astrophysics Data System (ADS)

You, Xiaoqian; Wu, Yang

2018-05-01

As a new type network architecture, software defined network has the key idea of separating the control place of the network from the transmission plane, to manage and control the network in a concentrated way; in addition, the network interface is opened on the control layer and the data layer, so as to achieve programmable control of the network. Considering that only the single shortest route is taken into the calculation of traditional network data flow transmission, and congestion and resource consumption caused by excessive load of link circuits are ignored, a link circuit load based flow media business QoS gurantee system is proposed in this article to divide the flow in the network into ordinary data flow and QoS flow. In this way, it supervises the link circuit load with the controller so as to calculate reasonable route rapidly and issue the flow table to the exchanger, to finish rapid data transmission. In addition, it establishes a simulation platform to acquire optimized result through simulation experiment.

Two modelling approaches to water-quality simulation in a flooded iron-ore mine (Saizerais, Lorraine, France): a semi-distributed chemical reactor model and a physically based distributed reactive transport pipe network model.

PubMed

Hamm, V; Collon-Drouaillet, P; Fabriol, R

2008-02-19

The flooding of abandoned mines in the Lorraine Iron Basin (LIB) over the past 25 years has degraded the quality of the groundwater tapped for drinking water. High concentrations of dissolved sulphate have made the water unsuitable for human consumption. This problematic issue has led to the development of numerical tools to support water-resource management in mining contexts. Here we examine two modelling approaches using different numerical tools that we tested on the Saizerais flooded iron-ore mine (Lorraine, France). A first approach considers the Saizerais Mine as a network of two chemical reactors (NCR). The second approach is based on a physically distributed pipe network model (PNM) built with EPANET 2 software. This approach considers the mine as a network of pipes defined by their geometric and chemical parameters. Each reactor in the NCR model includes a detailed chemical model built to simulate quality evolution in the flooded mine water. However, in order to obtain a robust PNM, we simplified the detailed chemical model into a specific sulphate dissolution-precipitation model that is included as sulphate source/sink in both a NCR model and a pipe network model. Both the NCR model and the PNM, based on different numerical techniques, give good post-calibration agreement between the simulated and measured sulphate concentrations in the drinking-water well and overflow drift. The NCR model incorporating the detailed chemical model is useful when a detailed chemical behaviour at the overflow is needed. The PNM incorporating the simplified sulphate dissolution-precipitation model provides better information of the physics controlling the effect of flow and low flow zones, and the time of solid sulphate removal whereas the NCR model will underestimate clean-up time due to the complete mixing assumption. In conclusion, the detailed NCR model will give a first assessment of chemical processes at overflow, and in a second time, the PNM model will provide more detailed information on flow and chemical behaviour (dissolved sulphate concentrations, remaining mass of solid sulphate) in the network. Nevertheless, both modelling methods require hydrological and chemical parameters (recharge flow rate, outflows, volume of mine voids, mass of solids, kinetic constants of the dissolution-precipitation reactions), which are commonly not available for a mine and therefore call for calibration data.

Flow Pattern Identification of Horizontal Two-Phase Refrigerant Flow Using Neural Networks

DTIC Science & Technology

2015-12-31

AFRL-RQ-WP-TP-2016-0079 FLOW PATTERN IDENTIFICATION OF HORIZONTAL TWO-PHASE REFRIGERANT FLOW USING NEURAL NETWORKS (POSTPRINT) Abdeel J...Journal Article Postprint 01 October 2013 – 22 June 2015 4. TITLE AND SUBTITLE FLOW PATTERN IDENTIFICATION OF HORIZONTAL TWO-PHASE REFRIGERANT FLOW USING...networks were used to automatically identify two-phase flow patterns for refrigerant R-134a flowing in a horizontal tube. In laboratory experiments

An experimental study of the Online Information Paradox: Does en-route information improve road network performance?

PubMed Central

2017-01-01

This study investigates the empirical presence of a theoretical transportation paradox, defined as the “Online Information Paradox” (OIP). The paradox suggests that, for certain road networks, the provision of online information deteriorate travel conditions for all users of that network relative to the situation where no online information is provided to users. The analytical presence of the paradox was derived for a specific network structure by using two equilibrium models, the first being the Expected User Equilibrium (EUE) solution (no information scenario) and the other being the User Equilibrium with Recourse (UER) solution (with information scenario). An incentivised computerised route choice game was designed using the concepts of experimental economics and administered in a controlled laboratory environment to investigate the physical presence of the paradox. Aggregate statistics of path flows and Total System Travel Costs (TSTC) were used to compare the experimental results with the theoretical findings. A total of 12 groups of 12 participants completed the experiment and the OIP and the occurrence of the OIP being significant was observed in 11 of the 12 cases. Though information increased travel costs for users on average, it reduced the volatility of travel costs experienced in the no information scenario indicating that information can achieve a more reliable system. Further replications of similar experiments and more importantly field based identification of the phenomena will force transport professionals to be aware of the emergence of the paradox. In addition, studies such as this emphasise the need for the adoption of adaptive traffic assignment techniques to appropriately model the acquisition of information on a road network. PMID:28902854

Ab Initio and Monte Carlo Approaches For the MagnetocaloricEffect in Co- and In-Doped Ni-Mn-Ga Heusler Alloys

NASA Astrophysics Data System (ADS)

Sokolovskiy, Vladimir; Grünebohm, Anna; Buchelnikov, Vasiliy; Entel, Peter

2014-09-01

This special issue collects contributions from the participants of the "Information in Dynamical Systems and Complex Systems" workshop, which cover a wide range of important problems and new approaches that lie in the intersection of information theory and dynamical systems. The contributions include theoretical characterization and understanding of the different types of information flow and causality in general stochastic processes, inference and identification of coupling structure and parameters of system dynamics, rigorous coarse-grain modeling of network dynamical systems, and exact statistical testing of fundamental information-theoretic quantities such as the mutual information. The collective efforts reported herein reflect a modern perspective of the intimate connection between dynamical systems and information flow, leading to the promise of better understanding and modeling of natural complex systems and better/optimal design of engineering systems.

Modelling rapid subsurface flow at the hillslope scale with explicit representation of preferential flow paths

NASA Astrophysics Data System (ADS)

Wienhöfer, J.; Zehe, E.

2012-04-01

Rapid lateral flow processes via preferential flow paths are widely accepted to play a key role for rainfall-runoff response in temperate humid headwater catchments. A quantitative description of these processes, however, is still a major challenge in hydrological research, not least because detailed information about the architecture of subsurface flow paths are often impossible to obtain at a natural site without disturbing the system. Our study combines physically based modelling and field observations with the objective to better understand how flow network configurations influence the hydrological response of hillslopes. The system under investigation is a forested hillslope with a small perennial spring at the study area Heumöser, a headwater catchment of the Dornbirnerach in Vorarlberg, Austria. In-situ points measurements of field-saturated hydraulic conductivity and dye staining experiments at the plot scale revealed that shrinkage cracks and biogenic macropores function as preferential flow paths in the fine-textured soils of the study area, and these preferential flow structures were active in fast subsurface transport of artificial tracers at the hillslope scale. For modelling of water and solute transport, we followed the approach of implementing preferential flow paths as spatially explicit structures of high hydraulic conductivity and low retention within the 2D process-based model CATFLOW. Many potential configurations of the flow path network were generated as realisations of a stochastic process informed by macropore characteristics derived from the plot scale observations. Together with different realisations of soil hydraulic parameters, this approach results in a Monte Carlo study. The model setups were used for short-term simulation of a sprinkling and tracer experiment, and the results were evaluated against measured discharges and tracer breakthrough curves. Although both criteria were taken for model evaluation, still several model setups produced acceptable matches to the observed behaviour. These setups were selected for long-term simulation, the results of which were compared against water level measurements at two piezometers along the hillslope and the integral discharge response of the spring to reject some non-behavioural model setups and further reduce equifinality. The results of this study indicate that process-based modelling can provide a means to distinguish preferential flow networks on the hillslope scale when complementary measurements to constrain the range of behavioural model setups are available. These models can further be employed as a virtual reality to investigate the characteristics of flow path architectures and explore effective parameterisations for larger scale applications.

Listening to the Noise: Random Fluctuations Reveal Gene Network Parameters

NASA Astrophysics Data System (ADS)

Munsky, Brian; Trinh, Brooke; Khammash, Mustafa

2010-03-01

The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations exhibit cell-to-cell variability that can manifest significant prototypical differences. Noise induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified using flow cytometry, single molecule fluorescence in situ hybridization, time lapse fluorescence microscopy and other single cell and single molecule measurement techniques. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. We use theoretical investigations to establish experimental guidelines for the identification of gene regulatory networks, and we apply these guideline to experimentally identify predictive models for different regulatory mechanisms in bacteria and yeast.

Semantic Space as a Metapopulation System: Modelling the Wikipedia Information Flow Network

NASA Astrophysics Data System (ADS)

Masucci, A. Paolo; Kalampokis, Alkiviadis; Eguíluz, Víctor M.; Hernández-García, Emilio

The meaning of a word can be defined as an indefinite set of interpretants, which are other words that circumscribe the semantic content of the word they represent (Derrida 1982). In the same way each interpretant has a set of interpretants representing it and so on. Hence the indefinite chain of meaning assumes a rhizomatic shape that can be represented and analysed via the modern techniques of network theory (Dorogovtsev and Mendes 2013).

Analysis of Traffic Signals on a Software-Defined Network for Detection and Classification of a Man-in-the-Middle Attack

DTIC Science & Technology

2017-09-01

unique characteristics of reported anomalies in the collected traffic signals to build a classification framework. Other cyber events, such as a...Furthermore, we identify unique characteristics of reported anomalies in the collected traffic signals to build a classification framework. Other cyber...2]. The applications build flow rules using network topology information provided by the control plane [1]. Since the control plane is able to

Coupling the Near and Far Field Models for Performance Assessment of Repositories for Spent Nuclear Fuel

NASA Astrophysics Data System (ADS)

Liu, L.; Neretnieks, I.

2006-12-01

ABSTRACT In our conceptualisation, water flows in channels in fractures in fractured rocks such as granites. In the Swedish concept for a repository for spent nuclear fuel the canisters containing the spent fuel are embedded in a buffer in holes below the floor of tunnels. The deposition holes can be intersected by fractures with channels with flowing water. The flow in individual channels is determined by the transmissivity properties of the network of the channels. The flowrate around a deposition hole and in the excavation damaged zone around the tunnels will control the rate of mass transfer of corrosive agents and of escaping nuclides. We call the carrying capacity of the solutes an equivalent flowrate. An escaping nuclide will reach the flowing water in the channel and be transported further into the channel network, mixing with water from other channels at some channel intersections and dividing into several channels at other intersection. In order to follow a nuclide from any leaking canister to the effluent points at the ground surface we have integrated our channel network model CHAN3D with our near field mass transfer model NUCTRAN. The NUCTRAN code, based on a compartment model can calculate the release of nuclides from a defective canister through different pathways into the near field of a repository from the local flowrates in the channels near the deposition hole obtained from CHAN3D. The network model CHAN3D uses observed transmissivity distributions and flowing fracture frequencies in boreholes to set up the 3-dimensional network of stochastic fractures. Deterministic fracture zones are described as such with their hydraulic, properties, sizes, locations and extensions. When available, information on fracture length distributions e.g. power law distributions and correlations between sizes and transmissivities are included in the network model. Once flowrates in all channels in the network have been calculated all equivalent flowrates for all canister positions can be calculated. The rate of transport of corrosive agents to and the releases of nuclides from any damaged canister are then calculated. For any given canister location the channel network model is then used to calculate the paths of the nuclides from the canister through the network by particle tracking. A large number of particles are released one by one from the canister and followed from one channel intersection to the next. A mixing rule is used at an intersection to decide which exit the particle takes. We mostly assume full mixing at intersections. The residence time and the ratio of flow wetted surface to flowrate along every path the particles traverse is summed. This information is sufficient to determine the residence time distribution (RTD) of the nuclides along that path also when they are subject to retardation by surface sorption and matrix diffusion. Actually this information is also sufficient to determine the RTD of arbitrary length decay chains subject to some minor (unimportant) simplifying assumptions. In this paper, we discuss in detail the coupling concept of how to integrate the near and far field models, together with the method of how to include transmissive fractures following a power law length distribution and fracture zones into CHAN3D in order to significantly decrease the computer time without loss of important features of the far field. The simulation results regarding a hypothetical repository located at the Forsmark area, Sweden, are also presented and discussed. Our study suggests that the integrated model can be used as an efficient tool to simulate the release of nuclides, including decay chains, from a repository and the transport to recipients.

Visualizing Mobility of Public Transportation System.

PubMed

Zeng, Wei; Fu, Chi-Wing; Arisona, Stefan Müller; Erath, Alexander; Qu, Huamin

2014-12-01

Public transportation systems (PTSs) play an important role in modern cities, providing shared/massive transportation services that are essential for the general public. However, due to their increasing complexity, designing effective methods to visualize and explore PTS is highly challenging. Most existing techniques employ network visualization methods and focus on showing the network topology across stops while ignoring various mobility-related factors such as riding time, transfer time, waiting time, and round-the-clock patterns. This work aims to visualize and explore passenger mobility in a PTS with a family of analytical tasks based on inputs from transportation researchers. After exploring different design alternatives, we come up with an integrated solution with three visualization modules: isochrone map view for geographical information, isotime flow map view for effective temporal information comparison and manipulation, and OD-pair journey view for detailed visual analysis of mobility factors along routes between specific origin-destination pairs. The isotime flow map linearizes a flow map into a parallel isoline representation, maximizing the visualization of mobility information along the horizontal time axis while presenting clear and smooth pathways from origin to destinations. Moreover, we devise several interactive visual query methods for users to easily explore the dynamics of PTS mobility over space and time. Lastly, we also construct a PTS mobility model from millions of real passenger trajectories, and evaluate our visualization techniques with assorted case studies with the transportation researchers.

Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information

PubMed Central

Wang, Jianxin; Chen, Bo; Wang, Yaqun; Wang, Ningtao; Garbey, Marc; Tran-Son-Tay, Roger; Berceli, Scott A.; Wu, Rongling

2013-01-01

The capacity of an organism to respond to its environment is facilitated by the environmentally induced alteration of gene and protein expression, i.e. expression plasticity. The reconstruction of gene regulatory networks based on expression plasticity can gain not only new insights into the causality of transcriptional and cellular processes but also the complex regulatory mechanisms that underlie biological function and adaptation. We describe an approach for network inference by integrating expression plasticity into Shannon’s mutual information. Beyond Pearson correlation, mutual information can capture non-linear dependencies and topology sparseness. The approach measures the network of dependencies of genes expressed in different environments, allowing the environment-induced plasticity of gene dependencies to be tested in unprecedented details. The approach is also able to characterize the extent to which the same genes trigger different amounts of expression in response to environmental changes. We demonstrated the usefulness of this approach through analysing gene expression data from a rabbit vein graft study that includes two distinct blood flow environments. The proposed approach provides a powerful tool for the modelling and analysis of dynamic regulatory networks using gene expression data from distinct environments. PMID:23470995

Neuron-Like Networks Between Ribosomal Proteins Within the Ribosome

NASA Astrophysics Data System (ADS)

Poirot, Olivier; Timsit, Youri

2016-05-01

From brain to the World Wide Web, information-processing networks share common scale invariant properties. Here, we reveal the existence of neural-like networks at a molecular scale within the ribosome. We show that with their extensions, ribosomal proteins form complex assortative interaction networks through which they communicate through tiny interfaces. The analysis of the crystal structures of 50S eubacterial particles reveals that most of these interfaces involve key phylogenetically conserved residues. The systematic observation of interactions between basic and aromatic amino acids at the interfaces and along the extension provides new structural insights that may contribute to decipher the molecular mechanisms of signal transmission within or between the ribosomal proteins. Similar to neurons interacting through “molecular synapses”, ribosomal proteins form a network that suggest an analogy with a simple molecular brain in which the “sensory-proteins” innervate the functional ribosomal sites, while the “inter-proteins” interconnect them into circuits suitable to process the information flow that circulates during protein synthesis. It is likely that these circuits have evolved to coordinate both the complex macromolecular motions and the binding of the multiple factors during translation. This opens new perspectives on nanoscale information transfer and processing.

A Survey on Security and Privacy in Emerging Sensor Networks: From Viewpoint of Close-Loop

PubMed Central

Zhang, Lifu; Zhang, Heng

2016-01-01

Nowadays, as the next generation sensor networks, Cyber-Physical Systems (CPSs) refer to the complex networked systems that have both physical subsystems and cyber components, and the information flow between different subsystems and components is across a communication network, which forms a closed-loop. New generation sensor networks are found in a growing number of applications and have received increasing attention from many inter-disciplines. Opportunities and challenges in the design, analysis, verification and validation of sensor networks co-exists, among which security and privacy are two important ingredients. This paper presents a survey on some recent results in the security and privacy aspects of emerging sensor networks from the viewpoint of the closed-loop. This paper also discusses several future research directions under these two umbrellas. PMID:27023559

eLoom and Flatland: specification, simulation and visualization engines for the study of arbitrary hierarchical neural architectures.

PubMed

Caudell, Thomas P; Xiao, Yunhai; Healy, Michael J

2003-01-01

eLoom is an open source graph simulation software tool, developed at the University of New Mexico (UNM), that enables users to specify and simulate neural network models. Its specification language and libraries enables users to construct and simulate arbitrary, potentially hierarchical network structures on serial and parallel processing systems. In addition, eLoom is integrated with UNM's Flatland, an open source virtual environments development tool to provide real-time visualizations of the network structure and activity. Visualization is a useful method for understanding both learning and computation in artificial neural networks. Through 3D animated pictorially representations of the state and flow of information in the network, a better understanding of network functionality is achieved. ART-1, LAPART-II, MLP, and SOM neural networks are presented to illustrate eLoom and Flatland's capabilities.

Development of an Aeromedical Scientific Information System for Aviation Safety

DTIC Science & Technology

2008-01-01

math- ematics, engineering, computer hardware, software , and networking, was assembled to glean the most knowledge from the complicated aeromedical...9, SPlus Enterprise Developer 8, and Insightful Miner version 7. Process flow charts were done with SmartDraw Suite Edition version 7. Static and

Concept development and needs identification for Intelligent Network Flow Optimization (INFLO) : assessment of relevant prior and ongoing research.

DOT National Transportation Integrated Search

2012-03-01

Through the USDOT Dynamic Mobility Applications (DMA) program, a number of high-priority mobility applications have been assessed and identified that can connect vehicles, travelers, and infrastructure in order to provide better information to travel...

A Framework for Information Theoretic Cooperative Sensing and Predictive Control

DTIC Science & Technology

2012-09-11

Miroslav Barić and Francesco Borelli , Decentralized Robust Control Invariance for a Network of Integrators, Proceeding of American Control...from http: //www.mpc.berkeley.edu. P4 Miroslav Barić and Francesco Borelli , Distributed Averaging with Flow Constraints, Proceeding of American Control

Inference in the brain: Statistics flowing in redundant population codes

PubMed Central

Pitkow, Xaq; Angelaki, Dora E

2017-01-01

It is widely believed that the brain performs approximate probabilistic inference to estimate causal variables in the world from ambiguous sensory data. To understand these computations, we need to analyze how information is represented and transformed by the actions of nonlinear recurrent neural networks. We propose that these probabilistic computations function by a message-passing algorithm operating at the level of redundant neural populations. To explain this framework, we review its underlying concepts, including graphical models, sufficient statistics, and message-passing, and then describe how these concepts could be implemented by recurrently connected probabilistic population codes. The relevant information flow in these networks will be most interpretable at the population level, particularly for redundant neural codes. We therefore outline a general approach to identify the essential features of a neural message-passing algorithm. Finally, we argue that to reveal the most important aspects of these neural computations, we must study large-scale activity patterns during moderately complex, naturalistic behaviors. PMID:28595050

Mobile phone collection, reuse and recycling in the UK

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ongondo, F.O.; Williams, I.D., E-mail: idw@soton.ac.uk

Highlights: > We characterized the key features of the voluntary UK mobile phone takeback network via a survey. > We identified 3 flows: information; product (handsets and accessories); and incentives. > There has been a significant rise in the number of UK takeback schemes since 1997. > Most returned handsets are low quality; little data exists on quantities of mobile phones collected. > Takeback schemes increasingly divert EoL mobile phones from landfill and enable reuse/recycling. - Abstract: Mobile phones are the most ubiquitous electronic product on the globe. They have relatively short lifecycles and because of their (perceived) in-built obsolescence,more » discarded mobile phones represent a significant and growing problem with respect to waste electrical and electronic equipment (WEEE). An emerging and increasingly important issue for industry is the shortage of key metals, especially the types of metals found in mobile phones, and hence the primary aim of this timely study was to assess and evaluate the voluntary mobile phone takeback network in the UK. The study has characterised the information, product and incentives flows in the voluntary UK mobile phone takeback network and reviewed the merits and demerits of the incentives offered. A survey of the activities of the voluntary mobile phone takeback schemes was undertaken in 2008 to: identify and evaluate the takeback schemes operating in the UK; determine the target groups from whom handsets are collected; and assess the collection, promotion and advertising methods used by the schemes. In addition, the survey sought to identify and critically evaluate the incentives offered by the takeback schemes, evaluate their ease and convenience of use; and determine the types, qualities and quantities of mobile phones they collect. The study has established that the UK voluntary mobile phone takeback network can be characterised as three distinctive flows: information flow; product flow (handsets and related accessories); and incentives flow. Over 100 voluntary schemes offering online takeback of mobile phone handsets were identified. The schemes are operated by manufacturers, retailers, mobile phone network service operators, charities and by mobile phone reuse, recycling and refurbishing companies. The latter two scheme categories offer the highest level of convenience and ease of use to their customers. Approximately 83% of the schemes are either for-profit/commercial-oriented and/or operate to raise funds for charities. The voluntary schemes use various methods to collect mobile phones from consumers, including postal services, courier and in-store. The majority of schemes utilise and finance pre-paid postage to collect handsets. Incentives offered by the takeback schemes include monetary payments, donation to charity and entry into prize draws. Consumers from whom handsets and related equipment are collected include individuals, businesses, schools, colleges, universities, charities and clubs with some schemes specialising on collecting handsets from one target group. The majority (84.3%) of voluntary schemes did not provide information on their websites about the quantities of mobile phones they collect. The operations of UK takeback schemes are decentralised in nature. Comparisons are made between the UK's decentralised collection system versus Australia's centralised network for collection of mobile phones. The significant principal conclusions from the study are: there has been a significant rise in the number of takeback schemes operating in the UK since the initial scheme was launched in 1997; the majority of returned handsets seem to be of low quality; and there is very little available information on the quantities of mobile phones collected by the various schemes. Irrespective of their financial motives, UK takeback schemes increasingly play an important role in sustainable waste management by diverting EoL mobile phones from landfills and encouraging reuse and recycling. Recommendations for future actions to improve the management of end-of-life mobile phone handsets and related accessories are made.« less

Peak-flow frequency relations and evaluation of the peak-flow gaging network in Nebraska

USGS Publications Warehouse

Soenksen, Philip J.; Miller, Lisa D.; Sharpe, Jennifer B.; Watton, Jason R.

