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The prismatic gas-cooled micro reactor core is composed of graphite prismatic blocks with gaps between them. The coolant passes through the gap to form flows that affect the flow and temperature distribution of the core. Conventionally, computational fluid dynamics methods have been used for prismatic gas-cooled reactor core analysis. However, they require considerable computational time and cost. For rapid and accurate calculation, in this study, a flow and heat transfer network analysis method is developed for evaluation of the core flow and temperature distribution. Finally, the calculated results of the flow heat transfer network analysis method are compared with the calculated results of computational fluid dynamics. The results show that the results of the flow heat transfer network analysis method are in good agreement with the results of computational fluid dynamics. [ABSTRACT FROM AUTHOR]

Bogie is a pivotal system and plays a critical part in the safety and reliability management of high-speed rail. However, the available bogie system reliability analysis methods lack the consideration of multi-state characteristics, and the common multi-state reliability analysis methods, being an NP-hard problem, lead to low efficiency. In order to overcome these drawbacks, this paper proposes a novel multi-state rail train bogie system reliability analysis approach based on the extended d -MC model. Three different function interactions within the bogie system are considered to build the multi-state bogie system flow network model. Meanwhile, an extended d -MC model is established to remove unnecessary d -MC candidates and duplicates, which greatly enhances the computing efficiency. The bogie system reliability is calculated, and examples are provided. Numerical experiments are carried out for the different operational conditions of the bogie system and are used to test the practicability of the method proposed in this article; it is found that this method outperforms a newly developed method in solving multi-state reliability problems. [ABSTRACT FROM AUTHOR]

It is known that the ensemble of random pure quantum states, constructed by bipartite maximally entangled states and random unitary matrices generated according to the Haar measure, exhibits an average entanglement entropy that obeys an area law. Our goal is to explore the entanglement entropy between the two subsystems in more detail. By employing techniques from Weingarten calculus and flow problems, we derive inequalities related to permutations. These inequalities lead to the conclusion that entanglement entropy almost surely follows an area law. We assert that these results persist even when replacing the Haar unitary random matrix with the Gaussian unitary ensemble. Finally, we illustrate our main results through two concrete examples. [ABSTRACT FROM AUTHOR]

Abstract The graph traversal edit distance (GTED), introduced by Ebrahimpour Boroojeny et al. (2018), is an elegant distance measure defined as the minimum edit distance between strings reconstructed from Eulerian trails in two edge-labeled graphs. GTED can be used to infer evolutionary relationships between species by comparing de Bruijn graphs directly without the computationally costly and error-prone process of genome assembly. Ebrahimpour Boroojeny et al. (2018) propose two ILP formulations for GTED and claim that GTED is polynomially solvable because the linear programming relaxation of one of the ILPs always yields optimal integer solutions. The claim that GTED is polynomially solvable is contradictory to the complexity results of existing string-to-graph matching problems. We resolve this conflict in complexity results by proving that GTED is NP-complete and showing that the ILPs proposed by Ebrahimpour Boroojeny et al. do not solve GTED but instead solve for a lower bound of GTED and are not solvable in polynomial time. In addition, we provide the first two, correct ILP formulations of GTED and evaluate their empirical efficiency. These results provide solid algorithmic foundations for comparing genome graphs and point to the direction of heuristics. The source code to reproduce experimental results is available at https://github.com/Kingsford-Group/gtednewilp/ .

