In epidemic prevention and control efforts, the rational allocation of medical resources has consistently been a focal point of attention for professionals in the field. In order to investigate the practical effectiveness of various immune measures in epidemic prevention during the process of pandemic spread, this study introduces an infectious disease model within complex networks that considers bidirectional immune interventions. Through theoretical analysis and numerical simulations of the model, we delve into a detailed discussion on the impact of immune measures targeted at different population groups on the transmission of the virus. In the theoretical analysis, the stability of the disease-free equilibrium point in the model is examined through the incorporation of the basic reproduction number analysis. In numerical simulations, the impact of bidirectional immunization and population mobility on the spread of infectious diseases is scrutinized through Monte Carlo simulations within the context of complex networks. Simulation results indicate that, compared to enhancing the recovery rate of infected individuals, increasing the immunization rate among susceptible individuals can more effectively reduce the scale of infectious diseases.

In order to reveal the characteristics of multilayer complex networks, three kinds of evolution models of three-layer complex networks are proposed, and a probability is defined to quantitatively describe the interlayer dependence of multilayer networks. The experimental results show that different random probabilities represent the degree distribution of three-layer networks with the characteristics of single peak, double peak and three peak. When the probabilities tends to 10^-3, the three-layer scale free networks show the characteristics of power law and single peak co-occurrence; the influence of probability on the clustering coefficient and average path length of three-layer networks is relatively small.

In order to study the impact of stop-loss trading on traders′ behavior and asset prices in the market, this paper constructs a multi-agent market model with stop-loss strategy based on the method of agent-based computational finance. The model simulation results show that when the stop-loss threshold is touched in the market, it is easy to trigger continuous stop-loss trading, resulting in a behavioral cascade between traders. This kind of transaction cascading leads to an increase in the convergence of trader behavior, an imbalance between sell orders and buy orders in the market, an abnormal collapse in market prices and a liquidity black hole.

Complex criminal cases today present systematic characteristics of multi-factor coupling and dynamic evolution, and their investigation process faces the challenge of nonlinear information integration. Criminal suspects use anti-detection methods such as changing clothes and shoes, facial obstruction, and posture camouflage, combined with complex environmental interference, which significantly reduces the practical effectiveness of single technical means such as face recognition and video structuring. In order to resolve this problem, this article focuses on the actual needs of suspect identification and tracking, breaks through the recognition bottleneck of a single modality, systematically integrates multi-information such as video structuring, face recognition, and gait recognition, and proposes a multi-information fusion video investigation system with gait recognition as the core, which realizes the dual characterization of suspect behavior patterns and identity characteristics, and provides a new technical path for improving identity recognition capabilities and the efficiency of solving complex cases.

The conditions for the spontaneous emergence of cooperation under the complex human behavior model have become the focus of many disciplines. Exploring the conditions for cooperation has both important scientific significance and theoretical value for understanding the institutional arrangements in human society. The social dilemma games provide a theoretical prototype for studying cooperation issues between multiple individuals. As a dynamic analysis method that can describe individuals′ learning and strategy adjustment processes, evolutionary game theory has been one of the most effective frameworks for studying the evolution of cooperation. This review article systematically summarizes the research progress of using the evolutionary game method to study the issues of group cooperation in social dilemma games. Specifically, the following topics are included: research progress of (1) social dilemma game models, evolutionary game theory and equilibrium analysis methods, (2) social dilemma games and the evolution of cooperation under reward/punishment mechanism and reputation mechanism, (3) social dilemma games and the evolution of cooperation with separation strategy and extortion strategy, and (4) social dilemma games and the evolution of cooperation under the network reciprocity. Finally, prospects for further research issues in this area are presented.

In order to provide effective decision-making basis for improvement of complex network invulnerability and protection of important nodes, this paper establishes a complex network invulnerability and node importance evaluation model based on redundancy. Firstly, the redundancy of complex networks is defined. At the same time, based on the redundancy, the invulnerability of the network is quantified. Then, this paper uses the global attribute of redundancy to evaluate the importance of each node in the network by means of node deletion. Finally, this paper uses actual networks for simulation experiments. The results show that the model and algorithm can provide a solution to the problem of high invulnerability network construction under some cost constraints, and at the same time they are effective and superior for evaluating the importance of nodes in larger networks.

Aiming at the problem that the traditional propagation model is difficult to accurately describe the law of information propagation in the blockchain social network, based on the information propagation characteristics of blockchain social networks, this paper considers the influence of the incentive mechanism of blockchain social networks on information propagation, uses evolutionary games to define the state transition probability and proposes a new blockchain social network information propagation model. The influence of group density, state transition probability, and incentive policies on information propagation is analyzed through simulation experiments. Experimental results show that the model can accurately describe the law of information propagation in blockchain social networks. The model proposed in this paper can effectively restrain the propagation of low-quality information in social networks and further build a good network public opinion environment.

Based on the Lyapunov stability theory, the theoretical analysis shows that under appropriate conditions, the drive-response network can achieve global synchronization through adaptive pinning control method. This method is more general and convenient. At the same time, this paper designs a secure communication system based on chaotic masking technology, which can realize one-to-many real-time transmission of information, and has the characteristics of fast decryption speed and high security. Finally, the correctness of the theoretical results is verified by numerical simulation.

In this paper, we use generative adversarial network (GAN) to improve semantic segmentation of images. The model is composed of a semantic segmentation network and a discriminant network, where the segmentation network responses for generating semantic segmentation result while the discriminant network responses for detecting the difference between the generated result and the labels on the global structure level and improving the segmentation effect. In order to extract context information, we adopt the spatial pyramid pooling module in the segmentation network, which could perform pooling operation on multiple levels of sub-regions. Meanwhile, in order to solve the problem of a large number of manual annotations needed in the semantic segmentation data set, we use the discriminant network to generate pseudo labels and realize semi-supervision in the training of the segmentation network. The model has been tested using PASCAL VOC2012 dataset, and the results show that supervised and semi-supervised approaches proposed in this paper are superior to the existing methods.

The topological characteristics and resilience analysis of public transportation systems are of great significance in urban management to ensure its safe and sustainable operation. This paper constructs a bus-metro interdependent network model based on the passenger transfer relationship and uses deep learning to identify their network topology attributes. A comprehensive importance indicator of the nodes is established by entropy weight-technique for order preference by similarity to ideal solution (EWM-TOPSIS), and the resilience of the network under different recovery strategies are analyzed. In order to verify the applicability and accuracy of the method, this study takes Wuhan's public transportation network as an example, which has practical guiding significance for the post-disaster recovery and operation management of the urban public transportation system.