scholarly journals Network Robustness Analysis Based on Maximum Flow

2021 ◽  
Vol 9 ◽  
Author(s):  
Meng Cai ◽  
Jiaqi Liu ◽  
Ying Cui
Keyword(s):  
Structural Integrity ◽  
Random Network ◽  
Robustness Analysis ◽  
Maximum Flow ◽  
Indicator System ◽  
Average Degree ◽  
Network Robustness ◽  
Damage Rate ◽  
Flow Capacity ◽  
Critical Damage

Network robustness is the ability of a network to maintain a certain level of structural integrity and its original functions after being attacked, and it is the key to whether the damaged network can continue to operate normally. We define two types of robustness evaluation indicators based on network maximum flow: flow capacity robustness, which assesses the ability of the network to resist attack, and flow recovery robustness, which assesses the ability to rebuild the network after an attack on the network. To verify the effectiveness of the robustness indicators proposed in this study, we simulate four typical networks and analyze their robustness, and the results show that a high-density random network is stronger than a low-density network in terms of connectivity and resilience; the growth rate parameter of scale-free network does not have a significant impact on robustness changes in most cases; the greater the average degree of a regular network, the greater the robustness; the robustness of small-world network increases with the increase in the average degree. In addition, there is a critical damage rate (when the node damage rate is less than this critical value, the damaged nodes and edges can almost be completely recovered) when examining flow recovery robustness, and the critical damage rate is around 20%. Flow capacity robustness and flow recovery robustness enrich the network structure indicator system and more comprehensively describe the structural stability of real networks.

scholarly journals Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1716
Author(s):  
Adrian Marius Deaconu ◽  
Delia Spridon
Keyword(s):  
Network Flow ◽  
Large Scale ◽  
Random Network ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Network Flow Problems ◽  
Commodity Flow ◽  
Vast Number ◽  
Flow Problems ◽  
Execution Speed

Algorithms for network flow problems, such as maximum flow, minimum cost flow, and multi-commodity flow problems, are continuously developed and improved, and so, random network generators become indispensable to simulate the functionality and to test the correctness and the execution speed of these algorithms. For this purpose, in this paper, the well-known Erdős–Rényi model is adapted to generate random flow (transportation) networks. The developed algorithm is fast and based on the natural property of the flow that can be decomposed into directed elementary s-t paths and cycles. So, the proposed algorithm can be used to quickly build a vast number of networks as well as large-scale networks especially designed for s-t flows.

scholarly journals Interdependency and Vulnerability of Multipartite Networks under Target Node Attacks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Qing Cai ◽  
Mahardhika Pratama ◽  
Sameer Alam
Keyword(s):  
Complex Networks ◽  
Robustness Analysis ◽  
Network Models ◽  
Continuous Phase ◽  
Structure Design ◽  
Network Robustness ◽  
Multilayer Networks ◽  
Component Theory ◽  
Centrality Metrics ◽  
Entire Network

Complex networks in reality may suffer from target attacks which can trigger the breakdown of the entire network. It is therefore pivotal to evaluate the extent to which a network could withstand perturbations. The research on network robustness has proven as a potent instrument towards that purpose. The last two decades have witnessed the enthusiasm on the studies of network robustness. However, existing studies on network robustness mainly focus on multilayer networks while little attention is paid to multipartite networks which are an indispensable part of complex networks. In this study, we investigate the robustness of multipartite networks under intentional node attacks. We develop two network models based on the largest connected component theory to depict the cascading failures on multipartite networks under target attacks. We then investigate the robustness of computer-generated multipartite networks with respect to eight node centrality metrics. We discover that the robustness of multipartite networks could display either discontinuous or continuous phase transitions. Interestingly, we discover that larger number of partite sets of a multipartite network could increase its robustness which is opposite to the phenomenon observed on multilayer networks. Our findings shed new lights on the robust structure design of complex systems. We finally present useful discussions on the applications of existing percolation theories that are well studied for network robustness analysis to multipartite networks. We show that existing percolation theories are not amenable to multipartite networks. Percolation on multipartite networks still deserves in-depth efforts.

A new methodology for preliminary design of centrifugal impellers with prewhirl

2019 ◽  
Vol 234 (3) ◽  
pp. 251-262 ◽  
Author(s):  
Xiaojian Li ◽  
Yijia Zhao ◽  
Zhengxian Liu ◽  
Hua Chen
Keyword(s):  
Centrifugal Compressor ◽  
Maximum Flow ◽  
High Flow ◽  
Centrifugal Impeller ◽  
Preliminary Design ◽  
Design Specification ◽  
Vaned Diffuser ◽  
Flow Capacity ◽  
Compressor Design ◽  
Aerodynamic Parameters

The overall trend of centrifugal compressor design is to strive for high aerodynamic performance and high flow capacity products. A new methodology is derived to implement a preliminary design for high flow capacity centrifugal impeller with and without prewhirl. First, several new non-dimensional equations connecting impeller geometric and aerodynamic parameters are derived for the maximum flow capacity. The effects of prewhirl on mass flow function, inlet diameter ratio and work coefficient are discussed, respectively. Then, based on these equations, a series of design diagrams are drawn to extract the universal rules in centrifugal impeller design with prewhirl. Some physical limits of design maps are also discussed. Finally, the throat area of impeller is discussed under prewhirl, and the matching principle between prewhirl impeller and vaned diffuser is derived and validated. The proposed method can be used to design a new centrifugal compressor, or to evaluate the design feasibility and the challenge of a given design specification.