The graph traversal edit distance (GTED), introduced by Ebrahimpour Boroojeny et al. (2018), is an elegant distance measure defined as the minimum edit distance between strings reconstructed from Eulerian trails in two edge-labeled graphs. GTED can be used to infer evolutionary relationships between species by comparing de Bruijn graphs directly without the computationally costly and error-prone process of genome assembly. Ebrahimpour Boroojeny et al. (2018) propose two ILP formulations for GTED and claim that GTED is polynomially solvable because the linear programming relaxation of one of the ILPs always yields optimal integer solutions. The claim that GTED is polynomially solvable is contradictory to the complexity results of existing string-to-graph matching problems. We resolve this conflict in complexity results by proving that GTED is NP-complete and showing that the ILPs proposed by Ebrahimpour Boroojeny et al. do not solve GTED but instead solve for a lower bound of GTED and are not solvable in polynomial time. In addition, we provide the first two, correct ILP formulations of GTED and evaluate their empirical efficiency. These results provide solid algorithmic foundations for comparing genome graphs and point to the direction of heuristics. The source code to reproduce experimental results is available at https://github.com/Kingsford-Group/gtednewilp/. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To alleviate the 'curse of dimensionality' and speed up the calculation of the total supply capability curve (TSC curve), this paper proposes a novel critical flow-based fast algorithm for distribution networks. Firstly, the critical flow and the TSC curve are found to have good correspondence through the comparison of their concepts and models. Secondly, a critical flow-based algorithm with three phases is proposed for calculating the TSC curve: (i). Determine the equivalent flow network of the distribution network; (ii). Solve critical flows of the equivalent flow network through a dimension reduction-sampling algorithm and obtain the corresponding TSC curve operating points; (iii). Calculate the flow value of the critical flow and plot the TSC curve. Finally, the proposed algorithm is verified by the expanded IEEE RBTS BUS4 case with soft open point (SOP). The application of the TSC curve in distribution network planning is demonstrated. In addition, the correctness and rapidity of the proposed algorithm are verified by comparing it with the existing sampling algorithm, and the reasons for the performance improvement are investigated. This paper applies the network flow theory to calculating the power supply capability of distribution networks for the first time, which simplifies the calculation of the TSC curve and increases the computational efficiency significantly. [ABSTRACT FROM AUTHOR]

Robust design problems in flow networks involve determining the optimal capacity assignments that enable the network to operate effectively even in the case of events' occurrence such as arcs or nodes' failures. Multi-source multi-sink flow networks (MMSFNs) are frequent in many real-life systems such as computer and telecommunication, logistics and supply-chain, and urban traffic. Although numerous studies on the design of MMSFNs have been conducted, the robust design problem for multi-source multi-sink stochastic-flow networks (MMSFNs) remains unexplored. To contribute to this field, this study addresses the robust design problem for MMSFNs using an approach of two steps. First, the problem is mathematically formulated as an optimization problem and second, a sub-optimal solution is proposed based on a genetic algorithm (GA) involving two components. The first component, an outer genetic algorithm, is employed to search the optimal capacity assigned to the network components with minimum sum. The second component, an inner genetic algorithm, is used to find the optimal flow vectors that maximize the system's reliability. Through extensive experimentation on three different networks with different topologies, the proposed solution has been found to be efficient. [ABSTRACT FROM AUTHOR]

This paper investigates video frame extrapolation, which can predict future frames from current and past frames. Although there have been many studies on video frame extrapolation in recent years, most of them suffer from the unsatisfactory image quality of the predicted frames such as severe blurring because it is difficult to predict the movement of future pixels for multi-modal video frames, especially with fast changing frames. An additional process such as frame alignment or recurrent prediction can improve the quality of the predicted frames, but it hinders real-time extrapolation. Motivated by the significant progress in video frame interpolation using deep learning-based flow estimation, a simplified video frame extrapolation scheme using deep learning-based uni-directional flow estimation is proposed to reduce the processing time compared to conventional video frame extrapolation schemes without compromising the image quality of the predicted frames. In the proposed scheme, the uni-directional flow is first estimated from the current and past frames through a flow network consisting of four flow blocks and the current frame is forward-warped through the estimated flow to predict a future frame. The proposed flow network is trained and evaluated using the Vimeo-90K triplet dataset. The performance of the proposed scheme is analyzed using the trained flow network in terms of prediction time as well as the similarity between predicted and ground truth frames such as the structural similarity index measure and mean absolute error of pixels, and compared to that of the state-of-the-art schemes such as Iterative and cycleGAN schemes. Extensive experiments show that the proposed scheme improves prediction quality by 2.1% and reduces prediction time by 99.7% compared to the state-of-the-art scheme.

