Finding Elephant Flows for Optical Networks

NARCIS (Netherlands)

Fioreze, Tiago; Oude Wolbers, Mattijs; van de Meent, R.; Pras, Aiko

2007-01-01

Optical networks are fast and reliable networks that enable, amongst others, dedicated light paths to be established for elephant IP flows. Elephant IP flows are characterized by being small in number, but long in time and high in traffic volume. Moving these flows from the general IP network to

Power laws and fragility in flow networks.

Science.gov (United States)

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

2013-01-01

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

Activity flow over resting-state networks shapes cognitive task activations.

Science.gov (United States)

Cole, Michael W; Ito, Takuya; Bassett, Danielle S; Schultz, Douglas H

2016-12-01

Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-state FC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allowed prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals) via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations.

Flow whitelisting in SCADA networks

DEFF Research Database (Denmark)

Barbosa, Rafael Ramos Regis; Sadre, Ramin; Pras, Aiko

2013-01-01

and the Internet. This paper describes an approach for improving the security of SCADA networks using flow whitelisting. A flow whitelist describes legitimate traffic based on four properties of network packets: client address, server address, server-side port and transport protocol. The proposed approach...

Hardness and Approximation for Network Flow Interdiction

OpenAIRE

Chestnut, Stephen R.; Zenklusen, Rico

2015-01-01

In the Network Flow Interdiction problem an adversary attacks a network in order to minimize the maximum s-t-flow. Very little is known about the approximatibility of this problem despite decades of interest in it. We present the first approximation hardness, showing that Network Flow Interdiction and several of its variants cannot be much easier to approximate than Densest k-Subgraph. In particular, any $n^{o(1)}$-approximation algorithm for Network Flow Interdiction would imply an $n^{o(1)}...

Flows in networks under fuzzy conditions

CERN Document Server

Bozhenyuk, Alexander Vitalievich; Kacprzyk, Janusz; Rozenberg, Igor Naymovich

2017-01-01

This book offers a comprehensive introduction to fuzzy methods for solving flow tasks in both transportation and networks. It analyzes the problems of minimum cost and maximum flow finding with fuzzy nonzero lower flow bounds, and describes solutions to minimum cost flow finding in a network with fuzzy arc capacities and transmission costs. After a concise introduction to flow theory and tasks, the book analyzes two important problems. The first is related to determining the maximum volume for cargo transportation in the presence of uncertain network parameters, such as environmental changes, measurement errors and repair work on the roads. These parameters are represented here as fuzzy triangular, trapezoidal numbers and intervals. The second problem concerns static and dynamic flow finding in networks under fuzzy conditions, and an effective method that takes into account the network’s transit parameters is presented here. All in all, the book provides readers with a practical reference guide to state-of-...

Monitoring individual traffic flows within the ATLAS TDAQ network

CERN Document Server

Sjoen, R; Ciobotaru, M; Batraneanu, S M; Leahu, L; Martin, B; Al-Shabibi, A

2010-01-01

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 a...

Application of artificial neural networks to identify equilibration in computer simulations

Science.gov (United States)

Leibowitz, Mitchell H.; Miller, Evan D.; Henry, Michael M.; Jankowski, Eric

2017-11-01

Determining which microstates generated by a thermodynamic simulation are representative of the ensemble for which sampling is desired is a ubiquitous, underspecified problem. Artificial neural networks are one type of machine learning algorithm that can provide a reproducible way to apply pattern recognition heuristics to underspecified problems. Here we use the open-source TensorFlow machine learning library and apply it to the problem of identifying which hypothetical observation sequences from a computer simulation are “equilibrated” and which are not. We generate training populations and test populations of observation sequences with embedded linear and exponential correlations. We train a two-neuron artificial network to distinguish the correlated and uncorrelated sequences. We find that this simple network is good enough for > 98% accuracy in identifying exponentially-decaying energy trajectories from molecular simulations.

Identifying and characterizing key nodes among communities based on electrical-circuit networks.

Science.gov (United States)

Zhu, Fenghui; Wang, Wenxu; Di, Zengru; Fan, Ying

2014-01-01

Complex networks with community structures are ubiquitous in the real world. Despite many approaches developed for detecting communities, we continue to lack tools for identifying overlapping and bridging nodes that play crucial roles in the interactions and communications among communities in complex networks. Here we develop an algorithm based on the local flow conservation to effectively and efficiently identify and distinguish the two types of nodes. Our method is applicable in both undirected and directed networks without a priori knowledge of the community structure. Our method bypasses the extremely challenging problem of partitioning communities in the presence of overlapping nodes that may belong to multiple communities. Due to the fact that overlapping and bridging nodes are of paramount importance in maintaining the function of many social and biological networks, our tools open new avenues towards understanding and controlling real complex networks with communities accompanied with the key nodes.

Fluid Flow in a Porous Tree-Shaped Network

OpenAIRE

Miguel, A. F.

2014-01-01

Tree-shaped flow networks connect one point to an infinity of points and are everywhere in Nature. These networks often own minimal flow resistance and vessel sizes obey to scaling power-laws. In this paper presents a model for fluid flow through a tree-shaped network with porous tubes. Hagen–Poiseuille flow is assumed for tubes and Darcy flow for the porous wall.

Organization of signal flow in directed networks

International Nuclear Information System (INIS)

Bányai, M; Bazsó, F; Négyessy, L

2011-01-01

Confining an answer to the question of whether and how the coherent operation of network elements is determined by the network structure is the topic of our work. We map the structure of signal flow in directed networks by analysing the degree of edge convergence and the overlap between the in- and output sets of an edge. Definitions of convergence degree and overlap are based on the shortest paths, thus they encapsulate global network properties. Using the defining notions of convergence degree and overlapping set we clarify the meaning of network causality and demonstrate the crucial role of chordless circles. In real-world networks the flow representation distinguishes nodes according to their signal transmitting, processing and control properties. The analysis of real-world networks in terms of flow representation was in accordance with the known functional properties of the network nodes. It is shown that nodes with different signal processing, transmitting and control properties are randomly connected at the global scale, while local connectivity patterns depart from randomness. The grouping of network nodes according to their signal flow properties was unrelated to the network's community structure. We present evidence that the signal flow properties of small-world-like, real-world networks cannot be reconstructed by algorithms used to generate small-world networks. Convergence degree values were calculated for regular oriented trees, and the probability density function for networks grown with the preferential attachment mechanism. For Erdos–Rényi graphs we calculated the probability density function of both convergence degrees and overlaps

Hodge Decomposition of Information Flow on Small-World Networks.

Science.gov (United States)

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

Directory of Open Access Journals (Sweden)

Taichi Haruna

2016-09-01

Full Text Available 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.

Flow rate of transport network controls uniform metabolite supply to tissue.

Science.gov (United States)

Meigel, Felix J; Alim, Karen

2018-05-01

Life and functioning of higher organisms depends on the continuous supply of metabolites to tissues and organs. What are the requirements on the transport network pervading a tissue to provide a uniform supply of nutrients, minerals or hormones? To theoretically answer this question, we present an analytical scaling argument and numerical simulations on how flow dynamics and network architecture control active spread and uniform supply of metabolites by studying the example of xylem vessels in plants. We identify the fluid inflow rate as the key factor for uniform supply. While at low inflow rates metabolites are already exhausted close to flow inlets, too high inflow flushes metabolites through the network and deprives tissue close to inlets of supply. In between these two regimes, there exists an optimal inflow rate that yields a uniform supply of metabolites. We determine this optimal inflow analytically in quantitative agreement with numerical results. Optimizing network architecture by reducing the supply variance over all network tubes, we identify patterns of tube dilation or contraction that compensate sub-optimal supply for the case of too low or too high inflow rate. © 2018 The Authors.

Airport Network Flow Simulator

Science.gov (United States)

1978-10-01

The Airport Network Flow Simulator is a FORTRAN IV simulation of the flow of air traffic in the nation's 600 commercial airports. It calculates for any group of selected airports: (a) the landing and take-off (Type A) delays; and (b) the gate departu...

Monitoring individual traffic flows within the ATLAS TDAQ network

International Nuclear Information System (INIS)

Sjoen, R; Batraneanu, S M; Leahu, L; Martin, B; Al-Shabibi, A; Stancu, S; Ciobotaru, M

2010-01-01

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.

Flow Regime Identification of Co-Current Downward Two-Phase Flow With Neural Network Approach

International Nuclear Information System (INIS)

Hiroshi Goda; Seungjin Kim; Ye Mi; Finch, Joshua P.; Mamoru Ishii; Jennifer Uhle

2002-01-01

Flow regime identification for an adiabatic vertical co-current downward air-water two-phase flow in the 25.4 mm ID and the 50.8 mm ID round tubes was performed by employing an impedance void meter coupled with the neural network classification approach. This approach minimizes the subjective judgment in determining the flow regimes. The signals obtained by an impedance void meter were applied to train the self-organizing neural network to categorize these impedance signals into a certain number of groups. The characteristic parameters set into the neural network classification included the mean, standard deviation and skewness of impedance signals in the present experiment. The classification categories adopted in the present investigation were four widely accepted flow regimes, viz. bubbly, slug, churn-turbulent, and annular flows. These four flow regimes were recognized based upon the conventional flow visualization approach by a high-speed motion analyzer. The resulting flow regime maps classified by the neural network were compared with the results obtained through the flow visualization method, and consequently the efficiency of the neural network classification for flow regime identification was demonstrated. (authors)

CUFID-query: accurate network querying through random walk based network flow estimation.

Science.gov (United States)

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2017-12-28

Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes. Through extensive

Impedance void-meter and neural networks for vertical two-phase flows

International Nuclear Information System (INIS)

Mi, Y.; Li, M.; Xiao, Z.; Tsoukalas, L.H.; Ishii, M.

1998-01-01

Most two-phase flow measurements, including void fraction measurements, depend on correct flow regime identification. There are two steps towards successful identification of flow regimes: one is to develop a non-intrusive instrument to demonstrate area-averaged void fluctuations, the other to develop a non-linear mapping approach to perform objective identification of flow regimes. A non-intrusive impedance void-meter provides input signals to a neural mapping approach used to identify flow regimes. After training, both supervised and self-organizing neural network learning paradigms performed flow regime identification successfully. The methodology presented holds considerable promise for multiphase flow diagnostic and measurement applications. (author)

Linear Programming and Network Flows

CERN Document Server

Bazaraa, Mokhtar S; Sherali, Hanif D

2011-01-01

The authoritative guide to modeling and solving complex problems with linear programming-extensively revised, expanded, and updated The only book to treat both linear programming techniques and network flows under one cover, Linear Programming and Network Flows, Fourth Edition has been completely updated with the latest developments on the topic. This new edition continues to successfully emphasize modeling concepts, the design and analysis of algorithms, and implementation strategies for problems in a variety of fields, including industrial engineering, management science, operations research

Semantic integration to identify overlapping functional modules in protein interaction networks

Directory of Open Access Journals (Sweden)

Ramanathan Murali

2007-07-01

Full Text Available Abstract Background The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. Results We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. Conclusion The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.

Opportunistic Beacon Networks: Information Dissemination via Wireless Network Identifiers

NARCIS (Netherlands)

Türkes, Okan; Scholten, Johan; Havinga, Paul J.M.

2016-01-01

This paper presents OBN, a universal opportunistic ad hoc networking model particularly intended for smart mobile devices. It enables fast and lightweight data dissemination in wireless community networks through the utilization of universally-available wireless network identifiers. As a ubiquitous

Identifying influential directors in the United States corporate governance network

Science.gov (United States)

Huang, Xuqing; Vodenska, Irena; Wang, Fengzhong; Havlin, Shlomo; Stanley, H. Eugene

2011-10-01

The influence of directors has been one of the most engaging topics recently, but surprisingly little research has been done to quantitatively evaluate the influence and power of directors. We analyze the structure of the US corporate governance network for the 11-year period 1996-2006 based on director data from the Investor Responsibility Research Center director database, and we develop a centrality measure named the influence factor to estimate the influence of directors quantitatively. The US corporate governance network is a network of directors with nodes representing directors and links between two directors representing their service on common company boards. We assume that information flows in the network through information-sharing processes among linked directors. The influence factor assigned to a director is based on the level of information that a director obtains from the entire network. We find that, contrary to commonly accepted belief that directors of large companies, measured by market capitalization, are the most powerful, in some instances, the directors who are influential do not necessarily serve on boards of large companies. By applying our influence factor method to identify the influential people contained in the lists created by popular magazines such as Fortune, Networking World, and Treasury and Risk Management, we find that the influence factor method is consistently either the best or one of the two best methods in identifying powerful people compared to other general centrality measures that are used to denote the significance of a node in complex network theory.

Identifying influential directors in the United States corporate governance network.

Science.gov (United States)

Huang, Xuqing; Vodenska, Irena; Wang, Fengzhong; Havlin, Shlomo; Stanley, H Eugene

2011-10-01

The influence of directors has been one of the most engaging topics recently, but surprisingly little research has been done to quantitatively evaluate the influence and power of directors. We analyze the structure of the US corporate governance network for the 11-year period 1996-2006 based on director data from the Investor Responsibility Research Center director database, and we develop a centrality measure named the influence factor to estimate the influence of directors quantitatively. The US corporate governance network is a network of directors with nodes representing directors and links between two directors representing their service on common company boards. We assume that information flows in the network through information-sharing processes among linked directors. The influence factor assigned to a director is based on the level of information that a director obtains from the entire network. We find that, contrary to commonly accepted belief that directors of large companies, measured by market capitalization, are the most powerful, in some instances, the directors who are influential do not necessarily serve on boards of large companies. By applying our influence factor method to identify the influential people contained in the lists created by popular magazines such as Fortune, Networking World, and Treasury and Risk Management, we find that the influence factor method is consistently either the best or one of the two best methods in identifying powerful people compared to other general centrality measures that are used to denote the significance of a node in complex network theory.

Multilevel Bloom Filters for P2P Flows Identification Based on Cluster Analysis in Wireless Mesh Network

Directory of Open Access Journals (Sweden)

Xia-an Bi

2015-01-01

Full Text Available With the development of wireless mesh networks and distributed computing, lots of new P2P services have been deployed and enrich the Internet contents and applications. The rapid growth of P2P flows brings great pressure to the regular network operation. So the effective flow identification and management of P2P applications become increasingly urgent. In this paper, we build a multilevel bloom filters data structure to identify the P2P flows through researches on the locality characteristics of P2P flows. Different level structure stores different numbers of P2P flow rules. According to the characteristics values of the P2P flows, we adjust the parameters of the data structure of bloom filters. The searching steps of the scheme traverse from the first level to the last level. Compared with the traditional algorithms, our method solves the drawbacks of previous schemes. The simulation results demonstrate that our algorithm effectively enhances the performance of P2P flows identification. Then we deploy our flow identification algorithm in the traffic monitoring sensors which belong to the network traffic monitoring system at the export link in the campus network. In the real environment, the experiment results demonstrate that our algorithm has a fast speed and high accuracy to identify the P2P flows; therefore, it is suitable for actual deployment.

Extraction of Multithread Channel Networks With a Reduced-Complexity Flow Model

Science.gov (United States)

Limaye, Ajay B.

2017-10-01

Quantitative measures of channel network geometry inform diverse applications in hydrology, sediment transport, ecology, hazard assessment, and stratigraphic prediction. These uses require a clear, objectively defined channel network. Automated techniques for extracting channels from topography are well developed for convergent channel networks and identify flow paths based on land-surface gradients. These techniques—even when they allow multiple flow paths—do not consistently capture channel networks with frequent bifurcations (e.g., in rivers, deltas, and alluvial fans). This paper uses multithread rivers as a template to develop a new approach for channel extraction suitable for channel networks with divergences. Multithread channels are commonly mapped using observed inundation extent, and I generalize this approach using a depth-resolving, reduced-complexity flow model to map inundation patterns for fixed topography across an arbitrary range of discharge. A case study for the Platte River, Nebraska, reveals that (1) the number of bars exposed above the water surface, bar area, and the number of wetted channel threads (i.e., braiding index) peak at intermediate discharge; (2) the anisotropic scaling of bar dimensions occurs for a range of discharge; and (3) the maximum braiding index occurs at a corresponding reference discharge that provides an objective basis for comparing the planform geometry of multithread rivers. Mapping by flow depth overestimates braiding index by a factor of 2. The new approach extends channel network extraction from topography to the full spectrum of channel patterns, with the potential for comparing diverse channel patterns at scales from laboratory experiments to natural landscapes.

Towards Effective Intra-flow Network Coding in Software Defined Wireless Mesh Networks

Directory of Open Access Journals (Sweden)

Donghai Zhu

2016-01-01

Full Text Available Wireless Mesh Networks (WMNs have potential to provide convenient broadband wireless Internet access to mobile users.With the support of Software-Defined Networking (SDN paradigm that separates control plane and data plane, WMNs can be easily deployed and managed. In addition, by exploiting the broadcast nature of the wireless medium and the spatial diversity of multi-hop wireless networks, intra-flow network coding has shown a greater benefit in comparison with traditional routing paradigms in data transmission for WMNs. In this paper, we develop a novel OpenCoding protocol, which combines the SDN technique with intra-flow network coding for WMNs. Our developed protocol can simplify the deployment and management of the network and improve network performance. In OpenCoding, a controller that works on the control plane makes routing decisions for mesh routers and the hop-by-hop forwarding function is replaced by network coding functions in data plane. We analyze the overhead of OpenCoding. Through a simulation study, we show the effectiveness of the OpenCoding protocol in comparison with existing schemes. Our data shows that OpenCoding outperforms both traditional routing and intra-flow network coding schemes.

Predicting Information Flows in Network Traffic.

Science.gov (United States)

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)

Identifying influential spreaders in interconnected networks

International Nuclear Information System (INIS)

Zhao, Dawei; Li, Lixiang; Huo, Yujia; Yang, Yixian; Li, Shudong

2014-01-01

Identifying the most influential spreaders in spreading dynamics is of the utmost importance for various purposes for understanding or controlling these processes. The existing relevant works are limited to a single network. Most real networks are actually not isolated, but typically coupled and affected by others. The properties of epidemic spreading have recently been found to have some significant differences in interconnected networks from those in a single network. In this paper, we focus on identifying the influential spreaders in interconnected networks. We find that the well-known k-shell index loses effectiveness; some insignificant spreaders in a single network become the influential spreaders in the whole interconnected networks while some influential spreaders become no longer important. The simulation results show that the spreading capabilities of the nodes not only depend on their influence for the network topology, but also are dramatically influenced by the spreading rate. Based on this perception, a novel index is proposed for measuring the influential spreaders in interconnected networks. We then support the efficiency of this index with numerical simulations. (paper)

Process efficiency. Redesigning social networks to improve surgery patient flow.

Science.gov (United States)

Samarth, Chandrika N; Gloor, Peter A

2009-01-01

We propose a novel approach to improve throughput of the surgery patient flow process of a Boston area teaching hospital. A social network analysis was conducted in an effort to demonstrate that process efficiency gains could be achieved through redesign of social network patterns at the workplace; in conjunction with redesign of organization structure and the implementation of workflow over an integrated information technology system. Key knowledge experts and coordinators in times of crisis were identified and a new communication structure more conducive to trust and knowledge sharing was suggested. The new communication structure is scalable without compromising on coordination required among key roles in the network for achieving efficiency gains.

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

Science.gov (United States)

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.

RECOVERY ACT - Robust Optimization for Connectivity and Flows in Dynamic Complex Networks

Energy Technology Data Exchange (ETDEWEB)

Balasundaram, Balabhaskar [Oklahoma State Univ., Stillwater, OK (United States); Butenko, Sergiy [Texas A & M Univ., College Station, TX (United States); Boginski, Vladimir [Univ. of Florida, Gainesville, FL (United States); Uryasev, Stan [Univ. of Florida, Gainesville, FL (United States)

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

Numerical simulation for gas-liquid two-phase flow in pipe networks

International Nuclear Information System (INIS)

Li Xiaoyan; Kuang Bo; Zhou Guoliang; Xu Jijun

1998-01-01

The complex pipe network characters can not directly presented in single phase flow, gas-liquid two phase flow pressure drop and void rate change model. Apply fluid network theory and computer numerical simulation technology to phase flow pipe networks carried out simulate and compute. Simulate result shows that flow resistance distribution is non-linear in two phase pipe network

Spike Code Flow in Cultured Neuronal Networks.

Science.gov (United States)

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.

Understanding "Understanding" Flow for Network-Centric Warfare: Military Knowledge-Flow Mechanics

National Research Council Canada - National Science Library

Nissen, Mark

2002-01-01

Network-centric warfare (NCW) emphasizes information superiority for battlespace efficacy, but it is clear that the mechanics of how knowledge flows are just as important as those pertaining to the networks and communication...

Reliability-oriented multi-resource allocation in a stochastic-flow network

International Nuclear Information System (INIS)

Hsieh, C.-C.; Lin, M.-H.

2003-01-01

A stochastic-flow network consists of a set of nodes, including source nodes which supply various resources and sink nodes at which resource demands take place, and a collection of arcs whose capacities have multiple operational states. The network reliability of such a stochastic-flow network is the probability that resources can be successfully transmitted from source nodes through multi-capacitated arcs to sink nodes. Although the evaluation schemes of network reliability in stochastic-flow networks have been extensively studied in the literature, how to allocate various resources at source nodes in a reliable means remains unanswered. In this study, a resource allocation problem in a stochastic-flow network is formulated that aims to determine the optimal resource allocation policy at source nodes subject to given resource demands at sink nodes such that the network reliability of the stochastic-flow network is maximized, and an algorithm for computing the optimal resource allocation is proposed that incorporates the principle of minimal path vectors. A numerical example is given to illustrate the proposed algorithm

Report of the European Myeloma Network on multiparametric flow cytometry in multiple myeloma and related disorders

DEFF Research Database (Denmark)

Rawstron, Andy C; Orfao, Alberto; Beksac, Meral

2008-01-01

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...

Development of objective flow regime identification method using self-organizing neural network

International Nuclear Information System (INIS)

Lee, Jae Young; Kim, Nam Seok; Kwak, Nam Yee

2004-01-01

Two-phase flow shows various flow patterns according to the amount of the void and its relative velocity to the liquid flow. This variation directly affect the interfacial transfer which is the key factor for the design or analysis of the phase change systems. Especially the safety analysis of the nuclear power plant has been performed based on the numerical code furnished with the proper constitutive relations depending highly upon the flow regimes. Heavy efforts have been focused to identify the flow regime and at this moment we stand on relative very stable engineering background compare to the other research field. However, the issues related to objectiveness and transient flow regime are still open to study. Lee et al. and Ishii developed the method for the objective and instantaneous flow regime identification based on the neural network and new index of probability distribution of the flow regime which allows just one second observation for the flow regime identification. In the present paper, we developed the self-organized neural network for more objective approach to this problem. Kohonen's Self-Organizing Map (SOM) has been used for clustering, visualization, and abstraction. The SOM is trained through unsupervised competitive learning using a 'winner takes it all' policy. Therefore, its unsupervised training character delete the possible interference of the regime developer to the neural network training. After developing the computer code, we evaluate the performance of the code with the vertically upward two-phase flow in the pipes of 25.4 and 50.4 cmm I.D. Also, the sensitivity of the number of the clusters to the flow regime identification was made

Flow distributions and spatial correlations in human brain capillary networks

Science.gov (United States)

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.

Distributed Multi-Commodity Network Flow Algorithm for Energy Optimal Routing in Wireless Sensor Networks.

Directory of Open Access Journals (Sweden)

J. Trdlicka

2010-12-01

Full Text Available This work proposes a distributed algorithm for energy optimal routing in a wireless sensor network. The routing problem is described as a mathematical problem by the minimum-cost multi-commodity network flow problem. Due to the separability of the problem, we use the duality theorem to derive the distributed algorithm. The algorithm computes the energy optimal routing in the network without any central node or knowledge of the whole network structure. Each node only needs to know the flow which is supposed to send or receive and the costs and capacities of the neighboring links. An evaluation of the presented algorithm on benchmarks for the energy optimal data flow routing in sensor networks with up to 100 nodes is presented.

Use of demand for and spatial flow of ecosystem services to identify priority areas.

Science.gov (United States)

Verhagen, Willem; Kukkala, Aija S; Moilanen, Atte; van Teeffelen, Astrid J A; Verburg, Peter H

2017-08-01

Policies and research increasingly focus on the protection of ecosystem services (ESs) through priority-area conservation. Priority areas for ESs should be identified based on ES capacity and ES demand and account for the connections between areas of ES capacity and demand (flow) resulting in areas of unique demand-supply connections (flow zones). We tested ways to account for ES demand and flow zones to identify priority areas in the European Union. We mapped the capacity and demand of a global (carbon sequestration), a regional (flood regulation), and 3 local ESs (air quality, pollination, and urban leisure). We used Zonation software to identify priority areas for ESs based on 6 tests: with and without accounting for ES demand and 4 tests that accounted for the effect of ES flow zone. There was only 37.1% overlap between the 25% of priority areas that encompassed the most ESs with and without accounting for ES demand. The level of ESs maintained in the priority areas increased from 23.2% to 57.9% after accounting for ES demand, especially for ESs with a small flow zone. Accounting for flow zone had a small effect on the location of priority areas and level of ESs maintained but resulted in fewer flow zones without ES maintained relative to ignoring flow zones. Accounting for demand and flow zones enhanced representation and distribution of ESs with local to regional flow zones without large trade-offs relative to the global ES. We found that ignoring ES demand led to the identification of priority areas in remote regions where benefits from ES capacity to society were small. Incorporating ESs in conservation planning should therefore always account for ES demand to identify an effective priority network for ESs. © 2016 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology.

Computer program for compressible flow network analysis

Science.gov (United States)

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.

Spike Code Flow in Cultured Neuronal Networks

Directory of Open Access Journals (Sweden)

Shinichi Tamura

2016-01-01

Full Text Available 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.

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

Science.gov (United States)

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

2018-01-01

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

Interest communities and flow roles in directed networks: the Twitter network of the UK riots.

Science.gov (United States)

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.

Network innovation through OpenFlow and SDN principles and design

CERN Document Server

Hu, Fei

2014-01-01

FUNDAMENTALSSDN /OpenFlow: Concepts and Applications; Ashley Gerrity and Fei HuAn OpenFlow Network Design Cycle; Pedro A. Aranda Gutiřrez and Diego R. LopezDESIGNIP Source Address Validation Solution with OpenFlow Extension and OpenRouter; Jun BiLanguage and Programming in SDN /OpenFlow; Muhammad Farooq and Fei HuControl and Management Software for SDNs; Natalia Castro Fernandes and Luiz Claudio Schara MagalhêsController Architecture and Performance in Software-Defined Networks; Ting Zhang and Fei HuMobile Applications on Global Clouds Using OpenFlow and Software-Defined Networking; Subharth

Visualization and Hierarchical Analysis of Flow in Discrete Fracture Network Models

Science.gov (United States)

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

2013-12-01

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

Modelling flow dynamics in water distribution networks using ...

African Journals Online (AJOL)

One such approach is the Artificial Neural Networks (ANNs) technique. The advantage of ANNs is that they are robust and can be used to model complex linear and non-linear systems without making implicit assumptions. ANNs can be trained to forecast flow dynamics in a water distribution network. Such flow dynamics ...

Generalized network modeling of capillary-dominated two-phase flow.

Science.gov (United States)

Raeini, Ali Q; Bijeljic, Branko; Blunt, Martin J

2018-02-01

We present a generalized network model for simulating capillary-dominated two-phase flow through porous media at the pore scale. Three-dimensional images of the pore space are discretized using a generalized network-described in a companion paper [A. Q. Raeini, B. Bijeljic, and M. J. Blunt, Phys. Rev. E 96, 013312 (2017)2470-004510.1103/PhysRevE.96.013312]-which comprises pores that are divided into smaller elements called half-throats and subsequently into corners. Half-throats define the connectivity of the network at the coarsest level, connecting each pore to half-throats of its neighboring pores from their narrower ends, while corners define the connectivity of pore crevices. The corners are discretized at different levels for accurate calculation of entry pressures, fluid volumes, and flow conductivities that are obtained using direct simulation of flow on the underlying image. This paper discusses the two-phase flow model that is used to compute the averaged flow properties of the generalized network, including relative permeability and capillary pressure. We validate the model using direct finite-volume two-phase flow simulations on synthetic geometries, and then present a comparison of the model predictions with a conventional pore-network model and experimental measurements of relative permeability in the literature.

