Flow-Based Network Analysis of the Caenorhabditis elegans Connectome
Bacik, Karol A.; Schaub, Michael T.; Billeh, Yazan N.; Barahona, Mauricio
2016-01-01
We exploit flow propagation on the directed neuronal network of the nematode C. elegans to reveal dynamically relevant features of its connectome. We find flow-based groupings of neurons at different levels of granularity, which we relate to functional and anatomical constituents of its nervous system. A systematic in silico evaluation of the full set of single and double neuron ablations is used to identify deletions that induce the most severe disruptions of the multi-resolution flow structure. Such ablations are linked to functionally relevant neurons, and suggest potential candidates for further in vivo investigation. In addition, we use the directional patterns of incoming and outgoing network flows at all scales to identify flow profiles for the neurons in the connectome, without pre-imposing a priori categories. The four flow roles identified are linked to signal propagation motivated by biological input-response scenarios. PMID:27494178
An artificial neural network based groundwater flow and transport simulator
Krom, T.D.; Rosbjerg, D.
1998-07-01
Artificial neural networks are investigated as a tool for the simulation of contaminant loss and recovery in three-dimensional heterogeneous groundwater flow and contaminant transport modeling. These methods have useful applications in expert system development, knowledge base development and optimization of groundwater pollution remediation. The numerical model runs used to develop the artificial neural networks can be re-used to develop artificial neural networks to address alternative optimization problems or changed formulations of the constraints and or objective function under optimization. Artificial neural networks have been analyzed with the goal of estimating objectives which normally require the use of traditional flow and transport codes: such as contaminant recovery, contaminant loss (unrecovered) and remediation failure. The inputs to the artificial neutral networks are variable pumping withdrawal rates at fairly unconstrained 3-D locations. A forward-feed backwards error propagation artificial neural network architecture is used. The significance of the size of the training set, network architecture, and network weight optimization algorithm with respect to the estimation accuracy and objective are shown to be important. Finally, the quality of the weight optimization is studied via cross-validation techniques. This is demonstrated to be a useful method for judging training performance for strongly under-described systems.
1988-12-01
Researchers have suggested other solution strategies, using ideas from nonlinear progamming for solving this general separable convex cost flow problems. Some...plane methods and branch and bound procedures of integer programming, primal-dual methods of linear and nonlinear programming, and polyhedral methods...Combinatorial Optimization: Networks and Matroids), Bazaraa and Jarvis [1978] (Linear Programming and Network Flows), Minieka [1978] (Optimization Algorithms for
International Trade Modelling Using Open Flow Networks: A Flow-Distance Based Analysis.
Shen, Bin; Zhang, Jiang; Li, Yixiao; Zheng, Qiuhua; Li, Xingsen
2015-01-01
This paper models and analyzes international trade flows using open flow networks (OFNs) with the approaches of flow distances, which provide a novel perspective and effective tools for the study of international trade. We discuss the establishment of OFNs of international trade from two coupled viewpoints: the viewpoint of trading commodity flow and that of money flow. Based on the novel model with flow distance approaches, meaningful insights are gained. First, by introducing the concepts of trade trophic levels and niches, countries' roles and positions in the global supply chains (or value-added chains) can be evaluated quantitatively. We find that the distributions of trading "trophic levels" have the similar clustering pattern for different types of commodities, and summarize some regularities between money flow and commodity flow viewpoints. Second, we find that active and competitive countries trade a wide spectrum of products, while inactive and underdeveloped countries trade a limited variety of products. Besides, some abnormal countries import many types of goods, which the vast majority of countries do not need to import. Third, harmonic node centrality is proposed and we find the phenomenon of centrality stratification. All the results illustrate the usefulness of the model of OFNs with its network approaches for investigating international trade flows.
International Trade Modelling Using Open Flow Networks: A Flow-Distance Based Analysis
Shen, Bin; Zhang, Jiang; Li, Yixiao; Zheng, Qiuhua; Li, Xingsen
2015-01-01
This paper models and analyzes international trade flows using open flow networks (OFNs) with the approaches of flow distances, which provide a novel perspective and effective tools for the study of international trade. We discuss the establishment of OFNs of international trade from two coupled viewpoints: the viewpoint of trading commodity flow and that of money flow. Based on the novel model with flow distance approaches, meaningful insights are gained. First, by introducing the concepts of trade trophic levels and niches, countries’ roles and positions in the global supply chains (or value-added chains) can be evaluated quantitatively. We find that the distributions of trading “trophic levels” have the similar clustering pattern for different types of commodities, and summarize some regularities between money flow and commodity flow viewpoints. Second, we find that active and competitive countries trade a wide spectrum of products, while inactive and underdeveloped countries trade a limited variety of products. Besides, some abnormal countries import many types of goods, which the vast majority of countries do not need to import. Third, harmonic node centrality is proposed and we find the phenomenon of centrality stratification. All the results illustrate the usefulness of the model of OFNs with its network approaches for investigating international trade flows. PMID:26569618
Water flow based geometric active deformable model for road network
NASA Astrophysics Data System (ADS)
Leninisha, Shanmugam; Vani, Kaliaperumal
2015-04-01
A width and color based geometric active deformable model is proposed for road network extraction from remote sensing images with minimal human interception. Orientation and width of road are computed from a single manual seed point, from which the propagation starts both right and left hand directions of the starting point, which extracts the interconnected road network from the aerial or high spatial resolution satellite image automatically. Here the propagation (like water flow in canal with defined boundary) is restricted with color and width of the road. Road extraction is done for linear, curvilinear (U shape and S shape) roads first, irrespective of width and color. Then, this algorithm is improved to extract road with junctions in a shape of L, T and X along with center line. Roads with small break or disconnected roads are also extracts by a modified version of this same algorithm. This methodology is tested and evaluated with various remote sensing images. The experimental results show that the proposed method is efficient and extracting roads accurately with less computation time. However, in complex urban areas, the identification accuracy declines due to the various sizes of obstacles, over bridges, multilane etc.
Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias
2013-04-24
Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.
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.
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.
NASA Astrophysics Data System (ADS)
Yang, Hui; Cheng, Lei; Yuan, Jian; Zhang, Jie; Zhao, Yongli; Lee, Young
2015-06-01
With the rapid growth of data center services, the elastic optical network is a very promising networking architecture to interconnect data centers because it can elastically allocate spectrum tailored for various bandwidth requirements. In case of a link failure, to ensure a high-level quality of service (QoS) for user request after the failure becomes a research focus. In light of it, in this paper, we propose and experimentally demonstrate multipath protection for data center services in OpenFlow-based software defined elastic optical network testbed aiming at improving network reliability. We first propose an OpenFlow-based software defined elastic optical network architecture for data center service protection. Then, based on the proposed architecture, multipath protection scheme is figured based on the importance level of the service. To implement the proposed scheme in the architecture, OpenFlow protocol is extended to support multipath protection in elastic optical network. The performance of our proposed multipath protection scheme is evaluated by means of experiment on our OpenFlow-based testbed. The feasibility of our proposed scheme is also demonstrated in software defined elastic optical networks.
Predicting commuter flows in spatial networks using a radiation model based on temporal ranges.
Ren, Yihui; Ercsey-Ravasz, Mária; Wang, Pu; González, Marta C; Toroczkai, Zoltán
2014-11-06
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.
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.
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.
NASA Astrophysics Data System (ADS)
Hsiao, Kuang-Ting; Devillard, Mathieu; Advani, Suresh G.
2004-05-01
In the vacuum assisted resin transfer moulding (VARTM) process, using a flow distribution network such as flow channels and high permeability fabrics can accelerate the resin infiltration of the fibre reinforcement during the manufacture of composite parts. The flow distribution network significantly influences the fill time and fill pattern and is essential for the process design. The current practice has been to cover the top surface of the fibre preform with the distribution media with the hope that the resin will flood the top surface immediately and penetrate through the thickness. However, this approach has some drawbacks. One is when the resin finds its way to the vent before it has penetrated the preform entirely, which results in a defective part or resin wastage. Also, if the composite structure contains ribs or inserts, this approach invariably results in dry spots. Instead of this intuitive approach, we propose a science-based approach to design the layout of the distribution network. Our approach uses flow simulation of the resin into the network and the preform and a genetic algorithm to optimize the flow distribution network. An experimental case study of a co-cured rib structure is conducted to demonstrate the design procedure and validate the optimized flow distribution network design. Good agreement between the flow simulations and the experimental results was observed. It was found that the proposed design algorithm effectively optimized the flow distribution network of the part considered in our case study and hence should prove to be a useful tool to extend the VARTM process to manufacture of complex structures with effective use of the distribution network layup.
NASA Technical Reports Server (NTRS)
Schallhorn, Paul; Majumdar, Alok
2012-01-01
This paper describes a finite volume based numerical algorithm that allows multi-dimensional computation of fluid flow within a system level network flow analysis. There are several thermo-fluid engineering problems where higher fidelity solutions are needed that are not within the capacity of system level codes. The proposed algorithm will allow NASA's Generalized Fluid System Simulation Program (GFSSP) to perform multi-dimensional flow calculation within the framework of GFSSP s typical system level flow network consisting of fluid nodes and branches. The paper presents several classical two-dimensional fluid dynamics problems that have been solved by GFSSP's multi-dimensional flow solver. The numerical solutions are compared with the analytical and benchmark solution of Poiseulle, Couette and flow in a driven cavity.
Network-based representation of energy transfer in unsteady separated flow
NASA Astrophysics Data System (ADS)
Nair, Aditya; Taira, Kunihiko
2015-11-01
We construct a network-based representation of energy pathways in unsteady separated flows using a POD-Galerkin projection model. In this formulation, we regard the POD modes as the network nodes and the energy transfer between the modes as the network edges. Based on the energy transfer analysis performed by Noack et al. (2008), edge weights are characterized on the interaction graph. As an example, we examine the energy transfer within the two-dimensional incompressible flow over a circular cylinder. In particular, we analyze the energy pathways involved in flow transition from the unstable symmetric steady state to periodic shedding cycle. The growth of perturbation energy over the network is examined to highlight key features of flow physics and to determine how the energy transfer can be influenced. Furthermore, we implement closed-loop flow control on the POD-Galerkin model to alter the energy interaction path and modify the global behavior of the wake dynamics. The insights gained will be used to perform further network analysis on fluid flows with added complexity. Work supported by US Army Research Office (W911NF-14-1-0386) and US Air Force Office of Scientific Research (YIP: FA9550-13-1-0183).
A power flow based model for the analysis of vulnerability in power networks
NASA Astrophysics Data System (ADS)
Wang, Zhuoyang; Chen, Guo; Hill, David J.; Dong, Zhao Yang
2016-10-01
An innovative model which considers power flow, one of the most important characteristics in a power system, is proposed for the analysis of power grid vulnerability. Moreover, based on the complex network theory and the Max-Flow theorem, a new vulnerability index is presented to identify the vulnerable lines in a power grid. In addition, comparative simulations between the power flow based model and existing models are investigated on the IEEE 118-bus system. The simulation results demonstrate that the proposed model and the index are more effective in power grid vulnerability analysis.
A neural network-based power system stabilizer using power flow characteristics
Park, Y.M.; Choi, M.S.; Lee, K.Y.
1996-06-01
A neural network-based Power System Stabilizer (Neuro-PSS) is designed for a generator connected to a multi-machine power system utilizing the nonlinear power flow dynamics. The uses of power flow dynamics provide a PSS for a wide range operation with reduced size neutral networks. The Neuro-PSS consists of two neutral networks: Neuro-Identifier and Neuro-Controller. The low-frequency oscillation is modeled by the Neuro-Identifier using the power flow dynamics, then a Generalized Backpropagation-Thorough-Time (GBTT) algorithm is developed to train the Neuro-Controller. The simulation results show that the Neuro-PSS designed in this paper performs well with good damping in a wide operation range compared with the conventional PSS.
Entropy-based snow network design for spring peak flow forecasting
NASA Astrophysics Data System (ADS)
Keum, J.; Coulibaly, P. D.; Tapsoba, D.
2015-12-01
In northern regions the dominant phase of precipitation is snow, this precipitation persists and accumulates throughout the winter season until freshet. Quantitative information on snow, such as snow water equivalent and snow cover extent, is essential for water resources management in northern regions. Due to the inaccessibility and remoteness of snow course locations, snow surveys are usually expensive. Therefore an efficient network design strategy is required to provide a maximum amount of information while also minimizing the network cost. In this study, an entropy-based multiobjective optimization method is applied to design a snow network by adding new stations to the existing network in the La Grande River Basin of Quebec, Canada. Three hydrologic models, Sacramento, HBV, and HSAMI, are calibrated to 12 subwatersheds in the La Grande River Basin. Pareto optimal networks are given by the multiobjective optimization by maximizing joint entropy and minimizing total correlation. Each of the potential optimal networks is then evaluated using the calibrated hydrologic models to determine the most appropriate network for spring peak flow forecasting. The proposed methodology provides useful information for designing snow network appropriate for spring peak flow forecasting, which is essential for reservoir operation.
NASA Astrophysics Data System (ADS)
Yang, Hui; Zhang, Jie; Zhao, Yongli; Ji, Yuefeng; Han, Jianrui; Lin, Yi; Qiu, Shaofeng; Lee, Young
2014-06-01
Nowadays, most service providers offer their services and support their applications through federated sets of data centers which need to be interconnected using high-capacity optical networks in intra-datacenter networks. Many datacenter applications in the environment require lower delay and higher availability with the end-to-end guaranteed quality of service. In this paper, we propose a novel time-aware software defined networking (TaSDN) architecture for OpenFlow-based intra-datacenter optical interconnection networks. Based on the proposed architecture, a time-aware service scheduling (TaSS) strategy is introduced to allocate the network and datacenter resources optimally, which considers the datacenter service scheduling with flexible service time and service bandwidth according to the various time sensitivity requirements. The TaSDN can arrange and accommodate the applications with required QoS considering the time factor, and enhance the data center responsiveness to quickly provide for intra-datacenter service demands. The overall feasibility of the proposed architecture is experimentally verified on our testbed with real OpenFlow-enabled tunable optical modules. The performance of TaSS strategy under heavy traffic load scenario is also evaluated based on TaSDN architecture in terms of blocking probability and resource occupation rate.
Characterization of Horizontal Gas-Liquid Two-Phase Flow Using Markov Model-Based Complex Network
NASA Astrophysics Data System (ADS)
Hu, Li-Dan; Jin, Ning-De; Gao, Zhong-Ke
2013-05-01
Horizontal gas-liquid two-phase flow widely exists in many physical systems and chemical engineering processes. Compared with vertical upward gas-liquid two-phase flow, investigations on dynamic behavior underlying horizontal gas-liquid flows are quite limited. Complex network provides a powerful framework for time series analysis of complex dynamical systems. We use a network generation method based on Markov transition probability to infer directed weighted complex networks from signals measured from horizontal gas-liquid two-phase flow experiment and find that the networks corresponding to different flow patterns exhibit different network structure. To investigate the dynamic characteristics of horizontal gas-liquid flows, we construct a number of complex networks under different flow conditions, and explore the network indices for each constructed network. In addition, we investigate the sample entropy of different flow patterns. Our results suggest that the network statistic can well represent the complexity in the transition among different flow patterns and further allows characterizing the interface fluctuation behavior in horizontal gas-liquid two-phase flow.
MEDUSA - An overset grid flow solver for network-based parallel computer systems
NASA Technical Reports Server (NTRS)
Smith, Merritt H.; Pallis, Jani M.
1993-01-01
Continuing improvement in processing speed has made it feasible to solve the Reynolds-Averaged Navier-Stokes equations for simple three-dimensional flows on advanced workstations. Combining multiple workstations into a network-based heterogeneous parallel computer allows the application of programming principles learned on MIMD (Multiple Instruction Multiple Data) distributed memory parallel computers to the solution of larger problems. An overset-grid flow solution code has been developed which uses a cluster of workstations as a network-based parallel computer. Inter-process communication is provided by the Parallel Virtual Machine (PVM) software. Solution speed equivalent to one-third of a Cray-YMP processor has been achieved from a cluster of nine commonly used engineering workstation processors. Load imbalance and communication overhead are the principal impediments to parallel efficiency in this application.
MEDUSA - An overset grid flow solver for network-based parallel computer systems
NASA Technical Reports Server (NTRS)
Smith, Merritt H.; Pallis, Jani M.
1993-01-01
Continuing improvement in processing speed has made it feasible to solve the Reynolds-Averaged Navier-Stokes equations for simple three-dimensional flows on advanced workstations. Combining multiple workstations into a network-based heterogeneous parallel computer allows the application of programming principles learned on MIMD (Multiple Instruction Multiple Data) distributed memory parallel computers to the solution of larger problems. An overset-grid flow solution code has been developed which uses a cluster of workstations as a network-based parallel computer. Inter-process communication is provided by the Parallel Virtual Machine (PVM) software. Solution speed equivalent to one-third of a Cray-YMP processor has been achieved from a cluster of nine commonly used engineering workstation processors. Load imbalance and communication overhead are the principal impediments to parallel efficiency in this application.
Flow distances on open flow networks
NASA Astrophysics Data System (ADS)
Guo, Liangzhu; Lou, Xiaodan; Shi, Peiteng; Wang, Jun; Huang, Xiaohan; Zhang, Jiang
2015-11-01
An open flow network is a weighted directed graph with a source and a sink, depicting flux distributions on networks in the steady state mode of an open flow system. Energetic food webs, economic input-output networks, and international trade networks are open flow network models of energy flows between species, money or value flows between industrial sectors, and goods flows between countries, respectively. An open flow network is different from a closed flow network because it considers the flows from or to the environment (the source and the sink). For instance, in energetic food webs, species obtain energy not only from other species but also from the environment (sunlight), and species also dissipate energy to the environment. Flow distances between any two nodes i and j are defined as the average number of transition steps of a random walker along the network from i to j. The conventional method for the calculation of the random walk distance on closed flow networks cannot be applied to open flow networks. Therefore, we derive novel explicit expressions for flow distances of open flow networks according to their underlying Markov matrix of the network in this paper. We apply flow distances to two types of empirical open flow networks, including energetic food webs and economic input-output networks. In energetic food webs, we visualize the trophic level of each species and compare flow distances with other distance metrics on the graph. In economic input-output networks, we rank sectors according to their average flow distances and cluster sectors into different industrial groups with strong connections. Other potential applications and mathematical properties are also discussed. To summarize, flow distance is a useful and powerful tool to study open flow systems.
1987-12-01
Telecomunications Networks This work appeared in: ., Serfozo, R.f. (1986). Equitable transit charges for multi-administration ? telecommunications...solved an analogous problem for a single server that can serve one of several separate flows of customers; the question being which flow should the
Design and Evaluation of a Proxy-Based Monitoring System for OpenFlow Networks
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
Design and Evaluation of a Proxy-Based Monitoring System for OpenFlow Networks.
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.
Maximum entropy analysis of flow networks
NASA Astrophysics Data System (ADS)
Niven, Robert K.; Abel, Markus; Schlegel, Michael; Waldrip, Steven H.
2014-12-01
This study examines a generalised maximum entropy (MaxEnt) analysis of a flow network, involving flow rates and potential differences on the network, connected by resistance functions. The analysis gives a generic derivation based on an explicit form of the resistance functions. Accounting for the constraints also leads to an extended form of Gibbs' phase rule, applicable to flow networks.
Ainsworth, Nathan G; Grijalva, Prof. Santiago
2013-01-01
Recent works have shown that networks of voltagesource inverters implementing frequency droop control may be analyzed as consensus-like networks. Based on this understanding, we show that enforcement of network line power flows can be viewed as an edge-preservation problem in a -disk dynamic interaction graph. Inspired by other works solving similar problems in other domains, we propose a line weighted frequency droop controller such that a network of all active buses implementing this controller enforces the specified line power flow constraints without need for communication. We provide simulation results verifying that our proposed controller limits line power to enforce constraints, and otherwise acts as a traditional droop controller.
Bobyshev, A.; Crawford, M.; DeMar, P.; Grigaliunas, V.; Grigoriev, M.; Moibenko, A.; Petravick, D.; Rechenmacher, R.; Newman, H.; Bunn, J.; Van Lingen, F.; Nae, D.; Ravot, S.; Steenberg, C.; Su, X.; Thomas, M.; Xia, Y.; /Caltech
2006-08-01
Lambda Station is an ongoing project of Fermi National Accelerator Laboratory and the California Institute of Technology. The goal of this project is to design, develop and deploy network services for path selection, admission control and flow based forwarding of traffic among data-intensive Grid applications such as are used in High Energy Physics and other communities. Lambda Station deals with the last-mile problem in local area networks, connecting production clusters through a rich array of wide area networks. Selective forwarding of traffic is controlled dynamically at the demand of applications. This paper introduces the motivation of this project, design principles and current status. Integration of Lambda Station client API with the essential Grid middleware such as the dCache/SRM Storage Resource Manager is also described. Finally, the results of applying Lambda Station services to development and production clusters at Fermilab and Caltech over advanced networks such as DOE's UltraScience Net and NSF's UltraLight is covered.
Flow optimization in vascular networks.
Cascaval, Radu C; D'Apice, Ciro; D'Arienzo, Maria Pia; Manzo, Rosanna
2017-06-01
The development of mathematical models for studying phenomena observed in vascular networks is very useful for its potential applications in medicine and physiology. Detailed 3D studies of flow in the arterial system based on the Navier-Stokes equations require high computational power, hence reduced models are often used, both for the constitutive laws and the spatial domain. In order to capture the major features of the phenomena under study, such as variations in arterial pressure and flow velocity, the resulting PDE models on networks require appropriate junction and boundary conditions. Instead of considering an entire network, we simulate portions of the latter and use inflow and outflow conditions which realistically mimic the behavior of the network that has not been included in the spatial domain. The resulting PDEs are solved numerically using a discontinuous Galerkin scheme for the spatial and Adam-Bashforth method for the temporal discretization. The aim is to study the effect of truncation to the flow in the root edge of a fractal network, the effect of adding or subtracting an edge to a given network, and optimal control strategies on a network in the event of a blockage or unblockage of an edge or of an entire subtree.
NASA Astrophysics Data System (ADS)
Olender, M.; Krenczyk, D.
2016-08-01
Modern enterprises have to react quickly to dynamic changes in the market, due to changing customer requirements and expectations. One of the key area of production management, that must continuously evolve by searching for new methods and tools for increasing the efficiency of manufacturing systems is the area of production flow planning and control. These aspects are closely connected with the ability to implement the concept of Virtual Enterprises (VE) and Virtual Manufacturing Network (VMN) in which integrated infrastructure of flexible resources are created. In the proposed approach, the players role perform the objects associated with the objective functions, allowing to solve the multiobjective production flow planning problems based on the game theory, which is based on the theory of the strategic situation. For defined production system and production order models ways of solving the problem of production route planning in VMN on computational examples for different variants of production flow is presented. Possible decision strategy to use together with an analysis of calculation results is shown.
Stochastic cycle selection in active flow networks
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
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.
A Network Flow-Based Method to Predict Anticancer Drug Sensitivity
Qin, Yufang; Chen, Ming; Wang, Haiyun; Zheng, Xiaoqi
2015-01-01
Predicting anticancer drug sensitivity can enhance the ability to individualize patient treatment, thus making development of cancer therapies more effective and safe. In this paper, we present a new network flow-based method, which utilizes the topological structure of pathways, for predicting anticancer drug sensitivities. Mutations and copy number alterations of cancer-related genes are assumed to change the pathway activity, and pathway activity difference before and after drug treatment is used as a measure of drug response. In our model, Contributions from different genetic alterations are considered as free parameters, which are optimized by the drug response data from the Cancer Genome Project (CGP). 10-fold cross validation on CGP data set showed that our model achieved comparable prediction results with existing elastic net model using much less input features. PMID:25992881
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.
Zhang, Xuejun; Lei, Jiaxing
2015-01-01
Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology. PMID:26180840
A Network Flow-based Analysis of Cognitive Reserve in Normal Ageing and Alzheimer's Disease.
Wook Yoo, Sang; Han, Cheol E; Shin, Joseph S; Won Seo, Sang; Na, Duk L; Kaiser, Marcus; Jeong, Yong; Seong, Joon-Kyung
2015-05-20
Cognitive reserve is the ability to sustain cognitive function even with a certain amount of brain damages. Here we investigate the neural compensation mechanism of cognitive reserve from the perspective of structural brain connectivity. Our goal was to show that normal people with high education levels (i.e., cognitive reserve) maintain abundant pathways connecting any two brain regions, providing better compensation or resilience after brain damage. Accordingly, patients with high education levels show more deterioration in structural brain connectivity than those with low education levels before symptoms of Alzheimer's disease (AD) become apparent. To test this hypothesis, we use network flow measuring the number of alternative paths between two brain regions in the brain network. The experimental results show that for normal aging, education strengthens network reliability, as measured through flow values, in a subnetwork centered at the supramarginal gyrus. For AD, a subnetwork centered at the left middle frontal gyrus shows a negative correlation between flow and education, which implies more collapse in structural brain connectivity for highly educated patients. We conclude that cognitive reserve may come from the ability of network reorganization to secure the information flow within the brain network, therefore making it more resistant to disease progress.
Uncovering hidden flows in physical networks
NASA Astrophysics Data System (ADS)
Wang, Chengwei; Grebogi, Celso; Baptista, Murilo S.
2017-06-01
Understanding the interactions among nodes in a complex network is of great importance, since they disclose how these nodes are cooperatively supporting the functioning of the network. Scientists have developed numerous methods to uncover the underlying adjacent physical connectivity based on measurements of functional quantities of the nodes states. Often, the physical connectivity, the adjacency matrix, is available. Yet, little is known about how this adjacent connectivity impacts on the “hidden” flows being exchanged between any two arbitrary nodes, after travelling longer non-adjacent paths. In this letter, we show that hidden physical flows in conservative flow networks, a quantity that is usually inaccessible to measurements, can be determined by the interchange of physical flows between any pair of adjacent nodes. Our approach applies to steady or dynamic state of either linear or non-linear complex networks that can be modelled by conservative flow networks, such as gas supply networks, water supply networks and power grids.
A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem
Cai, Kaiquan; Jia, Yaoguang; Zhu, Yanbo; Xiao, Mingming
2015-01-01
Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity. PMID:26180842
A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem.
Cai, Kaiquan; Jia, Yaoguang; Zhu, Yanbo; Xiao, Mingming
2015-01-01
Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.
Network Adaptive Deadband: NCS Data Flow Control for Shared Networks
Díaz-Cacho, Miguel; Delgado, Emma; Prieto, José A. G.; López, Joaquín
2012-01-01
This paper proposes a new middleware solution called Network Adaptive Deadband (NAD) for long time operation of Networked Control Systems (NCS) through the Internet or any shared network based on IP technology. The proposed middleware takes into account the network status and the NCS status, to improve the global system performance and to share more effectively the network by several NCS and sensor/actuator data flows. Relationship between network status and NCS status is solved with a TCP-friendly transport flow control protocol and the deadband concept, relating deadband value and transmission throughput. This creates a deadband-based flow control solution. Simulation and experiments in shared networks show that the implemented network adaptive deadband has better performance than an optimal constant deadband solution in the same circumstances. PMID:23208556
Spring-network-based model of a red blood cell for simulating mesoscopic blood flow.
Nakamura, Masanori; Bessho, Sadao; Wada, Shigeo
2013-01-01
We developed a mechanical model of a red blood cell (RBC) that is capable of expressing its characteristic behaviors in shear flows. The RBC was modeled as a closed shell membrane consisting of spring networks in the framework of the energy minimum concept. The fluid forces acting on RBCs were modeled from Newton's viscosity law and the conservation of momentum. In a steady shear flow, the RBC model exhibited various behaviors, depending on the shear rate; it tumbled, tank-treaded, or both. The transition from tumbling to tank-treading occurred at a shear rate of 20 s( - 1). The simulation of an RBC in steady and unsteady parallel shear flows (Couette flows) showed that the deformation parameters of the RBC were consistent with experimental results. The RBC in Poiseuille flow migrated radially towards the central axis of the flow channel. Axial migration became faster with an increase in the viscosity of the media, qualitatively consistent with experimental results. These results demonstrate that the proposed model satisfies the essential conditions for simulating RBC behavior in blood flow. Finally, a large-scale RBC flow simulation was implemented to show the capability of the proposed model for analyzing the mesoscopic nature of blood flow.
Understanding Networks of Computing Chemical Droplet Neurons Based on Information Flow.
Gruenert, Gerd; Gizynski, Konrad; Escuela, Gabi; Ibrahim, Bashar; Gorecki, Jerzy; Dittrich, Peter
2015-11-01
In this paper, we present general methods that can be used to explore the information processing potential of a medium composed of oscillating (self-exciting) droplets. Networks of Belousov-Zhabotinsky (BZ) droplets seem especially interesting as chemical reaction-diffusion computers because their time evolution is qualitatively similar to neural network activity. Moreover, such networks can be self-generated in microfluidic reactors. However, it is hard to track and to understand the function performed by a medium composed of droplets due to its complex dynamics. Corresponding to recurrent neural networks, the flow of excitations in a network of droplets is not limited to a single direction and spreads throughout the whole medium. In this work, we analyze the operation performed by droplet systems by monitoring the information flow. This is achieved by measuring mutual information and time delayed mutual information of the discretized time evolution of individual droplets. To link the model with reality, we use experimental results to estimate the parameters of droplet interactions. We exemplarily investigate an evolutionary generated droplet structure that operates as a NOR gate. The presented methods can be applied to networks composed of at least hundreds of droplets.
Modeling information flow in biological networks.
Kim, Yoo-Ah; Przytycki, Jozef H; Wuchty, Stefan; Przytycka, Teresa M
2011-06-01
Large-scale molecular interaction networks are being increasingly used to provide a system level view of cellular processes. Modeling communications between nodes in such huge networks as information flows is useful for dissecting dynamical dependences between individual network components. In the information flow model, individual nodes are assumed to communicate with each other by propagating the signals through intermediate nodes in the network. In this paper, we first provide an overview of the state of the art of research in the network analysis based on information flow models. In the second part, we describe our computational method underlying our recent work on discovering dysregulated pathways in glioma. Motivated by applications to inferring information flow from genotype to phenotype in a very large human interaction network, we generalized previous approaches to compute information flows for a large number of instances and also provided a formal proof for the method.
NASA Astrophysics Data System (ADS)
Mohamad-Saleh, J.; Hoyle, B. S.
2002-12-01
Artificial neural networks (ANNs) have been used to investigate their capabilities at estimating key parameters for the characterization of flow processes, based on electrical capacitance-sensed tomographic (ECT) data. The estimations of the parameters are made directly, without recourse to tomographic images. The parameters of interest include component height and interface orientation of two-component flows, and component fractions of two-component and three-component flows. Separate multi-layer perceptron networks were trained with patterns consisting of pairs of simulated ECT data and the corresponding component heights, interface orientations and component fractions. The networks were then tested with patterns consisting of unlearned simulated ECT data of various flows and with real ECT data of gas-water flows. The neural systems provided estimations having mean absolute errors of less than 1% for oil and water heights and fractions and less than 10° for interface orientations. When tested with real plant ECT data, the mean absolute errors were less than 4% for water height, less than 15° for gas-water interface orientation and less than 3% for water fraction, respectively. The results demonstrate the feasibility of the application of ANNs for flow process parameter estimations based upon tomography data.
Gagnon, Louis; Smith, Amy F.; Boas, David A.; Devor, Anna; Secomb, Timothy W.; Sakadžić, Sava
2016-01-01
Oxygen is delivered to brain tissue by a dense network of microvessels, which actively control cerebral blood flow (CBF) through vasodilation and contraction in response to changing levels of neural activity. Understanding these network-level processes is immediately relevant for (1) interpretation of functional Magnetic Resonance Imaging (fMRI) signals, and (2) investigation of neurological diseases in which a deterioration of neurovascular and neuro-metabolic physiology contributes to motor and cognitive decline. Experimental data on the structure, flow and oxygen levels of microvascular networks are needed, together with theoretical methods to integrate this information and predict physiologically relevant properties that are not directly measurable. Recent progress in optical imaging technologies for high-resolution in vivo measurement of the cerebral microvascular architecture, blood flow, and oxygenation enables construction of detailed computational models of cerebral hemodynamics and oxygen transport based on realistic three-dimensional microvascular networks. In this article, we review state-of-the-art optical microscopy technologies for quantitative in vivo imaging of cerebral microvascular structure, blood flow and oxygenation, and theoretical methods that utilize such data to generate spatially resolved models for blood flow and oxygen transport. These “bottom-up” models are essential for the understanding of the processes governing brain oxygenation in normal and disease states and for eventual translation of the lessons learned from animal studies to humans. PMID:27630556
Gagnon, Louis; Smith, Amy F; Boas, David A; Devor, Anna; Secomb, Timothy W; Sakadžić, Sava
2016-01-01
Oxygen is delivered to brain tissue by a dense network of microvessels, which actively control cerebral blood flow (CBF) through vasodilation and contraction in response to changing levels of neural activity. Understanding these network-level processes is immediately relevant for (1) interpretation of functional Magnetic Resonance Imaging (fMRI) signals, and (2) investigation of neurological diseases in which a deterioration of neurovascular and neuro-metabolic physiology contributes to motor and cognitive decline. Experimental data on the structure, flow and oxygen levels of microvascular networks are needed, together with theoretical methods to integrate this information and predict physiologically relevant properties that are not directly measurable. Recent progress in optical imaging technologies for high-resolution in vivo measurement of the cerebral microvascular architecture, blood flow, and oxygenation enables construction of detailed computational models of cerebral hemodynamics and oxygen transport based on realistic three-dimensional microvascular networks. In this article, we review state-of-the-art optical microscopy technologies for quantitative in vivo imaging of cerebral microvascular structure, blood flow and oxygenation, and theoretical methods that utilize such data to generate spatially resolved models for blood flow and oxygen transport. These "bottom-up" models are essential for the understanding of the processes governing brain oxygenation in normal and disease states and for eventual translation of the lessons learned from animal studies to humans.
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.
Hettiarachchi, Imali T; Mohamed, Shady; Nyhof, Luke; Nahavandi, Saeid
2013-01-01
Recently effective connectivity studies have gained significant attention among the neuroscience community as Electroencephalography (EEG) data with a high time resolution can give us a wider understanding of the information flow within the brain. Among other tools used in effective connectivity analysis Granger Causality (GC) has found a prominent place. The GC analysis, based on strictly causal multivariate autoregressive (MVAR) models does not account for the instantaneous interactions among the sources. If instantaneous interactions are present, GC based on strictly causal MVAR will lead to erroneous conclusions on the underlying information flow. Thus, the work presented in this paper applies an extended MVAR (eMVAR) model that accounts for the zero lag interactions. We propose a constrained adaptive Kalman filter (CAKF) approach for the eMVAR model identification and demonstrate that this approach performs better than the short time windowing-based adaptive estimation when applied to information flow analysis.
Hour-Glass Neural Network Based Daily Money Flow Estimation for Automatic Teller Machines
NASA Astrophysics Data System (ADS)
Karungaru, Stephen; Akashi, Takuya; Nakano, Miyoko; Fukumi, Minoru
Monetary transactions using Automated Teller Machines (ATMs) have become a normal part of our daily lives. At ATMs, one can withdraw, send or debit money and even update passbooks among many other possible functions. ATMs are turning the banking sector into a ubiquitous service. However, while the advantages for the ATM users (financial institution customers) are many, the financial institution side faces an uphill task in management and maintaining the cash flow in the ATMs. On one hand, too much money in a rarely used ATM is wasteful, while on the other, insufficient amounts would adversely affect the customers and may result in a lost business opportunity for the financial institution. Therefore, in this paper, we propose a daily cash flow estimation system using neural networks that enables better daily forecasting of the money required at the ATMs. The neural network used in this work is a five layered hour glass shaped structure that achieves fast learning, even for the time series data for which seasonality and trend feature extraction is difficult. Feature extraction is carried out using the Akamatsu Integral and Differential transforms. This work achieves an average estimation accuracy of 92.6%.
NASA Astrophysics Data System (ADS)
Frampton, A.; Cvetkovic, V.
2008-12-01
Implementing site characterization data to models for simulating flow and transport still remains a formidable challenge, in particular for sparsely fracture rock environments. We present advective flow and particle transport simulations in three-dimensional discrete fracture networks based on Laxemar site characterisation data in Sweden, which is a candidate repository site for high level radioactive waste in the Swedish nuclear waste management program. Field measurements have revealed at least five background fracture sets based on statistically significant orientation data, exhibiting power-law behaviour for fracture size and inferred transmissivity distributions. We study the effect of various interpretations of these background fracture populations, all consistent with the field data, and expose their impact on the behaviour of small scale advective particle transport. In particular, we analyse the inferred correlation between fracture size and transmissivity, together with implications on particle injection mode (flux and resident) and transport law. Furthermore, a fundamental aspect towards understanding tracer migration in subsurface sparsely fractured rock formations is the relationship between the Eulerian flow distribution at a sub-fracture scale with the Lagrangian flow distribution at a characteristic model domain scale. We present a novel approach of accurately inferring the segment-scale Lagrangian distributions from Eulerian distributions obtained from flow simulations. Also, we discuss the potential link to field measurements of fracture specific flow, and how such approaches can be used to improve confidence in model assessment.
Intra-Urban Movement Flow Estimation Using Location Based Social Networking Data
NASA Astrophysics Data System (ADS)
Kheiri, A.; Karimipour, F.; Forghani, M.
2015-12-01
In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook, which have attracted an increasing number of users and greatly enriched their urban experience. Location-based social network data, as a new travel demand data source, seems to be an alternative or complement to survey data in the study of mobility behavior and activity analysis because of its relatively high access and low cost. In this paper, three OD estimation models have been utilized in order to investigate their relative performance when using Location-Based Social Networking (LBSN) data. For this, the Foursquare LBSN data was used to analyze the intra-urban movement behavioral patterns for the study area, Manhattan, the most densely populated of the five boroughs of New York city. The outputs of models are evaluated using real observations based on different criterions including distance distribution, destination travel constraints. The results demonstrate the promising potential of using LBSN data for urban travel demand analysis and monitoring.
2012-08-01
the name of the server (all externally facing SMTP servers will have a DNS record and associated IP address). Telnetting to each IP address at port...38 5.6 Remote Services This section discusses how to profile three remote service protocols: Telnet , SSH, and FTP. Telnet is an older protocol...show, the sample network has two potential FTP servers, three potential SSH servers, and no Telnet servers. 2. To find clients on the network
Ecosystem flow networks: loaded dice?
Ulanowicz, R E; Wolff, W F
1991-02-01
An information-theoretic comparison of the topologies of observed ecosystem transfers and randomly constructed networks reveals that it is not easy to separate the members of the two sets. The distribution of ecosystem flow magnitudes, however, is seen to differ markedly from ordinary probability functions and to resemble the Cauchy or Pareto distributions. The agencies that impart such structure to ecological flow networks are not obvious, but one strong possibility is that autocatalysis, or indirect mutualism, promotes certain pathways at the expense of others, thereby enlarging the tail of the distribution of flow magnitudes.
NASA Astrophysics Data System (ADS)
Tang, Jinjun; Liu, Fang; Zhang, Weibin; Zhang, Shen; Wang, Yinhai
2016-05-01
A new method based on complex network theory is proposed to analyze traffic flow time series in different states. We use the data collected from loop detectors on freeway to establish traffic flow model and classify the flow into three states based on K-means method. We then introduced two widely used methods to convert time series into networks: phase space reconstruction and visibility graph. Furthermore, in phase space reconstruction, we discuss how to determine delay time constant and embedding dimension and how to select optimal critical threshold in terms of cumulative degree distribution. In the visibility graph, we design a method to construct network from multi-variables time series based on logical OR. Finally, we study and compare the statistic features of the networks converted from original traffic time series in three states based on phase space and visibility by using the degree distribution, network structure, correlation of the cluster coefficient to betweenness and degree-degree correlation.
NASA Astrophysics Data System (ADS)
Abbasi Hoseini, Afshin; Zavareh, Zahra; Lundell, Fredrik; Anderson, Helge I.
2014-04-01
A matching algorithm based on self-organizing map (SOM) neural network is proposed for tracking rod-like particles in 2D optical measurements of dispersed two-phase flows. It is verified by both synthetic images of elongated particles mimicking 2D suspension flows and direct numerical simulations-based results of prolate particles dispersed in a turbulent channel flow. Furthermore, the potential benefit of this algorithm is evaluated by applying it to the experimental data of rod-like fibers tracking in wall turbulence. The study of the behavior of elongated particles suspended in turbulent flows has a practical importance and covers a wide range of applications in engineering and science. In experimental approach, particle tracking velocimetry of the dispersed phase has a key role together with particle image velocimetry of the carrier phase to obtain the velocities of both phases. The essential parts of particle tracking are to identify and match corresponding particles correctly in consecutive images. The present study is focused on the development of an algorithm for pairing non-spherical particles that have one major symmetry axis. The novel idea in the algorithm is to take the orientation of the particles into account for matching in addition to their positions. The method used is based on the SOM neural network that finds the most likely matching link in images on the basis of feature extraction and clustering. The fundamental concept is finding corresponding particles in the images with the nearest characteristics: position and orientation. The most effective aspect of this two-frame matching algorithm is that it does not require any preliminary knowledge of neither the flow field nor the particle behavior. Furthermore, using one additional characteristic of the non-spherical particles, namely their orientation, in addition to its coordinate vector, the pairing is improved both for more reliable matching at higher concentrations of dispersed particles and
NASA Astrophysics Data System (ADS)
Tanaka, Fumihiko
A simple transient network model is introduced to describe creation and annihilation of junctions in the networks of associating polymers. Stationary non-linear viscosity is calculated by the theory and by Monte Carlo simulation to study shear thickening. The dynamic mechanical moduli are calculated as functions of the frequency and the chain disengagement rate. From the peak of the loss modulus, the lifetime τx of the junction is estimated, and from the high frequency plateau of the storage modulus, the number of elastically effective chains in the network is found. Transient phenomena such as stress relaxation and stress overshoot are also theoretically studied. Results are compared with the recent experimental reports on the rheological study of hydrophobically modified water-soluble polymeters.
Image flow management in a PACS network
NASA Astrophysics Data System (ADS)
Chen, Doris T.; Ho, Bruce K. T.; Chao, Woodrew; Taira, Ricky K.
1994-05-01
The purpose of this project is to develop a graphical tool to analyze and optimize the image flow pattern in a hierarchical, large scale PACS local area network. The PACS network is characterized as heavy image traffic, long image flow path, heterogeneous computer nodes, multiple protocols, and physical media. A smooth operation of the PACS network depends on a centralized, timely report of image flow pattern, trouble spots, and an alarm mechanism for critical conditions. Crucial timing parameters such as queue delay from acquisition to display, processing time for archiving and reformatting, and retrieval speed, can be locally collected and reported to the network manager for statistical characterization. Moreover, network traffic information and system component failures anywhere in the image flow can be reported graphically. High urgency failure trigger an immediate alarm to the designed operator. Our network management software with user friendly graphical interface is easy to use even by non-computer personnel such as hospital administrators. It achieves total automation in trouble reporting and thus leads to improvement of the network layout and operation condition based on the analysis of image flow pattern.
Mode Selection in Compressible Active Flow Networks
NASA Astrophysics Data System (ADS)
Forrow, Aden; Woodhouse, Francis G.; Dunkel, Jörn
2017-07-01
Coherent, large-scale dynamics in many nonequilibrium physical, biological, or information transport networks are driven by small-scale local energy input. Here, we introduce and explore an analytically tractable nonlinear model for compressible active flow networks. In contrast to thermally driven systems, we find that active friction selects discrete states with a limited number of oscillation modes activated at distinct fixed amplitudes. Using perturbation theory, we systematically predict the stationary states of noisy networks and find good agreement with a Bayesian state estimation based on a hidden Markov model applied to simulated time series data. Our results suggest that the macroscopic response of active network structures, from actomyosin force networks to cytoplasmic flows, can be dominated by a significantly reduced number of modes, in contrast to energy equipartition in thermal equilibrium. The model is also well suited to study topological sound modes and spectral band gaps in active matter.
A complex network representation of wind flows
NASA Astrophysics Data System (ADS)
Gelbrecht, Maximilian; Boers, Niklas; Kurths, Jürgen
2017-03-01
Climate networks have proven to be a valuable method to investigate spatial connectivity patterns of the climate system. However, so far such networks have mostly been applied to scalar observables. In this study, we propose a new method for constructing networks from atmospheric wind fields on two-dimensional isobaric surfaces. By connecting nodes along a spatial environment based on the local wind flow, we derive a network representation of the low-level circulation that captures its most important characteristics. In our approach, network links are placed according to a suitable statistical null model that takes into account the direction and magnitude of the flow. We compare a simulation-based (numerically costly) and a semi-analytical (numerically cheaper) approach to determine the statistical significance of possible connections, and find that both methods yield qualitatively similar results. As an application, we choose the regional climate system of South America and focus on the monsoon season in austral summer. Monsoon systems are generally characterized by substantial changes in the large-scale wind directions, and therefore provide ideal applications for the proposed wind networks. Based on these networks, we are able to reveal the key features of the low-level circulation of the South American Monsoon System, including the South American Low-Level Jet. Networks of the dry and the wet season are compared with each other and their differences are consistent with the literature on South American climate.
Methodologies and techniques for analysis of network flow data
Bobyshev, A.; Grigoriev, M.; /Fermilab
2004-12-01
Network flow data gathered at the border routers and core switches is used at Fermilab for statistical analysis of traffic patterns, passive network monitoring, and estimation of network performance characteristics. Flow data is also a critical tool in the investigation of computer security incidents. Development and enhancement of flow based tools is an on-going effort. This paper describes the most recent developments in flow analysis at Fermilab.
Carbon Emission Flow in Networks
Kang, Chongqing; Zhou, Tianrui; Chen, Qixin; Xu, Qianyao; Xia, Qing; Ji, Zhen
2012-01-01
As the human population increases and production expands, energy demand and anthropogenic carbon emission rates have been growing rapidly, and the need to decrease carbon emission levels has drawn increasing attention. The link between energy production and consumption has required the large-scale transport of energy within energy transmission networks. Within this energy flow, there is a virtual circulation of carbon emissions. To understand this circulation and account for the relationship between energy consumption and carbon emissions, this paper introduces the concept of “carbon emission flow in networks” and establishes a method to calculate carbon emission flow in networks. Using an actual analysis of China's energy pattern, the authors discuss the significance of this new concept, not only as a feasible approach but also as an innovative theoretical perspective. PMID:22761988
NASA Astrophysics Data System (ADS)
Frampton, A.
2007-12-01
We study particle transport in a 3D DFN scenario based on the Laxemar site characterisation data in Sweden, which is a candidate repository site for high level radioactive waste in the Swedish nuclear waste management program. The site characterisation data has revealed several interesting geometric and hydraulic fracture properties, such as power-law distributed fracture sizes and transmissivities. Our study involves investigating the relationship between the resulting Eulerian flow field at a segment (sub- fracture) scale with Lagrangian trajectories at the characteristic (model domain) transport scale. We present results from a new technique for upscaling particle transitions obtained from Eulerian flow statistics to predictions of tracer discharge at the characteristic transport scale, based on previously developed methods used for 2D DFN's. This includes a mapping algorithm for transforming Eulerian into Lagrangian flow statistics without a priori knowledge of network connectivity, and by retaining the correlation between the water residence time τ and the hydrodynamic control of retention β we present accurate tracer discharge predictions. These results are illustrated using the unlimited diffusion model, and for some hypothetical tracers with properties designed to capture the behaviour of many common radionuclides. Finally we emphasise the importance of capturing the early arrival and peak of tracer breakthrough curves, i.e. to capture the bulk of the tracer mass arrival, in order to make accurate and conservative predictions.
Li, Wei; Zhang, Yan; Cui, Lijuan; Zhang, Manyin; Wang, Yifei
2015-08-01
A horizontal subsurface flow constructed wetland (HSSF-CW) was designed to improve the water quality of an artificial lake in Beijing Wildlife Rescue and Rehabilitation Center, Beijing, China. Artificial neural networks (ANNs), including multilayer perceptron (MLP) and radial basis function (RBF), were used to model the removal of total phosphorus (TP). Four variables were selected as the input parameters based on the principal component analysis: the influent TP concentration, water temperature, flow rate, and porosity. In order to improve model accuracy, alternative ANNs were developed by incorporating meteorological variables, including precipitation, air humidity, evapotranspiration, solar heat flux, and barometric pressure. A genetic algorithm and cross-validation were used to find the optimal network architectures for the ANNs. Comparison of the observed data and the model predictions indicated that, with careful variable selection, ANNs appeared to be an efficient and robust tool for predicting TP removal in the HSSF-CW. Comparison of the accuracy and efficiency of MLP and RBF for predicting TP removal showed that the RBF with additional meteorological variables produced the most accurate results, indicating a high potentiality for modeling TP removal in the HSSF-CW.
Channegowda, M; Nejabati, R; Rashidi Fard, M; Peng, S; Amaya, N; Zervas, G; Simeonidou, D; Vilalta, R; Casellas, R; Martínez, R; Muñoz, R; Liu, L; Tsuritani, T; Morita, I; Autenrieth, A; Elbers, J P; Kostecki, P; Kaczmarek, P
2013-03-11
Software defined networking (SDN) and flexible grid optical transport technology are two key technologies that allow network operators to customize their infrastructure based on application requirements and therefore minimizing the extra capital and operational costs required for hosting new applications. In this paper, for the first time we report on design, implementation & demonstration of a novel OpenFlow based SDN unified control plane allowing seamless operation across heterogeneous state-of-the-art optical and packet transport domains. We verify and experimentally evaluate OpenFlow protocol extensions for flexible DWDM grid transport technology along with its integration with fixed DWDM grid and layer-2 packet switching.