Identifying the spatiotemporal interaction pattern of agricultural product circulation (APC) is crucial for agricultural resource adjustment and food security. Current studies are mostly based on static statistical data over an entire year or a specific period, which cannot describe the spatial pattern of APC and its seasonal variation on a fine spatiotemporal scale. Thus, this study extracts an APC trip chain based on national truck trajectory data and constructs the flow network of the Beijing APC with the city as the spatial unit and the season as the temporal unit. The spatial interaction pattern and seasonal variation in APC are then analyzed from the network spatial form, city node function role, and transportation corridors. The results are as follows: (1) Compared with methods based on static statistical data, the proposed method provides a more complete and refined depiction of the spatiotemporal interaction pattern of APC. (2) The flow network of the Beijing APC involves 316 cities in China, of which 143 cities play a major role with typical seasonal characteristics. These cities can be divided into perennial core cities, perennial major cities, core cities in winter-spring, major cities in winter-spring, core cities in summer-autumn, and major cities in summer-autumn, contributing 2.6%–40.3% to the Beijing APC. (3) There are 6 transportation corridors for the Beijing APC. The Beijing-Tianjin-Hebei corridor and coastal corridor contribute 53.5% and 12.8% of the annual supply, respectively, with a balanced supply in all seasons. The Beijing-Kunming corridor and Beijing-Guangzhou corridor contribute 14.3% and 9.0%, respectively, with much higher supplies in winter and spring. The northeast and northwest corridors contribute 7.3% and 3.3%, respectively, mainly in the summer and autumn. These results help deepen the understanding of agricultural product supply patterns and provide a reference for the design and optimization of agricultural product transportation routes. [ABSTRACT FROM AUTHOR]

This paper is devoted to dynamic network flows. A phenomenon called dynamic network flow splash is studied (splash is an exceeding of flow value over maximum feasible static flow for the same network). Parallel and tree-structured network topology classes are defined. Conditions for occurring network flow splash in the parallel and tree-structured networks are discovered. A formula for computing a value of maximum dynamic network flow splash is obtained. An algorithm for calculating the formula values is presented. [ABSTRACT FROM AUTHOR]

Analyzing the spatial agglomeration characteristics and local spatial structure stability of the inflow and outflow areas of water resources on the basis of quantifying the flow network of water resources can provide theoretical support for formulating safer and more reasonable water resource regulation policies. This paper used the multi-regional input-output model (MRIO) and the exploratory spatiotemporal data analysis method (ESTDA) to calculate the inflow and outflow water footprints of 30 provinces in China from 2002 to 2017, and analyzed China' s water resource flow pattern as well as the spatial and temporal evolution characteristics of the spatial agglomeration patterns of the inflow and outflow water footprints. The results showed that: ① The local water footprints accounted for the largest proportion of the total water footprints and showed a steady growth trend, and the inflow and outflow water footprints had an upward-downward trend from 2002 to 2017. ② From 2002 to 2012, the total number of high-level flow paths in the inter-provincial water resource flow patterns in China was larger and more prominent in the network, while in 2017, high-level flow paths were concentrated on a few link paths and in the eastern and western regions, but the breadth of associations did not weaken significantly. ③ The hot spots of the outflow water footprints of China' s provinces gradually shifted from the water-rich areas to the water-scarce areas, while the hot spots of the inflow water footprints were concentrated in the rich-water areas. ④ The LISA time path showed that the local spatial structure of the inflow and outflow water footprints in China' s provinces was relatively stable, and the effect of spatial and temporal dependence was small, showing a trend of coordinated changes. The spatial cohesion of the outflow water footprints was higher than that of the inflow water footprints, but the degree of transfer between the two was relatively large. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI), often known as machine learning, is a powerful tool for solving engineering problems. The evaluation of the network reliability of a flow network is a NP-hard problem, with computational effort growing exponentially with the number of nodes and arcs in the network. Also, the components assignment issue is NP-hard, and the computational effort increases with the number of available components. Many candidate solutions are typically examined during optimal components or optimal capacity assignment, each requiring reliability calculation. Consequently, this paper proposes an artificial neural network (ANN) predictive model to evaluate the flow network reliability. The neural network is one of the artificial intelligence tools constructed, trained, and validated using the maximum capacity of each component input and the network reliability as the target. The proposed ANN model provides empirical proof that neural networks can accurately estimate reliability by modeling the connection between the maximum capacities of network components and the reliability value.

The paper contains a description and research results of the proposal for distributed QoS extensions for Flow-Based Networking. These QoS extensions let the network accept or reject flows based on current network load and QoS promises for each of the flows. Proposed solution consists of two distributed components, each of them performing in every node, measurement system and access control. The solution could be applied in any network architecture that is able to distinguish flows and routers in this architecture contains flow state table. Proposed approach was verified by simulation, in FSA architecture. Verification was done for six different network structures servicing two traffic classes (MRS, ARS). The results of the simulation tests have confirmed that the average time delay and packet loss ratio in the network with proposed extensions are below thresholds and meet the requirements recommended by ITU-T.

