Discrete Dynamics in Nature and Society

Accurate risk assessment of international scientific and technological (S&T) cooperation is significant to international cooperation. In this paper, 5 risk dimensions with respect to the political, economic, social, cultural as well as technological of the regional comprehensive economic partnership countries (RCEP) are selected to establish a proper S&T cooperation index system for risk assessment. To calculate the weight of each index, a cross-entropy combination weighting method is proposed based on the combination weighting method of game theory. Furthermore, the standard cloud model is constructed by using the golden ratio, and the risk of S&T cooperation among the RCEP countries is analyzed by the cloud model. The results show that the combination weighting method proposed in this paper is effective, and its calculation is simpler than that of the game theory combination weighting method. Besides, compared with political, social, cultural, and technological indicators, economic indicators have a greater impact on S&T cooperation risk. Furthermore, it is also obtained from the results that the risk of S&T cooperation with China and the Philippines is at a lower to low level and medium to higher level, respectively, and the risk of S&T cooperation with other countries is all at a lower to medium level.

The synchronization behaviors of coupled oscillators under time-varying couplings are of both theoretical and practical significance. While recent studies show that synchronization is suppressed by time-varying coupling in general, the underlying mechanism is still not very clear. Here, by the kernel of sinusoidal coupling function, we revisit the effects of periodic coupling on the synchronization of networked phase oscillators. It is found that the suppressed synchronization by periodic coupling is attributed to the formation of synchronization clusters in the transition from desynchronization to global synchronization. The clusters are different in size and frequency but are all locked to the frequency of the periodic coupling. We demonstrate this phenomenon numerically in different network models and conduct a theoretical analysis on the numerical results based on the method of dimension reduction. The findings extend our knowledge on the dynamical responses of a complex network to external drivings, and shed lights on the mechanism of suppressed synchronization in periodically coupled oscillators.

This paper considers a more general eventually time-varying Beverton–Holt equation for species evolution which can include a harvesting action and a penalty for overpopulation numbers. The harvesting action may be positive (typically consisting of hunting or fishing) or negative which refers to repopulation within the environment. One considers also a penalty of quadratic type on the overpopulation and the introduction of a term related to Allee effect to take account of small levels of population. The intrinsic growth rate is assumed either to exceed unity or to be under unity. In the second case, the extinction point is a locally stable attractor while the other positive equilibrium point is unstable contrarily to the commonly studied case of intrinsic growth rate exceeding unity where the above roles are inverted. This consequence implies that the extinction point is also globally asymptotically stable for any given finite initial condition. In the case when the eventual overpopulation is penalized with a sufficiently large coefficient which exceeds a prescribed threshold, to quantify such an excess, only a globally asymptotically stable extinction attractor is present and no other positive equilibrium points exist. In the case of a positive moderate quadratic evaluation term for such an overpopulation, one or two positive equilibrium points coexist with the extinction one. The smaller one is unstable contrarily to the extinction equilibrium which is locally asymptotically stable. If it exists a second largest positive equilibrium point, being distinct to the above-given one, then it can be unstable or locally stable depending on the parameterization. Also, some methods of monitoring the population evolution through control laws on the harvesting action are discussed.

Improvements in hyperspectral image technology, diversification methods, and cost reductions have increased the convenience of hyperspectral data acquisitions. However, because of their multiband and multiredundant characteristics, hyperspectral data processing is still complex. Two feature extraction algorithms, the autoencoder (AE) and restricted Boltzmann machine (RBM), were used to optimize the classification model parameters. The optimal classification model was obtained by comparing a stacked autoencoder (SAE) and a deep belief network (DBN). Finally, the SAE was further optimized by adding sparse representation constraints and GPU parallel computation to improve classification accuracy and speed. The research results show that the SAE enhanced by deep learning is superior to the traditional feature extraction algorithm. The optimal classification model based on deep learning, namely, the stacked sparse autoencoder, achieved 93.41% and 94.92% classification accuracy using two experimental datasets. The use of parallel computing increased the model’s training speed by more than seven times, solving the model’s lengthy training time limitation.

This paper considers a repairable M/M/1 retrial queueing model with setup times. Once the system is empty, the server will be closed down to reduce operating costs. And the system will be activated only when a new customer arrives. The customer who activates the server will enter the retrial orbit waiting to reapply for service. The server may break down during the busy period. First, the steady-state probability of the model is obtained by using the probability generating function method. And we derive performance measures of the system such as the queue length of the orbit, the numerical examples are given to show the sensitivity of the performance measures. Second, the cost function is established to find the minimum cost of the system, and we study the effects of some parameters on the cost by numerical examples. Finally, from the perspective of the customer and social planner, we construct the individual utility function and the social welfare function in the almost and fully unobservable cases, and then the optimal strategy of the customers is analyzed.

Compared with the past, public opinion in the we-media era has become more difficult. How to incentivize social networking providers (SNPs) to participate in network public opinion governance and guide we-media practitioners (WPs) to standardize their dissemination are prominent problems that urgently need to be solved in response to network public opinion. This article supplements the perspective of network public opinion governance research and constructs a tripartite evolutionary game model including government, SNPs, and WPs. Then, after analyzing the influencing factors of the evolution of different agents’ strategies through model solving and numerical simulation, this article finds that reasonable rewards and punishments can promote SNPs to participate in network public opinion governance. Finally, this article proposes that the government should build a social governance system for network public opinion, which will effectively reduce governance costs and improve governance efficiency.