Structural Modeling and Characteristics Analysis of Flow Interaction Networks in the Internet

International Nuclear Information System (INIS)

Wu Xiao-Yu; Gu Ren-Tao; Pan Zhuo-Ya; Jin Wei-Qi; Ji Yue-Feng

2015-01-01

Applying network duality and elastic mechanics, we investigate the interactions among Internet flows by constructing a weighted undirected network, where the vertices and the edges represent the flows and the mutual dependence between flows, respectively. Based on the obtained flow interaction network, we find the existence of ‘super flow’ in the Internet, indicating that some flows have a great impact on a huge number of other flows; moreover, one flow can spread its influence to another through a limited quantity of flows (less than 5 in the experimental simulations), which shows strong small-world characteristics like the social network. To reflect the flow interactions in the physical network congestion evaluation, the ‘congestion coefficient’ is proposed as a new metric which shows a finer observation on congestion than the conventional one. (paper)

Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow.

Science.gov (United States)

Gao, Zhong-Ke; Zhang, Xin-Wang; Jin, Ning-De; Marwan, Norbert; Kurths, Jürgen

2013-09-01

Characterizing complex patterns arising from horizontal oil-water two-phase flows is a contemporary and challenging problem of paramount importance. We design a new multisector conductance sensor and systematically carry out horizontal oil-water two-phase flow experiments for measuring multivariate signals of different flow patterns. We then infer multivariate recurrence networks from these experimental data and investigate local cross-network properties for each constructed network. Our results demonstrate that a cross-clustering coefficient from a multivariate recurrence network is very sensitive to transitions among different flow patterns and recovers quantitative insights into the flow behavior underlying horizontal oil-water flows. These properties render multivariate recurrence networks particularly powerful for investigating a horizontal oil-water two-phase flow system and its complex interacting components from a network perspective.

Flow Oriented Channel Assignment for Multi-radio Wireless Mesh Networks

Directory of Open Access Journals (Sweden)

Niu Zhisheng

2010-01-01

Full Text Available We investigate channel assignment for a multichannel wireless mesh network backbone, where each router is equipped with multiple interfaces. Of particular interest is the development of channel assignment heuristics for multiple flows. We present an optimization formulation and then propose two iterative flow oriented heuristics for the conflict-free and interference-aware cases, respectively. To maximize the aggregate useful end-to-end flow rates, both algorithms identify and resolve congestion at instantaneous bottleneck link in each iteration. Then the link rate is optimally allocated among contending flows that share this link by solving a linear programming (LP problem. A thorough performance evaluation is undertaken as a function of the number of channels and interfaces/node and the number of contending flows. The performance of our algorithm is shown to be significantly superior to best known algorithm in its class in multichannel limited radio scenarios.

Information Flows in Networked Engineering Design Projects

DEFF Research Database (Denmark)

Parraguez, Pedro; Maier, Anja

Complex engineering design projects need to manage simultaneously multiple information flows across design activities associated with different areas of the design process. Previous research on this area has mostly focused on either analysing the “required information flows” through activity...... networks at the project level or in studying the social networks that deliver the “actual information flow”. In this paper we propose and empirically test a model and method that integrates both social and activity networks into one compact representation, allowing to compare actual and required...... information flows between design spaces, and to assess the influence that these misalignments could have on the performance of engineering design projects....

Software defined networking with OpenFlow

CERN Document Server

Azodolmolky, Siamak

2013-01-01

A step-by-step, example-based guide which will help you gain hands-on experience with the platforms and debugging tools on OpenFlow.If you are a network engineer, architect, junior researcher or an application developer, this book is ideal for you. You will need to have some level of network experience, knowledge of broad networking concepts, and some familiarity with day- to- day operation of computer networks. Ideally, you should also be familiar with programing scripting/languages (especially Python and Java), and system virtualization.

Numerical Modeling of Interstitial Fluid Flow Coupled with Blood Flow through a Remodeled Solid Tumor Microvascular Network.

Science.gov (United States)

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.

Numerical Modeling of Interstitial Fluid Flow Coupled with Blood Flow through a Remodeled Solid Tumor Microvascular Network.

Directory of Open Access Journals (Sweden)

M Soltani

Full Text Available 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.

Identifying PHM market and network opportunities.

Science.gov (United States)

Grube, Mark E; Krishnaswamy, Anand; Poziemski, John; York, Robert W

2015-11-01

Two key processes for healthcare organizations seeking to assume a financially sustainable role in population health management (PHM), after laying the groundwork for the effort, are to identify potential PHM market opportunities and determine the scope of the PHM network. Key variables organizations should consider with respect to market opportunities include the patient population, the overall insurance/employer market, and available types of insurance products. Regarding the network's scope, organizations should consider both traditional strategic criteria for a viable network and at least five additional criteria: network essentiality and PHM care continuum, network adequacy, service distribution right-sizing, network growth strategy, and organizational agility.

Identifiability of location and magnitude of flow barriers in slightly compressible flow

NARCIS (Netherlands)

Kahrobaei, S.; Mansoori Habib Abadi, M.; Joosten, G.J.P.; Hof, Van den P.M.J.; Jansen, J.D.

2015-01-01

Classic identifiability analysis of flow barriers in incompressible single-phase flow reveals that it is not possible to identify the location and permeability of low-permeability barriers from production data (wellbore pressures and rates), and that only averaged reservoir properties in between

Identifiability of location and magnitude of flow barriers in slightly compressible flow

NARCIS (Netherlands)

Kahrobaei, S.; Mansoori Habib Abadi, M.; Joosten, G.J.P.; Van den Hof, P.; Jansen, J.D.

2016-01-01

Classic identifiability analysis of flow barriers in incompressible single-phase flow reveals that it is not possible to identify the location and permeability of low-permeability barriers from production data (wellbore pressures and rates), and that only averaged reservoir properties in between

Self-sustained oscillations in blood flow through a honeycomb capillary network.

Science.gov (United States)

Davis, J M; Pozrikidis, C

2014-09-01

Numerical simulations of unsteady blood flow through a honeycomb network originating at multiple inlets and terminating at multiple outlets are presented and discussed under the assumption that blood behaves as a continuum with variable constitution. Unlike a tree network, the honeycomb network exhibits both diverging and converging bifurcations between branching capillary segments. Numerical results based on a finite difference method demonstrate that as in the case of tree networks considered in previous studies, the cell partitioning law at diverging bifurcations is an important parameter in both steady and unsteady flow. Specifically, a steady flow may spontaneously develop self-sustained oscillations at critical conditions by way of a Hopf bifurcation. Contrary to tree-like networks comprised entirely of diverging bifurcations, the critical parameters for instability in honeycomb networks depend weakly on the system size. The blockage of one or more network segments due to the presence of large cells or the occurrence of capillary constriction may cause flow reversal or trigger a transition to unsteady flow.

Concept development and needs identification for intelligent network flow optimization (INFLO) : concept of operations.

Science.gov (United States)

2012-06-01

The purpose of this project is to develop for the Intelligent Network Flow Optimization (INFLO), which is one collection (or bundle) of high-priority transformative applications identified by the United States Department of Transportation (USDOT) Mob...

Concept development and needs identification for intelligent network flow optimization (INFLO) : test readiness assessment.

Science.gov (United States)

2012-11-01

The purpose of this project is to develop for the Intelligent Network Flow Optimization (INFLO), which is one collection (or bundle) of high-priority transformative applications identified by the United States Department of Transportation (USDOT) Mob...

A flow level model for wireless multihop ad hoc network throughput

NARCIS (Netherlands)

Coenen, Tom Johannes Maria; van den Berg, Hans Leo; Boucherie, Richardus J.

2005-01-01

A flow level model for multihop wireless ad hoc networks is presented in this paper. Using a flow level view, we show the main properties and modeling challenges for ad hoc networks. Considering different scenarios, a multihop WLAN and a serial network with a TCP-like flow control protocol, we

Validating User Flows to Protect Software Defined Network Environments

Directory of Open Access Journals (Sweden)

Ihsan H. Abdulqadder

2018-01-01

Full Text Available Software Defined Network is a promising network paradigm which has led to several security threats in SDN applications that involve user flows, switches, and controllers in the network. Threats as spoofing, tampering, information disclosure, Denial of Service, flow table overloading, and so on have been addressed by many researchers. In this paper, we present novel SDN design to solve three security threats: flow table overloading is solved by constructing a star topology-based architecture, unsupervised hashing method mitigates link spoofing attack, and fuzzy classifier combined with L1-ELM running on a neural network for isolating anomaly packets from normal packets. For effective flow migration Discrete-Time Finite-State Markov Chain model is applied. Extensive simulations using OMNeT++ demonstrate the performance of our proposed approach, which is better at preserving holding time than are other state-of-the-art works from the literature.

Neural network modeling for near wall turbulent flow

International Nuclear Information System (INIS)

Milano, Michele; Koumoutsakos, Petros

2002-01-01

A neural network methodology is developed in order to reconstruct the near wall field in a turbulent flow by exploiting flow fields provided by direct numerical simulations. The results obtained from the neural network methodology are compared with the results obtained from prediction and reconstruction using proper orthogonal decomposition (POD). Using the property that the POD is equivalent to a specific linear neural network, a nonlinear neural network extension is presented. It is shown that for a relatively small additional computational cost nonlinear neural networks provide us with improved reconstruction and prediction capabilities for the near wall velocity fields. Based on these results advantages and drawbacks of both approaches are discussed with an outlook toward the development of near wall models for turbulence modeling and control

Energy flow models for the estimation of technical losses in distribution network

International Nuclear Information System (INIS)

Au, Mau Teng; Tan, Chin Hooi

2013-01-01

This paper presents energy flow models developed to estimate technical losses in distribution network. Energy flow models applied in this paper is based on input energy and peak demand of distribution network, feeder length and peak demand, transformer loading capacity, and load factor. Two case studies, an urban distribution network and a rural distribution network are used to illustrate application of the energy flow models. Results on technical losses obtained for the two distribution networks are consistent and comparable to network of similar types and characteristics. Hence, the energy flow models are suitable for practical application.

Scaling of peak flows with constant flow velocity in random self-similar networks

Science.gov (United States)

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

Information transmission and signal permutation in active flow networks

Science.gov (United States)

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

2018-03-01

Recent experiments show that both natural and artificial microswimmers in narrow channel-like geometries will self-organise to form steady, directed flows. This suggests that networks of flowing active matter could function as novel autonomous microfluidic devices. However, little is known about how information propagates through these far-from-equilibrium systems. Through a mathematical analogy with spin-ice vertex models, we investigate here the input–output characteristics of generic incompressible active flow networks (AFNs). Our analysis shows that information transport through an AFN is inherently different from conventional pressure or voltage driven networks. Active flows on hexagonal arrays preserve input information over longer distances than their passive counterparts and are highly sensitive to bulk topological defects, whose presence can be inferred from marginal input–output distributions alone. This sensitivity further allows controlled permutations on parallel inputs, revealing an unexpected link between active matter and group theory that can guide new microfluidic mixing strategies facilitated by active matter and aid the design of generic autonomous information transport networks.

Recurrence network analysis of experimental signals from bubbly oil-in-water flows

Energy Technology Data Exchange (ETDEWEB)

Gao, Zhong-Ke; Zhang, Xin-Wang; Du, Meng [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China); Jin, Ning-De, E-mail: ndjin@tju.edu.cn [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)

2013-02-04

Based on the signals from oil–water two-phase flow experiment, we construct and analyze recurrence networks to characterize the dynamic behavior of different flow patterns. We first take a chaotic time series as an example to demonstrate that the local property of recurrence network allows characterizing chaotic dynamics. Then we construct recurrence networks for different oil-in-water flow patterns and investigate the local property of each constructed network, respectively. The results indicate that the local topological statistic of recurrence network is very sensitive to the transitions of flow patterns and allows uncovering the dynamic flow behavior associated with chaotic unstable periodic orbits.

Recurrence network analysis of experimental signals from bubbly oil-in-water flows

International Nuclear Information System (INIS)

Gao, Zhong-Ke; Zhang, Xin-Wang; Du, Meng; Jin, Ning-De

2013-01-01

Based on the signals from oil–water two-phase flow experiment, we construct and analyze recurrence networks to characterize the dynamic behavior of different flow patterns. We first take a chaotic time series as an example to demonstrate that the local property of recurrence network allows characterizing chaotic dynamics. Then we construct recurrence networks for different oil-in-water flow patterns and investigate the local property of each constructed network, respectively. The results indicate that the local topological statistic of recurrence network is very sensitive to the transitions of flow patterns and allows uncovering the dynamic flow behavior associated with chaotic unstable periodic orbits.

Transitions from Trees to Cycles in Adaptive Flow Networks

DEFF Research Database (Denmark)

Martens, Erik Andreas; Klemm, Konstantin

2017-01-01

-world transport networks exhibit both tree-like motifs and cycles. When the network is subject to load fluctuations, the presence of cyclic motifs may help to reduce flow fluctuations and, thus, render supply in the network more robust. While previous studies considered network topology via optimization...... principles, here, we take a dynamical systems approach and study a simple model of a flow network with dynamically adapting weights (conductances). We assume a spatially non-uniform distribution of rapidly fluctuating loads in the sinks and investigate what network configurations are dynamically stable......Transport networks are crucial to the functioning of natural and technological systems. Nature features transport networks that are adaptive over a vast range of parameters, thus providing an impressive level of robustness in supply. Theoretical and experimental studies have found that real...

International Trade Modelling Using Open Flow Networks: A Flow-Distance Based Analysis.

Science.gov (United States)

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.

Micro/Nano-pore Network Analysis of Gas Flow in Shale Matrix.

Science.gov (United States)

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.

Flow-based vulnerability measures for network component importance: Experimentation with preparedness planning

International Nuclear Information System (INIS)

Nicholson, Charles D.; Barker, Kash; Ramirez-Marquez, Jose E.

2016-01-01

This work develops and compares several flow-based vulnerability measures to prioritize important network edges for the implementation of preparedness options. These network vulnerability measures quantify different characteristics and perspectives on enabling maximum flow, creating bottlenecks, and partitioning into cutsets, among others. The efficacy of these vulnerability measures to motivate preparedness options against experimental geographically located disruption simulations is measured. Results suggest that a weighted flow capacity rate, which accounts for both (i) the contribution of an edge to maximum network flow and (ii) the extent to which the edge is a bottleneck in the network, shows most promise across four instances of varying network sizes and densities. - Highlights: • We develop new flow-based measures of network vulnerability. • We apply these measures to determine the importance of edges after disruptions. • Networks of varying size and density are explored.

Generalized network modeling of capillary-dominated two-phase flow

Science.gov (United States)

Raeini, Ali Q.; Bijeljic, Branko; Blunt, Martin J.

2018-02-01

We present a generalized network model for simulating capillary-dominated two-phase flow through porous media at the pore scale. Three-dimensional images of the pore space are discretized using a generalized network—described in a companion paper [A. Q. Raeini, B. Bijeljic, and M. J. Blunt, Phys. Rev. E 96, 013312 (2017), 10.1103/PhysRevE.96.013312]—which comprises pores that are divided into smaller elements called half-throats and subsequently into corners. Half-throats define the connectivity of the network at the coarsest level, connecting each pore to half-throats of its neighboring pores from their narrower ends, while corners define the connectivity of pore crevices. The corners are discretized at different levels for accurate calculation of entry pressures, fluid volumes, and flow conductivities that are obtained using direct simulation of flow on the underlying image. This paper discusses the two-phase flow model that is used to compute the averaged flow properties of the generalized network, including relative permeability and capillary pressure. We validate the model using direct finite-volume two-phase flow simulations on synthetic geometries, and then present a comparison of the model predictions with a conventional pore-network model and experimental measurements of relative permeability in the literature.

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

Energy Technology Data Exchange (ETDEWEB)

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

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

International Nuclear Information System (INIS)

Khayrat, Karim; Jenny, Patrick

2017-01-01

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.

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

Science.gov (United States)

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

2018-04-01

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

The stationary flow in a heterogeneous compliant vessel network

International Nuclear Information System (INIS)

Filoche, Marcel; Florens, Magali

2011-01-01

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.

Selective pumping in a network: insect-style microscale flow transport

International Nuclear Information System (INIS)

Aboelkassem, Yasser; Staples, Anne E

2013-01-01

A new paradigm for selective pumping of fluids in a complex network of channels in the microscale flow regime is presented. The model is inspired by internal flow distributions produced by the rhythmic wall contractions observed in many insect tracheal networks. The approach presented here is a natural extension of previous two-dimensional modeling of insect-inspired microscale flow transport in a single channel, and aims to manipulate fluids efficiently in microscale networks without the use of any mechanical valves. This selective pumping approach enables fluids to be transported, controlled and precisely directed into a specific branch in a network while avoiding other possible routes. In order to present a quantitative analysis of the selective pumping approach presented here, the velocity and pressure fields and the time-averaged net flow that are induced by prescribed wall contractions are calculated numerically using the method of fundamental solutions. More specifically, the Stokeslets-meshfree method is used in this study to solve the Stokes equations that govern the flow motions in a network with moving wall contractions. The results presented here might help in understanding some features of the insect respiratory system function and guide efforts to fabricate novel microfluidic devices for flow transport and mixing, and targeted drug delivery applications. (paper)

Brain network clustering with information flow motifs

NARCIS (Netherlands)

Märtens, M.; Meier, J.M.; Hillebrand, Arjan; Tewarie, Prejaas; Van Mieghem, P.F.A.

2017-01-01

Recent work has revealed frequency-dependent global patterns of information flow by a network analysis of magnetoencephalography data of the human brain. However, it is unknown which properties on a small subgraph-scale of those functional brain networks are dominant at different frequencies bands.

Identifying the community structure of the food-trade international multi-network

Science.gov (United States)

Torreggiani, S.; Mangioni, G.; Puma, M. J.; Fagiolo, G.

2018-05-01

Achieving international food security requires improved understanding of how international trade networks connect countries around the world through the import-export flows of food commodities. The properties of international food trade networks are still poorly documented, especially from a multi-network perspective. In particular, nothing is known about the multi-network’s community structure. Here we find that the individual crop-specific layers of the multi-network have densely connected trading groups, a consistent characteristic over the period 2001–2011. Further, the multi-network is characterized by low variability over this period but with substantial heterogeneity across layers in each year. In particular, the layers are mostly assortative: more-intensively connected countries tend to import from and export to countries that are themselves more connected. We also fit econometric models to identify social, economic and geographic factors explaining the probability that any two countries are co-present in the same community. Our estimates indicate that the probability of country pairs belonging to the same food trade community depends more on geopolitical and economic factors—such as geographical proximity and trade-agreement co-membership—than on country economic size and/or income. These community-structure findings of the multi-network are especially valuable for efforts to understand past and emerging dynamics in the global food system, especially those that examine potential ‘shocks’ to global food trade.

Markov transition probability-based network from time series for characterizing experimental two-phase flow

International Nuclear Information System (INIS)

Gao Zhong-Ke; Hu Li-Dan; Jin Ning-De

2013-01-01

We generate a directed weighted complex network by a method based on Markov transition probability to represent an experimental two-phase flow. We first systematically carry out gas—liquid two-phase flow experiments for measuring the time series of flow signals. Then we construct directed weighted complex networks from various time series in terms of a network generation method based on Markov transition probability. We find that the generated network inherits the main features of the time series in the network structure. In particular, the networks from time series with different dynamics exhibit distinct topological properties. Finally, we construct two-phase flow directed weighted networks from experimental signals and associate the dynamic behavior of gas-liquid two-phase flow with the topological statistics of the generated networks. The results suggest that the topological statistics of two-phase flow networks allow quantitative characterization of the dynamic flow behavior in the transitions among different gas—liquid flow patterns. (general)

Application of the load flow and random flow models for the analysis of power transmission networks

International Nuclear Information System (INIS)

Zio, Enrico; Piccinelli, Roberta; Delfanti, Maurizio; Olivieri, Valeria; Pozzi, Mauro

2012-01-01

In this paper, the classical load flow model and the random flow model are considered for analyzing the performance of power transmission networks. The analysis concerns both the system performance and the importance of the different system elements; this latter is computed by power flow and random walk betweenness centrality measures. A network system from the literature is analyzed, representing a simple electrical power transmission network. The results obtained highlight the differences between the LF “global approach” to flow dispatch and the RF local approach of randomized node-to-node load transfer. Furthermore, computationally the LF model is less consuming than the RF model but problems of convergence may arise in the LF calculation.

Mass transport enhancement in redox flow batteries with corrugated fluidic networks

Science.gov (United States)

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.

Towards Effective Intra-flow Network Coding in Software Defined Wireless Mesh Networks

OpenAIRE

Donghai Zhu; Xinyu Yang Yang; Peng Zhao; Wei Yu

2016-01-01

Wireless Mesh Networks (WMNs) have potential to provide convenient broadband wireless Internet access to mobile users.With the support of Software-Defined Networking (SDN) paradigm that separates control plane and data plane, WMNs can be easily deployed and managed. In addition, by exploiting the broadcast nature of the wireless medium and the spatial diversity of multi-hop wireless networks, intra-flow network coding has shown a greater benefit in comparison with traditional routing paradigm...

Separation prediction in two dimensional boundary layer flows using artificial neural networks

International Nuclear Information System (INIS)

Sabetghadam, F.; Ghomi, H.A.

2003-01-01

In this article, the ability of artificial neural networks in prediction of separation in steady two dimensional boundary layer flows is studied. Data for network training is extracted from numerical solution of an ODE obtained from Von Karman integral equation with approximate one parameter Pohlhousen velocity profile. As an appropriate neural network, a two layer radial basis generalized regression artificial neural network is used. The results shows good agreements between the overall behavior of the flow fields predicted by the artificial neural network and the actual flow fields for some cases. The method easily can be extended to unsteady separation and turbulent as well as compressible boundary layer flows. (author)

Scaling of peak flows with constant flow velocity in random self-similar networks

Directory of Open Access Journals (Sweden)

R. Mantilla

2011-07-01

Full Text Available 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 p_i and p_e, 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

Solving Minimum Cost Multi-Commodity Network Flow Problem ...

African Journals Online (AJOL)

ADOWIE PERE

2018-03-23

Mar 23, 2018 ... network-based modeling framework for integrated fixed and mobile ... Minimum Cost Network Flow Problem (MCNFP) and some ..... Unmanned Aerial Vehicle Routing in Traffic. Incident ... Ph.D. Thesis, Dept. of Surveying &.

On Event Detection and Localization in Acyclic Flow Networks

KAUST Repository

Suresh, Mahima Agumbe

2013-05-01

Acyclic flow networks, present in many infrastructures of national importance (e.g., oil and gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures have been proven costly and imprecise, particularly when dealing with large-scale distribution systems. In this article, to the best of our knowledge, for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. We propose the idea of using sensors that move along the edges of the network and detect events (i.e., attacks). To localize the events, sensors detect proximity to beacons, which are devices with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensors and beacons deployed) in a predetermined zone of interest, while ensuring a degree of coverage by sensors and a required accuracy in locating events using beacons. We propose algorithms for solving the aforementioned problem and demonstrate their effectiveness with results obtained from a realistic flow network simulator.

Structural parameter identifiability analysis for dynamic reaction networks

DEFF Research Database (Denmark)

Davidescu, Florin Paul; Jørgensen, Sten Bay

2008-01-01

method based on Lie derivatives. The proposed systematic two phase methodology is illustrated on a mass action based model for an enzymatically catalyzed reaction pathway network where only a limited set of variables is measured. The methodology clearly pinpoints the structurally identifiable parameters...... where for a given set of measured variables it is desirable to investigate which parameters may be estimated prior to spending computational effort on the actual estimation. This contribution addresses the structural parameter identifiability problem for the typical case of reaction network models....... The proposed analysis is performed in two phases. The first phase determines the structurally identifiable reaction rates based on reaction network stoichiometry. The second phase assesses the structural parameter identifiability of the specific kinetic rate expressions using a generating series expansion...

Scaling-Laws of Flow Entropy with Topological Metrics of Water Distribution Networks

OpenAIRE

Giovanni Francesco Santonastaso; Armando Di Nardo; Michele Di Natale; Carlo Giudicianni; Roberto Greco

2018-01-01

Robustness of water distribution networks is related to their connectivity and topological structure, which also affect their reliability. Flow entropy, based on Shannon’s informational entropy, has been proposed as a measure of network redundancy and adopted as a proxy of reliability in optimal network design procedures. In this paper, the scaling properties of flow entropy of water distribution networks with their size and other topological metrics are studied. To such aim, flow entropy, ma...

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

Science.gov (United States)

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

2017-05-01

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

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

Science.gov (United States)

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

2015-02-04

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

Introduction to Focus Issue: Complex network perspectives on flow systems.

Science.gov (United States)

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.

Abnormal network flow detection based on application execution patterns from Web of Things (WoT) platforms.

Science.gov (United States)

Yoon, Young; Jung, Hyunwoo; Lee, Hana

2018-01-01

In this paper, we present a research work on a novel methodology of identifying abnormal behaviors at the underlying network monitor layer during runtime based on the execution patterns of Web of Things (WoT) applications. An execution pattern of a WoT application is a sequence of profiled time delays between the invocations of involved Web services, and it can be obtained from WoT platforms. We convert the execution pattern to a time sequence of network flows that are generated when the WoT applications are executed. We consider such time sequences as a whitelist. This whitelist reflects the valid application execution patterns. At the network monitor layer, our applied RETE algorithm examines whether any given runtime sequence of network flow instances does not conform to the whitelist. Through this approach, it is possible to interpret a sequence of network flows with regard to application logic. Given such contextual information, we believe that the administrators can detect and reason about any abnormal behaviors more effectively. Our empirical evaluation shows that our RETE-based algorithm outperforms the baseline algorithm in terms of memory usage.

Tests of peak flow scaling in simulated self-similar river networks

Science.gov (United States)

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.

Employment growth through labor flow networks.

Science.gov (United States)

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.

Employment growth through labor flow networks.

Directory of Open Access Journals (Sweden)

Omar A Guerrero

Full Text Available 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.

Altered Cerebral Blood Flow Covariance Network in Schizophrenia.

Science.gov (United States)

Liu, Feng; Zhuo, Chuanjun; Yu, Chunshui

2016-01-01

Many studies have shown abnormal cerebral blood flow (CBF) in schizophrenia; however, it remains unclear how topological properties of CBF network are altered in this disorder. Here, arterial spin labeling (ASL) MRI was employed to measure resting-state CBF in 96 schizophrenia patients and 91 healthy controls. CBF covariance network of each group was constructed by calculating across-subject CBF covariance between 90 brain regions. Graph theory was used to compare intergroup differences in global and nodal topological measures of the network. Both schizophrenia patients and healthy controls had small-world topology in CBF covariance networks, implying an optimal balance between functional segregation and integration. Compared with healthy controls, schizophrenia patients showed reduced small-worldness, normalized clustering coefficient and local efficiency of the network, suggesting a shift toward randomized network topology in schizophrenia. Furthermore, schizophrenia patients exhibited altered nodal centrality in the perceptual-, affective-, language-, and spatial-related regions, indicating functional disturbance of these systems in schizophrenia. This study demonstrated for the first time that schizophrenia patients have disrupted topological properties in CBF covariance network, which provides a new perspective (efficiency of blood flow distribution between brain regions) for understanding neural mechanisms of schizophrenia.

Efficient network-matrix architecture for general flow transport inspired by natural pinnate leaves.

Science.gov (United States)

Hu, Liguo; Zhou, Han; Zhu, Hanxing; Fan, Tongxiang; Zhang, Di

2014-11-14

Networks embedded in three dimensional matrices are beneficial to deliver physical flows to the matrices. Leaf architectures, pervasive natural network-matrix architectures, endow leaves with high transpiration rates and low water pressure drops, providing inspiration for efficient network-matrix architectures. In this study, the network-matrix model for general flow transport inspired by natural pinnate leaves is investigated analytically. The results indicate that the optimal network structure inspired by natural pinnate leaves can greatly reduce the maximum potential drop and the total potential drop caused by the flow through the network while maximizing the total flow rate through the matrix. These results can be used to design efficient networks in network-matrix architectures for a variety of practical applications, such as tissue engineering, cell culture, photovoltaic devices and heat transfer.

The Determinants of International News Flow: A Network Analysis.

Science.gov (United States)

Kim, Kyungmo; Barnett, George A.