NASA Astrophysics Data System (ADS)
Grana Romay, Manuel; Echave, Imanol
1999-08-01
In this paper we prose the application of the codebook computed by the Self Organizing Map as a smoothing filter, the QV Bayesian Filter, for the preprocessing of the image sequences. The optical flow is then robustly and efficiently computed over the filtered imags applying a correlation approach at the pixel level.
NASA Astrophysics Data System (ADS)
Lee, Won Cheol; Lee, Hoseok; Lim, Jaeheung; Park, Young June
2016-11-01
We report a simple and efficient electrical sensing scheme that can be used to overcome the "diffusion limit" of affinity-based biosensors by incorporating the structural advantage of a concentric electrode biosensor platform and the microstirring effect of AC electrothermal flow (ACEF). To prove the effect of ACEF on the biosensor performance, we performed both simulations and experiments for the detection of cardiac troponin-I, which is a biomarker for acute myocardial infarction. The finite element simulation results indicate that AC bias to the electrode (which has a concentric structure in our device) can induce fast convection flow, which facilitates the transport of the target molecules to the binding region located between the two electrodes. In our device, the channel region made of a carbon nanotube network decorated with gold nanoparticles, which act as the attaching sites of the probe molecules, is used as a highly sensitive electrical channel. We find that the electrical sensing method exhibited extremely fast sensing speeds compared with those under no bias (diffusion-limited) conditions.
Flow Control Using Neural Networks
2007-11-02
FEB 93 - 31 DEC 96 4. TITLE AND SUBTITLE 5 . FUNDING NUMBERS FLOW CONTROL USING NEURAL NETWORKS F49620-93-1-0135 61102F 6. AUTHOR(S) 2307/BS THORWALD...OFFICE OF SCIENTIFIC RESEARCH (AFOSRO AGENCY REPORT NUMBER 110 DUNCAN AVENUE, ROOM B115 BOLLING AFB DC 20332- 8050 11. SUPPLEMENTARY NOTES 12a...signals. Figure 5 shows a time series for an actuator that performs a ramp motion in the streamwise direction over about 1 % of the TS period and remains
Jia, Limin
2017-01-01
Aimed at the complicated problems of attraction characteristics regarding passenger flow in urban rail transit network, the concept of the gravity field of passenger flow is proposed in this paper. We establish the computation methods of field strength and potential energy to reveal the potential attraction relationship among stations from the perspective of the collection and distribution of passenger flow and the topology of network. As for the computation methods of field strength, an optimum path concept is proposed to define betweenness centrality parameter. Regarding the computation of potential energy, Compound Simpson’s Rule Formula is applied to get a solution to the function. Taking No. 10 Beijing Subway as a practical example, an analysis of simulation and verification is conducted, and the results shows in the following ways. Firstly, the bigger field strength value between two stations is, the stronger passenger flow attraction is, and the greater probability of the formation of the largest passenger flow of section is. Secondly, there is the greatest passenger flow volume and circulation capacity between two zones of high potential energy. PMID:28863175
Li, Man; Wang, Yanhui; Jia, Limin
2017-01-01
Aimed at the complicated problems of attraction characteristics regarding passenger flow in urban rail transit network, the concept of the gravity field of passenger flow is proposed in this paper. We establish the computation methods of field strength and potential energy to reveal the potential attraction relationship among stations from the perspective of the collection and distribution of passenger flow and the topology of network. As for the computation methods of field strength, an optimum path concept is proposed to define betweenness centrality parameter. Regarding the computation of potential energy, Compound Simpson's Rule Formula is applied to get a solution to the function. Taking No. 10 Beijing Subway as a practical example, an analysis of simulation and verification is conducted, and the results shows in the following ways. Firstly, the bigger field strength value between two stations is, the stronger passenger flow attraction is, and the greater probability of the formation of the largest passenger flow of section is. Secondly, there is the greatest passenger flow volume and circulation capacity between two zones of high potential energy.
Optimization of Ramified Flow Networks
NASA Astrophysics Data System (ADS)
Singleton, Martin; Hubler, Alfred; Heiss, Gregor
2009-03-01
A class of Ramified graphs (RG) is introduced as Iterated Function Systems (IFS) to optimally design networks for efficient reverse osmosis desalination in deep seawater. Ramified flow networks of absorbers, ranging from simple structures with constant weights, branch angles, and branch ratios, to fully optimized binary networks are considered. A contracting IFS with fixed overall length is presented for the generation of RG's which serve as candidates for optimality in terms of desalination performance criteria. Using the analogy to electrostatics, the diffusion equation is solved for the desalination systems under three different boundary conditions, i) all nodes having the same pressure difference across the absorbers, ii) all nodes producing permeate at identical rates, and iii) each node having the same salinity. Optimal branching angles and branch length ratios will be found by phase-space methods for each boundary condition, which either maximize production of permeate or minimize expenditure of energy for different fixed numbers of absorbers. For constant salinity absorbers, we give the total water production rate as functions of branching angle and branching ratio for up to 10 branching generations. Both optimal angle and optimal ratios are found to be decreasing functions of generation for constant salinity absorbers.
Borst, Alexander; Weber, Franz
2011-01-01
Optic flow based navigation is a fundamental way of visual course control described in many different species including man. In the fly, an essential part of optic flow analysis is performed in the lobula plate, a retinotopic map of motion in the environment. There, the so-called lobula plate tangential cells possess large receptive fields with different preferred directions in different parts of the visual field. Previous studies demonstrated an extensive connectivity between different tangential cells, providing, in principle, the structural basis for their large and complex receptive fields. We present a network simulation of the tangential cells, comprising most of the neurons studied so far (22 on each hemisphere) with all the known connectivity between them. On their dendrite, model neurons receive input from a retinotopic array of Reichardt-type motion detectors. Model neurons exhibit receptive fields much like their natural counterparts, demonstrating that the connectivity between the lobula plate tangential cells indeed can account for their complex receptive field structure. We describe the tuning of a model neuron to particular types of ego-motion (rotation as well as translation around/along a given body axis) by its ‘action field’. As we show for model neurons of the vertical system (VS-cells), each of them displays a different type of action field, i.e., responds maximally when the fly is rotating around a particular body axis. However, the tuning width of the rotational action fields is relatively broad, comparable to the one with dendritic input only. The additional intra-lobula-plate connectivity mainly reduces their translational action field amplitude, i.e., their sensitivity to translational movements along any body axis of the fly. PMID:21305019
Neural network system for traffic flow management
NASA Astrophysics Data System (ADS)
Gilmore, John F.; Elibiary, Khalid J.; Petersson, L. E. Rickard
1992-09-01
Atlanta will be the home of several special events during the next five years ranging from the 1996 Olympics to the 1994 Super Bowl. When combined with the existing special events (Braves, Falcons, and Hawks games, concerts, festivals, etc.), the need to effectively manage traffic flow from surface streets to interstate highways is apparent. This paper describes a system for traffic event response and management for intelligent navigation utilizing signals (TERMINUS) developed at Georgia Tech for adaptively managing special event traffic flows in the Atlanta, Georgia area. TERMINUS (the original name given Atlanta, Georgia based upon its role as a rail line terminating center) is an intelligent surface street signal control system designed to manage traffic flow in Metro Atlanta. The system consists of three components. The first is a traffic simulation of the downtown Atlanta area around Fulton County Stadium that models the flow of traffic when a stadium event lets out. Parameters for the surrounding area include modeling for events during various times of day (such as rush hour). The second component is a computer graphics interface with the simulation that shows the traffic flows achieved based upon intelligent control system execution. The final component is the intelligent control system that manages surface street light signals based upon feedback from control sensors that dynamically adapt the intelligent controller's decision making process. The intelligent controller is a neural network model that allows TERMINUS to control the configuration of surface street signals to optimize the flow of traffic away from special events.
Cascades in interdependent flow networks
NASA Astrophysics Data System (ADS)
Scala, Antonio; De Sanctis Lucentini, Pier Giorgio; Caldarelli, Guido; D'Agostino, Gregorio
2016-06-01
In this manuscript, we investigate the abrupt breakdown behavior of coupled distribution grids under load growth. This scenario mimics the ever-increasing customer demand and the foreseen introduction of energy hubs interconnecting the different energy vectors. We extend an analytical model of cascading behavior due to line overloads to the case of interdependent networks and find evidence of first order transitions due to the long-range nature of the flows. Our results indicate that the foreseen increase in the couplings between the grids has two competing effects: on the one hand, it increases the safety region where grids can operate without withstanding systemic failures; on the other hand, it increases the possibility of a joint systems' failure.
Computer program for compressible flow network analysis
NASA Technical Reports Server (NTRS)
Wilton, M. E.; Murtaugh, J. P.
1973-01-01
Program solves problem of an arbitrarily connected one dimensional compressible flow network with pumping in the channels and momentum balancing at flow junctions. Program includes pressure drop calculations for impingement flow and flow through pin fin arrangements, as currently found in many air cooled turbine bucket and vane cooling configurations.
Predicting Information Flows in Network Traffic.
ERIC Educational Resources Information Center
Hinich, Melvin J.; Molyneux, Robert E.
2003-01-01
Discusses information flow in networks and predicting network traffic and describes a study that uses time series analysis on a day's worth of Internet log data. Examines nonlinearity and traffic invariants, and suggests that prediction of network traffic may not be possible with current techniques. (Author/LRW)
Predicting Information Flows in Network Traffic.
ERIC Educational Resources Information Center
Hinich, Melvin J.; Molyneux, Robert E.
2003-01-01
Discusses information flow in networks and predicting network traffic and describes a study that uses time series analysis on a day's worth of Internet log data. Examines nonlinearity and traffic invariants, and suggests that prediction of network traffic may not be possible with current techniques. (Author/LRW)
NASA Astrophysics Data System (ADS)
Hajduczenia, Marek; da Silva, Henrique J.; Monteiro, Paulo P.
2006-09-01
We present a novel proposal for the extension of the currently approved multipoint control protocol (MPCP), as defined in the IEEE 802.3ah standard, clause 64, in the form of two new MPCP messages (extended GATE and extended REPORT), maintaining full backward compatibility with the already-deployed Ethernet passive optical network (EPON) systems and allowing for coexistence with legacy MPCP flow-control messages within the same network structure. Both currently existing logical-link identifier (LLID) assignment systems [namely, one LLID per optical network unit (ONU) and one LLID per queue] are examined in depth, and a missing scenario (one LLID per multiqueue) is discussed. Simulation results conducted using C++ based implementation of standard EPON networks with support for extended GATE-REPORT MPCP data units (DUs) prove that the design assumptions for both new flow-control messages were met to their fullest extent. The eGATE/eREPORT MPCP DUs allow for per-queue scheduling from the central packet controller in the optical line terminal (OLT) at the cost of ONU-based operation, thereby maintaining the benefits of two standard solutions. The obtained simulation results indicate superiority of the proposed IEEE 802.3ah, clause 64 extension in terms of network resource management, bandwidth efficiency, and system setup flexibility.
Neural Flows in Hopfield Network Approach
NASA Astrophysics Data System (ADS)
Ionescu, Carmen; Panaitescu, Emilian; Stoicescu, Mihai
2013-12-01
In most of the applications involving neural networks, the main problem consists in finding an optimal procedure to reduce the real neuron to simpler models which still express the biological complexity but allow highlighting the main characteristics of the system. We effectively investigate a simple reduction procedure which leads from complex models of Hodgkin-Huxley type to very convenient binary models of Hopfield type. The reduction will allow to describe the neuron interconnections in a quite large network and to obtain information concerning its symmetry and stability. Both cases, on homogeneous voltage across the membrane and inhomogeneous voltage along the axon will be tackled out. Few numerical simulations of the neural flow based on the cable-equation will be also presented.
An extended signal control strategy for urban network traffic flow
NASA Astrophysics Data System (ADS)
Yan, Fei; Tian, Fuli; Shi, Zhongke
2016-03-01
Traffic flow patterns are in general repeated on a daily or weekly basis. To improve the traffic conditions by using the inherent repeatability of traffic flow, a novel signal control strategy for urban networks was developed via iterative learning control (ILC) approach. Rigorous analysis shows that the proposed learning control method can guarantee the asymptotic convergence. The impacts of the ILC-based signal control strategy on the macroscopic fundamental diagram (MFD) were analyzed by simulations on a test road network. The results show that the proposed ILC strategy can evenly distribute the accumulation in the network and improve the network mobility.
NASA Technical Reports Server (NTRS)
Sinha, Neeraj; Brinckman, Kevin; Jansen, Bernard; Seiner, John
2011-01-01
A method was developed of obtaining propulsive base flow data in both hot and cold jet environments, at Mach numbers and altitude of relevance to NASA launcher designs. The base flow data was used to perform computational fluid dynamics (CFD) turbulence model assessments of base flow predictive capabilities in order to provide increased confidence in base thermal and pressure load predictions obtained from computational modeling efforts. Predictive CFD analyses were used in the design of the experiments, available propulsive models were used to reduce program costs and increase success, and a wind tunnel facility was used. The data obtained allowed assessment of CFD/turbulence models in a complex flow environment, working within a building-block procedure to validation, where cold, non-reacting test data was first used for validation, followed by more complex reacting base flow validation.
Information flow analysis of interactome networks.
Missiuro, Patrycja Vasilyev; Liu, Kesheng; Zou, Lihua; Ross, Brian C; Zhao, Guoyan; Liu, Jun S; Ge, Hui
2009-04-01
Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein-protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression
Lagrangian Flow Network approach to an open flow model
NASA Astrophysics Data System (ADS)
Ser-Giacomi, Enrico; Rodríguez-Méndez, Víctor; López, Cristóbal; Hernández-García, Emilio
2017-06-01
Concepts and tools from network theory, the so-called Lagrangian Flow Network framework, have been successfully used to obtain a coarse-grained description of transport by closed fluid flows. Here we explore the application of this methodology to open chaotic flows, and check it with numerical results for a model open flow, namely a jet with a localized wave perturbation. We find that network nodes with high values of out-degree and of finite-time entropy in the forward-in-time direction identify the location of the chaotic saddle and its stable manifold, whereas nodes with high in-degree and backwards finite-time entropy highlight the location of the saddle and its unstable manifold. The cyclic clustering coefficient, associated to the presence of periodic orbits, takes non-vanishing values at the location of the saddle itself.
Computational inference of neural information flow networks.
Smith, V Anne; Yu, Jing; Smulders, Tom V; Hartemink, Alexander J; Jarvis, Erich D
2006-11-24
Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.
Network-Theoretic Modeling of Fluid Flow
2015-07-29
Final Report STIR: Network-Theoretic Modeling of Fluid Flow ARO Grant W911NF-14-1-0386 Program manager: Dr. Samuel Stanton ( August 1, 2014–April 30...Morzyński, M., and Comte , P., “A finite-time thermodynamics of unsteady fluid flows,” Journal of Non-Equilibrium Thermody- namics, Vol. 33, No. 2
Song, Rui; Liu, Jianjun; Cui, Mengmeng
2016-01-01
Due to the intricate structure of porous rocks, relationships between porosity or saturation and petrophysical transport properties classically used for reservoir evaluation and recovery strategies are either very complex or nonexistent. Thus, the pore network model extracted from the natural porous media is emphasized as a breakthrough to predict the fluid transport properties in the complex micro pore structure. This paper presents a modified method of extracting the equivalent pore network model from the three-dimensional micro computed tomography images based on the maximum ball algorithm. The partition of pore and throat are improved to avoid tremendous memory usage when extracting the equivalent pore network model. The porosity calculated by the extracted pore network model agrees well with the original sandstone sample. Instead of the Poiseuille's law used in the original work, the Lattice-Boltzmann method is employed to simulate the single- and two- phase flow in the extracted pore network. Good agreements are acquired on relative permeability saturation curves of the simulation against the experiment results.
NASA Technical Reports Server (NTRS)
Venkatapathy, Ethiraj; Prabhu, Dinesh K.; Palmer, Grant
1991-01-01
Hypersonic wake flows behind the Aeroassist Flight Experiment (AFE) geometry are analyzed using two Navier-Stokes flow solvers. Many of the AFE wake features observed in ballistic-range shadowgraphs are simulated using a simple, two-dimensional semicylinder geometry at moderate angles of attack. At free-stream conditions corresponding to a Hypersonic Free Flight Facility (HFFF) AFE experiment, the three-dimensional base flow for the AFE geometry is computed using an ideal-gas, Navier-Stokes solver. The computed results agree reasonably well with the shadowgraphs taken at the HFFF. An ideal-gas and a nonequilibrium Navier-Stokes solver have been coupled and applied to the complete flow around the AFE vehicle at the free-stream conditions corresponding to a nomial trajectory point. Limitations of the coupled ideal-gas and nonequilibrium solution are discussed. The nonequilibrium base flow solution is analyzed for the wake radiation and the radiation profiles along various lines of sight are compared. Finally, the wake unsteadiness is predicted using experimental correlations and the numerical solutions. An adaptive grid code, SAGE, has been used in all the simulations to enhance the solution accuracy. The grid adaptation is found to be necessary in obtaining base flow solutions with accurate flow features.
NASA Astrophysics Data System (ADS)
Calabretta, Nicola; Miao, Wang; Dorren, Harm
2016-03-01
Traffic in data centers networks (DCNs) is steadily growing to support various applications and virtualization technologies. Multi-tenancy enabling efficient resource utilization is considered as a key requirement for the next generation DCs resulting from the growing demands for services and applications. Virtualization mechanisms and technologies can leverage statistical multiplexing and fast switch reconfiguration to further extend the DC efficiency and agility. We present a novel high performance flat DCN employing bufferless and distributed fast (sub-microsecond) optical switches with wavelength, space, and time switching operation. The fast optical switches can enhance the performance of the DCNs by providing large-capacity switching capability and efficiently sharing the data plane resources by exploiting statistical multiplexing. Benefiting from the Software-Defined Networking (SDN) control of the optical switches, virtual DCNs can be flexibly created and reconfigured by the DCN provider. Numerical and experimental investigations of the DCN based on the fast optical switches show the successful setup of virtual network slices for intra-data center interconnections. Experimental results to assess the DCN performance in terms of latency and packet loss show less than 10^-5 packet loss and 640ns end-to-end latency with 0.4 load and 16- packet size buffer. Numerical investigation on the performance of the systems when the port number of the optical switch is scaled to 32x32 system indicate that more than 1000 ToRs each with Terabit/s interface can be interconnected providing a Petabit/s capacity. The roadmap to photonic integration of large port optical switches will be also presented.
Edge anisotropy and the geometric perspective on flow networks
NASA Astrophysics Data System (ADS)
Molkenthin, Nora; Kutza, Hannes; Tupikina, Liubov; Marwan, Norbert; Donges, Jonathan F.; Feudel, Ulrike; Kurths, Jürgen; Donner, Reik V.
2017-03-01
Spatial networks have recently attracted great interest in various fields of research. While the traditional network-theoretic viewpoint is commonly restricted to their topological characteristics (often disregarding the existing spatial constraints), this work takes a geometric perspective, which considers vertices and edges as objects in a metric space and quantifies the corresponding spatial distribution and alignment. For this purpose, we introduce the concept of edge anisotropy and define a class of measures characterizing the spatial directedness of connections. Specifically, we demonstrate that the local anisotropy of edges incident to a given vertex provides useful information about the local geometry of geophysical flows based on networks constructed from spatio-temporal data, which is complementary to topological characteristics of the same flow networks. Taken both structural and geometric viewpoints together can thus assist the identification of underlying flow structures from observations of scalar variables.
Cycle and flow trusses in directed networks
Yoshida, Yuichi
2016-01-01
When we represent real-world systems as networks, the directions of links often convey valuable information. Finding module structures that respect link directions is one of the most important tasks for analysing directed networks. Although many notions of a directed module have been proposed, no consensus has been reached. This lack of consensus results partly because there might exist distinct types of modules in a single directed network, whereas most previous studies focused on an independent criterion for modules. To address this issue, we propose a generic notion of the so-called truss structures in directed networks. Our definition of truss is able to extract two distinct types of trusses, named the cycle truss and the flow truss, from a unified framework. By applying the method for finding trusses to empirical networks obtained from a wide range of research fields, we find that most real networks contain both cycle and flow trusses. In addition, the abundance of (and the overlap between) the two types of trusses may be useful to characterize module structures in a wide variety of empirical networks. Our findings shed light on the importance of simultaneously considering different types of modules in directed networks. PMID:28018610
Cycle and flow trusses in directed networks
NASA Astrophysics Data System (ADS)
Takaguchi, Taro; Yoshida, Yuichi
2016-11-01
When we represent real-world systems as networks, the directions of links often convey valuable information. Finding module structures that respect link directions is one of the most important tasks for analysing directed networks. Although many notions of a directed module have been proposed, no consensus has been reached. This lack of consensus results partly because there might exist distinct types of modules in a single directed network, whereas most previous studies focused on an independent criterion for modules. To address this issue, we propose a generic notion of the so-called truss structures in directed networks. Our definition of truss is able to extract two distinct types of trusses, named the cycle truss and the flow truss, from a unified framework. By applying the method for finding trusses to empirical networks obtained from a wide range of research fields, we find that most real networks contain both cycle and flow trusses. In addition, the abundance of (and the overlap between) the two types of trusses may be useful to characterize module structures in a wide variety of empirical networks. Our findings shed light on the importance of simultaneously considering different types of modules in directed networks.
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.
Cascading dynamics with local weighted flow redistribution in interdependent networks
NASA Astrophysics Data System (ADS)
Qiu, Yuzhuo
2013-07-01
We study load cascading dynamics in a system composed of coupled interdependent networks while adopting a local weighted flow redistribution rule. We find that when the intra- or inter-connectivity increases, robustness against the cascade of load failures in the symmetrically coupled interdependent networks increases. In addition, when a failed link has to first split its flow asymmetrically to its neighbouring link groups according to the link types, even though there exists an optimal split, the robustness is lowered in contrast with the non-split situation. Furthermore, the optimal weighting mechanism in an isolated network no longer holds in interdependent networks. Finally, robustness against the cascade of load failures is not guaranteed to increase by making the distribution of the degree of intra-connectivity broader. We confirm these phenomena by theoretical analysis based on mean-field theory. Our findings might have great implications for preventing load-failure-induced local cascades in symmetrically coupled interdependent networks.
Schmidt, Volker J.; Covi, Jennifer M.; Koepple, Christoph; Hilgert, Johannes G.; Polykandriotis, Elias; Bigdeli, Amir K.; Distel, Luitpold V.; Horch, Raymund E.; Kneser, Ulrich
2017-01-01
Background The arteriovenous (AV) loop model enables axial vascularization to gain a functional microcirculatory system in tissue engineering constructs in vivo. These constructs might replace surgical flaps for the treatment of complex wounds in the future. Today, free flaps are often exposed to high-dose radiation after defect coverage, according to guideline-oriented treatment plans. Vascular response of AV loop-based constructs has not been evaluated after radiation, although it is of particular importance. It is further unclear whether the interposed venous AV loop graft is crucial for the induction of angiogenesis. Material/Methods We exposed the grafted vein to a single radiation dose of 2 Gy prior to loop construction to alter intrinsic and angio-inductive properties specifically within the graft. Vessel loops were embedded in a fibrin-filled chamber for 15 days and radiation-induced effects on flow-mediated vascularization were assessed by micro-CT and two-dimensional histological analysis. Results Vessel amount was significantly impaired when an irradiated vein graft was used for AV loop construction. However, vessel growth and differentiation were still present. In contrast to vessel density, which was homogeneously diminished in constructs containing irradiated veins, vessel diameter was primarily decreased in the more peripheral regions. Conclusions Vascular luminal sprouts were significantly diminished in irradiated venous grafts, suggesting that the interposing vein constitutes a vital part of the AV loop model and is essential to initiate flow-mediate angiogenesis. These results add to the current understanding of AV loop-based neovascularization and suggest clinical implications for patients requiring combined AV loop-based tissue transfer and adjuvant radiotherapy. PMID:28199294
Schmidt, Volker J; Covi, Jennifer M; Koepple, Christoph; Hilgert, Johannes G; Polykandriotis, Elias; Bigdeli, Amir K; Distel, Luitpold V; Horch, Raymund E; Kneser, Ulrich
2017-02-15
BACKGROUND The arteriovenous (AV) loop model enables axial vascularization to gain a functional microcirculatory system in tissue engineering constructs in vivo. These constructs might replace surgical flaps for the treatment of complex wounds in the future. Today, free flaps are often exposed to high-dose radiation after defect coverage, according to guideline-oriented treatment plans. Vascular response of AV loop-based constructs has not been evaluated after radiation, although it is of particular importance. It is further unclear whether the interposed venous AV loop graft is crucial for the induction of angiogenesis. MATERIAL AND METHODS We exposed the grafted vein to a single radiation dose of 2 Gy prior to loop construction to alter intrinsic and angio-inductive properties specifically within the graft. Vessel loops were embedded in a fibrin-filled chamber for 15 days and radiation-induced effects on flow-mediated vascularization were assessed by micro-CT and two-dimensional histological analysis. RESULTS Vessel amount was significantly impaired when an irradiated vein graft was used for AV loop construction. However, vessel growth and differentiation were still present. In contrast to vessel density, which was homogeneously diminished in constructs containing irradiated veins, vessel diameter was primarily decreased in the more peripheral regions. CONCLUSIONS Vascular luminal sprouts were significantly diminished in irradiated venous grafts, suggesting that the interposing vein constitutes a vital part of the AV loop model and is essential to initiate flow-mediate angiogenesis. These results add to the current understanding of AV loop-based neovascularization and suggest clinical implications for patients requiring combined AV loop-based tissue transfer and adjuvant radiotherapy.
Spike Code Flow in Cultured Neuronal Networks.
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.
Hierarchical social networks and information flow
NASA Astrophysics Data System (ADS)
López, Luis; F. F. Mendes, Jose; Sanjuán, Miguel A. F.
2002-12-01
Using a simple model for the information flow on social networks, we show that the traditional hierarchical topologies frequently used by companies and organizations, are poorly designed in terms of efficiency. Moreover, we prove that this type of structures are the result of the individual aim of monopolizing as much information as possible within the network. As the information is an appropriate measurement of centrality, we conclude that this kind of topology is so attractive for leaders, because the global influence each actor has within the network is completely determined by the hierarchical level occupied.
Macropore Network Connectivity and Preferential Flow
NASA Astrophysics Data System (ADS)
Jarvis, N.; Koestel, J. K.; Larsbo, M.
2016-12-01
The increasingly widespread application of non-invasive X-ray imaging has led to the realization that macropores usually comprise complex multi-scale networks of multiple inter-connected and disconnected pathways, including dead-end pores and bottleneck constrictions. Combining X-ray scanning with flow and transport experiments carried out on the same soil columns may further our understanding of how network characteristics control preferential flow. We carried out tracer breakthrough experiments at a flow rate of 2 mm/hour on forty topsoil columns sampled from five different sites in east-central Sweden (four cultivated and one under long-term grass-clover ley) that had previously been imaged by X-ray tomography at a resolution of 0.3 mm. The results suggest that preferential flow is increasingly triggered as the macroporosity decreases towards a `percolation threshold' (i.e. the macroporosity that is required to establish long-range network connectivity). Sample size effects on the connectivity of the X-ray imaged macropore networks were also analyzed at two different resolutions (local pore thicknesses > 0.3 mm or > 1 mm) with two different methods: i.) a traditional technique for estimation of the REV using the statistics of the connection probability (the probability that two pore voxels belong to the same cluster) and ii.) a finite-size scaling analysis of the percolation probabilities. The analyses show that a cube of side length 8 cm is representative for imaged porosity and connectivity in these soil horizons. We conclude that percolation concepts could be used to account for the effects of macropore network connectivity in Darcy-scale models of preferential flow, although similar analyses should first be performed in subsoil horizons more susceptible to macropore flow.
Do Brain Networks Evolve by Maximizing Their Information Flow Capacity?
Antonopoulos, Chris G; Srivastava, Shambhavi; Pinto, Sandro E de S; Baptista, Murilo S
2015-08-01
We propose a working hypothesis supported by numerical simulations that brain networks evolve based on the principle of the maximization of their internal information flow capacity. We find that synchronous behavior and capacity of information flow of the evolved networks reproduce well the same behaviors observed in the brain dynamical networks of Caenorhabditis elegans and humans, networks of Hindmarsh-Rose neurons with graphs given by these brain networks. We make a strong case to verify our hypothesis by showing that the neural networks with the closest graph distance to the brain networks of Caenorhabditis elegans and humans are the Hindmarsh-Rose neural networks evolved with coupling strengths that maximize information flow capacity. Surprisingly, we find that global neural synchronization levels decrease during brain evolution, reflecting on an underlying global no Hebbian-like evolution process, which is driven by no Hebbian-like learning behaviors for some of the clusters during evolution, and Hebbian-like learning rules for clusters where neurons increase their synchronization.
Do Brain Networks Evolve by Maximizing Their Information Flow Capacity?
Antonopoulos, Chris G.; Srivastava, Shambhavi; Pinto, Sandro E. de S.; Baptista, Murilo S.
2015-01-01
We propose a working hypothesis supported by numerical simulations that brain networks evolve based on the principle of the maximization of their internal information flow capacity. We find that synchronous behavior and capacity of information flow of the evolved networks reproduce well the same behaviors observed in the brain dynamical networks of Caenorhabditis elegans and humans, networks of Hindmarsh-Rose neurons with graphs given by these brain networks. We make a strong case to verify our hypothesis by showing that the neural networks with the closest graph distance to the brain networks of Caenorhabditis elegans and humans are the Hindmarsh-Rose neural networks evolved with coupling strengths that maximize information flow capacity. Surprisingly, we find that global neural synchronization levels decrease during brain evolution, reflecting on an underlying global no Hebbian-like evolution process, which is driven by no Hebbian-like learning behaviors for some of the clusters during evolution, and Hebbian-like learning rules for clusters where neurons increase their synchronization. PMID:26317592
Cycle flows and multistability in oscillatory networks
NASA Astrophysics Data System (ADS)
Manik, Debsankha; Timme, Marc; Witthaut, Dirk
2017-08-01
We study multistability in phase locked states in networks of phase oscillators under both Kuramoto dynamics and swing equation dynamics—a popular model for studying coarse-scale dynamics of an electrical AC power grid. We first establish the existence of geometrically frustrated states in such systems—where although a steady state flow pattern exists, no fixed point exists in the dynamical variables of phases due to geometrical constraints. We then describe the stable fixed points of the system with phase differences along each edge not exceeding π/2 in terms of cycle flows—constant flows along each simple cycle—as opposed to phase angles or flows. The cycle flow formalism allows us to compute tight upper and lower bounds to the number of fixed points in ring networks. We show that long elementary cycles, strong edge weights, and spatially homogeneous distribution of natural frequencies (for the Kuramoto model) or power injections (for the oscillator model for power grids) cause such networks to have more fixed points. We generalize some of these bounds to arbitrary planar topologies and derive scaling relations in the limit of large capacity and large cycle lengths, which we show to be quite accurate by numerical computation. Finally, we present an algorithm to compute all phase locked states—both stable and unstable—for planar networks.
Interest communities and flow roles in directed networks: the Twitter network of the UK riots.
Beguerisse-Díaz, Mariano; Garduño-Hernández, Guillermo; Vangelov, Borislav; Yaliraki, Sophia N; Barahona, Mauricio
2014-12-06
Directionality is a crucial ingredient in many complex networks in which information, energy or influence are transmitted. In such directed networks, analysing flows (and not only the strength of connections) is crucial to reveal important features of the network that might go undetected if the orientation of connections is ignored. We showcase here a flow-based approach for community detection through the study of the network of the most influential Twitter users during the 2011 riots in England. Firstly, we use directed Markov Stability to extract descriptions of the network at different levels of coarseness in terms of interest communities, i.e. groups of nodes within which flows of information are contained and reinforced. Such interest communities reveal user groupings according to location, profession, employer and topic. The study of flows also allows us to generate an interest distance, which affords a personalized view of the attention in the network as viewed from the vantage point of any given user. Secondly, we analyse the profiles of incoming and outgoing long-range flows with a combined approach of role-based similarity and the novel relaxed minimum spanning tree algorithm to reveal that the users in the network can be classified into five roles. These flow roles go beyond the standard leader/follower dichotomy and differ from classifications based on regular/structural equivalence. We then show that the interest communities fall into distinct informational organigrams characterized by a different mix of user roles reflecting the quality of dialogue within them. Our generic framework can be used to provide insight into how flows are generated, distributed, preserved and consumed in directed networks.
Interest communities and flow roles in directed networks: the Twitter network of the UK riots
Beguerisse-Díaz, Mariano; Garduño-Hernández, Guillermo; Vangelov, Borislav; Yaliraki, Sophia N.; Barahona, Mauricio
2014-01-01
Directionality is a crucial ingredient in many complex networks in which information, energy or influence are transmitted. In such directed networks, analysing flows (and not only the strength of connections) is crucial to reveal important features of the network that might go undetected if the orientation of connections is ignored. We showcase here a flow-based approach for community detection through the study of the network of the most influential Twitter users during the 2011 riots in England. Firstly, we use directed Markov Stability to extract descriptions of the network at different levels of coarseness in terms of interest communities, i.e. groups of nodes within which flows of information are contained and reinforced. Such interest communities reveal user groupings according to location, profession, employer and topic. The study of flows also allows us to generate an interest distance, which affords a personalized view of the attention in the network as viewed from the vantage point of any given user. Secondly, we analyse the profiles of incoming and outgoing long-range flows with a combined approach of role-based similarity and the novel relaxed minimum spanning tree algorithm to reveal that the users in the network can be classified into five roles. These flow roles go beyond the standard leader/follower dichotomy and differ from classifications based on regular/structural equivalence. We then show that the interest communities fall into distinct informational organigrams characterized by a different mix of user roles reflecting the quality of dialogue within them. Our generic framework can be used to provide insight into how flows are generated, distributed, preserved and consumed in directed networks. PMID:25297320
NASA Astrophysics Data System (ADS)
Zeng, L.; Zhao, T. S.; Wei, L.; Zeng, Y. K.; Zhang, Z. H.
2016-09-01
Vanadium redox flow batteries (VRFBs) with their high flexibility in configuration and operation, as well as long cycle life are competent for the requirement of future energy storage systems. Nevertheless, due to the application of perfluorinated membranes, VRFBs are plagued by not only the severe migration issue of vanadium ions, but also their high cost. Herein, we fabricate semi-interpenetrating polymer networks (SIPNs), consisting of cross-linked polyvinylpyrrolidone (PVP) and polysulfone (PSF), as alternative membranes for VRFBs. It is demonstrated that the PVP-based SIPNs exhibit extremely low vanadium permeabilities, which contribute to the well-established hydrophilic/hydrophobic microstructures and the Donnan exclusion effect. As a result, the coulombic efficiencies of VRFBs with PVP-based SIPNs reach almost 100% at 40 mA cm-2 to 100 mA cm-2; the energy efficiencies are more than 3% higher than those of VRFBs with Nafion 212. More importantly, the PVP-based SIPNs exhibit a superior chemical stability, as demonstrated both by an ex situ immersion test and continuously cycling test. Hence, all the characterizations and performance tests reported here suggest that PVP-based SIPNs are a promising alternative membrane for redox flow batteries to achieve superior cell performance and excellent cycling stability at the fraction of the cost of perfluorinated membranes.
The stochastic link equilibrium strategy and algorithm for flow assignment in communication networks
NASA Astrophysics Data System (ADS)
Tao, Yang; Zhou, Xia
2005-11-01
Based on the mature user equilibrium (UE) theory in transportation field as well as the similarity of network flow between transportation and communication, in this paper, the user equilibrium theory was applied to communication networks, and how to apply the stochastic user equilibrium (SUE) to flow assigning in generalized communication networks was further studied. The stochastic link equilibrium (SLE) flow assignment strategy was proposed in this paper, the algorithm of SLE flow assignment was also provided. Both analyses and simulation based on the given algorithm proved that the optimal flow assignment in networks can be achieved by using this algorithm.
Sample EP Flow Analysis of Severely Damaged Networks
Werley, Kenneth Alan; McCown, Andrew William
2016-10-12
These are slides for a presentation at the working group meeting of the WESC SREMP Software Product Integration Team on sample EP flow analysis of severely damaged networks. The following topics are covered: ERCOT EP Transmission Model; Zoomed in to Houston and Overlaying StreetAtlas; EMPACT Solve/Dispatch/Shedding Options; QACS BaseCase Power Flow Solution; 3 Substation Contingency; Gen. & Load/100 Optimal Dispatch; Dispatch Results; Shed Load for Low V; Network Damage Summary; Estimated Service Areas (Potential); Estimated Outage Areas (potential).
Ries, Kernell G.; Eng, Ken
2010-01-01
The U.S. Geological Survey, in cooperation with the Maryland Department of the Environment, operated a network of 20 low-flow partial-record stations during 2008 in a region that extends from southwest of Baltimore to the northeastern corner of Maryland to obtain estimates of selected streamflow statistics at the station locations. The study area is expected to face a substantial influx of new residents and businesses as a result of military and civilian personnel transfers associated with the Federal Base Realignment and Closure Act of 2005. The estimated streamflow statistics, which include monthly 85-percent duration flows, the 10-year recurrence-interval minimum base flow, and the 7-day, 10-year low flow, are needed to provide a better understanding of the availability of water resources in the area to be affected by base-realignment activities. Streamflow measurements collected for this study at the low-flow partial-record stations and measurements collected previously for 8 of the 20 stations were related to concurrent daily flows at nearby index streamgages to estimate the streamflow statistics. Three methods were used to estimate the streamflow statistics and two methods were used to select the index streamgages. Of the three methods used to estimate the streamflow statistics, two of them--the Moments and MOVE1 methods--rely on correlating the streamflow measurements at the low-flow partial-record stations with concurrent streamflows at nearby, hydrologically similar index streamgages to determine the estimates. These methods, recommended for use by the U.S. Geological Survey, generally require about 10 streamflow measurements at the low-flow partial-record station. The third method transfers the streamflow statistics from the index streamgage to the partial-record station based on the average of the ratios of the measured streamflows at the partial-record station to the concurrent streamflows at the index streamgage. This method can be used with as few as
On information flow in relay networks
NASA Astrophysics Data System (ADS)
El Gamal, A.
Preliminary investigations conducted by El Gamal and Cover (1980) have shown that a max-flow min-cut interpretation for the capacity expressions of the classes of degraded and semideterministic relay channels can be found. In this paper it is shown that such an interpretation can also be found for fairly general classes of discrete memoryless relay networks. Cover and El Gamal (1979) have obtained general lower and upper bounds to capacity. However, the capacity of the general relay channel is not known. Past results are here extended to establish the capacity of deterministic relay networks with no interference and degraded relay networks. A general upper bound is given to the capacity of any relay network with this upper bound being a natural generalization of Theorem 4 in the study conducted by Cover and El Gamal (1979).
Scaling of Peak Flows with Constant Flow Velocity in Random Self-Similar Networks
NASA Astrophysics Data System (ADS)
Mantilla, Ricardo; Gupta, Vijay K.; Troutman, Brent M.
2010-05-01
We present a methodology to understand the role of the statistical self-similar topology of real river networks on flow hydrographs for rainfall-runoff events. Monte Carlo generated ensembles of 1000 Random Self-similar Networks (RSNs) with geometrically distributed interior and exterior generators are created. These networks emulate the topology of real networks. Hydrographs for every link in each of these networks are obtained by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated RSNs and hydrographs, the scaling parameters for the peak of the width function β and the hydrograph peak flow ? are estimated. It was found that ? > β, which supports a similar finding first reported for the Walnut Gulch basin, Arizona, and that is in qualitatively different from previous results on idealized river networks (e.g. Peano Network, Mandelbrot- Viscek Network). The use of numerical simulations is necessary as theoretical estimation of β and ? in RSNs is a complex mathematical open problem. However, other scaling features of the average width function can the average hydrograph are calculated analytically and compared with the estimation from the RSN-ensemble. Scaling of peak flows during individual rainfall runoff events is a new area of research that offers a path to understand regional scaling of flood quantiles, an important open problem in river network hydrology. For example, our results show an interesting connection between unit-hydrograph theory and flow dynamics. In addition, our methodology provides a reference framework to study scaling exponents under more complex scenarios of flow dynamics and runoff generation processes using ensembles of RSNs.
Scaling of Peak Flows with Constant Flow Velocity in Random Self-Similar Networks
NASA Astrophysics Data System (ADS)
Mantilla, R.; Gupta, V. K.; Troutman, B. M.
2010-12-01
We present a methodology to understand the role of the statistical self-similar topology of real river networks on flow hydrographs for rainfall-runoff events. Monte Carlo generated ensembles of 1000 Random Self-similar Networks (RSNs) with geometrically distributed interior and exterior generators are created. We show how these networks emulate the statistical self-similarity present in real networks by presenting results for 30 river networks in the continental USA. Hydrographs for every link in each of these networks are obtained by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated hydrographs for an ensemble of RSNs, the scaling parameters for the peak of the width function (β) and the hydrograph peak flow (φ) are estimated. It was found that φ > β, which supports a similar finding first reported for the Walnut Gulch basin, Arizona, and that is qualitatively different from previous results on idealized river networks (e.g. Peano Network, Mandelbrot- Viscek Network). Scaling of peak flows for individual rainfall runoff events is a new area of research that offers a path to physically understand regional scaling of flood quantiles. It addresses an important open problem in river network hydrology through studying the statistics of ensembles of multiple events in RSNs. In addition, our methodology provides a reference framework to study scaling exponents and intercepts under more complex scenarios of flow dynamics and runoff generation processes using ensembles of RSNs. Preliminary examples of such scenarios will also be given.
A Maximal Flow Approach to Dynamic Routing in Communication Networks,
1980-05-01
of nodes. In Appendix B we provide a computer program in Fortran for finding the maximal flow in these networks, based on the algorithm of Edmons and... Edmons and Karp is implemented by a Fortran Subroutine called MAXFL. The algorithm finds the shortest path between source and destination on which an
2007-11-02
S) AND ADDRESS(ES) DCW Industries, Inc. 5354 Palm Drive La Canada, CA 91011 8. PERFORMING ORGANIZATION...REPORT NUMBER DCW -38-R-05 9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) U. S. Army Research Office...Turbulence Modeling for CFD, Second Edition, DCW Industries, Inc., La Cañada, CA. Wilcox, D. C. (2001), “Projectile Base Flow Analysis,” DCW
NASA Astrophysics Data System (ADS)
Warsito, W.; Fan, L.-S.
2001-12-01
In this work a new image reconstruction technique for imaging two- and three-phase flows using electrical capacitance tomography (ECT) has been developed. A brief review on reconstruction techniques developed recently for ECT is also given. The reconstruction technique proposed here is based on multi-criterion optimization using an analogue neural network, hereafter referred to as neural network multi-criteria optimization image reconstruction (NN-MOIRT). The reconstruction technique is a combination between a multi-criterion optimization image reconstruction technique for linear tomography, and the so-called linear back projection (LBP) technique commonly used for capacitance tomography. The multi-criterion optimization image reconstruction problem is solved using Hopfield model dynamic neural network computing. For three-component imaging, the single-step sigmoid function in the Hopfield networks is replaced by a double-step sigmoid function, allowing the neural computation to converge to three distinct stable regions in the output space corresponding to the three components, enabling differentiation among the single phases. The technique has been tested on a capacitance data set obtained from simulated measurement as well as experiment using a 12-electrode sensor. The performance of the technique has been compared with other commonly used iterative techniques, i.e. iterative linear back projection technique (ILBP) and the simultaneous image reconstruction technique (SIRT) for two-phase system imaging, and has shown great improvements in the accuracy and the consistency as compared with those techniques. The technique has also shown the capability of three-phase image reconstruction with high accuracy.
Liu, Lei; Peng, Wei-Ren; Casellas, Ramon; Tsuritani, Takehiro; Morita, Itsuro; Martínez, Ricardo; Muñoz, Raül; Yoo, S J B
2014-01-13
Optical Orthogonal Frequency Division Multiplexing (O-OFDM), which transmits high speed optical signals using multiple spectrally overlapped lower-speed subcarriers, is a promising candidate for supporting future elastic optical networks. In contrast to previous works which focus on Coherent Optical OFDM (CO-OFDM), in this paper, we consider the direct-detection optical OFDM (DDO-OFDM) as the transport technique, which leads to simpler hardware and software realizations, potentially offering a low-cost solution for elastic optical networks, especially in metro networks, and short or medium distance core networks. Based on this network scenario, we design and deploy a software-defined networking (SDN) control plane enabled by extending OpenFlow, detailing the network architecture, the routing and spectrum assignment algorithm, OpenFlow protocol extensions and the experimental validation. To the best of our knowledge, it is the first time that an OpenFlow-based control plane is reported and its performance is quantitatively measured in an elastic optical network with DDO-OFDM transmission.
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.
Complex networks renormalization: flows and fixed points.
Radicchi, Filippo; Ramasco, José J; Barrat, Alain; Fortunato, Santo
2008-10-03
Recently, it has been claimed that some complex networks are self-similar under a convenient renormalization procedure. We present a general method to study renormalization flows in graphs. We find that the behavior of some variables under renormalization, such as the maximum number of connections of a node, obeys simple scaling laws, characterized by critical exponents. This is true for any class of graphs, from random to scale-free networks, from lattices to hierarchical graphs. Therefore, renormalization flows for graphs are similar as in the renormalization of spin systems. An analysis of classic renormalization for percolation and the Ising model on the lattice confirms this analogy. Critical exponents and scaling functions can be used to classify graphs in universality classes, and to uncover similarities between graphs that are inaccessible to a standard analysis.
Coevolution of functional flow processing networks
NASA Astrophysics Data System (ADS)
Kaluza, Pablo
2017-04-01
We present a study about the construction of functional flow processing networks that produce prescribed output patterns (target functions). The constructions are performed with a process of mutations and selections by an annealing-like algorithm. We consider the coevolution of the prescribed target functions during the optimization processes. We propose three different paths for these coevolutions in order to evolve from a simple initial function to a more complex final one. We compute several network properties during the optimizations by using the different path-coevolutions as mean values over network ensembles. As a function of the number of iterations of the optimization we find a similar behavior like a phase transition in the network structures. This result can be seen clearly in the mean motif distributions of the constructed networks. Coevolution allows to identify that feed-forward loops are responsible for the development of the temporal response of these systems. Finally, we observe that with a large number of iterations the optimized networks present similar properties despite the path-coevolution we employed.
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
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.
From invasion percolation to flow in rock fracture networks
NASA Astrophysics Data System (ADS)
Wettstein, Salomon J.; Wittel, Falk K.; Araújo, Nuno A. M.; Lanyon, Bill; Herrmann, Hans J.
2012-01-01
The main purpose of this work is to simulate two-phase flow in the form of immiscible displacement through anisotropic, three-dimensional (3D) discrete fracture networks (DFN). The considered DFNs are artificially generated, based on a general distribution function or are conditioned on measured data from deep geological investigations. We introduce several modifications to the invasion percolation (MIP) to incorporate fracture inclinations, intersection lines, as well as the hydraulic path length inside the fractures. Additionally a trapping algorithm is implemented that forbids any advance of the invading fluid into a region, where the defending fluid is completely encircled by the invader and has no escape route. We study invasion, saturation, and flow through artificial fracture networks, with varying anisotropy and size and finally compare our findings to well studied, conditioned fracture networks.
Radl, B.J.; Roland, W. Jr.
1995-12-31
The optimization system provides on-line, real-time air flow balancing without extensive testing or large complex physical models. NO{sub x} emissions and unit heat rate are very sensitive to air distribution and turbulence in the combustion zone. These issues are continuously changing due to ambient conditions, coal quality and the condition of plant equipment. This report discusses applying on-line, real-time and neural network to adjust secondary air flow and overfire air flow to reduce NO{sub x} and improve heat rate on various coal fired boiler designs.
Enhancing debris flow modeling parameters integrating Bayesian networks
NASA Astrophysics Data System (ADS)
Graf, C.; Stoffel, M.; Grêt-Regamey, A.
2009-04-01
Applied debris-flow modeling requires suitably constraint input parameter sets. Depending on the used model, there is a series of parameters to define before running the model. Normally, the data base describing the event, the initiation conditions, the flow behavior, the deposition process and mainly the potential range of possible debris flow events in a certain torrent is limited. There are only some scarce places in the world, where we fortunately can find valuable data sets describing event history of debris flow channels delivering information on spatial and temporal distribution of former flow paths and deposition zones. Tree-ring records in combination with detailed geomorphic mapping for instance provide such data sets over a long time span. Considering the significant loss potential associated with debris-flow disasters, it is crucial that decisions made in regard to hazard mitigation are based on a consistent assessment of the risks. This in turn necessitates a proper assessment of the uncertainties involved in the modeling of the debris-flow frequencies and intensities, the possible run out extent, as well as the estimations of the damage potential. In this study, we link a Bayesian network to a Geographic Information System in order to assess debris-flow risk. We identify the major sources of uncertainty and show the potential of Bayesian inference techniques to improve the debris-flow model. We model the flow paths and deposition zones of a highly active debris-flow channel in the Swiss Alps using the numerical 2-D model RAMMS. Because uncertainties in run-out areas cause large changes in risk estimations, we use the data of flow path and deposition zone information of reconstructed debris-flow events derived from dendrogeomorphological analysis covering more than 400 years to update the input parameters of the RAMMS model. The probabilistic model, which consistently incorporates this available information, can serve as a basis for spatial risk
TCP flow control using link layer information in mobile networks
NASA Astrophysics Data System (ADS)
Koga, Hiroyuki; Kawahara, Kenji; Oie, Yuji
2002-07-01
Mobile Networks have been expanding and IMT-2000 further increases their available bandwidth over wireless links. However, TCP, which is a reliable end-to-end transport protocol, is tuned to perform well in wired networks where bit error rates are very low and packet loss occurs mostly because of congestion. Although a TCP sender can execute flow control to utilize as much available bandwidth as possible in wired networks, it cannot work well in wireless networks characterized by high bit error rates. In the next generation mobile systems, sophisticated error recovery technologies of FEC and ARQ are indeed employed over wireless links, i.e., over Layer 2, to avoid performance degradation of upper layers. However, multiple retransmissions by Layer 2 ARQ can adversely increase transmission delay of TCP segments, which will further make TCP unnecessarily increase RTO (Retransmission TimeOut). Furthermore, a link bandwidth assigned to TCP flows can change in response to changing air conditions to use wireless links efficiently. TCP thus has to adapt its transmission rate according to the changing available bandwidth. The major goal of this study is to develop a receiver-based effective TCP flow control without any modification on TCP senders, which are probably connected with wired networks. For this end, we propose a TCP flow control employing some Layer 2 information on a wireless link at the mobile station. Our performance evaluation of the proposed TCP shows that the receiver-based TCP flow control can moderate the performance degradation very well even if FER on Layer 2 is high.
