Vector-borne diseases account for a high proportion of the infectious diseases in China, which constitutes a serious public health risk. Thus the research on dynamic behavior of vector-borne disease model has been widely concerned by a number of scholars. Aiming at a kind of vector-borne transmission disease. Firstly, a collaborative communication model of three-layer scale-free associated networks is established based on the heterogeneous mean field theory. Secondly, based on the established model, the relative values of global propagation threshold and each isolated subnetworks threshold are analyzed by Cauchy’s interleaving theorem and perturbation theory under the condition that the recovery rates of each subsystem are equal or at least two are unequal. It was found that collaborative communication reduced the transmission threshold and promoted the spread of the disease.

This paper investigates the generalized outer synchronization between two complex dynamic networks with different-dimensional nodes and unknown coupling strengths. Compared to the existing literature, the connection relations between nodes in the networks do not have to satisfy the dissipative condition. Supposing the corresponding nodes have similar dynamic behaviors and focusing on the error estimation of the coupling strength between two networks, the adaptive synchronization controller is synthesized based on Lyapunov stability theory. Finally, a simulation example is given to verify the effectiveness of the method proposed in this paper.

In this paper, the K-shell index is extended to the hypernetwork to avoid the influence of the nodes with larger hyperdegree but located at the edge of the hypernetwork on the mining of vital nodes. Due to the limitation of K-shell method, the result of node sorting is too rough. In order to solve this problem, this paper proposes a k^d_s (complex K-shell and degree) index to identify the vital nodes of the hypernetwork by combining the hyperdegree and K-shell (k_s) value and using the Euclidean distance formula, and verifies it by using the protein complex hypernetwork. Experiments show that k^d_s index can accurately and effectively identify the vital nodes in the hypernetwork.

The top 300 Chinese surnames are analyzed with the spatial autocorrelation method to confirm the characteristics of Chinese surnames’ concentration. A new index, diffusion distance, is proposed to quantify the spatial ranges of the concentration characteristics and the results are qualitatively explained from the perspective of the population mobility in Chinese history. Our specific results for the top 300 Chinese surnames are the following: The significant spatial autocorrelation of the relative frequencies generally exists within a short spatial range. About half of the surnames have a diffusion distance between 400 kilometers and 800 kilometers. There is a positive correlation between the diffusion distances of the surnames and their population sizes. That is, the diffusion ranges of larger-scale surnames are wider, which may be related to their longer history.

To solve the problem of path tracking control for three wheeled robots with nonholonomic dynamics characteristics, a multi robot cooperative formation control strategy is proposed by using Backstepping method. Firstly, the formation problem of multi robot system which based on pilot following mode is transformed into the problem of tracking the virtual leader's trajectory by following robot, and then the differential equation of pose error of multi robot system kinematics is obtained; then, the Backstepping method is used to construct the robot cooperative formation control strategy of nonlinear system. Lyapunov function and other knowledge are applied to analyze the robot dynamic system. It is concluded that the multi robot system is stable and can realize formation control. Finally, we verify the availability of the formation method through simulation experiments.

In this paper, the passenger evacuation movement model based on the improved cellular automata model is proposed to study the evacuation behaviors of passengers in the metro carriage under emergency. Considering passenger′s psychology of seeking advantages and avoiding harm and herding behaviors in emergency evacuation, the door selection mechanism of passengers is established on the basis of fuzzy logic theory. The improved cellular automata model is used to complete the modeling and simulation of passenger evacuation process. The effect of the fire source position in the metro carriage under a sudden fire, the effective utilization width of the metro door and the seat arrangement on the passenger evacuation under emergency are studied through the simulation experiment. Simulation results are as follows: When the fire source is located in the middle metro carriage, the evacuation efficiency will be higher than that located in the first or last metro carriage. The increase of the effective utilization width of the metro door within a certain range is conducive to improve the efficiency of passenger evacuation. The seat arrangement with single row longitudinal can ensure the efficiency of passenger evacuation on the basis of satisfying the comfort requirements of passengers to the maximum extent. The model proposed in this paper makes full use of the advantages of cellular automata model with less computation and the fuzzy logic theory which does not need to establish a complex mathematical model. This model can be used to predict the metro door selection and movement process of passengers, and then guides passengers to evacuate quickly under emergency.