1996-01-01

Examines the structure of international news flow and its determinants. Reveals inequality of international news flow between core and periphery, with Western industrialized countries at the center. Finds that the news flow network is structured into eight geographic-linguistic groups. Indicates flow is influenced by a country's economic…

Field-effect Flow Control in Polymer Microchannel Networks

Science.gov (United States)

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.

CIME course on Modelling and Optimisation of Flows on Networks

CERN Document Server

Ambrosio, Luigi; Helbing, Dirk; Klar, Axel; Zuazua, Enrique

2013-01-01

In recent years flows in networks have attracted the interest of many researchers from different areas, e.g. applied mathematicians, engineers, physicists, economists. The main reason for this ubiquity is the wide and diverse range of applications, such as vehicular traffic, supply chains, blood flow, irrigation channels, data networks and others. This book presents an extensive set of notes by world leaders on the main mathematical techniques used to address such problems, together with investigations into specific applications. The main focus is on partial differential equations in networks, but ordinary differential equations and optimal transport are also included. Moreover, the modeling is completed by analysis, numerics, control and optimization of flows in networks. The book will be a valuable resource for every researcher or student interested in the subject.

Solution of weakly compressible isothermal flow in landfill gas collection networks

Science.gov (United States)

Nec, Y.; Huculak, G.

2017-12-01

Pipe networks collecting gas in sanitary landfills operate under the regime of a weakly compressible isothermal flow of ideal gas. The effect of compressibility has been traditionally neglected in this application in favour of simplicity, thereby creating a conceptual incongruity between the flow equations and thermodynamic equation of state. Here the flow is solved by generalisation of the classic Darcy-Weisbach equation for an incompressible steady flow in a pipe to an ordinary differential equation, permitting continuous variation of density, viscosity and related fluid parameters, as well as head loss or gain due to gravity, in isothermal flow. The differential equation is solved analytically in the case of ideal gas for a single edge in the network. Thereafter the solution is used in an algorithm developed to construct the flow equations automatically for a network characterised by an incidence matrix, and determine pressure distribution, flow rates and all associated parameters therein.

Using Alloy to Formally Model and Reason About an OpenFlow Network Switch

OpenAIRE

Mirzaei, Saber; Bahargam, Sanaz; Skowyra, Richard; Kfoury, Assaf; Bestavros, Azer

2016-01-01

Openflow provides a standard interface for separating a network into a data plane and a programmatic control plane. This enables easy network reconfiguration, but introduces the potential for programming bugs to cause network effects. To study OpenFlow switch behavior, we used Alloy to create a software abstraction describing the internal state of a network and its OpenFlow switches. This work is an attempt to model the static and dynamic behaviour a network built using OpenFlow switches.

Radial basis function neural network for power system load-flow

International Nuclear Information System (INIS)

Karami, A.; Mohammadi, M.S.

2008-01-01

This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)

Constraints of nonresponding flows based on cross layers in the networks

Science.gov (United States)

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.

Solution of weakly compressible isothermal flow in landfill gas collection networks

Energy Technology Data Exchange (ETDEWEB)

Nec, Y [Thompson Rivers University, Kamloops, British Columbia (Canada); Huculak, G, E-mail: cranberryana@gmail.com, E-mail: greg@gnhconsulting.ca [GNH Consulting, Delta, British Columbia (Canada)

2017-12-15

Pipe networks collecting gas in sanitary landfills operate under the regime of a weakly compressible isothermal flow of ideal gas. The effect of compressibility has been traditionally neglected in this application in favour of simplicity, thereby creating a conceptual incongruity between the flow equations and thermodynamic equation of state. Here the flow is solved by generalisation of the classic Darcy–Weisbach equation for an incompressible steady flow in a pipe to an ordinary differential equation, permitting continuous variation of density, viscosity and related fluid parameters, as well as head loss or gain due to gravity, in isothermal flow. The differential equation is solved analytically in the case of ideal gas for a single edge in the network. Thereafter the solution is used in an algorithm developed to construct the flow equations automatically for a network characterised by an incidence matrix, and determine pressure distribution, flow rates and all associated parameters therein. (paper)

Solution of weakly compressible isothermal flow in landfill gas collection networks

International Nuclear Information System (INIS)

Nec, Y; Huculak, G

2017-01-01

Pipe networks collecting gas in sanitary landfills operate under the regime of a weakly compressible isothermal flow of ideal gas. The effect of compressibility has been traditionally neglected in this application in favour of simplicity, thereby creating a conceptual incongruity between the flow equations and thermodynamic equation of state. Here the flow is solved by generalisation of the classic Darcy–Weisbach equation for an incompressible steady flow in a pipe to an ordinary differential equation, permitting continuous variation of density, viscosity and related fluid parameters, as well as head loss or gain due to gravity, in isothermal flow. The differential equation is solved analytically in the case of ideal gas for a single edge in the network. Thereafter the solution is used in an algorithm developed to construct the flow equations automatically for a network characterised by an incidence matrix, and determine pressure distribution, flow rates and all associated parameters therein. (paper)

Non-invasive classification of gas–liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

International Nuclear Information System (INIS)

Abbagoni, Baba Musa; Yeung, Hoi

2016-01-01

The identification of flow pattern is a key issue in multiphase flow which is encountered in the petrochemical industry. It is difficult to identify the gas–liquid flow regimes objectively with the gas–liquid two-phase flow. This paper presents the feasibility of a clamp-on instrument for an objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and an artificial neural network, which records and processes the ultrasonic signals reflected from the two-phase flow. Experimental data is obtained on a horizontal test rig with a total pipe length of 21 m and 5.08 cm internal diameter carrying air-water two-phase flow under slug, elongated bubble, stratified-wavy and, stratified flow regimes. Multilayer perceptron neural networks (MLPNNs) are used to develop the classification model. The classifier requires features as an input which is representative of the signals. Ultrasound signal features are extracted by applying both power spectral density (PSD) and discrete wavelet transform (DWT) methods to the flow signals. A classification scheme of ‘1-of-C coding method for classification’ was adopted to classify features extracted into one of four flow regime categories. To improve the performance of the flow regime classifier network, a second level neural network was incorporated by using the output of a first level networks feature as an input feature. The addition of the two network models provided a combined neural network model which has achieved a higher accuracy than single neural network models. Classification accuracies are evaluated in the form of both the PSD and DWT features. The success rates of the two models are: (1) using PSD features, the classifier missed 3 datasets out of 24 test datasets of the classification and scored 87.5% accuracy; (2) with the DWT features, the network misclassified only one data point and it was able to classify the flow patterns up to 95.8% accuracy. This approach has demonstrated the

Non-invasive classification of gas-liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

Science.gov (United States)

Musa Abbagoni, Baba; Yeung, Hoi

2016-08-01

The identification of flow pattern is a key issue in multiphase flow which is encountered in the petrochemical industry. It is difficult to identify the gas-liquid flow regimes objectively with the gas-liquid two-phase flow. This paper presents the feasibility of a clamp-on instrument for an objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and an artificial neural network, which records and processes the ultrasonic signals reflected from the two-phase flow. Experimental data is obtained on a horizontal test rig with a total pipe length of 21 m and 5.08 cm internal diameter carrying air-water two-phase flow under slug, elongated bubble, stratified-wavy and, stratified flow regimes. Multilayer perceptron neural networks (MLPNNs) are used to develop the classification model. The classifier requires features as an input which is representative of the signals. Ultrasound signal features are extracted by applying both power spectral density (PSD) and discrete wavelet transform (DWT) methods to the flow signals. A classification scheme of ‘1-of-C coding method for classification’ was adopted to classify features extracted into one of four flow regime categories. To improve the performance of the flow regime classifier network, a second level neural network was incorporated by using the output of a first level networks feature as an input feature. The addition of the two network models provided a combined neural network model which has achieved a higher accuracy than single neural network models. Classification accuracies are evaluated in the form of both the PSD and DWT features. The success rates of the two models are: (1) using PSD features, the classifier missed 3 datasets out of 24 test datasets of the classification and scored 87.5% accuracy; (2) with the DWT features, the network misclassified only one data point and it was able to classify the flow patterns up to 95.8% accuracy. This approach has demonstrated the

Transnational cocaine and heroin flow networks in western Europe: A comparison.

Science.gov (United States)

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.

Flow enforcement algorithms for ATM networks

DEFF Research Database (Denmark)

Dittmann, Lars; Jacobsen, Søren B.; Moth, Klaus

1991-01-01

Four measurement algorithms for flow enforcement in asynchronous transfer mode (ATM) networks are presented. The algorithms are the leaky bucket, the rectangular sliding window, the triangular sliding window, and the exponentially weighted moving average. A comparison, based partly on teletraffic...

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

Science.gov (United States)

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

2016-01-01

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

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

Directory of Open Access Journals (Sweden)

Yoshiaki Taniguchi

2016-01-01

Full Text Available 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.

Identifying three-dimensional nested groundwater flow systems in a Tóthian basin

Science.gov (United States)

Wang, Xu-Sheng; Wan, Li; Jiang, Xiao-Wei; Li, Hailong; Zhou, Yangxiao; Wang, Junzhi; Ji, Xiaohui

2017-10-01

Nested groundwater flow systems have been revealed in Tóth's theory as the structural property of basin-scale groundwater circulation but were only well known with two-dimensional (2D) profile models. The method of searching special streamlines across stagnation points for partitioning flow systems, which has been successfully applied in the 2D models, has never been implemented for three-dimensional (3D) Tóthian basins because of the difficulty in solving the dual stream functions. Alternatively, a new method is developed to investigate 3D nested groundwater flow systems without determination of stagnation points. Connective indices are defined to quantify the connection between individual recharge and discharge zones along streamlines. Groundwater circulation cells (GWCCs) are identified according to the distribution of the connective indices and then grouped into local, intermediate and regional flow systems. This method requires existing solution of the flow velocity vector and is implemented via particle tracking technique. It is applied in a hypothetical 3D Tóthian basin with an analytical solution of the flow field and in a real-world basin with a numerical modeling approach. Different spatial patterns of flow systems compared to 2D profile models are found. The outcrops boundaries of GWCCs on water table may significantly deviate from and are not parallel to the nearby water table divides. Topological network is proposed to represent the linked recharge-discharge zones through closed and open GWCCs. Sensitivity analysis indicates that the development of GWCCs depends on the basin geometry, hydraulic parameters and water table shape.

Loan and nonloan flows in the Australian interbank network

Science.gov (United States)

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.

FLOWNET: A Computer Program for Calculating Secondary Flow Conditions in a Network of Turbomachinery

Science.gov (United States)

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.

Horizontal two phase flow pattern identification by neural networks

International Nuclear Information System (INIS)

Crivelaro, Kelen Cristina Oliveira; Seleghim Junior, Paulo; Hervieu, Eric

1999-01-01

A multiphase fluid can flow according to several flow regimes. The problem associated with multiphase systems are basically related to the behavior of macroscopic parameters, such as pressure drop, thermal exchanges and so on, and their strong correlation to the flow regime. From the industrial applications point of view, the safety and longevity of equipment and systems can only be assured when they work according to the flow regimes for which they were designed to. This implies in the need to diagnose flow regimes in real time. The automatic diagnosis of flow regimes represents an objective of extreme importance, mainly for applications on nuclear and petrochemical industries. In this work, a neural network is used in association to a probe of direct visualization for the identification of a gas-liquid flow horizontal regimes, developed in an experimental circuit. More specifically, the signals produced by the probe are used to compose a qualitative image of the flow, which is promptly sent to the network for the recognition of the regimes. Results are presented for different transitions among the flow regimes, which demonstrate the extremely satisfactory performance of the diagnosis system. (author)

Flow control and routing techniques for integrated voice and data networks

Science.gov (United States)

Ibe, O. C.

1981-10-01

We consider a model of integrated voice and data networks. In this model the network flow problem is formulated as a convex optimization problem. The objective function comprises two types of cost functions: the congestion cost functions, which limit the average input traffic to values compatible with the network conditions; and the rate limitation cost functions, which ensure that all conversations are fairly treated. A joint flow control and routing algorithm is constructed which determines the routes for each conversation, and effects flow control by setting voice packet lengths and data input rates in a manner that achieves optimal tradeoff between each user's satisfaction and the cost of network congestion. An additional congestion control protocol is specified which could be used in conjunction with the algorithm to make the latter respond more dynamically to network congestion.

Application of artificial neural network to identify nuclear materials

International Nuclear Information System (INIS)

Xu Peng; Wang Zhe; Li Tiantuo

2005-01-01

Applying the neutral network, the article studied the technology of identifying the gamma spectra of the nuclear material in the nuclear components. In the article, theory of the network identifying the spectra is described, and the results of identification of gamma spectra are given.(authors)

Understanding characteristics in multivariate traffic flow time series from complex network structure

Science.gov (United States)

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.

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

NARCIS (Netherlands)

Laumanns, M.; Lefeber, A.A.J.

2006-01-01

We develop a model based on stochastic discrete-time controlleddynamical systems in order to derive optimal policies for controllingthe material flow in supply networks. Each node in the network isdescribed as a transducer such that the dynamics of the material andinformation flows within the entire

Performance of Flow-Aware Networking in LTE backbone

DEFF Research Database (Denmark)

Sniady, Aleksander; Soler, José

2012-01-01

technologies, such as Long Term Evolution (LTE). This paper proposes usage of a modified Flow Aware Networking (FAN) technique for enhancing Quality of Service (QoS) in the all-IP transport networks underlying LTE backbone. The results obtained with OPNET Modeler show that FAN, in spite of being relatively...

A network flow algorithm to position tiles for LAMOST

International Nuclear Information System (INIS)

Li Guangwei; Zhao Gang

2009-01-01

We introduce the network flow algorithm used by the Sloan Digital Sky Survey (SDSS) into the sky survey of the Large sky Area Multi-Object fiber Spectroscopic Telescope (LAMOST) to position tiles. Because fibers in LAMOST's focal plane are distributed uniformly, we cannot use SDSS' method directly. To solve this problem, firstly we divide the sky into many small blocks, and we also assume that all the targets that are in the same block have the same position, which is the center of the block. Secondly, we give a value to limit the number of the targets that the LAMOST focal plane can collect in one square degree so that it cannot collect too many targets in one small block. Thirdly, because the network flow algorithm used in this paper is a bipartite network, we do not use the general solution algorithm that was used by SDSS. Instead, we give our new faster solution method for this special network. Compared with the Convergent Mean Shift Algorithm, the network flow algorithm can decrease observation times with improved mean imaging quality. This algorithm also has a very fast running speed. It can distribute millions of targets in a few minutes using a common personal computer.

Sonification of network traffic flow for monitoring and situational awareness

Science.gov (United States)

2018-01-01

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

Sonification of network traffic flow for monitoring and situational awareness.

Science.gov (United States)

Debashi, Mohamed; Vickers, Paul

2018-01-01

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

A microfluidic device for simultaneous measurement of viscosity and flow rate of blood in a complex fluidic network.

Science.gov (United States)

Jun Kang, Yang; Yeom, Eunseop; Lee, Sang-Joon

2013-01-01

Blood viscosity has been considered as one of important biophysical parameters for effectively monitoring variations in physiological and pathological conditions of circulatory disorders. Standard previous methods make it difficult to evaluate variations of blood viscosity under cardiopulmonary bypass procedures or hemodialysis. In this study, we proposed a unique microfluidic device for simultaneously measuring viscosity and flow rate of whole blood circulating in a complex fluidic network including a rat, a reservoir, a pinch valve, and a peristaltic pump. To demonstrate the proposed method, a twin-shaped microfluidic device, which is composed of two half-circular chambers, two side channels with multiple indicating channels, and one bridge channel, was carefully designed. Based on the microfluidic device, three sequential flow controls were applied to identify viscosity and flow rate of blood, with label-free and sensorless detection. The half-circular chamber was employed to achieve mechanical membrane compliance for flow stabilization in the microfluidic device. To quantify the effect of flow stabilization on flow fluctuations, a formula of pulsation index (PI) was analytically derived using a discrete fluidic circuit model. Using the PI formula, the time constant contributed by the half-circular chamber is estimated to be 8 s. Furthermore, flow fluctuations resulting from the peristaltic pumps are completely removed, especially under periodic flow conditions within short periods (T viscosity with respect to varying flow rate conditions [(a) known blood flow rate via a syringe pump, (b) unknown blood flow rate via a peristaltic pump]. As a result, the flow rate and viscosity of blood can be simultaneously measured with satisfactory accuracy. In addition, the proposed method was successfully applied to identify the viscosity of rat blood, which circulates in a complex fluidic network. These observations confirm that the proposed method can be used for

Non-invasive classification of gas–liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

OpenAIRE

Abbagoni, Baba Musa; Yeung, Hoi

2016-01-01

The identification of flow pattern is a key issue in multiphase flow which is encountered in the petrochemical industry. It is difficult to identify the gas–liquid flow regimes objectively with the gas–liquid two-phase flow. This paper presents the feasibility of a clamp-on instrument for an objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and an artificial neural network, which records and processes the ultrasonic signals reflected from the two-phase ...

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

Science.gov (United States)

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.

New approach for simulating groundwater flow in discrete fracture network

Science.gov (United States)

Fang, H.; Zhu, J.

2017-12-01

In this study, we develop a new approach to calculate groundwater flowrate and hydraulic head distribution in two-dimensional discrete fracture network (DFN) where both laminar and turbulent flows co-exist in individual fractures. The cubic law is used to calculate hydraulic head distribution and flow behaviors in fractures where flow is laminar, while the Forchheimer's law is used to quantify turbulent flow behaviors. Reynolds number is used to distinguish flow characteristics in individual fractures. The combination of linear and non-linear equations is solved iteratively to determine flowrates in all fractures and hydraulic heads at all intersections. We examine potential errors in both flowrate and hydraulic head from the approach of uniform flow assumption. Applying the cubic law in all fractures regardless of actual flow conditions overestimates the flowrate when turbulent flow may exist while applying the Forchheimer's law indiscriminately underestimate the flowrate when laminar flows exist in the network. The contrast of apertures of large and small fractures in the DFN has significant impact on the potential errors of using only the cubic law or the Forchheimer's law. Both the cubic law and Forchheimer's law simulate similar hydraulic head distributions as the main difference between these two approaches lies in predicting different flowrates. Fracture irregularity does not significantly affect the potential errors from using only the cubic law or the Forchheimer's law if network configuration remains similar. Relative density of fractures does not significantly affect the relative performance of the cubic law and Forchheimer's law.

Modeling of pulsatile flow-dependent nitric oxide regulation in a realistic microvascular network.

Science.gov (United States)

Wang, Ruofan; Pan, Qing; Kuebler, Wolfgang M; Li, John K-J; Pries, Axel R; Ning, Gangmin

2017-09-01

Hemodynamic pulsatility has been reported to regulate microcirculatory function. To quantitatively assess the impact of flow pulsatility on the microvasculature, a mathematical model was first developed to simulate the regulation of NO production by pulsatile flow in the microcirculation. Shear stress and pressure pulsatility were selected as regulators of endothelial NO production and NO-dependent vessel dilation as feedback to control microvascular hemodynamics. The model was then applied to a real microvascular network of the rat mesentery consisting of 546 microvessels. As compared to steady flow conditions, pulsatile flow increased the average NO concentration in arterioles from 256.8±93.1nM to 274.8±101.1nM (Pflow as compared to steady flow conditions. Network perfusion and flow heterogeneity were improved under pulsatile flow conditions, and vasodilation within the network was more sensitive to heart rate changes than pulse pressure amplitude. The proposed model simulates the role of flow pulsatility in the regulation of a complex microvascular network in terms of NO concentration and hemodynamics under varied physiological conditions. Copyright © 2017 Elsevier Inc. All rights reserved.

Time and flow-dependent changes in the p27(kip1) gene network drive maladaptive vascular remodeling.

Science.gov (United States)

DeSart, Kenneth M; Butler, Khayree; O'Malley, Kerri A; Jiang, Zhihua; Berceli, Scott A

2015-11-01

Although clinical studies have identified that a single nucleotide polymorphism in the p27(kip1) gene is associated with success or failure after vein bypass grafting, the underlying mechanisms for this difference are not well defined. Using a high-throughput approach in a flow-dependent vein graft model, we explored the differences in p27(kip1)-related genes that drive the enhanced hyperplastic response under low-flow conditions. Bilateral rabbit carotid artery interposition grafts with jugular vein were placed with a unilateral distal outflow branch ligation to create differential flow states. Grafts were harvested at 2 hours and at 1, 3, 7, 14, and 28 days after implantation, measured for neointimal area, and assayed for cell proliferation. Whole-vessel messenger RNA was isolated and analyzed using an Affymetrix (Santa Clara, Calif) gene array platform. Ingenuity Pathway Analysis (Ingenuity, Redwood City, Calif) was used to identify the gene networks surrounding p27(kip1). This gene set was then analyzed for temporal expression changes after graft placement and for differential expression in the alternate flow conditions. Outflow branch ligation resulted in an eightfold difference in mean flow rates throughout the 28-day perfusion period (P Flow reduction led to a robust hyperplastic response, resulting in a significant increase in intimal area by 7 days (0.13 ± 0.04 mm(2) vs 0.014 ± 0.006 mm(2); P flow grafts demonstrated a burst of actively dividing intimal cells (36.4 ± 9.4 cells/mm(2) vs 11.5 ± 1.9 cells/mm(2); P = .04). Sixty-five unique genes within the microarray were identified as components of the p27(kip1) network. At a false discovery rate of 0.05, 26 genes demonstrated significant temporal changes, and two dominant patterns of expression were identified. Class comparison analysis identified differential expression of 11 genes at 2 hours and seven genes and 14 days between the high-flow and low-flow grafts (P flow and shear stress result in

Optimal scheduling for distribution network with redox flow battery storage

International Nuclear Information System (INIS)

Hosseina, Majid; Bathaee, Seyed Mohammad Taghi

2016-01-01

Highlights: • A novel method for optimal scheduling of storages in radial network is presented. • Peak shaving and load leveling are the main objectives. • Vanadium redox flow battery is considered as the energy storage unit. • Real data is used for simulation. - Abstract: There are many advantages to utilize storages in electric power system. Peak shaving, load leveling, load frequency control, integration of renewable, energy trading and spinning reserve are the most important of them. Batteries, especially redox flow batteries, are one of the appropriate storages for utilization in distribution network. This paper presents a novel, heuristic and practical method for optimal scheduling in distribution network with flow battery storage. This heuristic method is more suitable for scheduling and operation of distribution networks which require installation of storages. Peak shaving and load leveling is considered as the main objective in this paper. Several indices are presented in this paper for determine the place of storages and also scheduling for optimal use of energy in them. Simulations of this paper are based on real information of distribution network substation that located in Semnan, Iran.

Transitions from Trees to Cycles in Adaptive Flow Networks

Directory of Open Access Journals (Sweden)

Erik A. Martens

2017-11-01

Full Text Available Transport networks are crucial to the functioning of natural and technological systems. Nature features transport networks that are adaptive over a vast range of parameters, thus providing an impressive level of robustness in supply. Theoretical and experimental studies have found that real-world transport networks exhibit both tree-like motifs and cycles. When the network is subject to load fluctuations, the presence of cyclic motifs may help to reduce flow fluctuations and, thus, render supply in the network more robust. While previous studies considered network topology via optimization principles, here, we take a dynamical systems approach and study a simple model of a flow network with dynamically adapting weights (conductances. We assume a spatially non-uniform distribution of rapidly fluctuating loads in the sinks and investigate what network configurations are dynamically stable. The network converges to a spatially non-uniform stable configuration composed of both cyclic and tree-like structures. Cyclic structures emerge locally in a transcritical bifurcation as the amplitude of the load fluctuations is increased. The resulting adaptive dynamics thus partitions the network into two distinct regions with cyclic and tree-like structures. The location of the boundary between these two regions is determined by the amplitude of the fluctuations. These findings may explain why natural transport networks display cyclic structures in the micro-vascular regions near terminal nodes, but tree-like features in the regions with larger veins.

Information flow in a network of dispersed signalers-receivers

Science.gov (United States)

Halupka, Konrad

2017-11-01

I consider a stochastic model of multi-agent communication in regular network. The model describes how dispersed animals exchange information. Each agent can initiate and transfer the signal to its nearest neighbors, who may pass it farther. For an external observer of busy networks, signaling activity may appear random, even though information flow actually thrives. Only when signal initiation and transfer are at low levels do spatiotemporal autocorrelations emerge as clumping signaling activity in space and pink noise time series. Under such conditions, the costs of signaling are moderate, but the signaler can reach a large audience. I propose that real-world networks of dispersed signalers-receivers may self-organize into this state and the flow of information maintains their integrity.

Three-Phase Unbalanced Load Flow Tool for Distribution Networks

DEFF Research Database (Denmark)

Demirok, Erhan; Kjær, Søren Bækhøj; Sera, Dezso

2012-01-01

This work develops a three-phase unbalanced load flow tool tailored for radial distribution networks based on Matlab®. The tool can be used to assess steady-state voltage variations, thermal limits of grid components and power losses in radial MV-LV networks with photovoltaic (PV) generators where...... most of the systems are single phase. New ancillary service such as static reactive power support by PV inverters can be also merged together with the load flow solution tool and thus, the impact of the various reactive power control strategies on the steady-state grid operation can be simply...... investigated. Performance of the load flow solution tool in the sense of resulting bus voltage magnitudes is compared and validated with IEEE 13-bus test feeder....

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

Science.gov (United States)

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.

Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behavior.

Science.gov (United States)

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.

Analysis of network-wide transit passenger flows based on principal component analysis

NARCIS (Netherlands)

Luo, D.; Cats, O.; van Lint, J.W.C.

2017-01-01

Transit networks are complex systems in which the passenger flow dynamics are difficult to capture and understand. While there is a growing ability to monitor and record travelers' behavior in the past decade, knowledge on network-wide passenger flows, which are essentially high-dimensional

Network flow of mobile agents enhances the evolution of cooperation

Science.gov (United States)

Ichinose, G.; Satotani, Y.; Nagatani, T.

2018-01-01

We study the effect of contingent movement on the persistence of cooperation on complex networks with empty nodes. Each agent plays the Prisoner's Dilemma game with its neighbors and then it either updates the strategy depending on the payoff difference with neighbors or it moves to another empty node if not satisfied with its own payoff. If no neighboring node is empty, each agent stays at the same site. By extensive evolutionary simulations, we show that the medium density of agents enhances cooperation where the network flow of mobile agents is also medium. Moreover, if the movements of agents are more frequent than the strategy updating, cooperation is further promoted. In scale-free networks, the optimal density for cooperation is lower than other networks because agents get stuck at hubs. Our study suggests that keeping a smooth network flow is significant for the persistence of cooperation in ever-changing societies.

On-line validation of feedwater flow rate in nuclear power plants using neural networks

International Nuclear Information System (INIS)

Khadem, M.; Ipakchi, A.; Alexandro, F.J.; Colley, R.W.

1994-01-01

On-line calibration of feedwater flow rate measurement in nuclear power plants provides a continuous realistic value of feedwater flow rate. It also reduces the manpower required for periodic calibration needed due to the fouling and defouling of the venturi meter surface condition. This paper presents a method for on-line validation of feedwater flow rate in nuclear power plants. The method is an improvement of the previously developed method which is based on the use of a set of process variables dynamically related to the feedwater flow rate. The online measurements of this set of variables are used as inputs to a neural network to obtain an estimate of the feedwater flow rate reading. The difference between the on-line feedwater flow rate reading, and the neural network estimate establishes whether there is a need to apply a correction factor to the feedwater flow rate measurement for calculation of the actual reactor power. The method was applied to the feedwater flow meters in the two feedwater flow loops of the TMI-1 nuclear power plant. The venturi meters used for flow measurements are susceptible to frequent fouling that degrades their measurement accuracy. The fouling effects can cause an inaccuracy of up to 3% relative error in feedwater flow rate reading. A neural network, whose inputs were the readings of a set of reference instruments, was designed to predict both feedwater flow rates simultaneously. A multi-layer feedforward neural network employing the backpropagation algorithm was used. A number of neural network training tests were performed to obtain an optimum filtering technique of the input/output data of the neural networks. The result of the selection of the filtering technique was confirmed by numerous Fast Fourier Transform (FFT) tests. Training and testing were done on data from TMI-1 nuclear power plant. The results show that the neural network can predict the correct flow rates with an absolute relative error of less than 2%

Bridging Inter-flow and Intra-flow Network Coding for Video Applications

DEFF Research Database (Denmark)

Hansen, Jonas; Krigslund, Jeppe; Roetter, Daniel Enrique Lucani

2013-01-01

transmission approach to decide how much and when to send redundancy in the network, and a minimalistic feedback mechanism to guarantee delivery of generations of the different flows. Given the delay constraints of video applications, we proposed a simple yet effective coding mechanism, Block Coding On The Fly...