Enhanced Flow in Small-World Networks
NASA Astrophysics Data System (ADS)
Oliveira, Cláudio L. N.; Morais, Pablo A.; Moreira, André A.; Andrade, José S.
2014-04-01
The proper addition of shortcuts to a regular substrate can lead to the formation of a complex network with a highly efficient structure for navigation [J. M. Kleinberg, Nature 406, 845 (2000)]. Here we show that enhanced flow properties can also be observed in these small-world topologies. Precisely, our model is a network built from an underlying regular lattice over which long-range connections are randomly added according to the probability, Pij˜rij-α, where rij is the Manhattan distance between nodes i and j, and the exponent α is a controlling parameter. The mean two-point global conductance of the system is computed by considering that each link has a local conductance given by gij∝rij-C, where C determines the extent of the geographical limitations (costs) on the long-range connections. Our results show that the best flow conditions are obtained for C =0 with α=0, while for C≫1 the overall conductance always increases with α. For C≈1, α=d becomes the optimal exponent, where d is the topological dimension of the substrate. Interestingly, this exponent is identical to the one obtained for optimal navigation in small-world networks using decentralized algorithms.
Flow-network adaptation in Physarum amoebae.
Tero, Atsushi; Yumiki, Kenji; Kobayashi, Ryo; Saigusa, Tetsu; Nakagaki, Toshiyuki
2008-06-01
Understanding how biological systems solve problems could aid the design of novel computational methods. Information processing in unicellular eukaryotes is of particular interest, as these organisms have survived for more than a billion years using a simple system. The large amoeboid plasmodium of Physarum is able to solve a maze and to connect multiple food locations via a smart network. This study examined how Physarum amoebae compute these solutions. The mechanism involves the adaptation of the tubular body, which appears to be similar to a network, based on cell dynamics. Our model describes how the network of tubes expands and contracts depending on the flux of protoplasmic streaming, and reproduces experimental observations of the behavior of the organism. The proposed algorithm based on Physarum is simple and powerful.
Hodge Decomposition of Information Flow on Small-World Networks.
Haruna, Taichi; Fujiki, Yuuya
2016-01-01
We investigate the influence of the small-world topology on the composition of information flow on networks. By appealing to the combinatorial Hodge theory, we decompose information flow generated by random threshold networks on the Watts-Strogatz model into three components: gradient, harmonic and curl flows. The harmonic and curl flows represent globally circular and locally circular components, respectively. The Watts-Strogatz model bridges the two extreme network topologies, a lattice network and a random network, by a single parameter that is the probability of random rewiring. The small-world topology is realized within a certain range between them. By numerical simulation we found that as networks become more random the ratio of harmonic flow to the total magnitude of information flow increases whereas the ratio of curl flow decreases. Furthermore, both quantities are significantly enhanced from the level when only network structure is considered for the network close to a random network and a lattice network, respectively. Finally, the sum of these two ratios takes its maximum value within the small-world region. These findings suggest that the dynamical information counterpart of global integration and that of local segregation are the harmonic flow and the curl flow, respectively, and that a part of the small-world region is dominated by internal circulation of information flow.
Hodge Decomposition of Information Flow on Small-World Networks
Haruna, Taichi; Fujiki, Yuuya
2016-01-01
We investigate the influence of the small-world topology on the composition of information flow on networks. By appealing to the combinatorial Hodge theory, we decompose information flow generated by random threshold networks on the Watts-Strogatz model into three components: gradient, harmonic and curl flows. The harmonic and curl flows represent globally circular and locally circular components, respectively. The Watts-Strogatz model bridges the two extreme network topologies, a lattice network and a random network, by a single parameter that is the probability of random rewiring. The small-world topology is realized within a certain range between them. By numerical simulation we found that as networks become more random the ratio of harmonic flow to the total magnitude of information flow increases whereas the ratio of curl flow decreases. Furthermore, both quantities are significantly enhanced from the level when only network structure is considered for the network close to a random network and a lattice network, respectively. Finally, the sum of these two ratios takes its maximum value within the small-world region. These findings suggest that the dynamical information counterpart of global integration and that of local segregation are the harmonic flow and the curl flow, respectively, and that a part of the small-world region is dominated by internal circulation of information flow. PMID:27733817
Employment Growth through Labor Flow Networks
Guerrero, Omar A.; Axtell, Robert L.
2013-01-01
It is conventional in labor economics to treat all workers who are seeking new jobs as belonging to a labor pool, and all firms that have job vacancies as an employer pool, and then match workers to jobs. Here we develop a new approach to study labor and firm dynamics. By combining the emerging science of networks with newly available employment micro-data, comprehensive at the level of whole countries, we are able to broadly characterize the process through which workers move between firms. Specifically, for each firm in an economy as a node in a graph, we draw edges between firms if a worker has migrated between them, possibly with a spell of unemployment in between. An economy's overall graph of firm-worker interactions is an object we call the labor flow network (LFN). This is the first study that characterizes a LFN for an entire economy. We explore the properties of this network, including its topology, its community structure, and its relationship to economic variables. It is shown that LFNs can be useful in identifying firms with high growth potential. We relate LFNs to other notions of high performance firms. Specifically, it is shown that fewer than 10% of firms account for nearly 90% of all employment growth. We conclude with a model in which empirically-salient LFNs emerge from the interaction of heterogeneous adaptive agents in a decentralized labor market. PMID:23658682
Channel networks within lava flows: Formation, evolution, and implications for flow behavior
NASA Astrophysics Data System (ADS)
Dietterich, Hannah R.; Cashman, Katharine V.
2014-08-01
New high-resolution maps of Hawaiian lava flows highlight complex topographically controlled channel networks. Network geometries range from distributary systems dominated by branching around local obstacles, to tributary systems constricted by topography. We combine 2-D network analysis tools developed for river systems and neural networks with 3-D lidar morphologic analysis and historical records of flow emplacement to investigate both the origins of channel networks and their influence on flow morphology and behavior. We find that network complexity is a function of underlying slope and that the degree of flow branching, network connectivity, and longevity of individual channels all influence the final flow morphology (flow and channel widths and levee heights). Furthermore, because channel networks govern the distribution of lava supply within a flow, changes in the channel topology can dramatically alter the effective volumetric flux in any one branch, which affects both flow length and advance rate. Specifically, branching will slow and shorten flows, while merging can accelerate and lengthen them. Consideration of channel networks is thus important for predicting lava flow behavior and mitigating flow hazards with diversion barriers. Observed relationships between network geometry, flow parameters, and morphology also offer insight into the interpretation of these features elsewhere on Earth and other terrestrial planets.
Optimizing information flow in biological networks
NASA Astrophysics Data System (ADS)
Bialek, William
2009-03-01
The generation of physicists who turned to the phenomena of life in the 1930s realized that to understand these phenomena one would need to track not just the flow of energy (as in inanimate systems) but also the flow of information. It would take more than a decade before Shannon provided the tools to formalize this intuition, making precise the connection between entropy and information. Since Shannon, many investigators have explored the possibility that biological mechanisms are selected to maximize the efficiency with which information is transmitted or represented, subject to fundamental physical constraints. I will survey these efforts, emphasizing that the same principles are being used in thinking about biological systems at very different levels of organization, from bacteria to brains. Although sometimes submerged under concerns about particular systems, the idea that information flow is optimized provides us with a candidate for a real theory of biological networks, rather than just a collection of parameterized models. I will try to explain why I think the time is right to focus on this grand theoretical goal, pointing to some key open problems and opportunities for connection to emerging experiments.
Monitoring individual traffic flows within the ATLAS TDAQ network
NASA Astrophysics Data System (ADS)
Sjoen, R.; Stancu, S.; Ciobotaru, M.; Batraneanu, S. M.; Leahu, L.; Martin, B.; Al-Shabibi, A.
2010-04-01
The ATLAS data acquisition system consists of four different networks interconnecting up to 2000 processors using up to 200 edge switches and five multi-blade chassis devices. The architecture of the system has been described in [1] and its operational model in [2]. Classical, SNMP-based, network monitoring provides statistics on aggregate traffic, but for performance monitoring and troubleshooting purposes there was an imperative need to identify and quantify single traffic flows. sFlow [3] is an industry standard based on statistical sampling which attempts to provide a solution to this. Due to the size of the ATLAS network, the collection and analysis of the sFlow data from all devices generates a data handling problem of its own. This paper describes how this problem is addressed by making it possible to collect and store data either centrally or distributed according to need. The methods used to present the results in a relevant fashion for system analysts are discussed and we explore the possibilities and limitations of this diagnostic tool, giving an example of its use in solving system problems that arise during the ATLAS data taking.
A perturbation-theoretic approach to Lagrangian flow networks
NASA Astrophysics Data System (ADS)
Fujiwara, Naoya; Kirchen, Kathrin; Donges, Jonathan F.; Donner, Reik V.
2017-03-01
Complex network approaches have been successfully applied for studying transport processes in complex systems ranging from road, railway, or airline infrastructures over industrial manufacturing to fluid dynamics. Here, we utilize a generic framework for describing the dynamics of geophysical flows such as ocean currents or atmospheric wind fields in terms of Lagrangian flow networks. In this approach, information on the passive advection of particles is transformed into a Markov chain based on transition probabilities of particles between the volume elements of a given partition of space for a fixed time step. We employ perturbation-theoretic methods to investigate the effects of modifications of transport processes in the underlying flow for three different problem classes: efficient absorption (corresponding to particle trapping or leaking), constant input of particles (with additional source terms modeling, e.g., localized contamination), and shifts of the steady state under probability mass conservation (as arising if the background flow is perturbed itself). Our results demonstrate that in all three cases, changes to the steady state solution can be analytically expressed in terms of the eigensystem of the unperturbed flow and the perturbation itself. These results are potentially relevant for developing more efficient strategies for coping with contaminations of fluid or gaseous media such as ocean and atmosphere by oil spills, radioactive substances, non-reactive chemicals, or volcanic aerosols.
Random fracture networks: percolation, geometry and flow
NASA Astrophysics Data System (ADS)
Adler, P. M.; Thovert, J. F.; Mourzenko, V. V.
2015-12-01
This paper reviews some of the basic properties of fracture networks. Most of the data can only be derived numerically, and to be useful they need to be rationalized, i.e., a large set of numbers should be replaced by a simple formula which is easy to apply for estimating orders of magnitude. Three major tools are found useful in this rationalization effort. First, analytical results can usually be derived for infinite fractures, a limit which corresponds to large densities. Second, the excluded volume and the dimensionless density prove crucial to gather data obtained at intermediate densities. Finally, shape factors can be used to further reduce the influence of fracture shapes. Percolation of fracture networks is of primary importance since this characteristic controls transport properties such as permeability. Recent numerical studies for various types of fracture networks (isotropic, anisotropic, heterogeneous in space, polydisperse, mixture of shapes) are summarized; the percolation threshold rho is made dimensionless by means of the excluded volume. A general correlation for rho is proposed as a function of the gyration radius. The statistical characteristics of the blocks which are cut in the solid matrix by the network are presented, since they control transfers between the porous matrix and the fractures. Results on quantities such as the volume, surface and number of faces are given and semi empirical relations are proposed. The possible intersection of a percolating network and of a cubic cavity is also summarized. This might be of importance for the underground storage of wastes. An approximate reasoning based on the excluded volume of the percolating cluster and of the cubic cavity is proposed. Finally, consequences on the permeability of fracture networks are briefly addressed. An empirical formula which verifies some theoretical properties is proposed.
Flow networks: A characterization of geophysical fluid transport
NASA Astrophysics Data System (ADS)
Ser-Giacomi, Enrico; Rossi, Vincent; López, Cristóbal; Hernández-García, Emilio
2015-03-01
We represent transport between different regions of a fluid domain by flow networks, constructed from the discrete representation of the Perron-Frobenius or transfer operator associated to the fluid advection dynamics. The procedure is useful to analyze fluid dynamics in geophysical contexts, as illustrated by the construction of a flow network associated to the surface circulation in the Mediterranean sea. We use network-theory tools to analyze the flow network and gain insights into transport processes. In particular, we quantitatively relate dispersion and mixing characteristics, classically quantified by Lyapunov exponents, to the degree of the network nodes. A family of network entropies is defined from the network adjacency matrix and related to the statistics of stretching in the fluid, in particular, to the Lyapunov exponent field. Finally, we use a network community detection algorithm, Infomap, to partition the Mediterranean network into coherent regions, i.e., areas internally well mixed, but with little fluid interchange between them.
Generalized network flow model with application to power supply-demand problems
Liu, C.
1982-08-01
A generalization of the conventional network flow model to a very general F-flow model is provided. The max-flow-min-cut theorem is then generalized. The theorem is used to derive a necessary and sufficient condition for feasibility of the multi-terminal supply-demand problem based on the F-flow model. As an application, the electric power supply-demand problem is discussed from the F-flow point of view.
Analysis of HRCT-derived xylem network reveals reverse flow in some vessels
USDA-ARS?s Scientific Manuscript database
Flow in xylem vessels is modeled based on constructions of three dimensional xylem networks derived from High Resolution Computed Tomography (HRCT) images of grapevine (Vitis vinifera) stems. Flow in 6-14% of the vessels was found to be oriented in the opposite direction to the bulk flow under norma...
Evolution of weighted complex bus transit networks with flow
NASA Astrophysics Data System (ADS)
Huang, Ailing; Xiong, Jie; Shen, Jinsheng; Guan, Wei
2016-02-01
Study on the intrinsic properties and evolutional mechanism of urban public transit networks (PTNs) has great significance for transit planning and control, particularly considering passengers’ dynamic behaviors. This paper presents an empirical analysis for exploring the complex properties of Beijing’s weighted bus transit network (BTN) based on passenger flow in L-space, and proposes a bi-level evolution model to simulate the development of transit routes from the view of complex network. The model is an iterative process that is driven by passengers’ travel demands and dual-controlled interest mechanism, which is composed of passengers’ spatio-temporal requirements and cost constraint of transit agencies. Also, the flow’s dynamic behaviors, including the evolutions of travel demand, sectional flow attracted by a new link and flow perturbation triggered in nearby routes, are taken into consideration in the evolutional process. We present the numerical experiment to validate the model, where the main parameters are estimated by using distribution functions that are deduced from real-world data. The results obtained have proven that our model can generate a BTN with complex properties, such as the scale-free behavior or small-world phenomenon, which shows an agreement with our empirical results. Our study’s results can be exploited to optimize the real BTN’s structure and improve the network’s robustness.
A methodology for detecting routing events in discrete flow networks.
Garcia, H. E.; Yoo, T.; Nuclear Technology
2004-01-01
A theoretical framework for formulating and implementing model-based monitoring of discrete flow networks is discussed. Possible flows of items are described as the sequence of discrete-event (DE) traces. Each trace defines the DE sequence(s) that are triggered when an entity follows a given flow-path and visits tracking locations distributed within the monitored system. Given the set of possible discrete flows, a possible-behavior model - an interacting set of automata - is constructed, where each automaton models the discrete flow of items at each tracking location. Event labels or symbols contain all the information required to unambiguously distinguish each discrete flow. Within the possible behavior, there is a special sub-behavior whose occurrence is required to be detected. The special behavior may be specified by the occurrence of routing events, such as faults. These intermittent or non-persistent events may occur repeatedly. An observation mask is then defined, characterizing the actual observation configuration available for collecting item tracking data. The analysis task is then to determine whether this observation configuration is capable of detecting the identified special behavior. The assessment is accomplished by evaluating several observability notions, such as detectability and diagnosability. If the corresponding property is satisfied, associated formal observers are constructed to perform the monitoring task at hand. The synthesis of an optimal observation mask may also be conducted to suggest an appropriate observation configuration guaranteeing the detection of the special events and to construct associated monitoring agents. The proposed framework, modeling methodology, and supporting techniques for discrete flow networks monitoring are presented and illustrated with an example.
Leandro, Jorge; Martins, Ricardo
2016-01-01
Pluvial flooding in urban areas is characterized by a gradually varying inundation process caused by surcharge of the sewer manholes. Therefore urban flood models need to simulate the interaction between the sewer network and the overland flow in order to accurately predict the flood inundation extents. In this work we present a methodology for linking 2D overland flow models with the storm sewer model SWMM 5. SWMM 5 is a well-known free open-source code originally developed in 1971. The latest major release saw its structure re-written in C ++ allowing it to be compiled as a command line executable or through a series of calls made to function inside a dynamic link library (DLL). The methodology developed herein is written inside the same DLL in C + +, and is able to simulate the bi-directional interaction between both models during simulation. Validation is done in a real case study with an existing urban flood coupled model. The novelty herein is that the new methodology can be added to SWMM without the need for editing SWMM's original code. Furthermore, it is directly applicable to other coupled overland flow models aiming to use SWMM 5 as the sewer network model.
Laminar flow of two miscible fluids in a simple network
NASA Astrophysics Data System (ADS)
Karst, Casey M.; Storey, Brian D.; Geddes, John B.
2013-03-01
When a fluid comprised of multiple phases or constituents flows through a network, nonlinear phenomena such as multiple stable equilibrium states and spontaneous oscillations can occur. Such behavior has been observed or predicted in a number of networks including the flow of blood through the microcirculation, the flow of picoliter droplets through microfluidic devices, the flow of magma through lava tubes, and two-phase flow in refrigeration systems. While the existence of nonlinear phenomena in a network with many inter-connections containing fluids with complex rheology may seem unsurprising, this paper demonstrates that even simple networks containing Newtonian fluids in laminar flow can demonstrate multiple equilibria. The paper describes a theoretical and experimental investigation of the laminar flow of two miscible Newtonian fluids of different density and viscosity through a simple network. The fluids stratify due to gravity and remain as nearly distinct phases with some mixing occurring only by diffusion. This fluid system has the advantage that it is easily controlled and modeled, yet contains the key ingredients for network nonlinearities. Experiments and 3D simulations are first used to explore how phases distribute at a single T-junction. Once the phase separation at a single junction is known, a network model is developed which predicts multiple equilibria in the simplest of networks. The existence of multiple stable equilibria is confirmed experimentally and a criterion for existence is developed. The network results are generic and could be applied to or found in different physical systems.
Dynamics of blood flow in a microfluidic ladder network
NASA Astrophysics Data System (ADS)
Maddala, Jeevan; Zilberman-Rudenko, Jevgenia; McCarty, Owen
The dynamics of a complex mixture of cells and proteins, such as blood, in perturbed shear flow remains ill-defined. Microfluidics is a promising technology for improving the understanding of blood flow under complex conditions of shear; as found in stent implants and in tortuous blood vessels. We model the fluid dynamics of blood flow in a microfluidic ladder network with dimensions mimicking venules. Interaction of blood cells was modeled using multiagent framework, where cells of different diameters were treated as spheres. This model served as the basis for predicting transition regions, collision pathways, re-circulation zones and residence times of cells dependent on their diameters and device architecture. Based on these insights from the model, we were able to predict the clot formation configurations at various locations in the device. These predictions were supported by the experiments using whole blood. To facilitate platelet aggregation, the devices were coated with fibrillar collagen and tissue factor. Blood was perfused through the microfluidic device for 9 min at a physiologically relevant venous shear rate of 600 s-1. Using fluorescent microscopy, we observed flow transitions near the channel intersections and at the areas of blood flow obstruction, which promoted larger thrombus formation. This study of integrating model predictions with experimental design, aids in defining the dynamics of blood flow in microvasculature and in development of novel biomedical devices.
Hierarchicality of trade flow networks reveals complexity of products.
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
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
Effective contaminant detection networks in uncertain groundwater flow fields.
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.
The simplicity of fractal-like flow networks for effective heat and mass transport
Pence, Deborah
2010-05-15
A variety of applications using disk-shaped fractal-like flow networks and the status of one and two-dimensional predictive models for these applications are summarized. Applications discussed include single-phase and two-phase heat sinks and heat exchangers, two-phase flow separators, desorbers, and passive micromixers. Advantages of using these fractal-like flow networks versus parallel-flow networks include lower pressure drop, lower maximum wall temperature, inlet plenum symmetry, alternate flow paths, and pressure recovery at the bifurcation. The compact nature of microscale fractal-like branching heat exchangers makes them ideal for modularity. Differences between fractal-like and constructal approaches applied to disk-shaped heat sink designs are highlighted, and the importance of including geometric constraints, including fabrication constraints, in flow network design optimization is discussed. Finally, a simple pencil and paper procedure for designing single-phase heat sinks with fractal-like flow networks based solely on geometric constraints is outlined. Benefit-to-cost ratios resulting from geometric-based designs are compared with those from flow networks determined using multivariable optimization. Results from the two network designs are within 11%. (author)
OpenFlow Extensions for Programmable Quantum Networks
2017-06-19
ARL-TR-8043 JUN 2017 US Army Research Laboratory OpenFlow Extensions for Programmable Quantum Networks by Venkat Dasari...Extensions for Programmable Quantum Networks by Venkat Dasari, Nikolai Snow, and Billy Geerhart Computational and Information Sciences Directorate...DATES COVERED (From - To) June 2015–March 2017 4. TITLE AND SUBTITLE OpenFlow Extensions for Programmable Quantum Networks 5a. CONTRACT NUMBER 5b
Network-Based Management Procedures.
ERIC Educational Resources Information Center
Buckner, Allen L.
Network-based management procedures serve as valuable aids in organizational management, achievement of objectives, problem solving, and decisionmaking. Network techniques especially applicable to educational management systems are the program evaluation and review technique (PERT) and the critical path method (CPM). Other network charting…
NASA Astrophysics Data System (ADS)
De Domenico, Manlio; Lancichinetti, Andrea; Arenas, Alex; Rosvall, Martin
2015-01-01
To comprehend interconnected systems across the social and natural sciences, researchers have developed many powerful methods to identify functional modules. For example, with interaction data aggregated into a single network layer, flow-based methods have proven useful for identifying modular dynamics in weighted and directed networks that capture constraints on flow processes. However, many interconnected systems consist of agents or components that exhibit multiple layers of interactions, possibly from several different processes. Inevitably, representing this intricate network of networks as a single aggregated network leads to information loss and may obscure the actual organization. Here, we propose a method based on a compression of network flows that can identify modular flows both within and across layers in nonaggregated multilayer networks. Our numerical experiments on synthetic multilayer networks, with some layers originating from the same interaction process, show that the analysis fails in aggregated networks or when treating the layers separately, whereas the multilayer method can accurately identify modules across layers that originate from the same interaction process. We capitalize on our findings and reveal the community structure of two multilayer collaboration networks with topics as layers: scientists affiliated with the Pierre Auger Observatory and scientists publishing works on networks on the arXiv. Compared to conventional aggregated methods, the multilayer method uncovers connected topics and reveals smaller modules with more overlap that better capture the actual organization.
Scaling of peak flows with constant flow velocity in random self-similar networks
Troutman, Brent M.; Mantilla, Ricardo; Gupta, Vijay K.
2011-01-01
A methodology is presented to understand the role of the statistical self-similar topology of real river networks on scaling, or power law, in peak flows for rainfall-runoff events. We created Monte Carlo generated sets of ensembles of 1000 random self-similar networks (RSNs) with geometrically distributed interior and exterior generators having parameters pi and pe, respectively. The parameter values were chosen to replicate the observed topology of real river networks. We calculated flow hydrographs in each of these networks by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated RSNs and hydrographs, the scaling exponents β and φ characterizing power laws with respect to drainage area, and corresponding to the width functions and flow hydrographs respectively, were estimated. We found that, in general, φ > β, which supports a similar finding first reported for simulations in the river network of the Walnut Gulch basin, Arizona. Theoretical estimation of β and φ in RSNs is a complex open problem. Therefore, using results for a simpler problem associated with the expected width function and expected hydrograph for an ensemble of RSNs, we give heuristic arguments for theoretical derivations of the scaling exponents β(E) and φ(E) that depend on the Horton ratios for stream lengths and areas. These ratios in turn have a known dependence on the parameters of the geometric distributions of RSN generators. Good agreement was found between the analytically conjectured values of β(E) and φ(E) and the values estimated by the simulated ensembles of RSNs and hydrographs. The independence of the scaling exponents φ(E) and φ with respect to the value of flow velocity and runoff intensity implies an interesting connection between unit hydrograph theory and flow dynamics. Our results provide a reference framework to study scaling exponents under more complex scenarios
Directory Enabled Policy Based Networking
KELIIAA, CURTIS M.
2001-10-01
This report presents a discussion of directory-enabled policy-based networking with an emphasis on its role as the foundation for securely scalable enterprise networks. A directory service provides the object-oriented logical environment for interactive cyber-policy implementation. Cyber-policy implementation includes security, network management, operational process and quality of service policies. The leading network-technology vendors have invested in these technologies for secure universal connectivity that transverses Internet, extranet and intranet boundaries. Industry standards are established that provide the fundamental guidelines for directory deployment scalable to global networks. The integration of policy-based networking with directory-service technologies provides for intelligent management of the enterprise network environment as an end-to-end system of related clients, services and resources. This architecture allows logical policies to protect data, manage security and provision critical network services permitting a proactive defense-in-depth cyber-security posture. Enterprise networking imposes the consideration of supporting multiple computing platforms, sites and business-operation models. An industry-standards based approach combined with principled systems engineering in the deployment of these technologies allows these issues to be successfully addressed. This discussion is focused on a directory-based policy architecture for the heterogeneous enterprise network-computing environment and does not propose specific vendor solutions. This document is written to present practical design methodology and provide an understanding of the risks, complexities and most important, the benefits of directory-enabled policy-based networking.
Fractal scaling and fluid flow in fracture networks in rock
Barton, C.C.
1996-12-31
Recovery of oil and gas resources and injection of toxic waste materials requires quantitative models to describe and predict the movement of fluids in rock. Existing models based on pore-space flow are inappropriate for study of the more rapid process of fluid flow through fracture networks. This type of flow is not a simple function of the fracture characteristics at any particular scale, but rather the integration of fracture contributions at all scales. The mathematical constructs of fractal geometry are well suited to quantify and model relationships within complex systems that are statistically self-similar over a wide range of scales. Analyses show that fracture traces mapped on two-dimensional slices through three-dimensional nature fracture networks in rock follow a fractal scaling law over six orders of magnitude. Detailed measurements of 17 two-dimensional samples of fracture networks (at diverse scales in rocks of dissimilar age, lithology, and tectonic setting) show similar fractal dimensions in the range 1.3-1.7. The range in fractal dimension implies that a single physical process of rock fracturing operates over a wide range of scales, from microscopic cracks to large, regional fault systems. The knowledge that rock-fracture networks are fractal allows the use of data from a one-dimensional drill-hole sample to predict the two- and three-dimensional scaling of the fracture system. The spacing of fractures in drill holes is a fractal Cantor distribution, and the range of fractal dimension is 0.4-0.6, which is an integer dimension less than that of fracture-trace patterns exposed on two-dimensional, planar sections. A reconstruction of the fracture history at the point of initial connectivity across the network (percolation) has a fractal dimension of 1.35 as compared to a dimension of 1.9 for the percolation cluster in a two-dimensional model. Paleo flow was mapped based on the deposition of aqueous minerals on the fracture surface.
Fractal scaling and fluid flow in fracture networks in rock
Barton, C.C. )
1996-01-01
Recovery of oil and gas resources and injection of toxic waste materials requires quantitative models to describe and predict the movement of fluids in rock. Existing models based on pore-space flow are inappropriate for study of the more rapid process of fluid flow through fracture networks. This type of flow is not a simple function of the fracture characteristics at any particular scale, but rather the integration of fracture contributions at all scales. The mathematical constructs of fractal geometry are well suited to quantify and model relationships within complex systems that are statistically self-similar over a wide range of scales. Analyses show that fracture traces mapped on two-dimensional slices through three-dimensional nature fracture networks in rock follow a fractal scaling law over six orders of magnitude. Detailed measurements of 17 two-dimensional samples of fracture networks (at diverse scales in rocks of dissimilar age, lithology, and tectonic setting) show similar fractal dimensions in the range 1.3-1.7. The range in fractal dimension implies that a single physical process of rock fracturing operates over a wide range of scales, from microscopic cracks to large, regional fault systems. The knowledge that rock-fracture networks are fractal allows the use of data from a one-dimensional drill-hole sample to predict the two- and three-dimensional scaling of the fracture system. The spacing of fractures in drill holes is a fractal Cantor distribution, and the range of fractal dimension is 0.4-0.6, which is an integer dimension less than that of fracture-trace patterns exposed on two-dimensional, planar sections. A reconstruction of the fracture history at the point of initial connectivity across the network (percolation) has a fractal dimension of 1.35 as compared to a dimension of 1.9 for the percolation cluster in a two-dimensional model. Paleo flow was mapped based on the deposition of aqueous minerals on the fracture surface.
Dynamics-based centrality for directed networks
NASA Astrophysics Data System (ADS)
Masuda, Naoki; Kori, Hiroshi
2010-11-01
Determining the relative importance of nodes in directed networks is important in, for example, ranking websites, publications, and sports teams, and for understanding signal flows in systems biology. A prevailing centrality measure in this respect is the PageRank. In this work, we focus on another class of centrality derived from the Laplacian of the network. We extend the Laplacian-based centrality, which has mainly been applied to strongly connected networks, to the case of general directed networks such that we can quantitatively compare arbitrary nodes. Toward this end, we adopt the idea used in the PageRank to introduce global connectivity between all the pairs of nodes with a certain strength. Numerical simulations are carried out on some networks. We also offer interpretations of the Laplacian-based centrality for general directed networks in terms of various dynamical and structural properties of networks. Importantly, the Laplacian-based centrality defined as the stationary density of the continuous-time random walk with random jumps is shown to be equivalent to the absorption probability of the random walk with sinks at each node but without random jumps. Similarly, the proposed centrality represents the importance of nodes in dynamics on the original network supplied with sinks but not with random jumps.
Consistent maximum entropy representations of pipe flow networks
NASA Astrophysics Data System (ADS)
Waldrip, Steven H.; Niven, Robert K.; Abel, Markus; Schlegel, Michael
2017-06-01
The maximum entropy method is used to predict flows on water distribution networks. This analysis extends the water distribution network formulation of Waldrip et al. (2016) Journal of Hydraulic Engineering (ASCE), by the use of a continuous relative entropy defined on a reduced parameter set. This reduction in the parameters that the entropy is defined over ensures consistency between different representations of the same network. The performance of the proposed reduced parameter method is demonstrated with a one-loop network case study.
Analyzing the International Exergy Flow Network of Ferrous Metal Ores
Qi, Hai; An, Haizhong; Hao, Xiaoqing; Zhong, Weiqiong; Zhang, Yanbing
2014-01-01
This paper employs an un-weighted and weighted exergy network to study the properties of ferrous metal ores in countries worldwide and their evolution from 2002 to 2012. We find that there are few countries controlling most of the ferrous metal ore exports in terms of exergy and that the entire exergy flow network is becoming more heterogeneous though the addition of new nodes. The increasing of the average clustering coefficient indicates that the formation of an international exergy flow system and regional integration is improving. When we contrast the average out strength of exergy and the average out strength of currency, we find both similarities and differences. Prices are affected largely by human factors; thus, the growth rate of the average out strength of currency has fluctuated acutely in the eleven years from 2002 to 2012. Exergy is defined as the maximum work that can be extracted from a system and can reflect the true cost in the world, and this parameter fluctuates much less. Performing an analysis based on the two aspects of exergy and currency, we find that the network is becoming uneven. PMID:25188407
Analyzing Information Flow in Brain Networks with Nonparametric Granger Causality
Dhamala, Mukeshwar; Rangarajan, Govindan; Ding, Mingzhou
2009-01-01
Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furnish this capability. The estimation of Granger causality currently requires autoregressive modeling of neural data. Here, we propose a nonparametric approach based on widely used Fourier and wavelet transforms to estimate Granger causality, eliminating the need of explicit autoregressive data modeling. We demonstrate the effectiveness of this approach by applying it to synthetic data generated by network models with known connectivity and to local field potentials recorded from monkeys performing a sensorimotor task. PMID:18394927
Analyzing information flow in brain networks with nonparametric Granger causality.
Dhamala, Mukeshwar; Rangarajan, Govindan; Ding, Mingzhou
2008-06-01
Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furnish this capability. The estimation of Granger causality currently requires autoregressive modeling of neural data. Here, we propose a nonparametric approach based on widely used Fourier and wavelet transforms to estimate both pairwise and conditional measures of Granger causality, eliminating the need of explicit autoregressive data modeling. We demonstrate the effectiveness of this approach by applying it to synthetic data generated by network models with known connectivity and to local field potentials recorded from monkeys performing a sensorimotor task.
Neural networks for BEM analysis of steady viscous flows
NASA Astrophysics Data System (ADS)
Mai-Duy, Nam; Tran-Cong, Thanh
2003-03-01
This paper presents a new neural network-boundary integral approach for analysis of steady viscous fluid flows. Indirect radial basis function networks (IRBFNs) which perform better than element-based methods for function interpolation, are introduced into the BEM scheme to represent the variations of velocity and traction along the boundary from the nodal values. In order to assess the effect of IRBFNs, the other features used in the present work remain the same as those used in the standard BEM. For example, Picard-type scheme is utilized in the iterative procedure to deal with the non-linear convective terms while the calculation of volume integrals and velocity gradients are based on the linear finite element-based method. The proposed IRBFN-BEM is verified on the driven cavity viscous flow problem and can achieve a moderate Reynolds number of 1400 using a relatively coarse uniform mesh. The results obtained such as the velocity profiles along the horizontal and vertical centrelines as well as the properties of the primary vortex are in very good agreement with the benchmark solution. Furthermore, the secondary vortices are also captured by the present method. Thus, it appears that an ability to represent the boundary solution accurately can significantly improve the overall solution accuracy of the BEM.
Baber, C; Stanton, N A; Atkinson, J; McMaster, R; Houghton, R J
2013-01-01
The concept of common operational pictures (COPs) is explored through the application of social network analysis (SNA) and agent-based modelling to a generic search and rescue (SAR) scenario. Comparing the command structure that might arise from standard operating procedures with the sort of structure that might arise from examining information-in-common, using SNA, shows how one structure could be more amenable to 'command' with the other being more amenable to 'control' - which is potentially more suited to complex multi-agency operations. An agent-based model is developed to examine the impact of information sharing with different forms of COPs. It is shown that networks using common relevant operational pictures (which provide subsets of relevant information to groups of agents based on shared function) could result in better sharing of information and a more resilient structure than networks that use a COP. SNA and agent-based modelling are used to compare different forms of COPs for maritime SAR operations. Different forms of COP change the communications structures in the socio-technical systems in which they operate, which has implications for future design and development of a COP.
Information Flow Between Resting-State Networks
Diez, Ibai; Erramuzpe, Asier; Escudero, Iñaki; Mateos, Beatriz; Cabrera, Alberto; Marinazzo, Daniele; Sanz-Arigita, Ernesto J.; Stramaglia, Sebastiano
2015-01-01
Abstract The resting brain dynamics self-organize into a finite number of correlated patterns known as resting-state networks (RSNs). It is well known that techniques such as independent component analysis can separate the brain activity at rest to provide such RSNs, but the specific pattern of interaction between RSNs is not yet fully understood. To this aim, we propose here a novel method to compute the information flow (IF) between different RSNs from resting-state magnetic resonance imaging. After hemodynamic response function blind deconvolution of all voxel signals, and under the hypothesis that RSNs define regions of interest, our method first uses principal component analysis to reduce dimensionality in each RSN to next compute IF (estimated here in terms of transfer entropy) between the different RSNs by systematically increasing k (the number of principal components used in the calculation). When k=1, this method is equivalent to computing IF using the average of all voxel activities in each RSN. For k≥1, our method calculates the k multivariate IF between the different RSNs. We find that the average IF among RSNs is dimension dependent, increasing from k=1 (i.e., the average voxel activity) up to a maximum occurring at k=5 and to finally decay to zero for k≥10. This suggests that a small number of components (close to five) is sufficient to describe the IF pattern between RSNs. Our method—addressing differences in IF between RSNs for any generic data—can be used for group comparison in health or disease. To illustrate this, we have calculated the inter-RSN IF in a data set of Alzheimer's disease (AD) to find that the most significant differences between AD and controls occurred for k=2, in addition to AD showing increased IF w.r.t. controls. The spatial localization of the k=2 component, within RSNs, allows the characterization of IF differences between AD and controls. PMID:26177254
Backbone of complex networks of corporations: The flow of control
NASA Astrophysics Data System (ADS)
Glattfelder, J. B.; Battiston, S.
2009-09-01
We present a methodology to extract the backbone of complex networks based on the weight and direction of links, as well as on nontopological properties of nodes. We show how the methodology can be applied in general to networks in which mass or energy is flowing along the links. In particular, the procedure enables us to address important questions in economics, namely, how control and wealth are structured and concentrated across national markets. We report on the first cross-country investigation of ownership networks, focusing on the stock markets of 48 countries around the world. On the one hand, our analysis confirms results expected on the basis of the literature on corporate control, namely, that in Anglo-Saxon countries control tends to be dispersed among numerous shareholders. On the other hand, it also reveals that in the same countries, control is found to be highly concentrated at the global level, namely, lying in the hands of very few important shareholders. Interestingly, the exact opposite is observed for European countries. These results have previously not been reported as they are not observable without the kind of network analysis developed here.
Backbone of complex networks of corporations: the flow of control.
Glattfelder, J B; Battiston, S
2009-09-01
We present a methodology to extract the backbone of complex networks based on the weight and direction of links, as well as on nontopological properties of nodes. We show how the methodology can be applied in general to networks in which mass or energy is flowing along the links. In particular, the procedure enables us to address important questions in economics, namely, how control and wealth are structured and concentrated across national markets. We report on the first cross-country investigation of ownership networks, focusing on the stock markets of 48 countries around the world. On the one hand, our analysis confirms results expected on the basis of the literature on corporate control, namely, that in Anglo-Saxon countries control tends to be dispersed among numerous shareholders. On the other hand, it also reveals that in the same countries, control is found to be highly concentrated at the global level, namely, lying in the hands of very few important shareholders. Interestingly, the exact opposite is observed for European countries. These results have previously not been reported as they are not observable without the kind of network analysis developed here.
Flow distributions and spatial correlations in human brain capillary networks
NASA Astrophysics Data System (ADS)
Lorthois, Sylvie; Peyrounette, Myriam; Larue, Anne; Le Borgne, Tanguy
2015-11-01
The vascular system of the human brain cortex is composed of a space filling mesh-like capillary network connected upstream and downstream to branched quasi-fractal arterioles and venules. The distribution of blood flow rates in these networks may affect the efficiency of oxygen transfer processes. Here, we investigate the distribution and correlation properties of blood flow velocities from numerical simulations in large 3D human intra-cortical vascular network (10000 segments) obtained from an anatomical database. In each segment, flow is solved from a 1D non-linear model taking account of the complex rheological properties of blood flow in microcirculation to deduce blood pressure, blood flow and red blood cell volume fraction distributions throughout the network. The network structural complexity is found to impart broad and spatially correlated Lagrangian velocity distributions, leading to power law transit time distributions. The origins of this behavior (existence of velocity correlations in capillary networks, influence of the coupling with the feeding arterioles and draining veins, topological disorder, complex blood rheology) are studied by comparison with results obtained in various model capillary networks of controlled disorder. ERC BrainMicroFlow GA615102, ERC ReactiveFronts GA648377.
NLS nozzle base flow characteristics
NASA Technical Reports Server (NTRS)
Erhart, John J.
1992-01-01
The flow characteristics of the National Launch System (NLS) nozzle base area need to be determined in order for heat transfer rates to be estimated. The objective of this work is to calculate these flow characteristics using computational fluid dynamics (CFD). A full Navier-Stokes code in an axisymmetric mode, using a kappa-epsilon model with wall functions is applied. Calculations were completed at an altitude of 3,250 and 80,000 feet in the flight trajectory. The results show flow features which can affect vehicle design. Calibration of a 3-D case with data is underway. Information is given in viewgraph form.
Multi-frequency complex network from time series for uncovering oil-water flow structure
Gao, Zhong-Ke; Yang, Yu-Xuan; Fang, Peng-Cheng; Jin, Ning-De; Xia, Cheng-Yi; Hu, Li-Dan
2015-01-01
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. PMID:25649900
Polysulfide flow batteries enabled by percolating nanoscale conductor networks.
Fan, Frank Y; Woodford, William H; Li, Zheng; Baram, Nir; Smith, Kyle C; Helal, Ahmed; McKinley, Gareth H; Carter, W Craig; Chiang, Yet-Ming
2014-01-01
A new approach to flow battery design is demonstrated wherein diffusion-limited aggregation of nanoscale conductor particles at ∼1 vol % concentration is used to impart mixed electronic-ionic conductivity to redox solutions, forming flow electrodes with embedded current collector networks that self-heal after shear. Lithium polysulfide flow cathodes of this architecture exhibit electrochemical activity that is distributed throughout the volume of flow electrodes rather than being confined to surfaces of stationary current collectors. The nanoscale network architecture enables cycling of polysulfide solutions deep into precipitation regimes that historically have shown poor capacity utilization and reversibility and may thereby enable new flow battery designs of higher energy density and lower system cost. Lithium polysulfide half-flow cells operating in both continuous and intermittent flow mode are demonstrated for the first time.
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.
Constructing minimum-cost flow-dependent networks
NASA Astrophysics Data System (ADS)
Thomas, Doreen A.; Weng, Jia F.
2002-09-01
In the construction of a communication network, the length of the network is an important but not unique factor determining the cost of the network. Among many possible network models, Gilbert proposed a flow-dependent model in which flow demands are assigned between each pair of points in a given point set A, and the cost per unit length of a link in the network is a function of the flow through the link. In this paper we first investigate the properties of this Gilbert model: the concavity of the cost function, decomposition, local minimality, the number of Steiner points and the maximum degree of Steiner points. Then we propose three heuristics for constructing minimum cost Gilbert networks. Two of them come from the fact that generally a minimum cost Gilbert network stands between two extremes: the complete network G(A) on A and the edge-weighted Steiner minimal tree W(A) on A. The first heuristic starts with G(A) and reduces the cost by splitting angles; the second one starts with both G(A) and W(A), and reduces the cost by selecting low cost paths. As a generalisation of the second heuristic, the third heuristic constructs a new Gilbert network of less cost by hybridising known Gilbert networks. Finally we discuss some considerations in practical applications.
Tanaka, Takafumi; Hirano, Akira; Jinno, Masahiko
2014-01-13
To evaluate the cost efficiency of IP over elastic optical network architectures, we use a multi-layer network design scheme that covers network to node equipment level. An evaluation in a static traffic environment shows that the multi-flow optical transponder-based elastic optical network reduces total cost as well as equipment counts compared to other elastic network models based on fixed-rate, mixed-line-rate and bandwidth-variable transponders.
One dimensional modeling of blood flow in large networks
NASA Astrophysics Data System (ADS)
Wang, Xiaofei; Lagree, Pierre-Yves; Fullana, Jose-Maria; Lorthois, Sylvie; Institut de Mecanique des Fluides de Toulouse Collaboration
2014-11-01
A fast and valid simulation of blood flow in large networks of vessels can be achieved with a one-dimensional viscoelastic model. In this paper, we developed a parallel code with this model and computed several networks: a circle of arteries, a human systemic network with 55 arteries and a vascular network of mouse kidney with more than one thousand segments. The numerical results were verified and the speedup of parallel computing was tested on multi-core computers. The evolution of pressure distributions in all the networks were visualized and we can see clearly the propagation patterns of the waves. This provides us a convenient tool to simulate blood flow in networks.
Cell motion, contractile networks, and the physics of interpenetrating reactive flow
Dembo, M.; Harlow, F.
1986-07-01
In this paper the authors propose a physical model of contractile biological polymer networks based on the notion of reactive interpenetrating flow. They show how their model leads to a mathematical formulation of the dynamical laws governing the behavior of contractile networks. They also develop estimates of the various parameters that appear in their equations, and discuss some elementary predictions of the model concerning the general scaling principles that pertain to the motions of contractile networks.
Peak-flow frequency relations and evaluation of the peak-flow gaging network in Nebraska
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
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)
Huang, Hongyun; Oizumi, Shunsuke; Kojima, Nobuhiko; Niino, Toshiki; Sakai, Yasuyuki
2007-09-01
To engineer implantable liver tissues, we designed a novel scaffold with a three-dimensional (3D) branching and joining flow-channel network comprising multiple tetrahedral units (4-mm edge length). For the fabrication of this network, biodegradable polycaprolactone (PCL) and 80% (w/w) NaCl salt particles serving as porogen were thoroughly mixed and applied in a selective laser sintering (SLS) process, a technique adapted to rapid prototyping. We thus obtained a scaffold that had high (89%) porosity with a pore size of 100-200 microm and 3D flow channels. To evaluate its biocompatibility, human hepatoma Hep G2 cells were seeded into the scaffold using avidin-biotin (AB) binding and cultured in a perfusion system for 9 days. The results demonstrated that such 3D flow channels are essential to the cells' growth and function. In addition, the AB binding-based seeding remarkably improved the overall performance of the cell-loaded scaffolds. The fabrication of a much finer scaffold, having a 500 cm(3) scale, based on the same design and the use of human hepatocyte progenitors, may, in the near future, lead to the development of an implantable liver tissue equivalent for use in humans.
Ramification of Channel Networks Incised by Groundwater Flow
NASA Astrophysics Data System (ADS)
Yi, R. S.; Seybold, H. F.; Petroff, A. P.; Devauchelle, O.; Rothman, D.
2011-12-01
The geometry of channel networks has been a source of fascination since at least Leonardo da Vinci's time. Yet a comprehensive understanding of ramification---the mechanism of branching by which a stream network acquires its geometric complexity---remains elusive. To investigate the mechanisms of ramification and network growth, we consider channel growth driven by groundwater flow as a model system, analogous to a medical scientist's laboratory rat. We test our theoretical predictions through analysis of a particularly compelling example found on the Florida Panhandle north of Bristol. As our ultimate goal is to understand ramification and growth dynamics of the entire network, we build a computational model based on the following growth hypothesis: Channels grow in the direction that captures the maximum water flux. When there are two such directions, tips bifurcate. The direction of growth can be determined from the expansion of the ground water field around each tip, where each coefficient in this expansion has a physical interpretation. The first coefficient in the expansion determines the ground water discharge, leading to a straight growth of the channel. The second term describes the asymmetry in the water field leading to a bending of the stream in the direction of maximal water flux. The ratio between the first and the third coefficient determines a critical distance rc over which the tip feels inhomogeneities in the ground water table. This initiates then the splitting of the tip. In order to test our growth hypothesis and to determine rc, we grow the Florida network backward. At each time step we calculate the solution of the ground water field and determine the appropriate expansion coefficients around each tip. Comparing this simulation result to the predicted values provides us with a stringent measure for rc and the significance of our growth hypothesis.
NASA Astrophysics Data System (ADS)
Zhao, P. X.; Zhao, S. M.
2016-06-01
In this study, we analyze urban traffic flow using taxi trajectory data to understand the characteristics of traffic flow from the network centrality perspective at point (intersection), line (road), and area (community) granularities. The entire analysis process comprises three steps. The first step utilizes the taxi trajectory data to evaluate traffic flow at different granularities. Second, the centrality indices are calculated based on research units at different granularities. Third, correlation analysis between the centrality indices and corresponding urban traffic flow is performed. Experimental results indicate that urbaxperimental results indicate that urbaxperimental results indicate that urban traffic flow is relatively influenced by the road network structure. However, urban traffic flow also depends on the research unit size. Traditional centralities and traffic flow exhibit a low correlation at point granularity but exhibit a high correlation at line and area granularities. Furthermore, the conclusions of this study reflect the universality of the modifiable areal unit problem.
SIPSON--simulation of interaction between pipe flow and surface overland flow in networks.
Djordjević, S; Prodanović, D; Maksimović, C; Ivetić, M; Savić, D
2005-01-01
The new simulation model, named SIPSON, based on the Preissmann finite difference method and the conjugate gradient method, is presented in the paper. This model simulates conditions when the hydraulic capacity of a sewer system is exceeded, pipe flow is pressurized, the water flows out from the piped system to the streets, and the inlets cannot capture all the runoff. In the mathematical model, buried structures and pipelines, together with surface channels, make a horizontally and vertically looped network involving a complex interaction of flows. In this paper, special internal boundary conditions related to equivalent inlets are discussed. Procedures are described for the simulation of manhole cover loss, basement flooding, the representation of street geometry, and the distribution of runoff hydrographs between surface and underground networks. All these procedures are built into the simulation model. Relevant issues are illustrated on a set of examples, focusing on specific parameters and comparison with field measurements of flooding of the Motilal ki Chal catchment (Indore, India). Satisfactory agreement of observed and simulated hydrographs and maximum surface flooding levels is obtained. It is concluded that the presented approach is an improvement compared to the standard "virtual reservoir" approach commonly applied in most of the models.
