Intervention against information diffusion in static and temporal coupling networks

Information diffusion in complex networks has become quite an active research topic.As an important part of this field,intervention against information diffusion processes is attracting ever-increasing attention from network and control engineers.In particular,it is urgent to design intervention schemes for the coevolutionary dynamics between information diffusion processes and coupled networks.For this purpose,we comprehensively study the problem of information diffusion intervention over static and temporal coupling networks.First,individual interactions are described by a modified activitydriven network(ADN)model.Then,we establish a novel node-based susceptible-infected-recovered-susceptible(SIRS)model to characterize the information diffusion dynamics.On these bases,three synergetic intervention strategies are formulated.Second,we derive the critical threshold of the controlled-SIRS system via stability analysis.Accordingly,we exploit a spectral optimization scheme to minimize the outbreak risk or the required budget.Third,we develop an optimal control scheme of dynamically allocating resources to minimize both system loss and intervention expense,in which the optimal intervention inputs are obtained through optimal control theory and a forward-backward sweep algorithm.Finally,extensive simulation results validate the accuracy of theoretical derivation and the performance of our proposed intervention schemes.

Manager Mobility and Private Equity Syndications from the Perspective of Coupling Networks:Evidence from China’s Private Equity Industry

This study explores whether manager mobility can influence syndications between private equity(PE)firms by constructing coupling network models.Using data from China’s private equity market from 1993 to 2017,we found that driving forces,resistant forces,and network structure play significant roles in determining resource flows between PE firms.Specifically,driving forces indicate that managers moving from domestic and foreign PE firms to state-owned PE firms are more likely to induce syndications.Furthermore,if the manager is promoted when changing jobs,mobility is likely to enhance the flow of resources.Resistant forces indicate that increased geographical distance reduces syndications.As for the influence of structure,if managers leave PE firms with higher status,they are more likely to induce syndications.This study contributes to the coupling network literature by providing a clarified three-factor framework.By exploring the characteristic of managers in state-owned private equity firms,we specified the syndication theory in China.This study can help private equity firms hire valuable managers and expand syndication networks in practice.

Synchronization in multilayer networks through different coupling mechanisms

In recent years,most studies of complex networks have focused on a single network and ignored the interaction of multiple networks,much less the coupling mechanisms between multiplex networks.In this paper we investigate synchronization phenomena in multilayer networks with nonidentical topological structures based on three specific coupling mechanisms:assortative,disassortative,and anti-assortative couplings.We find rich and complex synchronous dynamic phenomena in coupled networks.We also study the behavior of effective frequencies for layers I and II to understand the underlying microscopic dynamics occurring under the three different coupling mechanisms.In particular,the coupling mechanisms proposed here have strong robustness and effectiveness and can produce abundant synchronization phenomena in coupled networks.

Epidemic Spreading–Information Dissemination Coupling Mechanism in Heterogeneous Areas

With COVID-19 continuing to rage around the world,there is a spread of epidemic-related information on social networking platforms.This phenomenon may inhibit or promote the scale of epidemic transmission.This study constructed a double-layer epidemic spreading–information dissemination network based on the movements of individuals across regions to analyze the dynamic evolution and coupling mechanism of information dissemination and epidemic transmission.We also proposed measures to control the spread of the epidemic by analyzing the factors affecting dynamic transmission.We constructed a state probability equation based on Markov chain theory and performed Monte Carlo simulations to demonstrate the interaction between information dissemination and epidemic transmission.The simulation results showed that the higher the information dissemination rate,the larger the scale of information dissemination and the smaller the scale of epidemic transmission.In addition,the higher the recovery rate of the epidemic or the lower the infection rate of the epidemic,the smaller the scale of information dissemination and the smaller the scale of epidemic transmission.Moreover,the greater the probability of individuals moving across regions,the larger the spread of the epidemic and information.Finally,the higher the probability of an individual taking preventive behavior,the smaller the spread of the epidemic and information.Therefore,it is possible to suppress epidemic spread by increasing the information dissemination rate,epidemic recovery rate,and probability of individuals taking preventive behavior,while also reducing the infection rate of the epidemic and appropriately implementing regional blockades.

