The widespread of rumors is no doubt harmful to information security and social stability. Therefore, it is necessary to control it effectively. In this paper, a dynamic model with a double-layer interaction process is proposed to explore the impact of network interaction and dynamic evolution. Considering the individual heterogeneity and time-varying, the probability of node state transformation is not a fixed parameter, but closely related to the degree and hyperdegree of each node in the time step . Then, the theoretical analysis is conducted by Microscopic Markov Chain Approach and the impact of each parameter is tested. The simulation results show that the rumor diffusion is not only related to the interaction of the network, but also affected by the dynamic evolution of the network structure. Finally, according to the simulation results, the corresponding control strategies are proposed. The model is universal and can be reduced to traditional double-layer network model or single-layer dynamic hypernetwork model.

In the diagnosis of epileptic seizures, classification is an important step that directly affects the results. Visual inspection of Electroencephalogram (EEG) is a relatively common analytic method of epilepsy, but it is costly, time-consuming and relies on the experiences of the doctor. Therefore, the development of an efficient and accurate epileptic seizure automatic diagnosis system suitable for clinical diagnosis has become an urgent task. In order to better solve the problem of early diagnosis of epileptic and bring timely treatment to patients, the comprehensive representation of k nearest neighbors for multi-distance decision making (CRMKNN) is proposed in this research. In the proposed scheme, Euclidean distance and Hassanat distance are firstly used to select neighbors. Subsequently, the similarity distance is obtained through the linear representation of the nearest neighbors, and calculate the distribution of nearest neighbors in the category to get the discrete distance. Finally, the distance based on the comprehensive representation of the category is used to determine the category of the query EEG signal. In order to verify the method, we used the EEG signals from Bonn university public database and conducted experiments on six kinds of EEG combinations. Experimental results showed that our method could automatically detect seizure in all situations with an accuracy of not less than 99.50%. At the same time, compared with the classification results of existing methods, this method is more effective.

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With the global warming, soil erosion and a series of environmental problems are worsening. Green sustainable development has increasingly become a major issue of human concern. This study established a dynamic interaction model to explore the influence of green information and the social relations. Due to the continuous evolution of the network and the heterogeneity of individuals, the transition probability of each node is combined with time-varying parameters that reflect its current state. Then, guided by hypernetwork theory and microscopic Markov chain approach, this model is analyzed. Furthermore, the effects of various adjustment parameters on the green behaviors are compared and tested. The results show that behavior diffusion is not only affected by the diffusion of green information, but also closely related to the change of social relations. Finally, combined with the diffusion of green information, a propagation mechanism that simulates damped harmonic motion is proposed to maximize the green behavior. The results show that the final practice fraction of green behavior has been significantly improved.

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