Urban traffic-network performance: flow theory and simulation experiments
Williams, J.C.
1986-01-01
Performance models for urban street networks were developed to describe the response of a traffic network to given travel-demand levels. The three basic traffic flow variables, speed, flow, and concentration, are defined at the network level, and three model systems are proposed. Each system consists of a series of interrelated, consistent functions between the three basic traffic-flow variables as well as the fraction of stopped vehicles in the network. These models are subsequently compared with the results of microscopic simulation of a small test network. The sensitivity of one of the model systems to a variety of network features was also explored. Three categories of features were considered, with the specific features tested listed in parentheses: network topology (block length and street width), traffic control (traffic signal coordination), and traffic characteristics (level of inter-vehicular interaction). Finally, a fundamental issue concerning the estimation of two network-level parameters (from a nonlinear relation in the two-fluid theory) was examined. The principal concern was that of comparability of these parameters when estimated with information from a single vehicle (or small group of vehicles), as done in conjunction with previous field studies, and when estimated with network-level information (i.e., all the vehicles), as is possible with simulation.
Underground stope optimization with network flow method
NASA Astrophysics Data System (ADS)
Bai, Xiaoyu; Marcotte, Denis; Simon, Richard
2013-03-01
A new algorithm to optimize stope design for the sublevel stoping mining method is described. The model is based on a cylindrical coordinate defined around the initial vertical raise. Geotechnical constraints on hanging wall and footwall slopes are translated as precedence relations between blocks in the cylindrical coordinate system. Two control parameters with clear engineering meaning are defined to further constrain the solution: (a) the maximum distance of a block from the raise and (b) the horizontal width required to bring the farthest block to the raise. The graph obtained is completed by the addition of a source and a sink node allowing to transform the optimization program to a problem of maximum flow over the graph. The (conditional) optimal stope corresponding to the current raise location and height is obtained. The best location and height for the raise are determined by global optimization. The performance of the algorithm is evaluated with three simple synthetic deposits and one real deposit. Comparison is made with the floating stope technique. The results show that the algorithm effectively meets the geotechnical constraints and control parameters, and produce realistic optimal stope for engineering use.
Maximum entropy analysis of flow and reaction networks
NASA Astrophysics Data System (ADS)
Niven, Robert K.; Abel, Markus; Schlegel, Michael; Waldrip, Steven H.
2015-01-01
We present a generalised MaxEnt method to infer the stationary state of a flow network, subject to "observable" constraints on expectations of various parameters, as well as "physical" constraints arising from frictional properties (resistance functions) and conservation laws (Kirchhoff laws). The method invokes an entropy defined over all uncertainties in the system, in this case the internal and external flow rates and potential differences. The proposed MaxEnt framework is readily extendable to the analysis of networks with uncertainty in the network structure itself.
Bridging Minds: A Mixed Methodology to Assess Networked Flow.
Galimberti, Carlo; Chirico, Alice; Brivio, Eleonora; Mazzoni, Elvis; Riva, Giuseppe; Milani, Luca; Gaggioli, Andrea
2015-01-01
The main goal of this contribution is to present a methodological framework to study Networked Flow, a bio-psycho-social theory of collective creativity applying it on creative processes occurring via a computer network. First, we draw on the definition of Networked Flow to identify the key methodological requirements of this model. Next, we present the rationale of a mixed methodology, which aims at combining qualitative, quantitative and structural analysis of group dynamics to obtain a rich longitudinal dataset. We argue that this integrated strategy holds potential for describing the complex dynamics of creative collaboration, by linking the experiential features of collaborative experience (flow, social presence), with the structural features of collaboration dynamics (network indexes) and the collaboration outcome (the creative product). Finally, we report on our experience with using this methodology in blended collaboration settings (including both face-to-face and virtual meetings), to identify open issues and provide future research directions.
Stability and dynamical properties of material flow systems on random networks
NASA Astrophysics Data System (ADS)
Anand, K.; Galla, T.
2009-04-01
The theory of complex networks and of disordered systems is used to study the stability and dynamical properties of a simple model of material flow networks defined on random graphs. In particular we address instabilities that are characteristic of flow networks in economic, ecological and biological systems. Based on results from random matrix theory, we work out the phase diagram of such systems defined on extensively connected random graphs, and study in detail how the choice of control policies and the network structure affects stability. We also present results for more complex topologies of the underlying graph, focussing on finitely connected Erdös-Réyni graphs, Small-World Networks and Barabási-Albert scale-free networks. Results indicate that variability of input-output matrix elements, and random structures of the underlying graph tend to make the system less stable, while fast price dynamics or strong responsiveness to stock accumulation promote stability.
Base flow and ground water in upper Sweetwater Valley, Tennessee
Evaldi, R.D.; Lewis, J.G.
1983-01-01
Base flow measurements showed interbasin transfer of water among sub-basins of upper Sweetwater Valley. In general, topographically higher sub-basins have deficient surface outflow unless significant spring flow occurs in the basin. Topographically lower areas adjacent to the main channel of Sweetwater Creek generally have surplus flow. Major flow surpluses were associated with areas in which the majority of flow originated at a spring. Unusual outflow was related to geology to hypothesize a ground-water flow network. Areas of ground-water flow up-gradient of large springs were hypothesized as likely areas for significant ground-water reservoirs. A water budget study indicated that during dry years approximately three-fourths of the annual flow to Sweetwater Creek may be derived from ground-water sources. Streamflow records were analyzed to estimate the frequency of low-flow of Sweetwater Creek. (USGS)
Flow model for open-channel reach or network
Schaffranek, R.W.
1987-01-01
Formulation of a one-dimensional model for simulating unsteady flow in a single open-channel reach or in a network of interconnected channels is presented. The model is both general and flexible in that it can be used to simulate a wide range of flow conditions for various channel configurations. It is based on a four-point (box), implicit, finite-difference approximation of the governing nonlinear flow equations with user-definable weighting coefficients to permit varying the solution scheme from box-centered to fully forward. Unique transformation equations are formulated that permit correlation of the unknowns at the extremities of the channels, thereby reducing coefficient matrix and execution time requirements. Discharges and water-surface elevations computed at intermediate locations within a channel are determined following solution of the transformation equations. The matrix of transformation and boundary-condition equations is solved by Gauss elimination using maximum pivot strategy. Two diverse applications of the model are presented to illustrate its broad utility. (USGS)
Clustering coefficient and periodic orbits in flow networks
NASA Astrophysics Data System (ADS)
Rodríguez-Méndez, Victor; Ser-Giacomi, Enrico; Hernández-García, Emilio
2017-03-01
We show that the clustering coefficient, a standard measure in network theory, when applied to flow networks, i.e., graph representations of fluid flows in which links between nodes represent fluid transport between spatial regions, identifies approximate locations of periodic trajectories in the flow system. This is true for steady flows and for periodic ones in which the time interval τ used to construct the network is the period of the flow or a multiple of it. In other situations, the clustering coefficient still identifies cyclic motion between regions of the fluid. Besides the fluid context, these ideas apply equally well to general dynamical systems. By varying the value of τ used to construct the network, a kind of spectroscopy can be performed so that the observation of high values of mean clustering at a value of τ reveals the presence of periodic orbits of period 3 τ , which impact phase space significantly. These results are illustrated with examples of increasing complexity, namely, a steady and a periodically perturbed model two-dimensional fluid flow, the three-dimensional Lorenz system, and the turbulent surface flow obtained from a numerical model of circulation in the Mediterranean sea.
Confidence intervals in Flow Forecasting by using artificial neural networks
NASA Astrophysics Data System (ADS)
Panagoulia, Dionysia; Tsekouras, George
2014-05-01
One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input
Analysis of HRCT-derived xylem network reveals reverse flow in some vessels.
Lee, Eric F; Matthews, Mark A; McElrone, Andrew J; Phillips, Ronald J; Shackel, Kenneth A; Brodersen, Craig R
2013-09-21
Long distance water and nutrient transport in plants is dependent on the proper functioning of xylem networks, a series of interconnected pipe-like cells that are vulnerable to hydraulic dysfunction as a result of drought-induced embolism and/or xylem-dwelling pathogens. Here, flow in xylem vessels was modeled to determine the role of vessel connectivity by using three dimensional xylem networks derived from High Resolution Computed Tomography (HRCT) images of grapevine (Vitis vinifera cv. 'Chardonnay') stems. Flow in 4-27% of the vessel segments (i.e. any section of vessel elements between connection points associated with intervessel pits) was found to be oriented in the direction opposite to the bulk flow under normal transpiration conditions. In order for the flow in a segment to be in the reverse direction, specific requirements were determined for the location of connections, distribution of vessel endings, diameters of the connected vessels, and the conductivity of the connections. Increasing connectivity and decreasing vessel length yielded increasing numbers of reverse flow segments until a maximum value was reached, after which more interconnected networks and smaller average vessel lengths yielded a decrease in the number of reverse flow segments. Xylem vessel relays also encouraged the formation of reverse flow segments. Based on the calculated flow rates in the xylem network, the downward spread of Xylella fastidiosa bacteria in grape stems was modeled, and reverse flow was shown to be an additional mechanism for the movement of bacteria to the trunk of grapevine.
Efficient path routing strategy for flows with multiple priorities on scale-free networks
Zhou, Zhili; Cheng, Dong
2017-01-01
In real networks, traffic flows are different in amount as well as their priorities. However, the latter priority has rarely been examined in routing strategy studies. In this paper, a novel routing algorithm, which is based on the efficient path routing strategy (EP), is proposed to overcome network congestion problem caused by large amount of traffic flows with different priorities. In this scheme, traffic flows with different priorities are transmitted through different routing paths, which are based on EP with different parameters. Simulation results show that the traffic capacity for flows with different priorities can be enhanced by 12% with this method, compared with EP. In addition, the new method contributes to more balanced network traffic load distribution and reduces average transmission jump and delay of packets. PMID:28199382
Semi-automatic simulation model generation of virtual dynamic networks for production flow planning
NASA Astrophysics Data System (ADS)
Krenczyk, D.; Skolud, B.; Olender, M.
2016-08-01
Computer modelling, simulation and visualization of production flow allowing to increase the efficiency of production planning process in dynamic manufacturing networks. The use of the semi-automatic model generation concept based on parametric approach supporting processes of production planning is presented. The presented approach allows the use of simulation and visualization for verification of production plans and alternative topologies of manufacturing network configurations as well as with automatic generation of a series of production flow scenarios. Computational examples with the application of Enterprise Dynamics simulation software comprising the steps of production planning and control for manufacturing network have been also presented.
Exact Convex Relaxation of Optimal Power Flow in Radial Networks
Gan, LW; Li, N; Topcu, U; Low, SH
2015-01-01
The optimal power flow (OPF) problem determines a network operating point that minimizes a certain objective such as generation cost or power loss. It is nonconvex. We prove that a global optimum of OPF can be obtained by solving a second-order cone program, under a mild condition after shrinking the OPF feasible set slightly, for radial power networks. The condition can be checked a priori, and holds for the IEEE 13, 34, 37, 123-bus networks and two real-world networks.
Microbubble transport through a bifurcating vessel network with pulsatile flow.
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.
NASA Astrophysics Data System (ADS)
Liu, Zhiyong; Törnros, Tobias; Menzel, Lucas
2016-08-01
A general probabilistic prediction network is proposed for hydrological drought examination and environmental flow assessment. This network consists of three major components. First, we present the joint streamflow drought indicator (JSDI) to describe the hydrological dryness/wetness conditions. The JSDI is established based on a high-dimensional multivariate probabilistic model. In the second part, a drought-based environmental flow assessment method is introduced, which provides dynamic risk-based information about how much flow (the environmental flow target) is required for drought recovery and its likelihood under different hydrological drought initial situations. The final part involves estimating the conditional probability of achieving the required environmental flow under different precipitation scenarios according to the joint dependence structure between streamflow and precipitation. Three watersheds from different countries (Germany, China, and the United States) with varying sizes from small to large were used to examine the usefulness of this network. The results show that the JSDI can provide an assessment of overall hydrological dryness/wetness conditions and performs well in identifying both drought onset and persistence. This network also allows quantitative prediction of targeted environmental flow required for hydrological drought recovery and estimation of the corresponding likelihood. Moreover, the results confirm that the general network can estimate the conditional probability associated with the required flow under different precipitation scenarios. The presented methodology offers a promising tool for water supply planning and management and for drought-based environmental flow assessment. The network has no restrictions that would prevent it from being applied to other basins worldwide.
Impact of fracture network geometry on free convective flow patterns
NASA Astrophysics Data System (ADS)
Vujević, Katharina; Graf, Thomas; Simmons, Craig T.; Werner, Adrian D.
2014-09-01
The effect of fracture network geometry on free convection in fractured rock is studied using numerical simulations. We examine the structural properties of fracture networks that control the onset and strength of free convection and the patterns of density-dependent flow. Applicability of the equivalent porous medium approach (EPM) is also tested, and recommendations are given, for which situations the EPM approach is valid. To date, the structural properties of fracture networks that determine free convective flow are examined only in few, predominantly simplified regular fracture networks. We consider fracture networks containing continuous, discontinuous, orthogonal and/or inclined discrete fractures embedded in a low-permeability rock matrix. The results indicate that bulk permeability is not adequate to infer the occurrence and magnitude of free convection in fractured rock. Fracture networks can inhibit or promote convection depending on the fracture network geometry. Continuous fracture circuits are the crucial geometrical feature of fracture networks, because large continuous fracture circuits with a large vertical extent promote convection. The likelihood of continuous fracture circuits and thus of free convection increases with increasing fracture density and fracture length, but individual fracture locations may result in great deviances in strength of convection between statistically equivalent fracture networks such that prediction remains subject to large uncertainty.
River flow mass exponents with fractal channel networks and rainfall
Troutman, B.M.; Over, T.M.
2001-01-01
An important problem in hydrologic science is understanding how river flow is influenced by rainfall properties and drainage basin characteristics. In this paper we consider one approach, the use of mass exponents, in examining the relation of river flow to rainfall and the channel network, which provides the primary conduit for transport of water to the outlet in a large basin. Mass exponents, which characterize the power-law behavior of moments as a function of scale, are ideally suited for defining scaling behavior of processes that exhibit a high degree of variability or intermittency. The main result in this paper is an expression relating the mass exponent of flow resulting from an instantaneous burst of rainfall to the mass exponents of spatial rainfall and that of the network width function. Spatial rainfall is modeled as a random multiplicative cascade and the channel network as a recursive replacement tree; these fractal models reproduce certain types of self-similar behavior seen in actual rainfall and networks. It is shown that under these modeling assumptions the scaling behavior of flow mirrors that of rainfall if rainfall is highly variable in space, and on the other hand flow mirrors the structure of the network if rainfall is not so highly variable. ?? 2001 Elsevier Science Ltd. All rights reserved.
Flow Localization in Non-Linear Random Networks
NASA Astrophysics Data System (ADS)
Donev, Aleksandar; Phillip, Duxbury
2001-03-01
Local instabilities occur in the flow-potential characterstics of many complex materials, such as superconductors, dielectrics and porous materials. We describe a study of large networks in which each bond has a flow-potential characteristic having a threshold behavior. The thresholds vary randomly with values drawn from a variety of probability distributions. These large networks also exhibit a threshold-type response. The macroscopic onset occurs through a geometrical localization of the flow in the networks. For superconducting materials the critical current occurs when a surface of saturated arcs occurs transverse to the direction of flow. For porous materials on the other hand, at the macroscopic critical pressure a linear flow path begins across the network. We find these geometrical structures at the critical threshold using combinatorial graph algoritmhs (such as Dijkstra or push-relabel algorithms). The overall macroscopic problem is a convex, separable, minimal-cost network optimization problem. We have developed an efficient parallel algorithm for this problem and describe some preliminary results.
Introduction to Focus Issue: Complex network perspectives on flow systems
NASA Astrophysics Data System (ADS)
Donner, Reik V.; Hernández-García, Emilio; Ser-Giacomi, Enrico
2017-03-01
During the last few years, complex network approaches have demonstrated their great potentials as versatile tools for exploring the structural as well as dynamical properties of dynamical systems from a variety of different fields. Among others, recent successful examples include (i) functional (correlation) network approaches to infer hidden statistical interrelationships between macroscopic regions of the human brain or the Earth's climate system, (ii) Lagrangian flow networks allowing to trace dynamically relevant fluid-flow structures in atmosphere, ocean or, more general, the phase space of complex systems, and (iii) time series networks unveiling fundamental organization principles of dynamical systems. In this spirit, complex network approaches have proven useful for data-driven learning of dynamical processes (like those acting within and between sub-components of the Earth's climate system) that are hidden to other analysis techniques. This Focus Issue presents a collection of contributions addressing the description of flows and associated transport processes from the network point of view and its relationship to other approaches which deal with fluid transport and mixing and/or use complex network techniques.
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.
Optical disk based neural network
NASA Astrophysics Data System (ADS)
Lu, Taiwei; Choi, Kyusun; Wu, Shudong; Xu, Xin; Yu, Francis T. S.
1989-11-01
An optical disk (OD)-based optical neural network architecture for high-speed and large-capacity associative processing is proposed. The information storage by the OD is described, and an optical neural network using an OD for large-capacity storage of interconnection weight matrices (IWMs) is shown and discussed. The ways that optical interconnections are established between the IWM and the input pattern is shown, as is the way that the loop is closed. The operation of the OD in the network is examined.
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.
Building the Material Flow Networks of Aluminum in the 2007 U.S. Economy.
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.
Zubek, Julian; Denkiewicz, Michał; Barański, Juliusz; Wróblewski, Przemysław; Rączaszek-Leonardi, Joanna; Plewczynski, Dariusz
2017-01-01
This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems.
Denkiewicz, Michał; Barański, Juliusz; Wróblewski, Przemysław; Rączaszek-Leonardi, Joanna; Plewczynski, Dariusz
2017-01-01
This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems. PMID:28809957
The stationary flow in a heterogeneous compliant vessel network
NASA Astrophysics Data System (ADS)
Filoche, Marcel; Florens, Magali
2011-09-01
We introduce a mathematical model of the hydrodynamic transport into systems consisting in a network of connected flexible pipes. In each pipe of the network, the flow is assumed to be steady and one-dimensional. The fluid-structure interaction is described through tube laws which relate the pipe diameter to the pressure difference across the pipe wall. We show that the resulting one-dimensional differential equation describing the flow in the pipe can be exactly integrated if one is able to estimate averages of the Reynolds number along the pipe. The differential equation is then transformed into a non linear scalar equation relating pressures at both ends of the pipe and the flow rate in the pipe. These equations are coupled throughout the network with mass conservation equations for the flow and zero pressure losses at the branching points of the network. This allows us to derive a general model for the computation of the flow into very large inhomogeneous networks consisting of several thousands of flexible pipes. This model is then applied to perform numerical simulations of the human lung airway system at exhalation. The topology of the system and the tube laws are taken from morphometric and physiological data in the literature. We find good qualitative and quantitative agreement between the simulation results and flow-volume loops measured in real patients. In particular, expiratory flow limitation which is an essential characteristic of forced expiration is found to be well reproduced by our simulations. Finally, a mathematical model of a pathology (Chronic Obstructive Pulmonary Disease) is introduced which allows us to quantitatively assess the influence of a moderate or severe alteration of the airway compliances.
Executable Code Recognition in Network Flows Using Instruction Transition Probabilities
NASA Astrophysics Data System (ADS)
Kim, Ikkyun; Kang, Koohong; Choi, Yangseo; Kim, Daewon; Oh, Jintae; Jang, Jongsoo; Han, Kijun
The ability to recognize quickly inside network flows to be executable is prerequisite for malware detection. For this purpose, we introduce an instruction transition probability matrix (ITPX) which is comprised of the IA-32 instruction sets and reveals the characteristics of executable code's instruction transition patterns. And then, we propose a simple algorithm to detect executable code inside network flows using a reference ITPX which is learned from the known Windows Portable Executable files. We have tested the algorithm with more than thousands of executable and non-executable codes. The results show that it is very promising enough to use in real world.
Field-effect Flow Control in Polymer Microchannel Networks
NASA Technical Reports Server (NTRS)
Sniadecki, Nathan; Lee, Cheng S.; Beamesderfer, Mike; DeVoe, Don L.
2003-01-01
A new Bio-MEMS electroosmotic flow (EOF) modulator for plastic microchannel networks has been developed. The EOF modulator uses field-effect flow control (FEFC) to adjust the zeta potential at the Parylene C microchannel wall. By setting a differential EOF pumping rate in two of the three microchannels at a T-intersection with EOF modulators, the induced pressure at the intersection generated pumping in the third, field-free microchannel. The EOF modulators are able to change the magnitude and direction of the pressure pumping by inducing either a negative or positive pressure at the intersection. The flow velocity is tracked by neutralized fluorescent microbeads in the microchannels. The proof-of-concept of the EOF modulator described here may be applied to complex plastic ,microchannel networks where individual microchannel flow rates are addressable by localized induced-pressure pumping.
Field-effect Flow Control in Polymer Microchannel Networks
NASA Technical Reports Server (NTRS)
Sniadecki, Nathan; Lee, Cheng S.; Beamesderfer, Mike; DeVoe, Don L.
2003-01-01
A new Bio-MEMS electroosmotic flow (EOF) modulator for plastic microchannel networks has been developed. The EOF modulator uses field-effect flow control (FEFC) to adjust the zeta potential at the Parylene C microchannel wall. By setting a differential EOF pumping rate in two of the three microchannels at a T-intersection with EOF modulators, the induced pressure at the intersection generated pumping in the third, field-free microchannel. The EOF modulators are able to change the magnitude and direction of the pressure pumping by inducing either a negative or positive pressure at the intersection. The flow velocity is tracked by neutralized fluorescent microbeads in the microchannels. The proof-of-concept of the EOF modulator described here may be applied to complex plastic ,microchannel networks where individual microchannel flow rates are addressable by localized induced-pressure pumping.
Community discovery and information flow in networks
NASA Astrophysics Data System (ADS)
Huberman, Bernardo A.
2005-03-01
The dynamics of information within social groups is relevant to issues of productivity, innovation and the sorting out of useful ideas from the general chatter of a community. How information spreads and is aggregated determines the speed with which individuals and organizations can act and plan their future activities. This talk will describe new mechanisms for automatically identifying communities of practice within organizations and for elucidating the spread of information within those communities. Many of these mechanisms rely on the scale-free nature of social networks.
A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.
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.
A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks
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
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.
Ivanciuc, Ovidiu; Ivanciuc, Teodora; Klein, Douglas J
2013-06-01
Usual quantitative structure-activity relationship (QSAR) models are computed from unstructured input data, by using a vector of molecular descriptors for each chemical in the dataset. Another alternative is to consider the structural relationships between the chemical structures, such as molecular similarity, presence of certain substructures, or chemical transformations between compounds. We defined a class of network-QSAR models based on molecular networks induced by a sequence of substitution reactions on a chemical structure that generates a partially ordered set (or poset) oriented graph that may be used to predict various molecular properties with quantitative superstructure-activity relationships (QSSAR). The network-QSAR interpolation models defined on poset graphs, namely average poset, cluster expansion, and spline poset, were tested with success for the prediction of several physicochemical properties for diverse chemicals. We introduce the flow network QSAR, a new poset regression model in which the dataset of chemicals, represented as a reaction poset, is transformed into an oriented network of electrical resistances in which the current flow results in a potential at each node. The molecular property considered in the QSSAR model is represented as the electrical potential, and the value of this potential at a particular node is determined by the electrical resistances assigned to each edge and by a system of batteries. Each node with a known value for the molecular property is attached to a battery that sets the potential on that node to the value of the respective molecular property, and no external battery is attached to nodes from the prediction set, representing chemicals for which the values of the molecular property are not known or are intended to be predicted. The flow network QSAR algorithm determines the values of the molecular property for the prediction set of molecules by applying Ohm's law and Kirchhoff's current law to the poset
Lagrangian Flow networks: a new way to characterize transport and connectivity in geophysical flows
NASA Astrophysics Data System (ADS)
Ser-Giacomi, Enrico; Hernandez-Garcia, Emilio; Lopez, Cristobal; Rossi, Vincent; Vasile, Ruggero
2015-04-01
Water and air transport are among the basic processes shaping the climate of our planet. Heat and salinity fluxes change sea water density, and thus drive the global thermohaline circulation. Atmospheric winds force the ocean motion, and also transport moisture, heat or chemicals, impacting the regional climate. We describe transport among different regions of the ocean or the atmosphere by flow networks, giving a discrete and robust representation of the fluid advection dynamics. We use network-theory tools to gain insights into transport problem. Local and global features of the networks are extracted from many numerical experiments to give a time averaged description of the system. Classical concepts like dispersion, mixing and connectivity are finally related to a set of network-like objects contributing to build a "dictionary" between network measures and physical quantities in geophysical flows.
Evolution of karst conduit networks in transition from pressurised flow to free surface flow
NASA Astrophysics Data System (ADS)
Perne, M.; Covington, M. D.; Gabrovšek, F.
2014-06-01
We present a novel modelling approach to study the evolution of conduit networks in soluble rocks. Unlike the models presented so far, the model allows a transition from pressurised (pipe) flow to a free surface (open channel) flow in evolving discrete conduit networks. It calculates flow, solute transport and dissolutional enlargement within each time step and steps through time until a stable flow pattern establishes. The flow in each time step is calculated by calling the EPA Storm Water Management Model (EPA SWMM), which efficiently solves the 1-D Saint Venant equations in a network of conduits. We present several cases with low dip and sub-vertical networks to demonstrate mechanisms of flow pathway selection. In low dip models the inputs were randomly distributed to several junctions. The evolution of pathways progresses upstream: initially pathways linking outlets to the closest inputs evolve fastest because the gradient along these pathways is largest. When a pathway efficiently drains the available recharge, the head drop along the pathway attracts flow from the neighbouring upstream junctions and new connecting pathways evolve. The mechanism progresses from the output boundary inwards until all inputs are connected to the stable flow system. In the pressurised phase, each junction is drained by at least one conduit, but only one conduit remains active in the vadose phase. The selection depends on the initial geometry of a junction, initial distribution of diameters, the evolution in a pressurised regime, and on the dip of the conduits, which plays an important role in vadose entrenchment. In high dip networks, the vadose zone propagates downwards and inwards from the rim of the massif. When a network with randomly distributed initial diameters is supplied with concentrated recharge from the adjacent area, the sink point regresses up upstream along junctions connected to the prominent pathways. Large conductive structures provide deep penetration of high
Resistive Network Optimal Power Flow: Uniqueness and Algorithms
Tan, CW; Cai, DWH; Lou, X
2015-01-01
The optimal power flow (OPF) problem minimizes the power loss in an electrical network by optimizing the voltage and power delivered at the network buses, and is a nonconvex problem that is generally hard to solve. By leveraging a recent development on the zero duality gap of OPF, we propose a second-order cone programming convex relaxation of the resistive network OPF, and study the uniqueness of the optimal solution using differential topology, especially the Poincare-Hopf Index Theorem. We characterize the global uniqueness for different network topologies, e.g., line, radial, and mesh networks. This serves as a starting point to design distributed local algorithms with global behaviors that have low complexity, are computationally fast, and can run under synchronous and asynchronous settings in practical power grids.
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
Relationship between supergranulation flows, magnetic cancellation and network flares
NASA Astrophysics Data System (ADS)
Attie, R.; Innes, D. E.; Solanki, S. K.; Glassmeier, K. H.
2016-11-01
Context. Photospheric flows create a network of often mixed-polarity magnetic field in the quiet Sun, where small-scale eruptions and network flares are commonly seen. Aims: The aim of this paper is (1) to describe the characteristics of the flows that lead to these energy releases, (2) to quantify the energy build up due to photospheric flows acting on the magnetic field, and (3) to assess its contribution to the energy of small-scale, short-lived X-ray flares in the quiet Sun. Methods: We used photospheric and X-ray data from the SoHO and Hinode spacecraft combined with tracking algorithms to analyse the evolution of five network flares. The energy of the X-ray emitting thermal plasma is compared with an estimate of the energy built up due to converging and sheared flux. Results: Quiet-Sun network flares occur above sites of converging opposite-polarity magnetic flux that are often found on the outskirts of network cell junctions, sometimes with observable vortex-like motion. In all studied flares the thermal energy was more than an order of magnitude higher than the magnetic free energy of the converging flux model. The energy in the sheared field was always higher than in the converging flux but still lower than the thermal energy. Conclusions: X-ray network flares occur at sites of magnetic energy dissipation. The energy is probably built up by supergranular flows causing systematic shearing of the magnetic field. This process appears more efficient near the junction of the network lanes. Since this work relies on 11 case studies, our results call for a follow-up statistical analysis to test our hypothesis throughout the quiet Sun.
Toward an optimal design principle in symmetric and asymmetric tree flow networks.
Miguel, Antonio F
2016-01-21
Fluid flow in tree-shaped networks plays an important role in both natural and engineered systems. This paper focuses on laminar flows of Newtonian and non-Newtonian power law fluids in symmetric and asymmetric bifurcating trees. Based on the constructal law, we predict the tree-shaped architecture that provides greater access to the flow subjected to the total network volume constraint. The relationships between the sizes of parent and daughter tubes are presented both for symmetric and asymmetric branching tubes. We also approach the wall-shear stresses and the flow resistance in terms of first tube size, degree of asymmetry between daughter branches, and rheological behavior of the fluid. The influence of tubes obstructing the fluid flow is also accounted for. The predictions obtained by our theory-driven approach find clear support in the findings of previous experimental studies. Copyright © 2015 Elsevier Ltd. All rights reserved.
Regional hydrologic mapping of flows in stream networks
NASA Astrophysics Data System (ADS)
Saghafian, Bahram
2009-10-01
River low flow (LF) is an important hydrological characteristic used in management of the quantity and quality of water resources. A common way to transpose at-station low flow quantiles to ungaged locations involves development of regional multivariate regression models. In site selection studies of water control facilities, need arises to automate the mapping of LF characteristics onto the stream network in a continuous manner. This would allow estimation of LF at any desired ungaged location. A mapping algorithm is introduced that determines the value of regression model's independent variables (input components) over the drainage area of each stream pixel and calculates the flow (output). The mapping algorithm relies mainly on the digital elevation model (DEM) and its derivatives, such as flow direction and flow accumulation. Furthermore, the contribution of each independent variable of the regional model to the total flow may be plotted to represent the flow profiles along the streams. A case study involving LF mapping in rivers of Gilan province, Iran, where LF quantiles are required for issuing water withdrawal permits as well as maintaining water quality standards, is also described in the paper. Overall, the maps and profiles of flow statistics in the region of interest provide convenient visualization and assessment tools for water resource and environmental engineers. The transposition algorithm may also be applied in regional mapping of other flow characteristics such as flood or average flows.
Plume base flow simulation technology
NASA Technical Reports Server (NTRS)
Roberts, B. B.; Wallace, R. O.; Sims, J. L.
1983-01-01
A combined analytical/empirical approach was studied in an effort to define the plume simulation parameters for base flow. For design purposes, rocket exhaust simulation (i.e., plume simulation) is determined by wind tunnel testing. Cold gas testing was concluded to be a cost and schedule effective data base of substantial scope. The results fell short of the target, although work conducted was conclusive and advanced the state of the art. Comparisons of wind tunnel predictions with Space Transportation System (STS) flight data showed considerable differences. However, a review of the technology program data base has yielded an additional parameter that may correlate flight and cold gas test data. Data from the plume technology program and the NASA test flights are presented to substantiate the proposed simulation parameters.
Encounter Complexes for Clustering Network Flow (Briefing Charts)
2015-01-01
JAN 2015 2. REPORT TYPE 3. DATES COVERED 00-00-2015 to 00-00-2015 4. TITLE AND SUBTITLE Encounter Complexes For Clustering Network Flow 5a...2015. 14. ABSTRACT 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT Same as Report (SAR) 18. NUMBER OF PAGES 37
Any Time, Every Place: The Networked Societies of War Fighters in a Battlespace of Flows
2015-10-01
incongruous with place -based conceptions of work . An employee might be more productive by splitting a would-be two-hour commute between additional work ...September–October 2015 | 15 Any Time, Every Place The Networked Societies of War Fighters in a Battlespace of Flows Maj Dave Blair, USAF In a world...REPORT DATE OCT 2015 2. REPORT TYPE 3. DATES COVERED 00-00-2015 to 00-00-2015 4. TITLE AND SUBTITLE Any Time, Every Place : The Networked
NASA Astrophysics Data System (ADS)
Gao, Zhong-Ke; Dang, Wei-Dong; Xue, Le; Zhang, Shan-Shan
2017-03-01
Characterizing the flow structure underlying the evolution of oil-in-water bubbly flow remains a contemporary challenge of great interests and complexity. In particular, the oil droplets dispersing in a water continuum with diverse size make the study of oil-in-water bubbly flow really difficult. To study this issue, we first design a novel complex impedance sensor and systematically conduct vertical oil-water flow experiments. Based on the multivariate complex impedance measurements, we define modalities associated with the spatial transient flow structures and construct modality transition-based network for each flow condition to study the evolution of flow structures. In order to reveal the unique flow structures underlying the oil-in-water bubbly flow, we filter the inferred modality transition-based network by removing the edges with small weight and resulting isolated nodes. Then, the weighted clustering coefficient entropy and weighted average path length are employed for quantitatively assessing the original network and filtered network. The differences in network measures enable to efficiently characterize the evolution of the oil-in-water bubbly flow structures.
Experiments and network model of flow of oil-water emulsion in porous media
NASA Astrophysics Data System (ADS)
Romero, Mao Illich; Carvalho, Marcio S.; Alvarado, Vladimir
2011-10-01
Transport of emulsions in porous media is relevant to several subsurface applications. Many enhanced oil recovery (EOR) processes lead to emulsion formation and as a result conformance originating in the flow of a dispersed phase may arise. In some EOR processes, emulsion is injected directly as a mobility control agent. Modeling the flow of emulsion in porous media is extremely challenging due to the complex nature of the associated flows and numerous interfaces. The descriptions based on effective viscosity are not valid when the drop size is of the same order of magnitude as the pore-throat characteristic length scale. An accurate model of emulsion flow through porous media should describe this local change in mobility. The available filtration models do not take into account the variation of the straining and capturing rates with the local capillary number. In this work, we present experiments of emulsion flow through sandstone cores of different permeability and a first step on a capillary network model that uses experimentally determined pore-level constitutive relationships between flow rate and pressure drop in constricted capillaries to obtain representative macroscopic flow behavior emerging from microscopic emulsion flow at the pore level. A parametric analysis is conducted to study the effect of the permeability and dispersed phase droplet size on the flow response to emulsion flooding in porous media. The network model predictions qualitatively describe the oil-water emulsion flow behavior observed in the experiments.
Quantification of blood flow and topology in developing vascular networks.
Kloosterman, Astrid; Hierck, Beerend; Westerweel, Jerry; Poelma, Christian
2014-01-01
Since fluid dynamics plays a critical role in vascular remodeling, quantification of the hemodynamics is crucial to gain more insight into this complex process. Better understanding of vascular development can improve prediction of the process, and may eventually even be used to influence the vascular structure. In this study, a methodology to quantify hemodynamics and network structure of developing vascular networks is described. The hemodynamic parameters and topology are derived from detailed local blood flow velocities, obtained by in vivo micro-PIV measurements. The use of such detailed flow measurements is shown to be essential, as blood vessels with a similar diameter can have a large variation in flow rate. Measurements are performed in the yolk sacs of seven chicken embryos at two developmental stages between HH 13+ and 17+. A large range of flow velocities (1 µm/s to 1 mm/s) is measured in blood vessels with diameters in the range of 25-500 µm. The quality of the data sets is investigated by verifying the flow balances in the branching points. This shows that the quality of the data sets of the seven embryos is comparable for all stages observed, and the data is suitable for further analysis with known accuracy. When comparing two subsequently characterized networks of the same embryo, vascular remodeling is observed in all seven networks. However, the character of remodeling in the seven embryos differs and can be non-intuitive, which confirms the necessity of quantification. To illustrate the potential of the data, we present a preliminary quantitative study of key network topology parameters and we compare these with theoretical design rules.
NASA Astrophysics Data System (ADS)
Jia, Pin; Cheng, Linsong; Huang, Shijun; Wu, Yonghui
2016-06-01
This paper presents a semi-analytical model for the flow behavior of naturally fractured formations with multi-scale fracture networks. The model dynamically couples an analytical dual-porosity model with a numerical discrete fracture model. The small-scale fractures with the matrix are idealized as a dual-porosity continuum and an analytical flow solution is derived based on source functions in Laplace domain. The large-scale fractures are represented explicitly as the major fluid conduits and the flow is numerically modeled, also in Laplace domain. This approach allows us to include finer details of the fracture network characteristics while keeping the computational work manageable. For example, the large-scale fracture network may have complex geometry and varying conductivity, and the computations can be done at predetermined, discrete times, without any grids in the dual-porosity continuum. The validation of the semi-analytical model is demonstrated in comparison to the solution of ECLIPSE reservoir simulator. The simulation is fast, gridless and enables rapid model setup. On the basis of the model, we provide detailed analysis of the flow behavior of a horizontal production well in fractured reservoir with multi-scale fracture networks. The study has shown that the system may exhibit six flow regimes: large-scale fracture network linear flow, bilinear flow, small-scale fracture network linear flow, pseudosteady-state flow, interporosity flow and pseudoradial flow. During the first four flow periods, the large-scale fracture network behaves as if it only drains in the small-scale fracture network; that is, the effect of the matrix is negligibly small. The characteristics of the bilinear flow and the small-scale fracture network linear flow are predominantly determined by the dimensionless large-scale fracture conductivity. And low dimensionless fracture conductivity will generate large pressure drops in the large-scale fractures surrounding the wellbore. With
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...
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...
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...
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...
A time-shift scheduling-enabled optical flow switched network architecture and its performance.
Huang, Shaowei; Katsukawa, Yosuke; Tajima, Akio; Araki, Soichiro; Kitayama, Ken-ichi
2011-12-19
Optical flow switching (OFS) has been proposed as a simple and cost-effective transport technology for users with large transactions (>1 second). In previous studies, a fast wavelength reservation method was deployed for flow transmission in OFS-based networks. However, reserving a single wavelength for users with small transactions encounters a very common problem: inefficient wavelength utilization. In this paper, a flow transmission cycle is introduced to each wavelength, and each cycle consists of multiple slots, so that flows of different transactions can be multiplexed onto a single wavelength. It is assumed that inter-metropolitan area network (MAN) traffic is transported over wide area network (WAN). A global time-shift scheduling methodology taking into account propagation delays in MAN is designed to avoid potential contentions occurring among different flows which are carried by the same wavelength in WAN. The contributions of this paper are, first it provides a new OFS network architecture which can achieve better throughput and average wavelength utilization performance without losing the feature of simple transport structure provided by OFS; second, it is the first time that issues of how OFS networks are managed and controlled are addressed from a system point of view.
The sensitivity of stratified flow stability to base flow modifications
NASA Astrophysics Data System (ADS)
Chen, Kevin; Spedding, Geoffrey
2016-11-01
We present a novel theory that determines the sensitivity of linear stability to changes in the density or velocity of a base flow. The sensitivity is based on global direct and adjoint eigenmodes of the linearized Boussinesq equation, and is inspired by constant-density sensitivity analysis. The theory can be applied broadly to incompressible flows with small density variations, but it specifically provides new insight into the stability of density-stratified flows. Examples are given for the flows around a transverse thin plate at a Reynolds number of 30, a Prandtl number of 7.19, and Froude numbers of ∞ and 1. In the unstratified flow, the sensitivity is largest in the recirculation bubble; the stratified flow, however, exhibits high sensitivity in regions immediately upstream and downstream of the bluff body. Supported by the Viterbi Postdoctoral Fellowship, provided by the Viterbi School of Engineering at the University of Southern California.
NASA Astrophysics Data System (ADS)
Wu, Yating; Kuang, Bin; Wang, Tao; Zhang, Qianwu; Wang, Min
2015-12-01
This paper presents a minimum cost maximum flow (MCMF) based upstream bandwidth allocation algorithm, which supports differentiated QoS for orthogonal frequency division multiple access passive optical networks (OFDMA-PONs). We define a utility function as the metric to characterize the satisfaction degree of an ONU on the obtained bandwidth. The bandwidth allocation problem is then formulated as maximizing the sum of the weighted total utility functions of all ONUs. By constructing a flow network graph, we obtain the optimized bandwidth allocation using the MCMF algorithm. Simulation results show that the proposed scheme improves the performance in terms of mean packet delay, packet loss ratio and throughput.
Go with the Flow: Selling Social Networking
ERIC Educational Resources Information Center
Boule, Michelle
2008-01-01
A librarian may be comfortable with the idea of using a blog, wiki, or other social application with students, but other stakeholders in his or her community may not be so sure. Any technology maverick looking to sell the use of Web-based applications will, at some point, have to make the case. This article presents some of the most common…
Unified pipe network method for simulation of water flow in fractured porous rock
NASA Astrophysics Data System (ADS)
Ren, Feng; Ma, Guowei; Wang, Yang; Li, Tuo; Zhu, Hehua
2017-04-01
Rock masses are often conceptualized as dual-permeability media containing fractures or fracture networks with high permeability and porous matrix that is less permeable. In order to overcome the difficulties in simulating fluid flow in a highly discontinuous dual-permeability medium, an effective unified pipe network method is developed, which discretizes the dual-permeability rock mass into a virtual pipe network system. It includes fracture pipe networks and matrix pipe networks. They are constructed separately based on equivalent flow models in a representative area or volume by taking the advantage of the orthogonality of the mesh partition. Numerical examples of fluid flow in 2-D and 3-D domain including porous media and fractured porous media are presented to demonstrate the accuracy, robustness, and effectiveness of the proposed unified pipe network method. Results show that the developed method has good performance even with highly distorted mesh. Water recharge into the fractured rock mass with complex fracture network is studied. It has been found in this case that the effect of aperture change on the water recharge rate is more significant in the early stage compared to the fracture density change.
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
NASA Astrophysics Data System (ADS)
Zhou, Bao-Rong; Liu, Si-Liang; Zhang, Yong-Jun; Yi, Ying-Qi; Lin, Xiao-Ming
2017-05-01
To mitigate the impact on the distribution networks caused by the stochastic characteristic and high penetration of photovoltaic, a multi-objective optimal power flow model is proposed in this paper. The regulation capability of capacitor, inverter of photovoltaic and energy storage system embedded in active distribution network are considered to minimize the expected value of active power the T loss and probability of voltage violation in this model. Firstly, a probabilistic power flow based on cumulant method is introduced to calculate the value of the objectives. Secondly, NSGA-II algorithm is adopted for optimization to obtain the Pareto optimal solutions. Finally, the best compromise solution can be achieved through fuzzy membership degree method. By the multi-objective optimization calculation of IEEE34-node distribution network, the results show that the model can effectively improve the voltage security and economy of the distribution network on different levels of photovoltaic penetration.
Availability Improvement of Layer 2 Seamless Networks Using OpenFlow
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
The prescribed output pattern regulates the modular structure of flow networks
NASA Astrophysics Data System (ADS)
Emanuel Beber, Moritz; Armbruster, Dieter; Hütt, Marc-Thorsten
2013-11-01
Modules are common functional and structural properties of many social, technical and biological networks. Especially for biological systems it is important to understand how modularity is related to function and how modularity evolves. It is known that time-varying or spatially organized goals can lead to modularity in a simulated evolution of signaling networks. Here, we study a minimal model of material flow in networks. We discuss the relation between the shared use of nodes, i.e., the cooperativity of modules, and the orthogonality of a prescribed output pattern. We study the persistence of cooperativity through an evolution of robustness against local damages. We expect the results to be valid for a large class of flow-based biological and technical networks. Supplementary material in the form of one pdf file available from the Journal web page at http://dx.doi.org/10.1140/epjb/e2013-40672-3
The prescribed output pattern regulates the modular structure of flow networks
NASA Astrophysics Data System (ADS)
Beber, Moritz Emanuel; Armbruster, Dieter; Hütt, Marc-Thorsten
2013-11-01
Modules are common functional and structural properties of many social, technical and biological networks. Especially for biological systems it is important to understand how modularity is related to function and how modularity evolves. It is known that time-varying or spatially organized goals can lead to modularity in a simulated evolution of signaling networks. Here, we study a minimal model of material flow in networks. We discuss the relation between the shared use of nodes, i.e., the cooperativity of modules, and the orthogonality of a prescribed output pattern. We study the persistence of cooperativity through an evolution of robustness against local damages. We expect the results to be valid for a large class of flow-based biological and technical networks.
Availability improvement of layer 2 seamless networks using OpenFlow.
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.
Network fingerprint: a knowledge-based characterization of biomedical networks
Cui, Xiuliang; He, Haochen; He, Fuchu; Wang, Shengqi; Li, Fei; Bo, Xiaochen
2015-01-01
It can be difficult for biomedical researchers to understand complex molecular networks due to their unfamiliarity with the mathematical concepts employed. To represent molecular networks with clear meanings and familiar forms for biomedical researchers, we introduce a knowledge-based computational framework to decipher biomedical networks by making systematic comparisons to well-studied “basic networks”. A biomedical network is characterized as a spectrum-like vector called “network fingerprint”, which contains similarities to basic networks. This knowledge-based multidimensional characterization provides a more intuitive way to decipher molecular networks, especially for large-scale network comparisons and clustering analyses. As an example, we extracted network fingerprints of 44 disease networks in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The comparisons among the network fingerprints of disease networks revealed informative disease-disease and disease-signaling pathway associations, illustrating that the network fingerprinting framework will lead to new approaches for better understanding of biomedical networks. PMID:26307246
Cheung, Kit; Schultz, Simon R.; Luk, Wayne
2016-01-01
NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation. PMID:26834542
Cheung, Kit; Schultz, Simon R; Luk, Wayne
2015-01-01
NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.
Effect of fracture network geometry on density-driven flow in fractured porous rock
NASA Astrophysics Data System (ADS)
Vujevic, Katharina; Graf, Thomas
2013-04-01
Density-driven flow can be a highly efficient transport mechanism in hydrogeological systems, especially if head gradients as a driving force for groundwater movement are absent. Unstable density layering may lead to variable-density, free-convective flow. Convection cells may form whose number and shape depends on the prevailing concentration and temperature gradients. The presence of open fractures may complicate the free convective flow pattern because fractures represent preferential pathways where water flow velocities can be considerably larger than in the rock matrix. Therefore, the purpose of this study is to provide insight into the structural properties of fracture networks that determine flow and transport patterns and to make a statement on the applicability of the equivalent porous medium approach (EPM). We systematically study free convective flow in continuous, discontinuous, orthogonal and inclined fracture networks embedded in a low-permeability rock matrix. Layer stability and convection patterns for different fracture networks are compared to each other and to an unfractured base case representing an EPM. We examine rates of solute transport by monitoring the mass flux at the solute source and relate it to the critical structural properties of the fracture networks. Simulations are performed using the numerical variable-density groundwater flow and transport model HydroGeoSphere. Fractures are represented as discrete fractures, whose geometric properties are explicitly defined. Fracture permeability is calculated using the cubic law. Results show that for free convective flow, the EPM approach is not able to reliably represent a fractured porous medium if fracture permeability is more than 5 orders of magnitude larger than matrix permeability. Nonetheless the EPM approach can be a reasonable approximation if the fracture network (i) evenly covers the simulated rock, (ii) is of high fracture density, (iii) is well-connected, (iv) contains
Tracking Inter-Regional Carbon Flows: A Hybrid Network Model.
Chen, Shaoqing; Chen, Bin
2016-05-03
The mitigation of anthropogenic carbon emissions has moved beyond the local scale because they diffuse across boundaries, and the consumption that triggers emissions has become regional and global. A precondition of effective mitigation is to explicitly assess inter-regional transfer of emissions. This study presents a hybrid network model to track inter-regional carbon flows by combining network analysis and input-output analysis. The direct, embodied, and controlled emissions associated with regions are quantified for assessing various types of carbon flow. The network-oriented metrics called "controlled emissions" is proposed to cover the amount of carbon emissions that can be mitigated within a region by adjusting its consumption. The case study of the Jing-Jin-Ji Area suggests that CO2 emissions embodied in products are only partially controlled by a region from a network perspective. Controlled carbon accounted for about 70% of the total embodied carbon flows, while household consumption only controlled about 25% of Beijing's emissions, much lower than its proportion of total embodied carbon. In addition to quantifying emissions, the model can pinpoint the dominant processes and sectors of emissions transfer across regions. This technique is promising for searching efficient pathways of coordinated emissions control across various regions connected by trade.
Simulation model for flow of neutrophils in pulmonary capillary network.
Shirai, Atsushi; Fujita, Ryo; Hayase, Toshiyuki
2005-01-01
The concentration of neutrophils in the pulmonary microvasculature is higher than in systemic large vessels. It is thought that the high concentration of neutrophils facilitates their effective recruitment to sites of inflammation. Thus, in order to understand the role of neutrophils in the immune system, it is important to clarify their flow characteristics in the pulmonary microvasculature. In previous studies, we numerically investigated the motion of a neutrophil through a single capillary segment modeled by a moderate axisymmetric constriction in a straight pipe, developing a mathematical model for the prediction of the transit time of the cell through the segment. In the present study, this model was extended for application to network simulation of the motion of neutrophils. First, we numerically investigated shape recovery of a neutrophil after expulsion from a narrow capillary segment. This process was modeled in two different phases: elastic recovery and viscous recovery. The resulting model was combined with the previously developed models to simulate motion of the cells and plasma flow in a capillary network. A numerical simulation of the motion of neutrophils and plasma flow in a simple lattice capillary network showed that neutrophils were widely dispersed in the network with an increased concentration.