ADAPTIVE PINNING SYNCHRONIZATION OF COUPLED NEURAL NETWORKS WITH MIXED DELAYS AND VECTOR-FORM STOCHASTIC PERTURBATIONS

In this article, we consider the global chaotic synchronization of general cou- pled neural networks, in which subsystems have both discrete and distributed delays. Stochastic perturbations between subsystems are also considered. On the basis of two sim- ple adaptive pinning feedback control schemes, Lyapunov functional method, and stochas- tic analysis approach, several sufficient conditions are developed to guarantee global syn- chronization of the coupled neural networks with two kinds of delay couplings, even if only partial states of the nodes are coupled. The outer-coupling matrices may be symmetric or asymmetric. Unlike existing results that an isolate node is introduced as the pinning target, we pin to help the network realizing synchronization without introducing any iso- late node when the network is not synchronized. As a by product, sufficient conditions under which the network realizes synchronization without control are derived. Numerical simulations confirm the effectiveness of the obtained results.

Anti-noise performance of the pulse coupled neural network applied in discrimination of neutron and gamma-ray

In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination field.The experiments were conducted in two groups.In the first group,radiation pulse signals were pre-processed using a Fourier filter to reduce the original noise in the signals,whereas in the second group,the original noise was left untouched to simulate an extremely high-noise scenario.For each part,artificial Gaussian noise with different intensity levels was added to the signals prior to the discrimination process.In the aforementioned conditions,the performance of the PCNN was evaluated and compared with five other commonly used methods of n-γdiscrimination:(1)zero crossing,(2)charge comparison,(3)vector projection,(4)falling edge percentage slope,and(5)frequency gradient analysis.The experimental results showed that the PCNN method significantly outperforms other methods with outstanding FoM-value at all noise levels.Furthermore,the fluctuations in FoM-value of PCNN were significantly better than those obtained via other methods at most noise levels and only slightly worse than those obtained via the charge comparison and zerocrossing methods under extreme noise conditions.Additionally,the changing patterns and fluctuations of the FoMvalue were evaluated under different noise conditions.Hence,based on the results,the parameter selection strategy of the PCNN was presented.In conclusion,the PCNN method is suitable for use in high-noise application scenarios for n-γdiscrimination because of its stability and remarkable discrimination performance.It does not rely on strict parameter settings and can realize satisfactory performance over a wide parameter range.

Taming desynchronized bursting with delays in the Macaque cortical network

Inhibitory coupled bursting Hindmarsh-Rose neurons are considered as constitutive units of the Macaque corti- cal network. In the absence of information transmission delay the bursting activity is desynchronized, giving rise to spatiotemporally disordered dynamics. This paper shows that the introduction of finite delays can lead to the synchro- nization of bursting and thus to the emergence of coherent propagating fronts of excitation in the space-time domain. Moreover, it shows that the type of synchronous bursting is uniquely determined by the delay length, with the transi- tions from one type to the other occurring in a step-like manner depending on the delay. Interestingly, as the delay is tuned close to the transition points, the synchronization deteriorates, which implies the coexistence of different bursting attractors. These phenomena can be observed by different but fixed coupling strengths, thus indicating a new role for information transmission delays in realistic neuronal networks.

Adaptive synchronization in an array of asymmetric coupled neural networks

This paper investigates the global synchronization in an array of linearly coupled neural networks with constant and delayed coupling. By a simple combination of adaptive control and linear feedback with the updated laws, some sufficient conditions are derived for global synchronization of the coupled neural networks. The coupling configuration matrix is assumed to be asymmetric, which is more coincident with the realistic network. It is shown that the approaches developed here extend and improve the earlier works. Finally, numerical simulations are presented to demonstrate the effectiveness of the theoretical results.

Exponential synchronization of coupled memristive neural networks via pinning control

This paper is concerned with the exponential synchronization problem of coupled memristive neural networks. In contrast to general neural networks, memristive neural networks exhibit state-dependent switching behaviors due to the physical properties of memristors. Under a mild topology condition, it is proved that a small fraction of controlled sub- systems can efficiently synchronize the coupled systems. The pinned subsystems are identified via a search algorithm. Moreover, the information exchange network needs not to be undirected or strongly connected. Finally, two numerical simulations are performed to verify the usefulness and effectiveness of our results.