1999-01-01

Estimates of peak-flow magnitude and frequency are required for the efficient design of structures that convey flood flows or occupy floodways, such as bridges, culverts, and roads. The U.S. Geological Survey, in cooperation with the Nebraska Department of Roads, conducted a study to update peak-flow frequency analyses for selected streamflow-gaging stations, develop a new set of peak-flow frequency relations for ungaged streams, and evaluate the peak-flow gaging-station network for Nebraska. Data from stations located in or within about 50 miles of Nebraska were analyzed using guidelines of the Interagency Advisory Committee on Water Data in Bulletin 17B. New generalized skew relations were developed for use in frequency analyses of unregulated streams. Thirty-three drainage-basin characteristics related to morphology, soils, and precipitation were quantified using a geographic information system, related computer programs, and digital spatial data.For unregulated streams, eight sets of regional regression equations relating drainage-basin to peak-flow characteristics were developed for seven regions of the state using a generalized least squares procedure. Two sets of regional peak-flow frequency equations were developed for basins with average soil permeability greater than 4 inches per hour, and six sets of equations were developed for specific geographic areas, usually based on drainage-basin boundaries. Standard errors of estimate for the 100-year frequency equations (1percent probability) ranged from 12.1 to 63.8 percent. For regulated reaches of nine streams, graphs of peak flow for standard frequencies and distance upstream of the mouth were estimated.The regional networks of streamflow-gaging stations on unregulated streams were analyzed to evaluate how additional data might affect the average sampling errors of the newly developed peak-flow equations for the 100-year frequency occurrence. Results indicated that data from new stations, rather than more data from existing stations, probably would produce the greatest reduction in average sampling errors of the equations.

Information security system quality assessment through the intelligent tools

NASA Astrophysics Data System (ADS)

Trapeznikov, E. V.

2018-04-01

The technology development has shown the automated system information security comprehensive analysis necessity. The subject area analysis indicates the study relevance. The research objective is to develop the information security system quality assessment methodology based on the intelligent tools. The basis of the methodology is the information security assessment model in the information system through the neural network. The paper presents the security assessment model, its algorithm. The methodology practical implementation results in the form of the software flow diagram are represented. The practical significance of the model being developed is noted in conclusions.

A Multi-Technology Communication Platform for Urban Mobile Sensing

PubMed Central

Almeida, Rodrigo; Oliveira, Rui

2018-01-01

A common concern in smart cities is the focus on sensing procedures to provide city-wide information to city managers and citizens. To meet the growing demands of smart cities, the network must provide the ability to handle a large number of mobile sensors/devices, with high heterogeneity and unpredictable mobility, by collecting and delivering the sensed information for future treatment. This work proposes a multi-wireless technology communication platform for opportunistic data gathering and data exchange with respect to smart cities. Through the implementation of a proprietary long-range (LoRa) network and an urban sensor network, our platform addresses the heterogeneity of Internet of Things (IoT) devices while conferring communications in an opportunistic manner, increasing the interoperability of our platform. It implements and evaluates a medium access communication (MAC) protocol for LoRa networks with multiple gateways. It also implements mobile Opportunistic VEhicular (mOVE), a delay-tolerant network (DTN)-based architecture to address the mobility dimension. The platform provides vehicle-to-everything (V2X) communication with support for highly reliable and actionable information flows. Moreover, taking into account the high mobility pattern that a smart city scenario presents, we propose and evaluate two forwarding strategies for the opportunistic sensor network. PMID:29649175

Electric Current Flow Through Two-Dimensional Networks

NASA Astrophysics Data System (ADS)

Gaspard, Mallory

In modern nanotechnology, two-dimensional atomic network structures boast promising applications as nanoscale circuit boards to serve as the building blocks of more sustainable and efficient, electronic devices. However, properties associated with the network connectivity can be beneficial or deleterious to the current flow. Taking a computational approach, we will study large uniform networks, as well as large random networks using Kirchhoff's Equations in conjunction with graph theoretical measures of network connectedness and flows, to understand how network connectivity affects overall ability for successful current flow throughout a network. By understanding how connectedness affects flow, we may develop new ways to design more efficient two-dimensional materials for the next generation of nanoscale electronic devices, and we will gain a deeper insight into the intricate balance between order and chaos in the universe. Rensselaer Polytechnic Institute, SURP Institutional Grant.

LinkMind: link optimization in swarming mobile sensor networks.

PubMed

Ngo, Trung Dung

2011-01-01

A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.

LinkMind: Link Optimization in Swarming Mobile Sensor Networks

PubMed Central

Ngo, Trung Dung

2011-01-01

A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation. PMID:22164070

[Characterization of access to normal childbirth care in Bahia State, Brazil, based on graph theory].

PubMed

Sousa, Ludmilla Monfort Oliveira; Araújo, Edna Maria de; Miranda, José Garcia Vivas

2017-12-18

Origin-destination flow is a phenomenon that can be modeled as a network. Graph theory is a mathematical tool to characterize a network and thus allows studying the topological properties and temporal and spatial development of a set of related elements. The article aims to estimate the topological evolution of an inter-municipal network of normal deliveries. We selected the admissions for normal deliveries in the Hospital Information System of the Brazilian Unified National Health System, from 2008 to 2014, for women residing in Bahia State, Brazil. The following indices were applied: entry degree (from how many municipalities the women came for childbirth), exit degree (to how many municipalities they left), entry flow (how many women came), exit flow (how many women left), and the mean size of the exit edge (distance traveled). Analyses between macro-regions used the following indicators: proportion of normal deliveries performed outside the municipality of residence and mean size of the exit edge. The results indicate an increase in deliveries performed outside the municipality of residence, in addition to the persistence of concentration of deliveries in the hub municipalities in the Health Regions, and an increase in the distance between the municipality of residence and the municipality where the delivery took place. The organization of networks for normal childbirth poses an on-going challenge. It is important to analyze the flow of women for childbirth care in order to support the establishment of inter-municipal references to guarantee safe labor and childbirth. In conclusion, it is necessary to develop a regionalized network to meet the demand by pregnant women in the territory with universal and equitable coverage.

The influence of underlying topography on lava channel networks and flow behavior (Invited)

NASA Astrophysics Data System (ADS)

Dietterich, H. R.; Cashman, K. V.; Rust, A.

2013-12-01

New high resolution mapping of historical lava flows in Hawai';i reveals complex topographically controlled channel networks. Network morphologies range from distributary systems dominated by branching around local obstacles, to tributary systems constricted by topographic confinement. Because channel networks govern the distribution of lava within the flow, they can dramatically alter the effective volumetric flux, which affects both flow length and advance rate. The influence of flow bifurcations is evidenced by (1) channelized flows from Pu';u ';O';o episodes 1-20 at Kilauea Volcano, where flow front velocities decreased by approximately half each time a flow split, and (2) the length of confined flows, such as the Mauna Loa 1859 flow, which traveled twice as far as the distributary Mauna Loa 1984 flow, despite similar effusion rates and durations. To study the underlying controls on flow bifurcations, we have undertaken a series of analogue experiments with golden syrup (a Newtonian fluid) to better understand the physics of obstacle interaction and its influence on flow behavior and morphology. Controlling the effusion rate and surface slope, we extrude flows onto a surface with a cylindrical or V-shaped obstacle of variable angle. When the flow is sufficiently fast, a stationary wave forms upslope of the obstacle; if the stationary wave is sufficiently high, the flow can overtop, rather than split around, the obstacle. The stationary wave height increases with flow velocity and with the effective obstacle width. Evidence for stationary waves in Hawaiian lava flows comes from both photographs of active flows and waveforms frozen into solidified flows. We have also performed a preliminary set of similar experiments with molten basalt to identify the effect of cooling and investigate flow merging. In these experiments, a stationary wave develops upslope of the obstacle, which allows the surface to cool and thicken. After splitting, the syrup experiments show minimal impact of the split on flow advance, except in cases where the flow is very thin, and surface tension controls the flow behavior. In contrast, the experiments with molten basalt slow markedly, as measured by both flow front and surface velocities. This difference demonstrates the effect of cooling and crust formation on flowing lava. Crust formation also controls the ability of split flows to merge below an obstacle, such that flows can converge only at high flow rates, which limits time for crust formation, and at narrow obstacle angles, which limits the lateral spreading required for convergence. Our experiments qualitatively support theoretical descriptions of crustal controls on flow spreading and levee development, but our poor knowledge of the appropriate parameter values, particularly that of the strength of the viscoelastic crust, prevents a quantitative comparison. These experiments, and our observations from natural systems, have significant implications for predicting lava flow behavior and inform efforts to mitigate flow hazards with diversion barriers.

Dynamics of comb-of-comb-network polymers in random layered flows

NASA Astrophysics Data System (ADS)

Katyal, Divya; Kant, Rama

2016-12-01

We analyze the dynamics of comb-of-comb-network polymers in the presence of external random flows. The dynamics of such structures is evaluated through relevant physical quantities, viz., average square displacement (ASD) and the velocity autocorrelation function (VACF). We focus on comparing the dynamics of the comb-of-comb network with the linear polymer. The present work displays an anomalous diffusive behavior of this flexible network in the random layered flows. The effect of the polymer topology on the dynamics is analyzed by varying the number of generations and branch lengths in these networks. In addition, we investigate the influence of external flow on the dynamics by varying flow parameters, like the flow exponent α and flow strength Wα. Our analysis highlights two anomalous power-law regimes, viz., subdiffusive (intermediate-time polymer stretching and flow-induced diffusion) and superdiffusive (long-time flow-induced diffusion). The anomalous long-time dynamics is governed by the temporal exponent ν of ASD, viz., ν =2 -α /2 . Compared to a linear polymer, the comb-of-comb network shows a shorter crossover time (from the subdiffusive to superdiffusive regime) but a reduced magnitude of ASD. Our theory displays an anomalous VACF in the random layered flows that scales as t-α /2. We show that the network with greater total mass moves faster.

Random matrix theory for analyzing the brain functional network in attention deficit hyperactivity disorder

NASA Astrophysics Data System (ADS)

Wang, Rong; Wang, Li; Yang, Yong; Li, Jiajia; Wu, Ying; Lin, Pan

2016-11-01

Attention deficit hyperactivity disorder (ADHD) is the most common childhood neuropsychiatric disorder and affects approximately 6 -7 % of children worldwide. Here, we investigate the statistical properties of undirected and directed brain functional networks in ADHD patients based on random matrix theory (RMT), in which the undirected functional connectivity is constructed based on correlation coefficient and the directed functional connectivity is measured based on cross-correlation coefficient and mutual information. We first analyze the functional connectivity and the eigenvalues of the brain functional network. We find that ADHD patients have increased undirected functional connectivity, reflecting a higher degree of linear dependence between regions, and increased directed functional connectivity, indicating stronger causality and more transmission of information among brain regions. More importantly, we explore the randomness of the undirected and directed functional networks using RMT. We find that for ADHD patients, the undirected functional network is more orderly than that for normal subjects, which indicates an abnormal increase in undirected functional connectivity. In addition, we find that the directed functional networks are more random, which reveals greater disorder in causality and more chaotic information flow among brain regions in ADHD patients. Our results not only further confirm the efficacy of RMT in characterizing the intrinsic properties of brain functional networks but also provide insights into the possibilities RMT offers for improving clinical diagnoses and treatment evaluations for ADHD patients.

Assessment of topographic and drainage network controls on debris-flow travel distance along the west coast of the United States

USGS Publications Warehouse

Coe, Jeffrey A.; Reid, Mark E.; Brien, Dainne L.; Michael, John A.

2011-01-01

To better understand controls on debris-flow entrainment and travel distance, we examined topographic and drainage network characteristics of initiation locations in two separate debris-flow prone areas located 700 km apart along the west coast of the U.S. One area was located in northern California, the other in southern Oregon. In both areas, debris flows mobilized from slides during large storms, but, when stratified by number of contributing initiation locations, median debris-flow travel distances in Oregon were 5 to 8 times longer than median distances in California. Debris flows in Oregon readily entrained channel material; entrainment in California was minimal. To elucidate this difference, we registered initiation locations to high-resolution airborne LiDAR, and then examined travel distances with respect to values of slope, upslope contributing area, planform curvature, distance from initiation locations to the drainage network, and number of initiation areas that contributed to flows. Results show distinct differences in the topographic and drainage network characteristics of debris-flow initiation locations between the two study areas. Slope and planform curvature of initiation locations (landslide headscarps), commonly used to predict landslide-prone areas, were not useful for predicting debris-flow travel distances. However, a positive, power-law relation exists between median debris-flow travel distance and the number of contributing debris-flow initiation locations. Moreover, contributing area and the proximity of the initiation locations to the drainage network both influenced travel distances, but proximity to the drainage network was the better predictor of travel distance. In both study areas, flows that interacted with the drainage network flowed significantly farther than those that did not. In California, initiation sites within 60 m of the network were likely to reach the network and generate longtraveled flows; in Oregon, the threshold was 80 m.

"It's Not My Community"--Insights from Social Identity Theory Explaining Community-Failure

ERIC Educational Resources Information Center

Mueller, Julia; Renzl, Birgit; Kaar, Alexandra

2008-01-01

The availability of new Information and Communication Technologies (ICT) has led organisations to implement different kinds of electronic knowledge networks and communities. While the technological infrastructure connects employees in different locations and allows for learning and the flow of knowledge across traditional organisational…

Controllability of flow-conservation networks

NASA Astrophysics Data System (ADS)

Zhao, Chen; Zeng, An; Jiang, Rui; Yuan, Zhengzhong; Wang, Wen-Xu

2017-07-01

The ultimate goal of exploring complex networks is to control them. As such, controllability of complex networks has been intensively investigated. Despite recent advances in studying the impact of a network's topology on its controllability, a comprehensive understanding of the synergistic impact of network topology and dynamics on controllability is still lacking. Here, we explore the controllability of flow-conservation networks, trying to identify the minimal number of driver nodes that can guide the network to any desirable state. We develop a method to analyze the controllability on flow-conservation networks based on exact controllability theory, transforming the original analysis on adjacency matrix to Laplacian matrix. With this framework, we systematically investigate the impact of some key factors of networks, including link density, link directionality, and link polarity, on the controllability of these networks. We also obtain the analytical equations by investigating the network's structural properties approximatively and design the efficient tools. Finally, we consider some real networks with flow dynamics, finding that their controllability is significantly different from that predicted by only considering the topology. These findings deepen our understanding of network controllability with flow-conservation dynamics and provide a general framework to incorporate real dynamics in the analysis of network controllability.

Optimizing Power–Frequency Droop Characteristics of Distributed Energy Resources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guggilam, Swaroop S.; Zhao, Changhong; Dall Anese, Emiliano

This paper outlines a procedure to design power-frequency droop slopes for distributed energy resources (DERs) installed in distribution networks to optimally participate in primary frequency response. In particular, the droop slopes are engineered such that DERs respond in proportion to their power ratings and they are not unfairly penalized in power provisioning based on their location in the distribution network. The main contribution of our approach is that a guaranteed level of frequency regulation can be guaranteed at the feeder head, while ensuring that the outputs of individual DERs conform to some well-defined notion of fairness. The approach we adoptmore » leverages an optimization-based perspective and suitable linearizations of the power-flow equations to embed notions of fairness and information regarding the physics of the power flows within the distribution network into the droop slopes. Time-domain simulations from a differential algebraic equation model of the 39-bus New England test-case system augmented with three instances of the IEEE 37-node distribution-network with frequency-sensitive DERs are provided to validate our approach.« less

Enhanced reconstruction of weighted networks from strengths and degrees

NASA Astrophysics Data System (ADS)

Mastrandrea, Rossana; Squartini, Tiziano; Fagiolo, Giorgio; Garlaschelli, Diego

2014-04-01

Network topology plays a key role in many phenomena, from the spreading of diseases to that of financial crises. Whenever the whole structure of a network is unknown, one must resort to reconstruction methods that identify the least biased ensemble of networks consistent with the partial information available. A challenging case, frequently encountered due to privacy issues in the analysis of interbank flows and Big Data, is when there is only local (node-specific) aggregate information available. For binary networks, the relevant ensemble is one where the degree (number of links) of each node is constrained to its observed value. However, for weighted networks the problem is much more complicated. While the naïve approach prescribes to constrain the strengths (total link weights) of all nodes, recent counter-intuitive results suggest that in weighted networks the degrees are often more informative than the strengths. This implies that the reconstruction of weighted networks would be significantly enhanced by the specification of both strengths and degrees, a computationally hard and bias-prone procedure. Here we solve this problem by introducing an analytical and unbiased maximum-entropy method that works in the shortest possible time and does not require the explicit generation of reconstructed samples. We consider several real-world examples and show that, while the strengths alone give poor results, the additional knowledge of the degrees yields accurately reconstructed networks. Information-theoretic criteria rigorously confirm that the degree sequence, as soon as it is non-trivial, is irreducible to the strength sequence. Our results have strong implications for the analysis of motifs and communities and whenever the reconstructed ensemble is required as a null model to detect higher-order patterns.

Process Network Approach to Understanding How Forest Ecosystems Adapt to Changes

NASA Astrophysics Data System (ADS)

Kim, J.; Yun, J.; Hong, J.; Kwon, H.; Chun, J.

2011-12-01

Sustainability challenges are transforming science and its role in society. Complex systems science has emerged as an inevitable field of education and research, which transcends disciplinary boundaries and focuses on understanding of the dynamics of complex social-ecological systems (SES). SES is a combined system of social and ecological components and drivers that interact and give rise to results, which could not be understood on the basis of sociological or ecological considerations alone. However, both systems may be viewed as a network of processes, and such a network hierarchy may serve as a hinge to bridge social and ecological systems. As a first step toward such effort, we attempted to delineate and interpret such process networks in forest ecosystems, which play a critical role in the cycles of carbon and water from local to global scales. These cycles and their variability, in turn, play an important role in the emergent and self-organizing interactions between forest ecosystems and their environment. Ruddell and Kumar (2009) define a process network as a network of feedback loops and the related time scales, which describe the magnitude and direction of the flow of energy, matter, and information between the different variables in a complex system. Observational evidence, based on micrometeorological eddy covariance measurements, suggests that heterogeneity and disturbances in forest ecosystems in monsoon East Asia may facilitate to build resilience for adaptation to change. Yet, the principles that characterize the role of variability in these interactions remain elusive. In this presentation, we report results from the analysis of multivariate ecohydrologic and biogeochemical time series data obtained from temperate forest ecosystems in East Asia based on information flow statistics.

Architecture Design and Experimental Platform Demonstration of Optical Network based on OpenFlow Protocol

NASA Astrophysics Data System (ADS)

Xing, Fangyuan; Wang, Honghuan; Yin, Hongxi; Li, Ming; Luo, Shenzi; Wu, Chenguang

2016-02-01

With the extensive application of cloud computing and data centres, as well as the constantly emerging services, the big data with the burst characteristic has brought huge challenges to optical networks. Consequently, the software defined optical network (SDON) that combines optical networks with software defined network (SDN), has attracted much attention. In this paper, an OpenFlow-enabled optical node employed in optical cross-connect (OXC) and reconfigurable optical add/drop multiplexer (ROADM), is proposed. An open source OpenFlow controller is extended on routing strategies. In addition, the experiment platform based on OpenFlow protocol for software defined optical network, is designed. The feasibility and availability of the OpenFlow-enabled optical nodes and the extended OpenFlow controller are validated by the connectivity test, protection switching and load balancing experiments in this test platform.