Abstract Complex networks are emerging in low-Earth-orbit as the communication architectures of inter-linked space systems. These data transfer networks vary based on spacecraft interaction with targets and ground stations, which respectively represent source and sink nodes for data flowing through the network. We demonstrate how networks can be used to identify effective sink node selections that in combination provide source coverage, high data throughput, and low latency connections for intermittently connected, store-and-forward space systems. The challenge in this work is to account for the changing data transfer network that varies significantly depending on the ground stations selected—given a system where data is downlinked by spacecraft at the first opportunity. Therefore, passed-on networks are created to capture the redistribution of data following a sink node’s removal from the system, a problem of relevance to traffic management in a variety of flow network applications. Modelling the system using consensus dynamics, enables sink node selections to be evaluated in terms of their source coverage and data throughput. While restrictions in the depth of propagation when defining passed-on networks, ensures the optimisation implicitly rewards lower latency connections. This is a beneficial by-product for both space system design and store-and-forward data networks in general. The passed-on networks also provide an insight into the relationship between sink nodes, with eigenvector embedding-based communities identifying sink node divisions that correspond with differences in source node coverage.

To reveal the complete limits of a flow network, this paper proposes the concept of critical flow. Firstly, it is found that a flow network is in a critical state when it reaches its maximum flow, and the network cannot afford any flow increase. In fact, not only maximum flow but many other network flows, i.e., blocking flows, indicate the critical states of a flow network. From a new viewpoint, the concept of critical flow is proposed, which unifies the maximum flow and the blocking flow. Secondly, the mathematical model, solution algorithms, and visualization method for critical flow are proposed. An analytical algorithm and a numerical algorithm are proposed to calculate the complete critical flows. In addition, a critical flow curve is designed to visualize the complete limits of a network, with the top point being the maximum flow. Finally, case studies are conducted to illustrate the critical flow and its typical applications in flow networks and real networks. It is concluded that the complete limits of a flow network are the range of critical flows, not a single maximum flow value. The proposed critical flows describe the complete limits of a flow network for the first time. Critical flow can be used for network monitoring and network planning. In monitoring, it reflects the margin of the flow network more accurately than maximum flow. In planning, it evaluates the flow transmission capability more accurately than maximum flow. [ABSTRACT FROM AUTHOR]

Robust and accurate visual feature tracking is essential for good pose estimation in visual odometry. However, in fast-moving scenes, feature point extraction and matching are unstable because of blurred images and large image disparity. In this paper, we propose an unsupervised monocular visual odometry framework based on a fusion of features extracted from two sources, that is, the optical flow network and the traditional point feature extractor. In the training process, point features are generated for scene images and the outliers of matched point pairs are filtered by FlannMatch. Meanwhile, the optical flow network constrained by the principle of forward–backward flow consistency is used to select another group of corresponding point pairs. The Euclidean distance between the matching points found by FlannMatch and the corresponding point pairs by the flow network is added to the loss function of the flow network. Compared with SURF, the trained flow network shows more robust performance in complicated fast-motion scenarios. Furthermore, we propose the AvgFlow estimation module, which selects one group of the matched point pairs generated by the two methods according to the scene motion. The camera pose is then recovered by Perspective-n-Point (PnP) or the epipolar geometry. Experiments conducted on the KITTI Odometry dataset verify the effectiveness of the trajectory estimation of our approach, especially in fast-moving scenarios. [ABSTRACT FROM AUTHOR]

Robust design problems in flow networks involve determining the optimal capacity assignments that enable the network to operate effectively even in the case of events’ occurrence such as arcs or nodes’ failures. Multi-source multi-sink flow networks (MMSFNs) are frequent in many real-life systems such as computer and telecommunication, logistics and supply-chain, and urban traffic. Although numerous studies on the design of MMSFNs have been conducted, the robust design problem for multi-source multi-sink stochastic-flow networks (MMSFNs) remains unexplored. To contribute to this field, this study addresses the robust design problem for MMSFNs using an approach of two steps. First, the problem is mathematically formulated as an optimization problem and second, a sub-optimal solution is proposed based on a genetic algorithm (GA) involving two components. The first component, an outer genetic algorithm, is employed to search the optimal capacity assigned to the network components with minimum sum. The second component, an inner genetic algorithm, is used to find the optimal flow vectors that maximize the system’s reliability. Through extensive experimentation on three different networks with different topologies, the proposed solution has been found to be efficient.

