In the majority of cases, networks are beneficial. However, many times it may also be harmful, such as terrorist networks and disease spreading networks. It has become an urgent challenging problem to disintegrate these harmful networks by various methods such as immunization, block, isolation, disturbance, and attack. The core task of network disintegration is to identify the “critical nodes (edges)”. This survey firstly gives the mathematical description of network disintegration. On this basis, this survey then reviews the status of network disintegration study in the fields of operations research, network science, and computer science based on mathematical programming, the centrality metrics, the heuristic algorithms, evolutionary computation, and machine learning, respectively. Lastly, this survey presents the prospects of network disintegration study from the aspects of the target network, disintegration model, and algorithm.

Synergy is ubiquitous in contagion processes on complex networks. Most existing studies have been focused on the continuous models, yet the discrete models received less attention. Motivated by this, we employ the generating function method to study a two-state (active or inactive) threshold model on complex networks with different synergistic effects. Compared to the case without synergy, the positive synergy enhances prevalence and weakens systematic robustness. The negative synergy, however, plays an opposite role. These effects are strengthened when the network is heterogeneous.

To clarify the positioning of the industrial chain of FPGA (Field programmable gate array) during the localization process, and to optimize the knowledge transfer network, the article proposes has the analytical models in the industrial chains, and the creation of the knowledge transfer network using complex network analysis method and the analysis of network structural features. The results have demonstrated that China occupies a low share in the industrial chains and its development is also unbalanced. The knowledge transfer network mainly focuses on the core enterprises characterized by small world and following power-law distribution. Core nodes exert a significant impact on the industrial evolution. Based on the perspective of network optimization, this paper puts forward policy suggestions such as establishing a modern knowledge transfer network development system and forming a new international competitive advantage covering the whole industrial chain.

After the outbreak of COVID-19, it is of great significance to find an appropriate dynamic model of COVID-19 epidemic in order to master its transmission law, predict its development trend, and provide corresponding prevention and control basis. In this paper, the SEIRV chamber model is adopted, and the dynamics model of infectious disease is established by combining the fractional derivative of Conformable. The fractional derivative differential equation of Conformable is discretized by numerical method and its numerical solution is obtained. In addition, numerical simulation was carried out on the confirmed data of Wuhan city from January 23, 2020 to February 11, 2020. At the same time, consider that the Wuhan municipal government revised the epidemic data on February 12, 2020, adding nearly 14,000 people. The order α value of SEIRV model is modified, and then the revised data is simulated. The simulation results are in good agreement with the published data. The results show that compared with the traditional integer order model, the fractional order model can simulate the modified data. This reflects the advantages of fractional infectious disease dynamics model, and can provide certain reference value for the prediction of COVID-19 model.

In order to explore the evolution mechanism of the cooperation behavior of general aviation enterprises on safety supervision, an evolution game model is proposed based on the perspective of complex network. With the model, this paper analyzes the influence of network size, network average degree, cumulative payoff weight, cost-income ratio, reward and punishment of regulators. The results show that the increase of network size and network average degree contributes to the increase of the equilibrium density of cooperators; the increase of cumulative payoff weight promotes the cooperation behavior in the network; however, the increase of cost-income ratio inhibits the game cooperation behavior in the network; government reward and punishment can promote the cooperation behavior, and the effect of punishment is more significant than reward. The regulatory strategies of general aviation safety can be concluded including increasing government support, building network communication platform, reducing enterprise operation cost, and building industry reward and punishment mechanism.

The disharmony and conflict of the physician-patient relationship are becoming a real dilemma that the medical industry and the whole society have to face, which seriously affects people′s sense of happiness and health. Starting from multiple dimensions such as information asymmetry, trust, moral hazard, and the rewards and punishments of arbitration institutions, this study used evolutionary game theory and replication dynamic equations to construct an evolutionary game model for the choice of cooperation strategy between physicians and patients and then explored the evolving situation of the physician-patient relationship. Finally, the study simulated the evolution path of physician-patient strategy by numerical simulation method and analyzed the results. The results showed that: the physician-patient relationship will eventually stabilize in two states of cooperation and conflict; trust level, degree of information asymmetry, moral hazard, and reward and punishment of arbitration institutions are important factors that can affect the evolution of cooperation path between doctors and patients. Increasing the trust level and the reward and punishment of arbitration institutions, reducing the degree of information asymmetry and the moral hazard for both doctors and patients will prompt the physicians and patients to shift from conflict to cooperation.