SDN Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Rhett

The SDN Project completed on time and on budget and successfully accomplished 100% of the scope of work outlined in the original Statement of Project Objective (SOPO). The SDN Project formed an alliance between Ameren Corporation, University of Illinois Urbana- Champaign (UIUC), Pacific Northwest National Laboratories (PNNL), and Schweitzer Engineering Laboratories, Inc. (SEL). The objective of the SDN Project is to address Topic Area of Interest 2: Sustain critical energy delivery functions while responding to a cyber-intrusion under Funding Opportunity Announcement DE-FOA-0000797. The goal of the project is to design and commercially release technology that provides a method to sustainmore » critical energy delivery functions during a cyber intrusion and to do this control system operators need the ability to quickly identify and isolate the affected network areas, and re-route critical information and control flows around. The objective of the SDN Project is to develop a Flow Controller that monitors, configures, and maintains the safe, reliable network traffic flows of all the local area networks (LANs) on a control system in the Energy sector. The SDN team identified the core attributes of a control system and produced an SDN flow controller that has the same core attributes enabling networks to be designed, configured and deployed that maximize the whitelisted, deny-bydefault and purpose built networks. This project researched, developed and commercially released technology that: Enables all field networks be to configured and monitored as if they are a single asset to be protected; Enables greatly improved and even precalculated response actions to reliability and cyber events; Supports pre-configured localized response actions tailored to provide resilience against failures and centralized response to cyber-attacks that improve network reliability and availability; Architecturally enables the right subject matter experts, who are usually the information technology and operational technology engineers, to be the ones centrally administering the technology and responding to events; Simplifies network configuration, improving deterministic Ethernet transport times, and providing instant visualization on where the communication circuits are and how all circuits are impacted when changes (e.g., configuration changes, failures or intrusions) happen, allowing operators to minimize downtime; and Improves the ability to identify deviations in network behavior resulting in detection and analysis of potential cyber intrusions and faster response times Results: This project has forever changed the way critical infrastructure networks are designed, secured, deployed and maintained. The cybersecurity and performance advantages achieved are significant, simply put traditional networking has been obsoleted while the team maintained Ethernet interoperability avoiding any legacy concerns. The team commercially released technology that accomplished all the cybersecurity goals outlined in the SOPO and completed it by executing the project management plan approved in the initial contract. The resulting Energy sector SDN flow controller model number is SEL-5056 and can be freely downloaded from the www.SELinc.com website. This technology not only improves the cybersecurity of control systems but has measured results that it improves the performance and reliability of the control system as well. This means the system owners can confidently apply it to their systems knowing that it will, “first do no harm” but actually improve the system as well. Success of the project is best measured by the sales and deployment of the technology. System owners in industrial, electric, defense, and oil and gas only months after commercial release have approved plans for deployment.« less

Information flow and work productivity through integrated information technology

NASA Technical Reports Server (NTRS)

Wigand, R. T.

1985-01-01

The work environment surrounding integrated office systems is reviewed. The known effects of automated office technologies is synthesized and their known impact on work efficiency is reviewed. These effects are explored with regard to their impact on networks, work flow/processes, as well as organizational structure and power. Particular emphasis is given to structural changes due to the introduction of newer information technologies in organizations. The new information technologies have restructed the average organization's middle banks and, as a consequence, they have shrunk drastically. Organizational pyramids have flattened with fewer levels since executives have realized that they can get ahold of the needed information via the new technologies quicker and directly and do not have to rely on middle-level managers. Power shifts are typically accompanied with the introduction of these technologies resulting in the generation of a new form of organizational power.

Gait Recognition Based on Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Sokolova, A.; Konushin, A.

2017-05-01

In this work we investigate the problem of people recognition by their gait. For this task, we implement deep learning approach using the optical flow as the main source of motion information and combine neural feature extraction with the additional embedding of descriptors for representation improvement. In order to find the best heuristics, we compare several deep neural network architectures, learning and classification strategies. The experiments were made on two popular datasets for gait recognition, so we investigate their advantages and disadvantages and the transferability of considered methods.

Integrative models of vascular remodeling during tumor growth

PubMed Central

Rieger, Heiko; Welter, Michael

2015-01-01

Malignant solid tumors recruit the blood vessel network of the host tissue for nutrient supply, continuous growth, and gain of metastatic potential. Angiogenesis (the formation of new blood vessels), vessel cooption (the integration of existing blood vessels into the tumor vasculature), and vessel regression remodel the healthy vascular network into a tumor-specific vasculature that is in many respects different from the hierarchically organized arterio-venous blood vessel network of the host tissues. Integrative models based on detailed experimental data and physical laws implement in silico the complex interplay of molecular pathways, cell proliferation, migration, and death, tissue microenvironment, mechanical and hydrodynamic forces, and the fine structure of the host tissue vasculature. With the help of computer simulations high-precision information about blood flow patterns, interstitial fluid flow, drug distribution, oxygen and nutrient distribution can be obtained and a plethora of therapeutic protocols can be tested before clinical trials. In this review, we give an overview over the current status of integrative models describing tumor growth, vascular remodeling, blood and interstitial fluid flow, drug delivery, and concomitant transformations of the microenvironment. © 2015 The Authors. WIREs Systems Biology and Medicine published by Wiley Periodicals, Inc. PMID:25808551

Mission informed needed information: discoverable, available sensing sources (MINI-DASS): the operators and process flows the magic rabbits must negotiate

NASA Astrophysics Data System (ADS)

Kolodny, Michael A.

2017-05-01

Today's battlefield space is extremely complex, dealing with an enemy that is neither well-defined nor well-understood. Adversaries are comprised of widely-distributed, loosely-networked groups engaging in nefarious activities. Situational understanding is needed by decision makers; understanding of adversarial capabilities and intent is essential. Information needed at any time is dependent on the mission/task at hand. Information sources potentially providing mission-relevant information are disparate and numerous; they include sensors, social networks, fusion engines, internet, etc. Management of these multi-dimensional informational sources is critical. This paper will present a new approach being undertaken to answer the challenge of enhancing battlefield understanding by optimizing the utilization of available informational sources (means) to required missions/tasks as well as determining the "goodness'" of the information acquired in meeting the capabilities needed. Requirements are usually expressed in terms of a presumed technology solution (e.g., imagery). A metaphor of the "magic rabbits" was conceived to remove presumed technology solutions from requirements by claiming the "required" technology is obsolete. Instead, intelligent "magic rabbits" are used to provide needed information. The question then becomes: "WHAT INFORMATION DO YOU NEED THE RABBITS TO PROVIDE YOU?" This paper will describe a new approach called Mission-Informed Needed Information - Discoverable, Available Sensing Sources (MINI-DASS) that designs a process that builds information acquisition missions and determines what the "magic rabbits" need to provide in a manner that is machine understandable. Also described is the Missions and Means Framework (MMF) model used, the process flow utilized, the approach to developing an ontology of information source means and the approach for determining the value of the information acquired.

Real-time image processing for particle tracking velocimetry

NASA Astrophysics Data System (ADS)

Kreizer, Mark; Ratner, David; Liberzon, Alex

2010-01-01

We present a novel high-speed particle tracking velocimetry (PTV) experimental system. Its novelty is due to the FPGA-based, real-time image processing "on camera". Instead of an image, the camera transfers to the computer using a network card, only the relevant information of the identified flow tracers. Therefore, the system is ideal for the remote particle tracking systems in research and industrial applications, while the camera can be controlled and data can be transferred over any high-bandwidth network. We present the hardware and the open source software aspects of the PTV experiments. The tracking results of the new experimental system has been compared to the flow visualization and particle image velocimetry measurements. The canonical flow in the central cross section of a a cubic cavity (1:1:1 aspect ratio) in our lid-driven cavity apparatus is used for validation purposes. The downstream secondary eddy (DSE) is the sensitive portion of this flow and its size was measured with increasing Reynolds number (via increasing belt velocity). The size of DSE estimated from the flow visualization, PIV and compressed PTV is shown to agree within the experimental uncertainty of the methods applied.

Gust prediction via artificial hair sensor array and neural network

NASA Astrophysics Data System (ADS)

Pankonien, Alexander M.; Thapa Magar, Kaman S.; Beblo, Richard V.; Reich, Gregory W.

2017-04-01

Gust Load Alleviation (GLA) is an important aspect of flight dynamics and control that reduces structural loadings and enhances ride quality. In conventional GLA systems, the structural response to aerodynamic excitation informs the control scheme. A phase lag, imposed by inertia, between the excitation and the measurement inherently limits the effectiveness of these systems. Hence, direct measurement of the aerodynamic loading can eliminate this lag, providing valuable information for effective GLA system design. Distributed arrays of Artificial Hair Sensors (AHS) are ideal for surface flow measurements that can be used to predict other necessary parameters such as aerodynamic forces, moments, and turbulence. In previous work, the spatially distributed surface flow velocities obtained from an array of artificial hair sensors using a Single-State (or feedforward) Neural Network were found to be effective in estimating the steady aerodynamic parameters such as air speed, angle of attack, lift and moment coefficient. This paper extends the investigation of the same configuration to unsteady force and moment estimation, which is important for active GLA control design. Implementing a Recurrent Neural Network that includes previous-timestep sensor information, the hair sensor array is shown to be capable of capturing gust disturbances with a wide range of periods, reducing predictive error in lift and moment by 68% and 52% respectively. The L2 norms of the first layer of the weight matrices were compared showing a 23% emphasis on prior versus current information. The Recurrent architecture also improves robustness, exhibiting only a 30% increase in predictive error when undertrained as compared to a 170% increase by the Single-State NN. This diverse, localized information can thus be directly implemented into a control scheme that alleviates the gusts without waiting for a structural response or requiring user-intensive sensor calibration.

Centralized versus decentralized decision-making for recycled material flows.

PubMed

Hong, I-Hsuan; Ammons, Jane C; Realff, Matthew J

2008-02-15

A reverse logistics system is a network of transportation logistics and processing functions that collect, consolidate, refurbish, and demanufacture end-of-life products. This paper examines centralized and decentralized models of decision-making for material flows and associated transaction prices in reverse logistics networks. We compare the application of a centralized model for planning reverse production systems, where a single planner is acquainted with all of the system information and has the authority to determine decision variables for the entire system, to a decentralized approach. In the decentralized approach, the entities coordinate between tiers of the system using a parametrized flow function and compete within tiers based on reaching a price equilibrium. We numerically demonstrate the increase in the total net profit of the centralized system relative to the decentralized one. This implies that one may overestimate the system material flows and profit if the system planner utilizes a centralized viewto predict behaviors of independent entities in the system and that decentralized contract mechanisms will require careful design to avoid losses in the efficiency and scope of these systems.

Urban networks among Chinese cities along "the Belt and Road": A case of web search activity in cyberspace

PubMed Central

Du, Hongru; Zhao, Yannan; Wu, Rongwei; Zhang, Xiaolei

2017-01-01

“The Belt and Road” initiative has been expected to facilitate interactions among numerous city centers. This initiative would generate a number of centers, both economic and political, which would facilitate greater interaction. To explore how information flows are merged and the specific opportunities that may be offered, Chinese cities along “the Belt and Road” are selected for a case study. Furthermore, urban networks in cyberspace have been characterized by their infrastructure orientation, which implies that there is a relative dearth of studies focusing on the investigation of urban hierarchies by capturing information flows between Chinese cities along “the Belt and Road”. This paper employs Baidu, the main web search engine in China, to examine urban hierarchies. The results show that urban networks become more balanced, shifting from a polycentric to a homogenized pattern. Furthermore, cities in networks tend to have both a hierarchical system and a spatial concentration primarily in regions such as Beijing-Tianjin-Hebei, Yangtze River Delta and the Pearl River Delta region. Urban hierarchy based on web search activity does not follow the existing hierarchical system based on geospatial and economic development in all cases. Moreover, urban networks, under the framework of “the Belt and Road”, show several significant corridors and more opportunities for more cities, particularly western cities. Furthermore, factors that may influence web search activity are explored. The results show that web search activity is significantly influenced by the economic gap, geographical proximity and administrative rank of the city. PMID:29200421

Rate-based congestion control in networks with smart links, revision. B.S. Thesis - May 1988

NASA Technical Reports Server (NTRS)

Heybey, Andrew Tyrrell

1990-01-01

The author uses a network simulator to explore rate-based congestion control in networks with smart links that can feed back information to tell senders to adjust their transmission rates. This method differs in a very important way from congestion control in which a congested network component just drops packets - the most commonly used method. It is clearly advantageous for the links in the network to communicate with the end users about the network capacity, rather than the users unilaterally picking a transmission rate. The components in the middle of the network, not the end users, have information about the capacity and traffic in the network. The author experiments with three different algorithms for calculating the control rate to feed back to the users. All of the algorithms exhibit problems in the form of large queues when simulated with a configuration modeling the dynamics of a packet-voice system. However, the problems are not with the algorithms themselves, but with the fact that feedback takes time. If the network steady-state utilization is low enough that it can absorb transients in the traffic through it, then the large queues disappear. If the users are modified to start sending slowly, to allow the network to adapt to a new flow without causing congestion, a greater portion of the network's bandwidth can be used.

Identifying influential directors in the United States corporate governance network

NASA Astrophysics Data System (ADS)

Huang, Xuqing; Vodenska, Irena; Wang, Fengzhong; Havlin, Shlomo; Stanley, H. Eugene

2011-10-01

The influence of directors has been one of the most engaging topics recently, but surprisingly little research has been done to quantitatively evaluate the influence and power of directors. We analyze the structure of the US corporate governance network for the 11-year period 1996-2006 based on director data from the Investor Responsibility Research Center director database, and we develop a centrality measure named the influence factor to estimate the influence of directors quantitatively. The US corporate governance network is a network of directors with nodes representing directors and links between two directors representing their service on common company boards. We assume that information flows in the network through information-sharing processes among linked directors. The influence factor assigned to a director is based on the level of information that a director obtains from the entire network. We find that, contrary to commonly accepted belief that directors of large companies, measured by market capitalization, are the most powerful, in some instances, the directors who are influential do not necessarily serve on boards of large companies. By applying our influence factor method to identify the influential people contained in the lists created by popular magazines such as Fortune, Networking World, and Treasury and Risk Management, we find that the influence factor method is consistently either the best or one of the two best methods in identifying powerful people compared to other general centrality measures that are used to denote the significance of a node in complex network theory.

Identifying influential directors in the United States corporate governance network.

PubMed

Huang, Xuqing; Vodenska, Irena; Wang, Fengzhong; Havlin, Shlomo; Stanley, H Eugene

2011-10-01

The influence of directors has been one of the most engaging topics recently, but surprisingly little research has been done to quantitatively evaluate the influence and power of directors. We analyze the structure of the US corporate governance network for the 11-year period 1996-2006 based on director data from the Investor Responsibility Research Center director database, and we develop a centrality measure named the influence factor to estimate the influence of directors quantitatively. The US corporate governance network is a network of directors with nodes representing directors and links between two directors representing their service on common company boards. We assume that information flows in the network through information-sharing processes among linked directors. The influence factor assigned to a director is based on the level of information that a director obtains from the entire network. We find that, contrary to commonly accepted belief that directors of large companies, measured by market capitalization, are the most powerful, in some instances, the directors who are influential do not necessarily serve on boards of large companies. By applying our influence factor method to identify the influential people contained in the lists created by popular magazines such as Fortune, Networking World, and Treasury and Risk Management, we find that the influence factor method is consistently either the best or one of the two best methods in identifying powerful people compared to other general centrality measures that are used to denote the significance of a node in complex network theory.

Numerical Modeling of Interstitial Fluid Flow Coupled with Blood Flow through a Remodeled Solid Tumor Microvascular Network

PubMed Central

Soltani, M.; Chen, P.

2013-01-01

Modeling of interstitial fluid flow involves processes such as fluid diffusion, convective transport in extracellular matrix, and extravasation from blood vessels. To date, majority of microvascular flow modeling has been done at different levels and scales mostly on simple tumor shapes with their capillaries. However, with our proposed numerical model, more complex and realistic tumor shapes and capillary networks can be studied. Both blood flow through a capillary network, which is induced by a solid tumor, and fluid flow in tumor’s surrounding tissue are formulated. First, governing equations of angiogenesis are implemented to specify the different domains for the network and interstitium. Then, governing equations for flow modeling are introduced for different domains. The conservation laws for mass and momentum (including continuity equation, Darcy’s law for tissue, and simplified Navier–Stokes equation for blood flow through capillaries) are used for simulating interstitial and intravascular flows and Starling’s law is used for closing this system of equations and coupling the intravascular and extravascular flows. This is the first study of flow modeling in solid tumors to naturalistically couple intravascular and extravascular flow through a network. This network is generated by sprouting angiogenesis and consisting of one parent vessel connected to the network while taking into account the non-continuous behavior of blood, adaptability of capillary diameter to hemodynamics and metabolic stimuli, non-Newtonian blood flow, and phase separation of blood flow in capillary bifurcation. The incorporation of the outlined components beyond the previous models provides a more realistic prediction of interstitial fluid flow pattern in solid tumors and surrounding tissues. Results predict higher interstitial pressure, almost two times, for realistic model compared to the simplified model. PMID:23840579

How does a U.S. Geological Survey streamgage work?

USGS Publications Warehouse

Lurry, Dee L.

2010-01-01

Information on the flow of rivers and streams is a vital national asset that safeguards lives, protects property, and ensures adequate water supplies for the future. The U.S. Geological Survey (USGS) operates a network of more than 9,000 streamgages nationwide with more than 500 in Texas.

Micro/Nano-pore Network Analysis of Gas Flow in Shale Matrix

PubMed Central

Zhang, Pengwei; Hu, Liming; Meegoda, Jay N.; Gao, Shengyan

2015-01-01

The gas flow in shale matrix is of great research interests for optimized shale gas extraction. The gas flow in the nano-scale pore may fall in flow regimes such as viscous flow, slip flow and Knudsen diffusion. A 3-dimensional nano-scale pore network model was developed to simulate dynamic gas flow, and to describe the transient properties of flow regimes. The proposed pore network model accounts for the various size distributions and low connectivity of shale pores. The pore size, pore throat size and coordination number obey normal distribution, and the average values can be obtained from shale reservoir data. The gas flow regimes were simulated using an extracted pore network backbone. The numerical results show that apparent permeability is strongly dependent on pore pressure in the reservoir and pore throat size, which is overestimated by low-pressure laboratory tests. With the decrease of reservoir pressure, viscous flow is weakening, then slip flow and Knudsen diffusion are gradually becoming dominant flow regimes. The fingering phenomenon can be predicted by micro/nano-pore network for gas flow, which provides an effective way to capture heterogeneity of shale gas reservoir. PMID:26310236

Micro/Nano-pore Network Analysis of Gas Flow in Shale Matrix.

PubMed

Zhang, Pengwei; Hu, Liming; Meegoda, Jay N; Gao, Shengyan

2015-08-27

The gas flow in shale matrix is of great research interests for optimized shale gas extraction. The gas flow in the nano-scale pore may fall in flow regimes such as viscous flow, slip flow and Knudsen diffusion. A 3-dimensional nano-scale pore network model was developed to simulate dynamic gas flow, and to describe the transient properties of flow regimes. The proposed pore network model accounts for the various size distributions and low connectivity of shale pores. The pore size, pore throat size and coordination number obey normal distribution, and the average values can be obtained from shale reservoir data. The gas flow regimes were simulated using an extracted pore network backbone. The numerical results show that apparent permeability is strongly dependent on pore pressure in the reservoir and pore throat size, which is overestimated by low-pressure laboratory tests. With the decrease of reservoir pressure, viscous flow is weakening, then slip flow and Knudsen diffusion are gradually becoming dominant flow regimes. The fingering phenomenon can be predicted by micro/nano-pore network for gas flow, which provides an effective way to capture heterogeneity of shale gas reservoir.

Millisecond-timescale local network coding in the rat primary somatosensory cortex.

PubMed

Eldawlatly, Seif; Oweiss, Karim G

2011-01-01

Correlation among neocortical neurons is thought to play an indispensable role in mediating sensory processing of external stimuli. The role of temporal precision in this correlation has been hypothesized to enhance information flow along sensory pathways. Its role in mediating the integration of information at the output of these pathways, however, remains poorly understood. Here, we examined spike timing correlation between simultaneously recorded layer V neurons within and across columns of the primary somatosensory cortex of anesthetized rats during unilateral whisker stimulation. We used bayesian statistics and information theory to quantify the causal influence between the recorded cells with millisecond precision. For each stimulated whisker, we inferred stable, whisker-specific, dynamic bayesian networks over many repeated trials, with network similarity of 83.3±6% within whisker, compared to only 50.3±18% across whiskers. These networks further provided information about whisker identity that was approximately 6 times higher than what was provided by the latency to first spike and 13 times higher than what was provided by the spike count of individual neurons examined separately. Furthermore, prediction of individual neurons' precise firing conditioned on knowledge of putative pre-synaptic cell firing was 3 times higher than predictions conditioned on stimulus onset alone. Taken together, these results suggest the presence of a temporally precise network coding mechanism that integrates information across neighboring columns within layer V about vibrissa position and whisking kinetics to mediate whisker movement by motor areas innervated by layer V.

The Role of Advanced Information System Technology in Remote Sensing for NASA's Earth Science Enterprise in the 21st Century

NASA Technical Reports Server (NTRS)

Prescott, Glenn; Komar, George (Technical Monitor)

2001-01-01

Future NASA Earth observing satellites will carry high-precision instruments capable of producing large amounts of scientific data. The strategy will be to network these instrument-laden satellites into a web-like array of sensors to facilitate the collection, processing, transmission, storage, and distribution of data and data products - the essential elements of what we refer to as "Information Technology." Many of these Information Technologies will enable the satellite and ground information systems to function effectively in real-time, providing scientists with the capability of customizing data collection activities on a satellite or group of satellites directly from the ground. In future systems, extremely large quantities of data collected by scientific instruments will require the fastest processors, the highest communication channel transfer rates, and the largest data storage capacity to insure that data flows smoothly from the satellite-based instrument to the ground-based archive. Autonomous systems will control all essential processes and play a key role in coordinating the data flow through space-based communication networks. In this paper, we will discuss those critical information technologies for Earth observing satellites that will support the next generation of space-based scientific measurements of planet Earth, and insure that data and data products provided by these systems will be accessible to scientists and the user community in general.

Characteristics of individuals with high information potential in government research and development organizations.

NASA Technical Reports Server (NTRS)

Holland, W. E.

1972-01-01

In order to study focal individuals within informal communications networks, a special variable was constructed: information potential (IP) was defined as the information-source value placed on an individual by his colleagues. Four hypotheses involving IP were tested in three R&D organizations using questionnaires and pencil-and-paper tests. Results indicated that the individual with high IP used more and different sources of technical information, was seen to be a credible information source and to have a strong ability to associate seemingly unrelated ideas, and was as approachable as the other members of his organization. Four tentative conclusions may be drawn from this study concerning the person with high IP. He is (1) an identifiable individual in several different kinds of organizations; (2) a distinctive information transceiver (transmitter and receiver); (3) both a producer and a catalyst in his own organization; and (4) an extender and an amplifier of information search. To affect the efficiency of informal information flow, the research manager's best hope for positively influencing informal networks lies in the identification and motivation of the special communicators in his organization.