Host Event Based Network Monitoring
Jonathan Chugg
2013-01-01
The purpose of INL’s research on this project is to demonstrate the feasibility of a host event based network monitoring tool and the effects on host performance. Current host based network monitoring tools work on polling which can miss activity if it occurs between polls. Instead of polling, a tool could be developed that makes use of event APIs in the operating system to receive asynchronous notifications of network activity. Analysis and logging of these events will allow the tool to construct the complete real-time and historical network configuration of the host while the tool is running. This research focused on three major operating systems commonly used by SCADA systems: Linux, WindowsXP, and Windows7. Windows 7 offers two paths that have minimal impact on the system and should be seriously considered. First is the new Windows Event Logging API, and, second, Windows 7 offers the ALE API within WFP. Any future work should focus on these methods.
Advanced Neural Network Modeling of Synthetic Jet Flow Fields
2006-03-01
The purpose of this research was to continue development of a neural network -based, lumped deterministic source term (LDST) approximation module for...main exploration involved the grid sensitivity of the neural network model. A second task was originally planned on the portability of the approach to
Invulnerability study of transit network based on complex network
NASA Astrophysics Data System (ADS)
Wang, Kui; Fu, Xiufen
2017-05-01
In order to ensure the normal operation of urban transit network (abbreviated as UTN) and improve the operating efficiency of UTN, this paper takes UTN of Guangzhou as an example, analyzes the topology characteristics of this transit network, studies the invulnerability of the network under random attack and deliberate attack. The simulation results show that under random attack and under attack based on node degree, continuously removing the number of nodes is almost no effect on network invulnerability, but under attack based on the node betweenness and efficiency, the number of consecutive removed nodes is less, the network invulnerability is worse; Influence on network invulnerability by attack based on node betweenness is more significant than influence on network invulnerability by both random attack and attack based on node degree and efficiency.
Regional myocardial flow heterogeneity explained with fractal networks
VAN BEEK, JOHANNES H. G. M.; ROGER, STEPHEN A.; BASSINGTHWAIGHTE, JAMES B.
2010-01-01
There is explain how the distribution of flow broadens with an increase in the spatial resolution of the measurement, we developed fractal models for vascular networks. A dichotomous branching network of vessels represents the arterial tree and connects to a similar venous network. A small difference in vessel lengths and radii between the two daughter vessels, with the same degree of asymmetry at each branch generation, predicts the dependence of the relative dispersion (mean ± SD) on spatial resolution of the perfusion measurement reasonably well. When the degree of asymmetry increases with successive branching, a better fit to data on sheep and baboons results. When the asymmetry is random, a satisfactory fit is found. These models show that a difference in flow of 20% between the daughter vessels at a branch point gives a relative dispersion of flow of ~30% when the heart is divided into 100–200 pieces. Although these simple models do not represent anatomic features accurately, they provide valuable insight on the heterogeneity of flow within the heart. PMID:2589520
NASA Astrophysics Data System (ADS)
Lemouzy, B.; Garnier, J.-C.; Neufeld, N.
2011-02-01
The goal of the LHCb readout upgrade is to accelerate the DAQ to 40 MHz. Such a DAQ system will certainly employ 10 Gigabit or similar technologies and might also need new networking protocols such as a customized, light-weight TCP or more specialized protocols. A test module is being implemented to be integrated in the existing LHCb infrastructure. It is a multiple 10-Gigabit traffic generator, driven by a Stratix IV FPGA, and flexible enough to generate LHCb's raw data packets. Traffic data are either internally generated or read from external storage via the network. We have implemented a light-weight industry standard protocol ATA over Ethernet (AoE) and we present an outlook of using a file-system on these network-exported disk-drivers.
Geometry of channel networks incised by subsurface flow
NASA Astrophysics Data System (ADS)
Rothman, D.; Cohen, Y.; Devauchelle, O.; Seybold, H. F.; Yi, R.
2016-12-01
When groundwater returns to the surface at springs, the material atthe surface may erode and channels may be incised. Over time,ramified networks can form. Alternatively, water flowing over landcan also incise channels. Networks formed by overland flow have beenmore widely studied, but, paradoxically, the geometric aspects ofgroundwater-fed networks, which depend only weakly on topography, maybe more straightforwardly understood from elementary fluid mechanics.Here we focus on two aspects of the geometry of growinggroundwater-fed networks: the direction in which channels advanceand the angle at which they branch. First, using a conventionaltwo-dimensional approximation of the groundwater flow, we remark thatthe direction taken by moving channel tips may be equivalentlyunderstood from the maintenance of local symmetry in the groundwaterfield, the maximization of flux to tips, or motion along thegroundwater streamline into tips. Next, we use these ideas toshow that a bifurcated tip that grows in the predicted directionsresults in a branching angle of 2 pi/5 = 72 degrees. Our studies of astream network on the Florida Panhandle that is unequivocally drivenby subsurface flow accords well with this prediction. We also presentthe results of an empirical study of branching angles throughout thecontiguous United States. Our results show that in humid climates,branching angles appear to asymptotically approach 72 degrees as theso-called aridity index--the ratio of rainfall rate to potentialevapotranspiration--increases. The tendency toward this angle foraridity indicies greater than one is evident over approximatelyone-half of the country, suggesting that groundwater may play asignificant role in channelization processes wherever climates aresufficiently humid.
Flow distribution in parallel microfluidic networks and its effect on concentration gradient
Guermonprez, Cyprien; Michelin, Sébastien; Baroud, Charles N.
2015-01-01
The architecture of microfluidic networks can significantly impact the flow distribution within its different branches and thereby influence tracer transport within the network. In this paper, we study the flow rate distribution within a network of parallel microfluidic channels with a single input and single output, using a combination of theoretical modeling and microfluidic experiments. Within the ladder network, the flow rate distribution follows a U-shaped profile, with the highest flow rate occurring in the initial and final branches. The contrast with the central branches is controlled by a single dimensionless parameter, namely, the ratio of hydrodynamic resistance between the distribution channel and the side branches. This contrast in flow rates decreases when the resistance of the side branches increases relative to the resistance of the distribution channel. When the inlet flow is composed of two parallel streams, one of which transporting a diffusing species, a concentration variation is produced within the side branches of the network. The shape of this concentration gradient is fully determined by two dimensionless parameters: the ratio of resistances, which determines the flow rate distribution, and the Péclet number, which characterizes the relative speed of diffusion and advection. Depending on the values of these two control parameters, different distribution profiles can be obtained ranging from a flat profile to a step distribution of solute, with well-distributed gradients between these two limits. Our experimental results are in agreement with our numerical model predictions, based on a simplified 2D advection-diffusion problem. Finally, two possible applications of this work are presented: the first one combines the present design with self-digitization principle to encapsulate the controlled concentration in nanoliter chambers, while the second one extends the present design to create a continuous concentration gradient within an open flow
Survivability of public transit network based on network structure entropy
NASA Astrophysics Data System (ADS)
Fu, Bai-Bai; Zhang, Lin; Li, Shu-Bin; Li, Yun-Xuan
2015-01-01
In this work, we have collected 195 bus routes and 1433 bus stations of Jinan city as sample date to build up the public transit geospatial network model by applying space L method, until May 2014. Then, by analyzing the topological properties of public transit geospatial network model, which include degree and degree distribution, average shortest path length, clustering coefficient and betweenness, we get the conclusion that public transit network is a typical complex network with scale-free and small-world characteristics. Furthermore, in order to analyze the survivability of public transit network, we define new network structure entropy based on betweenness importance, and prove its correctness by giving that the new network structure entropy has the same statistical characteristics with network efficiency. Finally, the "inflexion zone" is discovered, which can be taken as the momentous indicator to determine the public transit network failure.
Network Flow Simulation of Fluid Transients in Rocket Propulsion Systems
NASA Technical Reports Server (NTRS)
Bandyopadhyay, Alak; Hamill, Brian; Ramachandran, Narayanan; Majumdar, Alok
2011-01-01
Fluid transients, also known as water hammer, can have a significant impact on the design and operation of both spacecraft and launch vehicle propulsion systems. These transients often occur at system activation and shutdown. The pressure rise due to sudden opening and closing of valves of propulsion feed lines can cause serious damage during activation and shutdown of propulsion systems. During activation (valve opening) and shutdown (valve closing), pressure surges must be predicted accurately to ensure structural integrity of the propulsion system fluid network. In the current work, a network flow simulation software (Generalized Fluid System Simulation Program) based on Finite Volume Method has been used to predict the pressure surges in the feed line due to both valve closing and valve opening using two separate geometrical configurations. The valve opening pressure surge results are compared with experimental data available in the literature and the numerical results compared very well within reasonable accuracy (< 5%) for a wide range of inlet-to-initial pressure ratios. A Fast Fourier Transform is preformed on the pressure oscillations to predict the various modal frequencies of the pressure wave. The shutdown problem, i.e. valve closing problem, the simulation results are compared with the results of Method of Characteristics. Most rocket engines experience a longitudinal acceleration, known as "pogo" during the later stage of engine burn. In the shutdown example problem, an accumulator has been used in the feed system to demonstrate the "pogo" mitigation effects in the feed system of propellant. The simulation results using GFSSP compared very well with the results of Method of Characteristics.
Ultrasensitive quantification of cerebral capillary flow networks and dynamics
NASA Astrophysics Data System (ADS)
You, Jiang; Park, Ki; Du, Congwu; Pan, Yingtian
2015-03-01
Ultra-high resolution optical Doppler coherence tomography (μODT) is a promising tool for brain functional imaging. However, its sensitivity for detecting slow flows in capillary beds may limit its utility in visualizing and quantifying subtle changes in brain microcirculation. To address this limitation, we developed a novel method called contrast-enhanced μODT (c-μODT) in which intralipid is injected into mouse tail vein to enhance μODT detection sensitivity. We demonstrate that after intralipid injection, the flow detection sensitivity of μODT is dramatically enhanced by 230% as quantified by the fill factor (FF) of microvasculature. More importantly, we show that c-μODT preserves the quantitative properties for flow imaging, i.e., showing a comparable change ratio of hypercapnia-induced flow increase in the capillary network before and after injecting intralipid.
NASA Astrophysics Data System (ADS)
Garbin, Silvia; Alessi Celegon, Elisa; Fanton, Pietro; Botter, Gianluca
2017-04-01
The temporal variability of river flow regime is a key feature structuring and controlling fluvial ecological communities and ecosystem processes. In particular, streamflow variability induced by climate/landscape heterogeneities or other anthropogenic factors significantly affects the connectivity between streams with notable implication for river fragmentation. Hydrologic connectivity is a fundamental property that guarantees species persistence and ecosystem integrity in riverine systems. In riverine landscapes, most ecological transitions are flow-dependent and the structure of flow regimes may affect ecological functions of endemic biota (i.e., fish spawning or grazing of invertebrate species). Therefore, minimum flow thresholds must be guaranteed to support specific ecosystem services, like fish migration, aquatic biodiversity and habitat suitability. In this contribution, we present a probabilistic approach aiming at a spatially-explicit, quantitative assessment of hydrologic connectivity at the network-scale as derived from river flow variability. Dynamics of daily streamflows are estimated based on catchment-scale climatic and morphological features, integrating a stochastic, physically based approach that accounts for the stochasticity of rainfall with a water balance model and a geomorphic recession flow model. The non-exceedance probability of ecologically meaningful flow thresholds is used to evaluate the fragmentation of individual stream reaches, and the ensuing network-scale connectivity metrics. A multi-dimensional Poisson Process for the stochastic generation of rainfall is used to evaluate the impact of climate signature on reach-scale and catchment-scale connectivity. The analysis shows that streamflow patterns and network-scale connectivity are influenced by the topology of the river network and the spatial variability of climatic properties (rainfall, evapotranspiration). The framework offers a robust basis for the prediction of the impact of
Fluid Flow Induced Calcium Response in Bone Cell Network
Huo, Bo; Lu, Xin L.; Hung, Clark T.; Costa, Kevin D.; Xu, Qiaobing; Whitesides, George M.; Guo, X. Edward
2010-01-01
In our previous work, bone cell networks with controlled spacing and functional intercellular gap junctions had been successfully established by using microcontact printing and self assembled monolayers technologies [Guo, X. E., E. Takai, X. Jiang, Q. Xu, G. M. Whitesides, J. T. Yardley, C. T. Hung, E. M. Chow, T. Hantschel, and K. D. Costa. Mol. Cell. Biomech. 3:95–107, 2006]. The present study investigated the calcium response and the underlying signaling pathways in patterned bone cell networks exposed to a steady fluid flow. The glass slides with cell networks were separated into eight groups for treatment with specific pharmacological agents that inhibit pathways significant in bone cell calcium signaling. The calcium transients of the network were recorded and quantitatively evaluated with a set of network parameters. The results showed that 18α-GA (gap junction blocker), suramin (ATP inhibitor), and thapsigargin (depleting intracellular calcium stores) significantly reduced the occurrence of multiple calcium peaks, which were visually obvious in the untreated group. The number of responsive peaks also decreased slightly yet significantly when either the COX-2/PGE2 or the NOS/nitric oxide pathway was disrupted. Different from all other groups, cells treated with 18α-GA maintained a high concentration of intracellular calcium following the first peak. In the absence of calcium in the culture medium, the intracellular calcium concentration decreased slowly with fluid flow without any calcium transients observed. These findings have identified important factors in the flow mediated calcium signaling of bone cells within a patterned network. PMID:20852730
[Using network planning techniques for work flow analysis in a clinical environment].
Teichgräber, Ulf K M; Gillessen, Christoph; Neumann, Fabian; Clasen, Birthe; Ricke, Jens
2002-08-01
In the face of increasing financial pressure on our health care system, one way to reduce costs while maintaining or even improving outcome quality is to improve work flow efficiency. Network Planning Technique (NPT) is a tool for mapping and analyzing work flows. Designing a network plan requires four steps. Step 1 is concerned with the determination of the work flow structure. Step 2 deals with data acquisition. Based on the data retrieved a network plan is created in Step 3. Step 4 includes the calculation of the critical path and slack times under optimistic, realistic and pessimistic conditions. Applied to the ultrasound division in our department a total examination time of 34:14 minutes was calculated with 23:09 minutes total slack time for the technician under realistic conditions. Using NPT creates transparency in work flows and allows us to estimate resource demands. A comparison between two different divisions with a similar work flow structure but different resource allocation demonstrates this method's potential for improving work flows. Limitations of the NPT can be noted when considering cycle overlap in repetitive work flows and modeling non-regular activities.
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.
Smooth information flow in temperature climate network reflects mass transport.
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.
Information flow in interaction networks II: channels, path lengths, and potentials.
Stojmirović, Aleksandar; Yu, Yi-Kuo
2012-04-01
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.
Spontaneous oscillations of capillary blood flow in artificial microvascular networks.
Forouzan, Omid; Yang, Xiaoxi; Sosa, Jose M; Burns, Jennie M; Shevkoplyas, Sergey S
2012-09-01
Previous computational studies have suggested that the capillary blood flow oscillations frequently observed in vivo can originate spontaneously from the non-linear rheological properties of blood, without any regulatory input. Testing this hypothesis definitively in experiments involving real microvasculature has been difficult because in vivo the blood flow in capillaries is always actively controlled by the host. The objective of this study was to test the hypothesis experimentally and to investigate the relative contribution of different blood cells to the capillary blood flow dynamics under static boundary conditions and in complete isolation from the active regulatory mechanisms mediated by the blood vessels in vivo. To accomplish this objective, we passed whole blood and re-constituted blood samples (purified red blood cells suspended in buffer or in autologous plasma) through an artificial microvascular network (AMVN) comprising completely inert, microfabricated vessels with the architecture inspired by the real microvasculature. We found that the flow of blood in capillaries of the AMVN indeed oscillates with characteristic frequencies in the range of 0-0.6 Hz, which is in a very good agreement with previous computational studies and in vivo observations. We also found that the traffic of leukocytes through the network (typically neglected in computational modeling) plays an important role in generating the oscillations. This study represents the key piece of experimental evidence in support of the hypothesis that spontaneous, self-sustained oscillations of capillary blood flow can be generated solely by the non-linear rheological properties of blood flowing through microvascular networks, and provides an insight into the mechanism of this fundamentally important microcirculatory phenomenon. Copyright © 2012 Elsevier Inc. All rights reserved.
Flow based vs. demand based energy-water modelling
NASA Astrophysics Data System (ADS)
Rozos, Evangelos; Nikolopoulos, Dionysis; Efstratiadis, Andreas; Koukouvinos, Antonios; Makropoulos, Christos
2015-04-01
The water flow in hydro-power generation systems is often used downstream to cover other type of demands like irrigation and water supply. However, the typical case is that the energy demand (operation of hydro-power plant) and the water demand do not coincide. Furthermore, the water inflow into a reservoir is a stochastic process. Things become more complicated if renewable resources (wind-turbines or photovoltaic panels) are included into the system. For this reason, the assessment and optimization of the operation of hydro-power systems are challenging tasks that require computer modelling. This modelling should not only simulate the water budget of the reservoirs and the energy production/consumption (pumped-storage), but should also take into account the constraints imposed by the natural or artificial water network using a flow routing algorithm. HYDRONOMEAS, for example, uses an elegant mathematical approach (digraph) to calculate the flow in a water network based on: the demands (input timeseries), the water availability (simulated) and the capacity of the transmission components (properties of channels, rivers, pipes, etc.). The input timeseries of demand should be estimated by another model and linked to the corresponding network nodes. A model that could be used to estimate these timeseries is UWOT. UWOT is a bottom up urban water cycle model that simulates the generation, aggregation and routing of water demand signals. In this study, we explore the potentials of UWOT in simulating the operation of complex hydrosystems that include energy generation. The evident advantage of this approach is the use of a single model instead of one for estimation of demands and another for the system simulation. An application of UWOT in a large scale system is attempted in mainland Greece in an area extending over 130×170 km². The challenges, the peculiarities and the advantages of this approach are examined and critically discussed.
An Open-Access Modeled Passenger Flow Matrix for the Global Air Network in 2010
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.
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.
Overall Ventilation System Flow Network Calculation for Site Recommendation
Jeff J. Steinhoff
2001-08-02
The scope of this calculation is to determine ventilation system resistances, pressure drops, airflows, and operating cost estimates for the Site Recommendation (SR) design as detailed in the ''Site Recommendation Subsurface Layout'' (BSC (Bechtel SAIC Company) 2001a). The statutory limit for emplacement of waste in Yucca Mountain is 70,000 metric tons of uranium (MTU) and is considered the base case for this report. The objective is to determine the overall repository system ventilation flow network for the monitoring phase during normal operations and to provide a basis for the system description document design descriptions. Any values derived from this calculation will not be used to support construction, fabrication, or procurement. The work scope is identified in the ''Technical Work Plan for Subsurface Design Section FY01 Work Activities'' (CRWMS M&O 2001, pp. 6 and 13). In accordance with the technical work plan this calculation was prepared in accordance with AP-3.12Q, ''Calculations'' and other procedures invoked by AP-3.12Q. It also incorporates the procedure AP-SI1.Q, ''Software Management''.
Modeling dynamic functional information flows on large-scale brain networks.
Lv, Peili; Guo, Lei; Hu, Xintao; Li, Xiang; Jin, Changfeng; Han, Junwei; Li, Lingjiang; Liu, Tianming
2013-01-01
Growing evidence from the functional neuroimaging field suggests that human brain functions are realized via dynamic functional interactions on large-scale structural networks. Even in resting state, functional brain networks exhibit remarkable temporal dynamics. However, it has been rarely explored to computationally model such dynamic functional information flows on large-scale brain networks. In this paper, we present a novel computational framework to explore this problem using multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. Basically, recent literature reports including our own studies have demonstrated that the resting state brain networks dynamically undergo a set of distinct brain states. Within each quasi-stable state, functional information flows from one set of structural brain nodes to other sets of nodes, which is analogous to the message package routing on the Internet from the source node to the destination. Therefore, based on the large-scale structural brain networks constructed from DTI data, we employ a dynamic programming strategy to infer functional information transition routines on structural networks, based on which hub routers that most frequently participate in these routines are identified. It is interesting that a majority of those hub routers are located within the default mode network (DMN), revealing a possible mechanism of the critical functional hub roles played by the DMN in resting state. Also, application of this framework on a post trauma stress disorder (PTSD) dataset demonstrated interesting difference in hub router distributions between PTSD patients and healthy controls.
NASA Astrophysics Data System (ADS)
Çebi, A.; Akdoğan, E.; Celen, A.; Dalkilic, A. S.
2017-02-01
An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.
NASA Astrophysics Data System (ADS)
Çebi, A.; Akdoğan, E.; Celen, A.; Dalkilic, A. S.
2016-06-01
An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.
An Algorithm to Find Minimum Cost Flow in a Network.
The paper describes an algorithm to find a flow of minimum cost in a network and a computer program which performs this algorithm. The principle of...optimal. What might make this algorithm efficient is the fact that the information which has been gathered at some iteration to find a negative...circuit will be used to find a negative circuit at next iteration so that each iteration requires a relatively small amount of work. (Author)
Power flow tracing in a simplified highly renewable European electricity network
NASA Astrophysics Data System (ADS)
Tranberg, Bo; Thomsen, Anders B.; Rodriguez, Rolando A.; Andresen, Gorm B.; Schäfer, Mirko; Greiner, Martin
2015-10-01
The increasing transmission capacity needs in a future energy system raise the question of how associated costs should be allocated to the users of a strengthened power grid. In contrast to straightforward oversimplified methods, a flow tracing based approach provides a fair and consistent nodal usage and thus cost assignment of transmission investments. This technique follows the power flow through the network and assigns the link capacity usage to the respective sources or sinks using a diffusion-like process, thus taking into account the underlying network structure and injection pattern. As a showcase, we apply power flow tracing to a simplified model of the European electricity grid with a high share of renewable wind and solar power generation, based on long-term weather and load data with an hourly temporal resolution.
NASA Astrophysics Data System (ADS)
Park, J.; Obeysekera, J.; Vanzee, R.
2005-05-01
The Management Simulation Engine (MSE) component of the Regional Simulation Model (RSM) incorporates a multi-level hierarchical control architecture which emphasizes the decoupling of hydrological state information from the management information processing applied to the states. A crucial aspect of effectively storing and accessing state information for water resource management purposes is the maintenance of an efficient storage mechanism which associates hydrological state information with the proper managerial abstractions. In the RSM this is done by storing hydrological and managerial information relevant to a water control unit (WCU) in a data storage object defined in the MSE Network. The MSE Network is an abstraction of the stream flow network and control structures suited to the needs of water resource routing and decisions. It is based on a standard graph theory representation of a flow network comprised of arcs and nodes. The MSE Network data objects serve as state and process information repositories for management processes. They maintain appropriately filtered state information, parameter storage relevant to WCU or hydraulic structure managerial constraints and variables, and serve as an integrated data source for any MSE algorithm. It also provides a mathematical representation of a constrained, interconnected flow network which facilitates efficient graph theory solutions of network connectivity and flow algorithms. This paper describes the MSE Network implementation in the RSM, an integrated hydrological computation engine aimed at meeting the needs for comprehensive integration of management features in coupled hydrological models [1]. [1] Belaineh, G., Peralta, R. C., Hughes, T. C., Simulation/ Optimization Modeling for Water Resources Management, ASCE Journal Water Resources Planning Management, 125(3), p 154-61, 1999
Investigation on Online Multiphase Flow Meter in oilfield Based on Open Channel Flow
NASA Astrophysics Data System (ADS)
Meng, L. Y.; Wang, W. C.; Li, Y. X.; Zhang, J.; Dong, S. P.
2010-03-01
Flow metering of multiphase pipeline is an urgently problem needed to be solved in oilfield producing in China. Based on the principle of multiphase oil and gas flow in the open channel, four liquid metering models(Falling Model I, Falling Model II, Open Channel Model and Element Resistance Model) and one gas model were obtained to calculate the gas and liquid flow rate, in which the water cut was measured by the differential pressure. And then a new type of multiphase meter system was developed based on these models and neural networks were developed to improve the estimating results of gas and liquid flow rate with the new metering system. At last a lot of experiments of multiphase metering were finished in lab and field. According to the experiments, the results of the metering system show that the liquid flow rate error was no more than 10%, and gas flow rate error was no more than 15%, which can meet the demand of the field flow rate measurement. Furthermore the relationship between liquid and gas flow rate and characteristic signals was found out through the experiments so as to deepening the study on multiphase flow metering technology.
Dynamic QoS Provisioning for Ethernet-based Networks
NASA Astrophysics Data System (ADS)
Angelopoulos, J.; Kanonakis, K.; Leligou, H. C.; Orfanoudakis, Th.; Katsigiannis, M.
2008-11-01
The evolution towards packet-based access networks and the importance of quality of experience brings the need for access networks that support the offer of a wide range of multimedia services not currently available to the desired extent. Legacy networks based on circuit switching used explicit signalling that travelled to all nodes along the path to book resources before the launce of the media stream. This approach does not scale well and is not in line with the philosophy of packet networks. Still, the need to reserve resources in advance remains since real-time services have limited if any means of adjusting their rates to the prevailing network conditions and to preserve customer satisfaction the traditional preventive approach that needs accurate estimates of resource needs for the duration of the session is the only option. The paper describes a possible CAC solution based on measuring flows and enriches the network with implicit admission control (without obviating explicit control if available) and can manage resource allocation to protect quality-demanding services from degradation. The basis is a flow measurement system, which will estimate the traffic load produced by the flow and activate admission control. However, because in most cases these initial indication may well be misleading, it will be cross checked against a database of previously recorded flows per customer interface which can provide long term data on the flows leaving only a few cases that have to be corrected on the fly. The overall product is a self-learning autonomic system that supports QoS in the access network for services that do not communicate with the network layer such as, for example, peer-to-peer real-time multimedia applications.
Towards effective flow simulations in realistic discrete fracture networks
NASA Astrophysics Data System (ADS)
Berrone, Stefano; Pieraccini, Sandra; Scialò, Stefano
2016-04-01
We focus on the simulation of underground flow in fractured media, modeled by means of Discrete Fracture Networks. Focusing on a new recent numerical approach proposed by the authors for tackling the problem avoiding mesh generation problems, we further improve the new family of methods making a step further towards effective simulations of large, multi-scale, heterogeneous networks. Namely, we tackle the imposition of Dirichlet boundary conditions in weak form, in such a way that geometrical complexity of the DFN is not an issue; we effectively solve DFN problems with fracture transmissivities spanning many orders of magnitude and approaching zero; furthermore, we address several numerical issues for improving the numerical solution also in quite challenging networks.
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.
Location-Based Services in Vehicular Networks
ERIC Educational Resources Information Center
Wu, Di
2013-01-01
Location-based services have been identified as a promising communication paradigm in highly mobile and dynamic vehicular networks. However, existing mobile ad hoc networking cannot be directly applied to vehicular networking due to differences in traffic conditions, mobility models and network topologies. On the other hand, hybrid architectures…
Location-Based Services in Vehicular Networks
ERIC Educational Resources Information Center
Wu, Di
2013-01-01
Location-based services have been identified as a promising communication paradigm in highly mobile and dynamic vehicular networks. However, existing mobile ad hoc networking cannot be directly applied to vehicular networking due to differences in traffic conditions, mobility models and network topologies. On the other hand, hybrid architectures…
Renormalization Group Flows of Hamiltonians Using Tensor Networks
NASA Astrophysics Data System (ADS)
Bal, M.; Mariën, M.; Haegeman, J.; Verstraete, F.
2017-06-01
A renormalization group flow of Hamiltonians for two-dimensional classical partition functions is constructed using tensor networks. Similar to tensor network renormalization [G. Evenbly and G. Vidal, Phys. Rev. Lett. 115, 180405 (2015), 10.1103/PhysRevLett.115.180405; S. Yang, Z.-C. Gu, and X.-G. Wen, Phys. Rev. Lett. 118, 110504 (2017), 10.1103/PhysRevLett.118.110504], we obtain approximate fixed point tensor networks at criticality. Our formalism, however, preserves positivity of the tensors at every step and hence yields an interpretation in terms of Hamiltonian flows. We emphasize that the key difference between tensor network approaches and Kadanoff's spin blocking method can be understood in terms of a change of the local basis at every decimation step, a property which is crucial to overcome the area law of mutual information. We derive algebraic relations for fixed point tensors, calculate critical exponents, and benchmark our method on the Ising model and the six-vertex model.
Renormalization Group Flows of Hamiltonians Using Tensor Networks.
Bal, M; Mariën, M; Haegeman, J; Verstraete, F
2017-06-23
A renormalization group flow of Hamiltonians for two-dimensional classical partition functions is constructed using tensor networks. Similar to tensor network renormalization [G. Evenbly and G. Vidal, Phys. Rev. Lett. 115, 180405 (2015)PRLTAO0031-900710.1103/PhysRevLett.115.180405; S. Yang, Z.-C. Gu, and X.-G. Wen, Phys. Rev. Lett. 118, 110504 (2017)PRLTAO0031-900710.1103/PhysRevLett.118.110504], we obtain approximate fixed point tensor networks at criticality. Our formalism, however, preserves positivity of the tensors at every step and hence yields an interpretation in terms of Hamiltonian flows. We emphasize that the key difference between tensor network approaches and Kadanoff's spin blocking method can be understood in terms of a change of the local basis at every decimation step, a property which is crucial to overcome the area law of mutual information. We derive algebraic relations for fixed point tensors, calculate critical exponents, and benchmark our method on the Ising model and the six-vertex model.
Estimating surface flow paths on a digital elevation model using a triangular facet network
NASA Astrophysics Data System (ADS)
Zhou, Qiming; Pilesjö, Petter; Chen, Yumin
2011-07-01
This study attempts to develop a method for the simulation of surface flow paths on a digital elevation model (DEM). The objective is to use a facet-based algorithm to estimate the surface flow paths on a raster DEM. A grid DEM was used to create a triangular facet network (TFN) over which the surface flow paths were determined. Since each facet in the network has a constant slope and aspect, the estimations of, for example, flow direction and divergence/convergence are less complicated compared to traditional raster-based solutions. Experiments were undertaken by estimating the specific catchment area (SCA) over a number of mathematical surfaces, as well as on a real-world DEM. Comparisons were made between the derived SCA by the TFN algorithm with some algorithms reported in the literature. The results show that the TFN algorithm produced the closest outcomes to the theoretical values of the SCA compared with other algorithms, deriving more consistent outcomes and being less influenced by surface shapes. The real-world DEM test also shows that the TFN was capable of modeling flow distribution without noticeable "artifacts," and its ability of tracking flow paths makes it an appropriate platform for dynamic surface flow simulation.
Chen, Bor-Sen; Li, Cheng-Wei
2015-01-01
In general, it is very difficult to measure the information flow in a cellular network directly. In this study, based on an information flow model and microarray data, we measured the information flow in cellular networks indirectly by using a systems biology method. First, we used a recursive least square parameter estimation algorithm to identify the system parameters of coupling signal transduction pathways and the cellular gene regulatory network (GRN). Then, based on the identified parameters and systems theory, we estimated the signal transductivities of the coupling signal transduction pathways from the extracellular signals to each downstream protein and the information transductivities of the GRN between transcription factors in response to environmental events. According to the proposed method, the information flow, which is characterized by signal transductivity in coupling signaling pathways and information transductivity in the GRN, can be estimated by microarray temporal data or microarray sample data. It can also be estimated by other high-throughput data such as next-generation sequencing or proteomic data. Finally, the information flows of the signal transduction pathways and the GRN in leukemia cancer cells and non-leukemia normal cells were also measured to analyze the systematic dysfunction in this cancer from microarray sample data. The results show that the signal transductivities of signal transduction pathways change substantially from normal cells to leukemia cancer cells. PMID:26082788
Chen, Bor-Sen; Li, Cheng-Wei
2015-01-01
In general, it is very difficult to measure the information flow in a cellular network directly. In this study, based on an information flow model and microarray data, we measured the information flow in cellular networks indirectly by using a systems biology method. First, we used a recursive least square parameter estimation algorithm to identify the system parameters of coupling signal transduction pathways and the cellular gene regulatory network (GRN). Then, based on the identified parameters and systems theory, we estimated the signal transductivities of the coupling signal transduction pathways from the extracellular signals to each downstream protein and the information transductivities of the GRN between transcription factors in response to environmental events. According to the proposed method, the information flow, which is characterized by signal transductivity in coupling signaling pathways and information transductivity in the GRN, can be estimated by microarray temporal data or microarray sample data. It can also be estimated by other high-throughput data such as next-generation sequencing or proteomic data. Finally, the information flows of the signal transduction pathways and the GRN in leukemia cancer cells and non-leukemia normal cells were also measured to analyze the systematic dysfunction in this cancer from microarray sample data. The results show that the signal transductivities of signal transduction pathways change substantially from normal cells to leukemia cancer cells.
Economics-based optimization of unstable flows
NASA Astrophysics Data System (ADS)
Huberman, B. A.; Helbing, D.
1999-07-01
As an example for the optimization of unstable flows, we present an economics-based method for deciding the optimal rates at which vehicles are allowed to enter a highway. It exploits the naturally occurring fluctuations of traffic flow and is flexible enough to adapt in real time to the transient flow characteristics of road traffic. Simulations based on realistic parameter values show that this strategy is feasible for naturally occurring traffic, and that even far from optimality, injection policies can improve traffic flow. Moreover, the same method can be applied to the optimization of flows of gases and granular media.
Three-phase flow simulations in discrete fracture networks
NASA Astrophysics Data System (ADS)
Geiger, S.; Niessner, J.; Matthai, S. K.; Helmig, R.
2006-12-01
Fractures are often the key conduits for fluid flow in otherwise low permeability rocks. Their presence in hydrocarbon reservoirs leads to complex production histories, unpredictable coupling of wells, rapidly changing flow rates, possibly early water breakthrough, and low final recovery. Recently, it has been demonstrated that a combination of finite volume and finite element discretization is well suited to model incompressible, immiscible two-phase flow in 3D discrete fracture networks (DFN) representing complexly fractured rocks. Such an approach has been commercialized in Golder Associates' FracMan Reservoir Edition software. For realistic reservoir simulations, however, it would be desirable if a third compressible gas phase can be included which is often present at reservoir conditions. Here we present the extension of an existing node-centred finite volume - finite element (FEFV) discretization for the efficient and accurate simulations of three-component - three-phase flow in geologically realistic representations of fractured porous media. Two possible types of fracture networks can be used: In 2D, they are detailed geometrical representations of fractured rock masses mapped in field studies. In 3D, they are geologically constrained, stochastically generated discrete fracture networks. Flow and transport can be simulated for fractures only or for fractures and matrix combined. The governing equations are solved decoupled using an implicit-pressure, explicit-saturation (IMPES) approach. Flux and concentration terms can be treated with higher-order accuracy in the finite volume scheme to preserve shock fronts. The method is locally mass conservative and works on unstructured, spatially refined grids. Flash calculations are carried out by a new description of the Black-Oil model. Capillary and gravity effects are included in this formulation. The robustness and accuracy of this formulation is shown in several applications. First, grid convergence is
Structural Efficiency of Percolated Landscapes in Flow Networks
Serrano, M. Ángeles; De Los Rios, Paolo
2008-01-01
The large-scale structure of complex systems is intimately related to their functionality and evolution. In particular, global transport processes in flow networks rely on the presence of directed pathways from input to output nodes and edges, which organize in macroscopic connected components. However, the precise relation between such structures and functional or evolutionary aspects remains to be understood. Here, we investigate which are the constraints that the global structure of directed networks imposes on transport phenomena. We define quantitatively under minimal assumptions the structural efficiency of networks to determine how robust communication between the core and the peripheral components through interface edges could be. Furthermore, we assess that optimal topologies in terms of access to the core should look like “hairy balls” so to minimize bottleneck effects and the sensitivity to failures. We illustrate our investigation with the analysis of three real networks with very different purposes and shaped by very different dynamics and time-scales–the Internet customer-provider set of relationships, the nervous system of the worm Caenorhabditis elegans, and the metabolism of the bacterium Escherichia coli. Our findings prove that different global connectivity structures result in different levels of structural efficiency. In particular, biological networks seem to be close to the optimal layout. PMID:18985157
Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems
Mudunuru, Maruti Kumar; Karra, Satish; Makedonska, Nataliia; ...
2017-09-05
Subsurface applications, including geothermal, geological carbon sequestration, and oil and gas, typically involve maximizing either the extraction of energy or the storage of fluids. Fractures form the main pathways for flow in these systems, and locating these fractures is critical for predicting flow. However, fracture characterization is a highly uncertain process, and data from multiple sources, such as flow and geophysical are needed to reduce this uncertainty. We present a nonintrusive, sequential inversion framework for integrating data from geophysical and flow sources to constrain fracture networks in the subsurface. In this framework, we first estimate bounds on the statistics formore » the fracture orientations using microseismic data. These bounds are estimated through a combination of a focal mechanism (physics-based approach) and clustering analysis (statistical approach) of seismic data. Then, the fracture lengths are constrained using flow data. In conclusion, the efficacy of this inversion is demonstrated through a representative example.« less
Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks.
Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime
2016-01-01
It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a "signature" of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence.
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.
A network approach based on cliques
NASA Astrophysics Data System (ADS)
Fadigas, I. S.; Pereira, H. B. B.
2013-05-01
The characterization of complex networks is a procedure that is currently found in several research studies. Nevertheless, few studies present a discussion on networks in which the basic element is a clique. In this paper, we propose an approach based on a network of cliques. This approach consists not only of a set of new indices to capture the properties of a network of cliques but also of a method to characterize complex networks of cliques (i.e., some of the parameters are proposed to characterize the small-world phenomenon in networks of cliques). The results obtained are consistent with results from classical methods used to characterize complex networks.
NASA Astrophysics Data System (ADS)
Sochi, Taha
2016-09-01
Several deterministic and stochastic multi-variable global optimization algorithms (Conjugate Gradient, Nelder-Mead, Quasi-Newton and global) are investigated in conjunction with energy minimization principle to resolve the pressure and volumetric flow rate fields in single ducts and networks of interconnected ducts. The algorithms are tested with seven types of fluid: Newtonian, power law, Bingham, Herschel-Bulkley, Ellis, Ree-Eyring and Casson. The results obtained from all those algorithms for all these types of fluid agree very well with the analytically derived solutions as obtained from the traditional methods which are based on the conservation principles and fluid constitutive relations. The results confirm and generalize the findings of our previous investigations that the energy minimization principle is at the heart of the flow dynamics systems. The investigation also enriches the methods of computational fluid dynamics for solving the flow fields in tubes and networks for various types of Newtonian and non-Newtonian fluids.
Ntofon, Okung-Dike; Channegowda, Mayur P; Efstathiou, Nikolaos; Rashidi Fard, Mehdi; Nejabati, Reza; Hunter, David K; Simeonidou, Dimitra
2013-02-25
In this paper, a novel Software-Defined Networking (SDN) architecture is proposed for high-end Ultra High Definition (UHD) media applications. UHD media applications require huge amounts of bandwidth that can only be met with high-capacity optical networks. In addition, there are requirements for control frameworks capable of delivering effective application performance with efficient network utilization. A novel SDN-based Controller that tightly integrates application-awareness with network control and management is proposed for such applications. An OpenFlow-enabled test-bed demonstrator is reported with performance evaluations of advanced online and offline media- and network-aware schedulers.
Wet Channel Network Extraction based on LiDAR Data
NASA Astrophysics Data System (ADS)
Hooshyar, M.; Kim, S.; Wang, D.; Medeiros, S. C.
2015-12-01
The temporal dynamics of stream network is vitally important for understanding hydrologic processes including groundwater interactions and hydrograph recessions. However, observations are limited on flowing channel heads, which are usually located in headwater catchments and under canopy. Near infrared LiDAR data provides an opportunity to map the flowing channel network owing to the fine spatial resolution, canopy penetration, and strong absorption of the light energy by the water surface. A systematic method is developed herein to map flowing channel networks based on the signal intensity of ground LiDAR return, which is lower on water surfaces than on dry surfaces. Based on the selected sample sites where the wetness conditions are known, the signal intensities of ground returns are extracted from the LiDAR point data. The frequency distributions of wet surface and dry surface returns are constructed. With the aid of LiDAR-based ground elevation, the signal intensity thresholds are identified for mapping flowing channels. The developed method is applied to Lake Tahoe area based on eight LiDAR snapshots during recession periods in five watersheds. A power-law relationship between streamflow and flowing channel length during the recession period is derived based on the result.
Neural Network-Based Hyperspectral Algorithms
2016-06-07
Neural Network -Based Hyperspectral Algorithms Walter F. Smith, Jr. and Juanita Sandidge Naval Research Laboratory Code 7340, Bldg 1105 Stennis Space...combination of in-situ and model data of water column variables (IOP’s, depth, bottom type, upwelling radiance, etc.) a neural network non-linear... network (Lippman, 1987). Neural network -based algorithms have been demonstrated by the investigators for retrieval of water depth from Airborne Visible
Wang, Gang; Yuan, Cansheng; Fu, Boyi; He, Luye; Reichmanis, Elsa; Wang, Hongzhi; Zhang, Qinghong; Li, Yaogang
2015-09-30
Low-cost microfluidic devices are desirable for many chemical processes; however, access to robust, inert, and appropriately structured materials for the inner channel wall is severely limited. Here, the shear force within confined microchannels was tuned through control of reactant solution fluid-flow and shown to dramatically impact nano- through microstructure growth. Combined use of experimental results and simulations allowed controlled growth of 3D networked Zn(OH)F nanostructures with uniform pore distributions and large fluid contact areas on inner microchannel walls. These attributes facilitated subsequent preparation of uniformly distributed Pd and PdPt networks with high structural and chemical stability using a facile, in situ conversion method. The advantageous properties of the microchannel based catalytic system were demonstrated using microwave-assisted continuous-flow coupling as a representative reaction. High conversion rates and good recyclability were obtained. Controlling materials nanostructure via fluid-flow-enhanced growth affords a general strategy to optimize the structure of an inner microchannel wall for desired attributes. The approach provides a promising pathway toward versatile, high-performance, and low-cost microfluidic devices for continuous-flow chemical processes.
A Network Coding Based Routing Protocol for Underwater Sensor Networks
Wu, Huayang; Chen, Min; Guan, Xin
2012-01-01
Due to the particularities of the underwater environment, some negative factors will seriously interfere with data transmission rates, reliability of data communication, communication range, and network throughput and energy consumption of underwater sensor networks (UWSNs). Thus, full consideration of node energy savings, while maintaining a quick, correct and effective data transmission, extending the network life cycle are essential when routing protocols for underwater sensor networks are studied. In this paper, we have proposed a novel routing algorithm for UWSNs. To increase energy consumption efficiency and extend network lifetime, we propose a time-slot based routing algorithm (TSR).We designed a probability balanced mechanism and applied it to TSR. The theory of network coding is introduced to TSBR to meet the requirement of further reducing node energy consumption and extending network lifetime. Hence, time-slot based balanced network coding (TSBNC) comes into being. We evaluated the proposed time-slot based balancing routing algorithm and compared it with other classical underwater routing protocols. The simulation results show that the proposed protocol can reduce the probability of node conflicts, shorten the process of routing construction, balance energy consumption of each node and effectively prolong the network lifetime. PMID:22666045
Applications of flow-networks to opinion-dynamics
NASA Astrophysics Data System (ADS)
Tupikina, Liubov; Kurths, Jürgen
2015-04-01
Networks were successfully applied to describe complex systems, such as brain, climate, processes in society. Recently a socio-physical problem of opinion-dynamics was studied using network techniques. We present the toy-model of opinion-formation based on the physical model of advection-diffusion. We consider spreading of the opinion on the fixed subject, assuming that opinion on society is binary: if person has opinion then the state of the node in the society-network equals 1, if the person doesn't have opinion state of the node equals 0. Opinion can be spread from one person to another if they know each other, or in the network-terminology, if the nodes are connected. We include into the system governed by advection-diffusion equation the external field to model such effects as for instance influence from media. The assumptions for our model can be formulated as the following: 1.the node-states are influenced by the network structure in such a way, that opinion can be spread only between adjacent nodes (the advective term of the opinion-dynamics), 2.the network evolution can have two scenarios: -network topology is not changing with time; -additional links can appear or disappear each time-step with fixed probability which requires adaptive networks properties. Considering these assumptions for our system we obtain the system of equations describing our model-dynamics which corresponds well to other socio-physics models, for instance, the model of the social cohesion and the famous voter-model. We investigate the behavior of the suggested model studying "waiting time" of the system, time to get to the stable state, stability of the model regimes for different values of model parameters and network topology.
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.
Program for Analyzing Flows in a Complex Network
NASA Technical Reports Server (NTRS)
Majumdar, Alok Kumar
2006-01-01
Generalized Fluid System Simulation Program (GFSSP) version 4 is a general-purpose computer program for analyzing steady-state and transient flows in a complex fluid network. The program is capable of modeling compressibility, fluid transients (e.g., water hammers), phase changes, mixtures of chemical species, and such externally applied body forces as gravitational and centrifugal ones. A graphical user interface enables the user to interactively develop a simulation of a fluid network consisting of nodes and branches. The user can also run the simulation and view the results in the interface. The system of equations for conservation of mass, energy, chemical species, and momentum is solved numerically by a combination of the Newton-Raphson and successive-substitution methods.
König, Jochem; Krahn, Ulrike; Binder, Harald
2013-12-30
Network meta-analysis techniques allow for pooling evidence from different studies with only partially overlapping designs for getting a broader basis for decision support. The results are network-based effect estimates that take indirect evidence into account for all pairs of treatments. The results critically depend on homogeneity and consistency assumptions, which are sometimes difficult to investigate. To support such evaluation, we propose a display of the flow of evidence and introduce new measures that characterize the structure of a mixed treatment comparison. Specifically, a linear fixed effects model for network meta-analysis is considered, where the network estimates for two treatments are linear combinations of direct effect estimates comparing these or other treatments. The linear coefficients can be seen as the generalization of weights known from classical meta-analysis. We summarize properties of these coefficients and display them as a weighted directed acyclic graph, representing the flow of evidence. Furthermore, measures are introduced that quantify the direct evidence proportion, the mean path length, and the minimal parallelism of mixed treatment comparisons. The graphical display and the measures are illustrated for two published network meta-analyses. In these applications, the proposed methods are seen to render transparent the process of data pooling in mixed treatment comparisons. They can be expected to be more generally useful for guiding and facilitating the validity assessment in network meta-analysis.
NASA Astrophysics Data System (ADS)
Gao, Zhong-Ke; Dang, Wei-Dong; Yang, Yu-Xuan; Cai, Qing
2017-03-01
The exploration of the spatial dynamical flow behaviors of oil-water flows has attracted increasing interests on account of its challenging complexity and great significance. We first technically design a double-layer distributed-sector conductance sensor and systematically carry out oil-water flow experiments to capture the spatial flow information. Based on the well-established recurrence network theory, we develop a novel multiplex multivariate recurrence network (MMRN) to fully and comprehensively fuse our double-layer multi-channel signals. Then we derive the projection networks from the inferred MMRNs and exploit the average clustering coefficient and the spectral radius to quantitatively characterize the nonlinear recurrent behaviors related to the distinct flow patterns. We find that these two network measures are very sensitive to the change of flow states and the distributions of network measures enable to uncover the spatial dynamical flow behaviors underlying different oil-water flow patterns. Our method paves the way for efficiently analyzing multi-channel signals from multi-layer sensor measurement system.
Suspended sediment dynamics in a tidal channel network under peak river flow
NASA Astrophysics Data System (ADS)
Achete, Fernanda Minikowski; van der Wegen, Mick; Roelvink, Dano; Jaffe, Bruce
2016-05-01
Peak river flows transport fine sediment, nutrients, and contaminants that may deposit in the estuary. This study explores the importance of peak river flows on sediment dynamics with special emphasis on channel network configurations. The Sacramento-San Joaquin Delta, which is connected to San Francisco Bay (California, USA), motivates this study and is used as a validation case. Besides data analysis of observations, we applied a calibrated process-based model (D-Flow FM) to explore and analyze high-resolution (˜100 m, ˜1 h) dynamics. Peak river flows supply the vast majority of sediment into the system. Data analysis of six peak flows (between 2012 and 2014) shows that on average, 40 % of the input sediment in the system is trapped and that trapping efficiency depends on timing and magnitude of river flows. The model has 90 % accuracy reproducing these trapping efficiencies. Modeled deposition patterns develop as the result of peak river flows after which, during low river flow conditions, tidal currents are not able to significantly redistribute deposited sediment. Deposition is quite local and mainly takes place at a deep junction. Tidal movement is important for sediment resuspension, but river induced, tide residual currents are responsible for redistributing the sediment towards the river banks and to the bay. We applied the same forcing for four different channel configurations ranging from a full delta network to a schematization of the main river. A higher degree of network schematization leads to higher peak-sediment export downstream to the bay. However, the area of sedimentation is similar for all the configurations because it is mostly driven by geometry and bathymetry.
Identifying High-Rate Flows Based on Sequential Sampling
NASA Astrophysics Data System (ADS)
Zhang, Yu; Fang, Binxing; Luo, Hao
We consider the problem of fast identification of high-rate flows in backbone links with possibly millions of flows. Accurate identification of high-rate flows is important for active queue management, traffic measurement and network security such as detection of distributed denial of service attacks. It is difficult to directly identify high-rate flows in backbone links because tracking the possible millions of flows needs correspondingly large high speed memories. To reduce the measurement overhead, the deterministic 1-out-of-k sampling technique is adopted which is also implemented in Cisco routers (NetFlow). Ideally, a high-rate flow identification method should have short identification time, low memory cost and processing cost. Most importantly, it should be able to specify the identification accuracy. We develop two such methods. The first method is based on fixed sample size test (FSST) which is able to identify high-rate flows with user-specified identification accuracy. However, since FSST has to record every sampled flow during the measurement period, it is not memory efficient. Therefore the second novel method based on truncated sequential probability ratio test (TSPRT) is proposed. Through sequential sampling, TSPRT is able to remove the low-rate flows and identify the high-rate flows at the early stage which can reduce the memory cost and identification time respectively. According to the way to determine the parameters in TSPRT, two versions of TSPRT are proposed: TSPRT-M which is suitable when low memory cost is preferred and TSPRT-T which is suitable when short identification time is preferred. The experimental results show that TSPRT requires less memory and identification time in identifying high-rate flows while satisfying the accuracy requirement as compared to previously proposed methods.