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

Science.gov (United States)

Jia, Yanxin; Kiss, István Z.

2017-04-01

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

Experimental and Numerical Investigation of Preferential Flow in Fractured Network with Clogging Process

Directory of Open Access Journals (Sweden)

Xiaobing Chen

2014-01-01

Full Text Available In this study, physical experiments and numerical simulations are combined to provide a detailed understanding of flow dynamics in fracture network. Hydraulic parameters such as pressure head, velocity field, Reynolds number on certain monitoring cross points, and total flux rate are examined under various clogging conditions. Applying the COMSOL Multiphysics code to solve the Navier-Stokes equation instead of Reynolds equation and using the measured data to validate the model, the fluid flow in the horizontal 2D cross-sections of the fracture network was simulated. Results show that local clogging leads to a significant reshaping of the flow velocity field and a reduction of the transport capacity of the entire system. The flow rate distribution is highly influenced by the fractures connected to the dominant flow channels, although local disturbances in velocity field are unlikely to spread over the whole network. Also, modeling results indicate that water flow in a fracture network, compared with that in a single fracture, is likely to transit into turbulence earlier under the same hydraulic gradient due to the influence of fracture intersections.

FLOW-BASED NETWORK MEASURES OF BRAIN CONNECTIVITY IN ALZHEIMER'S DISEASE.

Science.gov (United States)

Prasad, Gautam; Joshi, Shantanu H; Nir, Talia M; Toga, Arthur W; Thompson, Paul M

2013-01-01

We present a new flow-based method for modeling brain structural connectivity. The method uses a modified maximum-flow algorithm that is robust to noise in the diffusion data and guided by biologically viable pathways and structure of the brain. A flow network is first created using a lattice graph by connecting all lattice points (voxel centers) to all their neighbors by edges. Edge weights are based on the orientation distribution function (ODF) value in the direction of the edge. The maximum-flow is computed based on this flow graph using the flow or the capacity between each region of interest (ROI) pair by following the connected tractography fibers projected onto the flow graph edges. Network measures such as global efficiency, transitivity, path length, mean degree, density, modularity, small world, and assortativity are computed from the flow connectivity matrix. We applied our method to diffusion-weighted images (DWIs) from 110 subjects (28 normal elderly, 56 with early and 11 with late mild cognitive impairment, and 15 with AD) and segmented co-registered anatomical MRIs into cortical regions. Experimental results showed better performance compared to the standard fiber-counting methods when distinguishing Alzheimer's disease from normal aging.

On Identifying which Intermediate Nodes Should Code in Multicast Networks

DEFF Research Database (Denmark)

Pinto, Tiago; Roetter, Daniel Enrique Lucani; Médard, Muriel

2013-01-01

the data packets. Previous work has shown that in lossless wireline networks, the performance of tree-packing mechanisms is comparable to network coding, albeit with added complexity at the time of computing the trees. This means that most nodes in the network need not code. Thus, mechanisms that identify...... intermediate nodes that do require coding is instrumental for the efficient operation of coded networks and can have a significant impact in overall energy consumption. We present a distributed, low complexity algorithm that allows every node to identify if it should code and, if so, through what output link...

Towards Optimal Event Detection and Localization in Acyclic Flow Networks

KAUST Repository

Agumbe Suresh, Mahima

2012-01-03

Acyclic flow networks, present in many infrastructures of national importance (e.g., oil & gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures, have been proven costly and imprecise, especially when dealing with large scale distribution systems. In this paper, to the best of our knowledge for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. Sensor nodes move along the edges of the network and detect events (i.e., attacks) and proximity to beacon nodes with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensor and beacon nodes deployed), while ensuring a degree of sensing coverage in a zone of interest and a required accuracy in locating events. We propose algorithms for solving these problems and demonstrate their effectiveness with results obtained from a high fidelity simulator.

Network modeling for reverse flows of end-of-life vehicles

International Nuclear Information System (INIS)

Ene, Seval; Öztürk, Nursel

2015-01-01

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

Network modeling for reverse flows of end-of-life vehicles

Energy Technology Data Exchange (ETDEWEB)

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.

Neural network modeling of chaotic dynamics in nuclear reactor flows

International Nuclear Information System (INIS)

Welstead, S.T.

1992-01-01

Neural networks have many scientific applications in areas such as pattern classification and time series prediction. The universal approximation property of these networks, however, can also be exploited to provide researchers with tool for modeling observed nonlinear phenomena. It has been shown that multilayer feed forward networks can capture important global nonlinear properties, such as chaotic dynamics, merely by training the network on a finite set of observed data. The network itself then provides a model of the process that generated the data. Characterizations such as the existence and general shape of a strange attractor and the sign of the largest Lyapunov exponent can then be extracted from the neural network model. In this paper, the author applies this idea to data generated from a nonlinear process that is representative of convective flows that can arise in nuclear reactor applications. Such flows play a role in forced convection heat removal from pressurized water reactors and boiling water reactors, and decay heat removal from liquid-metal-cooled reactors, either by natural convection or by thermosyphons

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

Science.gov (United States)

Kim, Jinyoung

2017-12-01

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

Identifying modular relations in complex brain networks

DEFF Research Database (Denmark)

Andersen, Kasper Winther; Mørup, Morten; Siebner, Hartwig

2012-01-01

We evaluate the infinite relational model (IRM) against two simpler alternative nonparametric Bayesian models for identifying structures in multi subject brain networks. The models are evaluated for their ability to predict new data and infer reproducible structures. Prediction and reproducibility...... and obtains comparable reproducibility and predictability. For resting state functional magnetic resonance imaging data from 30 healthy controls the IRM model is also superior to the two simpler alternatives, suggesting that brain networks indeed exhibit universal complex relational structure...

Dynamics of blood flow and thrombus formation in a multi-bypass microfluidic ladder network.

Science.gov (United States)

Zilberman-Rudenko, Jevgenia; Sylman, Joanna L; Lakshmanan, Hari H S; McCarty, Owen J T; Maddala, Jeevan

2017-02-01

The reaction dynamics of a complex mixture of cells and proteins, such as blood, in branched circulatory networks within the human microvasculature or extravascular therapeutic devices such as extracorporeal oxygenation machine (ECMO) remains ill-defined. In this report we utilize a multi-bypass microfluidics ladder network design with dimensions mimicking venules to study patterns of blood platelet aggregation and fibrin formation under complex shear. Complex blood fluid dynamics within multi-bypass networks under flow were modeled using COMSOL. Red blood cells and platelets were assumed to be non-interacting spherical particles transported by the bulk fluid flow, and convection of the activated coagulation factor II, thrombin, was assumed to be governed by mass transfer. This model served as the basis for predicting formation of local shear rate gradients, stagnation points and recirculation zones as dictated by the bypass geometry. Based on the insights from these models, we were able to predict the patterns of blood clot formation at specific locations in the device. Our experimental data was then used to adjust the model to account for the dynamical presence of thrombus formation in the biorheology of blood flow. The model predictions were then compared to results from experiments using recalcified whole human blood. Microfluidic devices were coated with the extracellular matrix protein, fibrillar collagen, and the initiator of the extrinsic pathway of coagulation, tissue factor. Blood was perfused through the devices at a flow rate of 2 µL/min, translating to physiologically relevant initial shear rates of 300 and 700 s -1 for main channels and bypasses, respectively. Using fluorescent and light microscopy, we observed distinct flow and thrombus formation patterns near channel intersections at bypass points, within recirculation zones and at stagnation points. Findings from this proof-of-principle ladder network model suggest a specific correlation between

Identifying Broadband Rotational Spectra with Neural Networks

Science.gov (United States)

Zaleski, Daniel P.; Prozument, Kirill

2017-06-01

A typical broadband rotational spectrum may contain several thousand observable transitions, spanning many species. Identifying the individual spectra, particularly when the dynamic range reaches 1,000:1 or even 10,000:1, can be challenging. One approach is to apply automated fitting routines. In this approach, combinations of 3 transitions can be created to form a "triple", which allows fitting of the A, B, and C rotational constants in a Watson-type Hamiltonian. On a standard desktop computer, with a target molecule of interest, a typical AUTOFIT routine takes 2-12 hours depending on the spectral density. A new approach is to utilize machine learning to train a computer to recognize the patterns (frequency spacing and relative intensities) inherit in rotational spectra and to identify the individual spectra in a raw broadband rotational spectrum. Here, recurrent neural networks have been trained to identify different types of rotational spectra and classify them accordingly. Furthermore, early results in applying convolutional neural networks for spectral object recognition in broadband rotational spectra appear promising. Perez et al. "Broadband Fourier transform rotational spectroscopy for structure determination: The water heptamer." Chem. Phys. Lett., 2013, 571, 1-15. Seifert et al. "AUTOFIT, an Automated Fitting Tool for Broadband Rotational Spectra, and Applications to 1-Hexanal." J. Mol. Spectrosc., 2015, 312, 13-21. Bishop. "Neural networks for pattern recognition." Oxford university press, 1995.

End-to-End Traffic Flow Modeling of the Integrated SCaN Network

Science.gov (United States)

Cheung, K.-M.; Abraham, D. S.

2012-05-01

In this article, we describe the analysis and simulation effort of the end-to-end traffic flow for the Integrated Space Communications and Navigation (SCaN) Network. Using the network traffic derived for the 30-day period of July 2018 from the Space Communications Mission Model (SCMM), we generate the wide-area network (WAN) bandwidths of the ground links for different architecture options of the Integrated SCaN Network. We also develop a new analytical scheme to model the traffic flow and buffering mechanism of a store-and-forward network. It is found that the WAN bandwidth of the Integrated SCaN Network is an important differentiator of different architecture options, as the recurring circuit costs of certain architecture options can be prohibitively high.

Curing critical links in oscillator networks as power flow models

International Nuclear Information System (INIS)

Rohden, Martin; Meyer-Ortmanns, Hildegard; Witthaut, Dirk; Timme, Marc

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. (paper)

Directed weighted network structure analysis of complex impedance measurements for characterizing oil-in-water bubbly flow.

Science.gov (United States)

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.

Analysis of Cisco Open Network Environment (ONE) OpenFlow Controller Implementation

Science.gov (United States)

2014-08-01

Software - Defined Networking ( SDN ), when fully realized, offer many improvements over the current rigid and...functionalities like handshake, connection setup, switch management, and security. 15. SUBJECT TERMS OpenFlow, software - defined networking , Cisco ONE, SDN ...innovating packet-forwarding technologies. Network device roles are strictly defined with little or no flexibility. In Software - Defined Networks ( SDNs ),

Evaluation of the reliability of transport networks based on the stochastic flow of moving objects

International Nuclear Information System (INIS)

Wu Weiwei; Ning, Angelika; Ning Xuanxi

2008-01-01

In transport networks, human beings are moving objects whose moving direction is stochastic in emergency situations. Based on this idea, a new model-stochastic moving network (SMN) is proposed. It is different from binary-state networks and stochastic-flow networks. The flow of SMNs has multiple-saturated states, that correspond to different flow values in each arc. In this paper, we try to evaluate the system reliability, defined as the probability that the saturated flow of the network is not less than a given demand d. Based on this new model, we obtain the flow probability distribution of every arc by simulation. An algorithm based on the blocking cutset of the SMN is proposed to evaluate the network reliability. An example is used to show how to calculate the corresponding reliabilities for different given demands of the SMN. Simulation experiments of different size were made and the system reliability precision was calculated. The precision of simulation results also discussed

Threshold-based generic scheme for encrypted and tunneled Voice Flows Detection over IP Networks

Directory of Open Access Journals (Sweden)

M. Mazhar U. Rathore

2015-07-01

Full Text Available VoIP usage is rapidly growing due to its cost effectiveness, dramatic functionality over the traditional telephone network and its compatibility with public switched telephone network (PSTN. In some countries, like Pakistan, the commercial usage of VoIP is prohibited. Internet service providers (ISPs and telecommunication authorities are interested in detecting VoIP calls to either block or prioritize them. So detection of VoIP calls is important for both types of authorities. Signature-based, port-based, and pattern-based VoIP detection techniques are inefficient due to complex and confidential security and tunneling mechanisms used by VoIP. In this paper, we propose a generic, robust, efficient, and practically implementable statistical analysis-based solution to identify encrypted, non-encrypted, or tunneled VoIP media (voice flows using threshold values of flow statistical parameters. We have made a comparison with existing techniques and evaluated our system with respect to accuracy and efficiency. Our system has 97.54% direct rate and .00015% false positive rate.

Flow regime identification methodology with MCNP-X code and artificial neural network

International Nuclear Information System (INIS)

Salgado, Cesar M.; Instituto de Engenharia Nuclear; Schirru, Roberto; Brandao, Luis E.B.; Pereira, Claudio M.N.A.

2009-01-01

This paper presents flow regimes identification methodology in multiphase system in annular, stratified and homogeneous oil-water-gas regimes. The principle is based on recognition of the pulse height distributions (PHD) from gamma-ray with supervised artificial neural network (ANN) systems. The detection geometry simulation comprises of two NaI(Tl) detectors and a dual-energy gamma-ray source. The measurement of scattered radiation enables the dual modality densitometry (DMD) measurement principle to be explored. Its basic principle is to combine the measurement of scattered and transmitted radiation in order to acquire information about the different flow regimes. The PHDs obtained by the detectors were used as input to ANN. The data sets required for training and testing the ANN were generated by the MCNP-X code from static and ideal theoretical models of multiphase systems. The ANN correctly identified the three different flow regimes for all data set evaluated. The results presented show that PHDs examined by ANN may be applied in the successfully flow regime identification. (author)

Transitions from Trees to Cycles in Adaptive Flow Networks

DEFF Research Database (Denmark)

Martens, Erik Andreas; Klemm, Konstantin

2017-01-01

. The network converges to a spatially non-uniform stable configuration composed of both cyclic and tree-like structures. Cyclic structures emerge locally in a transcritical bifurcation as the amplitude of the load fluctuations is increased. The resulting adaptive dynamics thus partitions the network into two......Transport networks are crucial to the functioning of natural and technological systems. Nature features transport networks that are adaptive over a vast range of parameters, thus providing an impressive level of robustness in supply. Theoretical and experimental studies have found that real......-world transport networks exhibit both tree-like motifs and cycles. When the network is subject to load fluctuations, the presence of cyclic motifs may help to reduce flow fluctuations and, thus, render supply in the network more robust. While previous studies considered network topology via optimization...

Identify Dynamic Network Modules with Temporal and Spatial Constraints

Energy Technology Data Exchange (ETDEWEB)

Jin, R; McCallen, S; Liu, C; Almaas, E; Zhou, X J

2007-09-24

Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of cellular activity remains a difficult task. The reason is that most biological data are static, or only correspond to snapshots of cellular activity. In this study, we explicitly attempt to detangle the temporal complexity of biological networks by using compilations of time-series gene expression profiling data.We define a dynamic network module to be a set of proteins satisfying two conditions: (1) they form a connected component in the protein-protein interaction (PPI) network; and (2) their expression profiles form certain structures in the temporal domain. We develop the first efficient mining algorithm to discover dynamic modules in a temporal network, as well as frequently occurring dynamic modules across many temporal networks. Using yeast as a model system, we demonstrate that the majority of the identified dynamic modules are functionally homogeneous. Additionally, many of them provide insight into the sequential ordering of molecular events in cellular systems. We further demonstrate that identifying frequent dynamic network modules can significantly increase the signal to noise separation, despite the fact that most dynamic network modules are highly condition-specific. Finally, we note that the applicability of our algorithm is not limited to the study of PPI systems, instead it is generally applicable to the combination of any type of network and time-series data.

Availability improvement of layer 2 seamless networks using OpenFlow.

Science.gov (United States)

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.

Identifying key nodes in multilayer networks based on tensor decomposition.

Science.gov (United States)

Wang, Dingjie; Wang, Haitao; Zou, Xiufen

2017-06-01

The identification of essential agents in multilayer networks characterized by different types of interactions is a crucial and challenging topic, one that is essential for understanding the topological structure and dynamic processes of multilayer networks. In this paper, we use the fourth-order tensor to represent multilayer networks and propose a novel method to identify essential nodes based on CANDECOMP/PARAFAC (CP) tensor decomposition, referred to as the EDCPTD centrality. This method is based on the perspective of multilayer networked structures, which integrate the information of edges among nodes and links between different layers to quantify the importance of nodes in multilayer networks. Three real-world multilayer biological networks are used to evaluate the performance of the EDCPTD centrality. The bar chart and ROC curves of these multilayer networks indicate that the proposed approach is a good alternative index to identify real important nodes. Meanwhile, by comparing the behavior of both the proposed method and the aggregated single-layer methods, we demonstrate that neglecting the multiple relationships between nodes may lead to incorrect identification of the most versatile nodes. Furthermore, the Gene Ontology functional annotation demonstrates that the identified top nodes based on the proposed approach play a significant role in many vital biological processes. Finally, we have implemented many centrality methods of multilayer networks (including our method and the published methods) and created a visual software based on the MATLAB GUI, called ENMNFinder, which can be used by other researchers.

Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network.

Science.gov (United States)

Kordmahalleh, Mina Moradi; Sefidmazgi, Mohammad Gorji; Harrison, Scott H; Homaifar, Abdollah

2017-01-01

The modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after a finite time delay, or as a combined outcome of simultaneous and time delayed interactions. Powerful biotechnologies have been rapidly and successfully measuring levels of genetic expression to illuminate different states of biological systems. This has led to an ensuing challenge to improve the identification of specific regulatory mechanisms through regulatory network reconstructions. Solutions to this challenge will ultimately help to spur forward efforts based on the usage of regulatory network reconstructions in systems biology applications. We have developed a hierarchical recurrent neural network (HRNN) that identifies time-delayed gene interactions using time-course data. A customized genetic algorithm (GA) was used to optimize hierarchical connectivity of regulatory genes and a target gene. The proposed design provides a non-fully connected network with the flexibility of using recurrent connections inside the network. These features and the non-linearity of the HRNN facilitate the process of identifying temporal patterns of a GRN. Our HRNN method was implemented with the Python language. It was first evaluated on simulated data representing linear and nonlinear time-delayed gene-gene interaction models across a range of network sizes and variances of noise. We then further demonstrated the capability of our method in reconstructing GRNs of the Saccharomyces cerevisiae synthetic network for in vivo benchmarking of reverse-engineering and modeling approaches (IRMA). We compared the performance of our method to TD-ARACNE, HCC-CLINDE, TSNI and ebdbNet across different network

Refrigerant flow through electronic expansion valve: Experiment and neural network modeling

International Nuclear Information System (INIS)

Cao, Xiang; Li, Ze-Yu; Shao, Liang-Liang; Zhang, Chun-Lu

2016-01-01

Highlights: • Experimental data from different sources were used in comparison of EEV models. • Artificial neural network in EEV modeling is superior to literature correlations. • Artificial neural network with 4-4-1 structure and S function is recommended. • Artificial neural network is flexible for EEV mass flow rate and opening prediction. - Abstract: Electronic expansion valve (EEV) plays a crucial role in controlling refrigerant mass flow rate of refrigeration or heat pump systems for energy savings. However, complexities in two-phase throttling process and geometry make accurate modeling of EEV flow characteristics more difficult. This paper developed an artificial neural network (ANN) model using refrigerant inlet and outlet pressures, inlet subcooling, EEV opening as ANN inputs, refrigerant mass flow rate as ANN output. Both linear and nonlinear transfer functions in hidden layer were used and compared to each other. Experimental data from multiple sources including in-house experiments of one EEV with R410A were used for ANN training and test. In addition, literature correlations were compared with ANN as well. Results showed that the ANN model with nonlinear transfer function worked well in all cases and it is much accurate than the literature correlations. In all cases, nonlinear ANN predicted refrigerant mass flow rates within ±0.4% average relative deviation (A.D.) and 2.7% standard deviation (S.D.), meanwhile it predicted the EEV opening at 0.1% A.D. and 2.1% S.D.

Modeling flow and transport in fracture networks using graphs

Science.gov (United States)

Karra, S.; O'Malley, D.; Hyman, J. D.; Viswanathan, H. S.; Srinivasan, G.

2018-03-01

Fractures form the main pathways for flow in the subsurface within low-permeability rock. For this reason, accurately predicting flow and transport in fractured systems is vital for improving the performance of subsurface applications. Fracture sizes in these systems can range from millimeters to kilometers. Although modeling flow and transport using the discrete fracture network (DFN) approach is known to be more accurate due to incorporation of the detailed fracture network structure over continuum-based methods, capturing the flow and transport in such a wide range of scales is still computationally intractable. Furthermore, if one has to quantify uncertainty, hundreds of realizations of these DFN models have to be run. To reduce the computational burden, we solve flow and transport on a graph representation of a DFN. We study the accuracy of the graph approach by comparing breakthrough times and tracer particle statistical data between the graph-based and the high-fidelity DFN approaches, for fracture networks with varying number of fractures and degree of heterogeneity. Due to our recent developments in capabilities to perform DFN high-fidelity simulations on fracture networks with large number of fractures, we are in a unique position to perform such a comparison. We show that the graph approach shows a consistent bias with up to an order of magnitude slower breakthrough when compared to the DFN approach. We show that this is due to graph algorithm's underprediction of the pressure gradients across intersections on a given fracture, leading to slower tracer particle speeds between intersections and longer travel times. We present a bias correction methodology to the graph algorithm that reduces the discrepancy between the DFN and graph predictions. We show that with this bias correction, the graph algorithm predictions significantly improve and the results are very accurate. The good accuracy and the low computational cost, with O (104) times lower times than

Distributed routing algorithms to manage power flow in agent-based active distribution network

NARCIS (Netherlands)

Nguyen, H.P.; Kling, W.L.; Georgiadis, G.; Papatriantafilou, M.; Anh-Tuan, L.; Bertling, L.

2010-01-01

The current transition from passive to active electric distribution networks comes with problems and challenges on bi-directional power flow in the network and the uncertainty in the forecast of power generation from grid-connected renewable and distributed energy sources. The power flow management

An Amorphous Network Model for Capillary Flow and Dispersion in a Partially Saturated Porous Medium

Science.gov (United States)

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

Smooth Information Flow in Temperature Climate Network Reflects Mass Transport

Czech Academy of Sciences Publication Activity Database

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

2017-01-01

Roč. 27, č. 3 (2017), č. článku 035811. ISSN 1054-1500 R&D Projects: GA ČR GCP103/11/J068; GA MŠk LH14001 Institutional support: RVO:67985807 Keywords : directed network * causal network * Granger causality * climate network * information flow * temperature network Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 2.283, year: 2016

Computer network prepared to handle massive data flow

CERN Multimedia

2006-01-01

"Massive quantities of data will soon begin flowing from the largest scientific instrument ever built into an internationl network of computer centers, including one operated jointly by the University of Chicago and Indiana University." (2 pages)

Phase-synchronisation in continuous flow models of production networks

Science.gov (United States)

Scholz-Reiter, Bernd; Tervo, Jan Topi; Freitag, Michael

2006-04-01

To improve their position at the market, many companies concentrate on their core competences and hence cooperate with suppliers and distributors. Thus, between many independent companies strong linkages develop and production and logistics networks emerge. These networks are characterised by permanently increasing complexity, and are nowadays forced to adapt to dynamically changing markets. This factor complicates an enterprise-spreading production planning and control enormously. Therefore, a continuous flow model for production networks will be derived regarding these special logistic problems. Furthermore, phase-synchronisation effects will be presented and their dependencies to the set of network parameters will be investigated.

Synthesis of a parallel data stream processor from data flow process networks

NARCIS (Netherlands)

Zissulescu-Ianculescu, Claudiu

2008-01-01

In this talk, we address the problem of synthesizing Process Network specifications to FPGA execution platforms. The process networks we consider are special cases of Kahn Process Networks. We call them COMPAAN Data Flow Process Networks (CDFPN) because they are provided by a translator called the

Collective network routing

Science.gov (United States)

Hoenicke, Dirk

2014-12-02

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

Operator splitting method for simulation of dynamic flows in natural gas pipeline networks

Science.gov (United States)

Dyachenko, Sergey A.; Zlotnik, Anatoly; Korotkevich, Alexander O.; Chertkov, Michael

2017-12-01

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 is 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.

Field effect control of electro-osmotic flow in microfluidic networks

NARCIS (Netherlands)

van der Wouden, E.J.

2006-01-01

This thesis describes the development of a Field Effect Flow Control (FEFC) system for the control of Electro Osmotic Flow (EOF) in microfluidic networks. For this several aspects of FEFC have been reviewed and a process to fabricate microfluidic channels with integrated electrodes has been

Characterization of IP Flows Eligible for Lambda-Connections in Optical Networks

NARCIS (Netherlands)

Fioreze, Tiago; Oude Wolbers, Mattijs; van de Meent, R.; Pras, Aiko

2008-01-01

The advance on data transmission in optical networks has allowed data forwarding decisions to be taken at multiple levels in the protocol stack (e.g., at network and optical levels). With such capability, big IP flows can be moved from the network level and switched completely at the optical level

Network modeling for reverse flows of end-of-life vehicles.

Science.gov (United States)

Ene, Seval; Öztürk, Nursel

2015-04-01

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. Copyright © 2015 Elsevier Ltd. All rights reserved.

Roads at risk - the impact of debris flows on road network reliability and vulnerability in southern Norway

Science.gov (United States)

Meyer, Nele Kristin; Schwanghart, Wolfgang; Korup, Oliver

2014-05-01

Norwegian's road network is frequently affected by debris flows. Both damage repair and traffic interruption generate high economic losses and necessitate a rigorous assessment of where losses are expected to be high and where preventive measures should be focused on. In recent studies, we have developed susceptibility and trigger probability maps that serve as input into a hazard calculation at the scale of first-order watersheds. Here we combine these results with graph theory to assess the impact of debris flows on the road network of southern Norway. Susceptibility and trigger probability are aggregated for individual road sections to form a reliability index that relates to the failure probability of a link that connects two network vertices, e.g., road junctions. We define link vulnerability as a function of traffic volume and additional link failure distance. Additional link failure distance is the extra length of the alternative path connecting the two associated link vertices in case the network link fails and is calculated by a shortest-path algorithm. The product of network reliability and vulnerability indices represent the risk index. High risk indices identify critical links for the Norwegian road network and are investigated in more detail. Scenarios demonstrating the impact of single or multiple debris flow events are run for the most important routes between seven large cities in southern Norway. First results show that the reliability of the road network is lowest in the central and north-western part of the study area. Road network vulnerability is highest in the mountainous regions in central southern Norway where the road density is low and in the vicinity of cities where the traffic volume is large. The scenarios indicate that city connections that have their shortest path via routes crossing the central part of the study area have the highest risk of route failure.

A network-flow based valve-switching aware binding algorithm for flow-based microfluidic biochips

DEFF Research Database (Denmark)

Tseng, Kai-Han; You, Sheng-Chi; Minhass, Wajid Hassan

2013-01-01

-flow based resource binding algorithm based on breadth-first search (BFS) and minimum cost maximum flow (MCMF) in architectural-level synthesis. The experimental results show that our methodology not only makes significant reduction of valve-switching activities but also diminishes the application completion......Designs of flow-based microfluidic biochips are receiving much attention recently because they replace conventional biological automation paradigm and are able to integrate different biochemical analysis functions on a chip. However, as the design complexity increases, a flow-based microfluidic...... biochip needs more chip-integrated micro-valves, i.e., the basic unit of fluid-handling functionality, to manipulate the fluid flow for biochemical applications. Moreover, frequent switching of micro-valves results in decreased reliability. To minimize the valve-switching activities, we develop a network...

MINET, Transient Fluid Flow and Heat Transfer Power Plant Network Analysis

International Nuclear Information System (INIS)

Van Tuyle, G.J.