A web-based information system for a regional public mental healthcare service network in Brazil.

PubMed

Yoshiura, Vinicius Tohoru; de Azevedo-Marques, João Mazzoncini; Rzewuska, Magdalena; Vinci, André Luiz Teixeira; Sasso, Ariane Morassi; Miyoshi, Newton Shydeo Brandão; Furegato, Antonia Regina Ferreira; Rijo, Rui Pedro Charters Lopes; Del-Ben, Cristina Marta; Alves, Domingos

2017-01-01

Regional networking between services that provide mental health care in Brazil's decentralized public health system is challenging, partly due to the simultaneous existence of services managed by municipal and state authorities and a lack of efficient and transparent mechanisms for continuous and updated communication between them. Since 2011, the Ribeirao Preto Medical School and the XIII Regional Health Department of the Sao Paulo state, Brazil, have been developing and implementing a web-based information system to facilitate an integrated care throughout a public regional mental health care network. After a profound on-site analysis, the structure of the network was identified and a web-based information system for psychiatric admissions and discharges was developed and implemented using a socio-technical approach. An information technology team liaised with mental health professionals, health-service managers, municipal and state health secretariats and judicial authorities. Primary care, specialized community services, general emergency and psychiatric wards services, that comprise the regional mental healthcare network, were identified and the system flow was delineated. The web-based system overcame the fragmentation of the healthcare system and addressed service specific needs, enabling: detailed patient information sharing; active coordination of the processes of psychiatric admissions and discharges; real-time monitoring; the patients' status reports; the evaluation of the performance of each service and the whole network. During a 2-year period of operation, it registered 137 services, 480 health care professionals and 4271 patients, with a mean number of 2835 accesses per month. To date the system is successfully operating and further expanding. We have successfully developed and implemented an acceptable, useful and transparent web-based information system for a regional mental healthcare service network in a medium-income country with a decentralized public health system. Systematic collaboration between an information technology team and a wide range of stakeholders is essential for the system development and implementation.

Research on the business QoS and trustworthy and controllable strategies mechanism for energy internet

NASA Astrophysics Data System (ADS)

Zhang, Geng; Lu, Meiling; Zhang, Dahua; Zhou, Liang; Li, Likang

2017-01-01

Energy internet is a kind of power sharing network, which can realize the bidirectional flow of energy information on the basis of the existing power grid. It puts forward higher requirements for reliability and controllability of information communication, and all kinds of business QoS of the backbone network. So the research of business QoS and trustworthy and controllable strategies mechanism have an important significance for the development of energy internet. This paper mainly studies how to use the software defined network (SDN) to achieve business QoS, and provide QoS support for all kinds of business of the energy internet. Combined with the current development situation of the energy internet in our country, this paper researches the trustworthy and controllable strategies mechanism for energy internet, and proposes the transition scheme of the IPv6 credible network architecture based on SDN. This coordinates the contradiction between the growing demand for energy internet applications and the limitations of the energy internet technology itself.

Population Connectivity Measures of Fishery-Targeted Coral Reef Species to Inform Marine Reserve Network Design in Fiji

PubMed Central

Eastwood, Erin K.; López, Elora H.; Drew, Joshua A.

2016-01-01

Coral reef fish serve as food sources to coastal communities worldwide, yet are vulnerable to mounting anthropogenic pressures like overfishing and climate change. Marine reserve networks have become important tools for mitigating these pressures, and one of the most critical factors in determining their spatial design is the degree of connectivity among different populations of species prioritized for protection. To help inform the spatial design of an expanded reserve network in Fiji, we used rapidly evolving mitochondrial genes to investigate connectivity patterns of three coral reef species targeted by fisheries in Fiji: Epinephelus merra (Serranidae), Halichoeres trimaculatus (Labridae), and Holothuria atra (Holothuriidae). The two fish species, E. merra and Ha. trimaculatus, exhibited low genetic structuring and high amounts of gene flow, whereas the sea cucumber Ho. atra displayed high genetic partitioning and predominantly westward gene flow. The idiosyncratic patterns observed among these species indicate that patterns of connectivity in Fiji are likely determined by a combination of oceanographic and ecological characteristics. Our data indicate that in the cases of species with high connectivity, other factors such as representation or political availability may dictate where reserves are placed. In low connectivity species, ensuring upstream and downstream connections is critical. PMID:26805954

The effect of individual variation on the structure and function of interaction networks in harvester ants

PubMed Central

Pinter-Wollman, Noa; Wollman, Roy; Guetz, Adam; Holmes, Susan; Gordon, Deborah M.

2011-01-01

Social insects exhibit coordinated behaviour without central control. Local interactions among individuals determine their behaviour and regulate the activity of the colony. Harvester ants are recruited for outside work, using networks of brief antennal contacts, in the nest chamber closest to the nest exit: the entrance chamber. Here, we combine empirical observations, image analysis and computer simulations to investigate the structure and function of the interaction network in the entrance chamber. Ant interactions were distributed heterogeneously in the chamber, with an interaction hot-spot at the entrance leading further into the nest. The distribution of the total interactions per ant followed a right-skewed distribution, indicating the presence of highly connected individuals. Numbers of ant encounters observed positively correlated with the duration of observation. Individuals varied in interaction frequency, even after accounting for the duration of observation. An ant's interaction frequency was explained by its path shape and location within the entrance chamber. Computer simulations demonstrate that variation among individuals in connectivity accelerates information flow to an extent equivalent to an increase in the total number of interactions. Individual variation in connectivity, arising from variation among ants in location and spatial behaviour, creates interaction centres, which may expedite information flow. PMID:21490001

Optimal route discovery for soft QOS provisioning in mobile ad hoc multimedia networks

NASA Astrophysics Data System (ADS)

Huang, Lei; Pan, Feng

2007-09-01

In this paper, we propose an optimal routing discovery algorithm for ad hoc multimedia networks whose resource keeps changing, First, we use stochastic models to measure the network resource availability, based on the information about the location and moving pattern of the nodes, as well as the link conditions between neighboring nodes. Then, for a certain multimedia packet flow to be transmitted from a source to a destination, we formulate the optimal soft-QoS provisioning problem as to find the best route that maximize the probability of satisfying its desired QoS requirements in terms of the maximum delay constraints. Based on the stochastic network resource model, we developed three approaches to solve the formulated problem: A centralized approach serving as the theoretical reference, a distributed approach that is more suitable to practical real-time deployment, and a distributed dynamic approach that utilizes the updated time information to optimize the routing for each individual packet. Examples of numerical results demonstrated that using the route discovered by our distributed algorithm in a changing network environment, multimedia applications could achieve better QoS statistically.

Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

PubMed Central

Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

Magnetic Skyrmion as a Nonlinear Resistive Element: A Potential Building Block for Reservoir Computing

NASA Astrophysics Data System (ADS)

Prychynenko, Diana; Sitte, Matthias; Litzius, Kai; Krüger, Benjamin; Bourianoff, George; Kläui, Mathias; Sinova, Jairo; Everschor-Sitte, Karin

2018-01-01

Inspired by the human brain, there is a strong effort to find alternative models of information processing capable of imitating the high energy efficiency of neuromorphic information processing. One possible realization of cognitive computing involves reservoir computing networks. These networks are built out of nonlinear resistive elements which are recursively connected. We propose that a Skyrmion network embedded in magnetic films may provide a suitable physical implementation for reservoir computing applications. The significant key ingredient of such a network is a two-terminal device with nonlinear voltage characteristics originating from magnetoresistive effects, such as the anisotropic magnetoresistance or the recently discovered noncollinear magnetoresistance. The most basic element for a reservoir computing network built from "Skyrmion fabrics" is a single Skyrmion embedded in a ferromagnetic ribbon. In order to pave the way towards reservoir computing systems based on Skyrmion fabrics, we simulate and analyze (i) the current flow through a single magnetic Skyrmion due to the anisotropic magnetoresistive effect and (ii) the combined physics of local pinning and the anisotropic magnetoresistive effect.

Splitting nodes and linking channels: A method for assembling biocircuits from stochastic elementary units

NASA Astrophysics Data System (ADS)

Ferwerda, Cameron; Lipan, Ovidiu

2016-11-01

Akin to electric circuits, we construct biocircuits that are manipulated by cutting and assembling channels through which stochastic information flows. This diagrammatic manipulation allows us to create a method which constructs networks by joining building blocks selected so that (a) they cover only basic processes; (b) it is scalable to large networks; (c) the mean and variance-covariance from the Pauli master equation form a closed system; and (d) given the initial probability distribution, no special boundary conditions are necessary to solve the master equation. The method aims to help with both designing new synthetic signaling pathways and quantifying naturally existing regulatory networks.

On understanding nuclear reaction network flows with branchings on directed graphs

NASA Astrophysics Data System (ADS)

Meyer, Bradley S.

2018-04-01

Nuclear reaction network flow diagrams are useful for understanding which reactions are governing the abundance changes at a particular time during nucleosynthesis. This is especially true when the flows are largely unidirectional, such as during the s-process of nucleosynthesis. In explosive nucleosynthesis, when reaction flows are large, and when forward reactions are nearly balanced by their reverses, reaction flows no longer give a clear picture of the abundance evolution in the network. This paper presents a way of understanding network evolution in terms of sums of branchings on a directed graph, which extends the concept of reaction flows to allow for multiple reaction pathways.

Non-invasive classification of gas-liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

NASA Astrophysics Data System (ADS)

Musa Abbagoni, Baba; Yeung, Hoi

2016-08-01

The identification of flow pattern is a key issue in multiphase flow which is encountered in the petrochemical industry. It is difficult to identify the gas-liquid flow regimes objectively with the gas-liquid two-phase flow. This paper presents the feasibility of a clamp-on instrument for an objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and an artificial neural network, which records and processes the ultrasonic signals reflected from the two-phase flow. Experimental data is obtained on a horizontal test rig with a total pipe length of 21 m and 5.08 cm internal diameter carrying air-water two-phase flow under slug, elongated bubble, stratified-wavy and, stratified flow regimes. Multilayer perceptron neural networks (MLPNNs) are used to develop the classification model. The classifier requires features as an input which is representative of the signals. Ultrasound signal features are extracted by applying both power spectral density (PSD) and discrete wavelet transform (DWT) methods to the flow signals. A classification scheme of ‘1-of-C coding method for classification’ was adopted to classify features extracted into one of four flow regime categories. To improve the performance of the flow regime classifier network, a second level neural network was incorporated by using the output of a first level networks feature as an input feature. The addition of the two network models provided a combined neural network model which has achieved a higher accuracy than single neural network models. Classification accuracies are evaluated in the form of both the PSD and DWT features. The success rates of the two models are: (1) using PSD features, the classifier missed 3 datasets out of 24 test datasets of the classification and scored 87.5% accuracy; (2) with the DWT features, the network misclassified only one data point and it was able to classify the flow patterns up to 95.8% accuracy. This approach has demonstrated the success of a clamp-on ultrasound sensor for flow regime classification that would be possible in industry practice. It is considerably more promising than other techniques as it uses a non-invasive and non-radioactive sensor.

Computer aided approximation of flow rate through systemic-pulmonary arterial shunts (SPAS).

PubMed

Vennemann, Peter; Montag, Michael; Peters, Franz; Merzkirch, Wolfgang

2012-02-22

The discrimination of flow rates through bronchial arteries that are affected by pathological SPAS today still happens solely qualitatively. A reproducible quantification of flow rates, however, would enable the comprehension of phenomena like the intensified shunt perfusion seen in cases of chronic inflammations or the characterization of SPAS that may cause cardiovascular problems. A computational program is developed, that allows the modeling of individual bronchial arteries on the basis of the information provided by angiography. Angiographic images are available from the standard clinical assessment of SPAS. The flow through continuous and geometrically measurable vessel segments and SPAS is given by the law of Hagen-Poiseuille. The discharge through healthy branches is calculated by means of allometric scaling laws. The simulation results are verified by flow experiments in artificial vessel networks made of glass and PE tubing. The experimental set-up mimics realistic, pulsating pressure and flow conditions. When applied to the artificial vessel networks, the model described herein provides results for the volumetric flow rate that differ from values measured in laboratory experiments by <6%. The computer model is also applied to real angiographic images. Due to inaccuracies during the deduction of the geometry and due to necessary simplifications of the model, we expect significant deviations between calculated and real flow rates in bronchial systems. Nevertheless, the presented method enables the physician to objectively estimate the order of magnitude of volumetric flow through individual SPAS fairly independently from his experience and without the need of measurements additional to the mandatory angiography.

Application of the PROMETHEE technique to determine depression outlet location and flow direction in DEM

NASA Astrophysics Data System (ADS)

Chou, Tien-Yin; Lin, Wen-Tzu; Lin, Chao-Yuan; Chou, Wen-Chieh; Huang, Pi-Hui

2004-02-01

With the fast growing progress of computer technologies, spatial information on watersheds such as flow direction, watershed boundaries and the drainage network can be automatically calculated or extracted from a digital elevation model (DEM). The stubborn problem that depressions exist in DEMs has been frequently encountered while extracting the spatial information of terrain. Several filling-up methods have been proposed for solving depressions. However, their suitability for large-scale flat areas is inadequate. This study proposes a depression watershed method coupled with the Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEEs) theory to determine the optimal outlet and calculate the flow direction in depressions. Three processing procedures are used to derive the depressionless flow direction: (1) calculating the incipient flow direction; (2) establishing the depression watershed by tracing the upstream drainage area and determining the depression outlet using PROMETHEE theory; (3) calculating the depressionless flow direction. The developed method was used to delineate the Shihmen Reservoir watershed located in Northern Taiwan. The results show that the depression watershed method can effectively solve the shortcomings such as depression outlet differentiating and looped flow direction between depressions. The suitability of the proposed approach was verified.

Role of Unchannelized Flow in Determining Bifurcation Angle in Distributary Channel Networks

NASA Astrophysics Data System (ADS)

Coffey, T.

2016-12-01

Distributary channel bifurcations on river deltas are important features in both modern systems, where the channels control water, sediment, and nutrient routing, and in ancient deltas, where the channel networks can dictate large-scale stratigraphic heterogeneity. Geometric features of distributary channels, such as channel dimensions and network structure, have long been thought to be defined by factors such as flow velocity, grain size, or channel aspect ratio where the channel enters the basin. We use theory originally developed for tributary networks fed by groundwater seepage to understand the dynamics of distributary channel bifurcations. Interestingly, bifurcations in groundwater-fed tributary networks have been shown to evolve dependent on the diffusive flow patterns around the channel network. These networks possess a characteristic bifurcation angle of 72°, due to Laplacian flow (gradient2h2=0, where h is water surface elevation) in the groundwater flow field near tributary channel tips. We develop and test the hypothesis that bifurcation angles in distributary channel networks are likewise dictated by the external flow field, in this case the shallow surface water surrounding the subaqueous portion of distributary channel bifurcations in a deltaic setting. We measured 130 unique distributary channel bifurcations in a single experimental delta and in 10 natural deltas, yielding a mean angle of 70.35°±2.59° (95% confidence interval), in line with the theoretical prediction. This similarity implies that flow outside of the distributary channel network is also Laplacian, which we use scaling arguments to justify. We conclude that the dynamics of the unchannelized flow control bifurcation formation in distributary networks.

Numerical Simulation of Sickle Cell Blood Flow in the Microcirculation

NASA Astrophysics Data System (ADS)

Berger, Stanley A.; Carlson, Brian E.

2001-11-01

A numerical simulation of normal and sickle cell blood flow through the transverse arteriole-capillary microcirculation is carried out to model the dominant mechanisms involved in the onset of vascular stasis in sickle cell disease. The transverse arteriole-capillary network is described by Strahler's network branching method, and the oxygen and blood transport in the capillaries is modeled by a Krogh cylinder analysis utilizing Lighthill's lubrication theory, as developed by Berger and King. Poiseuille's law is used to represent blood flow in the arterioles. Applying this flow and transport model and utilizing volumetric flow continuity at each network bifurcation, a nonlinear system of equations is obtained, which is solved iteratively using a steepest descent algorithm coupled with a Newton solver. Ten different networks are generated and flow results are calculated for normal blood and sickle cell blood without and with precapillary oxygen loss. We find that total volumetric blood flow through the network is greater in the two sickle cell blood simulations than for normal blood owing to the anemia associated with sickle cell disease. The percentage of capillary blockage in the network increases dramatically with decreasing pressure drop across the network in the sickle cell cases while there is no blockage when normal blood flows through simulated networks. It is concluded that, in sickle cell disease, without any vasomotor dilation response to decreasing oxygen concentrations in the blood, capillary blockage will occur in the microvasculature even at average pressure drops across the transverse arteriole-capillary networks.

Damage Response in Fluid Flow Networks

NASA Astrophysics Data System (ADS)

Gavrilchenko, Tatyana; Katifori, Eleni

The networks found in biological fluid flow systems such as leaf venation and animal vasculature are characterized by hierarchically nested loops. This structure allows the system to be resilient against fluctuations in the flow of fluid and to be robust against damage. We analytically and computationally investigate how this loopy hierarchy determines the extent of disruption in fluid flow in the vicinity of a damage site. Perturbing the network with the removal of a single edge results in the differential flow as a function of distance from the perturbation decaying as a power law. The power law exponent is generally around -2 in 2D, but we find that it varies due to edge effects, initial edge conductivity, and local topology. We expect that these network flow findings, directly applicable to plant and animal veins, will have analogues in electrical grids, traffic flow and other transport networks.

Design of Distributed Controllers Seeking Optimal Power Flow Solutions Under Communication Constraints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall'Anese, Emiliano; Simonetto, Andrea; Dhople, Sairaj

This paper focuses on power distribution networks featuring inverter-interfaced distributed energy resources (DERs), and develops feedback controllers that drive the DER output powers to solutions of time-varying AC optimal power flow (OPF) problems. Control synthesis is grounded on primal-dual-type methods for regularized Lagrangian functions, as well as linear approximations of the AC power-flow equations. Convergence and OPF-solution-tracking capabilities are established while acknowledging: i) communication-packet losses, and ii) partial updates of control signals. The latter case is particularly relevant since it enables asynchronous operation of the controllers where DER setpoints are updated at a fast time scale based on local voltagemore » measurements, and information on the network state is utilized if and when available, based on communication constraints. As an application, the paper considers distribution systems with high photovoltaic integration, and demonstrates that the proposed framework provides fast voltage-regulation capabilities, while enabling the near real-time pursuit of solutions of AC OPF problems.« less

Design of Distributed Controllers Seeking Optimal Power Flow Solutions under Communication Constraints: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall'Anese, Emiliano; Simonetto, Andrea; Dhople, Sairaj

This paper focuses on power distribution networks featuring inverter-interfaced distributed energy resources (DERs), and develops feedback controllers that drive the DER output powers to solutions of time-varying AC optimal power flow (OPF) problems. Control synthesis is grounded on primal-dual-type methods for regularized Lagrangian functions, as well as linear approximations of the AC power-flow equations. Convergence and OPF-solution-tracking capabilities are established while acknowledging: i) communication-packet losses, and ii) partial updates of control signals. The latter case is particularly relevant since it enables asynchronous operation of the controllers where DER setpoints are updated at a fast time scale based on local voltagemore » measurements, and information on the network state is utilized if and when available, based on communication constraints. As an application, the paper considers distribution systems with high photovoltaic integration, and demonstrates that the proposed framework provides fast voltage-regulation capabilities, while enabling the near real-time pursuit of solutions of AC OPF problems.« less

Information processing in the primate visual system - An integrated systems perspective

NASA Technical Reports Server (NTRS)

Van Essen, David C.; Anderson, Charles H.; Felleman, Daniel J.

1992-01-01

The primate visual system contains dozens of distinct areas in the cerebral cortex and several major subcortical structures. These subdivisions are extensively interconnected in a distributed hierarchical network that contains several intertwined processing streams. A number of strategies are used for efficient information processing within this hierarchy. These include linear and nonlinear filtering, passage through information bottlenecks, and coordinated use of multiple types of information. In addition, dynamic regulation of information flow within and between visual areas may provide the computational flexibility needed for the visual system to perform a broad spectrum of tasks accurately and at high resolution.

A Framework for Bridging Scientists, Knowledge Brokers and Local Decision Makers in State-level Climate Assessments

NASA Astrophysics Data System (ADS)

Galford, G. L.; Nash, J. L.

2016-12-01

Large-scale analyses like the National Climate Assessment (NCA) contain a wealth of information critical to national and regional responses to climate change but tend to be insufficiently detailed for action at state or local levels. Many states now develop assessments (SCAs) to provide relevant, actionable information to state and local authorities. These assessments generate new or additional primary information, build networks and inform stakeholders. Based on our experience in the Vermont Climate Assessment (VCA), we present a SCA framework to engage local decision makers, using a fluid network of scientific experts and knowledge brokers to conduct subject area prioritization, data analysis, and writing. Knowledge brokers bridged the scientific and stakeholder communities, providing a two-way flow of information by capitalizing on their existing networks. Rich citizen records of climate and climate change impacts associated a human voice, a memorable story, or personal observation with a climate record, improving climate information salience. This engagement process that created salient climate information perceived as credible and legitimate by local and state decision makers. We present this framework as an effective structure for SCAs to foster interaction among scientists, knowledge brokers and stakeholders. We include a qualitative impact evaluation and lessons learned for future SCAs.

Fault detection on a sewer network by a combination of a Kalman filter and a binary sequential probability ratio test

NASA Astrophysics Data System (ADS)

Piatyszek, E.; Voignier, P.; Graillot, D.