Traffic guidance and traffic control are effective means to alleviate traffic problems. Formulating effective traffic guidance measures can improve the utilization rate of road resources and optimize the performance of the entire traffic network. Assuming that the traffic coordinator can capture traffic flow information in real-time utilizing sensors installed on each road, we consider the strong resilience constraints to construct an optimal selection problem of balanced flow in the traffic network. Based on multi-agent modeling, each agent has access to the corresponding traffic information of each link. We design a distributed optimization algorithm to tackle this optimization problem. In addition to the inherent advantages of distributed multi-agent algorithms, the communication topology among the sensors is allowed to be time-varying, which is more consistent with reality. To prove the effectiveness of the proposed algorithm, we also give a numerical simulation in the multi-agent environment. It should be reiterated that the optimization problem studied in this paper mainly focuses on traffic managers' perspectives. The goal of the studied optimization problem is to minimize the overall cost of the traffic network by adjusting the optimal equilibrium traffic flow. This study provides a reference for solving congestion optimization in today's cities. • Formulate an equilibrium flow selection problem with resilience constraints. • The distributed algorithm allows the communication among agents to be time-varying. • Communication graph among agents allows for being B-connected. [ABSTRACT FROM AUTHOR]

Aharoni et al. (J Combinat Theory, Ser B 101:1–17, 2010) proved the max-flow min-cut theorem for countable networks, namely that in every countable network with finite edge capacities, there exists a flow and a cut such that the flow saturates all outgoing edges of the cut and is zero on all incoming edges. In this paper, we formalize their proof in Isabelle/HOL and thereby identify and fix several problems with their proof. We also provide a simpler proof for networks where the total outgoing capacity of all vertices other than the source and the sink is finite. This proof is based on the max-flow min-cut theorem for finite networks. As a use case, we formalize a characterization theorem for relation lifting on discrete probability distributions and two of its applications. [ABSTRACT FROM AUTHOR]

Cryogenic turbopumps are used in high-performance, lightweight liquid rocket engines for space applications. The development of bearings and shaft seals for cryogenic turbopumps requires detailed characterization of the internal flow, taking into account the effects of boiling and multi-component two-phase flow. Here, a flow network solver was developed to analyse the secondary flow circuit of a cryogenic turbopump where the propellant is mixed with high-temperature helium after bearing cooling. The network solver is based on an extension of a classic 1D homogeneous model, originally developed for a pure substance, to the case of two-phase multi-component flow. The solver is capable of predicting pressures, temperatures, flow rates, and species concentrations in a complex two-phase flow in the presence of non-condensable gases. The unsteady mass, momentum, and energy conservation equations are implemented in conjunction with the thermodynamic equations of state using a general-purpose finite volume formulation, where the pressure drop and the heat transfer are calculated using correlations. The numerical tool was validated by comparing its predictions with experimental data obtained during tests on the secondary circuit of an oxygen turbopump developed at Avio S.p.A. A number of engine operating conditions were considered (inlet helium temperature in the range of 250–280 K , helium/liquid oxygen drain in the range of 165–230 K). The predicted temperature values showed good agreement with the experimental data in most conditions. [ABSTRACT FROM AUTHOR]

In view of the water crises historically experienced by Brazil’s semi-arid region, it is necessary to search for methodological tools that allow us to propose efficient alternatives for managing water demand. In this regard, the present study developed and applied a hybrid approach of flow network modeling and the Analytic Hierarchy Process (AHP) combined with Shannon’s entropy for simulating and prioritizing water demand management alternatives in the Epitácio Pessoa Reservoir in the Brazilian semiarid region. The approach initially performed simulations of the hydro system flow network to explore rational alternatives that evaluated the following criteria: meet the demands of human supply, meet the demands for irrigation, maintain the reservoir above the target volume, and reduce consumption. Subsequently, Shannon's entropy was used to assess the importance of the criteria and the multicriteria analysis of ranking of alternatives was built. Finally, a sensitivity analysis of the model was performed regarding the dependence on respective weights of the criteria. The results made enabled evaluation of the hydro system’s water security, highlighting insufficient supply by the single measure of water management transposition of the São Francisco River, and based on the hierarchy of alternatives, suggesting actions to improve the sustainability of water use, such as reuse of water, reduction of losses in supply systems and increasing irrigation efficiency. The proposed hybrid approach permitted the synthesis of the simulation’s results, which may simplify and support the decision-making process in water-demand management.