2002-01-01

1 - Description of program or function: MINET (Momentum Integral Network) was developed for the transient analysis of intricate fluid flow and heat transfer networks, such as those found in the balance of plant in power generating facilities. It can be utilized as a stand-alone program or interfaced to another computer program for concurrent analysis. Through such coupling, a computer code limited by either the lack of required component models or large computational needs can be extended to more fully represent the thermal hydraulic system thereby reducing the need for estimating essential transient boundary conditions. The MINET representation of a system is one or more networks of volumes, segments, and boundaries linked together via heat exchangers only, i.e., heat can transfer between networks, but fluids cannot. Volumes are used to represent tanks or other volume components, as well as locations in the system where significant flow divisions or combinations occur. Segments are composed of one or more pipes, pumps, heat exchangers, turbines, and/or valves each represented by one or more nodes. Boundaries are simply points where the network interfaces with the user or another computer code. Several fluids can be simulated, including water, sodium, NaK, and air. 2 - Method of solution: MINET is based on a momentum integral network method. Calculations are performed at two levels, the network level (volumes) and the segment level. Equations conserving mass and energy are used to calculate pressure and enthalpy within volumes. An integral momentum equation is used to calculate the segment average flow rate. In-segment distributions of mass flow rate and enthalpy are calculated using local equations of mass and energy. The segment pressure is taken to be the linear average of the pressure at both ends. This method uses a two-plus equation representation of the thermal hydraulic behavior of a system of heat exchangers, pumps, pipes, valves, tanks, etc. With the

Complex network analysis of phase dynamics underlying oil-water two-phase flows

Science.gov (United States)

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

Correlation Networks from Flows. The Case of Forced and Time-Dependent Advection-Diffusion Dynamics.

Directory of Open Access Journals (Sweden)

Liubov Tupikina

Full Text Available Complex network theory provides an elegant and powerful framework to statistically investigate different types of systems such as society, brain or the structure of local and long-range dynamical interrelationships in the climate system. Network links in climate networks typically imply information, mass or energy exchange. However, the specific connection between oceanic or atmospheric flows and the climate network's structure is still unclear. We propose a theoretical approach for verifying relations between the correlation matrix and the climate network measures, generalizing previous studies and overcoming the restriction to stationary flows. Our methods are developed for correlations of a scalar quantity (temperature, for example which satisfies an advection-diffusion dynamics in the presence of forcing and dissipation. Our approach reveals that correlation networks are not sensitive to steady sources and sinks and the profound impact of the signal decay rate on the network topology. We illustrate our results with calculations of degree and clustering for a meandering flow resembling a geophysical ocean jet.

Identifying the Key Weaknesses in Network Security at Colleges.

Science.gov (United States)

Olsen, Florence

2000-01-01

A new study identifies and ranks the 10 security gaps responsible for most outsider attacks on college computer networks. The list is intended to help campus system administrators establish priorities as they work to increase security. One network security expert urges that institutions utilize multiple security layers. (DB)

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors.

Science.gov (United States)

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.

Modelling flow dynamics in water distribution networks using ...

African Journals Online (AJOL)

DR OKE

was used for modelling the flow and simulate water demand using a Matlab .... This process requires that the neural network compute the error derivative of the .... Furthermore, Matlab was used as a simulation tool; and the first step was ...

Identifying partial topology of complex dynamical networks via a pinning mechanism

Science.gov (United States)

Zhu, Shuaibing; Zhou, Jin; Lu, Jun-an

2018-04-01

In this paper, we study the problem of identifying the partial topology of complex dynamical networks via a pinning mechanism. By using the network synchronization theory and the adaptive feedback controlling method, we propose a method which can greatly reduce the number of nodes and observers in the response network. Particularly, this method can also identify the whole topology of complex networks. A theorem is established rigorously, from which some corollaries are also derived in order to make our method more cost-effective. Several numerical examples are provided to verify the effectiveness of the proposed method. In the simulation, an approach is also given to avoid possible identification failure caused by inner synchronization of the drive network.

Unified pipe network method for simulation of water flow in fractured porous rock

Science.gov (United States)

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.

Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network

Energy Technology Data Exchange (ETDEWEB)

Du, Zhimin; Jin, Xinqiao; Yang, Yunyu [School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai (China)

2009-09-15

Wavelet neural network, the integration of wavelet analysis and neural network, is presented to diagnose the faults of sensors including temperature, flow rate and pressure in variable air volume (VAV) systems to ensure well capacity of energy conservation. Wavelet analysis is used to process the original data collected from the building automation first. With three-level wavelet decomposition, the series of characteristic information representing various operation conditions of the system are obtained. In addition, neural network is developed to diagnose the source of the fault. To improve the diagnosis efficiency, three data groups based on several physical models or balances are classified and constructed. Using the data decomposed by three-level wavelet, the neural network can be well trained and series of convergent networks are obtained. Finally, the new measurements to diagnose are similarly processed by wavelet. And the well-trained convergent neural networks are used to identify the operation condition and isolate the source of the fault. (author)

Modeling Flow in Naturally Fractured Reservoirs : Effect of Fracture Aperture Distribution on Critical Sub-Network for Flow

NARCIS (Netherlands)

Gong, J.; Rossen, W.R.

2014-01-01

Fracture network connectivity and aperture (or conductivity) distribution are two crucial features controlling the flow behavior of fractured formations. The effect of connectivity on flow properties is well documented. We focus here on the influence of fracture aperture distribution. We model a

Modeling flow in naturally fractured reservoirs : effect of fracture aperture distribution on dominant sub-network for flow

NARCIS (Netherlands)

Gong, J.; Rossen, W.R.

2017-01-01

Fracture network connectivity and aperture (or conductivity) distribution are two crucial features controlling flow behavior of naturally fractured reservoirs. The effect of connectivity on flow properties is well documented. In this paper, however, we focus here on the influence of fracture

Optimization of flow modeling in fractured media with discrete fracture network via percolation theory

Science.gov (United States)

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

Structural identifiability of cyclic graphical models of biological networks with latent variables.

Science.gov (United States)

Wang, Yulin; Lu, Na; Miao, Hongyu

2016-06-13

Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and

Social network analysis in identifying influential webloggers: A preliminary study

Science.gov (United States)

Hasmuni, Noraini; Sulaiman, Nor Intan Saniah; Zaibidi, Nerda Zura

2014-12-01

In recent years, second generation of internet-based services such as weblog has become an effective communication tool to publish information on the Web. Weblogs have unique characteristics that deserve users' attention. Some of webloggers have seen weblogs as appropriate medium to initiate and expand business. These webloggers or also known as direct profit-oriented webloggers (DPOWs) communicate and share knowledge with each other through social interaction. However, survivability is the main issue among DPOW. Frequent communication with influential webloggers is one of the way to keep survive as DPOW. This paper aims to understand the network structure and identify influential webloggers within the network. Proper understanding of the network structure can assist us in knowing how the information is exchanged among members and enhance survivability among DPOW. 30 DPOW were involved in this study. Degree centrality and betweenness centrality measurement in Social Network Analysis (SNA) were used to examine the strength relation and identify influential webloggers within the network. Thus, webloggers with the highest value of these measurements are considered as the most influential webloggers in the network.

Social Network Analysis Identifies Key Participants in Conservation Development.

Science.gov (United States)

Farr, Cooper M; Reed, Sarah E; Pejchar, Liba

2018-05-01

Understanding patterns of participation in private lands conservation, which is often implemented voluntarily by individual citizens and private organizations, could improve its effectiveness at combating biodiversity loss. We used social network analysis (SNA) to examine participation in conservation development (CD), a private land conservation strategy that clusters houses in a small portion of a property while preserving the remaining land as protected open space. Using data from public records for six counties in Colorado, USA, we compared CD participation patterns among counties and identified actors that most often work with others to implement CDs. We found that social network characteristics differed among counties. The network density, or proportion of connections in the network, varied from fewer than 2 to nearly 15%, and was higher in counties with smaller populations and fewer CDs. Centralization, or the degree to which connections are held disproportionately by a few key actors, was not correlated strongly with any county characteristics. Network characteristics were not correlated with the prevalence of wildlife-friendly design features in CDs. The most highly connected actors were biological and geological consultants, surveyors, and engineers. Our work demonstrates a new application of SNA to land-use planning, in which CD network patterns are examined and key actors are identified. For better conservation outcomes of CD, we recommend using network patterns to guide strategies for outreach and information dissemination, and engaging with highly connected actor types to encourage widespread adoption of best practices for CD design and stewardship.

Identifiability of tree-child phylogenetic networks under a probabilistic recombination-mutation model of evolution.

Science.gov (United States)

Francis, Andrew; Moulton, Vincent

2018-06-07

Phylogenetic networks are an extension of phylogenetic trees which are used to represent evolutionary histories in which reticulation events (such as recombination and hybridization) have occurred. A central question for such networks is that of identifiability, which essentially asks under what circumstances can we reliably identify the phylogenetic network that gave rise to the observed data? Recently, identifiability results have appeared for networks relative to a model of sequence evolution that generalizes the standard Markov models used for phylogenetic trees. However, these results are quite limited in terms of the complexity of the networks that are considered. In this paper, by introducing an alternative probabilistic model for evolution along a network that is based on some ground-breaking work by Thatte for pedigrees, we are able to obtain an identifiability result for a much larger class of phylogenetic networks (essentially the class of so-called tree-child networks). To prove our main theorem, we derive some new results for identifying tree-child networks combinatorially, and then adapt some techniques developed by Thatte for pedigrees to show that our combinatorial results imply identifiability in the probabilistic setting. We hope that the introduction of our new model for networks could lead to new approaches to reliably construct phylogenetic networks. Copyright © 2018 Elsevier Ltd. All rights reserved.

Spatially dynamic recurrent information flow across long-range dorsal motor network encodes selective motor goals.

Science.gov (United States)

Yoo, Peter E; Hagan, Maureen A; John, Sam E; Opie, Nicholas L; Ordidge, Roger J; O'Brien, Terence J; Oxley, Thomas J; Moffat, Bradford A; Wong, Yan T

2018-03-08

Performing voluntary movements involves many regions of the brain, but it is unknown how they work together to plan and execute specific movements. We recorded high-resolution ultra-high-field blood-oxygen-level-dependent signal during a cued ankle-dorsiflexion task. The spatiotemporal dynamics and the patterns of task-relevant information flow across the dorsal motor network were investigated. We show that task-relevant information appears and decays earlier in the higher order areas of the dorsal motor network then in the primary motor cortex. Furthermore, the results show that task-relevant information is encoded in general initially, and then selective goals are subsequently encoded in specifics subregions across the network. Importantly, the patterns of recurrent information flow across the network vary across different subregions depending on the goal. Recurrent information flow was observed across all higher order areas of the dorsal motor network in the subregions encoding for the current goal. In contrast, only the top-down information flow from the supplementary motor cortex to the frontoparietal regions, with weakened recurrent information flow between the frontoparietal regions and bottom-up information flow from the frontoparietal regions to the supplementary cortex were observed in the subregions encoding for the opposing goal. We conclude that selective motor goal encoding and execution rely on goal-dependent differences in subregional recurrent information flow patterns across the long-range dorsal motor network areas that exhibit graded functional specialization. © 2018 Wiley Periodicals, Inc.

Effect of fluid friction on interstitial fluid flow coupled with blood flow through solid tumor microvascular network.

Science.gov (United States)

Sefidgar, Mostafa; Soltani, M; Raahemifar, Kaamran; Bazmara, Hossein

2015-01-01

A solid tumor is investigated as porous media for fluid flow simulation. Most of the studies use Darcy model for porous media. In Darcy model, the fluid friction is neglected and a few simplified assumptions are implemented. In this study, the effect of these assumptions is studied by considering Brinkman model. A multiscale mathematical method which calculates fluid flow to a solid tumor is used in this study to investigate how neglecting fluid friction affects the solid tumor simulation. The mathematical method involves processes such as blood flow through vessels and solute and fluid diffusion, convective transport in extracellular matrix, and extravasation from blood vessels. The sprouting angiogenesis model is used for generating capillary network and then fluid flow governing equations are implemented to calculate blood flow through the tumor-induced capillary network. Finally, the two models of porous media are used for modeling fluid flow in normal and tumor tissues in three different shapes of tumors. Simulations of interstitial fluid transport in a solid tumor demonstrate that the simplifications used in Darcy model affect the interstitial velocity and Brinkman model predicts a lower value for interstitial velocity than the values that Darcy model predicts.

Dynamic mobility applications policy analysis : policy and institutional issues for intelligent network flow optimization (INFLO).

Science.gov (United States)

2014-12-01

The report documents policy considerations for the Intelligent Network Flow Optimization (INFLO) connected vehicle applications : bundle. INFLO aims to optimize network flow on freeways and arterials by informing motorists of existing and impen...

Evolution of weighted complex bus transit networks with flow

Science.gov (United States)

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.

Identifying Tracks Duplicates via Neural Network

CERN Document Server

Sunjerga, Antonio; CERN. Geneva. EP Department

2017-01-01

The goal of the project is to study feasibility of state of the art machine learning techniques in track reconstruction. Machine learning techniques provide promising ways to speed up the pattern recognition of tracks by adding more intelligence in the algorithms. Implementation of neural network to process of track duplicates identifying will be discussed. Different approaches are shown and results are compared to method that is currently in use.

Intelligent Network Flow Optimization (INFLO) prototype acceptance test summary.

Science.gov (United States)

2015-05-01

This report summarizes the results of System Acceptance Testing for the implementation of the Intelligent Network : Flow Optimization (INFLO) Prototype bundle within the Dynamic Mobility Applications (DMA) portion of the Connected : Vehicle Program. ...

On Event Detection and Localization in Acyclic Flow Networks

KAUST Repository

Suresh, Mahima Agumbe; Stoleru, Radu; Zechman, Emily M.; Shihada, Basem

2013-01-01

Acyclic flow networks, present in many infrastructures of national importance (e.g., oil and gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against

SIPSON--simulation of interaction between pipe flow and surface overland flow in networks.

Science.gov (United States)

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.

Methods for discriminating gas-liquid two phase flow patterns based on gray neural networks and SVM

International Nuclear Information System (INIS)

Li Jingjing; Zhou Tao; Duan Jun; Zhang Lei

2013-01-01

Background: The flow patterns of two phase flow will directly influence the heat transfer and mass transfer of the flow. Purpose: By wavelet analysis of the pressure drop experimental data, the wavelet coefficients of different frequency can be obtained. Methods: Get the wavelet energy and then train them in the model of BP neural network to distinguish the flow patterns. Introduced the implant gray neural networks model and use it for the two phase flow for the first time. At the same time, set up the method of training the pressure data and wavelet energy data in the support vector machine. Results: Through treatment of the gray layer, the result of the neural network is more accuracy. It can obviously reduce the effect of data marginalization. The accuracy of the pressure drop Lib-SVM method is 95.2%. Conclusions: The results show that these three methods can make a distinction among the different flow patterns and the Lib-SVM method gets the best result, then the gray neural networks, and at last the BP neural networks. (authors)

Catchment organisation, free energy dynamics and network control on critical zone water flows

Science.gov (United States)

Zehe, E.; Ehret, U.; Kleidon, A.; Jackisch, C.; Scherer, U.; Blume, T.

2012-04-01

From a functional point of view the catchment system is compiled by patterns of permeable and less permeable textural elements - soils and mother rock. Theses textural elements provide a mechanical stabile matrix for growth of terrestrial biota and soil formation. They furthermore organize subsurface storage of water against gravity, dissolved nutrients and heat. Storage against gravity is only possible because water acts as wetting fluid and is thus attracted by capillary forces in the pores space. Capillarity increases non-linearly with decreasing pore size and is zero at local saturation. The pore size distribution of a soil is thus characteristic of its capability to store water against losses such as drainage, evaporation and root extraction and at the same time a fingerprint of the work that has been performed by physical, chemical and biological processes to weather solid mother rock and form a soil. A strong spatial covariance of soil hydraulic properties within the same soil type is due to a fingerprint of strong spatial organization at small scales. Spatial organization at the hillslope scale implies the existence of a typical soil catena i.e. that hillslopes exhibit the same/ downslope sequence of different soils types. Textural storage elements are separated by strikingly self-similar network like structures, we name them flow structures. These flow structures are created in a self-reinforcing manner by work performed either by biota like earth worms and plant roots or by dissipative processes such as soil cracking and water/fluvial erosion. Regardless of their different origin connected flow structures exhibit a highly similar functioning and similar characteristics: they allow for high mass flows at small driving potential gradients because specific flow resistance along the network is continuously very small. This implies temporal stability even during small extremes, due to the small amount of local momentum dissipation per unit mass flow, as well

A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

Science.gov (United States)

Huang, Darong; Bai, Xing-Rong

Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

Depth-dependent flow and pressure characteristics in cortical microvascular networks.

Directory of Open Access Journals (Sweden)

Franca Schmid

2017-02-01

Full Text Available 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.

A bio-inspired methodology of identifying influential nodes in complex networks.

Directory of Open Access Journals (Sweden)

Cai Gao

Full Text Available How to identify influential nodes is a key issue in complex networks. The degree centrality is simple, but is incapable to reflect the global characteristics of networks. Betweenness centrality and closeness centrality do not consider the location of nodes in the networks, and semi-local centrality, leaderRank and pageRank approaches can be only applied in unweighted networks. In this paper, a bio-inspired centrality measure model is proposed, which combines the Physarum centrality with the K-shell index obtained by K-shell decomposition analysis, to identify influential nodes in weighted networks. Then, we use the Susceptible-Infected (SI model to evaluate the performance. Examples and applications are given to demonstrate the adaptivity and efficiency of the proposed method. In addition, the results are compared with existing methods.

Social adaptation in multi-agent model of linguistic categorization is affected by network information flow.

Science.gov (United States)

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.

Integrated Power Flow and Short Circuit Calculation Method for Distribution Network with Inverter Based Distributed Generation

OpenAIRE

Yang, Shan; Tong, Xiangqian

2016-01-01

Power flow calculation and short circuit calculation are the basis of theoretical research for distribution network with inverter based distributed generation. The similarity of equivalent model for inverter based distributed generation during normal and fault conditions of distribution network and the differences between power flow and short circuit calculation are analyzed in this paper. Then an integrated power flow and short circuit calculation method for distribution network with inverte...

Exploring the potential of blood flow network data

NARCIS (Netherlands)

Poelma, C.

2015-01-01

To gain a better understanding of the role of haemodynamic forces during the development of the cardiovascular system, a series of studies have been reported recently that describe flow fields in the vasculature of model systems. Such data sets, in particular those reporting networks at multiple

Exploiting the Vulnerability of Flow Table Overflow in Software-Defined Network: Attack Model, Evaluation, and Defense

Directory of Open Access Journals (Sweden)

Yadong Zhou

2018-01-01

Full Text Available As the most competitive solution for next-generation network, SDN and its dominant implementation OpenFlow are attracting more and more interests. But besides convenience and flexibility, SDN/OpenFlow also introduces new kinds of limitations and security issues. Of these limitations, the most obvious and maybe the most neglected one is the flow table capacity of SDN/OpenFlow switches. In this paper, we proposed a novel inference attack targeting at SDN/OpenFlow network, which is motivated by the limited flow table capacities of SDN/OpenFlow switches and the following measurable network performance decrease resulting from frequent interactions between data and control plane when the flow table is full. To the best of our knowledge, this is the first proposed inference attack model of this kind for SDN/OpenFlow. We implemented an inference attack framework according to our model and examined its efficiency and accuracy. The evaluation results demonstrate that our framework can infer the network parameters (flow table capacity and usage with an accuracy of 80% or higher. We also proposed two possible defense strategies for the discovered vulnerability, including routing aggregation algorithm and multilevel flow table architecture. These findings give us a deeper understanding of SDN/OpenFlow limitations and serve as guidelines to future improvements of SDN/OpenFlow.

Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer.

Directory of Open Access Journals (Sweden)

Matthew Ruffalo

2015-12-01

Full Text Available Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these "silent players". For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation.

Concept development and needs identification for intelligent network flow optimization (INFLO) : functional and performance requirements, and high-level data and communication needs.

Science.gov (United States)

2012-11-01

The purpose of this project is to develop for the Intelligent Network Flow Optimization (INFLO), which is one collection (or bundle) of high-priority transformative applications identified by the United States Department of Transportation (USDOT) Mob...

Development of flow network analysis code for block type VHTR core by linear theory method

International Nuclear Information System (INIS)

Lee, J. H.; Yoon, S. J.; Park, J. W.; Park, G. C.

2012-01-01

VHTR (Very High Temperature Reactor) is high-efficiency nuclear reactor which is capable of generating hydrogen with high temperature of coolant. PMR (Prismatic Modular Reactor) type reactor consists of hexagonal prismatic fuel blocks and reflector blocks. The flow paths in the prismatic VHTR core consist of coolant holes, bypass gaps and cross gaps. Complicated flow paths are formed in the core since the coolant holes and bypass gap are connected by the cross gap. Distributed coolant was mixed in the core through the cross gap so that the flow characteristics could not be modeled as a simple parallel pipe system. It requires lot of effort and takes very long time to analyze the core flow with CFD analysis. Hence, it is important to develop the code for VHTR core flow which can predict the core flow distribution fast and accurate. In this study, steady state flow network analysis code is developed using flow network algorithm. Developed flow network analysis code was named as FLASH code and it was validated with the experimental data and CFD simulation results. (authors)

Technology and knowledge flow the power of networks

CERN Document Server

Trentin, Guglielmo

2011-01-01

This book outlines how network technology can support, foster and enhance the Knowledge Management, Sharing and Development (KMSD) processes in professional environments through the activation of both formal and informal knowledge flows. Understanding how ICT can be made available to such flows in the knowledge society is a factor that cannot be disregarded and is confirmed by the increasing interest of companies in new forms of software-mediated social interaction. The latter factor is in relation both to the possibility of accelerating internal communication and problem solving processes, an

Improved Road-Network-Flow Control Strategy Based on Macroscopic Fundamental Diagrams and Queuing Length in Connected-Vehicle Network

Directory of Open Access Journals (Sweden)

Xiaohui Lin

2017-01-01

Full Text Available Connected-vehicles network provides opportunities and conditions for improving traffic signal control, and macroscopic fundamental diagrams (MFD can control the road network at the macrolevel effectively. This paper integrated proposed real-time access to the number of mobile vehicles and the maximum road queuing length in the Connected-vehicles network. Moreover, when implementing a simple control strategy to limit the boundary flow of a road network based on MFD, we determined whether the maximum queuing length of each boundary section exceeds the road-safety queuing length in real-time calculations and timely adjusted the road-network influx rate to avoid the overflow phenomenon in the boundary section. We established a road-network microtraffic simulation model in VISSIM software taking a district as the experimental area, determined MFD of the region based on the number of mobile vehicles, and weighted traffic volume of the road network. When the road network was tending to saturate, we implemented a simple control strategy and our algorithm limits the boundary flow. Finally, we compared the traffic signal control indicators with three strategies: (1 no control strategy, (2 boundary control, and (3 boundary control with limiting queue strategy. The results show that our proposed algorithm is better than the other two.

Dynamic logics of networks: Information flow and the spread of opinion

NARCIS (Netherlands)

Christoff, Z.L.

2016-01-01

This thesis uses logical tools to investigate a number of basic features of social networks and their evolution over time, including flow of information and spread of opinions. Part I contains the preliminaries, including an introduction to the basic phenomena in social networks that call for a

Optimal Power Flow for resistive DC Network : A Port-Hamiltonian approach

NARCIS (Netherlands)

Benedito, Ernest; del Puerto-Flores, D.; Doria-Cerezo, A.; Scherpen, Jacquelien M.A.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri

This paper studies the optimal power flow problem for resistive DC networks. The gradient method algorithm is written in a port-Hamiltonian form and the stability of the resulting dynamics is studied. Stability conditions are provided for general cyclic networks and a solution, when these conditions

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

Science.gov (United States)

Grassi, Rosanna

2010-06-01

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

The influence of passenger flow on the topology characteristics of urban rail transit networks

Science.gov (United States)

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.

The Flow of International Students from a Macro Perspective: A Network Analysis

Science.gov (United States)

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,…

A constructive logic for services and information flow in computer networks

NARCIS (Netherlands)

Borghuis, V.A.J.; Feijs, L.M.G.

2000-01-01

In this paper we introduce a typed -calculus in which computer networks can be formalized and directed at situations where the services available on the network are stationary, while the information can flow freely. For this calculus, an analogue of the ‘propositions-as-types ’interpretation of

A service flow model for the liner shipping network design problem

DEFF Research Database (Denmark)

Plum, Christian Edinger Munk; Pisinger, David; Sigurd, Mikkel M.

2014-01-01

. The formulation alleviates issues faced by arc flow formulations with regards to handling multiple calls to the same port. A problem which has not been fully dealt with earlier by LSNDP formulations. Multiple calls are handled by introducing service nodes, together with port nodes in a graph representation...... of the network and a penalty for not flowed cargo. The model can be used to design liner shipping networks to utilize a container carrier’s assets efficiently and to investigate possible scenarios of changed market conditions. The model is solved as a Mixed Integer Program. Results are presented for the two...

Chaos in a dynamic model of urban transportation network flow based on user equilibrium states

International Nuclear Information System (INIS)

Xu Meng; Gao Ziyou

2009-01-01

In this study, we investigate the dynamical behavior of network traffic flow. We first build a two-stage mathematical model to analyze the complex behavior of network flow, a dynamical model, which is based on the dynamical gravity model proposed by Dendrinos and Sonis [Dendrinos DS, Sonis M. Chaos and social-spatial dynamic. Berlin: Springer-Verlag; 1990] is used to estimate the number of trips. Considering the fact that the Origin-Destination (O-D) trip cost in the traffic network is hard to express as a functional form, in the second stage, the user equilibrium network assignment model was used to estimate the trip cost, which is the minimum cost of used path when user equilibrium (UE) conditions are satisfied. It is important to use UE to estimate the O-D cost, since a connection is built among link flow, path flow, and O-D flow. The dynamical model describes the variations of O-D flows over discrete time periods, such as each day and each week. It is shown that even in a system with dimensions equal to two, chaos phenomenon still exists. A 'Chaos Propagation' phenomenon is found in the given model.

The influence of digital elevation model resolution on overland flow networks for modelling urban pluvial flooding.

Science.gov (United States)

Leitão, J P; Boonya-Aroonnet, S; Prodanović, D; Maksimović, C

2009-01-01

This paper presents the developments towards the next generation of overland flow modelling of urban pluvial flooding. Using a detailed analysis of the Digital Elevation Model (DEM) the developed GIS tools can automatically generate surface drainage networks which consist of temporary ponds (floodable areas) and flow paths and link them with the underground network through inlets. For different commercially-available Rainfall-Runoff simulation models, the tool will generate the overland flow network needed to model the surface runoff and pluvial flooding accurately. In this paper the emphasis is placed on a sensitivity analysis of ponds and preferential overland flow paths creation. Different DEMs for three areas were considered in order to compare the results obtained. The DEMs considered were generated using different acquisition techniques and hence represent terrain with varying levels of resolution and accuracy. The results show that DEMs can be used to generate surface flow networks reliably. As expected, the quality of the surface network generated is highly dependent on the quality and resolution of the DEMs and successful representation of buildings and streets.

Multivariate multiscale complex network analysis of vertical upward oil-water two-phase flow in a small diameter pipe.

Science.gov (United States)

Gao, Zhong-Ke; Yang, Yu-Xuan; Zhai, Lu-Sheng; Dang, Wei-Dong; Yu, Jia-Liang; Jin, Ning-De

2016-02-02

High water cut and low velocity vertical upward oil-water two-phase flow is a typical complex system with the features of multiscale, unstable and non-homogenous. We first measure local flow information by using distributed conductance sensor and then develop a multivariate multiscale complex network (MMCN) to reveal the dispersed oil-in-water local flow behavior. Specifically, we infer complex networks at different scales from multi-channel measurements for three typical vertical oil-in-water flow patterns. Then we characterize the generated multiscale complex networks in terms of network clustering measure. The results suggest that the clustering coefficient entropy from the MMCN not only allows indicating the oil-in-water flow pattern transition but also enables to probe the dynamical flow behavior governing the transitions of vertical oil-water two-phase flow.

Non-Newtonian fluid flow in 2D fracture networks

Science.gov (United States)

Zou, L.; Håkansson, U.; Cvetkovic, V.