Aiming at the stability of the Cournot duopoly quantum Nash equilibrium, using quantum game theory and nonlinear dynamics theory, a dynamic game model with quantum entanglement as a variable under different rational expectations is constructed. We analyze the equilibrium points and stability conditions of the model. It is concluded that the quantum equilibrium point is locally stable under certain parameter conditions. The adjustment speed of firm will cause the equilibrium point to exhibit complexity characteristics, and quantum entanglement can effectively control its stability. This paper makes a numerical simulation analysis of the model. When the parameters do not satisfy the stability conditions, chaotic characteristics such as bifurcation and strange attractors will appear.

In order to study the impact of risk aversion,We apply the system dynamics method to construct the model and simulate fresh food`s supply chain system. We subdivide the products into fresh products and discounted products so that the model is closer to the actual situation. Our experiment result show that retailers` order quantity decreases when the degree of risk aversion of retailers increases. Moreover the profits of both supply chain members and the overall supply chain decrease. It is proposed that suppliers adopt repurchase contracts and sharing the cost of retaining freshness. These methods can alleviate the negative impact of retailers′ risk aversion and improve the overall supply chain system performance.

Based on the two-state theory, under the adiabatic approximation condition, applying the properties of the stochastic potential, the system output signal-to-noise ratio (SNR) is obtained. It is shown that the SR phenomenon can be found as SNR varies with the damping coefficient, with the additive noise strength, as well as with the system parameters. With the increase of the correlation length of stochastic potential, the maximum value of SNR decreases monotonically; while with the increase of the amplitude of the stochastic potential, the maximum value of SNR increases monotonically. Numerical simulation results show that the output SNR of the system is consistent with the theoretical results.

In order to study the propagation mechanism of unsafe behaviors of university laboratory personnel and to improve the inspection of unsafe behaviors, a propagation model of unsafe behaviors was constructed based on system dynamics and SEIR propagation model. The state variables, rate variables and parameters were set up through Anylogic platform. Finally, the simulation of the model was applied to a laboratory of a university in Tianjin, and some guiding suggestions were provided. This research showed that increasing recovery rate, improvement rate, direct immunity rate, reducing exposed rate, infectious rate, forgetting rate effectively prevented and controlled the propagation of unsafe behaviors in university laboratories.

A distributed robust moving horizon estimation method is proposed for distributed network systems with random packet loss. Each node in the system can transmit data over the network to its neighbors, and when a packet is lost it uses Zero-order hold data to compensate for the measurements. Based on moving horizon principle, sensor node transforms the state estimation problem into a quadratic optimization problem in a fixed window by using the local measurement data and the data from the adjacent nodes, the performance index is fused with the method of scalar weighted linear minimum variance. The method guarantees the consistency of the system and reduces the calculation time while the accuracy of the results is guaranteed. Finally, the stability of the estimator is analyzed and compared with other estimation methods in simulation. The results show that this method has better effect.

With the increasing number of motor vehicles, people pay more and more attention to the efficiency of travel and environmental problems. To solve this problem, an improved fast non dominated sorting genetic algorithm (NSGA-Ⅱ) is proposed and applied to the optimization of regional traffic signal timing. The algorithm is a multi-objective optimization algorithm, which takes the average delay and exhaust emissions of regional traffic as the optimization objectives. After the algorithm optimization, a series of optimal values will be obtained, from which the comprehensive optimal value is selected. Finally, VISSIM microscopic traffic simulation software is used to build a simulation model for this case, and the optimization results of the method are verified. The effectiveness of the proposed method is verified by comparing it with the improved fast non dominated sorting genetic algorithm. The simulation results show that the optimized timing reduces the CO emission by 8.213% and the average vehicle delay by 19.023%.

Aiming at the problem of direct conflict between autonomous vehicles and other human-driven vehicles at intersections, an autonomous vehicle behavior decision model is built, and deep reinforcement learning is used to train autonomous vehicles when passing road intersections, allowing autonomous vehicles to make autonomous decisions and achieve fast control of complex scenarios,and the comparison with the non-dominated sorting genetic algorithm-Ⅱ verifies the stability of the autonomous vehicle.The simulation results show that the autonomous vehicle beha-vior decision-making method using the depth deterministic strategy gradient algorithm has better output speed to ensure the smooth changes of the throttle and brake values, and effectively solve the safety and comfort problems of autonomous vehicles.