Microbial methanogenesis produces a range of isotope (13C/12C and D/H) and isotopologue (13CH 3 D and 12CH 2 D 2) fractionations. Differential reversibility of enzymatic reactions qualitatively explains the isotope and isotopologue fractionations observed in both laboratory cultures and environmental samples. We applied pathway thermodynamics and isotopologue flow network models to quantitatively describe 13C/12C, D/H, 13CH 3 D, and 12CH 2 D 2 fractionations during hydrogenotrophic methanogenesis. The model consists of the 10 enzymatic reactions of the methanogenesis pathway and tracks mass balance of isotopologues by taking into account the reaction symmetries of singly- and doubly-deuterated isotopologues. Based on the thermodynamics and enzyme kinetic data, the model estimates the reversibilities of 8 reactions from predicted in vivo concentrations of 17 metabolites and cofactors. The isotopologue flow network model calculates the isotopologue composition of product methane as well as all intermediates as a function of reversibilities and prescribed fractionation factors. The model explains a number of observations for laboratory culture experiments, including the range of 13C/12C fractionation up to 80‰ between CH 4 and CO 2 , with increasing magnitudes while decreasing pH 2. Relatively constant D/H fractionations of 300 ± 40‰ between methane and water can be explained when methane is produced from three near-equilibrium H in methyl-coenzyme M with the addition of one kinetic D-depleted H during the last step of methanogenesis. Abundances of the doubly substituted isotopologues, 13CH 3 D and 12CH 2 D 2 , reflect kinetic and equilibrium end-members with additional complications due to non-linear mixing and/or combinatorial effect. Our model can make predictions for isotopologue fractionations under slow rates of methanogenesis in energy-limiting deep sedimentary environments, where a large quantity of methane is produced. Near-equilibrium isotopologue ratios, often observed in marine sedimentary environments, are produced when pH 2 is less than 10 Pa. Our model results indicate that methanogenesis does not occur or only proceeds at extremely slow rates at this low pH 2 because low concentration of methyl-tetrahydromethanopterin limits the rate and thermodynamic feasibility of methanogenesis. Accordingly, it is proposed that near-equilibrium methane isotopologue signals in deep marine sediments are produced by the catalytic reversibility of methyl-coenzyme M reductase, likely from anaerobic methanotrophic archaea performing either anaerobic methane oxidation or net methanogenesis. The pathway thermodynamics and isotopologue flow network model scheme presented herein can be applied and expanded to predict isotopologue fractionations for a range of metabolisms beyond methanogenesis. [ABSTRACT FROM AUTHOR]

In this article, an intelligent attacker is considered, which aims to prevent leader-following multiagent systems from achieving consensus. The attacker randomly injects denial-of-service (DoS) attacks to some communication channels in the network, which make the corresponding attacked edges disconnected. The minimum number of communication channels needed to be jammed by the attacker to guarantee the system fails to achieve the consensus is provided based on the Max-Flow Min-Cut lemma and an algorithm to generate the minimum attacked edges is proposed. Furthermore, a lower bound and an upper bound of the attack probability to destroy the consensusability of the system are provided, respectively. Finally, numerical simulations are given to illustrate the results. [ABSTRACT FROM AUTHOR]

The paper contains a description and research results of the proposal for distributed QoS extensions for Flow-Based Networking. These QoS extensions let the network accept or reject flows based on current network load and QoS promises for each of the flows. Proposed solution consists of two distributed components, each of them performing in every node, measurement system and access control. The solution could be applied in any network architecture that is able to distinguish flows and routers in this architecture contains flow state table. Proposed approach was verified by simulation, in FSA architecture. Verification was done for six different network structures servicing two traffic classes (MRS, ARS). The results of the simulation tests have confirmed that the average time delay and packet loss ratio in the network with proposed extensions are below thresholds and meet the requirements recommended by ITU-T. [ABSTRACT FROM AUTHOR]

Hierarchical organizations have to build the right human resource (HR) composition to fulfill their operational and strategic needs. One of the largest hierarchical organizations that face this challenge is the military organization. To tackle this challenge, managers rely on military HR planning that consists of strategic planning and operational planning. The general practice of focusing on the strategic planning then dealing with the operational planning could lead to an undesired manpower composition. Therefore, we propose an approach that allows simultaneous HR planning for both strategic and operational levels. Such simultaneous planning ensures building an adequate plan that considers the different aspects of personnel. We employ a flow network model to represent the military human resource system. To find an adequate solution that, we use mixed integer goal programming. [ABSTRACT FROM AUTHOR]

What is the origin of behaviour? Although typically associated with a nervous system, simple organisms also show complex behaviours. Among them, the slime mold Physarum polycephalum, a giant single cell, is ideally suited to study emergence of behaviour. Here, we show how locomotion and morphological adaptation behaviour emerge from self-organized patterns of rhythmic contractions of the actomyosin lining of the tubes making up the network-shaped organism. We quantify the spatio-temporal contraction dynamics by decomposing experimentally recorded contraction patterns into spatial contraction modes. Notably, we find a continuous spectrum of modes, as opposed to a few dominant modes. Our data suggests that the continuous spectrum of modes allows for dynamic transitions between a plethora of specific behaviours with transitions marked by highly irregular contraction states. By mapping specific behaviours to states of active contractions, we provide the basis to understand behaviour’s complexity as a function of biomechanical dynamics.