3D Seed-Germination-Like MXene with In Situ Growing CNTs/Ni Heterojunction for Enhanced Microwave Absorption via Polarization and Magnetization

Ti_(3)C_(2)Tx MXene is widely regarded as a potential micro-wave absorber due to its dielectric multi-layered structure.However,missing magnetic loss capability of pure MXene leads to the unmatched electromagnetic parameters and unsatisfied impedance matching condi-tion.Herein,with the inspiration from dielectric-magnetic synergy,this obstruction is solved by fabricating magnetic CNTs/Ni hetero-structure decorated MXene substrate via a facile in situ induced growth method.Ni2+ions are successfully attached on the surface and interlamination of each MXene unit by intensive electrostatic adsorption.Benefiting from the possible“seed-germination”effect,the“seeds”Ni^(2+)grow into“buds”Ni nanoparticles and“stem”carbon nanotubes(CNTs)from the enlarged“soil”of MXene skeleton.Due to the improved impedance matching con-dition,the MXene-CNTs/Ni hybrid holds a superior microwave absorp-tion performance of−56.4 dB at only 2.4 mm thickness.Such a distinctive 3D architecture endows the hybrids:(i)a large-scale 3D magnetic coupling network in each dielectric unit that leading to the enhanced magnetic loss capability,(ii)a massive multi-heterojunction interface structure that resulting in the reinforced polarization loss capability,confirmed by the off-axis electron holography.These outstanding results provide novel ideas for developing magnetic MXene-based absorbers.

PCNN based image processing and feature extraction of dual-bypass gas metal arc weld pool

In manual welding process, skilled welders can adjust the welding parameters to ensure the weld quality through their observation of the weld pool surface. In order to acquire useful information of the weld pool for control of the welding process and realizing the automatic welding, the measurement system of DB-GMA W process was established and the weld pool image was obtained by passive vision. Then, three image processing algorithms, Sobel, Canny, and pulse coupled neural network （PCNN） were detailed and applied to extracting the edge of the DB-GMA weld pool. In addition, a scheme was proposed for calculating the length, maximum width and superficial area of the weld pool under different welding conditions. The compared results show that the PCNN algorithm can be used for extracting the edge of the weld pool and the obtained information is more useful and accurate. The calculated results coincide with the actual measurement well, which demonstrates that the proposed algorithm is effective, its imaging processing time is required only 20 ms, which can completely meet the requirement of real-time control.

Image edge detection based on pulse coupled neural network and modulus maxima in non-subsampled contourlet domain

Edge is the intrinsic geometric structure of an image. Edge detection methods are the key technologies in the lleld of image processing. In this paper, a multi-scale image edge detection method is proposed to effectively extract image geometric features. A source image is decomposed into the high frequency directional sub-bands coefficients and the low frequency sub-bands coefficients by non-subampled contourlet transform （NSCT）. The high frequency sub-bands coefficients are used to detect the abundant details of the image edges by the modulus maxima （MM） algorithm. The low frequency sub-band coefficients are used to detect the basic contour line of the image edges by the pulse coupled neural network （PCNN）. The final edge detection image is reconstructed with detected edge information at different scales and different directional sub-bands in the NSCT domain. Experimental results demonstrate that the proposed method outperforms several state-of-art image edge detection methods in both visual effects and objective evaluation.

Pulse Coupled Neural Network Edge-Based Algorithm for Image Text Locating

This paper presents a method for locating text based on a simplified pulse coupled neural network （PCNN）. The PCNN generates a firings map in a similar way to the human visual system with non-linear image processing. The PCNN is used to segment the original image into different planes and edges detected using both the PCNN firings map and a phase congruency detector. The different edges are integrated using an automatically adjusted weighting coefficient. Both the simplified PCNN and the phase congruency energy model in the frequency domain imitate the human visual system. This paper shows how to use PCNN by changing the compute space from the spatial domain to the frequency domain for solving the text location problem. The algorithm is a simplified PCNN edge-based （PCNNE） algorithm. Three comparison tests are used to evaluate the algorithm. Tests on large data sets show PCNNE efficiently detects texts with various colors, font sizes, positions, and uneven illumination. This method outperforms several traditional methods both in text detection rate and text detection accuracy.