2017-12-01

Modeling of non-Newtonian fluid (e.g., drilling fluids and cement grouts) flow in fractured rocks is of interest in many geophysical and industrial practices, such as drilling operations, enhanced oil recovery and rock grouting. In fractured rock masses, the flow paths are dominated by fractures, which are often represented as discrete fracture networks (DFN). In the literature, many studies have been devoted to Newtonian fluid (e.g., groundwater) flow in fractured rock using the DFN concept, but few works are dedicated to non-Newtonian fluids.In this study, a generalized flow equation for common non-Newtonian fluids (such as Bingham, power-law and Herschel-Bulkley) in a single fracture is obtained from the analytical solutions for non-Newtonian fluid discharge between smooth parallel plates. Using Monte Carlo sampling based on site characterization data for the distribution of geometrical features (e.g., density, length, aperture and orientations) in crystalline fractured rock, a two dimensional (2D) DFN model is constructed for generic flow simulations. Due to complex properties of non-Newtonian fluids, the relationship between fluid discharge and the pressure gradient is nonlinear. A Galerkin finite element method solver is developed to iteratively solve the obtained nonlinear governing equations for the 2D DFN model. Using DFN realizations, simulation results for different geometrical distributions of the fracture network and different non-Newtonian fluid properties are presented to illustrate the spatial discharge distributions. The impact of geometrical structures and the fluid properties on the non-Newtonian fluid flow in 2D DFN is examined statistically. The results generally show that modeling non-Newtonian fluid flow in fractured rock as a DFN is feasible, and that the discharge distribution may be significantly affected by the geometrical structures as well as by the fluid constitutive properties.

Implementation of Finite Volume based Navier Stokes Algorithm Within General Purpose Flow Network Code

Science.gov (United States)

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.

77 FR 3544 - Meeting and Webinar on the Active Traffic and Demand Management and Intelligent Network Flow...

Science.gov (United States)

2012-01-24

... Intelligent Network Flow Optimization Operational Concepts; Notice of Public Meeting AGENCY: Research and... Demand Management (ADTM) and Intelligent Network Flow Optimization (INFLO) operational concepts. The ADTM... infrastructure. The vision for ATDM research is to allow transportation agencies to increase traffic flow...

Flow distribution analysis on the cooling tube network of ITER thermal shield

International Nuclear Information System (INIS)

Nam, Kwanwoo; Chung, Wooho; Noh, Chang Hyun; Kang, Dong Kwon; Kang, Kyoung-O; Ahn, Hee Jae; Lee, Hyeon Gon

2014-01-01

Thermal shield (TS) is to be installed between the vacuum vessel or the cryostat and the magnets in ITER tokamak to reduce the thermal radiation load to the magnets operating at 4.2K. The TS is cooled by pressurized helium gas at the inlet temperature of 80K. The cooling tube is welded on the TS panel surface and the composed flow network of the TS cooling tubes is complex. The flow rate in each panel should be matched to the thermal design value for effective radiation shielding. This paper presents one dimensional analysis on the flow distribution of cooling tube network for the ITER TS. The hydraulic cooling tube network is modeled by an electrical analogy. Only the cooling tube on the TS surface and its connecting pipe from the manifold are considered in the analysis model. Considering the frictional factor and the local loss in the cooling tube, the hydraulic resistance is expressed as a linear function with respect to mass flow rate. Sub-circuits in the TS are analyzed separately because each circuit is controlled by its own control valve independently. It is found that flow rates in some panels are insufficient compared with the design values. In order to improve the flow distribution, two kinds of design modifications are proposed. The first one is to connect the tubes of the adjacent panels. This will increase the resistance of the tube on the panel where the flow rate is excessive. The other design suggestion is that an orifice is installed at the exit of tube routing where the flow rate is to be reduced. The analysis for the design suggestions shows that the flow mal-distribution is improved significantly

Identifying the optimal supply temperature in district heating networks - A modelling approach

DEFF Research Database (Denmark)

Mohammadi, Soma; Bojesen, Carsten

2014-01-01

of this study is to develop a model for thermo-hydraulic calculation of low temperature DH system. The modelling is performed with emphasis on transient heat transfer in pipe networks. The pseudo-dynamic approach is adopted to model the District Heating Network [DHN] behaviour which estimates the temperature...... dynamically while the flow and pressure are calculated on the basis of steady state conditions. The implicit finite element method is applied to simulate the transient temperature behaviour in the network. Pipe network heat losses, pressure drop in the network and return temperature to the plant...... are calculated in the developed model. The model will serve eventually as a basis to find out the optimal supply temperature in an existing DHN in later work. The modelling results are used as decision support for existing DHN; proposing possible modifications to operate at optimal supply temperature....

Alternative approach to automated management of load flow in engineering networks considering functional reliability

Directory of Open Access Journals (Sweden)

Ирина Александровна Гавриленко

2016-02-01

Full Text Available The approach to automated management of load flow in engineering networks considering functional reliability was proposed in the article. The improvement of the concept of operational and strategic management of load flow in engineering networks was considered. The verbal statement of the problem for thesis research is defined, namely, the problem of development of information technology for exact calculation of the functional reliability of the network, or the risk of short delivery of purpose-oriented product for consumers

Achieving Fair Throughput among TCP Flows in Multi-Hop Wireless Mesh Networks

Science.gov (United States)

Hou, Ting-Chao; Hsu, Chih-Wei

Previous research shows that the IEEE 802.11 DCF channel contention mechanism is not capable of providing throughput fairness among nodes in different locations of the wireless mesh network. The node nearest the gateway will always strive for the chance to transmit data, causing fewer transmission opportunities for the nodes farther from the gateway, resulting in starvation. Prior studies modify the DCF mechanism to address the fairness problem. This paper focuses on the fairness study when TCP flows are carried over wireless mesh networks. By not modifying lower layer protocols, the current work identifies TCP parameters that impact throughput fairness and proposes adjusting those parameters to reduce frame collisions and improve throughput fairness. With the aid of mathematical formulation and ns2 simulations, this study finds that frame transmission from each node can be effectively controlled by properly controlling the delayed ACK timer and using a suitable advertised window. The proposed method reduces frame collisions and greatly improves TCP throughput fairness.

Direction of information flow in large-scale resting-state networks is frequency-dependent.

Science.gov (United States)

Hillebrand, Arjan; Tewarie, Prejaas; van Dellen, Edwin; Yu, Meichen; Carbo, Ellen W S; Douw, Linda; Gouw, Alida A; van Straaten, Elisabeth C W; Stam, Cornelis J

2016-04-05

Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-to-posterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequency-dependent reentry loops that are dominated by flow from parieto-occipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.

Flow-Based Network Management: A Report from the IRTF NMRG Workshop

NARCIS (Netherlands)

de Oliveira Schmidt, R.; Sadre, R.; Hendriks, Luuk

This is the report on the Workshop on Flow-Based Network Management, held within the 37th IRTF NMRG meeting, during IETF 93, on 24th July 2015, in Prague, Czech Republic. Following the tradition of the IRTF NMRG, the workshop focused on technologies, developments, and challenges of using flow-level

How to Identify Success Among Networks That Promote Active Living.

Science.gov (United States)

Litt, Jill; Varda, Danielle; Reed, Hannah; Retrum, Jessica; Tabak, Rachel; Gustat, Jeanette; O'Hara Tompkins, Nancy

2015-11-01

We evaluated organization- and network-level factors that influence organizations' perceived success. This is important for managing interorganizational networks, which can mobilize communities to address complex health issues such as physical activity, and for achieving change. In 2011, we used structured interview and network survey data from 22 states in the United States to estimate multilevel random-intercept models to understand organization- and network-level factors that explain perceived network success. A total of 53 of 59 "whole networks" met the criteria for inclusion in the analysis (89.8%). Coordinators identified 559 organizations, with 3 to 12 organizations from each network taking the online survey (response rate = 69.7%; range = 33%-100%). Occupying a leadership position (P Organizations' perceptions of success can influence decisions about continuing involvement and investment in networks designed to promote environment and policy change for active living. Understanding these factors can help leaders manage complex networks that involve diverse memberships, varied interests, and competing community-level priorities.

Spray flow-network flow transition of binary Lennard-Jones particle system

KAUST Repository

Inaoka, Hajime

2010-07-01

We simulate gas-liquid flows caused by rapid depressurization using a molecular dynamics model. The model consists of two types of Lennard-Jones particles, which we call liquid particles and gas particles. These two types of particles are distinguished by their mass and strength of interaction: a liquid particle has heavier mass and stronger interaction than a gas particle. By simulations with various initial number densities of these particles, we found that there is a transition from a spray flow to a network flow with an increase of the number density of the liquid particles. At the transition point, the size of the liquid droplets follows a power-law distribution, while it follows an exponential distribution when the number density of the liquid particles is lower than the critical value. The comparison between the transition of the model and that of models of percolation is discussed. The change of the average droplet size with the initial number density of the gas particles is also presented. © 2010 Elsevier B.V. All rights reserved.

Spray flow-network flow transition of binary Lennard-Jones particle system

KAUST Repository

Inaoka, Hajime; Yukawa, Satoshi; Ito, Nobuyasu

2010-01-01

We simulate gas-liquid flows caused by rapid depressurization using a molecular dynamics model. The model consists of two types of Lennard-Jones particles, which we call liquid particles and gas particles. These two types of particles are distinguished by their mass and strength of interaction: a liquid particle has heavier mass and stronger interaction than a gas particle. By simulations with various initial number densities of these particles, we found that there is a transition from a spray flow to a network flow with an increase of the number density of the liquid particles. At the transition point, the size of the liquid droplets follows a power-law distribution, while it follows an exponential distribution when the number density of the liquid particles is lower than the critical value. The comparison between the transition of the model and that of models of percolation is discussed. The change of the average droplet size with the initial number density of the gas particles is also presented. © 2010 Elsevier B.V. All rights reserved.

A probabilistic approach to quantifying spatial patterns of flow regimes and network-scale connectivity

Science.gov (United States)

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

A Network Flow-based Analysis of Cognitive Reserve in Normal Ageing and Alzheimer's Disease.

Science.gov (United States)

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.

The time constrained multi-commodity network flow problem and its application to liner shipping network design

DEFF Research Database (Denmark)

Karsten, Christian Vad; Pisinger, David; Røpke, Stefan

2015-01-01

-commodity network flow problem with transit time constraints which puts limits on the duration of the transit of the commodities through the network. It is shown that for the particular application it does not increase the solution time to include the transit time constraints and that including the transit time...... is essential to offer customers a competitive product. © 2015 Elsevier Ltd. All rights reserved....

The application of neural networks to flow regime identification

International Nuclear Information System (INIS)

Embrechts, M.; Yapo, T.C.; Lahey, R.T. Jr.

1993-01-01

This paper deals with the application of a Kohonen map for the identification of two-phase flow regimes where a mixture of gas and fluid flows through a horizontal tube. Depending on the relative flow velocities of the gas and the liquid phase, four distinct flow regimes can be identified: Wavy flow, plug flow, slug flow and annular flow. A schematic of these flow regimes is presented. The objective identification of two-phase flow regimes constitutes an important and challenging problem for the design of safe and reliable nuclear power plants. Previous attempts to classify these flow regimes are reviewed by Franca and Lahey. The authors describe how a Kohonen map can be applied to distinguish between flow regimes based on the Fourier power spectra and wavelet transforms of pressure drop fluctuations. The Fourier power spectra allowed the Kohonen map to identify the flow regimes successfully. In contrast, the Kohonen maps based on a wavelet transform could only distinguish between wavy and annular flows. An analysis of typical two-phase pressure drop data for an air/water mixture in a horizontal pipe is presented. Use of the wavelet transform and the Kohonen feature map are discussed

Computer-Aided Analysis of Flow in Water Pipe Networks after a Seismic Event

Directory of Open Access Journals (Sweden)

Won-Hee Kang

2017-01-01

Full Text Available This paper proposes a framework for a reliability-based flow analysis for a water pipe network after an earthquake. For the first part of the framework, we propose to use a modeling procedure for multiple leaks and breaks in the water pipe segments of a network that has been damaged by an earthquake. For the second part, we propose an efficient system-level probabilistic flow analysis process that integrates the matrix-based system reliability (MSR formulation and the branch-and-bound method. This process probabilistically predicts flow quantities by considering system-level damage scenarios consisting of combinations of leaks and breaks in network pipes and significantly reduces the computational cost by sequentially prioritizing the system states according to their likelihoods and by using the branch-and-bound method to select their partial sets. The proposed framework is illustrated and demonstrated by examining two example water pipe networks that have been subjected to a seismic event. These two examples consist of 11 and 20 pipe segments, respectively, and are computationally modeled considering their available topological, material, and mechanical properties. Considering different earthquake scenarios and the resulting multiple leaks and breaks in the water pipe segments, the water flows in the segments are estimated in a computationally efficient manner.

Numerical Simulation of Flow and Suspended Sediment Transport in the Distributary Channel Networks

Directory of Open Access Journals (Sweden)

Wei Zhang

2014-01-01

Full Text Available Flow and suspended sediment transport in distributary channel networks play an important role in the evolution of deltas and estuaries, as well as the coastal environment. In this study, a 1D flow and suspended sediment transport model is presented to simulate the hydrodynamics and suspended sediment transport in the distributary channel networks. The governing equations for river flow are the Saint-Venant equations and for suspended sediment transport are the nonequilibrium transport equations. The procedure of solving the governing equations is firstly to get the matrix form of the water level and suspended sediment concentration at all connected junctions by utilizing the transformation of the governing equations of the single channel. Secondly, the water level and suspended sediment concentration at all junctions can be obtained by solving these irregular spare matrix equations. Finally, the water level, discharge, and suspended sediment concentration at each river section can be calculated. The presented 1D flow and suspended sediment transport model has been applied to the Pearl River networks and can reproduce water levels, discharges, and suspended sediment concentration with good accuracy, indicating this that model can be used to simulate the hydrodynamics and suspended sediment concentration in the distributary channel networks.

Multivariate weighted recurrence network inference for uncovering oil-water transitional flow behavior in a vertical pipe.

Science.gov (United States)

Gao, Zhong-Ke; Yang, Yu-Xuan; Cai, Qing; Zhang, Shan-Shan; Jin, Ning-De

2016-06-01

Exploring the dynamical behaviors of high water cut and low velocity oil-water flows remains a contemporary and challenging problem of significant importance. This challenge stimulates us to design a high-speed cycle motivation conductance sensor to capture spatial local flow information. We systematically carry out experiments and acquire the multi-channel measurements from different oil-water flow patterns. Then we develop a novel multivariate weighted recurrence network for uncovering the flow behaviors from multi-channel measurements. In particular, we exploit graph energy and weighted clustering coefficient in combination with multivariate time-frequency analysis to characterize the derived complex networks. The results indicate that the network measures are very sensitive to the flow transitions and allow uncovering local dynamical behaviors associated with water cut and flow velocity. These properties render our method particularly useful for quantitatively characterizing dynamical behaviors governing the transition and evolution of different oil-water flow patterns.

The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks.

Science.gov (United States)

Gu, Weiwei; Gong, Li; Lou, Xiaodan; Zhang, Jiang

2017-10-13

Network embedding which encodes all vertices in a network as a set of numerical vectors in accordance with it's local and global structures, has drawn widespread attention. Network embedding not only learns significant features of a network, such as the clustering and linking prediction but also learns the latent vector representation of the nodes which provides theoretical support for a variety of applications, such as visualization, link prediction, node classification, and recommendation. As the latest progress of the research, several algorithms based on random walks have been devised. Although those algorithms have drawn much attention for their high scores in learning efficiency and accuracy, there is still a lack of theoretical explanation, and the transparency of those algorithms has been doubted. Here, we propose an approach based on the open-flow network model to reveal the underlying flow structure and its hidden metric space of different random walk strategies on networks. We show that the essence of embedding based on random walks is the latent metric structure defined on the open-flow network. This not only deepens our understanding of random- walk-based embedding algorithms but also helps in finding new potential applications in network embedding.

Motif distributions in phase-space networks for characterizing experimental two-phase flow patterns with chaotic features.

Science.gov (United States)

Gao, Zhong-Ke; Jin, Ning-De; Wang, Wen-Xu; Lai, Ying-Cheng

2010-07-01

The dynamics of two-phase flows have been a challenging problem in nonlinear dynamics and fluid mechanics. We propose a method to characterize and distinguish patterns from inclined water-oil flow experiments based on the concept of network motifs that have found great usage in network science and systems biology. In particular, we construct from measured time series phase-space complex networks and then calculate the distribution of a set of distinct network motifs. To gain insight, we first test the approach using time series from classical chaotic systems and find a universal feature: motif distributions from different chaotic systems are generally highly heterogeneous. Our main finding is that the distributions from experimental two-phase flows tend to be heterogeneous as well, suggesting the underlying chaotic nature of the flow patterns. Calculation of the maximal Lyapunov exponent provides further support for this. Motif distributions can thus be a feasible tool to understand the dynamics of realistic two-phase flow patterns.

Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

Science.gov (United States)

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2016-10-06

Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison. In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment. Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .

Network flow model of force transmission in unbonded and bonded granular media.

Science.gov (United States)

Tordesillas, Antoinette; Tobin, Steven T; Cil, Mehmet; Alshibli, Khalid; Behringer, Robert P

2015-06-01

An established aspect of force transmission in quasistatic deformation of granular media is the existence of a dual network of strongly versus weakly loaded particles. Despite significant interest, the regulation of strong and weak forces through the contact network remains poorly understood. We examine this aspect of force transmission using data on microstructural fabric from: (I) three-dimensional discrete element models of grain agglomerates of bonded subspheres constructed from in situ synchrotron microtomography images of silica sand grains under unconfined compression and (II) two-dimensional assemblies of unbonded photoelastic circular disks submitted to biaxial compression under constant volume. We model force transmission as a network flow and solve the maximum flow-minimum cost (MFMC) problem, the solution to which yields a percolating subnetwork of contacts that transmits the "maximum flow" (i.e., the highest units of force) at "least cost" (i.e., the dissipated energy from such transmission). We find the MFMC describes a two-tier hierarchical architecture. At the local level, it encapsulates intraconnections between particles in individual force chains and in their conjoined 3-cycles, with the most common configuration having at least one force chain contact experiencing frustrated rotation. At the global level, the MFMC encapsulates interconnections between force chains. The MFMC can be used to predict most of the force chain particles without need for any information on contact forces, thereby suggesting the network flow framework may have potential broad utility in the modeling of force transmission in unbonded and bonded granular media.

Direct numerical simulation of cellular-scale blood flow in microvascular networks

Science.gov (United States)

Balogh, Peter; Bagchi, Prosenjit

2017-11-01

A direct numerical simulation method is developed to study cellular-scale blood flow in physiologically realistic microvascular networks that are constructed in silico following published in vivo images and data, and are comprised of bifurcating, merging, and winding vessels. The model resolves large deformation of individual red blood cells (RBC) flowing in such complex networks. The vascular walls and deformable interfaces of the RBCs are modeled using the immersed-boundary methods. Time-averaged hemodynamic quantities obtained from the simulations agree quite well with published in vivo data. Our simulations reveal that in several vessels the flow rates and pressure drops could be negatively correlated. The flow resistance and hematocrit are also found to be negatively correlated in some vessels. These observations suggest a deviation from the classical Poiseuille's law in such vessels. The cells are observed to frequently jam at vascular bifurcations resulting in reductions in hematocrit and flow rate in the daughter and mother vessels. We find that RBC jamming results in several orders of magnitude increase in hemodynamic resistance, and thus provides an additional mechanism of increased in vivo blood viscosity as compared to that determined in vitro. Funded by NSF CBET 1604308.

Neural network analysis of head-flow curves in deep well pumps

International Nuclear Information System (INIS)

Goelcue, Mustafa

2006-01-01

In impellers with splitter blades, the difficulty in calculation of the flow area of the impeller is because of the unknown flow rate occurring in the two separate areas when the splitter blades are added. Experimental studies were made to investigate the effects of splitter blade length on deep well pump performance for different numbers of blades. Head-flow curves of deep well pump impellers with splitter blades were investigated using artificial neural networks (ANNs). Gradient descent (GD), Gradient descent with momentum (GDM) and Levenberg-Marquardt (LM) learning algorithms were used in the networks. Experimental studies were completed to obtain training and test data. Blade number (z), non-dimensional splitter blade length (L-bar ) and flow rate (Q) were used as the input layer, while the output is head (H m ). For the testing data, the root mean squared error (RMSE), fraction of variance (R 2 ) and mean absolute percentage error (MAPE) were found to be 0.1285, 0.9999 and 1.6821%, respectively. With these results, we believe that the ANN can be used for prediction of head-flow curves as an appropriate method in deep well pump impellers with splitter blades.

Identifying influential spreaders in complex networks through local effective spreading paths

Science.gov (United States)

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

2017-05-01

How to effectively identify a set of influential spreaders in complex networks is of great theoretical and practical value, which can help to inhibit the rapid spread of epidemics, promote the sales of products by word-of-mouth advertising, and so on. A naive strategy is to select the top ranked nodes as identified by some centrality indices, and other strategies are mainly based on greedy methods and heuristic methods. However, most of those approaches did not concern the connections between nodes. Usually, the distances between the selected spreaders are very close, leading to a serious overlapping of their influence. As a consequence, the global influence of the spreaders in networks will be greatly reduced, which largely restricts the performance of those methods. In this paper, a simple and efficient method is proposed to identify a set of discrete yet influential spreaders. By analyzing the spreading paths in the network, we present the concept of effective spreading paths and measure the influence of nodes via expectation calculation. The numerical analysis in undirected and directed networks all show that our proposed method outperforms many other centrality-based and heuristic benchmarks, especially in large-scale networks. Besides, experimental results on different spreading models and parameters demonstrates the stability and wide applicability of our method.

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

Science.gov (United States)

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.

Network analysis applications in hydrology

Science.gov (United States)

Price, Katie

2017-04-01

Applied network theory has seen pronounced expansion in recent years, in fields such as epidemiology, computer science, and sociology. Concurrent development of analytical methods and frameworks has increased possibilities and tools available to researchers seeking to apply network theory to a variety of problems. While water and nutrient fluxes through stream systems clearly demonstrate a directional network structure, the hydrological applications of network theory remain under­explored. This presentation covers a review of network applications in hydrology, followed by an overview of promising network analytical tools that potentially offer new insights into conceptual modeling of hydrologic systems, identifying behavioral transition zones in stream networks and thresholds of dynamical system response. Network applications were tested along an urbanization gradient in Atlanta, Georgia, USA. Peachtree Creek and Proctor Creek. Peachtree Creek contains a nest of five long­term USGS streamflow and water quality gages, allowing network application of long­term flow statistics. The watershed spans a range of suburban and heavily urbanized conditions. Summary flow statistics and water quality metrics were analyzed using a suite of network analysis techniques, to test the conceptual modeling and predictive potential of the methodologies. Storm events and low flow dynamics during Summer 2016 were analyzed using multiple network approaches, with an emphasis on tomogravity methods. Results indicate that network theory approaches offer novel perspectives for understanding long­ term and event­based hydrological data. Key future directions for network applications include 1) optimizing data collection, 2) identifying "hotspots" of contaminant and overland flow influx to stream systems, 3) defining process domains, and 4) analyzing dynamic connectivity of various system components, including groundwater­surface water interactions.

Flow network QSAR for the prediction of physicochemical properties by mapping an electrical resistance network onto a chemical reaction poset.

Science.gov (United States)

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

Identifying the default mode network structure using dynamic causal modeling on resting-state functional magnetic resonance imaging.

Science.gov (United States)

Di, Xin; Biswal, Bharat B

2014-02-01

The default mode network is part of the brain structure that shows higher neural activity and energy consumption when one is at rest. The key regions in the default mode network are highly interconnected as conveyed by both the white matter fiber tracing and the synchrony of resting-state functional magnetic resonance imaging signals. However, the causal information flow within the default mode network is still poorly understood. The current study used the dynamic causal modeling on a resting-state fMRI data set to identify the network structure underlying the default mode network. The endogenous brain fluctuations were explicitly modeled by Fourier series at the low frequency band of 0.01-0.08Hz, and those Fourier series were set as driving inputs of the DCM models. Model comparison procedures favored a model wherein the MPFC sends information to the PCC and the bilateral inferior parietal lobule sends information to both the PCC and MPFC. Further analyses provide evidence that the endogenous connectivity might be higher in the right hemisphere than in the left hemisphere. These data provided insight into the functions of each node in the DMN, and also validate the usage of DCM on resting-state fMRI data. © 2013.

Application of neural networks to validation of feedwater flow rate in a nuclear power plant

International Nuclear Information System (INIS)

Khadem, M.; Ipakchi, A.; Alexandro, F.J.; Colley, R.W.

1993-01-01

Feedwater flow rate measurement in nuclear power plants requires periodic calibration. This is due to the fact that the venturi surface condition of the feedwater flow rate sensor changes because of a chemical reaction between the surface coating material and the feedwater. Fouling of the venturi surface, due to this chemical reaction and the deposits of foreign materials, has been observed shortly after a clean venturi is put in operation. A fouled venturi causes an incorrect measurement of feedwater flow rate, which in turn results in an inaccurate calculation of the generated power. This paper presents two methods for verifying incipient and continuing fouling of the venturi of the feedwater flow rate sensors. Both methods are based on the use of a set of dissimilar process variables dynamically related to the feedwater flow rate variable. The first method uses a neural network to generate estimates of the feedwater flow rate readings. Agreement, within a given tolerance, of the feedwater flow rate instrument reading, and the corresponding neural network output establishes that the feedwater flow rate instrument is operating properly. The second method is similar to the first method except that the neural network predicts the core power which is calculated from measurements on the primary loop, rather than the feedwater flow rates. This core power is referred to the primary core power in this paper. A comparison of the power calculated from the feedwater flow measurements in the secondary loop, with the calculated and neural network predicted primary core power provides information from which it can be determined whether fouling is beginning to occur. The two methods were tested using data from the feedwater flow meters in the two feedwater flow loops of the TMI-1 nuclear power plant

Scientometric methods for identifying emerging technologies

Science.gov (United States)

Abercrombie, Robert K; Schlicher, Bob G; Sheldon, Frederick T

2015-11-03

Provided is a method of generating a scientometric model that tracks the emergence of an identified technology from initial discovery (via original scientific and conference literature), through critical discoveries (via original scientific, conference literature and patents), transitioning through Technology Readiness Levels (TRLs) and ultimately on to commercial application. During the period of innovation and technology transfer, the impact of scholarly works, patents and on-line web news sources are identified. As trends develop, currency of citations, collaboration indicators, and on-line news patterns are identified. The combinations of four distinct and separate searchable on-line networked sources (i.e., scholarly publications and citation, worldwide patents, news archives, and on-line mapping networks) are assembled to become one collective network (a dataset for analysis of relations). This established network becomes the basis from which to quickly analyze the temporal flow of activity (searchable events) for the example subject domain.

Integrated Power Flow and Short Circuit Calculation Method for Distribution Network with Inverter Based Distributed Generation

Directory of Open Access Journals (Sweden)

Shan Yang

2016-01-01

Full Text Available Power flow calculation and short circuit calculation are the basis of theoretical research for distribution network with inverter based distributed generation. The similarity of equivalent model for inverter based distributed generation during normal and fault conditions of distribution network and the differences between power flow and short circuit calculation are analyzed in this paper. Then an integrated power flow and short circuit calculation method for distribution network with inverter based distributed generation is proposed. The proposed method let the inverter based distributed generation be equivalent to Iθ bus, which makes it suitable to calculate the power flow of distribution network with a current limited inverter based distributed generation. And the low voltage ride through capability of inverter based distributed generation can be considered as well in this paper. Finally, some tests of power flow and short circuit current calculation are performed on a 33-bus distribution network. The calculated results from the proposed method in this paper are contrasted with those by the traditional method and the simulation method, whose results have verified the effectiveness of the integrated method suggested in this paper.

Identifying Typhoon Tracks based on Event Synchronization derived Spatially Embedded Climate Networks

Science.gov (United States)

Ozturk, Ugur; Marwan, Norbert; Kurths, Jürgen

2017-04-01

Complex networks are commonly used for investigating spatiotemporal dynamics of complex systems, e.g. extreme rainfall. Especially directed networks are very effective tools in identifying climatic patterns on spatially embedded networks. They can capture the network flux, so as the principal dynamics of spreading significant phenomena. Network measures, such as network divergence, bare the source-receptor relation of the directed networks. However, it is still a challenge how to catch fast evolving atmospheric events, i.e. typhoons. In this study, we propose a new technique, namely Radial Ranks, to detect the general pattern of typhoons forward direction based on the strength parameter of the event synchronization over Japan. We suggest to subset a circular zone of high correlation around the selected grid based on the strength parameter. Radial sums of the strength parameter along vectors within this zone, radial ranks are measured for potential directions, which allows us to trace the network flux over long distances. We employed also the delay parameter of event synchronization to identify and separate the frontal storms' and typhoons' individual behaviors.