Depth-dependent flow and pressure characteristics in cortical microvascular networks
Schmid, Franca; Kleinfeld, David; Jenny, Patrick; Weber, Bruno
2017-01-01
A better knowledge of the flow and pressure distribution in realistic microvascular networks is needed for improving our understanding of neurovascular coupling mechanisms and the related measurement techniques. Here, numerical simulations with discrete tracking of red blood cells (RBCs) are performed in three realistic microvascular networks from the mouse cerebral cortex. Our analysis is based on trajectories of individual RBCs and focuses on layer-specific flow phenomena until a cortical depth of 1 mm. The individual RBC trajectories reveal that in the capillary bed RBCs preferentially move in plane. Hence, the capillary flow field shows laminar patterns and a layer-specific analysis is valid. We demonstrate that for RBCs entering the capillary bed close to the cortical surface (< 400 μm) the largest pressure drop takes place in the capillaries (37%), while for deeper regions arterioles are responsible for 61% of the total pressure drop. Further flow characteristics, such as capillary transit time or RBC velocity, also vary significantly over cortical depth. Comparison of purely topological characteristics with flow-based ones shows that a combined interpretation of topology and flow is indispensable. Our results provide evidence that it is crucial to consider layer-specific differences for all investigations related to the flow and pressure distribution in the cortical vasculature. These findings support the hypothesis that for an efficient oxygen up-regulation at least two regulation mechanisms must be playing hand in hand, namely cerebral blood flow increase and microvascular flow homogenization. However, the contribution of both regulation mechanisms to oxygen up-regulation likely varies over depth. PMID:28196095
Maximum Flow in Planar Networks with Exponentially Distributed Arc Capacities.
1984-12-01
avoid constructing the dual, are described in Itai and Shiloach P 97 91. In this paper, we consider the maximum flow problem in (st) planar networks...use arc e and lies completely below P. If no such path exists we say P(e) - *. An algorithm tc construct P(e) given P and e is described in Itai and...suggested in Ford and Fulkerson [1956], developed in Berge and Ghouila-Houri [1962] and its time complexity is reduced to 0( IVI log IVI ) by Itai and
FLOWER IPv4/IPv6 Network Flow Summarization software
Nickless, Bill; Curtis, Darren; Christy, Jason; Younkin, Chance; Mount, Jason; Richard Griswold, Joe Lenaeus
2011-04-04
FLOWER was written as a refactoring/reimplementation of the existing Flo software used by the Cooperative Protection Program (CPP) to provide network flow summaries for analysis by the Operational Analysis Center (OAC) and other US Department of Energy cyber security elements. FLOWER is designed and tested to operate at 10 gigabits/second, nearly 10 times faster than competing solutions. FLOWER output is optimized for importation into SQL databases for categorization and analysis. FLOWER is written in C++ using current best software engineering practices.
Knowledge-based flow field zoning
NASA Technical Reports Server (NTRS)
Andrews, Alison E.
1988-01-01
Automation flow field zoning in two dimensions is an important step towards easing the three-dimensional grid generation bottleneck in computational fluid dynamics. A knowledge based approach works well, but certain aspects of flow field zoning make the use of such an approach challenging. A knowledge based flow field zoner, called EZGrid, was implemented and tested on representative two-dimensional aerodynamic configurations. Results are shown which illustrate the way in which EZGrid incorporates the effects of physics, shape description, position, and user bias in a flow field zoning.
Network modeling for reverse flows of end-of-life vehicles
Ene, Seval; Öztürk, Nursel
2015-04-15
Highlights: • We developed a network model for reverse flows of end-of-life vehicles. • The model considers all recovery operations for end-of-life vehicles. • A scenario-based model is used for uncertainty to improve real case applications. • The model is adequate to real case applications for end-of-life vehicles recovery. • Considerable insights are gained from the model by sensitivity analyses. - Abstract: Product recovery operations are of critical importance for the automotive industry in complying with environmental regulations concerning end-of-life products management. Manufacturers must take responsibility for their products over the entire life cycle. In this context, there is a need for network design methods for effectively managing recovery operations and waste. The purpose of this study is to develop a mathematical programming model for managing reverse flows in end-of-life vehicles’ recovery network. A reverse flow is the collection of used products from consumers and the transportation of these products for the purpose of recycling, reuse or disposal. The proposed model includes all operations in a product recovery and waste management network for used vehicles and reuse for vehicle parts such as collection, disassembly, refurbishing, processing (shredding), recycling, disposal and reuse of vehicle parts. The scope of the network model is to determine the numbers and locations of facilities in the network and the material flows between these facilities. The results show the performance of the model and its applicability for use in the planning of recovery operations in the automotive industry. The main objective of recovery and waste management is to maximize revenue and minimize pollution in end-of-life product operations. This study shows that with an accurate model, these activities may provide economic benefits and incentives in addition to protecting the environment.
Irreversibility and complex network behavior of stream flow fluctuations
NASA Astrophysics Data System (ADS)
Serinaldi, Francesco; Kilsby, Chris G.
2016-05-01
Exploiting the duality between time series and networks, directed horizontal visibility graphs (DHVGs) are used to perform an unprecedented analysis of the dynamics of stream flow fluctuations with focus on time irreversibility and long range dependence. The analysis relies on a large quality-controlled data set consisting of 699 daily time series recorded in the continental United States (CONUS) that are not affected by human activity and primarily reflects meteorological conditions. DHVGs allow a clear visualization and quantification of time irreversibility of flow dynamics, which can be interpreted as a signature of nonlinearity, and long range dependence resulting from the interaction of atmospheric, surface and underground processes acting at multiple spatio-temporal scales. Irreversibility is explored by mapping the time series into ingoing, outgoing, and undirected graphs and comparing the corresponding degree distributions. Using surrogate data preserving up to the second order linear temporal dependence properties of the observed series, DHVGs highlight the additional complexity introduced by nonlinearity into flow fluctuation dynamics. We show that the degree distributions do not decay exponentially as expected, but tend to follow a subexponential behavior, even though sampling uncertainty does not allow a clear distinction between apparent or true power law decay. These results confirm that the complexity of stream flow dynamics goes beyond a linear representation involving for instance the combination of linear processes with short and long range dependence, and requires modeling strategies accounting for temporal asymmetry and nonlinearity.
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.
Feedback mechanism in network dynamics with preferential flow
NASA Astrophysics Data System (ADS)
Fan, H.; Wang, Z.; Chen, L.; Aihara, K.
2009-02-01
We study complex systems or networks in which each node holds an internal dynamics and interacts with other nodes through some kinds of topologies. Collective behavior with dynamical fluctuations is analyzed in complex systems. The dynamical fluctuations of a node can be divided into two parts: one is the internal dynamical fluctuation of the node and the other is the external dynamical fluctuation caused by other nodes. Based on a theoretical analysis, a hidden feedback mechanism is identified in complex systems, which is illustrated in a macroeconomic network and in a city-population network. Furthermore, we study the effect of the topology of the networks on the feedback mechanism. The feedback mechanism is preserved for hub nodes in the networks with a scale-free topology as well as in the networks with an evolving topology. By the hidden feedback mechanism, the observation data can be utilized to judge directly whether the system of each node is with positive feedback or with negative feedback even without knowing its dynamical model.
Inference of Gene Regulatory Network Based on Local Bayesian Networks.
Liu, Fei; Zhang, Shao-Wu; Guo, Wei-Feng; Wei, Ze-Gang; Chen, Luonan
2016-08-01
The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce
FLOWNET: A Computer Program for Calculating Secondary Flow Conditions in a Network of Turbomachinery
NASA Technical Reports Server (NTRS)
Rose, J. R.
1978-01-01
The program requires the network parameters, the flow component parameters, the reservoir conditions, and the gas properties as input. It will then calculate all unknown pressures and the mass flow rate in each flow component in the network. The program can treat networks containing up to fifty flow components and twenty-five unknown network pressures. The types of flow components that can be treated are face seals, narrow slots, and pipes. The program is written in both structured FORTRAN (SFTRAN) and FORTRAN 4. The program must be run in an interactive (conversational) mode.
Computer-Based Information Networks: Selected Examples.
ERIC Educational Resources Information Center
Hardesty, Larry
The history, purpose, and operation of six computer-based information networks are described in general and nontechnical terms. In the introduction the many definitions of an information network are explored. Ohio College Library Center's network (OCLC) is the first example. OCLC began in 1963, and since early 1973 has been extending its services…
Threat Based Risk Assessment for Enterprise Networks
2016-02-15
Threat-Based Risk Assessment for Enterprise Networks Richard P. Lippmann and James F. Riordan Protecting enterprise networks requires... enterprises to make sure that risks from all current threats are addressed, many organizations adopt a best-practices approach by installing popular...effective when performed by skilled security practitioners who understand an enterprise network, can enumerate all threats and their likelihoods, and
Computer-Based Information Networks: Selected Examples.
ERIC Educational Resources Information Center
Hardesty, Larry
The history, purpose, and operation of six computer-based information networks are described in general and nontechnical terms. In the introduction the many definitions of an information network are explored. Ohio College Library Center's network (OCLC) is the first example. OCLC began in 1963, and since early 1973 has been extending its services…
NASA Astrophysics Data System (ADS)
Shin, Sung-Hee; Kim, Yekyung; Yun, Sung-Hyun; Maurya, Sandip; Moon, Seung-Hyeon
2015-11-01
We develop three types of organic-inorganic composite membranes based on a semi-interpenetrating polymer network (SIPN) to explore the effects of membrane structure on the possible operating current densities of a non-aqueous redox flow battery (RFB) system. Poly(vinylidene fluoride) (PVdF) is selected as a supporting polymer matrix for improving the chemical and thermal stability of the organic-inorganic composite membranes. We also introduce silica nanoparticles (5 wt% of PVdF) into the membranes to ensure the low crossover of active species. The fabrication of SIPN through the addition of glycidyl methacrylate, 4-vinylpyridine, or N-vinylcarbazole enables control of the membrane structure. Depending on monomer type, the membrane structure is determined to be either aliphatic or aromatic in terms of chemical properties and either dense or porous in terms of physical properties. These chemical and physical structures affect the electrochemical properties that correspond to charge/discharge performance and to the range of possible operating current densities. An important requirement is to examine charge/discharge performance at the possible range of operating current densities by using various membrane structures. This requirement is discussed in relation to a proposed design strategy for non-aqueous RFB membranes.
Controlling ill-behaved flows with active queue management in DiffServ networks
NASA Astrophysics Data System (ADS)
Shu, Yantai; Gao, Deyun; Yang, Oliver W. W.; Qi, Lantao
2003-08-01
In this paper, we propose a new active queue management mechanism called the RIO-SD (RED IN and OUT with Selective Dropping) to control ill-behaved flows in DiffServ networks. Under this scheme, core routers are not required to maintain per-flow state, and the ill-behaved flows can be identified based on the drop history of the "OUT-profile" virtual queue. Control is effected by placing two pre-filters in front of the "IN-profile" and "OUT-profile" virtual queues respectively. Simulation results indicate that our approach can also improve the performance of other normal flows. Our work demonstrates that our algorithm is robust and simple to use.
Dong, Jing; Mahmassani, Hani S.
2011-01-01
This paper proposes a methodology to produce random flow breakdown endogenously in a mesoscopic operational model, by capturing breakdown probability and duration. Based on previous research findings that probability of flow breakdown can be represented as a function of flow rate and the duration can be characterized by a hazard model. By generating random flow breakdown at various levels and capturing the traffic characteristics at the onset of the breakdown, the stochastic network simulation model provides a tool for evaluating travel time variability. The proposed model can be used for (1) providing reliability related traveler information; (2) designing ITS (intelligent transportation systems) strategies to improve reliability; and (3) evaluating reliability-related performance measures of the system.
Forecasting the short-term passenger flow on high-speed railway with neural networks.
Xie, Mei-Quan; Li, Xia-Miao; Zhou, Wen-Liang; Fu, Yan-Bing
2014-01-01
Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-term passenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway.
Forecasting the Short-Term Passenger Flow on High-Speed Railway with Neural Networks
Xie, Mei-Quan; Li, Xia-Miao; Zhou, Wen-Liang; Fu, Yan-Bing
2014-01-01
Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-term passenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway. PMID:25544838
Functional water flow pathways and hydraulic regulation in the xylem network of Arabidopsis.
Park, Joonghyuk; Kim, Hae Koo; Ryu, Jeongeun; Ahn, Sungsook; Lee, Sang Joon; Hwang, Ildoo
2015-03-01
In vascular plants, the xylem network constitutes a complex microfluidic system. The relationship between vascular network architecture and functional hydraulic regulation during actual water flow remains unexplored. Here, we developed a method to visualize individual xylem vessels of the 3D xylem network of Arabidopsis thaliana, and to analyze the functional activities of these vessels using synchrotron X-ray computed tomography with hydrophilic gold nanoparticles as flow tracers. We show how the organization of the xylem network changes dynamically throughout the plant, and reveal how the elementary units of this transport system are organized to ensure both long-distance axial water transport and local lateral water transport. Xylem vessels form distinct clusters that operate as functional units, and the activity of these units, which determines water flow pathways, is modulated not only by varying the number and size of xylem vessels, but also by altering their interconnectivity and spatial arrangement. Based on these findings, we propose a regulatory model of water transport that ensures hydraulic efficiency and safety.
Analysis and Visualization of Discrete Fracture Networks Using a Flow Topology Graph.
Aldrich, Garrett; Hyman, Jeffrey; Karra, Satish; Gable, Carl; Makedonska, Nataliia; Viswanathan, Hari; Woodring, Jonathan; Hamann, Bernd
2016-06-20
We present an analysis and visualization prototype using the concept of a flow topology graph (FTG) for characterization of flow in constrained networks, with a focus on discrete fracture networks (DFN), developed collaboratively by geoscientists and visualization scientists. Our method allows users to understand and evaluate flow and transport in DFN simulations by computing statistical distributions, segment paths of interest, and cluster particles based on their paths. The new approach enables domain scientists to evaluate the accuracy of the simulations, visualize features of interest, and compare multiple realizations over a specific domain of interest. Geoscientists can simulate complex transport phenomena modeling large sites for networks consisting of several thousand fractures without compromising the geometry of the network. However, few tools exist for performing higher-level analysis and visualization of simulated DFN data. The prototype system we present addresses this need. We demonstrate its effectiveness for increasingly complex examples of DFNs, covering two distinct use cases - hydrocarbon extraction from unconventional resources and transport of dissolved contaminant from a spent nuclear fuel repository.
Curing critical links in oscillator networks as power flow models
NASA Astrophysics Data System (ADS)
Rohden, Martin; Witthaut, Dirk; Timme, Marc; Meyer-Ortmanns, Hildegard
2017-01-01
Modern societies crucially depend on the robust supply with electric energy so that blackouts of power grids can have far reaching consequences. Typically, large scale blackouts take place after a cascade of failures: the failure of a single infrastructure component, such as a critical transmission line, results in several subsequent failures that spread across large parts of the network. Improving the robustness of a network to prevent such secondary failures is thus key for assuring a reliable power supply. In this article we analyze the nonlocal rerouting of power flows after transmission line failures for a simplified AC power grid model and compare different strategies to improve network robustness. We identify critical links in the grid and compute alternative pathways to quantify the grid’s redundant capacity and to find bottlenecks along the pathways. Different strategies are developed and tested to increase transmission capacities to restore stability with respect to transmission line failures. We show that local and nonlocal strategies typically perform alike: one can equally well cure critical links by providing backup capacities locally or by extending the capacities of bottleneck links at remote locations.
Directedness of information flow in mobile phone communication networks.
Peruani, Fernando; Tabourier, Lionel
2011-01-01
Without having direct access to the information that is being exchanged, traces of information flow can be obtained by looking at temporal sequences of user interactions. These sequences can be represented as causality trees whose statistics result from a complex interplay between the topology of the underlying (social) network and the time correlations among the communications. Here, we study causality trees in mobile-phone data, which can be represented as a dynamical directed network. This representation of the data reveals the existence of super-spreaders and super-receivers. We show that the tree statistics, respectively the information spreading process, are extremely sensitive to the in-out degree correlation exhibited by the users. We also learn that a given information, e.g., a rumor, would require users to retransmit it for more than 30 hours in order to cover a macroscopic fraction of the system. Our analysis indicates that topological node-node correlations of the underlying social network, while allowing the existence of information loops, they also promote information spreading. Temporal correlations, and therefore causality effects, are only visible as local phenomena and during short time scales. Consequently, the very idea that there is (intentional) information spreading beyond a small vecinity is called into question. These results are obtained through a combination of theory and data analysis techniques.
Esralew, Rachel A.; Lewis, Jason M.
2010-01-01
The U.S. Geological Survey, in cooperation with the Oklahoma Water Resources Board, investigated trends in base flow, total flow, and base-flow index of selected streams in Oklahoma and evaluated possible causes for trends. Thirty-seven streamflow-gaging stations that had unregulated or moderately regulated streamflow were selected for trend analysis. Statistical evaluation of trends in annual and seasonal (winter-spring and summer-autumn) base flow, total flow, and base-flow index at 37 selected streamflow-gaging stations in Oklahoma was performed by using a Kendall's tau trend test. This trend analysis also was performed for annual and seasonal precipitation for nine climate divisions in the study area, annual peak flows, the number of days where flow was zero or less than 1 cubic foot per second (both annually and seasonally), and annual winter groundwater levels for 35 shallow wells near the analyzed stations. Precipitation-adjusted trends using LOESS regressions and Kendall's tau were computed for annual and seasonal base-flow and total-flow volumes in order to identify the presence of underlying trends in streamflow that are not associated with annual or seasonal variations in precipitation. In general, upward trends in precipitation were detected for climate divisions in north-central Oklahoma and south-central and southeastern Kansas. More climate divisions had statistically significant upward trends in total precipitation for annual water years than in winter-spring or summer-autumn water years. Significant trends in annual or seasonal base-flow volume were detected for 22 stations, 19 of which had trends that were upward in direction. Significant trends in annual or seasonal total-flow volume were detected for 14 stations, 9 of which had trends that were upward in direction. Most stations that had significant upward trends in annual or seasonal total-flow volume also had significant upward trends in base-flow volume for the same period. Precipitation
A Minimax Network Flow Model for Characterizing the Impact of Slot Restrictions
NASA Technical Reports Server (NTRS)
Lee, Douglas W.; Patek, Stephen D.; Alexandrov, Natalia; Bass, Ellen J.; Kincaid, Rex K.
2010-01-01
This paper proposes a model for evaluating long-term measures to reduce congestion at airports in the National Airspace System (NAS). This model is constructed with the goal of assessing the global impacts of congestion management strategies, specifically slot restrictions. We develop the Minimax Node Throughput Problem (MINNTHRU), a multicommodity network flow model that provides insight into air traffic patterns when one minimizes the worst-case operation across all airports in a given network. MINNTHRU is thus formulated as a model where congestion arises from network topology. It reflects not market-driven airline objectives, but those of a regulatory authority seeking a distribution of air traffic beneficial to all airports, in response to congestion management measures. After discussing an algorithm for solving MINNTHRU for moderate-sized (30 nodes) and larger networks, we use this model to study the impacts of slot restrictions on the operation of an entire hub-spoke airport network. For both a small example network and a medium-sized network based on 30 airports in the NAS, we use MINNTHRU to demonstrate that increasing the severity of slot restrictions increases the traffic around unconstrained hub airports as well as the worst-case level of operation over all airports.
Network Medicine: A Network-based Approach to Human Diseases
NASA Astrophysics Data System (ADS)
Ghiassian, Susan Dina
With the availability of large-scale data, it is now possible to systematically study the underlying interaction maps of many complex systems in multiple disciplines. Statistical physics has a long and successful history in modeling and characterizing systems with a large number of interacting individuals. Indeed, numerous approaches that were first developed in the context of statistical physics, such as the notion of random walks and diffusion processes, have been applied successfully to study and characterize complex systems in the context of network science. Based on these tools, network science has made important contributions to our understanding of many real-world, self-organizing systems, for example in computer science, sociology and economics. Biological systems are no exception. Indeed, recent studies reflect the necessity of applying statistical and network-based approaches in order to understand complex biological systems, such as cells. In these approaches, a cell is viewed as a complex network consisting of interactions among cellular components, such as genes and proteins. Given the cellular network as a platform, machinery, functionality and failure of a cell can be studied with network-based approaches, a field known as systems biology. Here, we apply network-based approaches to explore human diseases and their associated genes within the cellular network. This dissertation is divided in three parts: (i) A systematic analysis of the connectivity patterns among disease proteins within the cellular network. The quantification of these patterns inspires the design of an algorithm which predicts a disease-specific subnetwork containing yet unknown disease associated proteins. (ii) We apply the introduced algorithm to explore the common underlying mechanism of many complex diseases. We detect a subnetwork from which inflammatory processes initiate and result in many autoimmune diseases. (iii) The last chapter of this dissertation describes the
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
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.
Simulating unsteady flow and sediment transport in vegetated channel network
NASA Astrophysics Data System (ADS)
Bai, Yang; Duan, Jennifer G.
2014-07-01
This paper presents a one-dimensional model for simulating flood routing and sediment transport over mobile alluvium in a vegetated channel network. The modified St. Venant equations together with the governing equations for suspended sediment and bed load transport were solved simultaneously to obtain flow properties and sediment transport rate. The Godunov-type finite volume method is employed to discretize the governing equations. Then, the Exner equation was solved for bed elevation change. Since sediment transport is non-equilibrium when bed is degrading or aggrading, a recovery coefficient for suspended sediment and an adaptation length for bed load transport were used to quantify the differences between equilibrium and non-equilibrium sediment transport rate. The influence of vegetation on floodplain and main channel was accounted for by adjusting resistance terms in the momentum equations for flow field. A procedure to separate the grain resistance from the total resistance was proposed and implemented to calculate sediment transport rate. The model was tested by a flume experiment case and an unprecedented flood event occurred in the Santa Cruz River, Tucson, Arizona, in July 2006. Simulated results of flow discharge and bed elevation changes showed satisfactory agreements with the measurements. The impacts of vegetation density on sediment transport and significance of non-equilibrium sediment transport model were discussed.
Adaptive Multi-Scale Pore Network Method for Two-Phase Flow in Porous Media
NASA Astrophysics Data System (ADS)
Meyer, D. W.; Khayrat, K.; Jenny, P.
2015-12-01
Dynamic pore network simulators are important tools in studying macroscopic quantities in two-phase flow through porous media. However, these simulators have a time complexity of order N2 for N pore bodies, which limits their usage to small domains. Quasi-static pore network simulators, which assume capillary dominated flow, are more efficient with a time complexity of order N log(N), but are unable to capture phenomena caused by viscous effects such as viscous fingering and stable displacement. It has been experimentally observed that, in several flow scenarios, capillary forces are dominant at the pore scale and viscous forces at larger scales. In order to take advantage of this behaviour and to reduce the time complexity of existing dynamic pore network simulators, we propose a multi-scale pore-network method for two phase flow. In our solution algorithm, the pore network is first divided into smaller subnetworks. The algorithm to advance the fluid interfaces within each subnetwork consists of three steps: 1) The saturation rate of each subnetwork is obtained by solving a two-phase meso-scale mass balance equation over the domain of subnetworks. Here, a multi-point flux scheme is used. 2) Depending on the local capillary number computed in the subnetwork, either an invasion percolation algorithm or a dynamic network algorithm is used to locally advance the fluid-fluid interfaces within each subnetwork until a new saturation value is matched. 3) The transmissibilities for the meso-scale equation are updated based on the updated fluid configurations in each subnetwork. For this purpose the methodoloy of the existing multi-scale finite volume (MSFV) method is employed. An important feature of the multi-scale pore-network method is that it maintains consistency of both fluid occupancy and fluxes at subnetwork interfaces. Viscous effects such as viscous fingering (see figure) can be captured at a decreased computational cost compared to dynamic pore network
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.
Reputation-based collaborative network biology.
Binder, Jean; Boue, Stephanie; Di Fabio, Anselmo; Fields, R Brett; Hayes, William; Hoeng, Julia; Park, Jennifer S; Peitsch, Manuel C
2015-01-01
A pilot reputation-based collaborative network biology platform, Bionet, was developed for use in the sbv IMPROVER Network Verification Challenge to verify and enhance previously developed networks describing key aspects of lung biology. Bionet was successful in capturing a more comprehensive view of the biology associated with each network using the collective intelligence and knowledge of the crowd. One key learning point from the pilot was that using a standardized biological knowledge representation language such as BEL is critical to the success of a collaborative network biology platform. Overall, Bionet demonstrated that this approach to collaborative network biology is highly viable. Improving this platform for de novo creation of biological networks and network curation with the suggested enhancements for scalability will serve both academic and industry systems biology communities.
Network repair based on community structure
NASA Astrophysics Data System (ADS)
Wang, Tianyu; Zhang, Jun; Sun, Xiaoqian; Wandelt, Sebastian
2017-06-01
Real-world complex systems are often fragile under disruptions. Accordingly, research on network repair has been studied intensively. Recently proposed efficient strategies for network disruption, based on collective influence, call for more research on efficient network repair strategies. Existing strategies are often designed to repair networks with local information only. However, the absence of global information impedes the creation of efficient repairs. Motivated by this limitation, we propose a concept of community-level repair, which leverages the community structure of the network during the repair process. Moreover, we devise a general framework of network repair, with in total six instances. Evaluations on real-world and random networks show the effectiveness and efficiency of the community-level repair approaches, compared to local and random repairs. Our study contributes to a better understanding of repair processes, and reveals that exploitation of the community structure improves the repair process on a disrupted network significantly.
Catchment organisation, free energy dynamics and network control on critical zone water flows
NASA Astrophysics Data System (ADS)
Zehe, E.; Ehret, U.; Kleidon, A.; Jackisch, C.; Scherer, U.; Blume, T.
2012-04-01
as that these flow structures organize and dominate flows of water, dissolved matter and sediments during rainfall driven conditions at various scales: - Surface connected vertical flow structures of anecic worm burrows or soil cracks organize and dominated vertical flows at the plot scale - this is usually referred to as preferential flow; - Rill networks at the soil surface organise and dominate hillslope scale overland flow response and sediment yields; - Subsurface pipe networks at the bedrock interface organize and dominate hillslope scale lateral subsurface water and tracer flows; - The river net organizes and dominates flows of water, dissolved matter and sediments to the catchment outlet and finally across continental gradients to the sea. Fundamental progress with respect to the parameterization of hydrological models, subscale flow networks and to understand the adaptation of hydro-geo ecosystems to change could be achieved by discovering principles that govern the organization of catchments flow networks in particular at least during steady state conditions. This insight has inspired various scientists to suggest principles for organization of ecosystems, landscapes and flow networks; as Bejans constructural law, Minimum Energy Expenditure , Maximum Entropy Production. In line with these studies we suggest that a thermodynamic/energetic treatment of the catchment is might be a key for understanding the underlying principles that govern organisation of flow and transport. Our approach is to employ a) physically based hydrological model that address at least all the relevant hydrological processes in the critical zone in a coupled way, behavioural representations of the observed organisation of flow structures and textural elements, that are consistent with observations in two well investigated research catchments and have been tested against distributed observations of soil moisture and catchment scale discharge; to simulate the full concert of hydrological
A Muskingum-Cunge Channel Flow Routing Method for Drainage Networks
1991-11-01
A AD-A247 020 US Army Corps of Engineers Hydrologic Engineering Center A Muskingum-Cunge Channel Flow Routing Method for Drainage Networks DTIC...Flow Routing Method for Drainage Networks November 1991 By Jurgen Garbrecht National Agricultural Water Quality Laboratory USDA - Agricultural...CA 95616-4687 (916) 756-1104 TP-135 2 SUMMARY A Muskingum-Cunge channel flow routing scheme is modified for application to large drainage networks with
Reciprocating flow-based centrifugal microfluidics mixer
NASA Astrophysics Data System (ADS)
Noroozi, Zahra; Kido, Horacio; Micic, Miodrag; Pan, Hansheng; Bartolome, Christian; Princevac, Marko; Zoval, Jim; Madou, Marc
2009-07-01
Proper mixing of reagents is of paramount importance for an efficient chemical reaction. While on a large scale there are many good solutions for quantitative mixing of reagents, as of today, efficient and inexpensive fluid mixing in the nanoliter and microliter volume range is still a challenge. Complete, i.e., quantitative mixing is of special importance in any small-scale analytical application because the scarcity of analytes and the low volume of the reagents demand efficient utilization of all available reaction components. In this paper we demonstrate the design and fabrication of a novel centrifugal force-based unit for fast mixing of fluids in the nanoliter to microliter volume range. The device consists of a number of chambers (including two loading chambers, one pressure chamber, and one mixing chamber) that are connected through a network of microchannels, and is made by bonding a slab of polydimethylsiloxane (PDMS) to a glass slide. The PDMS slab was cast using a SU-8 master mold fabricated by a two-level photolithography process. This microfluidic mixer exploits centrifugal force and pneumatic pressure to reciprocate the flow of fluid samples in order to minimize the amount of sample and the time of mixing. The process of mixing was monitored by utilizing the planar laser induced fluorescence (PLIF) technique. A time series of high resolution images of the mixing chamber were analyzed for the spatial distribution of light intensities as the two fluids (suspension of red fluorescent particles and water) mixed. Histograms of the fluorescent emissions within the mixing chamber during different stages of the mixing process were created to quantify the level of mixing of the mixing fluids. The results suggest that quantitative mixing was achieved in less than 3 min. This device can be employed as a stand alone mixing unit or may be integrated into a disk-based microfluidic system where, in addition to mixing, several other sample preparation steps may be
Reciprocating flow-based centrifugal microfluidics mixer.
Noroozi, Zahra; Kido, Horacio; Micic, Miodrag; Pan, Hansheng; Bartolome, Christian; Princevac, Marko; Zoval, Jim; Madou, Marc
2009-07-01
Proper mixing of reagents is of paramount importance for an efficient chemical reaction. While on a large scale there are many good solutions for quantitative mixing of reagents, as of today, efficient and inexpensive fluid mixing in the nanoliter and microliter volume range is still a challenge. Complete, i.e., quantitative mixing is of special importance in any small-scale analytical application because the scarcity of analytes and the low volume of the reagents demand efficient utilization of all available reaction components. In this paper we demonstrate the design and fabrication of a novel centrifugal force-based unit for fast mixing of fluids in the nanoliter to microliter volume range. The device consists of a number of chambers (including two loading chambers, one pressure chamber, and one mixing chamber) that are connected through a network of microchannels, and is made by bonding a slab of polydimethylsiloxane (PDMS) to a glass slide. The PDMS slab was cast using a SU-8 master mold fabricated by a two-level photolithography process. This microfluidic mixer exploits centrifugal force and pneumatic pressure to reciprocate the flow of fluid samples in order to minimize the amount of sample and the time of mixing. The process of mixing was monitored by utilizing the planar laser induced fluorescence (PLIF) technique. A time series of high resolution images of the mixing chamber were analyzed for the spatial distribution of light intensities as the two fluids (suspension of red fluorescent particles and water) mixed. Histograms of the fluorescent emissions within the mixing chamber during different stages of the mixing process were created to quantify the level of mixing of the mixing fluids. The results suggest that quantitative mixing was achieved in less than 3 min. This device can be employed as a stand alone mixing unit or may be integrated into a disk-based microfluidic system where, in addition to mixing, several other sample preparation steps may be
[Network-based continuing medical education].
Romanov, Kalle
2011-01-01
Network-based training can provide continuing medical education with methods, whose implementation by means of traditional training is difficult or practically impossible. By virtue of its chronological and geographical flexibility, educational application of the network may provide extra advantage for the trainee and the trainer. Implementation of network-based training is, however, demanding and laborious both technically and pedagogically, whereby organizations should strive for collaboration in organizing the training. In addition, the status of network-based continuing education in relation to the physician's working time should be clearly defined.
NASA Astrophysics Data System (ADS)
Hernández, Leonor; Juliá, José Enrique; Chiva, Sergio; Paranjape, Sidharth; Ishii, Mamoru
2007-06-01
Important aspects of the hydrodynamics and thus, the correct identification of the flow regime could enhance safety and overall performance in multiphase flow systems. Several works on flow regime identification have been carried out in the past. Most of them consist, in a first stage, on measuring certain flow parameters that can be used as good flow regime indicators and, then, developing a flow regime map using these indicators. In this work, a vertical two-phase flow loop facility was used, whereby local conductivity signals were recorded and utilized for the development of an Artificial Neural Network (ANN) based method for the flow regime classification. The experimental database consists of a total number of 125 test cases covering a wide range of situations in the loop working area. Each experiment flow regime was identified by visual inspection, and classified into bubbly (B), cap-bubbly (CB), slug (S), churn turbulent (CT) or annular (A). The bubble chord length cumulative probability function (CPDF), calculated from the measured conductivity signals was selected as flow regime indicator. Different ANN configurations were designed, trained and optimized. The ANN types and the statistical parameters of the CPFD used as inputs were two of the four parameters varied in the ANN optimization process. The temporal length in the conductivity signal used to calculate the CPDF, was also modified during this study. The range of variation of the temporal signal was from 1 to 60 seconds. Together with this temporal variation, the number of training patterns (increasing with decreasing CPDF processing times or not) was also modified. The modification of all these variables allowed the identification of the ANN configuration that better fit the requirements of the specific study of flow regime classification.
Parallel CFD design on network-based computer
NASA Technical Reports Server (NTRS)
Cheung, Samson
1995-01-01
Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advanced computational fluid dynamics codes, which can be computationally expensive on mainframe supercomputers. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computing environment utilizing a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package is applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.
CFD Optimization on Network-Based Parallel Computer System
NASA Technical Reports Server (NTRS)
Cheung, Samson H.; VanDalsem, William (Technical Monitor)
1994-01-01
Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advance computational fluid dynamics codes, which is computationally expensive in mainframe supercomputer. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computer on a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package has been applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.
Parallel CFD design on network-based computer
NASA Technical Reports Server (NTRS)
Cheung, Samson
1995-01-01
Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advanced computational fluid dynamics codes, which can be computationally expensive on mainframe supercomputers. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computing environment utilizing a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package is applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.
CFD Optimization on Network-Based Parallel Computer System
NASA Technical Reports Server (NTRS)
Cheung, Samson H.; Holst, Terry L. (Technical Monitor)
1994-01-01
Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advance computational fluid dynamics codes, which is computationally expensive in mainframe supercomputer. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computer on a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package has been applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.
RECOVERY ACT - Robust Optimization for Connectivity and Flows in Dynamic Complex Networks
Balasundaram, Balabhaskar; Butenko, Sergiy; Boginski, Vladimir; Uryasev, Stan
2013-12-25
The goal of this project was to study robust connectivity and flow patterns of complex multi-scale systems modeled as networks. Networks provide effective ways to study global, system level properties, as well as local, multi-scale interactions at a component level. Numerous applications from power systems, telecommunication, transportation, biology, social science, and other areas have benefited from novel network-based models and their analysis. Modeling and optimization techniques that employ appropriate measures of risk for identifying robust clusters and resilient network designs in networks subject to uncertain failures were investigated in this collaborative multi-university project. In many practical situations one has to deal with uncertainties associated with possible failures of network components, thereby affecting the overall efficiency and performance of the system (e.g., every node/connection has a probability of partial or complete failure). Some extreme examples include power grid component failures, airline hub failures due to weather, or freeway closures due to emergencies. These are also situations in which people, materials, or other resources need to be managed efficiently. Important practical examples include rerouting flow through power grids, adjusting flight plans, and identifying routes for emergency services and supplies, in the event network elements fail unexpectedly. Solutions that are robust under uncertainty, in addition to being economically efficient, are needed. This project has led to the development of novel models and methodologies that can tackle the optimization problems arising in such situations. A number of new concepts, which have not been previously applied in this setting, were investigated in the framework of the project. The results can potentially help decision-makers to better control and identify robust or risk-averse decisions in such situations. Formulations and optimal solutions of the considered problems need
Code Validation Study for Base Flows
NASA Technical Reports Server (NTRS)
Ascoli, Edward P.; Heiba, Adel H.; Lagnado, Ronald R.; Ungewitter, Ronald J.; Williams, Morgan
1993-01-01
New and old rocket launch concepts recommend the clustering of motors for improved lift capability. The flowfield of the base region of the rocket is very complex and can contain high temperature plume gases. These hot gases can cause catastrophic problems if not adequately designed for. To assess the base region characteristics, advanced computational fluid dynamics (CFD) is being used. As a precursor to these calculations the CFD code requires validation on base flows. The primary objective of this code validation study was to establish a high level of confidence in predicting base flows with the USA CFD code. USA has been extensively validated for fundamental flows and other applications. However, base heating flows have a number of unique characteristics so it was necessary to extend the existing validation for this class of problems. In preparation for the planned NLS 1.5 Stage base heating analysis, six case sets were studied to extend the USA code validation data base. This presentation gives a cursive review of three of these cases. The cases presented include a 2D axi-symmetric study, a 3D real nozzle study, and a 3D multi-species study. The results of all the studies show good general agreement with data with no adjustments to the base numerical algorithms or physical models in the code. The study proved the capability of the USA code for modeling base flows within the accuracy of available data.
Code validation study for base flows
NASA Astrophysics Data System (ADS)
Ascoli, Edward P.; Heiba, Adel H.; Lagnado, Ronald R.; Ungewitter, Ronald J.; Williams, Morgan
1993-07-01
New and old rocket launch concepts recommend the clustering of motors for improved lift capability. The flowfield of the base region of the rocket is very complex and can contain high temperature plume gases. These hot gases can cause catastrophic problems if not adequately designed for. To assess the base region characteristics, advanced computational fluid dynamics (CFD) is being used. As a precursor to these calculations the CFD code requires validation on base flows. The primary objective of this code validation study was to establish a high level of confidence in predicting base flows with the USA CFD code. USA has been extensively validated for fundamental flows and other applications. However, base heating flows have a number of unique characteristics so it was necessary to extend the existing validation for this class of problems. In preparation for the planned NLS 1.5 Stage base heating analysis, six case sets were studied to extend the USA code validation data base. This presentation gives a cursive review of three of these cases. The cases presented include a 2D axi-symmetric study, a 3D real nozzle study, and a 3D multi-species study. The results of all the studies show good general agreement with data with no adjustments to the base numerical algorithms or physical models in the code. The study proved the capability of the USA code for modeling base flows within the accuracy of available data.
Physical Origins of Statistical Scale Invariance or Scaling in Peak Flows in Real River Networks
NASA Astrophysics Data System (ADS)
Mantilla, R.; Gupta, V. K.; Furey, P.
2005-12-01
For nearly forty years, regional flood frequency analyses in unnested and in nested basins have shown that annual peak-flow quantiles can be related to drainage areas as power laws that arise from the property of scale invariance. This empirical feature has instigated a basic hydrologic question: Can power laws be obtained from physical processes governing rainfall-runoff transformations on real channel networks? There has been steady progress in answering this question since 1990. A physical understanding of peak flow scaling requires the time scales of individual rainfall-runoff events as a first step before going to longer time scales. We have used data from two Agriculture Research Service (ARS) experimental basins in the United States to test the physical basis of scaling in peak flows. The first basin is Goodwin Creek in Mississippi (21 km2), and the second one is Walnut Gulch in Arizona (150 km2). We have tested the hypothesis that scaling parameters of individual flood events on Goodwin Creek vary from one event to the next due to the effect of temporal rainfall variability. On the Walnut Gulch, we have tested the hypothesis that scaling in peak flows for short duration rainfall events is controlled by the river network topological and geometric configuration and the downstream hydraulic-geometric properties. Based on these results we present a gauging strategy to investigate peak flow scaling in the 1100 km2 Whitewater basin in Kansas.
Sediment transport mechanisms through the sustainable vegetated flow networks
NASA Astrophysics Data System (ADS)
Allen, Deonie; Haynes, Heather; Arthur, Scott
2016-04-01
Understanding the pollution treatment efficiency of a sustainable urban drainage (SuDS) asset or network requires the influx, transport, detention and discharge of the pollutant within the system. To date event specific monitoring of sediment (primarily total suspended solids) concentrations in the inflow and discharge from SuDS have been monitored. Long term analysis of where the sediment is transported to and the residency time of this pollutant within the SuDS asset or network have not been unraveled due to the difficulty in monitoring specific sediment particulate movement. Using REO tracing methodology, sediment particulate movement has become possible. In tracing sediment movement from an urban surface the internal residency and transportation of this sediment has illustrated SuDS asset differences in multi-event detention. Of key importance is the finding that sediment remains within the SuDS asset for extended periods of time, but that the location sediment detention changes. Thus, over multiple rainfall-runoff events sediment is seen to move through the SuDS assets and network proving the assumption that detained sediment is permanent and stationary to be inaccurate. Furthermore, mass balance analysis of SuDS sediment indicates that there is notable re-suspension and ongoing release of sediment from the SuDS over time and cumulative rainfall-runoff events. Continued monitoring of sediment deposition and concentration in suspension illustrates that sediment detention within SuDS decreases over time/multiple events, without stabilizing within a 12 month period. Repeated experiments show a consistent pattern of detention and release for the three SuDS networks monitored in Scotland. Through consideration of both rainfall and flow factors the drivers of sediment transport within the monitored SuDS have been identified. Within the limitation of this field study the key drivers to SuDS sediment detention efficiency (or transport of sediment through the system
Granular flows on a dissipative base
NASA Astrophysics Data System (ADS)
Louge, Michel Y.; Valance, Alexandre; Lancelot, Paul; Delannay, Renaud; Artières, Olivier
2015-08-01
We study inclined channel flows of sand over a sensor-enabled composite geotextile fabric base that dissipates granular fluctuation energy. We record strain of the fabric along the flow direction with imbedded fiber-optic Bragg gratings, flow velocity on the surface by correlating grain position in successive images, flow thickness with the streamwise shift of an oblique laser light sheet, velocity depth profile through a transparent side wall using a high-speed camera, and overall discharge rate. These independent measurements at inclinations between 33∘ and 37∘ above the angle of repose at 32.1 ±0 .8∘ are consistent with a mass flow rate scaling as the 3 /2 power of the flow depth, which is markedly different than flows on a rigid bumpy boundary. However, this power changes to 5 /2 when flows are forced on the sand bed below its angle of repose. Strain measurements imply that the mean solid volume fraction in the flowing layer above the angle of repose is 0.268 ±0.033 , independent of discharge rate or inclination.
Granular flows on a dissipative base.
Louge, Michel Y; Valance, Alexandre; Lancelot, Paul; Delannay, Renaud; Artières, Olivier
2015-08-01
We study inclined channel flows of sand over a sensor-enabled composite geotextile fabric base that dissipates granular fluctuation energy. We record strain of the fabric along the flow direction with imbedded fiber-optic Bragg gratings, flow velocity on the surface by correlating grain position in successive images, flow thickness with the streamwise shift of an oblique laser light sheet, velocity depth profile through a transparent side wall using a high-speed camera, and overall discharge rate. These independent measurements at inclinations between 33∘ and 37∘ above the angle of repose at 32.1±0.8∘ are consistent with a mass flow rate scaling as the 3/2 power of the flow depth, which is markedly different than flows on a rigid bumpy boundary. However, this power changes to 5/2 when flows are forced on the sand bed below its angle of repose. Strain measurements imply that the mean solid volume fraction in the flowing layer above the angle of repose is 0.268±0.033, independent of discharge rate or inclination.
Motion model identification of rescue robot based on optimized Jordan neural network
NASA Astrophysics Data System (ADS)
Zhang, Guangbin; Zhang, Runmei; Wang, Guangyin; Wu, Yulu
2017-06-01
Considering the influence of various factors, such as speed, angle, depth of water, weight, and water flow, on the underwater rescue robot, a method based on neural network is proposed. According to the characteristics of Elman and Jordan neural network, a new dynamic neural network is constructed. The network can be used to remember the state of the hidden layer and increase the feedback of the output node. The improved Jordan network is optimized by chaos particle swarm optimization algorithm. The optimized neural network is applied to identify the dynamic model of the underwater rescue robot. The simulation results show that the neural network has good convergence speed and accuracy.
Content-Based Networking: DTN, AMS, Sharednet
NASA Technical Reports Server (NTRS)
Burleigh, Scott
2006-01-01
A detailed viewgraph presentation on DTN, AMS, and Sharednet content-based networking is shown. The contents include: 1) DARPA Content-Based Networking Summary of Requirements; 2) Concept; 3) Key Features of AMS; 4) Overview of Sharednet; 5) SharedNet Deployment History; 6) SharedNet AMS DTN; 7) Detailed Structure; and 8) Bottom line.
Traffic Flow Density Distribution Based on FEM
NASA Astrophysics Data System (ADS)
Ma, Jing; Cui, Jianming
In analysis of normal traffic flow, it usually uses the static or dynamic model to numerical analyze based on fluid mechanics. However, in such handling process, the problem of massive modeling and data handling exist, and the accuracy is not high. Finite Element Method (FEM) is a production which is developed from the combination of a modern mathematics, mathematics and computer technology, and it has been widely applied in various domain such as engineering. Based on existing theory of traffic flow, ITS and the development of FEM, a simulation theory of the FEM that solves the problems existing in traffic flow is put forward. Based on this theory, using the existing Finite Element Analysis (FEA) software, the traffic flow is simulated analyzed with fluid mechanics and the dynamics. Massive data processing problem of manually modeling and numerical analysis is solved, and the authenticity of simulation is enhanced.
Multi-Commodity Network Flow for Tracking Multiple People.
Ben Shitrit, Horesh; Berclaz, Jérôme; Fleuret, Francois; Fua, Pascal
2014-08-01
In this paper, we show that tracking multiple people whose paths may intersect can be formulated as a multi-commodity network flow problem. Our proposed framework is designed to exploit image appearance cues to prevent identity switches. Our method is effective even when such cues are only available at distant time intervals. This is unlike many current approaches that depend on appearance being exploitable from frame-to-frame. Furthermore, our algorithm lends itself to a real-time implementation. We validate our approach on three publicly available datasets that contain long and complex sequences, the APIDIS basketball dataset, the ISSIA soccer dataset, and the PETS'09 pedestrian dataset. We also demonstrate its performance on a newer basketball dataset that features complete world championship basketball matches. In all cases, our approach preserves identity better than state-of-the-art tracking algorithms.
Multi-Commodity Network Flow for Tracking Multiple People.
Ben Shitrit, Horesh; Berclaz, Jérôme; Fleuret, François; Fua, Pascal
2013-10-17
n this paper, we show that tracking multiple people whose paths may intersect can be formulated as a multi-commodity network flow problem. Our proposed framework is designed to exploit image appearance cues to prevent identity switches. Our method is effective even when such cues are only available at distant time intervals. This is unlike many current approaches that depend on appearance being exploitable from frame to frame. Furthermore, our algorithm lends itself to a real-time implementation. We validate our approach on three publicly available datasets that contain long and complex sequences, the APIDIS basketball dataset, the ISSIA soccer dataset and the PETS’09 pedestrian dataset. We also demonstrate its performance on a newer basketball dataset that features complete world championship basketball matches. In all cases, our approach preserves identity better than state-of-the-art tracking algorithms.
Speed limit and ramp meter control for traffic flow networks
NASA Astrophysics Data System (ADS)
Goatin, Paola; Göttlich, Simone; Kolb, Oliver
2016-07-01
The control of traffic flow can be related to different applications. In this work, a method to manage variable speed limits combined with coordinated ramp metering within the framework of the Lighthill-Whitham-Richards (LWR) network model is introduced. Following a 'first-discretize-then-optimize' approach, the first order optimality system is derived and the switch of speeds at certain fixed points in time is explained, together with the boundary control for the ramp metering. Sequential quadratic programming methods are used to solve the control problem numerically. For application purposes, experimental setups are presented wherein variable speed limits are used as a traffic guidance system to avoid traffic jams on highway interchanges and on-ramps.
Fractal Analysis of Flow Resistance in Tree-Like Branching Networks with Roughened Microchannels
NASA Astrophysics Data System (ADS)
Yang, Shanshan; Fu, Huahua; Yu, Boming
In this work, the effective average height of the roughness elements, the relative increase of the pressure gradients, the relative decrease of the permeability are derived based on the fractal geometry theory and technique for laminar flow through tree-like branching networks with roughened channels. The relationships among the effective average height, the structural parameters and pressure drops as well as permeability are studied. It is found that the total pressure drop across a tree-like branching network with roughened channels is increased by a factor of 1/(1 ‑ ɛ)4, and the permeability for the network with roughened channels is decreased by a factor of (1 ‑ ɛ)2, where ɛ is the relative roughness of surfaces of channels, compared to those with smooth channels.
Information flow through threespine stickleback networks without social transmission
Atton, N.; Hoppitt, W.; Webster, M. M.; Galef, B. G.; Laland, K. N.
2012-01-01
Social networks can result in directed social transmission of learned information, thus influencing how innovations spread through populations. Here we presented shoals of threespine sticklebacks (Gasterosteous aculeatus) with two identical foraging tasks and applied network-based diffusion analysis (NBDA) to determine whether the order in which individuals in a social group contacted and solved the tasks was affected by the group's network structure. We found strong evidence for a social effect on discovery of the foraging tasks with individuals tending to discover a task sooner when others in their group had previously done so, and with the spread of discovery of the foraging tasks influenced by groups' social networks. However, the same patterns of association did not reliably predict spread of solution to the tasks, suggesting that social interactions affected the time at which the tasks were discovered, but not the latency to its solution following discovery. The present analysis, one of the first applications of NBDA to a natural animal system, illustrates how NBDA can lead to insight into the mechanisms supporting behaviour acquisition that more conventional statistical approaches might miss. Importantly, we provide the first compelling evidence that the spread of novel behaviours can result from social learning in the absence of social transmission, a phenomenon that we refer to as an untransmitted social effect on learning. PMID:22896644
Information flow through threespine stickleback networks without social transmission.