2000-05-01

One of the aims of sewer networks is the protection of population against floods and the reduction of pollution rejected to the receiving water during rainy events. To meet these goals, managers have to equip the sewer networks with and to set up real-time control systems. Unfortunately, a component fault (leading to intolerable behaviour of the system) or sensor fault (deteriorating the process view and disturbing the local automatism) makes the sewer network supervision delicate. In order to ensure an adequate flow management during rainy events it is essential to set up procedures capable of detecting and diagnosing these anomalies. This article introduces a real-time fault detection method, applicable to sewer networks, for the follow-up of rainy events. This method consists in comparing the sensor response with a forecast of this response. This forecast is provided by a model and more precisely by a state estimator: a Kalman filter. This Kalman filter provides not only a flow estimate but also an entity called 'innovation'. In order to detect abnormal operations within the network, this innovation is analysed with the binary sequential probability ratio test of Wald. Moreover, by crossing available information on several nodes of the network, a diagnosis of the detected anomalies is carried out. This method provided encouraging results during the analysis of several rains, on the sewer network of Seine-Saint-Denis County, France.

Searching for superspreaders of information in real-world social media.

PubMed

Pei, Sen; Muchnik, Lev; Andrade, José S; Zheng, Zhiming; Makse, Hernán A

2014-07-03

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.

Searching for superspreaders of information in real-world social media

NASA Astrophysics Data System (ADS)

Pei, Sen; Muchnik, Lev; Andrade, José S., Jr.; Zheng, Zhiming; Makse, Hernán A.

2014-07-01

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for ``viral'' information dissemination in relevant applications.

Searching for superspreaders of information in real-world social media

PubMed Central

Pei, Sen; Muchnik, Lev; Andrade, Jr., José S.; Zheng, Zhiming; Makse, Hernán A.

2014-01-01

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for “viral” information dissemination in relevant applications. PMID:24989148

Quality of service provision assessment in the healthcare information and telecommunications infrastructures.

PubMed

Babulak, Eduard

2006-01-01

The continuous increase in the complexity and the heterogeneity of corporate and healthcare telecommunications infrastructures will require new assessment methods of quality of service (QoS) provision that are capable of addressing all engineering and social issues with much faster speeds. Speed and accessibility to any information at any time from anywhere will create global communications infrastructures with great performance bottlenecks that may put in danger human lives, power supplies, national economy and security. Regardless of the technology supporting the information flows, the final verdict on the QoS is made by the end user. The users' perception of telecommunications' network infrastructure QoS provision is critical to the successful business management operation of any organization. As a result, it is essential to assess the QoS Provision in the light of user's perception. This article presents a cost effective methodology to assess the user's perception of quality of service provision utilizing the existing Staffordshire University Network (SUN) by adding a component of measurement to the existing model presented by Walker. This paper presents the real examples of CISCO Networking Solutions for Health Care givers and offers a cost effective approach to assess the QoS provision within the campus network, which could be easily adapted to any health care organization or campus network in the world.

Spectral features of solar plasma flows

NASA Astrophysics Data System (ADS)

Barkhatov, N. A.; Revunov, S. E.

2014-11-01

Research to the identification of plasma flows in the Solar wind by spectral characteristics of solar plasma flows in the range of magnetohydrodynamics is devoted. To do this, the wavelet skeleton pattern of Solar wind parameters recorded on Earth orbit by patrol spacecraft and then executed their neural network classification differentiated by bandwidths is carry out. This analysis of spectral features of Solar plasma flows in the form of magnetic clouds (MC), corotating interaction regions (CIR), shock waves (Shocks) and highspeed streams from coronal holes (HSS) was made. The proposed data processing and the original correlation-spectral method for processing information about the Solar wind flows for further classification as online monitoring of near space can be used. This approach will allow on early stages in the Solar wind flow detect geoeffective structure to predict global geomagnetic disturbances.

Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance

PubMed Central

Stringer, Clive; Beeknoo, Neeraj

2017-01-01

The topology of the patient flow network in a hospital is complex, comprising hundreds of overlapping patient journeys, and is a determinant of operational efficiency. To understand the network architecture of patient flow, we performed a data-driven network analysis of patient flow through two acute hospital sites of King’s College Hospital NHS Foundation Trust. Administration databases were queried for all intra-hospital patient transfers in an 18-month period and modelled as a dynamic weighted directed graph. A ‘core’ subnetwork containing only 13–17% of all edges channelled 83–90% of the patient flow, while an ‘ephemeral’ network constituted the remainder. Unsupervised cluster analysis and differential network analysis identified sub-networks where traffic is most associated with A&E performance. Increased flow to clinical decision units was associated with the best A&E performance in both sites. The component analysis also detected a weekend effect on patient transfers which was not associated with performance. We have performed the first data-driven hypothesis-free analysis of patient flow which can enhance understanding of whole healthcare systems. Such analysis can drive transformation in healthcare as it has in industries such as manufacturing. PMID:28968472

How can social network analysis contribute to social behavior research in applied ethology?

PubMed

Makagon, Maja M; McCowan, Brenda; Mench, Joy A

2012-05-01

Social network analysis is increasingly used by behavioral ecologists and primatologists to describe the patterns and quality of interactions among individuals. We provide an overview of this methodology, with examples illustrating how it can be used to study social behavior in applied contexts. Like most kinds of social interaction analyses, social network analysis provides information about direct relationships (e.g. dominant-subordinate relationships). However, it also generates a more global model of social organization that determines how individual patterns of social interaction relate to individual and group characteristics. A particular strength of this approach is that it provides standardized mathematical methods for calculating metrics of sociality across levels of social organization, from the population and group levels to the individual level. At the group level these metrics can be used to track changes in social network structures over time, evaluate the effect of the environment on social network structure, or compare social structures across groups, populations or species. At the individual level, the metrics allow quantification of the heterogeneity of social experience within groups and identification of individuals who may play especially important roles in maintaining social stability or information flow throughout the network.

Data collection framework for energy efficient privacy preservation in wireless sensor networks having many-to-many structures.

PubMed

Bahşi, Hayretdin; Levi, Albert

2010-01-01

Wireless sensor networks (WSNs) generally have a many-to-one structure so that event information flows from sensors to a unique sink. In recent WSN applications, many-to-many structures evolved due to the need for conveying collected event information to multiple sinks. Privacy preserved data collection models in the literature do not solve the problems of WSN applications in which network has multiple un-trusted sinks with different level of privacy requirements. This study proposes a data collection framework bases on k-anonymity for preventing record disclosure of collected event information in WSNs. Proposed method takes the anonymity requirements of multiple sinks into consideration by providing different levels of privacy for each destination sink. Attributes, which may identify an event owner, are generalized or encrypted in order to meet the different anonymity requirements of sinks at the same anonymized output. If the same output is formed, it can be multicasted to all sinks. The other trivial solution is to produce different anonymized outputs for each sink and send them to related sinks. Multicasting is an energy efficient data sending alternative for some sensor nodes. Since minimization of energy consumption is an important design criteria for WSNs, multicasting the same event information to multiple sinks reduces the energy consumption of overall network.

Effective contaminant detection networks in uncertain groundwater flow fields.

PubMed

Hudak, P F

2001-01-01

A mass transport simulation model tested seven contaminant detection-monitoring networks under a 40 degrees range of groundwater flow directions. Each monitoring network contained five wells located 40 m from a rectangular landfill. The 40-m distance (lag) was measured in different directions, depending upon the strategy used to design a particular monitoring network. Lagging the wells parallel to the central flow path was more effective than alternative design strategies. Other strategies allowed higher percentages of leaks to migrate between monitoring wells. Results of this study suggest that centrally lagged groundwater monitoring networks perform most effectively in uncertain groundwater-flow fields.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Chase

A number of Department of Energy (DOE) science applications, involving exascale computing systems and large experimental facilities, are expected to generate large volumes of data, in the range of petabytes to exabytes, which will be transported over wide-area networks for the purpose of storage, visualization, and analysis. The objectives of this proposal are to (1) develop and test the component technologies and their synthesis methods to achieve source-to-sink high-performance flows, and (2) develop tools that provide these capabilities through simple interfaces to users and applications. In terms of the former, we propose to develop (1) optimization methods that align andmore » transition multiple storage flows to multiple network flows on multicore, multibus hosts; and (2) edge and long-haul network path realization and maintenance using advanced provisioning methods including OSCARS and OpenFlow. We also propose synthesis methods that combine these individual technologies to compose high-performance flows using a collection of constituent storage-network flows, and realize them across the storage and local network connections as well as long-haul connections. We propose to develop automated user tools that profile the hosts, storage systems, and network connections; compose the source-to-sink complex flows; and set up and maintain the needed network connections.« less

3D Groundwater flow model at the Upper Rhine Graben scale to delineate preferential target areas for geothermal projects

NASA Astrophysics Data System (ADS)

Armandine Les Landes, Antoine; Guillon, Théophile; Peter-Borie, Mariane; Rachez, Xavier

2017-04-01

Any deep unconventional geothermal project remains risky because of the uncertainty regarding the presence of the geothermal resource at depth and the drilling costs increasing accordingly. That's why this resource must be located as precisely as possible to increase the chances of successful projects and their economic viability. To minimize the risk, as much information as possible should be gathered prior to any drilling. Usually, the position of the exploration wells of geothermal energy systems is chosen based on structural geology observations, geophysics measurements and geochemical analyses. Confronting these observations to results from additional disciplines should bring more objectivity in locating the region to explore and where to implant the geothermal system. The Upper Rhine Graben (URG) is a tectonically active rift system that corresponds to one branch of the European Cenozoic Rift System where the basin hosts a significant potential for geothermal energy. The large fault network inherited from a complex tectonic history and settled under the sedimentary deposits hosts fluid circulation patterns. Geothermal anomalies are strongly influenced by fluid circulations within permeable structures such as fault zones. In order to better predict the location of the geothermal resource, it is necessary to understand how it is influenced by heat transport mechanisms such as groundwater flow. The understanding of fluid circulation in hot fractured media at large scale can help in the identification of preferential zones at a finer scale where additional exploration can be carried out. Numerical simulations is a useful tool to deal with the issue of fluid circulations through large fault networks that enable the uplift of deep and hot fluids. Therefore, we build a numerical model to study groundwater flow at the URG scale (150 x 130km), which aims to delineate preferential zones. The numerical model is based on a hybrid method using a Discrete Fracture Network (DFN) and 3D elements to simulate groundwater flow in the 3D regional fault network and in sedimentary deposits, respectively. Firstly, the geometry of the 3D fracture network and its hydraulic connections with 3D elements (sedimentary cover) is built in accordance with the tectonic history and based on geological and geophysical evidences. Secondly, data from previous studies and site-specific geological knowledge provide information on the fault zones family sets and on respective hydraulic properties. Then, from the simulated 3D groundwater flow model and based on a particle tracking methodology, groundwater flow paths are constructed. The regional groundwater flow paths results are extracted and analysed to delineate preferential zones to explore at finer scale and so to define the potential positions of the exploration wells. This work is conducted in the framework of the IMAGE project (Integrated Methods for Advanced Geothermal Exploration, grant agreement No. 608553), which aims to develop new methods for better siting of exploitation wells.

Abstract Linguistic Structure Correlates with Temporal Activity during Naturalistic Comprehension

PubMed Central

Brennan, Jonathan R.; Stabler, Edward P.; Van Wagenen, Sarah E.; Luh, Wen-Ming; Hale, John T.

2016-01-01

Neurolinguistic accounts of sentence comprehension identify a network of relevant brain regions, but do not detail the information flowing through them. We investigate syntactic information. Does brain activity implicate a computation over hierarchical grammars or does it simply reflect linear order, as in a Markov chain? To address this question, we quantify the cognitive states implied by alternative parsing models. We compare processing-complexity predictions from these states against fMRI timecourses from regions that have been implicated in sentence comprehension. We find that hierarchical grammars independently predict timecourses from left anterior and posterior temporal lobe. Markov models are predictive in these regions and across a broader network that includes the inferior frontal gyrus. These results suggest that while linear effects are wide-spread across the language network, certain areas in the left temporal lobe deal with abstract, hierarchical syntactic representations. PMID:27208858

An Amorphous Network Model for Capillary Flow and Dispersion in a Partially Saturated Porous Medium

NASA Astrophysics Data System (ADS)

Simmons, C. S.; Rockhold, M. L.

2013-12-01

Network models of capillary flow are commonly used to represent conduction of fluids at pore scales. Typically, a flow system is described by a regular geometric lattice of interconnected tubes. Tubes constitute the pore throats, while connection junctions (nodes) are pore bodies. Such conceptualization of the geometry, however, is questionable for the pore scale, where irregularity clearly prevails, although prior published models using a regular lattice have demonstrated successful descriptions of the flow in the bulk medium. Here a network is allowed to be amorphous, and is not subject to any particular lattice structure. Few network flow models have treated partially saturated or even multiphase conditions. The research trend is toward using capillary tubes with triangular or square cross sections that have corners and always retain some fluid by capillarity when drained. In contrast, this model uses only circular capillaries, whose filled state is controlled by a capillary pressure rule for the junctions. The rule determines which capillary participate in the flow under an imposed matric potential gradient during steady flow conditions. Poiseuille's Law and Laplace equation are used to describe flow and water retention in the capillary units of the model. A modified conjugate gradient solution for steady flow that tracks which capillary in an amorphous network contribute to fluid conduction was devised for partially saturated conditions. The model thus retains the features of classical capillary models for determining hydraulic flow properties under unsaturated conditions based on distribution of non-interacting tubes, but now accounts for flow exchange at junctions. Continuity of the flow balance at every junction is solved simultaneously. The effective water retention relationship and unsaturated permeability are evaluated for an extensive enough network to represent a small bulk sample of porous medium. The model is applied for both a hypothetically randomly generate network and for a directly measured porous medium structure, by means of xray-CT scan. A randomly generated network has the benefit of providing ensemble averages for sample replicates of a medium's properties, whereas network structure measurements are expected to be more predictive. Dispersion of solute in a network flow is calculate by using particle tracking to determine the travel time breakthrough between inflow and outflow boundaries. The travel time distribution can exhibit substantial skewness that reflects both network velocity variability and mixing dilution at junctions. When local diffusion is not included, and transport is strictly advective, then the skew breakthrough is not due to mobile-immobile flow region behavior. The approach of dispersivity to its asymptotic value with sample size is examined, and may be only an indicator of particular stochastic flow variation. It is not proven that a simplified network flow model can accurately predict the hydraulic properties of a sufficiently large-size medium sample, but such a model can at least demonstrate macroscopic flow resulting from the interaction of physical processes at pore scales.

Spatio-temporal organization of dynamics in a two-dimensional periodically driven vortex flow: A Lagrangian flow network perspective.

PubMed

Lindner, Michael; Donner, Reik V

2017-03-01

We study the Lagrangian dynamics of passive tracers in a simple model of a driven two-dimensional vortex resembling real-world geophysical flow patterns. Using a discrete approximation of the system's transfer operator, we construct a directed network that describes the exchange of mass between distinct regions of the flow domain. By studying different measures characterizing flow network connectivity at different time-scales, we are able to identify the location of dynamically invariant structures and regions of maximum dispersion. Specifically, our approach allows us to delimit co-existing flow regimes with different dynamics. To validate our findings, we compare several network characteristics to the well-established finite-time Lyapunov exponents and apply a receiver operating characteristic analysis to identify network measures that are particularly useful for unveiling the skeleton of Lagrangian chaos.

Methods of information geometry in computational system biology (consistency between chemical and biological evolution).

PubMed

Astakhov, Vadim

2009-01-01

Interest in simulation of large-scale metabolic networks, species development, and genesis of various diseases requires new simulation techniques to accommodate the high complexity of realistic biological networks. Information geometry and topological formalisms are proposed to analyze information processes. We analyze the complexity of large-scale biological networks as well as transition of the system functionality due to modification in the system architecture, system environment, and system components. The dynamic core model is developed. The term dynamic core is used to define a set of causally related network functions. Delocalization of dynamic core model provides a mathematical formalism to analyze migration of specific functions in biosystems which undergo structure transition induced by the environment. The term delocalization is used to describe these processes of migration. We constructed a holographic model with self-poetic dynamic cores which preserves functional properties under those transitions. Topological constraints such as Ricci flow and Pfaff dimension were found for statistical manifolds which represent biological networks. These constraints can provide insight on processes of degeneration and recovery which take place in large-scale networks. We would like to suggest that therapies which are able to effectively implement estimated constraints, will successfully adjust biological systems and recover altered functionality. Also, we mathematically formulate the hypothesis that there is a direct consistency between biological and chemical evolution. Any set of causal relations within a biological network has its dual reimplementation in the chemistry of the system environment.

Scanning protocols dedicated to smart velocity ranging in spectral OCT.

PubMed

Grulkowski, Ireneusz; Gorczynska, Iwona; Szkulmowski, Maciej; Szlag, Daniel; Szkulmowska, Anna; Leitgeb, Rainer A; Kowalczyk, Andrzej; Wojtkowski, Maciej

2009-12-21

We introduce a new type of scanning protocols, called segmented protocols, which enable extracting multi-range flow velocity information from a single Spectral OCT data set. The protocols are evaluated using a well defined flow in a glass capillary. As an example of in vivo studies, we demonstrate two- and three-dimensional imaging of the retinal vascular system in the eyes of healthy volunteers. The flow velocity detection is performed using a method of Joint Spectral and Time domain OCT. Velocity ranging is demonstrated in imaging of retinal vasculature in the macular region and in the optic disk area characterized by different flow velocity values. Additionally, an enhanced visualization of retinal capillary network is presented in the close proximity to macula.

Computational Modeling and Analysis of Networked Organizational Planning in a Coalition Maritime Strike Environment

DTIC Science & Technology

2006-06-01

KMO ) for the CFMCC staff. That officer had a daily meeting with all of the CFMCC’s collateral duty knowledge managers (KM) to discuss information...analyses of process steps) and mentored by the KMO , could enhance knowledge creation and utilization while not jeopardizing work flows. Clearly in

Variations in Driver Behavior: An Analysis of Car-Following Behavior Heterogeneity as a Function of Road Type and Traffic Condition

DOT National Transportation Integrated Search

2017-11-15

Microsimulation modeling is a tool used by practitioners and researchers to predict and evaluate the flow of traffic on real transportation networks. These models are used in practice to inform decisions and thus must reflect a high level of accuracy...

Efficient quantum transmission in multiple-source networks.

PubMed

Luo, Ming-Xing; Xu, Gang; Chen, Xiu-Bo; Yang, Yi-Xian; Wang, Xiaojun

2014-04-02

A difficult problem in quantum network communications is how to efficiently transmit quantum information over large-scale networks with common channels. We propose a solution by developing a quantum encoding approach. Different quantum states are encoded into a coherent superposition state using quantum linear optics. The transmission congestion in the common channel may be avoided by transmitting the superposition state. For further decoding and continued transmission, special phase transformations are applied to incoming quantum states using phase shifters such that decoders can distinguish outgoing quantum states. These phase shifters may be precisely controlled using classical chaos synchronization via additional classical channels. Based on this design and the reduction of multiple-source network under the assumption of restricted maximum-flow, the optimal scheme is proposed for specially quantized multiple-source network. In comparison with previous schemes, our scheme can greatly increase the transmission efficiency.

Social networks and expertise development for Australian breast radiologists.

PubMed

Taba, Seyedamir Tavakoli; Hossain, Liaquat; Willis, Karen; Lewis, Sarah

2017-02-11

In this study, we explore the nexus between social networks and expertise development of Australian breast radiologists. Background literature has shown that a lack of appropriate social networks and interaction among certain professional group(s) may be an obstacle for knowledge acquisition, information flow and expertise sharing. To date there have not been any systematic studies investigating how social networks and expertise development are interconnected and whether this leads to improved performance for breast radiologists. This study explores the value of social networks in building expertise alongside with other constructs of performance for the Australian radiology workforce using semi-structured in-depth interviews with 17 breast radiologists. The findings from this study emphasise the influences of knowledge transfer and learning through social networks and interactions as well as knowledge acquisition and development through experience and feedback. The results also show that accessibility to learning resources and a variety of timely feedback on performance through the information and communication technologies (ICT) is likely to facilitate improved performance and build social support. We argue that radiologists' and, in particular, breast radiologists' work performance, needs to be explored not only through individual numerical characteristics but also by analysing the social context and peer support networks in which they operate and we identify multidisciplinary care as a core entity of social learning.

Social network and decision-making in primates: a report on Franco-Japanese research collaborations.

PubMed

Sueur, Cédric; Pelé, Marie

2016-07-01

Sociality is suggested to evolve as a strategy for animals to cope with challenges in their environment. Within a population, each individual can be seen as part of a network of social interactions that vary in strength, type and dynamics (Sueur et al. 2011a). The structure of this social network can strongly impact upon not only on the fitness of individuals and their decision-making, but also on the ecology of populations and the evolution of a species. Our Franco-Japanese collaboration allowed us to study social networks in several species (Japanese macaques, chimpanzees, colobines, etc.) and on different topics (social epidemiology, social evolution, information transmission). Individual attributes such as stress, rank or age can affect how individuals take decisions and the structure of the social network. This heterogeneity is linked to the assortativity of individuals and to the efficiency of the flow within a network. It is important, therefore, that this heterogeneity is integrated in the process or pattern under study in order to provide a better resolution of investigation and, ultimately, a better understanding of behavioural strategies, social dynamics and social evolution. How social information affects decision-making could be important to understand how social groups make collective decisions and how information may spread throughout the social group. In human beings, road-crossing behaviours in the presence of other individuals is a good way to study the influence of social information on individual behaviour and decision-making, for instance. Culture directly affects which information - personal vs social - individuals prefer to follow. Our collaboration contributed to the understanding of the relative influence of different factors, cultural and ecological, on primate, including human, sociality.

Cubic law with aperture-length correlation: implications for network scale fluid flow

NASA Astrophysics Data System (ADS)

Klimczak, Christian; Schultz, Richard A.; Parashar, Rishi; Reeves, Donald M.