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

Science.gov (United States)

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

2017-12-01

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

A network flow model for load balancing in circuit-switched multicomputers

Science.gov (United States)

Bokhari, Shahid H.

1990-01-01

In multicomputers that utilize circuit switching or wormhole routing, communication overhead depends largely on link contention - the variation due to distance between nodes is negligible. This has a major impact on the load balancing problem. In this case, there are some nodes with excess load (sources) and others with deficit load (sinks) and it is required to find a matching of sources to sinks that avoids contention. The problem is made complex by the hardwired routing on currently available machines: the user can control only which nodes communicate but not how the messages are routed. Network flow models of message flow in the mesh and the hypercube were developed to solve this problem. The crucial property of these models is the correspondence between minimum cost flows and correctly routed messages. To solve a given load balancing problem, a minimum cost flow algorithm is applied to the network. This permits one to determine efficiently a maximum contention free matching of sources to sinks which, in turn, tells one how much of the given imbalance can be eliminated without contention.

A distributed predictive control approach for periodic flow-based networks: application to drinking water systems

Science.gov (United States)

Grosso, Juan M.; Ocampo-Martinez, Carlos; Puig, Vicenç

2017-10-01

This paper proposes a distributed model predictive control approach designed to work in a cooperative manner for controlling flow-based networks showing periodic behaviours. Under this distributed approach, local controllers cooperate in order to enhance the performance of the whole flow network avoiding the use of a coordination layer. Alternatively, controllers use both the monolithic model of the network and the given global cost function to optimise the control inputs of the local controllers but taking into account the effect of their decisions over the remainder subsystems conforming the entire network. In this sense, a global (all-to-all) communication strategy is considered. Although the Pareto optimality cannot be reached due to the existence of non-sparse coupling constraints, the asymptotic convergence to a Nash equilibrium is guaranteed. The resultant strategy is tested and its effectiveness is shown when applied to a large-scale complex flow-based network: the Barcelona drinking water supply system.

Characterization of fracture networks for fluid flow analysis

International Nuclear Information System (INIS)

Long, J.C.S.; Billaux, D.; Hestir, K.; Majer, E.L.; Peterson, J.; Karasaki, K.; Nihei, K.; Gentier, S.; Cox, L.

1989-06-01

The analysis of fluid flow through fractured rocks is difficult because the only way to assign hydraulic parameters to fractures is to perform hydraulic tests. However, the interpretation of such tests, or ''inversion'' of the data, requires at least that we know the geometric pattern formed by the fractures. Combining a statistical approach with geophysical data may be extremely helpful in defining the fracture geometry. Cross-hole geophysics, either seismic or radar, can provide tomograms which are pixel maps of the velocity or attenuation anomalies in the rock. These anomalies are often due to fracture zones. Therefore, tomograms can be used to identify fracture zones and provide information about the structure within the fracture zones. This structural information can be used as the basis for simulating the degree of fracturing within the zones. Well tests can then be used to further refine the model. Because the fracture network is only partially connected, the resulting geometry of the flow paths may have fractal properties. We are studying the behavior of well tests under such geometry. Through understanding of this behavior, it may be possible to use inverse techniques to refine the a priori assignment of fractures and their conductances such that we obtain the best fit to a series of well test results simultaneously. The methodology described here is under development and currently being applied to several field sites. 4 refs., 14 figs

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

Science.gov (United States)

Guo, Wenzhang; Wang, Hao; Wu, Zhengping

2018-03-01

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

Control of Networked Traffic Flow Distribution - A Stochastic Distribution System Perspective

Energy Technology Data Exchange (ETDEWEB)

Wang, Hong [Pacific Northwest National Laboratory (PNNL); Aziz, H M Abdul [ORNL; Young, Stan [National Renewable Energy Laboratory (NREL); Patil, Sagar [Pacific Northwest National Laboratory (PNNL)

2017-10-01

Networked traffic flow is a common scenario for urban transportation, where the distribution of vehicle queues either at controlled intersections or highway segments reflect the smoothness of the traffic flow in the network. At signalized intersections, the traffic queues are controlled by traffic signal control settings and effective traffic lights control would realize both smooth traffic flow and minimize fuel consumption. Funded by the Energy Efficient Mobility Systems (EEMS) program of the Vehicle Technologies Office of the US Department of Energy, we performed a preliminary investigation on the modelling and control framework in context of urban network of signalized intersections. In specific, we developed a recursive input-output traffic queueing models. The queue formation can be modeled as a stochastic process where the number of vehicles entering each intersection is a random number. Further, we proposed a preliminary B-Spline stochastic model for a one-way single-lane corridor traffic system based on theory of stochastic distribution control.. It has been shown that the developed stochastic model would provide the optimal probability density function (PDF) of the traffic queueing length as a dynamic function of the traffic signal setting parameters. Based upon such a stochastic distribution model, we have proposed a preliminary closed loop framework on stochastic distribution control for the traffic queueing system to make the traffic queueing length PDF follow a target PDF that potentially realizes the smooth traffic flow distribution in a concerned corridor.

Identifying changes in the support networks of end-of-life carers using social network analysis.

Science.gov (United States)

Leonard, Rosemary; Horsfall, Debbie; Noonan, Kerrie

2015-06-01

End-of-life caring is often associated with reduced social networks for both the dying person and for the carer. However, those adopting a community participation and development approach, see the potential for the expansion and strengthening of networks. This paper uses Knox, Savage and Harvey's definitions of three generations social network analysis to analyse the caring networks of people with a terminal illness who are being cared for at home and identifies changes in these caring networks that occurred over the period of caring. Participatory network mapping of initial and current networks was used in nine focus groups. The analysis used key concepts from social network analysis (size, density, transitivity, betweenness and local clustering) together with qualitative analyses of the group's reflections on the maps. The results showed an increase in the size of the networks and that ties between the original members of the network strengthened. The qualitative data revealed the importance between core and peripheral network members and the diverse contributions of the network members. The research supports the value of third generation social network analysis and the potential for end-of-life caring to build social capital. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Volume fraction prediction in biphasic flow using nuclear technique and artificial neural network

International Nuclear Information System (INIS)

Salgado, Cesar M.; Brandao, Luis E.B.

2015-01-01

The volume fraction is one of the most important parameters used to characterize air-liquid two-phase flows. It is a physical value to determine other parameters, such as the phase's densities and to determine the flow rate of each phase. These parameters are important to predict the flow pattern and to determine a mathematical model for the system. To study, for example, heat transfer and pressure drop. This work presents a methodology for volume fractions prediction in water-gas stratified flow regime using the nuclear technique and artificial intelligence. The volume fractions calculate in biphasic flow systems is complex and the analysis by means of analytical equations becomes very difficult. The approach is based on gamma-ray pulse height distributions pattern recognition by means of the artificial neural network. The detection system uses appropriate broad beam geometry, comprised of a ( 137 Cs) energy gamma-ray source and a NaI(Tl) scintillation detector in order measure transmitted beam whose the counts rates are influenced by the phases composition. These distributions are directly used by the network without any parameterization of the measured signal. The ideal and static theoretical models for stratified regime have been developed using MCNP-X code, which was used to provide training, test and validation data for the network. The detector also was modeled with this code and the results were compared to experimental photopeak efficiency measurements of radiation sources. The proposed network could obtain with satisfactory prediction of the volume fraction in water-gas system, demonstrating to be a promising approach for this purpose. (author)

Volume fraction prediction in biphasic flow using nuclear technique and artificial neural network

Energy Technology Data Exchange (ETDEWEB)

Salgado, Cesar M.; Brandao, Luis E.B., E-mail: otero@ien.gov.br, E-mail: brandao@ien.gov.br [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)

2015-07-01

The volume fraction is one of the most important parameters used to characterize air-liquid two-phase flows. It is a physical value to determine other parameters, such as the phase's densities and to determine the flow rate of each phase. These parameters are important to predict the flow pattern and to determine a mathematical model for the system. To study, for example, heat transfer and pressure drop. This work presents a methodology for volume fractions prediction in water-gas stratified flow regime using the nuclear technique and artificial intelligence. The volume fractions calculate in biphasic flow systems is complex and the analysis by means of analytical equations becomes very difficult. The approach is based on gamma-ray pulse height distributions pattern recognition by means of the artificial neural network. The detection system uses appropriate broad beam geometry, comprised of a ({sup 137}Cs) energy gamma-ray source and a NaI(Tl) scintillation detector in order measure transmitted beam whose the counts rates are influenced by the phases composition. These distributions are directly used by the network without any parameterization of the measured signal. The ideal and static theoretical models for stratified regime have been developed using MCNP-X code, which was used to provide training, test and validation data for the network. The detector also was modeled with this code and the results were compared to experimental photopeak efficiency measurements of radiation sources. The proposed network could obtain with satisfactory prediction of the volume fraction in water-gas system, demonstrating to be a promising approach for this purpose. (author)

The Average Network Flow Problem: Shortest Path and Minimum Cost Flow Formulations, Algorithms, Heuristics, and Complexity

Science.gov (United States)

2012-09-13

46, 1989. [75] S. Melkote and M.S. Daskin . An integrated model of facility location and transportation network design. Transportation Research Part A ... a work of the U.S. Government and is not subject to copyright protection in the United States. AFIT/DS/ENS/12-09 THE AVERAGE NETWORK FLOW PROBLEM...focused thinking (VFT) are used sparingly, as is the case across the entirety of the supply chain literature. We provide a VFT tutorial for supply chain

Cilia driven flow networks in the brain

Science.gov (United States)

Wang, Yong; Faubel, Regina; Westendorf, Chrsitian; Eichele, Gregor; Bodenschatz, Eberhard

Neurons exchange soluble substances via the cerebrospinal fluid (CSF) that fills the ventricular system. The walls of the ventricular cavities are covered with motile cilia that constantly beat and thereby induce a directional flow. We recently discovered that cilia in the third ventricle generate a complex flow pattern leading to partitioning of the ventricular volume and site-directed transport paths along the walls. Transient and daily recurrent alterations in the cilia beating direction lead to changes in the flow pattern. This has consequences for delivery of CSF components along the near wall flow. The contribution of this cilia-induced flow to overall CSF flow remains to be investigated. The state-of-art lattice Boltzmann method is adapted for studying the CFS flow. The 3D geometry of the third ventricle at high resolution was reconstructed. Simulation of CSF flow without cilia in this geometry confirmed that the previous idea about unidirectional flow does not explain how different components of CSF can be delivered to their various target sites. We study the contribution of the cilia-induced flow pattern to overall CSF flow and identify target areas for site-specific delivery of CSF-constituents with respect to the temporal changes.

Simulation model for centrifugal pump in flow networks based on internal characteristics

International Nuclear Information System (INIS)

Sun, Ji-Lin; Xue, Ruo-Jun; Peng, Min-Jun

2018-01-01

For the simulation of centrifugal pump in flow network system, in general three approaches can be used, the fitting model, the numerical method and the internal characteristics model. The fitting model is simple and rapid thus widely used. The numerical method can provide more detailed information in comparison with the fitting model, but increases implementation complexity and computational cost. In real-time simulations of flow networks, to simulate the condition out of the rated condition, especially for the volume flow rate, which the accuracy of fitting model is incredible, a new method for simulating centrifugal pumps was proposed in this research. The method based on the theory head and hydraulic loss in centrifugal pumps, and cavitation is also to be considered. The simulation results are verified with experimental benchmark data from an actual pump. The comparison confirms that the proposed method could fit the flow-head curves well, and the responses of main parameters in dynamic-state operations are consistent with theoretical analyses.

Decompositions of injection patterns for nodal flow allocation in renewable electricity networks

Science.gov (United States)

Schäfer, Mirko; Tranberg, Bo; Hempel, Sabrina; Schramm, Stefan; Greiner, Martin

2017-08-01

The large-scale integration of fluctuating renewable power generation represents a challenge to the technical and economical design of a sustainable future electricity system. In this context, the increasing significance of long-range power transmission calls for innovative methods to understand the emerging complex flow patterns and to integrate price signals about the respective infrastructure needs into the energy market design. We introduce a decomposition method of injection patterns. Contrary to standard flow tracing approaches, it provides nodal allocations of link flows and costs in electricity networks by decomposing the network injection pattern into market-inspired elementary import/export building blocks. We apply the new approach to a simplified data-driven model of a European electricity grid with a high share of renewable wind and solar power generation.

Geometry of power flows and convex-relaxed power flows in distribution networks with high penetration of renewables

DEFF Research Database (Denmark)

Huang, Shaojun; Wu, Qiuwei; Zhao, Haoran

2016-01-01

Renewable energies are increasingly integrated in electric distribution networks and will cause severe overvoltage issues. Smart grid technologies make it possible to use coordinated control to mitigate the overvoltage issues and the optimal power flow (OPF) method is proven to be efficient...... in the applications such as curtailment management and reactive power control. Nonconvex nature of the OPF makes it difficult to solve and convex relaxation is a promising method to solve the OPF very efficiently. This paper investigates the geometry of the power flows and the convex-relaxed power flows when high...

Identifying Cancer Subtypes from miRNA-TF-mRNA Regulatory Networks and Expression Data.

Directory of Open Access Journals (Sweden)

Taosheng Xu

Full Text Available Identifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms. Therefore, there are great opportunities for developing methods that can utilise network information in identifying cancer subtypes.In this paper, we propose a method, weighted similarity network fusion (WSNF, to utilise the information in the complex miRNA-TF-mRNA regulatory network in identifying cancer subtypes. We firstly build the regulatory network where the nodes represent the features, i.e. the microRNAs (miRNAs, transcription factors (TFs and messenger RNAs (mRNAs and the edges indicate the interactions between the features. The interactions are retrieved from various interatomic databases. We then use the network information and the expression data of the miRNAs, TFs and mRNAs to calculate the weight of the features, representing the level of importance of the features. The feature weight is then integrated into a network fusion approach to cluster the samples (patients and thus to identify cancer subtypes. We applied our method to the TCGA breast invasive carcinoma (BRCA and glioblastoma multiforme (GBM datasets. The experimental results show that WSNF performs better than the other commonly used computational methods, and the information from miRNA-TF-mRNA regulatory network contributes to the performance improvement. The WSNF method successfully identified five breast cancer subtypes and three GBM subtypes which show significantly different survival patterns. We observed that the expression patterns of the features in some mi

Functional water flow pathways and hydraulic regulation in the xylem network of Arabidopsis.

Science.gov (United States)

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. © The Author 2014. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Incremental Optimization of Hub and Spoke Network for the Spokes’ Numbers and Flow

Directory of Open Access Journals (Sweden)

Yanfeng Wang

2015-01-01

Full Text Available Hub and spoke network problem is solved as part of a strategic decision making process which may have a profound effect on the future of enterprises. In view of the existing network structure, as time goes on, the number of spokes and the flow change because of different sources of uncertainty. Hence, the incremental optimization of hub and spoke network problem is considered in this paper, and the policy makers should adopt a series of strategies to cope with the change, such as setting up new hubs, adjusting the capacity level of original hubs, or closing some original hubs. The objective is to minimize the total cost, which includes the setup costs for the new hubs, the closure costs, and the adjustment costs for the original hubs as well as the flow routing costs. Two mixed-integer linear programming formulations are proposed and analyzed for this problem. China Deppon Logistics as an example is performed to present computational analysis, and we analyze the changes in the solutions driven by the number of spokes and the flow. The tests also allow an analysis to consider the effect of variation in parameters on network.

Queueing network model for obstetric patient flow in a hospital.

Science.gov (United States)

Takagi, Hideaki; Kanai, Yuta; Misue, Kazuo

2016-03-03

A queueing network is used to model the flow of patients in a hospital using the observed admission rate of patients and the histogram for the length of stay for patients in each ward. A complete log of orders for every movement of all patients from room to room covering two years was provided to us by the Medical Information Department of the University of Tsukuba Hospital in Japan. We focused on obstetric patients, who are generally hospitalized at random times throughout the year, and we analyzed the patient flow probabilistically. On admission, each obstetric patient is assigned to a bed in one of the two wards: one for normal delivery and the other for high-risk delivery. Then, the patient may be transferred between the two wards before discharge. We confirm Little's law of queueing theory for the patient flow in each ward. Next, we propose a new network model of M/G/ ∞ and M/M/ m queues to represent the flow of these patients, which is used to predict the probability distribution for the number of patients staying in each ward at the nightly census time. Although our model is a very rough and simplistic approximation of the real patient flow, the predicted probability distribution shows good agreement with the observed data. The proposed method can be used for capacity planning of hospital wards to predict future patient load in each ward.

Simulation of two-phase flow in horizontal fracture networks with numerical manifold method

Science.gov (United States)

Ma, G. W.; Wang, H. D.; Fan, L. F.; Wang, B.

2017-10-01

The paper presents simulation of two-phase flow in discrete fracture networks with numerical manifold method (NMM). Each phase of fluids is considered to be confined within the assumed discrete interfaces in the present method. The homogeneous model is modified to approach the mixed fluids. A new mathematical cover formation for fracture intersection is proposed to satisfy the mass conservation. NMM simulations of two-phase flow in a single fracture, intersection, and fracture network are illustrated graphically and validated by the analytical method or the finite element method. Results show that the motion status of discrete interface significantly depends on the ratio of mobility of two fluids rather than the value of the mobility. The variation of fluid velocity in each fracture segment and the driven fluid content are also influenced by the ratio of mobility. The advantages of NMM in the simulation of two-phase flow in a fracture network are demonstrated in the present study, which can be further developed for practical engineering applications.

Inter-flow and intra-flow interference mitigation routing in wireless mesh networks

OpenAIRE

Houaidia, Chiraz; Idoudi, Hanen; Van den Bossche, Adrien; Saidane, Leila; Val, Thierry

2017-01-01

In this paper, we address the problem of QoS support in an heterogeneous multi-rate wireless mesh network. We propose a new routing metric that provides information about link quality, based on PHY and MAC characteristics, including the link availability, the loss rate and the available bandwidth. This metric allows to apprehend inter-flow interferences and avoid bottleneck formation by balancing traffic load on the links. Based on the conflict graph model and calculation of maximal cliques, ...

Using Metaheuristic and Fuzzy System for the Optimization of Material Pull in a Push-Pull Flow Logistics Network

Directory of Open Access Journals (Sweden)

Afshin Mehrsai

2013-01-01

Full Text Available Alternative material flow strategies in logistics networks have crucial influences on the overall performance of the networks. Material flows can follow push, pull, or hybrid systems. To get the advantages of both push and pull flows in networks, the decoupling-point strategy is used as coordination mean. At this point, material pull has to get optimized concerning customer orders against pushed replenishment-rates. To compensate the ambiguity and uncertainty of both dynamic flows, fuzzy set theory can practically be applied. This paper has conceptual and mathematical parts to explain the performance of the push-pull flow strategy in a supply network and to give a novel solution for optimizing the pull side employing Conwip system. Alternative numbers of pallets and their lot-sizes circulating in the assembly system are getting optimized in accordance with a multi-objective problem; employing a hybrid approach out of meta-heuristics (genetic algorithm and simulated annealing and fuzzy system. Two main fuzzy sets as triangular and trapezoidal are applied in this technique for estimating ill-defined waiting times. The configured technique leads to smoother flows between push and pull sides in complex networks. A discrete-event simulation model is developed to analyze this thesis in an exemplary logistics network with dynamics.

Towards Mining Latent Client Identifiers from Network Traffic

Directory of Open Access Journals (Sweden)

Jain Sakshi

2016-04-01

Full Text Available Websites extensively track users via identifiers that uniquely map to client machines or user accounts. Although such tracking has desirable properties like enabling personalization and website analytics, it also raises serious concerns about online user privacy, and can potentially enable illicit surveillance by adversaries who broadly monitor network traffic.

Program for Analyzing Flows in a Complex Network

Science.gov (United States)

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.

Detecting Network Vulnerabilities Through Graph TheoreticalMethods

Energy Technology Data Exchange (ETDEWEB)

Cesarz, Patrick; Pomann, Gina-Maria; Torre, Luis de la; Villarosa, Greta; Flournoy, Tamara; Pinar, Ali; Meza Juan

2007-09-30

Identifying vulnerabilities in power networks is an important problem, as even a small number of vulnerable connections can cause billions of dollars in damage to a network. In this paper, we investigate a graph theoretical formulation for identifying vulnerabilities of a network. We first try to find the most critical components in a network by finding an optimal solution for each possible cutsize constraint for the relaxed version of the inhibiting bisection problem, which aims to find loosely coupled subgraphs with significant demand/supply mismatch. Then we investigate finding critical components by finding a flow assignment that minimizes the maximum among flow assignments on all edges. We also report experiments on IEEE 30, IEEE 118, and WSCC 179 benchmark power networks.

EVENT, Explosive Transients in Flow Networks

International Nuclear Information System (INIS)

Andrae, R.W.; Tang, P.K.; Bolstad, J.W.; Gregory, W.S.

1985-01-01

1 - Description of problem or function: A major concern of the chemical, nuclear, and mining industries is the occurrence of an explosion in one part of a facility and subsequent transmission of explosive effects through the ventilation system. An explosive event can cause performance degradation of the ventilation system or even structural failures. A more serious consequence is the release of hazardous materials to the environment if vital protective devices such as air filters, are damaged. EVENT was developed to investigate the effects of explosive transients through fluid-flow networks. Using the principles of fluid mechanics and thermodynamics, governing equations for the conservation of mass, energy, and momentum are formulated. These equations are applied to the complete network subdivided into two general components: nodes and branches. The nodes represent boundaries and internal junctions where the conservation of mass and energy applies. The branches can be ducts, valves, blowers, or filters. Since in EVENT the effect of the explosion, not the characteristics of the explosion itself, is of interest, the transient is simulated in the simplest possible way. A rapid addition of mass and energy to the system at certain locations is used. This representation is adequate for all of the network except the region where the explosion actually occurs. EVENT84 is a modification of EVENT which includes a new explosion chamber model subroutine based on the NOL BLAST program developed at the Naval Ordnance Laboratory, Silver Spring, Maryland. This subroutine calculates the confined explosion near-field parameters and supplies the time functions of energy and mass injection. Solid-phase or TNT-equivalent explosions (which simulate 'point source' explosions in nuclear facilities) as well as explosions in gas-air mixtures can be simulated. The four types of explosions EVENT84 simulates are TNT, hydrogen in air, acetylene in air, and tributyl phosphate (TBP or 'red oil

Imaging the Flow Networks from a Harmonic Pumping in a Karstic Field with an Inversion Algorithm

Science.gov (United States)

Fischer, P.; Lecoq, N.; Jardani, A.; Jourde, H.; Wang, X.; Chedeville, S.; Cardiff, M. A.

2017-12-01

Identifying flow paths within karstic fields remains a complex task because of the high dependency of the hydraulic responses to the relative locations between the observation boreholes and the karstic conduits and interconnected fractures that control the main flows of the hydrosystem. In this context, harmonic pumping is a new investigation tool that permits to inform on the flow paths connectivity between the boreholes. We have shown that the amplitude and phase offset values in the periodic responses of a hydrosystem to a harmonic pumping test characterize three different type of flow behavior between the measurement boreholes and the pumping borehole: a direct connectivity response (conduit flow), an indirect connectivity (conduit and short matrix flows), and an absence of connectivity (matrix). When the hydraulic responses to study are numerous and complex, the interpretation of the flow paths requires an inverse modeling. Therefore, we have recently developed a Cellular Automata-based Deterministic Inversion (CADI) approach that permits to infer the spatial distribution of field hydraulic conductivities in a structurally constrained model. This method distributes hydraulic conductivities along linear structures (i.e. karst conduits) and iteratively modifies the structural geometry of this conduits network to progressively match the observed responses to the modeled ones. As a result, this method produces a conductivity model that is composed of a discrete conduit network embedded in the background matrix, capable of producing the same flow behavior as the investigated hydrologic system. We applied the CADI approach in order to reproduce, in a model, the amplitude and phase offset values of a set of periodic responses generated from harmonic pumping tests conducted in different boreholes at the Terrieu karstic field site (Southern France). This association of oscillatory responses with the CADI method provides an interpretation of the flow paths within the

A method for identifying gas-liquid two-phase flow patterns on the basis of wavelet packet multi-scale information entropy and HMM

International Nuclear Information System (INIS)

Zhou Yunlong; Zhang Xueqing; Gao Yunpeng; Cheng Yue

2009-01-01

For studying flow regimes of gas/liquid two-phase in a vertical upward pipe, the conductance fluctuation information of four typical flow regimes was collected by a measuring the system with self-made multiple conductivity probes. Owing to the non-stationarity of conductance fluctuation signals of gas-liquid two-phase flow, a kind of' flow regime identification method based on wavelet packet Multi-scale Information Entropy and Hidden Markov Model (HMM) was put forward. First of all, the collected conductance fluctuation signals were decomposed into eight different frequency bands signals. Secondly, the wavelet packet multi-scale information entropy of different frequency bands signals were regarded as the input characteristic vectors of all states HMM which had been trained. In the end the regime identification of' the gas-liquid two-phase flow could be performed. The study showed that the method that HMM was applied to identify the flow regime was superior to the one that BP neural network was used, and the results proved that the method was efficient and feasible. (authors)

Identifying influential spreaders in complex networks based on kshell hybrid method

Science.gov (United States)

Namtirtha, Amrita; Dutta, Animesh; Dutta, Biswanath

2018-06-01

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

The Role of Small Impoundments on Flow Alteration Within River Networks

Science.gov (United States)

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

2017-12-01

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

Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China

Directory of Open Access Journals (Sweden)

C. W. Dawson

2002-01-01

Full Text Available While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP, and the radial basis function network (RBF. Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting

Explosive percolation on directed networks due to monotonic flow of activity

Science.gov (United States)

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

2017-07-01

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

Neural Network Predictive Control for Vanadium Redox Flow Battery

Directory of Open Access Journals (Sweden)

Hai-Feng Shen

2013-01-01

Full Text Available The vanadium redox flow battery (VRB is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.

A polynomial time algorithm for solving the maximum flow problem in directed networks

International Nuclear Information System (INIS)

Tlas, M.

2015-01-01

An efficient polynomial time algorithm for solving maximum flow problems has been proposed in this paper. The algorithm is basically based on the binary representation of capacities; it solves the maximum flow problem as a sequence of O(m) shortest path problems on residual networks with nodes and m arcs. It runs in O(m"2r) time, where is the smallest integer greater than or equal to log B , and B is the largest arc capacity of the network. A numerical example has been illustrated using this proposed algorithm.(author)

Variable disparity estimation based intermediate view reconstruction in dynamic flow allocation over EPON-based access networks

Science.gov (United States)

Bae, Kyung-Hoon; Lee, Jungjoon; Kim, Eun-Soo

2008-06-01

In this paper, a variable disparity estimation (VDE)-based intermediate view reconstruction (IVR) in dynamic flow allocation (DFA) over an Ethernet passive optical network (EPON)-based access network is proposed. In the proposed system, the stereoscopic images are estimated by a variable block-matching algorithm (VBMA), and they are transmitted to the receiver through DFA over EPON. This scheme improves a priority-based access network by converting it to a flow-based access network with a new access mechanism and scheduling algorithm, and then 16-view images are synthesized by the IVR using VDE. Some experimental results indicate that the proposed system improves the peak-signal-to-noise ratio (PSNR) to as high as 4.86 dB and reduces the processing time to 3.52 s. Additionally, the network service provider can provide upper limits of transmission delays by the flow. The modeling and simulation results, including mathematical analyses, from this scheme are also provided.

Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods

Science.gov (United States)

Cubuk, E. D.; Schoenholz, S. S.; Rieser, J. M.; Malone, B. D.; Rottler, J.; Durian, D. J.; Kaxiras, E.; Liu, A. J.

2015-03-01

We use machine-learning methods on local structure to identify flow defects—or particles susceptible to rearrangement—in jammed and glassy systems. We apply this method successfully to two very different systems: a two-dimensional experimental realization of a granular pillar under compression and a Lennard-Jones glass in both two and three dimensions above and below its glass transition temperature. We also identify characteristics of flow defects that differentiate them from the rest of the sample. Our results show it is possible to discern subtle structural features responsible for heterogeneous dynamics observed across a broad range of disordered materials.