Maximum airflow through the nose in humans

1991 ◽  
Vol 70 (3) ◽  
pp. 1369-1376 ◽  
Author(s):  
J. Pertuze ◽  
A. Watson ◽  
N. B. Pride
Keyword(s):  
Peak Flow ◽  
Maximum Flow ◽  
Nasal Breathing ◽  
Flow Capacity ◽  
Maximum Expiratory Flow ◽  
Maximum Effort ◽  
Flow Through ◽  
Expiratory Flow ◽  
Nasal Flow ◽  
Alpha Adrenergic Agonist

Inspiratory and expiratory flow via the nose and via the mouth during maximum-effort vital capacity (VC) maneuvers have been compared in 10 healthy subjects. Under baseline conditions maximum flow via the nose was lower than that via the mouth in the upper 50-60% of the VC on expiration and throughout the VC on inspiration. The mean ratio of maximum inspiratory to maximum expiratory flow at mid-VC was 1.38 during mouth breathing and 0.62 during nasal breathing. Inspiratory flow limitation with no increase in flow through the nose as driving pressure was increased above a critical value (usually between 12 and 30 cmH2O) was found in all six subjects studied. Stenting the alae nasi in seven subjects increased peak flow via the nose from a mean of 3.49 to 4.32 l/s on inspiration and from 4.83 to 5.61 l/s on expiration. Topical application of an alpha-adrenergic agonist in seven subjects increased mean peak nasal flow on inspiration from 3.25 to 3.89 l/s and on expiration from 5.03 to 7.09 l/s. Further increases in peak flow occurred with subsequent alan stenting. With the combination of stenting and topical mucosal vasoconstriction, nasal peak flow on expiration reached 81% and, on inspiration, 79% of corresponding peak flows via the mouth. The results demonstrate that narrowing of the alar vestibule and the state of the mucosal vasculature both influence maximum flow through the nose; under optimal conditions, nasal flow capacity is close to that via the mouth.

scholarly journals Voltage Degradation Model of Thin Film Capacitors

1985 ◽  
Vol 12 (1) ◽  
pp. 63-70
Author(s):  
T. M. Berlicki
Keyword(s):  
Thin Film ◽  
Experimental Data ◽  
Breakdown Voltage ◽  
Short Term ◽  
Degradation Model ◽  
Damage Rate ◽  
Thin Film Capacitors ◽  
Voltage Degradation ◽  
Critical Damage

A degradation model of thin film capacitors is presented. This model takes into consideration that: (a) the damage rate dD/dt is a function of the damage value D, and (b) the critical damage Dcis a function of working voltage. On the base of this model, the short term breakdown voltage and its distribution is defined. The experimental data presented conforms with the described model.

scholarly journals A multiple perspective method for urban subway network robustness analysis

AIP Advances ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. 075219 ◽  
Author(s):  
Shuliang Wang ◽  
Sen Nie ◽  
Longfeng Zhao ◽  
H. Eugene Stanley
Keyword(s):  
Robustness Analysis ◽  
Network Robustness ◽  
Multiple Perspective

scholarly journals Stock Market Temporal Complex Networks Construction, Robustness Analysis, and Systematic Risk Identification: A Case of CSI 300 Index

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Xiaole Wan ◽  
Zhen Zhang ◽  
Chi Zhang ◽  
Qingchun Meng
Keyword(s):  
Stock Market ◽  
Complex Networks ◽  
Robustness Analysis ◽  
Risk Identification ◽  
Network Robustness ◽  
Degree Centrality ◽  
Market Risks ◽  
Stationary Test ◽  
Systematic Risks ◽  
Quantitative Indexes

The Chinese stock 300 index (CSI 300) is widely accepted as an overall reflection of the general movements and trends of the Chinese A-share markets. Among the methodologies used in stock market research, the complex network as the extension of graph theory presents an edged tool for analyzing internal structure and dynamic involutions. So, the stock data of the CSI 300 were chosen and divided into two time series, prepared for analysis via network theory. After stationary test and coefficients calculated for daily amplitudes of stock, two “year-round” complex networks were constructed, respectively. Furthermore, the network indexes, including out degree centrality, in degree centrality, and betweenness centrality, were analyzed by taking negative correlations among stocks into account. The first 20 stocks in the market networks, termed “major players,” “gatekeeper,” and “vulnerable players,” were explored. On this basis, temporal networks were constructed and the algorithm to test robustness was designed. In addition, quantitative indexes of robustness and evaluation standards of network robustness were introduced and the systematic risks of the stock market were analyzed. This paper enriches the theory on temporal network robustness and provides an effective tool to prevent systematic stock market risks.

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