2010-06-01

Previous studies have computed and modeled fluid flow through fractured rock with the parallel plate approach where the volumetric flow per unit width normal to the direction of flow is proportional to the cubed aperture between the plates, referred to as the traditional cubic law. When combined with the square root relationship of displacement to length scaling of opening-mode fractures, total flow rates through natural opening-mode fractures are found to be proportional to apertures to the fifth power. This new relationship was explored by examining a suite of flow simulations through fracture networks using the discrete fracture network model (DFN). Flow was modeled through fracture networks with the same spatial distribution of fractures for both correlated and uncorrelated fracture length-to-aperture relationships. Results indicate that flow rates are significantly higher for correlated DFNs. Furthermore, the length-to-aperture relations lead to power-law distributions of network hydraulic conductivity which greatly influence equivalent permeability tensor values. These results confirm the importance of the correlated square root relationship of displacement to length scaling for total flow through natural opening-mode fractures and, hence, emphasize the role of these correlations for flow modeling.

Dual gene activation and knockout screen reveals directional dependencies in genetic networks. | Office of Cancer Genomics

Cancer.gov

Understanding the direction of information flow is essential for characterizing how genetic networks affect phenotypes. However, methods to find genetic interactions largely fail to reveal directional dependencies. We combine two orthogonal Cas9 proteins from Streptococcus pyogenes and Staphylococcus aureus to carry out a dual screen in which one gene is activated while a second gene is deleted in the same cell. We analyze the quantitative effects of activation and knockout to calculate genetic interaction and directionality scores for each gene pair.

Bus-based park-and-ride system: a stochastic model on multimodal network with congestion pricing schemes

NASA Astrophysics Data System (ADS)

Liu, Zhiyuan; Meng, Qiang

2014-05-01

This paper focuses on modelling the network flow equilibrium problem on a multimodal transport network with bus-based park-and-ride (P&R) system and congestion pricing charges. The multimodal network has three travel modes: auto mode, transit mode and P&R mode. A continuously distributed value-of-time is assumed to convert toll charges and transit fares to time unit, and the users' route choice behaviour is assumed to follow the probit-based stochastic user equilibrium principle with elastic demand. These two assumptions have caused randomness to the users' generalised travel times on the multimodal network. A comprehensive network framework is first defined for the flow equilibrium problem with consideration of interactions between auto flows and transit (bus) flows. Then, a fixed-point model with unique solution is proposed for the equilibrium flows, which can be solved by a convergent cost averaging method. Finally, the proposed methodology is tested by a network example.

Cross-frequency coupling in real and virtual brain networks

PubMed Central

Jirsa, Viktor; Müller, Viktor

2013-01-01

Information processing in the brain is thought to rely on the convergence and divergence of oscillatory behaviors of widely distributed brain areas. This information flow is captured in its simplest form via the concepts of synchronization and desynchronization and related metrics. More complex forms of information flow are transient synchronizations and multi-frequency behaviors with metrics related to cross-frequency coupling (CFC). It is supposed that CFC plays a crucial role in the organization of large-scale networks and functional integration across large distances. In this study, we describe different CFC measures and test their applicability in simulated and real electroencephalographic (EEG) data obtained during resting state. For these purposes, we derive generic oscillator equations from full brain network models. We systematically model and simulate the various scenarios of CFC under the influence of noise to obtain biologically realistic oscillator dynamics. We find that (i) specific CFC-measures detect correctly in most cases the nature of CFC under noise conditions, (ii) bispectrum (BIS) and bicoherence (BIC) correctly detect the CFCs in simulated data, (iii) empirical resting state EEG show a prominent delta-alpha CFC as identified by specific CFC measures and the more classic BIS and BIC. This coupling was mostly asymmetric (directed) and generally higher in the eyes closed (EC) than in the eyes open (EO) condition. In conjunction, these two sets of measures provide a powerful toolbox to reveal the nature of couplings from experimental data and as such allow inference on the brain state dependent information processing. Methodological advantages of using CFC measures and theoretical significance of delta and alpha interactions during resting and other brain states are discussed. PMID:23840188

Tests of peak flow scaling in simulated self-similar river networks

USGS Publications Warehouse

Menabde, M.; Veitzer, S.; Gupta, V.; Sivapalan, M.

2001-01-01

The effect of linear flow routing incorporating attenuation and network topology on peak flow scaling exponent is investigated for an instantaneously applied uniform runoff on simulated deterministic and random self-similar channel networks. The flow routing is modelled by a linear mass conservation equation for a discrete set of channel links connected in parallel and series, and having the same topology as the channel network. A quasi-analytical solution for the unit hydrograph is obtained in terms of recursion relations. The analysis of this solution shows that the peak flow has an asymptotically scaling dependence on the drainage area for deterministic Mandelbrot-Vicsek (MV) and Peano networks, as well as for a subclass of random self-similar channel networks. However, the scaling exponent is shown to be different from that predicted by the scaling properties of the maxima of the width functions. ?? 2001 Elsevier Science Ltd. All rights reserved.

The queueing perspective of asynchronous network coding in two-way relay network

NASA Astrophysics Data System (ADS)

Liang, Yaping; Chang, Qing; Li, Xianxu

2018-04-01

Asynchronous network coding (NC) has potential to improve the wireless network performance compared with a routing or the synchronous network coding. Recent researches concentrate on the optimization between throughput/energy consuming and delay with a couple of independent input flow. However, the implementation of NC requires a thorough investigation of its impact on relevant queueing systems where few work focuses on. Moreover, few works study the probability density function (pdf) in network coding scenario. In this paper, the scenario with two independent Poisson input flows and one output flow is considered. The asynchronous NC-based strategy is that a new arrival evicts a head packet holding in its queue when waiting for another packet from the other flow to encode. The pdf for the output flow which contains both coded and uncoded packets is derived. Besides, the statistic characteristics of this strategy are analyzed. These results are verified by numerical simulations.

MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers

PubMed Central

Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier

2017-01-01

Background The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. Objective MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. Methods MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. Results MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user’s specific interests and provides an efficient way to share information with collaborators. Furthermore, the user’s behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. Conclusions We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends. PMID:28623182

The designing and implementation of PE teaching information resource database based on broadband network

NASA Astrophysics Data System (ADS)

Wang, Jian

2017-01-01

In order to change traditional PE teaching mode and realize the interconnection, interworking and sharing of PE teaching resources, a distance PE teaching platform based on broadband network is designed and PE teaching information resource database is set up. The designing of PE teaching information resource database takes Windows NT 4/2000Server as operating system platform, Microsoft SQL Server 7.0 as RDBMS, and takes NAS technology for data storage and flow technology for video service. The analysis of system designing and implementation shows that the dynamic PE teaching information resource sharing platform based on Web Service can realize loose coupling collaboration, realize dynamic integration and active integration and has good integration, openness and encapsulation. The distance PE teaching platform based on Web Service and the design scheme of PE teaching information resource database can effectively solve and realize the interconnection, interworking and sharing of PE teaching resources and adapt to the informatization development demands of PE teaching.

PSAW/MicroSWIS [Microminiature Surface Acoustic Wave (SAW) based Wirelesss Instrumentation System

NASA Technical Reports Server (NTRS)

Heermann, Doug; Krug, Eric

2004-01-01

This Final Report for the PSAW/MicroSWIS Program is provided in compliance with contract number NAS3-01118. This report documents the overall progress of the program and presents project objectives, work carried out, and results obtained. Program Conceptual Design Package stated the following objectives: To develop a sensor/transceiver network that can support networking operations within spacecraft with sufficient bandwidth so that (1) flight control data, (2) avionics data, (3) payload/experiment data, and (4) prognostic health monitoring sensory information can flow to appropriate locations at frequencies that contain the maximum amount of information content but require minimum interconnect and power: a very high speed, low power, programmable modulation, spread-spectrum radio sensor/transceiver.

Final report for the Multiprotocol Label Switching (MPLS) control plane security LDRD project.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Torgerson, Mark Dolan; Michalski, John T.; Tarman, Thomas David

2003-09-01

As rapid Internet growth continues, global communications becomes more dependent on Internet availability for information transfer. Recently, the Internet Engineering Task Force (IETF) introduced a new protocol, Multiple Protocol Label Switching (MPLS), to provide high-performance data flows within the Internet. MPLS emulates two major aspects of the Asynchronous Transfer Mode (ATM) technology. First, each initial IP packet is 'routed' to its destination based on previously known delay and congestion avoidance mechanisms. This allows for effective distribution of network resources and reduces the probability of congestion. Second, after route selection each subsequent packet is assigned a label at each hop, whichmore » determines the output port for the packet to reach its final destination. These labels guide the forwarding of each packet at routing nodes more efficiently and with more control than traditional IP forwarding (based on complete address information in each packet) for high-performance data flows. Label assignment is critical in the prompt and accurate delivery of user data. However, the protocols for label distribution were not adequately secured. Thus, if an adversary compromises a node by intercepting and modifying, or more simply injecting false labels into the packet-forwarding engine, the propagation of improperly labeled data flows could create instability in the entire network. In addition, some Virtual Private Network (VPN) solutions take advantage of this 'virtual channel' configuration to eliminate the need for user data encryption to provide privacy. VPN's relying on MPLS require accurate label assignment to maintain user data protection. This research developed a working distributive trust model that demonstrated how to deploy confidentiality, authentication, and non-repudiation in the global network label switching control plane. Simulation models and laboratory testbed implementations that demonstrated this concept were developed, and results from this research were transferred to industry via standards in the Optical Internetworking Forum (OIF).« less

Solution of weakly compressible isothermal flow in landfill gas collection networks

NASA Astrophysics Data System (ADS)

Nec, Y.; Huculak, G.

2017-12-01

Pipe networks collecting gas in sanitary landfills operate under the regime of a weakly compressible isothermal flow of ideal gas. The effect of compressibility has been traditionally neglected in this application in favour of simplicity, thereby creating a conceptual incongruity between the flow equations and thermodynamic equation of state. Here the flow is solved by generalisation of the classic Darcy-Weisbach equation for an incompressible steady flow in a pipe to an ordinary differential equation, permitting continuous variation of density, viscosity and related fluid parameters, as well as head loss or gain due to gravity, in isothermal flow. The differential equation is solved analytically in the case of ideal gas for a single edge in the network. Thereafter the solution is used in an algorithm developed to construct the flow equations automatically for a network characterised by an incidence matrix, and determine pressure distribution, flow rates and all associated parameters therein.

Composition and Realization of Source-to-Sink High-Performance Flows: File Systems, Storage, Hosts, LAN and WAN

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Chase Qishi

A number of Department of Energy (DOE) science applications, involving exascale computing systems and large experimental facilities, are expected to generate large volumes of data, in the range of petabytes to exabytes, which will be transported over wide-area networks for the purpose of storage, visualization, and analysis. To support such capabilities, significant progress has been made in various components including the deployment of 100 Gbps networks with future 1 Tbps bandwidth, increases in end-host capabilities with multiple cores and buses, capacity improvements in large disk arrays, and deployment of parallel file systems such as Lustre and GPFS. High-performance source-to-sink datamore » flows must be composed of these component systems, which requires significant optimizations of the storage-to-host data and execution paths to match the edge and long-haul network connections. In particular, end systems are currently supported by 10-40 Gbps Network Interface Cards (NIC) and 8-32 Gbps storage Host Channel Adapters (HCAs), which carry the individual flows that collectively must reach network speeds of 100 Gbps and higher. Indeed, such data flows must be synthesized using multicore, multibus hosts connected to high-performance storage systems on one side and to the network on the other side. Current experimental results show that the constituent flows must be optimally composed and preserved from storage systems, across the hosts and the networks with minimal interference. Furthermore, such a capability must be made available transparently to the science users without placing undue demands on them to account for the details of underlying systems and networks. And, this task is expected to become even more complex in the future due to the increasing sophistication of hosts, storage systems, and networks that constitute the high-performance flows. The objectives of this proposal are to (1) develop and test the component technologies and their synthesis methods to achieve source-to-sink high-performance flows, and (2) develop tools that provide these capabilities through simple interfaces to users and applications. In terms of the former, we propose to develop (1) optimization methods that align and transition multiple storage flows to multiple network flows on multicore, multibus hosts; and (2) edge and long-haul network path realization and maintenance using advanced provisioning methods including OSCARS and OpenFlow. We also propose synthesis methods that combine these individual technologies to compose high-performance flows using a collection of constituent storage-network flows, and realize them across the storage and local network connections as well as long-haul connections. We propose to develop automated user tools that profile the hosts, storage systems, and network connections; compose the source-to-sink complex flows; and set up and maintain the needed network connections. These solutions will be tested using (1) 100 Gbps connection(s) between Oak Ridge National Laboratory (ORNL) and Argonne National Laboratory (ANL) with storage systems supported by Lustre and GPFS file systems with an asymmetric connection to University of Memphis (UM); (2) ORNL testbed with multicore and multibus hosts, switches with OpenFlow capabilities, and network emulators; and (3) 100 Gbps connections from ESnet and their Openflow testbed, and other experimental connections. This proposal brings together the expertise and facilities of the two national laboratories, ORNL and ANL, and UM. It also represents a collaboration between DOE and the Department of Defense (DOD) projects at ORNL by sharing technical expertise and personnel costs, and leveraging the existing DOD Extreme Scale Systems Center (ESSC) facilities at ORNL.« less

Establishment of a hydrological monitoring network in a tropical African catchment: An integrated participatory approach

NASA Astrophysics Data System (ADS)

Gomani, M. C.; Dietrich, O.; Lischeid, G.; Mahoo, H.; Mahay, F.; Mbilinyi, B.; Sarmett, J.

Sound decision making for water resources management has to be based on good knowledge of the dominant hydrological processes of a catchment. This information can only be obtained through establishing suitable hydrological monitoring networks. Research catchments are typically established without involving the key stakeholders, which results in instruments being installed at inappropriate places as well as at high risk of theft and vandalism. This paper presents an integrated participatory approach for establishing a hydrological monitoring network. We propose a framework with six steps beginning with (i) inception of idea; (ii) stakeholder identification; (iii) defining the scope of the network; (iv) installation; (v) monitoring; and (vi) feedback mechanism integrated within the participatory framework. The approach is illustrated using an example of the Ngerengere catchment in Tanzania. In applying the approach, the concept of establishing the Ngerengere catchment monitoring network was initiated in 2008 within the Resilient Agro-landscapes to Climate Change in Tanzania (ReACCT) research program. The main stakeholders included: local communities; Sokoine University of Agriculture; Wami Ruvu Basin Water Office and the ReACCT Research team. The scope of the network was based on expert experience in similar projects and lessons learnt from literature review of similar projects from elsewhere integrated with local expert knowledge. The installations involved reconnaissance surveys, detailed surveys, and expert consultations to identify best sites. First, a Digital Elevation Model, land use, and soil maps were used to identify potential monitoring sites. Local and expert knowledge was collected on flow regimes, indicators of shallow groundwater plant species, precipitation pattern, vegetation, and soil types. This information was integrated and used to select sites for installation of an automatic weather station, automatic rain gauges, river flow gauging stations, flow measurement sites and shallow groundwater wells. The network is now used to monitor hydro-meteorological parameters in collaboration with key stakeholders in the catchment. Preliminary results indicate that the network is working well. The benefits of this approach compared to conventional narrow scientific/technical approaches have been shown by gaining rapid insight into the hydrology of the catchment, identifying best sites for the instruments; and voluntary participation of stakeholders in installation, monitoring and safeguarding the installations. This approach has proved simple yet effective and yielded good results. Based on this experience gained in applying the approach in establishing the Ngerengere catchment monitoring network, we conclude that the integrated participatory approach helps to assimilate local and expert knowledge in catchments monitoring which consequently results in: (i) identifying best sites for the hydrologic monitoring; (ii) instilling the sense of ownership; (iii) providing security of the installed network; and (iv) minimizing costs for installation and monitoring.

The Vega balloons - A tool for studying atmosphere dynamics on Venus

NASA Technical Reports Server (NTRS)

Kremnev, R. S.; Selivanov, A. S.; Linkin, V. M.; Lipatov, A. N.; Tarnoruder, I. IA.; Puchkov, V. I.; Kustodiev, V. D.; Shurupov, A. A.; Ragent, B.; Preston, R. A.

1986-01-01

The Vega balloon experiment, designed to measure the dynamics of the Venus atmosphere, comprised the balloons themselves, their gondolas with on-board sensors and radio transmitters, and the radio telescope network on the earth. The structures and the physical parameters of the balloon probe are described, together with the instruments on the gondola, designed for the measurements of the atmospheric pressure, temperature, and vertical wind flows, and illumination, as well as possible flashes of lightning. Consideration is also given to the formatting of the information flow for the individual parameters measured.

GIS as a tool for efficient management of transport streams

NASA Astrophysics Data System (ADS)

Zatserkovnyi, V. I.; Kobrin, O. V.

2015-10-01

The transport network, which is an ideal object for the automation and the increase of efficiency using geographic information systems (GIS), is considered. The transport problems, which have a lot of mathematical models of the traffic flow for their solution, are enumerated. GIS analysis tools that allow one to build optimal routes in the real road network with its capabilities and limitations are presented. They can solve the extremely important problem of modern Ukraine - the rapid increase of the number of cars and the glut of road network vehicles. The intelligent transport systems, which are created and developed on the basis of GPS, GIS, modern communications and telecommunications facilities, are considered.

Toward two-dimensional search engines

NASA Astrophysics Data System (ADS)

Ermann, L.; Chepelianskii, A. D.; Shepelyansky, D. L.

2012-07-01

We study the statistical properties of various directed networks using ranking of their nodes based on the dominant vectors of the Google matrix known as PageRank and CheiRank. On average PageRank orders nodes proportionally to a number of ingoing links, while CheiRank orders nodes proportionally to a number of outgoing links. In this way, the ranking of nodes becomes two dimensional which paves the way for the development of two-dimensional search engines of a new type. Statistical properties of information flow on the PageRank-CheiRank plane are analyzed for networks of British, French and Italian universities, Wikipedia, Linux Kernel, gene regulation and other networks. A special emphasis is done for British universities networks using the large database publicly available in the UK. Methods of spam links control are also analyzed.

Where and why hyporheic exchange is important: Inferences from a parsimonious, physically-based river network model

NASA Astrophysics Data System (ADS)

Gomez-Velez, J. D.; Harvey, J. W.

2014-12-01

Hyporheic exchange has been hypothesized to have basin-scale consequences; however, predictions throughout river networks are limited by available geomorphic and hydrogeologic data as well as models that can analyze and aggregate hyporheic exchange flows across large spatial scales. We developed a parsimonious but physically-based model of hyporheic flow for application in large river basins: Networks with EXchange and Subsurface Storage (NEXSS). At the core of NEXSS is a characterization of the channel geometry, geomorphic features, and related hydraulic drivers based on scaling equations from the literature and readily accessible information such as river discharge, bankfull width, median grain size, sinuosity, channel slope, and regional groundwater gradients. Multi-scale hyporheic flow is computed based on combining simple but powerful analytical and numerical expressions that have been previously published. We applied NEXSS across a broad range of geomorphic diversity in river reaches and synthetic river networks. NEXSS demonstrates that vertical exchange beneath submerged bedforms dominates hyporheic fluxes and turnover rates along the river corridor. Moreover, the hyporheic zone's potential for biogeochemical transformations is comparable across stream orders, but the abundance of lower-order channels results in a considerably higher cumulative effect for low-order streams. Thus, vertical exchange beneath submerged bedforms has more potential for biogeochemical transformations than lateral exchange beneath banks, although lateral exchange through meanders may be important in large rivers. These results have implications for predicting outcomes of river and basin management practices.

Chaotic itinerancy within the coupled dynamics between a physical body and neural oscillator networks.

PubMed

Park, Jihoon; Mori, Hiroki; Okuyama, Yuji; Asada, Minoru

2017-01-01

Chaotic itinerancy is a phenomenon in which the state of a nonlinear dynamical system spontaneously explores and attracts certain states in a state space. From this perspective, the diverse behavior of animals and its spontaneous transitions lead to a complex coupled dynamical system, including a physical body and a brain. Herein, a series of simulations using different types of non-linear oscillator networks (i.e., regular, small-world, scale-free, random) with a musculoskeletal model (i.e., a snake-like robot) as a physical body are conducted to understand how the chaotic itinerancy of bodily behavior emerges from the coupled dynamics between the body and the brain. A behavior analysis (behavior clustering) and network analysis for the classified behavior are then applied. The former consists of feature vector extraction from the motions and classification of the movement patterns that emerged from the coupled dynamics. The network structures behind the classified movement patterns are revealed by estimating the "information networks" different from the given non-linear oscillator networks based on the transfer entropy which finds the information flow among neurons. The experimental results show that: (1) the number of movement patterns and their duration depend on the sensor ratio to control the balance of strength between the body and the brain dynamics and on the type of the given non-linear oscillator networks; and (2) two kinds of information networks are found behind two kinds movement patterns with different durations by utilizing the complex network measures, clustering coefficient and the shortest path length with a negative and a positive relationship with the duration periods of movement patterns. The current results seem promising for a future extension of the method to a more complicated body and environment. Several requirements are also discussed.

Numerical Experiments on Advective Transport in Large Three-Dimensional Discrete Fracture Networks

NASA Astrophysics Data System (ADS)

Makedonska, N.; Painter, S. L.; Karra, S.; Gable, C. W.