Applying the flow-capturing location-allocation model to an authentic network: Edmonton, Canada

NARCIS (Netherlands)

M.J. Hodgson (John); K.E. Rosing (Kenneth); A.L.G. Storrier (Leontien)

1996-01-01

textabstractTraditional location-allocation models aim to locate network facilities to optimally serve demand expressed as weights at nodes. For some types of facilities demand is not expressed at nodes, but as passing network traffic. The flow-capturing location-allocation model responds to this

Modeling Day-to-day Flow Dynamics on Degradable Transport Network

Science.gov (United States)

Gao, Bo; Zhang, Ronghui; Lou, Xiaoming

2016-01-01

Stochastic link capacity degradations are common phenomena in transport network which can cause travel time variations and further can affect travelers’ daily route choice behaviors. This paper formulates a deterministic dynamic model, to capture the day-to-day (DTD) flow evolution process in the presence of degraded link capacity degradations. The aggregated network flow dynamics are driven by travelers’ study of uncertain travel time and their choice of risky routes. This paper applies the exponential-smoothing filter to describe travelers’ study of travel time variations, and meanwhile formulates risk attitude parameter updating equation to reflect travelers’ endogenous risk attitude evolution schema. In addition, this paper conducts theoretical analyses to investigate several significant mathematical characteristics implied in the proposed DTD model, including fixed point existence, uniqueness, stability and irreversibility. Numerical experiments are used to demonstrate the effectiveness of the DTD model and verify some important dynamic system properties. PMID:27959903

A neural network to identify neutral mesons

International Nuclear Information System (INIS)

Lefevre, F.; Lautridou, P.; Marques, M.; Matulewicz, T.; Ostendorf, R.; Schutz, Y.

1994-01-01

Both π 0 and η mesons decay long before they can reach a detector. They predominantly decay by emission of two photons, and are identified by constructing the invariant mass of the photons. Misidentified mesons result from ambiguity in associating photons. Our work tries to select which pair is the most likely to be a physical one rather than a chance one. We first designed a Hopfield neural net, but all the activities converged rapidly towards zero except the highest one. To improve the solution we slew down the computation in order to let the network explore several states and to impose activities to converge towards one for all selected pairs. This was achieved by adding links connecting each cell to itself. The network performance is all the more interesting that the solid angle covered by the detector is greater than 15%. (D.L.). 5 refs

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

Science.gov (United States)

Zhao, Shuangming; Zhao, Pengxiang; Cui, Yunfan

2017-07-01

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

Flow model for open-channel reach or network

Science.gov (United States)

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)

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

Directory of Open Access Journals (Sweden)

Huan Chen

Full Text Available 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.

Science.gov (United States)

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.

Network analysis of translocated Takahe populations to identify disease surveillance targets.

Science.gov (United States)

Grange, Zoë L; VAN Andel, Mary; French, Nigel P; Gartrell, Brett D

2014-04-01

Social network analysis is being increasingly used in epidemiology and disease modeling in humans, domestic animals, and wildlife. We investigated this tool in describing a translocation network (area that allows movement of animals between geographically isolated locations) used for the conservation of an endangered flightless rail, the Takahe (Porphyrio hochstetteri). We collated records of Takahe translocations within New Zealand and used social network principles to describe the connectivity of the translocation network. That is, networks were constructed and analyzed using adjacency matrices with values based on the tie weights between nodes. Five annual network matrices were created using the Takahe data set, each incremental year included records of previous years. Weights of movements between connected locations were assigned by the number of Takahe moved. We calculated the number of nodes (i(total)) and the number of ties (t(total)) between the nodes. To quantify the small-world character of the networks, we compared the real networks to random graphs of the equivalent size, weighting, and node strength. Descriptive analysis of cumulative annual Takahe movement networks involved determination of node-level characteristics, including centrality descriptors of relevance to disease modeling such as weighted measures of in degree (k(i)(in)), out degree (k(i)(out)), and betweenness (B(i)). Key players were assigned according to the highest node measure of k(i)(in), k(i)(out), and B(i) per network. Networks increased in size throughout the time frame considered. The network had some degree small-world characteristics. Nodes with the highest cumulative tie weights connecting them were the captive breeding center, the Murchison Mountains and 2 offshore islands. The key player fluctuated between the captive breeding center and the Murchison Mountains. The cumulative networks identified the captive breeding center every year as the hub of the network until the final

A method for identifying hierarchical sub-networks / modules and weighting network links based on their similarity in sub-network / module affiliation

Directory of Open Access Journals (Sweden)

WenJun Zhang

2016-06-01

Full Text Available Some networks, including biological networks, consist of hierarchical sub-networks / modules. Based on my previous study, in present study a method for both identifying hierarchical sub-networks / modules and weighting network links is proposed. It is based on the cluster analysis in which between-node similarity in sets of adjacency nodes is used. Two matrices, linkWeightMat and linkClusterIDs, are achieved by using the algorithm. Two links with both the same weight in linkWeightMat and the same cluster ID in linkClusterIDs belong to the same sub-network / module. Two links with the same weight in linkWeightMat but different cluster IDs in linkClusterIDs belong to two sub-networks / modules at the same hirarchical level. However, a link with an unique cluster ID in linkClusterIDs does not belong to any sub-networks / modules. A sub-network / module of the greater weight is the more connected sub-network / modules. Matlab codes of the algorithm are presented.

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

Science.gov (United States)

Giang, Pham Thanh; Nakagawa, Kenji

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

Connectivity, flow and transport in network models of fractured media

International Nuclear Information System (INIS)

Robinson, P.C.

1984-10-01

In order to evaluate the safety of radioactive waste disposal underground it is important to understand the way in which radioactive material is transported through the rock to the surface. If the rock is fractured the usual models may not be applicable. In this work we look at three aspects of fracture networks: connectivity, flow and transport. These are studied numerically by generating fracture networks in a computer and modelling the processes which occur. Connectivity relates to percolation theory, and critical densities for fracture systems are found in two and three dimensions. The permeability of two-dimensional networks is studied. The way that permeability depends on fracture density, network size and spread of fracture length can be predicted using a cut lattice model. Transport through the fracture network by convection through the fractures and mixing at the intersections is studied. The Fickian dispersion equation does not describe the resulting hydrodynamic dispersion. Extensions to the techniques to three dimensions and to include other processes are discussed. (author)

Gaussian Graphical Models Identify Networks of Dietary Intake in a German Adult Population.

Science.gov (United States)

Iqbal, Khalid; Buijsse, Brian; Wirth, Janine; Schulze, Matthias B; Floegel, Anna; Boeing, Heiner

2016-03-01

Data-reduction methods such as principal component analysis are often used to derive dietary patterns. However, such methods do not assess how foods are consumed in relation to each other. Gaussian graphical models (GGMs) are a set of novel methods that can address this issue. We sought to apply GGMs to derive sex-specific dietary intake networks representing consumption patterns in a German adult population. Dietary intake data from 10,780 men and 16,340 women of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort were cross-sectionally analyzed to construct dietary intake networks. Food intake for each participant was estimated using a 148-item food-frequency questionnaire that captured the intake of 49 food groups. GGMs were applied to log-transformed intakes (grams per day) of 49 food groups to construct sex-specific food networks. Semiparametric Gaussian copula graphical models (SGCGMs) were used to confirm GGM results. In men, GGMs identified 1 major dietary network that consisted of intakes of red meat, processed meat, cooked vegetables, sauces, potatoes, cabbage, poultry, legumes, mushrooms, soup, and whole-grain and refined breads. For women, a similar network was identified with the addition of fried potatoes. Other identified networks consisted of dairy products and sweet food groups. SGCGMs yielded results comparable to those of GGMs. GGMs are a powerful exploratory method that can be used to construct dietary networks representing dietary intake patterns that reveal how foods are consumed in relation to each other. GGMs indicated an apparent major role of red meat intake in a consumption pattern in the studied population. In the future, identified networks might be transformed into pattern scores for investigating their associations with health outcomes. © 2016 American Society for Nutrition.

Higher frequency network activity flow predicts lower frequency node activity in intrinsic low-frequency BOLD fluctuations.

Science.gov (United States)

Bajaj, Sahil; Adhikari, Bhim Mani; Dhamala, Mukesh

2013-01-01

The brain remains electrically and metabolically active during resting conditions. The low-frequency oscillations (LFO) of the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI) coherent across distributed brain regions are known to exhibit features of this activity. However, these intrinsic oscillations may undergo dynamic changes in time scales of seconds to minutes during resting conditions. Here, using wavelet-transform based time-frequency analysis techniques, we investigated the dynamic nature of default-mode networks from intrinsic BOLD signals recorded from participants maintaining visual fixation during resting conditions. We focused on the default-mode network consisting of the posterior cingulate cortex (PCC), the medial prefrontal cortex (mPFC), left middle temporal cortex (LMTC) and left angular gyrus (LAG). The analysis of the spectral power and causal flow patterns revealed that the intrinsic LFO undergo significant dynamic changes over time. Dividing the frequency interval 0 to 0.25 Hz of LFO into four intervals slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz) and slow-2 (0.198-0.25 Hz), we further observed significant positive linear relationships of slow-4 in-out flow of network activity with slow-5 node activity, and slow-3 in-out flow of network activity with slow-4 node activity. The network activity associated with respiratory related frequency (slow-2) was found to have no relationship with the node activity in any of the frequency intervals. We found that the net causal flow towards a node in slow-3 band was correlated with the number of fibers, obtained from diffusion tensor imaging (DTI) data, from the other nodes connecting to that node. These findings imply that so-called resting state is not 'entirely' at rest, the higher frequency network activity flow can predict the lower frequency node activity, and the network activity flow can reflect underlying structural

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

Science.gov (United States)

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.

Guidelines on CV networking information flow optimization for Texas.

Science.gov (United States)

2017-03-01

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

Information flow in layered networks of non-monotonic units

International Nuclear Information System (INIS)

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

2015-01-01

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. (paper)

Information flow in layered networks of non-monotonic units

Science.gov (United States)

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.

Predictive model identifies key network regulators of cardiomyocyte mechano-signaling.

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Philip M Tan

2017-11-01

Full Text Available Mechanical strain is a potent stimulus for growth and remodeling in cells. Although many pathways have been implicated in stretch-induced remodeling, the control structures by which signals from distinct mechano-sensors are integrated to modulate hypertrophy and gene expression in cardiomyocytes remain unclear. Here, we constructed and validated a predictive computational model of the cardiac mechano-signaling network in order to elucidate the mechanisms underlying signal integration. The model identifies calcium, actin, Ras, Raf1, PI3K, and JAK as key regulators of cardiac mechano-signaling and characterizes crosstalk logic imparting differential control of transcription by AT1R, integrins, and calcium channels. We find that while these regulators maintain mostly independent control over distinct groups of transcription factors, synergy between multiple pathways is necessary to activate all the transcription factors necessary for gene transcription and hypertrophy. We also identify a PKG-dependent mechanism by which valsartan/sacubitril, a combination drug recently approved for treating heart failure, inhibits stretch-induced hypertrophy, and predict further efficacious pairs of drug targets in the network through a network-wide combinatorial search.

Effectively identifying user profiles in network and host metrics

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Murphy, John P.; Berk, Vincent H.; Gregorio-de Souza, Ian

2010-04-01

This work presents a collection of methods that is used to effectively identify users of computers systems based on their particular usage of the software and the network. Not only are we able to identify individual computer users by their behavioral patterns, we are also able to detect significant deviations in their typical computer usage over time, or compared to a group of their peers. For instance, most people have a small, and relatively unique selection of regularly visited websites, certain email services, daily work hours, and typical preferred applications for mandated tasks. We argue that these habitual patterns are sufficiently specific to identify fully anonymized network users. We demonstrate that with only a modest data collection capability, profiles of individual computer users can be constructed so as to uniquely identify a profiled user from among their peers. As time progresses and habits or circumstances change, the methods presented update each profile so that changes in user behavior can be reliably detected over both abrupt and gradual time frames, without losing the ability to identify the profiled user. The primary benefit of our methodology allows one to efficiently detect deviant behaviors, such as subverted user accounts, or organizational policy violations. Thanks to the relative robustness, these techniques can be used in scenarios with very diverse data collection capabilities, and data privacy requirements. In addition to behavioral change detection, the generated profiles can also be compared against pre-defined examples of known adversarial patterns.

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

Science.gov (United States)

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

2016-10-26

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

Capacity Expansion and Reliability Evaluation on the Networks Flows with Continuous Stochastic Functional Capacity

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F. Hamzezadeh

2014-01-01

Full Text Available In many systems such as computer network, fuel distribution, and transportation system, it is necessary to change the capacity of some arcs in order to increase maximum flow value from source s to sink t, while the capacity change incurs minimum cost. In real-time networks, some factors cause loss of arc’s flow. For example, in some flow distribution systems, evaporation, erosion or sediment in pipes waste the flow. Here we define a real capacity, or the so-called functional capacity, which is the operational capacity of an arc. In other words, the functional capacity of an arc equals the possible maximum flow that may pass through the arc. Increasing the functional arcs capacities incurs some cost. There is a certain resource available to cover the costs. First, we construct a mathematical model to minimize the total cost of expanding the functional capacities to the required levels. Then, we consider the loss of flow on each arc as a stochastic variable and compute the system reliability.

Analysis of multiphase flows using dual-energy gamma densitometry and neural networks

International Nuclear Information System (INIS)

Bishop, C.M.; James, G.D.

1993-01-01

Dual-energy gamma densitometry offers a powerful technique for the non-intrusive analysis of multiphase flows. By employing multiple beam lines, information on the phase configuration can be obtained. Once the configuration is known, it then becomes possible in principle to determine the phase fractions. In practice, however, the extraction of the phase fractions from the densitometer data is complicated by the wide variety of phase configurations which can arise, and by the considerable difficulties of modelling multiphase flows. In this paper we show that neural network techniques provide a powerful approach to the analysis of data from dual-energy gamma densitometers, allowing both the phase configuration and the phase fractions to be determined with high accuracy, whilst avoiding the uncertainties associated with modelling. The technique is well suited to the determination of oil, water and gas fractions in multiphase oil pipelines. Results from linear and non-linear network models are compared, and a new technique for validating the network output is described. (orig.)

Overall Ventilation System Flow Network Calculation for Site Recommendation

International Nuclear Information System (INIS)

Steinhoff, Jeff J.

2001-01-01

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 and 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''

A Visual Analytics Technique for Identifying Heat Spots in Transportation Networks

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Marian Sorin Nistor

2016-12-01

Full Text Available The decision takers of the public transportation system, as part of urban critical infrastructures, need to increase the system resilience. For doing so, we identified analysis tools for biological networks as an adequate basis for visual analytics in that domain. In the paper at hand we therefore translate such methods for transportation systems and show the benefits by applying them on the Munich subway network. Here, visual analytics is used to identify vulnerable stations from different perspectives. The applied technique is presented step by step. Furthermore, the key challenges in applying this technique on transportation systems are identified. Finally, we propose the implementation of the presented features in a management cockpit to integrate the visual analytics mantra for an adequate decision support on transportation systems.

Unscheduled load flow effect due to large variation in the distributed generation in a subtransmission network

Science.gov (United States)

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.

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

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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.

A new cut-based algorithm for the multi-state flow network reliability problem

International Nuclear Information System (INIS)

Yeh, Wei-Chang; Bae, Changseok; Huang, Chia-Ling

2015-01-01

Many real-world systems can be modeled as multi-state network systems in which reliability can be derived in terms of the lower bound points of level d, called d-minimal cuts (d-MCs). This study proposes a new method to find and verify obtained d-MCs with simple and useful found properties for the multi-state flow network reliability problem. The proposed algorithm runs in O(mσp) time, which represents a significant improvement over the previous O(mp 2 σ) time bound based on max-flow/min-cut, where p, σ and m denote the number of MCs, d-MC candidates and edges, respectively. The proposed algorithm also conquers the weakness of some existing methods, which failed to remove duplicate d-MCs in special cases. A step-by-step example is given to demonstrate how the proposed algorithm locates and verifies all d-MC candidates. As evidence of the utility of the proposed approach, we present extensive computational results on 20 benchmark networks in another example. The computational results compare favorably with a previously developed algorithm in the literature. - Highlights: • A new method is proposed to find all d-MCs for the multi-state flow networks. • The proposed method can prevent the generation of d-MC duplicates. • The proposed method is simpler and more efficient than the best-known algorithms

Creating Turbulent Flow Realizations with Generative Adversarial Networks

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King, Ryan; Graf, Peter; Chertkov, Michael

2017-11-01

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

Clustering and Flow Conservation Monitoring Tool for Software Defined Networks

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Jesús Antonio Puente Fernández

2018-04-01

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

Clustering and Flow Conservation Monitoring Tool for Software Defined Networks.

Science.gov (United States)

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

2018-04-03

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

Identifying and localizing network problems using the PuNDIT project

International Nuclear Information System (INIS)

Batista, Jorge; McKee, Shawn; Dovrolis, Constantine; Lee, Danny

2015-01-01

In today's world of distributed collaborations of scientists, there are many challenges to providing effective infrastructures to couple these groups of scientists with their shared computing and storage resources. The Pythia Network Diagnostic InfrasTructure (PuNDIT[1]) project is integrating and scaling research tools and creating robust code suitable for operational needs addressing the difficult challenge of automating the detection and location of network problems.PuNDIT is building upon the de-facto standard perfSONAR[2] network measurement infrastructure deployed in Open Science Grid(OSG)[3] and the Worldwide LHC Computing Grid(WLCG)[4]to gather and analyze complex real-world network topologies coupled with their corresponding network metrics to identify possible signatures of network problems from a set of symptoms. The PuNDIT Team is working closely with the perfSONAR developers from ESnet and Internet2 to integrate PuNDIT components as part of the perfSONAR Toolkit. A primary goal for PuNDIT is to convert complex network metrics into easily understood diagnoses in an automated way. We will report on the project progress to-date in working with the OSG and WLCG communities, describe the current implementation including some initial results and discuss future plans and the project timeline. (paper)

The application of particle swarm optimization to identify gamma spectrum with neural network

International Nuclear Information System (INIS)

Shi Dongsheng; Di Yuming; Zhou Chunlin

2006-01-01

Aiming at the shortcomings that BP algorithm is usually trapped to a local optimum and it has a low speed of convergence in the application of neural network to identify gamma spectrum, according to the advantage of the globe optimal searching of particle swarm optimization, this paper put forward a new algorithm for neural network training by combining BP algorithm and Particle Swarm Optimization-mixed PSO-BP algorithm. In the application to identify gamma spectrum, the new algorithm overcomes the shortcoming that BP algorithm is usually trapped to a local optimum and the neural network trained by it has a high ability of generalization with identification result of one hundred percent correct. Practical example shows that the mixed PSO-BP algorithm can effectively and reliably be used to identify gamma spectrum. (authors)

The use of Ethernet in the DataFlow of the ATLAS Trigger & DAQ

CERN Document Server

Stancu, Stefan; Dobinson, Bob; Korcyl, Krzysztof; Knezo, Emil; CHEP 2003 Computing in High Energy Physics

2003-01-01

The article analyzes a proposed network topology for the ATLAS DAQ DataFlow, and identifies the Ethernet features required for a proper operation of the network: MAC address table size, switch performance in terms of throughput and latency, the use of Flow Control, Virtual LANs and Quality of Service. We investigate these features on some Ethernet switches, and conclude on their usefulness for the ATLAS DataFlow network

Analyzing the role of social networks in mapping knowledge flows: A case of a pharmaceutical company in India

Directory of Open Access Journals (Sweden)

V. Murale

2014-03-01

Full Text Available Knowledge Management literature lays emphasis on the fact that a major chunk of knowledge dissemination occurs through the various forms of social networks that exist within the organizations. A social network is a simple structure comprising of set of actors or nodes that may have relationships ties with one another. The social network analysis (SNA will help in mapping and measuring formal and informal relationships to understand what facilitates or impedes the knowledge flows that bind interacting units. This paper aims at studying the knowledge flows that happen through the social networks. It first, provides a conceptual framework and review of literature on the recent research and application of knowledge mapping and SNA, followed by a discussion on application of SNA for mapping knowledge flows in a pharmaceutical firm. In the last part, Knowledge maps are presented to illustrate the actual knowledge flow in firm.

An effective fractal-tree closure model for simulating blood flow in large arterial networks.

Science.gov (United States)

Perdikaris, Paris; Grinberg, Leopold; Karniadakis, George Em

2015-06-01

The aim of the present work is to address the closure problem for hemodynamic simulations by developing a flexible and effective model that accurately distributes flow in the downstream vasculature and can stably provide a physiological pressure outflow boundary condition. To achieve this goal, we model blood flow in the sub-pixel vasculature by using a non-linear 1D model in self-similar networks of compliant arteries that mimic the structure and hierarchy of vessels in the meso-vascular regime (radii [Formula: see text]). We introduce a variable vessel length-to-radius ratio for small arteries and arterioles, while also addressing non-Newtonian blood rheology and arterial wall viscoelasticity effects in small arteries and arterioles. This methodology aims to overcome substantial cut-off radius sensitivities, typically arising in structured tree and linearized impedance models. The proposed model is not sensitive to outflow boundary conditions applied at the end points of the fractal network, and thus does not require calibration of resistance/capacitance parameters typically required for outflow conditions. The proposed model convergences to a periodic state in two cardiac cycles even when started from zero-flow initial conditions. The resulting fractal-trees typically consist of thousands to millions of arteries, posing the need for efficient parallel algorithms. To this end, we have scaled up a Discontinuous Galerkin solver that utilizes the MPI/OpenMP hybrid programming paradigm to thousands of computer cores, and can simulate blood flow in networks of millions of arterial segments at the rate of one cycle per 5 min. The proposed model has been extensively tested on a large and complex cranial network with 50 parent, patient-specific arteries and 21 outlets to which fractal trees where attached, resulting to a network of up to 4,392,484 vessels in total, and a detailed network of the arm with 276 parent arteries and 103 outlets (a total of 702,188 vessels

ComboCoding: Combined intra-/inter-flow network coding for TCP over disruptive MANETs

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Chien-Chia Chen

2011-07-01

Full Text Available TCP over wireless networks is challenging due to random losses and ACK interference. Although network coding schemes have been proposed to improve TCP robustness against extreme random losses, a critical problem still remains of DATA–ACK interference. To address this issue, we use inter-flow coding between DATA and ACK to reduce the number of transmissions among nodes. In addition, we also utilize a “pipeline” random linear coding scheme with adaptive redundancy to overcome high packet loss over unreliable links. The resulting coding scheme, ComboCoding, combines intra-flow and inter-flow coding to provide robust TCP transmission in disruptive wireless networks. The main contributions of our scheme are twofold; the efficient combination of random linear coding and XOR coding on bi-directional streams (DATA and ACK, and the novel redundancy control scheme that adapts to time-varying and space-varying link loss. The adaptive ComboCoding was tested on a variable hop string topology with unstable links and on a multipath MANET with dynamic topology. Simulation results show that TCP with ComboCoding delivers higher throughput than with other coding options in high loss and mobile scenarios, while introducing minimal overhead in normal operation.

A modified GO-FLOW methodology with common cause failure based on Discrete Time Bayesian Network

International Nuclear Information System (INIS)

Fan, Dongming; Wang, Zili; Liu, Linlin; Ren, Yi

2016-01-01

Highlights: • Identification of particular causes of failure for common cause failure analysis. • Comparison two formalisms (GO-FLOW and Discrete Time Bayesian network) and establish the correlation between them. • Mapping the GO-FLOW model into Bayesian network model. • Calculated GO-FLOW model with common cause failures based on DTBN. - Abstract: The GO-FLOW methodology is a success-oriented system reliability modelling technique for multi-phase missions involving complex time-dependent, multi-state and common cause failure (CCF) features. However, the analysis algorithm cannot easily handle the multiple shared signals and CCFs. In addition, the simulative algorithm is time consuming when vast multi-state components exist in the model, and the multiple time points of phased mission problems increases the difficulty of the analysis method. In this paper, the Discrete Time Bayesian Network (DTBN) and the GO-FLOW methodology are integrated by the unified mapping rules. Based on these rules, the multi operators can be mapped into DTBN followed by, a complete GO-FLOW model with complex characteristics (e.g. phased mission, multi-state, and CCF) can be converted to the isomorphic DTBN and easily analyzed by utilizing the DTBN. With mature algorithms and tools, the multi-phase mission reliability parameter can be efficiently obtained via the proposed approach without considering the shared signals and the various complex logic operation. Meanwhile, CCF can also arise in the computing process.

Two problems in multiphase biological flows: Blood flow and particulate transport in microvascular network, and pseudopod-driven motility of amoeboid cells

Science.gov (United States)

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.

Evaluate the performance of a stochastic-flow network with cost attribute in terms of minimal cuts

International Nuclear Information System (INIS)

Lin, Y.-K.

2006-01-01

This paper proposes a performance index to measure the quality level of a stochastic-flow network in which each node has a designated capacity, which will have different lower levels due to various partial and complete failures. The performance index is the probability that the maximum flow of the network equals the demand d without exceeding the budget b. A simple algorithm in terms of minimal cuts is first proposed to generate all upper boundary points for (d, b), and then the probability that the maximum flow is less than or equal to d can be calculated in terms of such points. The upper boundary point for (d, b) is a maximal vector representing the capacity of each arc such that the maximum flow of the network under the budget b is d. The performance index can be calculated by repeating the proposed algorithm to obtain all upper boundary point for (d-1, b). A benchmark example is shown to illustrate the solution procedure

An integrative -omics approach to identify functional sub-networks in human colorectal cancer.

Directory of Open Access Journals (Sweden)

Rod K Nibbe

2010-01-01

Full Text Available Emerging evidence indicates that gene products implicated in human cancers often cluster together in "hot spots" in protein-protein interaction (PPI networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a "proteomics-first" approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to "seed" a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC

ICan: an integrated co-alteration network to identify ovarian cancer-related genes.

Science.gov (United States)

Zhou, Yuanshuai; Liu, Yongjing; Li, Kening; Zhang, Rui; Qiu, Fujun; Zhao, Ning; Xu, Yan

2015-01-01

Over the last decade, an increasing number of integrative studies on cancer-related genes have been published. Integrative analyses aim to overcome the limitation of a single data type, and provide a more complete view of carcinogenesis. The vast majority of these studies used sample-matched data of gene expression and copy number to investigate the impact of copy number alteration on gene expression, and to predict and prioritize candidate oncogenes and tumor suppressor genes. However, correlations between genes were neglected in these studies. Our work aimed to evaluate the co-alteration of copy number, methylation and expression, allowing us to identify cancer-related genes and essential functional modules in cancer. We built the Integrated Co-alteration network (ICan) based on multi-omics data, and analyzed the network to uncover cancer-related genes. After comparison with random networks, we identified 155 ovarian cancer-related genes, including well-known (TP53, BRCA1, RB1 and PTEN) and also novel cancer-related genes, such as PDPN and EphA2. We compared the results with a conventional method: CNAmet, and obtained a significantly better area under the curve value (ICan: 0.8179, CNAmet: 0.5183). In this paper, we describe a framework to find cancer-related genes based on an Integrated Co-alteration network. Our results proved that ICan could precisely identify candidate cancer genes and provide increased mechanistic understanding of carcinogenesis. This work suggested a new research direction for biological network analyses involving multi-omics data.

Dynamics of Number of Packets in Transit in Free Flow State of Data Network

International Nuclear Information System (INIS)

Shengkun Xie; Lawniczak, A.T.

2011-01-01

We study how the dynamics of Number of Packets in Transit (NPT) is affected by the coupling of a routing type with a volume of incoming packet traffic in a data network model of packet switching type. The NPT is a network performance indicator of an aggregate type that measures in '' real time '', how many packets are in the network on their routes to their destinations. We conduct our investigation using a time-discrete simulation model that is an abstraction of the Network Layer of the ISO OSI Seven Layer Reference Model. This model focuses on packets and their routing. We consider a static routing and two different types of dynamic routings coupled with different volumes of incoming packet traffic in the network free flow state. Our study shows that the order of the values of the NPT mean value time series depends on the coupling of a routing type with a volume of incoming packet traffic and changes when the volume of incoming packet traffic increases and is closed to the critical source load values, i.e. when it is closed to the phase transition points from the network free flow state to its congested states. (authors)