A Potential Security Threat and Its Solution in Coupled Urban Power-traffic Networks with High Penetration of Electric Vehicles

The growing penetration of electric vehicles(EVs)and the popularity of fast charging stations(FCSs)have greatly strengthened the coupling of the urban power network(PN)and traffic network(TN).In this paper,a potential security threat of the PN-TN coupling is revealed.Different from traditional loads,a regional FCS outage can lead to both the spatial and temporal redistribution of EV charging loads due to EV mobility,which further leads to a power flow redistribution.To assess the resulting potential threats,an integrated PN-TN modeling framework is developed,where the PN is described by a direct current optimal power flow model,and the TN is depicted by an energy-constraint traffic assignment problem.To protect the privacy of the two networks,an FCS outage distribution factor is proposed to describe the spatial-temporal redistribution ratio of the charging load among the remaining I FCSs.Moreover,to protect the security of the coupled networks,a price-based preventive regulation method,based on the spatial demand elasticity of the EV charging load,is developed to reallocate the charging load as a solution for insecure situations.Numerical simulation results validate the existence of the PN-TN coupling threat and demonstrate the effectiveness of the regulation method to exploit the spatial flexibility of EV loads.

Output-threshold coupled neural network for solving the shortest path problems

This paper presents a coupled neural network, called output-threshold coupled neural network (OTCNN), which can mimic the autowaves in the present pulsed coupled neural networks (PCNNs), by the construction of mutual coupling between neuron outputs and the threshold of a neuron. Based on its autowaves, this paper presents a method for finding the shortest path in shortest time with OTCNNs. The method presented here features much fewer neurons needed, simplicity of the structure of the neurons and the networks, and large scale of parallel computation. It is shown that OTCNN is very effective in finding the shortest paths from a single start node to multiple destination nodes for asymmetric weighted graph, with a number of iterations proportional only to the length of the shortest paths, but independent of the complexity of the graph and the total number of existing paths in the graph. Finally, examples for finding the shortest path are presented.

An edge-based model for non-Markovian sexually transmitted infections in coupled network

In this paper,allowing for general transmission and recovery times distributions,we proposed an edge-based age-structured-like compartmental model for STIs(EBACMS)in a coupled nctwork.We considered sexual transmissions between men with also heterosexual contacts.Mathematically,we gave the general approach of proving the nonnegativity of solutions for the system coupling ordinary and partial differen-tial equations,which can be applied to all edge-based compartment models.We then analyzed the epidemic threshold Ro with different distributions which couples the thresholds of the single-layer and bipartite networks in the percolation theory.We also studied the global stability of disease-free equilibrium with R0<1 and the final epidemic size F(the proportion of the population experiencing infection during the epidemic)with R0>1.In addition,numerical simulations indicated that given a fixed exponential transmission distribution,a higher variance(with same mean)in general recovery distribution gives smaller R0 and F.Sensitivity analysis on Ro and F in terms of the parameters illustrated that male-to-male transmission routes have a greater impact on Rg and F than the heterosexual transmission routes for the Markovian transmission process and arbitrary recovery process.The results provide a good theoretical guideline to consider the distributions of real-world STIs.

Observation of chimera patterns in a network of symmetric chaotic finance systems

The economic and financial systems consist of many nonlinear factors that make them behave as the complex systems.Recently many chaotic finance systems have been proposed to study the complex dynamics of finance as a noticeable problem in economics.In fact,the intricate structure between financial institutions can be obtained by using a network of financial systems.Therefore,in this paper,we consider a ring network of coupled symmetric chaotic finance systems,and investigate its behavior by varying the coupling parameters.The results show that the coupling strength and range have significant effects on the behavior of the coupled systems,and various patterns such as the chimera and multi-chimera states are observed.Furthermore,changing the parameters'values,remarkably influences on the oscillators attractors.When several synchronous clusters are formed,the attractors of the synchronized oscillators are symmetric,but different from the single oscillator attractor.

Chimera state in a network of nonlocally coupled impact oscillators

Chimera state is a peculiar spatiotemporal pattern,wherein the coherence and incoherence coexist in the network of coupled identical oscillators.In this paper,we study the chimera states in a network of impact oscillators with nonlocal coupling.We investigate the effects of the coupling strength and the coupling range on the network behavior.The results reveal the emergence of the chimera state for significantly small values of coupling strength,and higher coupling strength values lead to unbounded motions in the oscillators.We also study the network in the case of excitation failure.We observe that the coupling helps in the maintenance of an oscillatory motion with a lower amplitude in the failed oscillator.