Robust and accurate visual feature tracking is essential for good pose estimation in visual odometry. However, in fast-moving scenes, feature point extraction and matching are unstable because of blurred images and large image disparity. In this paper, we propose an unsupervised monocular visual odometry framework based on a fusion of features extracted from two sources, that is, the optical flow network and the traditional point feature extractor. In the training process, point features are generated for scene images and the outliers of matched point pairs are filtered by FlannMatch. Meanwhile, the optical flow network constrained by the principle of forward–backward flow consistency is used to select another group of corresponding point pairs. The Euclidean distance between the matching points found by FlannMatch and the corresponding point pairs by the flow network is added to the loss function of the flow network. Compared with SURF, the trained flow network shows more robust performance in complicated fast-motion scenarios. Furthermore, we propose the AvgFlow estimation module, which selects one group of the matched point pairs generated by the two methods according to the scene motion. The camera pose is then recovered by Perspective-n-Point (PnP) or the epipolar geometry. Experiments conducted on the KITTI Odometry dataset verify the effectiveness of the trajectory estimation of our approach, especially in fast-moving scenarios.

The Segregation Index quantifies the degree of segregation of social groups or classes. Because of the increasing use of fine-grained spatiotemporal activity and flow data, the conventional segregation measurements' inclusiveness is challenged. We add population flow to the conventional place-based spatial exposure index to identify spatiotemporal segregation changes. Specifically, we considered the population-flow network, hierarchical structure, and time. In Chicago's demonstration case study, we first used the time-dependent Twitter Origin-Destination flow matrices and their hierarchical structure information to estimate interactions between areal units at the neighborhood level. Then we computed the new population composition of units based on their interactions with other units and estimated the proposed spatiotemporal exposure index for different times. Finally, we systematically compared their differences with the conventional indices at global and local scales to see how population-flow patterns affect the exposure index. The results show that the population-flow patterns reflect valuable information in neighborhood interactions in temporal and spatial dimensions, but it is missing information in the conventional segregation computations. Furthermore, we emphasize that the hierarchical structures of flow patterns and the choice of appropriate parameters are also important factors for a rational segregation evaluation. [ABSTRACT FROM AUTHOR]

Wireless sensor networks(WSN) connectivity is an important way to ensure network fault tolerance.Conventional multi-routing algorithms often have common paths,which will lead to premature connectivity failure of the whole WSN due to node damage,communication link interruption and other faults.Aiming at the common path problem,a solution was proposed to generate multiple paths on the common path through node splitting to support fault tolerance of WSN and ensure that each sensor node has K disjoint shortest paths of bounded length to the sinks.The simulation results show that the algorithm can provide better fault tolerance and ensure network connectivity.

Secure and stable cryptocurrency key management is important in modern cryptocurrency because the keys are the only way to access digital assets. Although many cryptocurrency wallet schemes have been proposed, some application limitations and inherent security risks still exist. In this paper, we propose a novel cryptocurrency wallet management scheme based on Decentralized Multi-Constrained Derangement (DMCD) to store the keys securely and stably in a decentralized network. Serving as the data distribution strategy, DMCD has high data dispersion and a better balance between the rate of storage space utilization and contribution, which can guarantee the security and stability of the key storage and recovery. In our scheme, to cope with the problem that nodes are frequently online and offline in the decentralized network, we employ a Shamir-Kademlia-Neighbor (SKN) redundancy strategy to ensure the high availability of stored key. Meanwhile, for achieving anonymous communication during DMCD data distribution, based on the Kademlia protocol, we change the Client/Server (C/S) mode of Hordes protocol to a decentralized version. All the proposed technologies can ensure that our scheme works well in a decentralized mode. The experiments and evaluations demonstrate that our scheme is efficient, stable, and secure in the decentralized network.