Atton, N; Hoppitt, W; Webster, M M; Galef, B G; Laland, K N
2012-10-22
Social networks can result in directed social transmission of learned information, thus influencing how innovations spread through populations. Here we presented shoals of threespine sticklebacks (Gasterosteous aculeatus) with two identical foraging tasks and applied network-based diffusion analysis (NBDA) to determine whether the order in which individuals in a social group contacted and solved the tasks was affected by the group's network structure. We found strong evidence for a social effect on discovery of the foraging tasks with individuals tending to discover a task sooner when others in their group had previously done so, and with the spread of discovery of the foraging tasks influenced by groups' social networks. However, the same patterns of association did not reliably predict spread of solution to the tasks, suggesting that social interactions affected the time at which the tasks were discovered, but not the latency to its solution following discovery. The present analysis, one of the first applications of NBDA to a natural animal system, illustrates how NBDA can lead to insight into the mechanisms supporting behaviour acquisition that more conventional statistical approaches might miss. Importantly, we provide the first compelling evidence that the spread of novel behaviours can result from social learning in the absence of social transmission, a phenomenon that we refer to as an untransmitted social effect on learning.
Low-flow, base-flow, and mean-flow regression equations for Pennsylvania streams
Stuckey, Marla H.
2006-01-01
Low-flow, base-flow, and mean-flow characteristics are an important part of assessing water resources in a watershed. These streamflow characteristics can be used by watershed planners and regulators to determine water availability, water-use allocations, assimilative capacities of streams, and aquatic-habitat needs. Streamflow characteristics are commonly predicted by use of regression equations when a nearby streamflow-gaging station is not available. Regression equations for predicting low-flow, base-flow, and mean-flow characteristics for Pennsylvania streams were developed from data collected at 293 continuous- and partial-record streamflow-gaging stations with flow unaffected by upstream regulation, diversion, or mining. Continuous-record stations used in the regression analysis had 9 years or more of data, and partial-record stations used had seven or more measurements collected during base-flow conditions. The state was divided into five low-flow regions and regional regression equations were developed for the 7-day, 10-year; 7-day, 2-year; 30-day, 10-year; 30-day, 2-year; and 90-day, 10-year low flows using generalized least-squares regression. Statewide regression equations were developed for the 10-year, 25-year, and 50-year base flows using generalized least-squares regression. Statewide regression equations were developed for harmonic mean and mean annual flow using weighted least-squares regression. Basin characteristics found to be significant explanatory variables at the 95-percent confidence level for one or more regression equations were drainage area, basin slope, thickness of soil, stream density, mean annual precipitation, mean elevation, and the percentage of glaciation, carbonate bedrock, forested area, and urban area within a basin. Standard errors of prediction ranged from 33 to 66 percent for the n-day, T-year low flows; 21 to 23 percent for the base flows; and 12 to 38 percent for the mean annual flow and harmonic mean, respectively. The
Robustness in semantic networks based on cliques
NASA Astrophysics Data System (ADS)
Grilo, M.; Fadigas, I. S.; Miranda, J. G. V.; Cunha, M. V.; Monteiro, R. L. S.; Pereira, H. B. B.
2017-04-01
Here, we present a study on how the structure of semantic networks based on cliques (specifically, article titles) behaves when vertex removal strategies (i.e., random and uniform vertex removal - RUR, highest degree vertex removal - HDR, and highest intermediation centrality vertex removal - HICR) are applied to this type of network. We propose a method for calculation of the average size of the small components and we identify the existence of a fraction (fp) where the topological structure of the network changes. Semantic networks based on cliques maintain the small-world phenomenon when subjected to RUR, HDR and HICR for fractions of removed vertices less than or equal to fp.
NASA Astrophysics Data System (ADS)
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.
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.
Parallel Computation of Unsteady Flows on a Network of Workstations
NASA Technical Reports Server (NTRS)
1997-01-01
Parallel computation of unsteady flows requires significant computational resources. The utilization of a network of workstations seems an efficient solution to the problem where large problems can be treated at a reasonable cost. This approach requires the solution of several problems: 1) the partitioning and distribution of the problem over a network of workstation, 2) efficient communication tools, 3) managing the system efficiently for a given problem. Of course, there is the question of the efficiency of any given numerical algorithm to such a computing system. NPARC code was chosen as a sample for the application. For the explicit version of the NPARC code both two- and three-dimensional problems were studied. Again both steady and unsteady problems were investigated. The issues studied as a part of the research program were: 1) how to distribute the data between the workstations, 2) how to compute and how to communicate at each node efficiently, 3) how to balance the load distribution. In the following, a summary of these activities is presented. Details of the work have been presented and published as referenced.
Reentrant Information Flow in Electrophysiological Rat Default Mode Network.
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.
Reentrant Information Flow in Electrophysiological Rat Default Mode Network
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. PMID:28289373
Transnational cocaine and heroin flow networks in western Europe: A comparison.
Chandra, Siddharth; Joba, Johnathan
2015-08-01
A comparison of the properties of drug flow networks for cocaine and heroin in a group of 17 western European countries is provided with the aim of understanding the implications of their similarities and differences for drug policy. Drug flow data for the cocaine and heroin networks were analyzed using the UCINET software package. Country-level characteristics including hub and authority scores, core and periphery membership, and centrality, and network-level characteristics including network density, the results of a triad census, and the final fitness of the core-periphery structure of the network, were computed and compared between the two networks. The cocaine network contains fewer path redundancies and a smaller, more tightly knit core than the heroin network. Authorities, hubs and countries central to the cocaine network tend to have higher hub, authority, and centrality scores than those in the heroin network. The core-periphery and hub-authority structures of the cocaine and heroin networks reflect the west-to-east and east-to-west patterns of flow of cocaine and heroin respectively across Europe. The key nodes in the cocaine and heroin networks are generally distinct from one another. The analysis of drug flow networks can reveal important structural features of trafficking networks that can be useful for the allocation of scarce drug control resources. The identification of authorities, hubs, network cores, and network-central nodes can suggest foci for the allocation of these resources. In the case of Europe, while some countries are important to both cocaine and heroin networks, different sets of countries occupy positions of prominence in the two networks. The distinct nature of the cocaine and heroin networks also suggests that a one-size-fits-all supply- and interdiction-focused policy may not work as well as an approach that takes into account the particular characteristics of each network. Copyright © 2015 Elsevier B.V. All rights reserved.
Bandwidth turbulence control based on flow community structure in the Internet
NASA Astrophysics Data System (ADS)
Wu, Xiaoyu; Gu, Rentao; Ji, Yuefeng
2016-10-01
Bursty flows vary rapidly in short period of time, and cause fierce bandwidth turbulence in the Internet. In this letter, we model the flow bandwidth turbulence process by constructing a flow interaction network (FIN network), with nodes representing flows and edges denoting bandwidth interactions among them. To restrain the bandwidth turbulence in FIN networks, an immune control strategy based on flow community structure is proposed. Flows in community boundary positions are immunized to cut off the inter-community turbulence spreading. By applying this control strategy in the first- and the second-level flow communities separately, 97.2% flows can effectively avoid bandwidth variations by immunizing 21% flows, and the average bandwidth variation degree reaches near zero. To achieve a similar result, about 70%-90% immune flows are needed with targeted control strategy based on flow degrees and random control strategy. Moreover, simulation results showed that the control effect of the proposed strategy improves significantly if the immune flow number is relatively smaller in each control step.
Modeling patient flows using a queuing network with blocking.
Koizumi, Naoru; Kuno, Eri; Smith, Tony E
2005-02-01
The downsizing and closing of state mental health institutions in Philadelphia in the 1990's led to the development of a continuum care network of residential-based services. Although the diversity of care settings increased, congestion in facilities caused many patients to unnecessarily spend extra days in intensive facilities. This study applies a queuing network system with blocking to analyze such congestion processes. "Blocking" denotes situations where patients are turned away from accommodations to which they are referred, and are thus forced to remain in their present facilities until space becomes available. Both mathematical and simulation results are presented and compared. Although queuing models have been used in numerous healthcare studies, the inclusion of blocking is still rare. We found that, in Philadelphia, the shortage of a particular type of facilities may have created "upstream blocking". Thus removal of such facility-specific bottlenecks may be the most efficient way to reduce congestion in the system as a whole.
NASA Astrophysics Data System (ADS)
Ryerson, F. J.; Ezzedine, S. M.; Glascoe, L. G.; Antoun, T. H.
2011-12-01
Fractures and fracture networks are the principle pathways for migration of water, heat and mass in enhanced geothermal systems, oil and gas reservoirs, CO2 leakage from saline aquifers, and radioactive and toxic industrial wastes from underground storage repositories. A major issue to overcome when characterizing a fractured reservoir is that of data limitation due to accessibility and affordability. Moreover, the ability to map discontinuities in the rock with available geological and geophysical tools tends to decrease particularly as the scale of the discontinuity goes down. Data collected are often reduced to probability distribution functions for predictive modeling and simulation in a stochastic framework such as stochastic discrete fracture network. Stochastic discrete fracture network models enable probabilistic assessment of flow, transport and geomechanical phenomena that are not adequately captured using continuum models. Despite the fundamental uncertainties inherited within the probabilistic reduction of the sparse data collected, very little work has been conducted on quantifying uncertainty on the reduced probabilistic distribution functions. In the current study, we investigate the impact of parameter uncertainties of the distribution functions that characterize discrete fracture networks on the flow, heat and mass transport and geomechanics. Numerical results of first, second and third moments, normalized to a base case scenario, are presented and compared to theoretical results extended from percolation theory. (Prepared by LLNL under Contract DE-AC52-07NA27344)
NASA Astrophysics Data System (ADS)
Donado-Garzon, L. D.; Pardo, Y.
2013-12-01
Fractured media are very heterogeneous systems where occur complex physical and chemical processes to model. One of the possible approaches to conceptualize this type of massifs is the Discrete Fracture Network (DFN). Donado et al., modeled flow and transport in a granitic batholith based on this approach and found good fitting with hydraulic and tracer tests, but the computational cost was excessive due to a gigantic amount of elements to model. We present in this work a methodology based on percolation theory for reducing the number of elements and in consequence, to reduce the bandwidth of the conductance matrix and the execution time of each network. DFN poses as an excellent representation of all the set of fractures of the media, but not all the fractures of the media are part of the conductive network. Percolation theory is used to identify which nodes or fractures are not conductive, based on the occupation probability or percolation threshold. In a fractured system, connectivity determines the flow pattern in the fractured rock mass. This volume of fluid is driven through connection paths formed by the fractures, when the permeability of the rock is negligible compared to the fractures. In a population of distributed fractures, each of this that has no intersection with any connected fracture do not contribute to generate a flow field. This algorithm also permits us to erase these elements however they are water conducting and hence, refine even more the backbone of the network. We used 100 different generations of DFN that were optimized in this study using percolation theory. In each of the networks calibrate hydrodynamic parameters as hydraulic conductivity and specific storage coefficient, for each of the five families of fractures, yielding a total of 10 parameters to estimate, at each generation. Since the effects of the distribution of fault orientation changes the value of the percolation threshold, but not the universal laws of classical
Controls on debris flow bulking in proglacial gully networks on Mount Rainier, WA
NASA Astrophysics Data System (ADS)
Legg, N. T.; Meigs, A.; Grant, G. E.; Kennard, P.
2012-12-01
Conversion of floodwaters to debris flows due to sediment bulking continues to be a poorly understood phenomenon. This study examines the initiation zone of a series of six debris flows that originated in proglacial areas of catchments on the flank of Mount Rainier during one storm in 2006. One-meter spatial resolution aerial photographs and LiDAR DEMs acquired before and after the storm reveal the lack of a single mass failure to explain the debris flow deposits. Rather, the imagery show appreciable gully widening along reaches up to approximately 1.5 km in length. Based on gully discharges estimated from rainfall rates and estimates of sediment contribution from gully wall width change, we find that the sediment volumes contributed from gully walls are sufficient to bulk floodwaters up to debris flow concentrations. Points in gullies where width change began (upstream limit) in 2006 have a power law trend (R2 = 0.58) in terms of slope-drainage area. Reaches with noticeable width change, which we refer to as bulking reaches (BR), plot along a similar trend with greater drainage areas and gentler slopes. We then extracted slope and drainage area of all proglacial drainage networks to examine differences in morphology between debris flow basins (DFB) and non-debris flow basins (NDFB), hypothesizing that DFB would have a greater portion of their drainage networks with similar morphology to BR than NDFB. A comparison of total network length with greater slope and area than BR reveals that the two basins types are not statistically different. Lengths of the longest reaches with greater slope and drainage area than the BR trend, however, are statistically longer in DFB than in the NDFBs (p<0.05). These results suggest that debris flow initiation by sediment bulking does not operate as a simple threshold phenomenon in slope-area space. Instead debris flow initiation via bulking depends upon slope, drainage area, and gully length. We suspect the dependence on length
Artificial Neural Network Modeling to Evaluate the Dynamic Flow Stress of 7050 Aluminum Alloy
NASA Astrophysics Data System (ADS)
Quan, Guo-zheng; Wang, Tong; Li, Yong-le; Zhan, Zong-yang; Xia, Yu-feng
2016-02-01
The flow stress data have been obtained by a set of isothermal hot compression tests, which were carried out in the temperature range of 573-723 K and strain rates of 0.01, 0.1, 1, and 10 s-1 with a reduction of 60% on a Gleeble-1500 thermo-mechanical simulator. On the basis of the experimental data, constitutive equation and an artificial neural network model were developed for the analysis and simulation of the flow behavior of the 7050 aluminum alloy. After training with standard back-propagation learning algorithm, the artificial neural network model has the ability to present the intrinsic relationship between the flow stress and the processing variables. In the present model, the temperature, strain, and strain rate were chosen as inputs, and the flow stress was chosen as output. By comparing the values of correlation coefficient and average absolute relative error, the prediction accuracy of the model and the improved Arrhenius-type model can be evaluated. The results indicated that the well-trained artificial neural network model is more accurate than the improved Arrhenius-type model in predicting the hot compressive behavior of the as-extruded 7050 aluminum alloy. Based on the predicted stress data and experimental stress data, the 3D continuous stress-strain maps at different strains, temperatures, and strain rates were plotted subsequently. Besides, the flow stress values at arbitrary temperature, strain rate, and strain are explicit on the 3D continuous stress-strain maps, which would be beneficial to articulate working processes more validly.
Network-based Approaches in Pharmacology.
Boezio, Baptiste; Audouze, Karine; Ducrot, Pierre; Taboureau, Olivier
2017-10-01
In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Mass transport enhancement in redox flow batteries with corrugated fluidic networks
NASA Astrophysics Data System (ADS)
Lisboa, Kleber Marques; Marschewski, Julian; Ebejer, Neil; Ruch, Patrick; Cotta, Renato Machado; Michel, Bruno; Poulikakos, Dimos
2017-08-01
We propose a facile, novel concept of mass transfer enhancement in flow batteries based on electrolyte guidance in rationally designed corrugated channel systems. The proposed fluidic networks employ periodic throttling of the flow to optimally deflect the electrolytes into the porous electrode, targeting enhancement of the electrolyte-electrode interaction. Theoretical analysis is conducted with channels in the form of trapezoidal waves, confirming and detailing the mass transport enhancement mechanism. In dilute concentration experiments with an alkaline quinone redox chemistry, a scaling of the limiting current with Re0.74 is identified, which compares favourably against the Re0.33 scaling typical of diffusion-limited laminar processes. Experimental IR-corrected polarization curves are presented for high concentration conditions, and a significant performance improvement is observed with the narrowing of the nozzles. The adverse effects of periodic throttling on the pumping power are compared with the benefits in terms of power density, and an improvement of up to 102% in net power density is obtained in comparison with the flow-by case employing straight parallel channels. The proposed novel concept of corrugated fluidic networks comes with facile fabrication and contributes to the improvement of the transport characteristics and overall performance of redox flow battery systems.
A Dynamic Space-Time Network Flow Model for City Traffic Congestion.
1983-10-01
both the constant demand and elastic demand models a static set of origin destination demands is assumed to exist. This may be a valid long run...determined for each situation. II. A TWO ATTRIBUTE DINAMIC NETWORK FLOW MODEL In this section a two attribute network flow model which is dynamic, i.e
Base flow in the Great Lakes Basin
Neff, B.P.; Day, S.M.; Piggott, A.R.; Fuller, L.M.
2005-01-01
Hydrograph separations were performed using the PART, HYSEP 1, 2, and 3, BFLOW and UKIH methods on 104,293 years of daily streamflow records from 3,936 streamflow-gaging stations in Ontario, Canada and the eight Great Lakes States of Illinois, Indiana, Michigan, Minnesota, New York, Ohio, Pennsylvania, and Wisconsin to estimate base-flow index (BFI) and base flow. BFI ranged an average of 0.24 BFI depending on which hydrograph-separation method was used. BFI data from 959 selected streamflow-gaging stations with a combined 28,784 years of daily streamflow data were used to relate BFI to surficial geology and the proportion of surface water within the gaged watersheds. This relation was then used to derive estimates of BFI throughout the Great Lakes, Ottawa River, and upper St. Lawrence River Basins at a scale of 8-digit hydrologic unit code (HUC) watersheds for the U.S. and tertiary watersheds in Canada. This process was repeated for each of the six hydrograph-separation methods used. When applied to gaged watersheds, model results predicted observed base flow within 0.2 BFI up to 94 percent of the time. Estimates of long-term (length of streamflow record) average annual streamflow in each HUC and tertiary watershed were calculated and used to determine average annual base flow from BFI estimates. Possibilities for future study based on results from this study include long-term trend analysis of base flow and improving the scale at which base-flow estimates can be made.
Network-Based Classrooms: Promises and Realities.
ERIC Educational Resources Information Center
Bruce, Bertram C., Ed.; And Others
Exploring how new technologies and new pedagogies transform and are transformed by existing institutions, this book presents 14 essays that discuss network-based classrooms in which students use communications software on computer networks to converse in writing. The first part of the book discusses general themes and issues of the ENFI…
Neural network based architectures for aerospace applications
NASA Technical Reports Server (NTRS)
Ricart, Richard
1987-01-01
A brief history of the field of neural networks research is given and some simple concepts are described. In addition, some neural network based avionics research and development programs are reviewed. The need for the United States Air Force and NASA to assume a leadership role in supporting this technology is stressed.
Influence of perched groundwater on base flow
Niswonger, R.G.; Fogg, G.E.
2008-01-01
Analysis with a three-dimensional variably saturated groundwater flow model provides a basic understanding of the interplay between streams and perched groundwater. A simplified, layered model of heterogeneity was used to explore these relationships. Base flow contribution from perched groundwater was evaluated with regard to varying hydrogeologic conditions, including the size and location of the fine-sediment unit and the hydraulic conductivity of the fine-sediment unit and surrounding coarser sediment. Simulated base flow was sustained by perched groundwater with a maximum monthly discharge in excess of 15 L/s (0.6 feet3/s) over the length of the 2000-m stream reach. Generally, the rate of perched-groundwater discharge to the stream was proportional to the hydraulic conductivity of sediment surrounding the stream, whereas the duration of discharge was proportional to the hydraulic conductivity of the fine-sediment unit. Other aspects of the perched aquifer affected base flow, such as the depth of stream penetration and the size of the fine-sediment unit. Greater stream penetration decreased the maximum base flow contribution but increased the duration of contribution. Perched groundwater provided water for riparian vegetation at the demand rate but reduced the duration of perched-groundwater discharge nearly 75%. Copyright 2008 by the American Geophysical Union.
Modeling the interdependent network based on two-mode networks
NASA Astrophysics Data System (ADS)
An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian
2017-10-01
Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.
Trust Based Routing in Ad Hoc Network
NASA Astrophysics Data System (ADS)
Talati, Mikita V.; Valiveti, Sharada; Kotecha, K.
Ad Hoc network often termed as an infrastructure-less, self- organized or spontaneous network.The execution and survival of an ad-hoc network is solely dependent upon the cooperative and trusting nature of its nodes. However, this naive dependency on intermediate nodes makes the ad-hoc network vulnerable to passive and active attacks by malicious nodes and cause inflict severe damage. A number of protocols have been developed to secure ad-hoc networks using cryptographic schemes, but all rely on the presence of trust authority. Due to mobility of nodes and limitation of resources in wireless network one interesting research area in MANET is routing. This paper offers various trust models and trust based routing protocols to improve the trustworthiness of the neighborhood.Thus it helps in selecting the most secure and trustworthy route from the available ones for the data transfer.
Network-Based Collaborative Education.
ERIC Educational Resources Information Center
Trentin, Guglielmo
1999-01-01
Discusses telematics and the use of computer networks to support collaborative education, both among teachers for training and planning and among students in their learning process. Highlights include traditional class groups and groups using computer-mediated communication; teachers' roles; learning circles for teachers; online teacher training;…
NASA Astrophysics Data System (ADS)
Piotrowski, Adam P.; Napiorkowski, Jarosław J.
2011-09-01
SummaryAlthough neural networks have been widely applied to various hydrological problems, including river flow forecasting, for at least 15 years, they have usually been trained by means of gradient-based algorithms. Recently nature inspired Evolutionary Computation algorithms have rapidly developed as optimization methods able to cope not only with non-differentiable functions but also with a great number of local minima. Some of proposed Evolutionary Computation algorithms have been tested for neural networks training, but publications which compare their performance with gradient-based training methods are rare and present contradictory conclusions. The main goal of the present study is to verify the applicability of a number of recently developed Evolutionary Computation optimization methods, mostly from the Differential Evolution family, to multi-layer perceptron neural networks training for daily rainfall-runoff forecasting. In the present paper eight Evolutionary Computation methods, namely the first version of Differential Evolution (DE), Distributed DE with Explorative-Exploitative Population Families, Self-Adaptive DE, DE with Global and Local Neighbors, Grouping DE, JADE, Comprehensive Learning Particle Swarm Optimization and Efficient Population Utilization Strategy Particle Swarm Optimization are tested against the Levenberg-Marquardt algorithm - probably the most efficient in terms of speed and success rate among gradient-based methods. The Annapolis River catchment was selected as the area of this study due to its specific climatic conditions, characterized by significant seasonal changes in runoff, rapid floods, dry summers, severe winters with snowfall, snow melting, frequent freeze and thaw, and presence of river ice - conditions which make flow forecasting more troublesome. The overall performance of the Levenberg-Marquardt algorithm and the DE with Global and Local Neighbors method for neural networks training turns out to be superior to other
Dynamic Baysesian state-space model with a neural network for an online river flow prediction
NASA Astrophysics Data System (ADS)
Ham, Jonghwa; Hong, Yoon-Seok
2013-04-01
-space model formulation. The nonlinear Monte Carlo filtering algorithm is based on recursively constructing the posterior probability density (distribution) of the state variable of neural network's weight, with respect to measured data (in our case, river flow), through a random trajectory of the state by entities called 'particles' in the dynamic state-space model formulation. A weight, which is the probability of the trajectory of the state, is assigned to each particle by a Bayesian correction term based on measurement. The algorithms differ in the way that the swarm of particles evolves and adapts to incoming online measurement data. In order to demonstrate the efficiency and usefulness of the proposed MLP-SMC, a practical application of hydrological modeling is carried out to predict the river flow sequentially in advance on the arrival of each new item of river flow data at intervals of 10 minutes. The performance of the proposed MLP-SMC is compared with the performance of a multi-layer perceptron (MLP) model trained using the back-propagation learning algorithm (MLP-BP) in which a batch off-line learning algorithm is implemented. The results show that the proposed MLP-SMC shows superiority in terms of model accuracy and computational cost compared with MLP-BP. The sequential Monte Carlo learning algorithm implemented in MLP-SMC is shown to have less sensitivity to noisy and sparsely distributed data compared to the batch off-line learning algorithm used in MLP-BP.
Detecting Communities Based on Network Topology
NASA Astrophysics Data System (ADS)
Liu, Wei; Pellegrini, Matteo; Wang, Xiaofan
2014-07-01
Network methods have had profound influence in many domains and disciplines in the past decade. Community structure is a very important property of complex networks, but the accurate definition of a community remains an open problem. Here we defined community based on three properties, and then propose a simple and novel framework to detect communities based on network topology. We analyzed 16 different types of networks, and compared our partitions with Infomap, LPA, Fastgreedy and Walktrap, which are popular algorithms for community detection. Most of the partitions generated using our approach compare favorably to those generated by these other algorithms. Furthermore, we define overlapping nodes that combine community structure with shortest paths. We also analyzed the E. Coli. transcriptional regulatory network in detail, and identified modules with strong functional coherence.
Natale, Fabrizio; Savini, Lara; Giovannini, Armando; Calistri, Paolo; Candeloro, Luca; Fiore, Gianluca
2011-02-01
A new method for the calculation of a centrality measure (Disease Flow Centrality, DFC), which takes into account temporal dynamics of livestock movement networks, is proposed. The method is based on a network traversal algorithm which represents an epidemic process more realistically compared with traditional graph traversal algorithms used in the calculation of centrality measures on static networks. The new approach was tested on networks generated from all the registered movements of cattle in Italy in the years 2007, 2008 and 2009 and the results were compared to those obtained by classical centrality measures. The results show that DFC values often differ substantially from those of other centrality measures and that these DFC values tend to be more unstable in time. The DFC offers several advantages for assessing risk and vulnerability of specific holdings and of an entire network, using recent movement data from national livestock databases. Some examples also indicate how the basic approach in the DFC calculation could be expanded into a more complex epidemic model by incorporating weights and how it could be combined with a geo-spatial perspective.
A Network Model of Channel Competition in Geochemical Flows
NASA Astrophysics Data System (ADS)
Szymczak, P.; Ladd, A. J.
2005-12-01
Dissolution in porous media introduces a positive feedback between fluid transport and chemical reactions at the mineral surfaces leading to the formation of pronounced wormhole-like channels [1]. As the dissolution proceeds, the channels interact and compete for the flow; eventually the growth of the shorter ones ceases. Thus the number of channels decreases with time while the characteristic distance between them increases, which leads to a scale-invariant power-law distribution of channel lengths. We present several resistor network models of the evolution of dissolving channels and explore them using computer simulation and theoretical methods such as a theory of branched growth [2]. First, the simplest case of two competing channels is considered, and the conditions under which one of them captures the flow from the other are explored. Next, the case of N competing channels is analyzed and the numerical results for N ~ 103 are presented. The results are compared with pore-scale simulations of fracture dissolution using the microscopic, three-dimensional numerical model developed in [3]. It is seen that despite their simplicity, the resistor models retain the essential features of the nonlinear interaction between the channels. [1] P. Ortoleva, J. Chadam, E. Merino, and A. Sen, Geochemical Self Organisation II: The Reactive-Infiltration Instability, Am. J. Sci., 287, 1008, 1987 [2] T.C. Halsey, M. Leibig, Theory of Branched Growth, Phys. Rev. A, 46, 7793, 1992 [3] P. Szymczak, A. J. C. Ladd, Microscopic Simulations of Fracture Dissolution, Geophys. Res. Lett., 31, L23606, doi:10.1029/2004GL021297, 2004
NASA Astrophysics Data System (ADS)
Wang, Wendong; Su, Yuliang; Zhang, Xiao; Sheng, Guanglong; Ren, Long
2015-03-01
This paper formulates a fractal-tree network model to address the challenging problem of characterizing the hydraulic fracture network in unconventional reservoirs. It has been proved that the seepage flow in tight/shale oil reservoirs is much more complicated to the conventional formation. To further understand the flow mechanisms in such a complex system, a semi-analytical model considering "branch network fractures" was established stage by stage using point source method and superposition principle. Fractal method was employed to generate and represent induced fracture network around bi-wing fractures. In addition, based on the new established model and solution, deterministic fractal-tree-like fracture network patterns and heterogeneity were carefully investigated and compared with the simulation model. Results show that the fractal dimension for the fracture network has significant effect on the connectivity of the stimulated reservoir. The proposed fractal model may capture the characteristics of the heterogeneous complex fracture network and help in understanding the flow and transport mechanisms of multiple fractured horizontal wells.
Combining neural networks and genetic algorithms for hydrological flow forecasting
NASA Astrophysics Data System (ADS)
Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr
2010-05-01
We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and
A multi-scale network method for two-phase flow in porous media
NASA Astrophysics Data System (ADS)
Khayrat, Karim; Jenny, Patrick
2017-08-01
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 within 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.
NASA Astrophysics Data System (ADS)
Xue, Jie; Gui, Dongwei; Zhao, Ying; Lei, Jiaqiang; Zeng, Fanjiang; Feng, Xinlong; Mao, Donglei; Shareef, Muhammad
2016-09-01
The competition for water resources between agricultural and natural oasis ecosystems has become an increasingly serious problem in oasis areas worldwide. Recently, the intensive extension of oasis farmland has led to excessive exploitation of water discharge, and consequently has resulted in a lack of water supply in natural oasis. To coordinate the conflicts, this paper provides a decision-making framework for modeling environmental flows in oasis areas using Bayesian networks (BNs). Three components are included in the framework: (1) assessment of agricultural economic loss due to meeting environmental flow requirements; (2) decision-making analysis using BNs; and (3) environmental flow decision-making under different water management scenarios. The decision-making criterion is determined based on intersection point analysis between the probability of large-level total agro-economic loss and the ratio of total to maximum agro-economic output by satisfying environmental flows. An application in the Qira oasis area of the Tarim Basin, Northwest China indicates that BNs can model environmental flow decision-making associated with agricultural economic loss effectively, as a powerful tool to coordinate water-use conflicts. In the case study, the environmental flow requirement is determined as 50.24%, 49.71% and 48.73% of the natural river flow in wet, normal and dry years, respectively. Without further agricultural economic loss, 1.93%, 0.66% and 0.43% of more river discharge can be allocated to eco-environmental water demands under the combined strategy in wet, normal and dry years, respectively. This work provides a valuable reference for environmental flow decision-making in any oasis area worldwide.
Incorporation of Condensation Heat Transfer in a Flow Network Code
NASA Technical Reports Server (NTRS)
Anthony, Miranda; Majumdar, Alok
2002-01-01
Pure water is distilled from waste water in the International Space Station. The distillation assembly consists of an evaporator, a compressor and a condenser. Vapor is periodically purged from the condenser to avoid vapor accumulation. Purged vapor is condensed in a tube by coolant water prior to entering the purge pump. The paper presents a condensation model of purged vapor in a tube. This model is based on the Finite Volume Method. In the Finite Volume Method, the flow domain is discretized into multiple control volumes and a simultaneous analysis is performed.
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.
Dynamics of comb-of-comb-network polymers in random layered flows.
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 W_{α}. 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.
Non-Markovian quantum feedback networks II: Controlled flows
NASA Astrophysics Data System (ADS)
Gough, John E.
2017-06-01
The concept of a controlled flow of a dynamical system, especially when the controlling process feeds information back about the system, is of central importance in control engineering. In this paper, we build on the ideas presented by Bouten and van Handel [Quantum Stochastics and Information: Statistics, Filtering and Control (World Scientific, 2008)] and develop a general theory of quantum feedback. We elucidate the relationship between the controlling processes, Z, and the measured processes, Y, and to this end we make a distinction between what we call the input picture and the output picture. We should note that the input-output relations for the noise fields have additional terms not present in the standard theory but that the relationship between the control processes and measured processes themselves is internally consistent—we do this for the two main cases of quadrature measurement and photon-counting measurement. The theory is general enough to include a modulating filter which post-processes the measurement readout Y before returning to the system. This opens up the prospect of applying very general engineering feedback control techniques to open quantum systems in a systematic manner, and we consider a number of specific modulating filter problems. Finally, we give a brief argument as to why most of the rules for making instantaneous feedback connections [J. Gough and M. R. James, Commun. Math. Phys. 287, 1109 (2009)] ought to apply for controlled dynamical networks as well.
Micro/Nano-pore Network Analysis of Gas Flow in Shale Matrix
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
The Flow of International Students from a Macro Perspective: A Network Analysis
ERIC Educational Resources Information Center
Barnett, George A.; Lee, Moosung; Jiang, Ke; Park, Han Woo
2016-01-01
This paper provides a network analysis of the international flow of students among 210 countries and the factors determining the structure of this flow. Among these factors, bilateral hyperlink connections between countries and the number of telephone minutes (communication variables) are the most important predictors of the flow's structure,…
The Flow of International Students from a Macro Perspective: A Network Analysis
ERIC Educational Resources Information Center
Barnett, George A.; Lee, Moosung; Jiang, Ke; Park, Han Woo
2016-01-01
This paper provides a network analysis of the international flow of students among 210 countries and the factors determining the structure of this flow. Among these factors, bilateral hyperlink connections between countries and the number of telephone minutes (communication variables) are the most important predictors of the flow's structure,…
Micro/Nano-pore Network Analysis of Gas Flow in Shale Matrix.
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.
Enterprise network control and management: traffic flow models
NASA Astrophysics Data System (ADS)
Maruyama, William; George, Mark S.; Hernandez, Eileen; LoPresto, Keith; Uang, Yea
1999-11-01
The exponential growth and dramatic increase in demand for network bandwidth is expanding the market for broadband satellite networks. It is critical to rapidly deliver ubiquitous satellite communication networks that are differentiated by lower cost and increased Quality of Service (QoS). There is a need to develop new network architectures, control and management systems to meet the future commercial and military traffic requirements, services and applications. The next generation communication networks must support legacy and emerging network traffic while providing user negotiated levels of QoS. Network resources control algorithms must be designed to provide the guaranteed performance levels for voice, video and data having different service requirements. To evaluate network architectures and performance, it is essential to understand the network traffic characteristics.
Base pressure in laminar supersonic flow.
NASA Technical Reports Server (NTRS)
Messiter, A. F.; Hough, G. R.; Feo, A.
1973-01-01
An asymptotic description is proposed for supersonic laminar flow over a wedge or a backward-facing step, for large Reynolds number and for a base or step height which is small compared with the boundary-layer length. The analysis is carried out for adiabatic wall conditions and a viscosity coefficient proportional to temperature. In a particular limit corresponding to a very thick boundary layer, a similarity law is obtained for the base pressure. For a thinner boundary layer an asymptotic form for the base pressure is obtained which shows the dependence on the parameters explicitly and which permits good agreement with experiment. This latter result is based on an inviscid-flow approximation for the corner expansion and for reattachment with viscous forces important primarily in a thin sublayer about the dividing streamline. A prediction of the pressure distribution at reattachment is given and the result is compared with experimental pressure distributions.
A Study on Market-based Strategic Procurement Planning in Convergent Supply Networks
NASA Astrophysics Data System (ADS)
Opadiji, Jayeola Femi; Kaihara, Toshiya
We present a market-based decentralized approach which uses a market-oriented programming algorithm to obtain Pareto-optimal allocation of resources traded among agents which represent enterprise units in a supply network. The proposed method divides the network into a series of Walrsian markets in order to obtain procurement budgets for enterprises in the network. An interaction protocol based on market value propagation is constructed to coordinate the flow of resources across the network layers. The method mitigates the effect of product complementarity in convergent network by allowing for enterprises to hold private valuations of resources in the markets.
Jing, Da; Lu, X. Lucas; Luo, Erping; Sajda, Paul; Leong, Pui L; Guo, X. Edward
2013-01-01
Mechanical stimuli can trigger intracellular calcium (Ca2+) responses in osteocytes and osteoblasts. Successful construction of bone cell networks necessitates more elaborate and systematic analysis for the spatiotemporal properties of Ca2+ signaling in the networks. In the present study, an unsupervised algorithm based on independent component analysis (ICA) was employed to extract the Ca2+ signals of bone cells in the network. We demonstrated that the ICA-based technology could yield higher signal fidelity than the manual region of interest (ROI) method. Second, the spatiotemporal properties of Ca2+ signaling in osteocyte-like MLO-Y4 and osteoblast-like MC3T3-E1 cell networks under laminar and steady fluid flow stimulation were systematically analyzed and compared. MLO-Y4 cells exhibited much more active Ca2+ transients than MC3T3-E1 cells, evidenced by more Ca2+ peaks, less time to the 1st peak and less time between the 1st and 2nd peaks. With respect to temporal properties, MLO-Y4 cells demonstrated higher spike rate and Ca2+ oscillating frequency. The spatial intercellular synchronous activities of Ca2+ signaling in MLO-Y4 cell networks were higher than those in MC3T3-E1 cell networks and also negatively correlated with the intercellular distance, revealing faster Ca2+ wave propagation in MLO-Y4 cell networks. Our findings show that the unsupervised ICA-based technique results in more sensitive and quantitative signal extraction than traditional ROI analysis, with the potential to be widely employed in Ca2+ signaling extraction in the cell networks. The present study also revealed a dramatic spatiotemporal difference in Ca2+ signaling for osteocytic and osteoblastic cell networks in processing the mechanical stimulus. The higher intracellular Ca2+ oscillatory behaviors and intercellular coordination of MLO-Y4 cells provided further evidences that osteocytes may behave as the major mechanical sensor in bone modeling and remodeling processes. PMID:23328496
Microbridge structures for uniform interval control of flowing droplets in microfluidic networks.
Lee, Do-Hyun; Lee, Wonhye; Um, Eujin; Park, Je-Kyun
2011-09-01
Precise temporal control of microfluidic droplets such as synchronization and combinatorial pairing of droplets is required to achieve a variety range of chemical and biochemical reactions inside microfluidic networks. Here, we present a facile and robust microfluidic platform enabling uniform interval control of flowing droplets for the precise temporal synchronization and pairing of picoliter droplets with a reagent. By incorporating microbridge structures interconnecting the droplet-carrying channel and the flow control channel, a fluidic pressure drop was derived between the two fluidic channels via the microbridge structures, reordering flowing droplets with a defined uniform interval. Through the adjustment of the control oil flow rate, the droplet intervals were flexibly and precisely adjustable. With this mechanism of droplet spacing, the gelation of the alginate droplets as well as control of the droplet interval was simultaneously achieved by additional control oil flow including calcified oleic acid. In addition, by parallel linking identical microfluidic modules with distinct sample inlet, controlled synchronization and pairing of two distinct droplets were demonstrated. This method is applicable to facilitate and develop many droplet-based microfluidic applications, including biological assay, combinatorial synthesis, and high-throughput screening.
NASDA knowledge-based network planning system
NASA Technical Reports Server (NTRS)
Yamaya, K.; Fujiwara, M.; Kosugi, S.; Yambe, M.; Ohmori, M.
1993-01-01
One of the SODS (space operation and data system) sub-systems, NP (network planning) was the first expert system used by NASDA (national space development agency of Japan) for tracking and control of satellite. The major responsibilities of the NP system are: first, the allocation of network and satellite control resources and, second, the generation of the network operation plan data (NOP) used in automated control of the stations and control center facilities. Up to now, the first task of network resource scheduling was done by network operators. NP system automatically generates schedules using its knowledge base, which contains information on satellite orbits, station availability, which computer is dedicated to which satellite, and how many stations must be available for a particular satellite pass or a certain time period. The NP system is introduced.
Tests of peak flow scaling in simulated self-similar river networks
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.
Constrained optimisation in granular network flows: Games with a loaded dice
NASA Astrophysics Data System (ADS)
Lin, Qun; Tordesillas, Antoinette
2013-06-01
Flows in real world networks are rarely the outcome of unconditional random allocations as, say, the roll of a dice. Think, for example, of force transmission through a contact network in a quasistatically deforming granular material. Forces `flow' through this network in a highly conditional manner. How much force is transmitted between two contacting particles is always conditional not only on all the other forces acting between the particles in question but also on those acting on the other particles in the system. Broadly, we are interested in the nature and extent to which flows through a contact network favour certain pathways over others, and how the mechanisms that govern such biased flows for a given imposed loading history determine the future evolution of the contact network. Our first step is to solve a selection of fundamental combinatorial optimisation problems on the contact network from the perspective of force transmission. Here we report on solutions to the Maximum Flow Minimum Cost Problem for a weighted contact network where the weights assigned to the links of the contact network are varied according to their contact types. We found that those pathways through which the maximum flow of force is transmitted, in the direction of the maximum principal stress, at minimum cost - pass through the great majority of the force chains. Although the majority of the contacts in these pathways are elastic, the plastic contacts bear an undue influence on the minimum cost.
Bipolar Membranes for Acid Base Flow Batteries
NASA Astrophysics Data System (ADS)
Anthamatten, Mitchell; Roddecha, Supacharee; Jorne, Jacob; Coughlan, Anna
2011-03-01
Rechargeable batteries can provide grid-scale electricity storage to match power generation with consumption and promote renewable energy sources. Flow batteries offer modular and flexible design, low cost per kWh and high efficiencies. A novel flow battery concept will be presented based on acid-base neutralization where protons (H+) and hydroxyl (OH-) ions react electrochemically to produce water. The large free energy of this highly reversible reaction can be stored chemically, and, upon discharge, can be harvested as usable electricity. The acid-base flow battery concept avoids the use of a sluggish oxygen electrode and utilizes the highly reversible hydrogen electrode, thus eliminating the need for expensive noble metal catalysts. The proposed flow battery is a hybrid of a battery and a fuel cell---hydrogen gas storing chemical energy is produced at one electrode and is immediately consumed at the other electrode. The two electrodes are exposed to low and high pH solutions, and these solutions are separated by a hybrid membrane containing a hybrid cation and anion exchange membrane (CEM/AEM). Membrane design will be discussed, along with ion-transport data for synthesized membranes.
MINET: transient analysis of fluid-flow and heat-transfer networks
Van Tuyle, G.J.; Guppy, J.G.; Nepsee, T.C.
1983-01-01
MINET, a computer code developed for the steady-state and transient analysis of fluid-flow and heat-transfer networks, is described. The code is based on a momentum integral network method, which offers significant computational advantages in the analysis of large systems, such as the balance of plant in a power-generating facility. An application is discussed in which MINET is coupled to the Super System Code (SSC), an advanced generic code for the transient analysis of loop- or pool-type LMFBR systems. In this application, the ability of the Clinch River Breeder Reactor Plant to operate in a natural circulation mode following an assumed loss of all electric power, was assessed. Results from the MINET portion of the calculations are compared against those generated independently by the Clinch River Project, using the DEMO code.
Modeling Patient Flows Using a Queuing Network with Blocking
KUNO, ERI; SMITH, TONY E.
2015-01-01
The downsizing and closing of state mental health institutions in Philadelphia in the 1990’s led to the development of a continuum care network of residential-based services. Although the diversity of care settings increased, congestion in facilities caused many patients to unnecessarily spend extra days in intensive facilities. This study applies a queuing network system with blocking to analyze such congestion processes. “Blocking” denotes situations where patients are turned away from accommodations to which they are referred, and are thus forced to remain in their present facilities until space becomes available. Both mathematical and simulation results are presented and compared. Although queuing models have been used in numerous healthcare studies, the inclusion of blocking is still rare. We found that, in Philadelphia, the shortage of a particular type of facilities may have created “upstream blocking”. Thus removal of such facility-specific bottlenecks may be the most efficient way to reduce congestion in the system as a whole. PMID:15782512
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.
A power function method for estimating base flow.
Lott, Darline A; Stewart, Mark T
2013-01-01
Analytical base flow separation techniques are often used to determine the base flow contribution to total stream flow. Most analytical methods derive base flow from discharge records alone without using basin-specific variables other than basin area. This paper derives a power function for estimating base flow, the form being aQ(b) + cQ, an analytical method calibrated against an integrated basin variable, specific conductance, relating base flow to total discharge, and is consistent with observed mathematical behavior of dissolved solids in stream flow with varying discharge. Advantages of the method are being uncomplicated, reproducible, and applicable to hydrograph separation in basins with limited specific conductance data. The power function relationship between base flow and discharge holds over a wide range of basin areas. It better replicates base flow determined by mass balance methods than analytical methods such as filters or smoothing routines that are not calibrated to natural tracers or empirical basin and gauge-specific variables. Also, it can be used with discharge during periods without specific conductance values, including separating base flow from quick flow for single events. However, it may overestimate base flow during very high flow events. Application of geochemical mass balance and power function base flow separation methods to stream flow and specific conductance records from multiple gauges in the same basin suggests that analytical base flow separation methods must be calibrated at each gauge. Using average values of coefficients introduces a potentially significant and unknown error in base flow as compared with mass balance methods.
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.
Rawstron, Andy C; Orfao, Alberto; Beksac, Meral; Bezdickova, Ludmila; Brooimans, Rik A; Bumbea, Horia; Dalva, Klara; Fuhler, Gwenny; Gratama, Jan; Hose, Dirk; Kovarova, Lucie; Lioznov, Michael; Mateo, Gema; Morilla, Ricardo; Mylin, Anne K; Omedé, Paola; Pellat-Deceunynck, Catherine; Perez Andres, Martin; Petrucci, Maria; Ruggeri, Marina; Rymkiewicz, Grzegorz; Schmitz, Alexander; Schreder, Martin; Seynaeve, Carine; Spacek, Martin; de Tute, Ruth M; Van Valckenborgh, Els; Weston-Bell, Nicky; Owen, Roger G; San Miguel, Jesús F; Sonneveld, Pieter; Johnsen, Hans E
2008-03-01
The European Myeloma Network (EMN) organized two flow cytometry workshops. The first aimed to identify specific indications for flow cytometry in patients with monoclonal gammopathies, and consensus technical approaches through a questionnaire-based review of current practice in participating laboratories. The second aimed to resolve outstanding technical issues and develop a consensus approach to analysis of plasma cells. The primary clinical applications identified were: differential diagnosis of neoplastic plasma cell disorders from reactive plasmacytosis; identifying risk of progression in patients with MGUS and detecting minimal residual disease. A range of technical recommendations were identified, including: 1) CD38, CD138 and CD45 should all be included in at least one tube for plasma cell identification and enumeration. The primary gate should be based on CD38 vs. CD138 expression; 2) after treatment, clonality assessment is only likely to be informative when combined with immunophenotype to detect abnormal cells. Flow cytometry is suitable for demonstrating a stringent complete remission; 3) for detection of abnormal plasma cells, a minimal panel should include CD19 and CD56. A preferred panel would also include CD20, CD117, CD28 and CD27; 4) discrepancies between the percentage of plasma cells detected by flow cytometry and morphology are primarily related to sample quality and it is, therefore, important to determine that marrow elements are present in follow-up samples, particularly normal plasma cells in MRD negative cases.
Geomorphic Signatures on Brutsaert Base Flow Recession Analysis
NASA Astrophysics Data System (ADS)
Rinaldo, Andrea; Mutzner, Raphael; Bertuzzo, Enrico; Tarolli, Paolo; Weijs, Steven; Ceola, Serena; Tomasic, Nevena; Rodríguez-Iturbe, Ignacio; Parlange, Marc
2013-04-01
This paper addresses the signatures of catchment geomorphology on base flow recession curves. Its relevance relates to the implied predictability of base flow features, which are central to catchment-scale transport phenomena and to ecohydrological function. Moving from the classical recession curve analysis method, originally applied to the Finger Lakes Region, a large set of recession curves has been analyzed from Swiss streamflow data relatively unaffected by snowmelt. For these catchments, digital terrain models have been accurately analyzed. Recent results aimed at the geomorphic origins of recession curves have been then applied to the Swiss dataset. The method links river network morphology, epitomized by time-varying geometry of saturated channel sites, with the classic parametrization of recession events, in particular by assimilating two scaling exponents, β and bG (i.e. |dQ/dt|?Qβwhere Q is at-a-station gauged flow rate; N(l) ?G(l)bG where l is the downstream distance from the channel heads receding in time, N(l) is the number of draining channel reaches located at distance l from their heads, and G(l) is the total drainage network length at a distance greater or equal to l, the active drainage network). Here, we confirm that the method provides good results, yet only in catchments where drainage density can be regarded as spatially constant. A correction to the method is proposed, which accounts for arbitrary local drainage densities affecting the local drainage inflow per unit channel length. Such corrections properly vanish should drainage density become spatially constant. A comparative analysis on the Swiss streamflow and Digital Elevation Model data shows that the proposed correction proves indeed statistically significant. Overall, definite geomorphic signatures are recognized for recession curves, with notable theoretical and practical implications.
Geomorphic signatures on Brutsaert base flow recession analysis
NASA Astrophysics Data System (ADS)
Mutzner, Raphaël.; Bertuzzo, Enrico; Tarolli, Paolo; Weijs, Steven V.; Nicotina, Ludovico; Ceola, Serena; Tomasic, Nevena; Rodriguez-Iturbe, Ignacio; Parlange, Marc B.; Rinaldo, Andrea
2013-09-01
This paper addresses the signatures of catchment geomorphology on base flow recession curves. Its relevance relates to the implied predictability of base flow features, which are central to catchment-scale transport processes and to ecohydrological function. Moving from the classical recession curve analysis method, originally applied in the Finger Lakes Region of New York, a large set of recession curves has been analyzed from Swiss streamflow data. For these catchments, digital elevation models have been precisely analyzed and a method aimed at the geomorphic origins of recession curves has been applied to the Swiss data set. The method links river network morphology, epitomized by time-varying distribution of contributing channel sites, with the classic parameterization of recession events. This is done by assimilating two scaling exponents, β and bG, with |dQ/dt| ∝ Qβ where Q is at-a-station gauged flow rate and N(l) ∝ N>(l>)∝G>(l>)bG where l is the downstream distance from the channel heads receding in time, N(l) is the number of draining channel reaches located at distance l from their heads, and G(l) is the total drainage network length at a distance greater or equal to l, the active drainage network. We find that the method provides good results in catchments where drainage density can be regarded as spatially constant. A correction to the method is proposed which accounts for arbitrary local drainage densities affecting the local drainage inflow per unit channel length. Such corrections properly vanish when the drainage density become spatially constant. Overall, definite geomorphic signatures are recognizable for recession curves, with notable theoretical and practical implications.