2013-12-01

Modeling of flow and solute transport in discrete fracture networks is an important approach for understanding the migration of contaminants in impermeable hard rocks such as granite, where fractures provide dominant flow and transport pathways. The discrete fracture network (DFN) model attempts to mimic discrete pathways for fluid flow through a fractured low-permeable rock mass, and may be combined with particle tracking simulations to address solute transport. However, experience has shown that it is challenging to obtain accurate transport results in three-dimensional DFNs because of the high computational burden and difficulty in constructing a high-quality unstructured computational mesh on simulated fractures. An integrated DFN meshing [1], flow, and particle tracking [2] simulation capability that enables accurate flow and particle tracking simulation on large DFNs has recently been developed. The new capability has been used in numerical experiments on advective transport in large DFNs with tens of thousands of fractures and millions of computational cells. The modeling procedure starts from the fracture network generation using a stochastic model derived from site data. A high-quality computational mesh is then generated [1]. Flow is then solved using the highly parallel PFLOTRAN [3] code. PFLOTRAN uses the finite volume approach, which is locally mass conserving and thus eliminates mass balance problems during particle tracking. The flow solver provides the scalar fluxes on each control volume face. From the obtained fluxes the Darcy velocity is reconstructed for each node in the network [4]. Velocities can then be continuously interpolated to any point in the domain of interest, thus enabling random walk particle tracking. In order to describe the flow field on fractures intersections, the control volume cells on intersections are split into four planar polygons, where each polygon corresponds to a piece of a fracture near the intersection line. Thus, computational nodes lying on fracture intersections have four associated velocities, one on each side of the intersection in each fracture plane [2]. This information is used to route particles arriving at the fracture intersection to the appropriate downstream fracture segment. Verified for small DFNs, the new simulation capability allows accurate particle tracking on more realistic representations of fractured rock sites. In the current work we focus on travel time statistics and spatial dispersion and show numerical results in DFNs of different sizes, fracture densities, and transmissivity distributions. [1] Hyman J.D., Gable C.W., Painter S.L., Automated meshing of stochastically generated discrete fracture networks, Abstract H33G-1403, 2011 AGU, San Francisco, CA, 5-9 Dec. [2] N. Makedonska, S. L. Painter, T.-L. Hsieh, Q.M. Bui, and C. W. Gable., Development and verification of a new particle tracking capability for modeling radionuclide transport in discrete fracture networks, Abstract, 2013 IHLRWM, Albuquerque, NM, Apr. 28 - May 3. [3] Lichtner, P.C., Hammond, G.E., Bisht, G., Karra, S., Mills, R.T., and Kumar, J. (2013) PFLOTRAN User's Manual: A Massively Parallel Reactive Flow Code. [4] Painter S.L., Gable C.W., Kelkar S., Pathline tracing on fully unstructured control-volume grids, Computational Geosciences, 16 (4), 2012, 1125-1134.

Quantifying Information Flow During Emergencies

NASA Astrophysics Data System (ADS)

Gao, Liang; Song, Chaoming; Gao, Ziyou; Barabási, Albert-László; Bagrow, James P.; Wang, Dashun

2014-02-01

Recent advances on human dynamics have focused on the normal patterns of human activities, with the quantitative understanding of human behavior under extreme events remaining a crucial missing chapter. This has a wide array of potential applications, ranging from emergency response and detection to traffic control and management. Previous studies have shown that human communications are both temporally and spatially localized following the onset of emergencies, indicating that social propagation is a primary means to propagate situational awareness. We study real anomalous events using country-wide mobile phone data, finding that information flow during emergencies is dominated by repeated communications. We further demonstrate that the observed communication patterns cannot be explained by inherent reciprocity in social networks, and are universal across different demographics.

Understanding characteristics in multivariate traffic flow time series from complex network structure

NASA Astrophysics Data System (ADS)

Yan, Ying; Zhang, Shen; Tang, Jinjun; Wang, Xiaofei

2017-07-01

Discovering dynamic characteristics in traffic flow is the significant step to design effective traffic managing and controlling strategy for relieving traffic congestion in urban cities. A new method based on complex network theory is proposed to study multivariate traffic flow time series. The data were collected from loop detectors on freeway during a year. In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights. We discuss how to select optimal critical threshold for networks at different hour in term of cumulative probability distribution of degree. Furthermore, two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow. The results demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours. Accordingly, we detect three traffic states: trough, peak and transitional hours, according to the correlation between two aforementioned properties. The classifying results of states can actually represent hourly fluctuation in traffic flow by analyzing annual average hourly values of traffic volume, occupancy and speed in corresponding hours.

Locating an imaging radar in Canada for identifying spaceborne objects

NASA Astrophysics Data System (ADS)

Schick, William G.

1992-12-01

This research presents a study of the maximal coverage p-median facility location problem as applied to the location of an imaging radar in Canada for imaging spaceborne objects. The classical mathematical formulation of the maximal coverage p-median problem is converted into network-flow with side constraint formulations that are developed using a scaled down version of the imaging radar location problem. Two types of network-flow with side constraint formulations are developed: a network using side constraints that simulates the gains in a generalized network; and a network resembling a multi-commodity flow problem that uses side constraints to force flow along identical arcs. These small formulations are expanded to encompass a case study using 12 candidate radar sites, and 48 satellites divided into three states. SAS/OR PROC NETFLOW was used to solve the network-flow with side constraint formulations. The case study show that potential for both formulations, although the simulated gains formulation encountered singular matrix computational difficulties as a result of the very organized nature of its side constraint matrix. The multi-commodity flow formulation, when combined with equi-distribution of flow constraints, provided solutions for various values of p, the number of facilities to be selected.

A proposal for an SDN-based SIEPON architecture

NASA Astrophysics Data System (ADS)

Khalili, Hamzeh; Sallent, Sebastià; Piney, José Ramón; Rincón, David

2017-11-01

Passive Optical Network (PON) elements such as Optical Line Terminal (OLT) and Optical Network Units (ONUs) are currently managed by inflexible legacy network management systems. Software-Defined Networking (SDN) is a new networking paradigm that improves the operation and management of networks. In this paper, we propose a novel architecture, based on the SDN concept, for Ethernet Passive Optical Networks (EPON) that includes the Service Interoperability standard (SIEPON). In our proposal, the OLT is partially virtualized and some of its functionalities are allocated to the core network management system, while the OLT itself is replaced by an OpenFlow (OF) switch. A new MultiPoint MAC Control (MPMC) sublayer extension based on the OpenFlow protocol is presented. This would allow the SDN controller to manage and enhance the resource utilization, flow monitoring, bandwidth assignment, quality-of-service (QoS) guarantees, and energy management of the optical network access, to name a few possibilities. The OpenFlow switch is extended with synchronous ports to retain the time-critical nature of the EPON network. OpenFlow messages are also extended with new functionalities to implement the concept of EPON Service Paths (ESPs). Our simulation-based results demonstrate the effectiveness of the new architecture, while retaining a similar (or improved) performance in terms of delay and throughput when compared to legacy PONs.

Role of Unchannelized Flow in Determining Bifurcation Angle in Distributary Channel Networks

NASA Astrophysics Data System (ADS)

Coffey, T.

2016-02-01

Distributary channel bifurcations on river deltas are important features in both actively prograding river deltas and in lithified deltas within the stratigraphic record. Attributes of distributary channels have long been thought to be defined by flow velocity, grain size and channel aspect ratio where the channel enters the basin. Interestingly, bifurcations in groundwater-fed tributary networks have been shown to grow and bifurcate independent of flow within the exposed channel network. These networks possess a characteristic bifurcation angle of 72°, based on Laplacian flow (water surface concavity equals zero) in the groundwater flow field near tributary channel tips. Based on the tributary channel model, we develop and test the hypothesis that bifurcation angles in distributary channels are likewise dictated by the external flow field, in this case the surface water surrounding the subaqueous portion of distributary channel tips in a deltaic setting. We measured 64 unique distributary bifurcations in an experimental delta, yielding a characteristic angle of 70.2°±2.2° (95% confidence interval), in line with the theoretical prediction for tributary channels. This similarity between bifurcation angles suggests that (A) flow directly outside of the distributary network is Laplacian, (B) the external flow field controls the bifurcation dynamics of distributary channels, and (C) that flow within the channel plays a secondary role in network dynamics.

Extrapolating regional probability of drying of headwater streams using discrete observations and gauging networks

NASA Astrophysics Data System (ADS)

Beaufort, Aurélien; Lamouroux, Nicolas; Pella, Hervé; Datry, Thibault; Sauquet, Eric

2018-05-01

Headwater streams represent a substantial proportion of river systems and many of them have intermittent flows due to their upstream position in the network. These intermittent rivers and ephemeral streams have recently seen a marked increase in interest, especially to assess the impact of drying on aquatic ecosystems. The objective of this paper is to quantify how discrete (in space and time) field observations of flow intermittence help to extrapolate over time the daily probability of drying (defined at the regional scale). Two empirical models based on linear or logistic regressions have been developed to predict the daily probability of intermittence at the regional scale across France. Explanatory variables were derived from available daily discharge and groundwater-level data of a dense gauging/piezometer network, and models were calibrated using discrete series of field observations of flow intermittence. The robustness of the models was tested using an independent, dense regional dataset of intermittence observations and observations of the year 2017 excluded from the calibration. The resulting models were used to extrapolate the daily regional probability of drying in France: (i) over the period 2011-2017 to identify the regions most affected by flow intermittence; (ii) over the period 1989-2017, using a reduced input dataset, to analyse temporal variability of flow intermittence at the national level. The two empirical regression models performed equally well between 2011 and 2017. The accuracy of predictions depended on the number of continuous gauging/piezometer stations and intermittence observations available to calibrate the regressions. Regions with the highest performance were located in sedimentary plains, where the monitoring network was dense and where the regional probability of drying was the highest. Conversely, the worst performances were obtained in mountainous regions. Finally, temporal projections (1989-2016) suggested the highest probabilities of intermittence (> 35 %) in 1989-1991, 2003 and 2005. A high density of intermittence observations improved the information provided by gauging stations and piezometers to extrapolate the temporal variability of intermittent rivers and ephemeral streams.

Mobile phone collection, reuse and recycling in the UK.

PubMed

Ongondo, F O; Williams, I D

2011-06-01

Mobile phones are the most ubiquitous electronic product on the globe. They have relatively short lifecycles and because of their (perceived) in-built obsolescence, discarded mobile phones represent a significant and growing problem with respect to waste electrical and electronic equipment (WEEE). An emerging and increasingly important issue for industry is the shortage of key metals, especially the types of metals found in mobile phones, and hence the primary aim of this timely study was to assess and evaluate the voluntary mobile phone takeback network in the UK. The study has characterised the information, product and incentives flows in the voluntary UK mobile phone takeback network and reviewed the merits and demerits of the incentives offered. A survey of the activities of the voluntary mobile phone takeback schemes was undertaken in 2008 to: identify and evaluate the takeback schemes operating in the UK; determine the target groups from whom handsets are collected; and assess the collection, promotion and advertising methods used by the schemes. In addition, the survey sought to identify and critically evaluate the incentives offered by the takeback schemes, evaluate their ease and convenience of use; and determine the types, qualities and quantities of mobile phones they collect. The study has established that the UK voluntary mobile phone takeback network can be characterised as three distinctive flows: information flow; product flow (handsets and related accessories); and incentives flow. Over 100 voluntary schemes offering online takeback of mobile phone handsets were identified. The schemes are operated by manufacturers, retailers, mobile phone network service operators, charities and by mobile phone reuse, recycling and refurbishing companies. The latter two scheme categories offer the highest level of convenience and ease of use to their customers. Approximately 83% of the schemes are either for-profit/commercial-oriented and/or operate to raise funds for charities. The voluntary schemes use various methods to collect mobile phones from consumers, including postal services, courier and in-store. The majority of schemes utilise and finance pre-paid postage to collect handsets. Incentives offered by the takeback schemes include monetary payments, donation to charity and entry into prize draws. Consumers from whom handsets and related equipment are collected include individuals, businesses, schools, colleges, universities, charities and clubs with some schemes specialising on collecting handsets from one target group. The majority (84.3%) of voluntary schemes did not provide information on their websites about the quantities of mobile phones they collect. The operations of UK takeback schemes are decentralised in nature. Comparisons are made between the UK's decentralised collection system versus Australia's centralised network for collection of mobile phones. The significant principal conclusions from the study are: there has been a significant rise in the number of takeback schemes operating in the UK since the initial scheme was launched in 1997; the majority of returned handsets seem to be of low quality; and there is very little available information on the quantities of mobile phones collected by the various schemes. Irrespective of their financial motives, UK takeback schemes increasingly play an important role in sustainable waste management by diverting EoL mobile phones from landfills and encouraging reuse and recycling. Recommendations for future actions to improve the management of end-of-life mobile phone handsets and related accessories are made. Copyright © 2011 Elsevier Ltd. All rights reserved.

What makes it likeable? A study on the reactions to messages in a digital social network: the case of Facebook in Farsi.

PubMed

Sabbar, Shaho; Hyun, Daiwon

2016-01-01

After a piece of information is put into a network, its fate depends on the behaviors of the nodes of the network; nodes that are equipped with the hardware and software of the age of information and are more powerful than any time in the past. This study suggests that a useful research for communication, marketing and advertising would be one that looks for patterns in the reactions of the nodes toward different pieces of information. This study has used Facebook to see how people have reacted to different types of messages in terms of liking, sharing and commenting. Rather than looking for universal, generalizable patterns we have tried to examine the practicality of the proposed method. The practical aspect of the study comes after a short theoretical discussion on the issue of flow of information in a digital world. The results revealed dozens of significant relations between the examined variables. This study, its theoretical discussion and results suggest that it would be practical to study the relations between the characteristics of Facebook messages and the type of reactions (liking, sharing and commenting) that they attract.

Contraction driven flow in the extended vein networks of Physarum polycephalum

NASA Astrophysics Data System (ADS)

Alim, Karen; Amselem, Gabriel; Peaudecerf, Francois; Pringle, Anne; Brenner, Michael P.

2011-11-01

The true slime mold Physarum polycephalum is a basal organism that forms an extended network of veins to forage for food. P. polycephalum is renown for its adaptive changes of vein structure and morphology in response to food sources. These rearrangements presumably occur to establish an efficient transport and mixing of resources throughout the networks thus presenting a prototype to design transport networks under the constraints of laminar flow. The physical flows of cytoplasmic fluid enclosed by the veins exhibit an oscillatory flow termed ``shuttle streaming.'' The flow exceed by far the volume required for growth at the margins suggesting that the additional energy cost for generating the flow is spent for efficient and/or targeted redistribution of resources. We show that the viscous shuttle flow is driven by the radial contractions of the veins that accompany the streaming. We present a model for the fluid flow and resource dispersion arising due to radial contractions. The transport and mixing properties of the flow are discussed.

Consciousness, cognition and brain networks: New perspectives.

PubMed

Aldana, E M; Valverde, J L; Fábregas, N

2016-10-01

A detailed analysis of the literature on consciousness and cognition mechanisms based on the neural networks theory is presented. The immune and inflammatory response to the anesthetic-surgical procedure induces modulation of neuronal plasticity by influencing higher cognitive functions. Anesthetic drugs can cause unconsciousness, producing a functional disruption of cortical and thalamic cortical integration complex. The external and internal perceptions are processed through an intricate network of neural connections, involving the higher nervous activity centers, especially the cerebral cortex. This requires an integrated model, formed by neural networks and their interactions with highly specialized regions, through large-scale networks, which are distributed throughout the brain collecting information flow of these perceptions. Functional and effective connectivity between large-scale networks, are essential for consciousness, unconsciousness and cognition. It is what is called the "human connectome" or map neural networks. Copyright © 2014 Sociedad Española de Anestesiología, Reanimación y Terapéutica del Dolor. Publicado por Elsevier España, S.L.U. All rights reserved.

Incorporation of Condensation Heat Transfer in a Flow Network Code

NASA Technical Reports Server (NTRS)

Anthony, Miranda; Majumdar, Alok; McConnaughey, Paul K. (Technical Monitor)

2001-01-01

In this paper we have investigated the condensation of water vapor in a short tube. A numerical model of condensation heat transfer was incorporated in a flow network code. The flow network code that we have used in this paper is Generalized Fluid System Simulation Program (GFSSP). GFSSP is a finite volume based flow network code. Four different condensation models were presented in the paper. Soliman's correlation has been found to be the most stable in low flow rates which is of particular interest in this application. Another highlight of this investigation is conjugate or coupled heat transfer between solid or fluid. This work was done in support of NASA's International Space Station program.

Bypass Flow Resistance in Prismatic Gas-Cooled Nuclear Reactors

DOE PAGES

McEligot, Donald M.; Johnson, Richard W.

2016-12-20

Available computational fluid dynamics (CFD) predictions of pressure distributions in the vertical bypass flow between blocks in a prismatic gas-cooled reactor (GCR) have been analyzed to deduce apparent friction factors and loss coefficients for systems and network codes. We performed calculations for vertical gap spacings "s" of 2, 6 and 10 mm, horizontal gaps between the blocks of two mm and two flow rates, giving a range of gap Reynolds numbers Re Dh of about 40 to 5300. Laminar predictions of the fully-developed friction factor f fd were about three to ten per cent lower than the classical infinitely-wide channelmore » In the entry region, the local apparent friction factor was slightly higher than the classic idealized case but the hydraulic entry length L hy was approximately the same. The per cent reduction in flow resistance was greater than the per cent increase in flow area at the vertical corners of the blocks. The standard k-ϵ model was employed for flows expected to be turbulent. Its predictions of f fd and flow resistance were significantly higher than direct numerical simulations for the classic case; the value of L hy was about thirty gap spacings. Initial quantitative information for entry coefficients and loss coefficients for the expansion-contraction junctions between blocks is also presented. Our study demonstrates how CFD predictions can be employed to provide integral quantities needed in systems and network codes.« less

Bypass Flow Resistance in Prismatic Gas-Cooled Nuclear Reactors

DOE Office of Scientific and Technical Information (OSTI.GOV)

McEligot, Donald M.; Johnson, Richard W.

Available computational fluid dynamics (CFD) predictions of pressure distributions in the vertical bypass flow between blocks in a prismatic gas-cooled reactor (GCR) have been analyzed to deduce apparent friction factors and loss coefficients for systems and network codes. We performed calculations for vertical gap spacings "s" of 2, 6 and 10 mm, horizontal gaps between the blocks of two mm and two flow rates, giving a range of gap Reynolds numbers Re Dh of about 40 to 5300. Laminar predictions of the fully-developed friction factor f fd were about three to ten per cent lower than the classical infinitely-wide channelmore » In the entry region, the local apparent friction factor was slightly higher than the classic idealized case but the hydraulic entry length L hy was approximately the same. The per cent reduction in flow resistance was greater than the per cent increase in flow area at the vertical corners of the blocks. The standard k-ϵ model was employed for flows expected to be turbulent. Its predictions of f fd and flow resistance were significantly higher than direct numerical simulations for the classic case; the value of L hy was about thirty gap spacings. Initial quantitative information for entry coefficients and loss coefficients for the expansion-contraction junctions between blocks is also presented. Our study demonstrates how CFD predictions can be employed to provide integral quantities needed in systems and network codes.« less

SDTCP: Towards Datacenter TCP Congestion Control with SDN for IoT Applications.

PubMed

Lu, Yifei; Ling, Zhen; Zhu, Shuhong; Tang, Ling

2017-01-08

The Internet of Things (IoT) has gained popularity in recent years. Today's IoT applications are now increasingly deployed in cloud platforms to perform Big Data analytics. In cloud data center networks (DCN), TCP incast usually happens when multiple senders simultaneously communicate with a single receiver. However, when TCP incast happens, DCN may suffer from both throughput collapse for TCP burst flows and temporary starvation for TCP background flows. In this paper, we propose a software defined network (SDN)-based TCP congestion control mechanism, referred to as SDTCP, to leverage the features, e.g., centralized control methods and the global view of the network, in order to solve the TCP incast problems. When we detect network congestion on an OpenFlow switch, our controller can select the background flows and reduce their bandwidth by adjusting the advertised window of TCP ACK packets of the corresponding background flows so as to reserve more bandwidth for burst flows. SDTCP is transparent to the end systems and can accurately decelerate the rate of background flows by leveraging the global view of the network gained via SDN. The experiments demonstrate that our SDTCP can provide high tolerance for burst flows and achieve better flow completion time for short flows. Therefore, SDTCP is an effective and scalable solution for the TCP incast problem.

A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

NASA Astrophysics Data System (ADS)

Huang, Darong; Bai, Xing-Rong

Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

Research on virtual network load balancing based on OpenFlow

NASA Astrophysics Data System (ADS)

Peng, Rong; Ding, Lei

2017-08-01

The Network based on OpenFlow technology separate the control module and data forwarding module. Global deployment of load balancing strategy through network view of control plane is fast and of high efficiency. This paper proposes a Weighted Round-Robin Scheduling algorithm for virtual network and a load balancing plan for server load based on OpenFlow. Load of service nodes and load balancing tasks distribution algorithm will be taken into account.

The Case Against Satellites. The Network Project. Notebook Number Seven.

ERIC Educational Resources Information Center

Columbia Univ., New York, NY. Network Project.

An analysis is presented of the dangers generated by the corporate ownership of a satellite communications technology powerful enough to centralize and control the flow of information. The first part of the report reprints the transcript of a radio documentary on satellite communications, one of a series of five MATRIX radio programs produced by…

Growth of White Matter in the Adolescent Brain: Myelin or Axon?

ERIC Educational Resources Information Center

Paus, Tomas

2010-01-01

White matter occupies almost half of the human brain. It contains axons connecting spatially segregated modules and, as such, it is essential for the smooth flow of information in functional networks. Structural maturation of white matter continues during adolescence, as reflected in age-related changes in its volume, as well as in its…

The Emotional Impact of Traditional and New Media in Social Events

ERIC Educational Resources Information Center

Salcudean, Minodora; Muresan, Raluca

2017-01-01

In past times, media were the sole vector to reflect in their entire complexity the events surrounding major world tragedies. Nowadays, social media are an essential component of the media process and classical press channels are connected to the social networking flow, where they can find information and, at the same time, tap into the emotional…

Dynamic hydro-climatic networks in pristine and regulated rivers

NASA Astrophysics Data System (ADS)

Botter, G.; Basso, S.; Lazzaro, G.; Doulatyari, B.; Biswal, B.; Schirmer, M.; Rinaldo, A.