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Cryogenic liquid propellants are used in liquid rocket engines to obtain high specific impulse. The flow rates are controlled by turbopumps that deliver liquid propellant to the engine at high pressure levels. Due to the very low saturation temperature of the cryogenic propellant, in the first phases of the transient operation, in which the engine is at ambient temperature, its surfaces are subject to boiling conditions. The effect of boiling on the heat transfer between the solid and the fluid needs to be well characterized in order to correctly predict the cryopump metal temperature temporal evolution and the necessary amount of propellant. With the aim of benchmarking numerical tools against experimental data, a representative test case was chosen. This consists of a stator-rotor-stator spinning disc reactor studied under single-phase and two-phase heat transfer conditions. The numerical approaches used are represented by a 1D network solver, where the pressure drop and heat transfer are calculated by correlations, and Computational Fluid Dynamics (CFD) simulations, carried out with ANSYS Fluent. Both the numerical tools returned a reasonable agreement in single-phase conditions, also thanks to the use of adequate correlations in the flow network solver and typical conditions for the CFD simulations. Two-phase conditions on the contrary are more challenging, with underpredictions up to 20% and 80%, respectively. The issues are ascribable to the use of correlations that are inadequate to capture the two-phase phenomena occurring in the srs reactor and numerical limitations in the actual implementation of the boiling model in the CFD solver.

Computer networks and power transmission networks are treated as capacitated flow networks. A capacitated flow network may partially fail due to maintenance. Therefore, the capacity of each edge should be optimally assigned to face critical situations--i.e., to keep the network functioning normally in the case of failure at one or more edges. The robust design problem (RDP) in a capacitated flow network is to search for the minimum capacity assignment of each edge such that the network still survived even under the edge's failure. The RDP is known as NP-hard. Thus, capacity assignment problem subject to system reliability and total capacity constraints is studied in this paper. The problem is formulated mathematically, and a genetic algorithm is proposed to determine the optimal solution. The optimal solution found by the proposed algorithm is characterized by maximum reliability and minimum total capacity. Some numerical examples are presented to illustrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Gas turbine combustor performance was explored by utilizing a 1-D flow network model. To obtain the preliminary performance of combustion chamber, three different flow network solvers were coupled with a chemical reactor network scheme. These flow solvers were developed via simplified, segregated and direct solutions of the nodal equations. Flow models were utilized to predict the flow field, pressure, density and temperature distribution inside the chamber network. The network model followed a segregated flow and chemical network scheme, and could supply information about the pressure drop, nodal pressure, average temperature, species distribution, and flow split. For the verification of the model's results, analyses were performed using CFD on a seven-stage annular test combustor from TUSAS Engine Industries, and the results were then compared with actual performance tests of the combustor. The results showed that the preliminary performance predictor code accurately estimated the flow distribution. Pressure distribution was also consistent with the CFD results, but with varying levels of conformity. The same was true for the average temperature predictions of the inner combustor at the dilution and exit zones. However, the reactor network predicted higher elemental temperatures at the entry zones. [ABSTRACT FROM AUTHOR]

Blood circulation in the human brain is supplied through a network of cerebral arteries. If a clinician suspects a patient has a stroke or other cerebrovascular condition, they order imaging tests. Neuroradiologists visually search the resulting scans for abnormalities. Their visual search tasks correspond to the abstract network analysis tasks of browsing and path following. To assist neuroradiologists in identifying cerebral artery abnormalities, we designed CerebroVis, a novel abstract—yet spatially contextualized—cerebral artery network visualization. In this design study, we contribute a novel framing and definition of the cerebral artery system in terms of network theory and characterize neuroradiologist domain goals as abstract visualization and network analysis tasks. Through an iterative, user-centered design process we developed an abstract network layout technique which incorporates cerebral artery spatial context. The abstract visualization enables increased domain task performance over 3D geometry representations, while including spatial context helps preserve the user's mental map of the underlying geometry. We provide open source implementations of our network layout technique and prototype cerebral artery visualization tool. We demonstrate the robustness of our technique by successfully laying out 61 open source brain scans. We evaluate the effectiveness of our layout through a mixed methods study with three neuroradiologists. In a formative controlled experiment our study participants used CerebroVis and a conventional 3D visualization to examine real cerebral artery imaging data to identify a simulated intracranial artery stenosis. Participants were more accurate at identifying stenoses using CerebroVis (absolute risk difference 13%). A free copy of this paper, the evaluation stimuli and data, and source code are available at osf.io/e5sxt. [ABSTRACT FROM AUTHOR]