Network based sky Brightness Monitor
NASA Astrophysics Data System (ADS)
McKenna, Dan; Pulvermacher, R.; Davis, D. R.
2009-01-01
We have developed and are currently testing an autonomous 2 channel photometer designed to measure the night sky brightness in the visual wavelengths over a multi-year campaign. The photometer uses a robust silicon sensor filtered with Hoya CM500 glass. The Sky brightness is measured every minute at two elevation angles typically zenith and 20 degrees to monitor brightness and transparency. The Sky Brightness monitor consists of two units, the remote photometer and a network interface. Currently these devices use 2.4 Ghz transceivers with a free space range of 100 meters. The remote unit is battery powered with day time recharging using a solar panel. Data received by the network interface transmits data via standard POP Email protocol. A second version is under development for radio sensitive areas using an optical fiber for data transmission. We will present the current comparison with the National Park Service sky monitoring camera. We will also discuss the calibration methods used for standardization and temperature compensation. This system is expected to be deployed in the next year and be operated by the International Dark Sky Association SKYMONITOR project.
NASA Astrophysics Data System (ADS)
Ezzedine, S. M.
2010-12-01
Fractures and fracture networks are the principle pathways for migration of water, heat and mass in enhanced geothermal systems, oil and gas reservoirs, CO2 leakage from saline aquifers, and radioactive and toxic industrial wastes from underground storage repositories. A major issue to overcome when characterizing a fractured reservoir is that of data limitation due to accessibility and affordability. Moreover, the ability to map discontinuities in the rock with available geological and geophysical tools tends to decrease particularly as the scale of the discontinuity goes down. Geological characterization data include measurements of fracture density, orientation, extent, and aperture, and are based on analysis of outcrops, borehole optical and acoustic televiewer logs, aerial photographs, and core samples among others. All of these measurements are taken at the field scale through a very sparse limited number of deep boreholes. These types of data are often reduced to probability distributions function for predictive modeling and simulation in a stochastic framework such as stochastic discrete fracture network. Stochastic discrete fracture network models enable, through Monte Carlo realizations and simulations, for probabilistic assessment of flow and transport phenomena that are not adequately captured using continuum models. Despite the fundamental uncertainties inherited within the probabilistic reduction of the sparse data collected, very little work has been conducted on quantifying uncertainty on the reduced probabilistic distribution functions. In the current study, using nested Monte Carlo simulations, we present the impact of parameter uncertainties of the distribution functions that characterize discrete fracture networks on the flow, heat and mass transport. Numerical results of first, second and third moments, normalized to a base case scenario, are presented and compared to theoretical results extended from percolation theory.
Busam, Sirisha; McNabb, Maia; Wackwitz, Anke; Senevirathna, Wasana; Beggah, Siham; Meer, Jan Roelof van der; Wells, Mona; Breuer, Uta; Harms, Hauke
2007-12-01
Genetically engineered bioreporters are an excellent complement to traditional methods of chemical analysis. The application of fluorescence flow cytometry to detection of bioreporter response enables rapid and efficient characterization of bacterial bioreporter population response on a single-cell basis. In the present study, intrapopulation response variability was used to obtain higher analytical sensitivity and precision. We have analyzed flow cytometric data for an arsenic-sensitive bacterial bioreporter using an artificial neural network-based adaptive clustering approach (a single-layer perceptron model). Results for this approach are far superior to other methods that we have applied to this fluorescent bioreporter (e.g., the arsenic detection limit is 0.01 microM, substantially lower than for other detection methods/algorithms). The approach is highly efficient computationally and can be implemented on a real-time basis, thus having potential for future development of high-throughput screening applications.
The influence of passenger flow on the topology characteristics of urban rail transit networks
NASA Astrophysics Data System (ADS)
Hu, Yingyue; Chen, Feng; Chen, Peiwen; Tan, Yurong
2017-05-01
Current researches on the network characteristics of metro networks are generally carried out on topology networks without passenger flows running on it, thus more complex features of the networks with ridership loaded on it cannot be captured. In this study, we incorporated the load of metro networks, passenger volume, into the exploration of network features. Thus, the network can be examined in the context of operation, which is the ultimate purpose of the existence of a metro network. To this end, section load was selected as an edge weight to demonstrate the influence of ridership on the network, and a weighted calculation method for complex network indicators and robustness were proposed to capture the unique behaviors of a metro network with passengers flowing in it. The proposed method was applied on Beijing Subway. Firstly, the passenger volume in terms of daily origin and destination matrix was extracted from exhausted transit smart card data. Using the established approach and the matrix as weighting, common indicators of complex network including clustering coefficient, betweenness and degree were calculated, and network robustness were evaluated under potential attacks. The results were further compared to that of unweighted networks, and it suggests indicators of the network with consideration of passenger volumes differ from that without ridership to some extent, and networks tend to be more vulnerable than that without load on it. The significance sequence for the stations can be changed. By introducing passenger flow weighting, actual operation status of the network can be reflected more accurately. It is beneficial to determine the crucial stations and make precautionary measures for the entire network’s operation security.
Web100-based Network Diagnostic Tool
Carlson, Richard A.
2003-03-20
NDT is a client/server based network diagnostic tool developed to aid in finding network performance and configuration problems. The tool measures data transfer rates between two internet hosts (client and server). It also gathers detailed TCP statistical variable counters supplied by the Web100 modified server and uses these TCP variables to compute the theoretical performance rate between the two internet hosts. It then compares these analytical results with the measured results to determine if performance or configuration problems exist and translates these results into plain text messages to aid users and network operators in resolving reported problems.
Solenoid micropump-based flow system for generalized calibration strategy.
Wieczorek, Marcin; Kościelniak, Paweł; Swit, Paweł; Paluch, Justyna; Kozak, Joanna
2015-02-01
Generalized calibration strategy (GCS) is one of the innovative approaches aimed at verification and improvement of accuracy of analytical determinations. It combines in a single procedure the interpolative and the extrapolative calibration approaches along with stepwise dilution of a sample with the use of a dedicated flow system. In the paper a simple solenoid micropump-based flow system designed for implementation of GCS has been described. The manifold consists of several modules fully operated by a computer and connected with each other in a properly designed network. Its performance and usefulness were tested on determination of calcium by FAAS in synthetic and natural samples containing strong interferents. It was shown how GCS can serve for detection, examination and elimination of the interference effects. It was demonstrated that the designed manifold enabled to perform GCS procedure with very good precision, in short time and with very low standard, sample and reagent consumption.
Suwannee River flow variability 1550-2005 CE reconstructed from a multispecies tree-ring network
NASA Astrophysics Data System (ADS)
Harley, Grant L.; Maxwell, Justin T.; Larson, Evan; Grissino-Mayer, Henri D.; Henderson, Joseph; Huffman, Jean
2017-01-01
Understanding the long-term natural flow regime of rivers enables resource managers to more accurately model water level variability. Models for managing water resources are important in Florida where population increase is escalating demand on water resources and infrastructure. The Suwannee River is the second largest river system in Florida and the least impacted by anthropogenic disturbance. We used new and existing tree-ring chronologies from multiple species to reconstruct mean March-October discharge for the Suwannee River during the period 1550-2005 CE and place the short period of instrumental flows (since 1927 CE) into historical context. We used a nested principal components regression method to maximize the use of chronologies with varying time coverage in the network. Modeled streamflow estimates indicated that instrumental period flow conditions do not adequately capture the full range of Suwannee River flow variability beyond the observational period. Although extreme dry and wet events occurred in the gage record, pluvials and droughts that eclipse the intensity and duration of instrumental events occurred during the 16-19th centuries. The most prolonged and severe dry conditions during the past 450 years occurred during the 1560s CE. In this prolonged drought period mean flow was estimated at 17% of the mean instrumental period flow. Significant peaks in spectral density at 2-7, 10, 45, and 85-year periodicities indicated the important influence of coupled oceanic-atmospheric processes on Suwannee River streamflow over the past four centuries, though the strength of these periodicities varied over time. Future water planning based on current flow expectations could prove devastating to natural and human systems if a prolonged and severe drought mirroring the 16th and 18th century events occurred. Future work in the region will focus on updating existing tree-ring chronologies and developing new collections from moisture-sensitive sites to improve
NASA Astrophysics Data System (ADS)
Chen, Miawjane; Yan, Shangyao; Wang, Sin-Siang; Liu, Chiu-Lan
2015-02-01
An effective project schedule is essential for enterprises to increase their efficiency of project execution, to maximize profit, and to minimize wastage of resources. Heuristic algorithms have been developed to efficiently solve the complicated multi-mode resource-constrained project scheduling problem with discounted cash flows (MRCPSPDCF) that characterize real problems. However, the solutions obtained in past studies have been approximate and are difficult to evaluate in terms of optimality. In this study, a generalized network flow model, embedded in a time-precedence network, is proposed to formulate the MRCPSPDCF with the payment at activity completion times. Mathematically, the model is formulated as an integer network flow problem with side constraints, which can be efficiently solved for optimality, using existing mathematical programming software. To evaluate the model performance, numerical tests are performed. The test results indicate that the model could be a useful planning tool for project scheduling in the real world.
Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch.
Huang, Tao; Yan, Siyu; Yang, Fan; Pan, Tian; Liu, Jiang
2016-01-19
Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture.
Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch
Huang, Tao; Yan, Siyu; Yang, Fan; Pan, Tian; Liu, Jiang
2016-01-01
Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture. PMID:26797616
Leo satellite-based telecommunication network concepts
NASA Technical Reports Server (NTRS)
Aiken, John G.; Swan, Peter A.; Leopold, Ray J.
1991-01-01
Design considerations are discussed for Low Earth Orbit (LEO) satellite based telecommunications networks. The satellites are assumed to be connected to each other via intersatellite links. They are connected to the end user either directly or through gateways to other networks. Frequency reuse, circuit switching, packet switching, call handoff, and routing for these systems are discussed by analogy with terrestrial cellular (mobile radio) telecommunication systems.
Bubble Eliminator Based on Centrifugal Flow
NASA Technical Reports Server (NTRS)
Gonda, Steve R.; Tsao, Yow-Min D.; Lee, Wenshan
2004-01-01
The fluid bubble eliminator (FBE) is a device that removes gas bubbles from a flowing liquid. The FBE contains no moving parts and does not require any power input beyond that needed to pump the liquid. In the FBE, the buoyant force for separating the gas from the liquid is provided by a radial pressure gradient associated with a centrifugal flow of the liquid and any entrained bubbles. A device based on a similar principle is described in Centrifugal Adsorption Cartridge System (MSC- 22863), which appears on page 48 of this issue. The FBE was originally intended for use in filtering bubbles out of a liquid flowing relatively slowly in a bioreactor system in microgravity. Versions that operate in normal Earth gravitation at greater flow speeds may also be feasible. The FBE (see figure) is constructed as a cartridge that includes two concentric cylinders with flanges at the ends. The outer cylinder is an impermeable housing; the inner cylinder comprises a gas-permeable, liquid-impermeable membrane covering a perforated inner tube. Multiple spiral disks that collectively constitute a spiral ramp are mounted in the space between the inner and outer cylinders. The liquid enters the FBE through an end flange, flows in the annular space between the cylinders, and leaves through the opposite end flange. The spiral disks channel the liquid into a spiral flow, the circumferential component of which gives rise to the desired centrifugal effect. The resulting radial pressure gradient forces the bubbles radially inward; that is, toward the inner cylinder. At the inner cylinder, the gas-permeable, liquid-impermeable membrane allows the bubbles to enter the perforated inner tube while keeping the liquid in the space between the inner and outer cylinders. The gas thus collected can be vented via an endflange connection to the inner tube. The centripetal acceleration (and thus the radial pressure gradient) is approximately proportional to the square of the flow speed and
A network analysis of food flows within the United States of America.
Lin, Xiaowen; Dang, Qian; Konar, Megan
2014-05-20
The world food system is globalized and interconnected, in which trade plays an increasingly important role in facilitating food availability. We present a novel application of network analysis to domestic food flows within the USA, a country with global importance as a major agricultural producer and trade power. We find normal node degree distributions and Weibull node strength and betweenness centrality distributions. An unassortative network structure with high clustering coefficients exists. These network properties indicate that the USA food flow network is highly social and well-mixed. However, a power law relationship between node betweenness centrality and node degree indicates potential network vulnerability to the disturbance of key nodes. We perform an equality analysis which serves as a benchmark for global food trade, where the Gini coefficient = 0.579, Lorenz asymmetry coefficient = 0.966, and Hoover index = 0.442. These findings shed insight into trade network scaling and proxy free trade and equitable network architectures.
Maximum flow-based resilience analysis: From component to system
Jin, Chong; Li, Ruiying; Kang, Rui
2017-01-01
Resilience, the ability to withstand disruptions and recover quickly, must be considered during system design because any disruption of the system may cause considerable loss, including economic and societal. This work develops analytic maximum flow-based resilience models for series and parallel systems using Zobel’s resilience measure. The two analytic models can be used to evaluate quantitatively and compare the resilience of the systems with the corresponding performance structures. For systems with identical components, the resilience of the parallel system increases with increasing number of components, while the resilience remains constant in the series system. A Monte Carlo-based simulation method is also provided to verify the correctness of our analytic resilience models and to analyze the resilience of networked systems based on that of components. A road network example is used to illustrate the analysis process, and the resilience comparison among networks with different topologies but the same components indicates that a system with redundant performance is usually more resilient than one without redundant performance. However, not all redundant capacities of components can improve the system resilience, the effectiveness of the capacity redundancy depends on where the redundant capacity is located. PMID:28545135
Calcium Response in Osteocytic Networks under Steady and Oscillatory Fluid Flow
Lu, X. Lucas; Huo, Bo; Park, Miri; Guo, X. Edward
2012-01-01
The fluid flow in the lacunar-canalicular system of bone is an essential mechanical stimulation on the osteocyte networks. Due to the complexity of human physical activities, the fluid shear stress on osteocyte bodies and processes consists of both steady and oscillatory components. In this study, we investigated and compared the intracellular calcium ([Ca2+]i) responses of osteocytic networks under steady and oscillatory fluid flows. An in vitro osteocytic network was built with MLO-Y4 osteocyte-like cells using micro-patterning techniques to simulate the in vivo orderly organization of osteocyte networks. Sinusoidal oscillating fluid flow or unidirectional steady flow was applied on the cell surface with 20 dyne/cm2 peak shear stress. It was found that the osteocytic networks were significantly more responsive to steady flow than to oscillatory flow. The osteocytes can release more calcium peaks with higher magnitudes at a faster speed under steady flow stimulation. The [Ca2+]i signaling transients under the steady and oscillatory flows have significantly different spatiotemporal characters, but a similar responsive percentage of cells. Further signaling pathway studies using inhibitors showed that endoplasmic reticulum (ER) calcium store, extracellular calcium source, ATP, PGE2 and NO related pathways play similar roles in the [Ca2+]i signaling of osteocytes under either steady or oscillating flow. The spatiotemporal characteristics of [Ca2+]i transients under oscillating fluid flow are affected more profoundly by pharmacological treatments than under the steady flow. Our findings support the hypothesis that the [Ca2+]i responses of osteocytic networks are significantly dependent on the profiles of fluid flow. PMID:22750013
NASA Astrophysics Data System (ADS)
Islam, Mujahidul
from the vast network. A path tracing methodology is developed to identify the power lines that are vulnerable to an unscheduled flow effect in the sub-transmission network. It is much harder to aggregate power system network sensitivity information or data from measuring load flow physically than to simulate in software. System dynamics is one of the key factors to determine an appropriate dynamic control mechanism at an optimum network location. Once a model of deterministic but variable power generator is used, the simulation can be meaningful in justifying this claim. The method used to model the variable generator is named the two-components phase distortion model. The model was validated from the high resolution data collected from three pilot photovoltaic sites in Florida - two in the city of St. Petersburg and one in the city of Tampa. The high resolution data was correlated with weather radar closest to the sites during the design stage of the model. Technically the deterministic model cannot replicate a stochastic model which is more realistically applicable for solar isolation and involves a Markov chain. The author justified the proposition based on the fact that for analysis of the response functions of different systems, the excitation function should be common for comparison. Moreover, there could be many possible simulation scenarios but fewer worst cases. Almost all commercial systems are protected against potential faults and contingencies to a certain extent. Hence, the proposed model for worst case studies was designed within a reasonable limit. The simulation includes steady state and transient mode using multiple software modules including MatlabRTM, PSCADRTM and Paladin Design BaseRTM. It is shown that by identifying vulnerable or sensitive branches in the network, the control mechanisms can be coordinated reliably. In the long run this can save money by preventing unscheduled power flow in the network and eventually stabilizing the energy market.
The flow of power law fluids in elastic networks and porous media.
Sochi, Taha
2016-02-01
The flow of power law fluids, which include shear thinning and shear thickening as well as Newtonian as a special case, in networks of interconnected elastic tubes is investigated using a residual-based pore scale network modeling method with the employment of newly derived formulae. Two relations describing the mechanical interaction between the local pressure and local cross-sectional area in distensible tubes of elastic nature are considered in the derivation of these formulae. The model can be used to describe shear dependent flows of mainly viscous nature. The behavior of the proposed model is vindicated by several tests in a number of special and limiting cases where the results can be verified quantitatively or qualitatively. The model, which is the first of its kind, incorporates more than one major nonlinearity corresponding to the fluid rheology and conduit mechanical properties, that is non-Newtonian effects and tube distensibility. The formulation, implementation, and performance indicate that the model enjoys certain advantages over the existing models such as being exact within the restricting assumptions on which the model is based, easy implementation, low computational costs, reliability, and smooth convergence. The proposed model can, therefore, be used as an alternative to the existing Newtonian distensible models; moreover, it stretches the capabilities of the existing modeling approaches to reach non-Newtonian rheologies.
Ye, Sheng; Covino, Timothy P.; Sivapalan, Murugesu; Basu, Nandita; Li, Hongyi; Wang, Shaowen
2012-06-30
In this paper, we use a dynamic network flow model, coupled with a transient storage zone biogeochemical model, to simulate dissolved nutrient removal processes at the channel network scale. We have explored several scenarios in respect of the combination of rainfall variability, and the biological and geomorphic characteristics of the catchment, to understand the dominant controls on removal and delivery of dissolved nutrients (e.g., nitrate). These model-based theoretical analyses suggested that while nutrient removal efficiency is lower during flood events compared to during baseflow periods, flood events contribute significantly to bulk nutrient removal, whereas bulk removal during baseflow periods is less. This is due to the fact that nutrient supply is larger during flood events; this trend is even stronger in large rivers. However, the efficiency of removal during both periods decreases in larger rivers, however, due to (i) increasing flow velocities and thus decreasing residence time, and (ii) increasing flow depth, and thus decreasing nutrient uptake rates. Besides nutrient removal processes can be divided into two parts: in the main channel and in the hyporheic transient storage zone. When assessing their relative contributions the size of the transient storage zone is a dominant control, followed by uptake rates in the main channel and in the transient storage zone. Increasing size of the transient storage zone with downstream distance affects the relative contributions to nutrient removal of the water column and the transient storage zone, which also impacts the way nutrient removal rates scale with increasing size of rivers. Intra-annual hydrologic variability has a significant impact on removal rates at all scales: the more variable the streamflow is, compared to mean discharge, the less nutrient is removed in the channel network. A scale-independent first order uptake coefficient, ke, estimated from model simulations, is highly dependent on the
NASA Astrophysics Data System (ADS)
Scarsoglio, Stefania; Cazzato, Fabio; Ridolfi, Luca
2017-09-01
A network-based approach is presented to investigate the cerebrovascular flow patterns during atrial fibrillation (AF) with respect to normal sinus rhythm (NSR). AF, the most common cardiac arrhythmia with faster and irregular beating, has been recently and independently associated with the increased risk of dementia. However, the underlying hemodynamic mechanisms relating the two pathologies remain mainly undetermined so far; thus, the contribution of modeling and refined statistical tools is valuable. Pressure and flow rate temporal series in NSR and AF are here evaluated along representative cerebral sites (from carotid arteries to capillary brain circulation), exploiting reliable artificially built signals recently obtained from an in silico approach. The complex network analysis evidences, in a synthetic and original way, a dramatic signal variation towards the distal/capillary cerebral regions during AF, which has no counterpart in NSR conditions. At the large artery level, networks obtained from both AF and NSR hemodynamic signals exhibit elongated and chained features, which are typical of pseudo-periodic series. These aspects are almost completely lost towards the microcirculation during AF, where the networks are topologically more circular and present random-like characteristics. As a consequence, all the physiological phenomena at the microcerebral level ruled by periodicity—such as regular perfusion, mean pressure per beat, and average nutrient supply at the cellular level—can be strongly compromised, since the AF hemodynamic signals assume irregular behaviour and random-like features. Through a powerful approach which is complementary to the classical statistical tools, the present findings further strengthen the potential link between AF hemodynamic and cognitive decline.
Chaos in a dynamic model of traffic flows in an origin-destination network
NASA Astrophysics Data System (ADS)
Zhang, Xiaoyan; Jarrett, David F.
1998-06-01
In this paper we investigate the dynamic behavior of road traffic flows in an area represented by an origin-destination (O-D) network. Probably the most widely used model for estimating the distribution of O-D flows is the gravity model, [J. de D. Ortuzar and L. G. Willumsen, Modelling Transport (Wiley, New York, 1990)] which originated from an analogy with Newton's gravitational law. The conventional gravity model, however, is static. The investigation in this paper is based on a dynamic version of the gravity model proposed by Dendrinos and Sonis by modifying the conventional gravity model [D. S. Dendrinos and M. Sonis, Chaos and Social-Spatial Dynamics (Springer-Verlag, Berlin, 1990)]. The dynamic model describes the variations of O-D flows over discrete-time periods, such as each day, each week, and so on. It is shown that when the dimension of the system is one or two, the O-D flow pattern either approaches an equilibrium or oscillates. When the dimension is higher, the behavior found in the model includes equilibria, oscillations, periodic doubling, and chaos. Chaotic attractors are characterized by (positive) Liapunov exponents and fractal dimensions.
On event-based optical flow detection
Brosch, Tobias; Tschechne, Stephan; Neumann, Heiko
2015-01-01
Event-based sensing, i.e., the asynchronous detection of luminance changes, promises low-energy, high dynamic range, and sparse sensing. This stands in contrast to whole image frame-wise acquisition by standard cameras. Here, we systematically investigate the implications of event-based sensing in the context of visual motion, or flow, estimation. Starting from a common theoretical foundation, we discuss different principal approaches for optical flow detection ranging from gradient-based methods over plane-fitting to filter based methods and identify strengths and weaknesses of each class. Gradient-based methods for local motion integration are shown to suffer from the sparse encoding in address-event representations (AER). Approaches exploiting the local plane like structure of the event cloud, on the other hand, are shown to be well suited. Within this class, filter based approaches are shown to define a proper detection scheme which can also deal with the problem of representing multiple motions at a single location (motion transparency). A novel biologically inspired efficient motion detector is proposed, analyzed and experimentally validated. Furthermore, a stage of surround normalization is incorporated. Together with the filtering this defines a canonical circuit for motion feature detection. The theoretical analysis shows that such an integrated circuit reduces motion ambiguity in addition to decorrelating the representation of motion related activations. PMID:25941470
A neural network based speech recognition system
NASA Astrophysics Data System (ADS)
Carroll, Edward J.; Coleman, Norman P., Jr.; Reddy, G. N.
1990-02-01
An overview is presented of the development of a neural network based speech recognition system. The two primary tasks involved were the development of a time invariant speech encoder and a pattern recognizer or detector. The speech encoder uses amplitude normalization and a Fast Fourier Transform to eliminate amplitude and frequency shifts of acoustic clues. The detector consists of a back-propagation network which accepts data from the encoder and identifies individual words. This use of neural networks offers two advantages over conventional algorithmic detectors: the detection time is no more than a few network time constants, and its recognition speed is independent of the number of the words in the vocabulary. The completed system has functioned as expected with high tolerance to input variation and with error rates comparable to a commercial system when used in a noisy environment.
Complex network analysis of phase dynamics underlying oil-water two-phase flows
Gao, Zhong-Ke; Zhang, Shan-Shan; Cai, Qing; Yang, Yu-Xuan; Jin, Ning-De
2016-01-01
Characterizing the complicated flow behaviors arising from high water cut and low velocity oil-water flows is an important problem of significant challenge. We design a high-speed cycle motivation conductance sensor and carry out experiments for measuring the local flow information from different oil-in-water flow patterns. We first use multivariate time-frequency analysis to probe the typical features of three flow patterns from the perspective of energy and frequency. Then we infer complex networks from multi-channel measurements in terms of phase lag index, aiming to uncovering the phase dynamics governing the transition and evolution of different oil-in-water flow patterns. In particular, we employ spectral radius and weighted clustering coefficient entropy to characterize the derived unweighted and weighted networks and the results indicate that our approach yields quantitative insights into the phase dynamics underlying the high water cut and low velocity oil-water flows. PMID:27306101
Complex network analysis of phase dynamics underlying oil-water two-phase flows
NASA Astrophysics Data System (ADS)
Gao, Zhong-Ke; Zhang, Shan-Shan; Cai, Qing; Yang, Yu-Xuan; Jin, Ning-De
2016-06-01
Characterizing the complicated flow behaviors arising from high water cut and low velocity oil-water flows is an important problem of significant challenge. We design a high-speed cycle motivation conductance sensor and carry out experiments for measuring the local flow information from different oil-in-water flow patterns. We first use multivariate time-frequency analysis to probe the typical features of three flow patterns from the perspective of energy and frequency. Then we infer complex networks from multi-channel measurements in terms of phase lag index, aiming to uncovering the phase dynamics governing the transition and evolution of different oil-in-water flow patterns. In particular, we employ spectral radius and weighted clustering coefficient entropy to characterize the derived unweighted and weighted networks and the results indicate that our approach yields quantitative insights into the phase dynamics underlying the high water cut and low velocity oil-water flows.
Operator splitting method for simulation of dynamic flows in natural gas pipeline networks
Dyachenko, Sergey A.; Zlotnik, Anatoly; Korotkevich, Alexander O.; ...
2017-09-19
Here, we develop an operator splitting method to simulate flows of isothermal compressible natural gas over transmission pipelines. The method solves a system of nonlinear hyperbolic partial differential equations (PDEs) of hydrodynamic type for mass flow and pressure on a metric graph, where turbulent losses of momentum are modeled by phenomenological Darcy-Weisbach friction. Mass flow balance is maintained through the boundary conditions at the network nodes, where natural gas is injected or withdrawn from the system. Gas flow through the network is controlled by compressors boosting pressure at the inlet of the adjoint pipe. Our operator splitting numerical scheme ismore » unconditionally stable and it is second order accurate in space and time. The scheme is explicit, and it is formulated to work with general networks with loops. We test the scheme over range of regimes and network configurations, also comparing its performance with performance of two other state of the art implicit schemes.« less
Road traffic: A case study of flow and path-dependency in weighted directed networks
NASA Astrophysics Data System (ADS)
Bono, Flavio; Gutiérrez, Eugenio; Poljansek, Karmen
2010-11-01
How much can we tell about flows through networks just from their topological properties? Whereas flow distributions of river basins, trees or cardiovascular systems come naturally to mind, more complex topologies are not so immediate, especially if the network is large and heterogeneously directed. Our study is motivated by the question of how the distribution of path-dependent trails in directed networks is correlated to the distribution of network flows. As an example we have studied the path-dependencies in closed trails in four metropolitan areas in England and the USA and computed their global and spatial correlations with measured traffic flows. We have found that the heterogeneous distribution of traffic intensity is mirrored by the distribution of agglomerate path-dependency and that high traffic roads are packed along corridors at short-to-medium trail lengths from the ensemble of nodes.
Perrin, Christian L; Tardy, Philippe M J; Sorbie, Ken S; Crawshaw, John C
2006-03-15
The in situ rheology of polymeric solutions has been studied experimentally in etched silicon micromodels which are idealizations of porous media. The rectangular channels in these etched networks have dimensions typical of pore sizes in sandstone rocks. Pressure drop/flow rate relations have been measured for water and non-Newtonian hydrolyzed-polyacrylamide (HPAM) solutions in both individual straight rectangular capillaries and in networks of such capillaries. Results from these experiments have been analyzed using pore-scale network modeling incorporating the non-Newtonian fluid mechanics of a Carreau fluid. Quantitative agreement is seen between the experiments and the network calculations in the Newtonian and shear-thinning flow regions demonstrating that the 'shift factor,'alpha, can be calculated a priori. Shear-thickening behavior was observed at higher flow rates in the micromodel experiments as a result of elastic effects becoming important and this remains to be incorporated in the network model.
Suen, Jonathan Y; Navlakha, Saket
2017-02-09
Controlling the flow and routing of data is a fundamental problem in many distributed networks, including transportation systems, integrated circuits, and the Internet. In the brain, synaptic plasticity rules have been discovered that regulate network activity in response to environmental inputs, which enable circuits to be stable yet flexible. Here, we develop a new neuro-inspired model for network flow control that depends only on modifying edge weights in an activity-dependent manner. We show how two fundamental plasticity rules, long-term potentiation and long-term depression, can be cast as a distributed gradient descent algorithm for regulating traffic flow in engineered networks. We then characterize, both by simulation and analytically, how different forms of edge-weight-update rules affect network routing efficiency and robustness. We find a close correspondence between certain classes of synaptic weight-update rules derived experimentally in the brain and rules commonly used in engineering, suggesting common principles to both.
NASA Astrophysics Data System (ADS)
Bagchi, Prosenjit
2016-11-01
In this talk, two problems in multiphase biological flows will be discussed. The first is the direct numerical simulation of whole blood and drug particulates in microvascular networks. Blood in microcirculation behaves as a dense suspension of heterogeneous cells. The erythrocytes are extremely deformable, while inactivated platelets and leukocytes are nearly rigid. A significant progress has been made in recent years in modeling blood as a dense cellular suspension. However, many of these studies considered the blood flow in simple geometry, e.g., straight tubes of uniform cross-section. In contrast, the architecture of a microvascular network is very complex with bifurcating, merging and winding vessels, posing a further challenge to numerical modeling. We have developed an immersed-boundary-based method that can consider blood cell flow in physiologically realistic and complex microvascular network. In addition to addressing many physiological issues related to network hemodynamics, this tool can be used to optimize the transport properties of drug particulates for effective organ-specific delivery. Our second problem is pseudopod-driven motility as often observed in metastatic cancer cells and other amoeboid cells. We have developed a multiscale hydrodynamic model to simulate such motility. We study the effect of cell stiffness on motility as the former has been considered as a biomarker for metastatic potential. Funded by the National Science Foundation.
Optical flow based finger stroke detection
NASA Astrophysics Data System (ADS)
Zhu, Zhongdi; Li, Bin; Wang, Kongqiao
2010-07-01
Finger stroke detection is an important topic in hand based Human Computer Interaction (HCI) system. Few research studies have carried out effective solutions to this problem. In this paper, we present a novel approach for stroke detection based on mono vision. Via analyzing the optical flow field within the finger area, our method is able to detect finger stroke under various camera position and visual angles. We present a thorough evaluation for each component of the algorithm, and show its efficiency and effectiveness on solving difficult stroke detection problems.
Schwarz-Based Algorithms for Compressible Flows
NASA Technical Reports Server (NTRS)
Tidriri, M. D.
1996-01-01
We investigate in this paper the application of Schwarz-based algorithms to compressible flows. First we study the combination of these methods with defect-correction procedures. We then study the effect on the Schwarz-based methods of replacing the explicit treatment of the boundary conditions by an implicit one. In the last part of this paper we study the combination of these methods with Newton-Krylov matrix-free methods. Numerical experiments that show the performance of our approaches are then presented.
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.
Application guide for AFINCH (Analysis of Flows in Networks of Channels) described by NHDPlus
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.
Autonomous robot behavior based on neural networks
NASA Astrophysics Data System (ADS)
Grolinger, Katarina; Jerbic, Bojan; Vranjes, Bozo
1997-04-01
The purpose of autonomous robot is to solve various tasks while adapting its behavior to the variable environment, expecting it is able to navigate much like a human would, including handling uncertain and unexpected obstacles. To achieve this the robot has to be able to find solution to unknown situations, to learn experienced knowledge, that means action procedure together with corresponding knowledge on the work space structure, and to recognize working environment. The planning of the intelligent robot behavior presented in this paper implements the reinforcement learning based on strategic and random attempts for finding solution and neural network approach for memorizing and recognizing work space structure (structural assignment problem). Some of the well known neural networks based on unsupervised learning are considered with regard to the structural assignment problem. The adaptive fuzzy shadowed neural network is developed. It has the additional shadowed hidden layer, specific learning rule and initialization phase. The developed neural network combines advantages of networks based on the Adaptive Resonance Theory and using shadowed hidden layer provides ability to recognize lightly translated or rotated obstacles in any direction.
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.
A network theory approach for a better understanding of overland flow connectivity
NASA Astrophysics Data System (ADS)
Masselink, Rens; Heckmann, Tobias; Temme, Arnaud; Anders, Niels; Keesstra, Saskia
2016-04-01
Hydrological connectivity describes the physical coupling, or linkages of different elements within a landscape regarding (sub)surface flows. A firm understanding of hydrological connectivity is important for catchment management applications, for e.g. habitat and species protection, and for flood resistance and resilience improvement. Thinking about (geomorphological) systems as networks can lead to new insights, which has been recognised within the scientific community as well, seeing the recent increase in the use of network (graph) theory within the geosciences. Network theory supports the analysis and understanding of complex systems by providing data structures for modelling objects and their linkages, and a versatile toolbox to quantitatively appraise network structure and properties. The objective of this study was to characterise overland flow connectivity dynamics on hillslopes in a humid sub-Mediterranean environment by using a combination of high-resolution digital-terrain models, overland flow sensors and a network approach. Results showed that there are significant differences between overland flow on agricultural areas and semi-natural shrubs areas. Positive correlations between connectivity and precipitation characteristics were found, while negative correlations between connectivity and soil moisture were found, probably due to soil water repellency. The combination of a structural network to determine potential connectivity with dynamic networks to determine the actual connectivity proved a powerful tool in analysing overland flow connectivity.
Zhou, Xing; Peng, Wei; Xu, Zhen; Yang, Bo
2015-01-01
Network robustness is the ability of a network to maintain performance after disruption, thus it is an important index for network designers to refer to. Every actual network has its own topology structure, flow magnitude (scale) and flow distribution. How the robustness relates to these factors still remains unresolved. To analyze the relations, we first established a robustness problem model, studied the hardness of a special case of the model, and generated a lot of representative network instances. We conducted experiments on these instances, deleting 5% to 50% edges on each instance and found that the robustness of a network has an approximate linearity to its structural entropy and flow entropy, when the correlation coefficient between the structure and flow is fixed. We also found that robustness is unlikely to have a relation to the flow scale and edge scale in our model. The empirical studies thus can provide a way of quickly estimating the robustness of real-world networks by using the regression coefficients we obtained during the experiments. We conducted computation on a real-world dataset and got favorable results, which exhibited the effectiveness of the estimation. PMID:26695517
Zhou, Xing; Peng, Wei; Xu, Zhen; Yang, Bo
2015-01-01
Network robustness is the ability of a network to maintain performance after disruption, thus it is an important index for network designers to refer to. Every actual network has its own topology structure, flow magnitude (scale) and flow distribution. How the robustness relates to these factors still remains unresolved. To analyze the relations, we first established a robustness problem model, studied the hardness of a special case of the model, and generated a lot of representative network instances. We conducted experiments on these instances, deleting 5% to 50% edges on each instance and found that the robustness of a network has an approximate linearity to its structural entropy and flow entropy, when the correlation coefficient between the structure and flow is fixed. We also found that robustness is unlikely to have a relation to the flow scale and edge scale in our model. The empirical studies thus can provide a way of quickly estimating the robustness of real-world networks by using the regression coefficients we obtained during the experiments. We conducted computation on a real-world dataset and got favorable results, which exhibited the effectiveness of the estimation.
Sossoe, K.S.; Lebacque, J-P.
2015-03-10
We present in this paper a model of vehicular traffic flow for a multimodal transportation road network. We introduce the notion of class of vehicles to refer to vehicles of different transport modes. Our model describes the traffic on highways (which may contain several lanes) and network transit for pubic transportation. The model is drafted with Eulerian and Lagrangian coordinates and uses a Logit model to describe the traffic assignment of our multiclass vehicular flow description on shared roads. The paper also discusses traffic streams on dedicated lanes for specific class of vehicles with event-based traffic laws. An Euler-Lagrangian-remap scheme is introduced to numerically approximate the model’s flow equations.
NASA Astrophysics Data System (ADS)
Mandal, Sumantra; Sivaprasad, P. V.; Venugopal, S.; Murthy, K. P. N.
2006-09-01
An artificial neural network (ANN) model is developed to predict the constitutive flow behaviour of austenitic stainless steels during hot deformation. The input parameters are alloy composition and process variables whereas flow stress is the output. The model is based on a three-layer feed-forward ANN with a back-propagation learning algorithm. The neural network is trained with an in-house database obtained from hot compression tests on various grades of austenitic stainless steels. The performance of the model is evaluated using a wide variety of statistical indices. Good agreement between experimental and predicted data is obtained. The correlation between individual alloying elements and high temperature flow behaviour is investigated by employing the ANN model. The results are found to be consistent with the physical phenomena. The model can be used as a guideline for new alloy development.
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
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.
Overlapping Community Detection based on Network Decomposition
NASA Astrophysics Data System (ADS)
Ding, Zhuanlian; Zhang, Xingyi; Sun, Dengdi; Luo, Bin
2016-04-01
Community detection in complex network has become a vital step to understand the structure and dynamics of networks in various fields. However, traditional node clustering and relatively new proposed link clustering methods have inherent drawbacks to discover overlapping communities. Node clustering is inadequate to capture the pervasive overlaps, while link clustering is often criticized due to the high computational cost and ambiguous definition of communities. So, overlapping community detection is still a formidable challenge. In this work, we propose a new overlapping community detection algorithm based on network decomposition, called NDOCD. Specifically, NDOCD iteratively splits the network by removing all links in derived link communities, which are identified by utilizing node clustering technique. The network decomposition contributes to reducing the computation time and noise link elimination conduces to improving the quality of obtained communities. Besides, we employ node clustering technique rather than link similarity measure to discover link communities, thus NDOCD avoids an ambiguous definition of community and becomes less time-consuming. We test our approach on both synthetic and real-world networks. Results demonstrate the superior performance of our approach both in computation time and accuracy compared to state-of-the-art algorithms.
Overlapping Community Detection based on Network Decomposition
Ding, Zhuanlian; Zhang, Xingyi; Sun, Dengdi; Luo, Bin
2016-01-01
Community detection in complex network has become a vital step to understand the structure and dynamics of networks in various fields. However, traditional node clustering and relatively new proposed link clustering methods have inherent drawbacks to discover overlapping communities. Node clustering is inadequate to capture the pervasive overlaps, while link clustering is often criticized due to the high computational cost and ambiguous definition of communities. So, overlapping community detection is still a formidable challenge. In this work, we propose a new overlapping community detection algorithm based on network decomposition, called NDOCD. Specifically, NDOCD iteratively splits the network by removing all links in derived link communities, which are identified by utilizing node clustering technique. The network decomposition contributes to reducing the computation time and noise link elimination conduces to improving the quality of obtained communities. Besides, we employ node clustering technique rather than link similarity measure to discover link communities, thus NDOCD avoids an ambiguous definition of community and becomes less time-consuming. We test our approach on both synthetic and real-world networks. Results demonstrate the superior performance of our approach both in computation time and accuracy compared to state-of-the-art algorithms. PMID:27066904
Mobility based multicast routing in wireless mesh networks
NASA Astrophysics Data System (ADS)
Jain, Sanjeev; Tripathi, Vijay S.; Tiwari, Sudarshan
2013-01-01
There exist two fundamental approaches to multicast routing namely minimum cost trees and shortest path trees. The (MCT's) minimum cost tree is one which connects receiver and sources by providing a minimum number of transmissions (MNTs) the MNTs approach is generally used for energy constraint sensor and mobile ad hoc networks. In this paper we have considered node mobility and try to find out simulation based comparison of the (SPT's) shortest path tree, (MST's) minimum steiner trees and minimum number of transmission trees in wireless mesh networks by using the performance metrics like as an end to end delay, average jitter, throughput and packet delivery ratio, average unicast packet delivery ratio, etc. We have also evaluated multicast performance in the small and large wireless mesh networks. In case of multicast performance in the small networks we have found that when the traffic load is moderate or high the SPTs outperform the MSTs and MNTs in all cases. The SPTs have lowest end to end delay and average jitter in almost all cases. In case of multicast performance in the large network we have seen that the MSTs provide minimum total edge cost and minimum number of transmissions. We have also found that the one drawback of SPTs, when the group size is large and rate of multicast sending is high SPTs causes more packet losses to other flows as MCTs.
The implementation of a standards based heterogeneous network
Eldridge, J.M.; Tolendino, L.F.
1991-08-05
Computer networks, supporting an organization's activities, are prevalent and very important to the organization's mission. Implementing a heterogenous organizational network allows the staff to select the computing environment that best supports their job requirements. This paper outlines the lessons learned implementing a heterogenous computer network based on networking standards such as TCP/IP and Ethernet. Such a network is a viable alternative to a proprietary, vendor supported network and can provide all the functionality customers expect in a computer network. 2 figs.
Motion detection based on recurrent network dynamics
Joukes, Jeroen; Hartmann, Till S.; Krekelberg, Bart
2014-01-01
The detection of visual motion requires temporal delays to compare current with earlier visual input. Models of motion detection assume that these delays reside in separate classes of slow and fast thalamic cells, or slow and fast synaptic transmission. We used a data-driven modeling approach to generate a model that instead uses recurrent network dynamics with a single, fixed temporal integration window to implement the velocity computation. This model successfully reproduced the temporal response dynamics of a population of motion sensitive neurons in macaque middle temporal area (MT) and its constituent parts matched many of the properties found in the motion processing pathway (e.g., Gabor-like receptive fields (RFs), simple and complex cells, spatially asymmetric excitation and inhibition). Reverse correlation analysis revealed that a simplified network based on first and second order space-time correlations of the recurrent model behaved much like a feedforward motion energy (ME) model. The feedforward model, however, failed to capture the full speed tuning and direction selectivity properties based on higher than second order space-time correlations typically found in MT. These findings support the idea that recurrent network connectivity can create temporal delays to compute velocity. Moreover, the model explains why the motion detection system often behaves like a feedforward ME network, even though the anatomical evidence strongly suggests that this network should be dominated by recurrent feedback. PMID:25565992
Fraser, Graham M; Goldman, Daniel; Ellis, Christopher G
2012-08-01
We describe a systematic approach to modeling blood flow using reconstructed capillary networks and in vivo hemodynamic measurements. Our goal was to produce flow solutions that represent convective O(2) delivery in vivo. Two capillary networks, I and II (84 × 168 × 342 and 70 × 157 × 268 μm(3)), were mapped using custom software. Total network red blood cell supply rate (SR) was calculated from in vivo data and used as a target metric for the flow model. To obtain inlet hematocrits, mass balances were applied recursively from downstream vessels. Pressure differences across the networks were adjusted to achieve target SR. Baseline flow solutions were used as inputs to existing O(2) transport models. To test the impact of flow redistribution, asymmetric flow solutions (Asym) were generated by applying a ± 20% pressure change to network outlets. Asym solutions produced a mean absolute difference in SR per capillary of 27.6 ± 33.3% in network I and 33.2 ± 40.1% in network II vs. baseline. The O(2) transport model calculated mean tissue PO(2) of 28.2 ± 4.8 and 28.1 ± 3.5 mmHg for baseline and 27.6 ± 5.2 and 27.7 ± 3.7 mmHg for Asym. This outcome illustrates that moderate changes in flow distribution within a capillary network have little impact on tissue PO(2) provided that total SR remains unchanged. © 2012 John Wiley & Sons Ltd.
NASA Astrophysics Data System (ADS)
Huang, Ailing; Zang, Guangzhi; He, Zhengbing; Guan, Wei
2017-05-01
Urban public transit system is a typical mixed complex network with dynamic flow, and its evolution should be a process coupling topological structure with flow dynamics, which has received little attention. This paper presents the R-space to make a comparative empirical analysis on Beijing’s flow-weighted transit route network (TRN) and we found that both the Beijing’s TRNs in the year of 2011 and 2015 exhibit the scale-free properties. As such, we propose an evolution model driven by flow to simulate the development of TRNs with consideration of the passengers’ dynamical behaviors triggered by topological change. The model simulates that the evolution of TRN is an iterative process. At each time step, a certain number of new routes are generated driven by travel demands, which leads to dynamical evolution of new routes’ flow and triggers perturbation in nearby routes that will further impact the next round of opening new routes. We present the theoretical analysis based on the mean-field theory, as well as the numerical simulation for this model. The results obtained agree well with our empirical analysis results, which indicate that our model can simulate the TRN evolution with scale-free properties for distributions of node’s strength and degree. The purpose of this paper is to illustrate the global evolutional mechanism of transit network that will be used to exploit planning and design strategies for real TRNs.
Global migration topology analysis and modeling of bilateral flow network 2006-2010
NASA Astrophysics Data System (ADS)
Porat, I.; Benguigui, L.
2016-07-01
Migration is one of the most dramatic and vast human processes in modern times. Migration is defined as people that leave their home and home-land and move to a new country. In this research we address the pattern of this massive human movement with the tools of network theory. The undirected global flow migration network (2006-2010) was identified as an exclusive disassortative network which combines two types of defined groups of large- and small-degree (D) countries with betweeness (Be) of Be˜D 3. This structure was modeled and simulated with synthetic networks of similar characteristics as the global flow migration network, and the results suggest that small-degree nodes have the topology of random networks, but the dominant part of the large-degree hubs controls this topology and shapes the network into an ultra-small world. This exclusive topology and the difference of the global flow migration network from scale-free and from Erdös-Rényi networks may be a result of two defined and different topologies of large- and small-degree countries.
Network based high performance concurrent computing
Sunderam, V.S.
1991-01-01
The overall objectives of this project are to investigate research issues pertaining to programming tools and efficiency issues in network based concurrent computing systems. The basis for these efforts is the PVM project that evolved during my visits to Oak Ridge Laboratories under the DOE Faculty Research Participation program; I continue to collaborate with researchers at Oak Ridge on some portions of the project.
Dissecting a Network-Based Education System
ERIC Educational Resources Information Center
Davis, Tiffany; Yoo, Seong-Moo; Pan, Wendi
2005-01-01
The Alabama Learning Exchange (ALEX; www.alex.state.al.us) is a network-based education system designed and implemented to help improve education in Alabama. It accomplishes this goal by providing a single location for the state's K-12 educators to find information that will help improve their classroom effectiveness. The ALEX system includes…
Neural network analysis of pulp flow speed in low coherence Doppler flowmetry measurement
NASA Astrophysics Data System (ADS)
Hannula, Manne; Alarousu, Erkki; Prykäri, Tuukka; Myllylä, Risto
2007-03-01
Low Coherence Doppler Flowmetry (LCDF) measurement produces a signal, which frequency domain characteristics are in connection to the speed of the flow. In this study a LCDF measurement data of pulp flow in a capillary was analyzed with a simple Artificial Neural Network (ANN) method to estimate the flow speed. The accuracy of the method proved to be good, validation of the method resulted in absolute error of 14 +/- 11 percentage units (mean+/-std) in flow speed estimation. The results of the study can be utilized in development of industrial pulp flow speed measurement instruments.
Secured network sensor-based defense system
NASA Astrophysics Data System (ADS)
Wei, Sixiao; Shen, Dan; Ge, Linqiang; Yu, Wei; Blasch, Erik P.; Pham, Khanh D.; Chen, Genshe
2015-05-01
Network sensor-based defense (NSD) systems have been widely used to defend against cyber threats. Nonetheless, if the adversary finds ways to identify the location of monitor sensors, the effectiveness of NSD systems can be reduced. In this paper, we propose both temporal and spatial perturbation based defense mechanisms to secure NSD systems and make the monitor sensor invisible to the adversary. The temporal-perturbation based defense manipulates the timing information of published data so that the probability of successfully recognizing monitor sensors can be reduced. The spatial-perturbation based defense dynamically redeploys monitor sensors in the network so that the adversary cannot obtain the complete information to recognize all of the monitor sensors. We carried out experiments using real-world traffic traces to evaluate the effectiveness of our proposed defense mechanisms. Our data shows that our proposed defense mechanisms can reduce the attack accuracy of recognizing detection sensors.
Flow version of statistical neurodynamics for oscillator neural networks
NASA Astrophysics Data System (ADS)
Uchiyama, Satoki
2012-04-01
We consider a neural network of Stuart-Landau oscillators as an associative memory. This oscillator network with N elements is a system of an N-dimensional differential equation, works as an attractor neural network, and is expected to have no Lyapunov functions. Therefore, the technique of equilibrium statistical physics is not applicable to the study of this system in the thermodynamic limit. However, the simplicity of this system allows us to extend statistical neurodynamics [S. Amari, K. Maginu, Neural Netw. 1 (1988) 63-73], which was originally developed to analyse the discrete time evolution of the Hopfield model, into the version for continuous time evolution. We have developed and attempted to apply this method in the analysis of the phase transition of our model network.
Electrical percolation networks of carbon nanotubes in a shear flow.
Kwon, Gyemin; Heo, Youhee; Shin, Kwanwoo; Sung, Bong June
2012-01-01
The effect of shear on the electrical percolation network of carbon nanotube (CNT)-polymer composites is investigated using computer simulations. Configurations of CNTs in a simple shear, obtaine