2014-12-01

Flow patterns observed at-a-station are the dynamical byproduct of a cascade of processes involving different compartments of the hydro-climatic network (e.g., climate, rainfall, soil, vegetation) that regulates the transformation of rainfall into streamflows. In complex branching rivers, flow regimes result from the heterogeneous arrangement around the stream network of multiple hydrologic cascades that simultaneously occur within distinct contributing areas. As such, flow regimes are seen as the integrated output of a complex "network of networks", which can be properly characterized by its degree of temporal variability and spatial heterogeneity. Hydrologic networks that generate river flow regimes are dynamic in nature. In pristine rivers, the time-variance naturally emerges at multiple timescales from climate variability (namely, seasonality and inter-annual fluctuations), implying that the magnitude (and the features) of the water flow between two nodes may be highly variable across different seasons and years. Conversely, the spatial distribution of river flow regimes within pristine rivers involves scale-dependent transport features, as well as regional climatic and soil use gradients, which in small and meso-scale catchments (A < 103 km2) are usually mild enough to guarantee quite uniform flow regimes and high spatial correlations. Human-impacted rivers, instead, constitute hybrid networks where observed spatio-temporal patterns are dominated by anthropogenic shifts, such as landscape alterations and river regulation. In regulated rivers, the magnitude and the features of water flows from node to node may change significantly through time due to damming and withdrawals. However, regulation may impact river regimes in a spatially heterogeneous manner (e.g. in localized river reaches), with a significant decrease of spatial correlations and network connectivity. Provided that the spatial and temporal dynamics of flow regimes in complex rivers may strongly impact important biotic processes involved in the river food web (e.g. biofilm and riparian vegetation dynamics), the study of rivers as dynamic networks provides important clues to water management strategies and freshwater ecosystem studies.

Quantifying the Effects of Spatial Uncertainty in Fracture Permeability on CO2 Leakage through Columbia River Basalt Flow Interiors

NASA Astrophysics Data System (ADS)

Gierzynski, A.; Pollyea, R.

2016-12-01

Recent studies suggest that continental flood basalts may be suitable for geologic carbon sequestration, due to fluid-rock reactions that mineralize injected CO2 on relatively short time-scales. Flood basalts also possess a morphological structure conducive to injection, with alternating high-permeability (flow margin) and low-permeability (flow interior) layers. However, little information exists on the behavior of CO2 migration within field-scale fracture networks, particularly within flow interiors and at conditions near the critical point for CO2. In this study, numerical simulation is used to investigate the influence of fracture permeability uncertainty during gravity-driven CO2 migration within a jointed basalt flow interior as CO2 undergoes phase change from supercritical fluid to a subcritical phase. The model domain comprises a 2D fracture network mapped with terrestrial LiDAR scans of Columbia River Basalt acquired near Starbuck, WA. The model domain is 5 m × 5 m with bimodal heterogeneity (fracture and matrix), and initial conditions corresponding to a hydrostatic pressure gradient between 750 and 755 m depth. Under these conditions, the critical point for CO2 occurs 1.5 m above the bottom of the domain. For this model scenario, CO2 enters the base of the fracture network at 0.5 MPa overpressure, and matrix permeability is assumed constant. Fracture permeability follows a lognormal distribution on the basis of fracture aperture values from literature. In order to account for spatial uncertainty, the lognormal fracture permeability distribution is randomly located in the model domain and CO2 migration is simulated within the same fracture network for 50 equally probable realizations. Model results suggest that fracture connectivity, which is independent of permeability distribution, governs the path taken by buoyant CO2 as it rises through the flow interior; however, the permeability distribution strongly governs the CO2 flux magnitude. In particular, this research shows that even where fracture networks are sufficiently connected, CO2 flux is often inhibited by a cell of lower permeability, analogous to an obstruction or asperity in a natural fracture. This impresses the importance of considering spatial uncertainty in fracture apertures when modeling CO2 leakage through a caprock.

Rayleigh Scattering Diagnostic for Simultaneous Measurements of Dynamic Density and Velocity

NASA Technical Reports Server (NTRS)

Seasholtz, Richard G.; Panda, J.

2000-01-01

A flow diagnostic technique based on the molecular Rayleigh scattering of laser light is used to obtain dynamic density and velocity data in turbulent flows. The technique is based on analyzing the Rayleigh scattered light with a Fabry-Perot interferometer and recording information about the interference pattern with a multiple anode photomultiplier tube (PMT). An artificial neural network is used to process the signals from the PMT to recover the velocity time history, which is then used to calculate the velocity power spectrum. The technique is illustrated using simulated data. The results of an experiment to measure the velocity power spectrum in a low speed (100 rn/sec) flow are also presented.

Transient PVT measurements and model predictions for vessel heat transfer. Part II.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Felver, Todd G.; Paradiso, Nicholas Joseph; Winters, William S., Jr.

2010-07-01

Part I of this report focused on the acquisition and presentation of transient PVT data sets that can be used to validate gas transfer models. Here in Part II we focus primarily on describing models and validating these models using the data sets. Our models are intended to describe the high speed transport of compressible gases in arbitrary arrangements of vessels, tubing, valving and flow branches. Our models fall into three categories: (1) network flow models in which flow paths are modeled as one-dimensional flow and vessels are modeled as single control volumes, (2) CFD (Computational Fluid Dynamics) models inmore » which flow in and between vessels is modeled in three dimensions and (3) coupled network/CFD models in which vessels are modeled using CFD and flows between vessels are modeled using a network flow code. In our work we utilized NETFLOW as our network flow code and FUEGO for our CFD code. Since network flow models lack three-dimensional resolution, correlations for heat transfer and tube frictional pressure drop are required to resolve important physics not being captured by the model. Here we describe how vessel heat transfer correlations were improved using the data and present direct model-data comparisons for all tests documented in Part I. Our results show that our network flow models have been substantially improved. The CFD modeling presented here describes the complex nature of vessel heat transfer and for the first time demonstrates that flow and heat transfer in vessels can be modeled directly without the need for correlations.« less

The Situation Awareness Weighted Network (SAWN) model and method: Theory and application.

PubMed

Kalloniatis, Alexander; Ali, Irena; Neville, Timothy; La, Phuong; Macleod, Iain; Zuparic, Mathew; Kohn, Elizabeth

2017-05-01

We introduce a novel model and associated data collection method to examine how a distributed organisation of military staff who feed a Common Operating Picture (COP) generates Situation Awareness (SA), a critical component in organisational performance. The proposed empirically derived Situation Awareness Weighted Network (SAWN) model draws on two scientific models of SA, by Endsley involving perception, comprehension and projection, and by Stanton et al. positing that SA exists across a social and semantic network of people and information objects in activities connected across a set of tasks. The output of SAWN is a representation as a weighted semi-bipartite network of the interaction between people ('human nodes') and information artefacts such as documents and system displays ('product nodes'); link weights represent the Endsley levels of SA that individuals acquire from or provide to information objects and other individuals. The SAWN method is illustrated with aggregated empirical data from a case study of Australian military staff undertaking their work during two very different scenarios, during steady-state operations and in a crisis threat context. A key outcome of analysis of the weighted networks is that we are able to quantify flow of SA through an organisation as staff seek to "value-add" in the conduct of their work. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Scalable software-defined optical networking with high-performance routing and wavelength assignment algorithms.

PubMed

Lee, Chankyun; Cao, Xiaoyuan; Yoshikane, Noboru; Tsuritani, Takehiro; Rhee, June-Koo Kevin

2015-10-19

The feasibility of software-defined optical networking (SDON) for a practical application critically depends on scalability of centralized control performance. The paper, highly scalable routing and wavelength assignment (RWA) algorithms are investigated on an OpenFlow-based SDON testbed for proof-of-concept demonstration. Efficient RWA algorithms are proposed to achieve high performance in achieving network capacity with reduced computation cost, which is a significant attribute in a scalable centralized-control SDON. The proposed heuristic RWA algorithms differ in the orders of request processes and in the procedures of routing table updates. Combined in a shortest-path-based routing algorithm, a hottest-request-first processing policy that considers demand intensity and end-to-end distance information offers both the highest throughput of networks and acceptable computation scalability. We further investigate trade-off relationship between network throughput and computation complexity in routing table update procedure by a simulation study.

Efficient Quantum Transmission in Multiple-Source Networks

PubMed Central

Luo, Ming-Xing; Xu, Gang; Chen, Xiu-Bo; Yang, Yi-Xian; Wang, Xiaojun

2014-01-01

A difficult problem in quantum network communications is how to efficiently transmit quantum information over large-scale networks with common channels. We propose a solution by developing a quantum encoding approach. Different quantum states are encoded into a coherent superposition state using quantum linear optics. The transmission congestion in the common channel may be avoided by transmitting the superposition state. For further decoding and continued transmission, special phase transformations are applied to incoming quantum states using phase shifters such that decoders can distinguish outgoing quantum states. These phase shifters may be precisely controlled using classical chaos synchronization via additional classical channels. Based on this design and the reduction of multiple-source network under the assumption of restricted maximum-flow, the optimal scheme is proposed for specially quantized multiple-source network. In comparison with previous schemes, our scheme can greatly increase the transmission efficiency. PMID:24691590

Effects of deception in social networks

PubMed Central

Iñiguez, Gerardo; Govezensky, Tzipe; Dunbar, Robin; Kaski, Kimmo; Barrio, Rafael A.

2014-01-01

Honesty plays a crucial role in any situation where organisms exchange information or resources. Dishonesty can thus be expected to have damaging effects on social coherence if agents cannot trust the information or goods they receive. However, a distinction is often drawn between prosocial lies (‘white’ lies) and antisocial lying (i.e. deception for personal gain), with the former being considered much less destructive than the latter. We use an agent-based model to show that antisocial lying causes social networks to become increasingly fragmented. Antisocial dishonesty thus places strong constraints on the size and cohesion of social communities, providing a major hurdle that organisms have to overcome (e.g. by evolving counter-deception strategies) in order to evolve large, socially cohesive communities. In contrast, white lies can prove to be beneficial in smoothing the flow of interactions and facilitating a larger, more integrated network. Our results demonstrate that these group-level effects can arise as emergent properties of interactions at the dyadic level. The balance between prosocial and antisocial lies may set constraints on the structure of social networks, and hence the shape of society as a whole. PMID:25056625

Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis

PubMed Central

Toppi, Jlenia; Astolfi, Laura; Risetti, Monica; Anzolin, Alessandra; Kober, Silvia E.; Wood, Guilherme; Mattia, Donatella

2018-01-01

Several non-invasive imaging methods have contributed to shed light on the brain mechanisms underlying working memory (WM). The aim of the present study was to depict the topology of the relevant EEG-derived brain networks associated to distinct operations of WM function elicited by the Sternberg Item Recognition Task (SIRT) such as encoding, storage, and retrieval in healthy, middle age (46 ± 5 years) adults. High density EEG recordings were performed in 17 participants whilst attending a visual SIRT. Neural correlates of WM were assessed by means of a combination of EEG signal processing methods (i.e., time-varying connectivity estimation and graph theory), in order to extract synthetic descriptors of the complex networks underlying the encoding, storage, and retrieval phases of WM construct. The group analysis revealed that the encoding phase exhibited a significantly higher small-world topology of EEG networks with respect to storage and retrieval in all EEG frequency oscillations, thus indicating that during the encoding of items the global network organization could “optimally” promote the information flow between WM sub-networks. We also found that the magnitude of such configuration could predict subject behavioral performance when memory load increases as indicated by the negative correlation between Reaction Time and the local efficiency values estimated during the encoding in the alpha band in both 4 and 6 digits conditions. At the local scale, the values of the degree index which measures the degree of in- and out- information flow between scalp areas were found to specifically distinguish the hubs within the relevant sub-networks associated to each of the three different WM phases, according to the different role of the sub-network of regions in the different WM phases. Our findings indicate that the use of EEG-derived connectivity measures and their related topological indices might offer a reliable and yet affordable approach to monitor WM components and thus theoretically support the clinical assessment of cognitive functions in presence of WM decline/impairment, as it occurs after stroke. PMID:29379425

PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology

PubMed Central

Gioutlakis, Aris; Klapa, Maria I.

2017-01-01

It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple heterogeneous primary datasets, presenting the PPIs at various genetic reference levels. Existing PPI meta-databases perform integration via normalization; namely, PPIs are merged after converted to a certain target level. Hence, the node set of the integrated network depends each time on the number and type of the combined datasets. Moreover, the irreversible a priori normalization process hinders the identification of normalization artifacts in the integrated network, which originate from the nonlinearity characterizing the genetic information flow. PICKLE (Protein InteraCtion KnowLedgebasE) 2.0 implements a new architecture for this recently introduced human PPI meta-database. Its main novel feature over the existing meta-databases is its approach to primary PPI dataset integration via genetic information ontology. Building upon the PICKLE principles of using the reviewed human complete proteome (RHCP) of UniProtKB/Swiss-Prot as the reference protein interactor set, and filtering out protein interactions with low probability of being direct based on the available evidence, PICKLE 2.0 first assembles the RHCP genetic information ontology network by connecting the corresponding genes, nucleotide sequences (mRNAs) and proteins (UniProt entries) and then integrates PPI datasets by superimposing them on the ontology network without any a priori transformations. Importantly, this process allows the resulting heterogeneous integrated network to be reversibly normalized to any level of genetic reference without loss of the original information, the latter being used for identification of normalization biases, and enables the appraisal of potential false positive interactions through PPI source database cross-checking. The PICKLE web-based interface (www.pickle.gr) allows for the simultaneous query of multiple entities and provides integrated human PPI networks at either the protein (UniProt) or the gene level, at three PPI filtering modes. PMID:29023571

Attention reorganizes connectivity across networks in a frequency specific manner.

PubMed

Kwon, Soyoung; Watanabe, Masataka; Fischer, Elvira; Bartels, Andreas

2017-01-01

Attention allows our brain to focus its limited resources on a given task. It does so by selective modulation of neural activity and of functional connectivity (FC) across brain-wide networks. While there is extensive literature on activity changes, surprisingly few studies examined brain-wide FC modulations that can be cleanly attributed to attention compared to matched visual processing. In contrast to prior approaches, we used an ultra-long trial design that avoided transients from trial onsets, included slow fluctuations (<0.1Hz) that carry important information on FC, and allowed for frequency-segregated analyses. We found that FC derived from long blocks had a nearly two-fold higher gain compared to FC derived from traditional (short) block designs. Second, attention enhanced intrinsic (negative or positive) correlations across networks, such as between the default-mode network (DMN), the dorsal attention network (DAN), and the visual system (VIS). In contrast attention de-correlated the intrinsically correlated visual regions. Third, the de-correlation within VIS was driven primarily by high frequencies, whereas the increase in DAN-VIS predominantly by low frequencies. These results pinpoint two fundamentally distinct effects of attention on connectivity. Information flow increases between distinct large-scale networks, and de-correlation within sensory cortex indicates decreased redundancy. Copyright © 2016 Elsevier Inc. All rights reserved.

The Geospatial Characteristics of a Social Movement Communication Network

PubMed Central

Conover, Michael D.; Davis, Clayton; Ferrara, Emilio; McKelvey, Karissa; Menczer, Filippo; Flammini, Alessandro

2013-01-01

Social movements rely in large measure on networked communication technologies to organize and disseminate information relating to the movements’ objectives. In this work we seek to understand how the goals and needs of a protest movement are reflected in the geographic patterns of its communication network, and how these patterns differ from those of stable political communication. To this end, we examine an online communication network reconstructed from over 600,000 tweets from a thirty-six week period covering the birth and maturation of the American anticapitalist movement, Occupy Wall Street. We find that, compared to a network of stable domestic political communication, the Occupy Wall Street network exhibits higher levels of locality and a hub and spoke structure, in which the majority of non-local attention is allocated to high-profile locations such as New York, California, and Washington D.C. Moreover, we observe that information flows across state boundaries are more likely to contain framing language and references to the media, while communication among individuals in the same state is more likely to reference protest action and specific places and times. Tying these results to social movement theory, we propose that these features reflect the movement’s efforts to mobilize resources at the local level and to develop narrative frames that reinforce collective purpose at the national level. PMID:23483885

The geospatial characteristics of a social movement communication network.

PubMed

Conover, Michael D; Davis, Clayton; Ferrara, Emilio; McKelvey, Karissa; Menczer, Filippo; Flammini, Alessandro

2013-01-01

Social movements rely in large measure on networked communication technologies to organize and disseminate information relating to the movements' objectives. In this work we seek to understand how the goals and needs of a protest movement are reflected in the geographic patterns of its communication network, and how these patterns differ from those of stable political communication. To this end, we examine an online communication network reconstructed from over 600,000 tweets from a thirty-six week period covering the birth and maturation of the American anticapitalist movement, Occupy Wall Street. We find that, compared to a network of stable domestic political communication, the Occupy Wall Street network exhibits higher levels of locality and a hub and spoke structure, in which the majority of non-local attention is allocated to high-profile locations such as New York, California, and Washington D.C. Moreover, we observe that information flows across state boundaries are more likely to contain framing language and references to the media, while communication among individuals in the same state is more likely to reference protest action and specific places and times. Tying these results to social movement theory, we propose that these features reflect the movement's efforts to mobilize resources at the local level and to develop narrative frames that reinforce collective purpose at the national level.

Design of pressure-driven microfluidic networks using electric circuit analogy.

PubMed

Oh, Kwang W; Lee, Kangsun; Ahn, Byungwook; Furlani, Edward P

2012-02-07

This article reviews the application of electric circuit methods for the analysis of pressure-driven microfluidic networks with an emphasis on concentration- and flow-dependent systems. The application of circuit methods to microfluidics is based on the analogous behaviour of hydraulic and electric circuits with correlations of pressure to voltage, volumetric flow rate to current, and hydraulic to electric resistance. Circuit analysis enables rapid predictions of pressure-driven laminar flow in microchannels and is very useful for designing complex microfluidic networks in advance of fabrication. This article provides a comprehensive overview of the physics of pressure-driven laminar flow, the formal analogy between electric and hydraulic circuits, applications of circuit theory to microfluidic network-based devices, recent development and applications of concentration- and flow-dependent microfluidic networks, and promising future applications. The lab-on-a-chip (LOC) and microfluidics community will gain insightful ideas and practical design strategies for developing unique microfluidic network-based devices to address a broad range of biological, chemical, pharmaceutical, and other scientific and technical challenges.

Evidence for Dynamic Network Regulation of Drosophila Photoreceptor Function from Mutants Lacking the Neurotransmitter Histamine

PubMed Central

Dau, An; Friederich, Uwe; Dongre, Sidhartha; Li, Xiaofeng; Bollepalli, Murali K.; Hardie, Roger C.; Juusola, Mikko

2016-01-01

Synaptic feedback from interneurons to photoreceptors can help to optimize visual information flow by balancing its allocation on retinal pathways under changing light conditions. But little is known about how this critical network operation is regulated dynamically. Here, we investigate this question by comparing signaling properties and performance of wild-type Drosophila R1–R6 photoreceptors to those of the hdcJK910 mutant, which lacks the neurotransmitter histamine and therefore cannot transmit information to interneurons. Recordings show that hdcJK910 photoreceptors sample similar amounts of information from naturalistic stimulation to wild-type photoreceptors, but this information is packaged in smaller responses, especially under bright illumination. Analyses reveal how these altered dynamics primarily resulted from network overload that affected hdcJK910 photoreceptors in two ways. First, the missing inhibitory histamine input to interneurons almost certainly depolarized them irrevocably, which in turn increased their excitatory feedback to hdcJK910 R1–R6s. This tonic excitation depolarized the photoreceptors to artificially high potentials, reducing their operational range. Second, rescuing histamine input to interneurons in hdcJK910 mutant also restored their normal phasic feedback modulation to R1–R6s, causing photoreceptor output to accentuate dynamic intensity differences at bright illumination, similar to the wild-type. These results provide mechanistic explanations of how synaptic feedback connections optimize information packaging in photoreceptor output and novel insight into the operation and design of dynamic network regulation of sensory neurons. PMID:27047343

The impact of neighboring infection on the computer virus spread in packets on scale-free networks

NASA Astrophysics Data System (ADS)

Lazfi, S.; Lamzabi, S.; Rachadi, A.; Ez-Zahraouy, H.

2017-12-01

In this paper, we introduce the effect of neighbors on the infection of packets by computer virus in the SI and SIR models using the minimal traffic routing protocol. We have applied this model to the Barabasi-Albert network to determine how intrasite and extrasite infection rates affect virus propagation through the traffic flow of information packets in both the free-flow and the congested phases. The numerical results show that when we change the intrasite infection rate λ1 while keeping constant the extrasite infection rate λ2, we get normal behavior in the congested phase: in the network, the proportion of infected packets increases to reach a peak and then decreases resulting in a simultaneous increase of the recovered packets. In contrast, when the intrasite infection rate λ1 is kept fixed, an increase of the extrasite infection rate results in two regimes: The first one is characterized by an increase of the proportion of infected packets until reaching some peak value and then decreases smoothly. The second regime is characterized by an increase of infected packets to some stationary value.

Spreading Effect in Industrial Complex Network Based on Revised Structural Holes Theory

PubMed Central

Ye, Qing; Guan, Jun

2016-01-01

This paper analyzed the spreading effect of industrial sectors with complex network model under perspective of econophysics. Input-output analysis, as an important research tool, focuses more on static analysis. However, the fundamental aim of industry analysis is to figure out how interaction between different industries makes impacts on economic development, which turns out to be a dynamic process. Thus, industrial complex network based on input-output tables from WIOD is proposed to be a bridge connecting accurate static quantitative analysis and comparable dynamic one. With application of revised structural holes theory, flow betweenness and random walk centrality were respectively chosen to evaluate industrial sectors’ long-term and short-term spreading effect process in this paper. It shows that industries with higher flow betweenness or random walk centrality would bring about more intensive industrial spreading effect to the industrial chains they stands in, because value stream transmission of industrial sectors depends on how many products or services it can get from the other ones, and they are regarded as brokers with bigger information superiority and more intermediate interests. PMID:27218468