In this paper, we study a class of synchronous control problems called Markov complex systems. First, according to the Lyapunov stability theorem and the linear matrix inequality method, this paper constructs a new augmented closed-loop functional, which considers the state information of the current sample and the previous sample at the same time. Thus, a lower conservative stability criterion for Markov complex systems is obtained. At the same time, a design method of data sampling controller is given. Finally, numerical and simulation examples are established, and the simulation results prove that the method proposed in this paper has obvious advantages over other methods.

It is important to obtain low intervention fire prevention and control strategy in dense building areas by detecting the key building that dominate the extent of fire spread. First, established the spread directed graph model based on the multi-scenario building group fire spread simulation results. Then,the complex network method was introduced to study the appropriate sorting method of important nodes. Furthermore, a search algorithm for the reconstruction sequence of important nodes is proposed, and the number of retrofit nodes is determined with fire loss expectation as the evaluation index. Finally, the above method is applied study the issues of fire spread prevention and control in wooden village buildings. The results show that the critical buildings and their fire protection priorities can be accurately determined by the method of this paper,but their effects are different. The risk of fire spread can be significantly reduced through improving the fire protection levels of a relatively small number of buildings.

The frequency of bin entry and exit is one of the key factors affecting the efficiency of the “part-to-picker” picking system based on the shuttle storage system. In case of sufficient goods in the bin, the bins of a certain kind of goods required by the same batch of orders only need to be shipped out once. To allocate similar orders to one batch and to reduce the number of bins in and out of the warehouse can improve the picking efficiency of the system. Taking the minimum number of bins out of the warehouse as the objective function, an order batching optimization model is established. According to the characteristics of the model, an improved genetic algorithm is designed. A hybrid crossover strategy is proposed. On the basis of elite retention, partial search is performed on part of the elite chromosomes of each generation with a certain probability to improve the convergence speed and solution accuracy of genetic algorithm. The simulation results show that the total number of outgoing of bins is reduced after optimization, and the picking efficiency of the system is improved and the approach proposed is valid.

Aiming at the problem that the multi-agent system structure will change randomly due to external disturbances, a mean-square consensus control algorithm is proposed for leader-follower multi-agent system when switching signals obey semi-Markov process. The stability of the control algorithm is analyzed by algebraic graph theory, the properties of the transition rate and the distribution function of the dwell time of semi-Markov procession and Lyapunov stability theory. The results show that sufficient conditions for the system to achieve mean square consensus is given by the designed impulsive control protocol. In addition, if the pulse interval has an upper bound, the mean square consensus can still be achieved. Finally, the validity of the proposed condition is verified by the numerical example.

This paper proposes a method to detect the compromised actuators and then determine the attack types. Based on sensor measurements, the actual control command conducted by the actuator can be estimated using maximum likelihood estimation method; and then by comparing the estimated command with the reference one which is sent from the on-board computer, we are able to figure out whether the control command is successfully conducted by the actuator or not. Experimental results based on simulation platform show that the detector can effectively detect error once the attack occurs and precisely identify the specific type of the sensor attack.

To improve the randomness of chaotic systems, two chaotic systems with anti-structures have been proposed that are combined into a new automatic switching chaotic system by virtue of a switching function. The basic dynamic behaviors and rich chaotic characteristics of the switched chaotic system are analyzed upon studying the stability of the equilibrium point, chaotic attractor phase diagram, Lyapunov exponent spectrum, bifurcation diagram, Poincaré cross section and power spectrum of the system. With the help of the calculation of the spectral entropy complexity (SE) and C0 complexity, the complexity of switched chaotic system is proved to be substantially higher than that of the sub-chaotic system. Based on Multisim software, the analog circuit simulation experiment of the system is designed. The simulation results show that the switched system is feasible. The synchronization of the two systems is realized by the coupling synchronization method, and it is applied to the chaotic masking secure communication. The experimental results show that this method is feasible and effective.

The high storage and computing cost limits the application of neural network model in low computing power platform. To overcome the above shortages, we proposed two combined probability optimization algorithms to combine multiple light neural networks in a continuous time window, which can significantly reduce computing load under similar accuracy. The proposed scheme gives a general way for related algorithm on embedded hardware. To verify its effectiveness, a group of experiment was executed on the pain expression recognition with continuous peak values. In comparison to other conventional algorithms, the model complexity, computing load and storage of proposed scheme decrease obviously under consistent accuracy.