This paper analyses the stress effect of cross‐regional transfer of resource‐exhausted enterprises from eastern China to central and Western China. A tripartite evolutionary game model including the central government, the local government of the operation recipient region, and the resource‐exhausted transfer enterprises is established under the assumption of limited rationality. By analysing the evolutionary equilibrium and using MATLAB, for example, analysis, the relationship between equilibrium probability and various parameters, as well as the key influencing factors of equilibrium strategy were explored. The research shows, first, that the degree of punishment imposed by the central government on the local governments, the implementation of regulation by the local governments, and the amount of rewards/punishments implemented by the local governments for transfer enterprises are the key factors affecting evolutionary stability. Second, it shows that the local governments’ penalty for transfer enterprises has a significant impact on the convergence speed of enterprises’ strategic choice to “Completely Control Pollution.” Finally, from the perspective of the relationships between the central government and the local governments, as well as with transfer enterprises, countermeasures and suggestions are put forward to effectively prevent the stress effect of the cross‐regional transfer behaviour of resource‐exhausted enterprises.

To build holistic protection against complex and blended network threats, multiple security features need to be integrated into unified security architecture, which requires in a unified threat management (UTM). However, most existing UTMs operate by simply stringing together a number of security applications working independently without system level optimization that streamlines processing flow and leverages shared information and resources to reach high performance. In this paper, a generic framework is proposed to optimize the performance of UTMs at both algorithmic and architectural aspects by exploring the idea of integrated protocol processing (IPP). The algorithm proposed in this paper improves overall protocol processing complexity of ACL and IDS from Theta(log(M) + log(N)) to Theta(log(M +N)) . Experiments on Intel IXP2850 network processor show that our scheme outperforms existing solutions with 30% increase of throughput.

Background: This study addresses the challenge of predicting data sequences characterized by a mix of partial linearity and partial nonlinearity. Traditional forecasting models often struggle to accurately capture the complex patterns of change within the data. Methods: To this end, this study introduces a novel polynomial-driven discrete grey power model (PFDPGM(1,1)) that includes time perturbation parameters, enabling a flexible representation of complex variation patterns in the data. The model aims to determine the accumulation order, nonlinear power exponent, time perturbation parameter, and polynomial degree to minimize the fitting error under various criteria. The estimation of unknown parameters is carried out by leveraging a hybrid optimization algorithm, which integrates Particle Swarm Optimization (PSO) and the Grey Wolf Optimization (GWO) algorithm. Results: To validate the effectiveness of the proposed model, the annual total renewable energy consumption in the BRICS countries is used as a case study. The results demonstrate that the newly constructed polynomial-driven discrete grey power model can adaptively fit and accurately predict data series with diverse trend change characteristics. Conclusions: This study has achieved a significant breakthrough by successfully developing a new forecasting model. This model is capable of handling data sequences with mixed trends effectively. As a result, it provides a new tool for predicting complex data change patterns.

The purpose of this study is to construct a gray relational model based on information diffusion to avoid rank reversal when the available decision information is insufficient, or the decision objects vary. Considering that the sample dependence of the ideal sequence selection in gray relational decision-making is based on case sampling, which causes the phenomenon of rank reversal, this study designs an ideal point diffusion method based on the development trend and distribution skewness of the sample information. In this method, a gray relational model for sample classification is constructed using a virtual-ideal sequence. Subsequently, an optimization model is established to obtain the criteria weights and classification radius values that minimize the deviation between the comprehensive relational degree of the classification object and the critical value. The rank-reversal problem in gray relational models could drive decision-makers away from using this method. The results of this study demonstrate that the proposed gray relational model based on information diffusion and virtual-ideal sequencing can effectively avoid rank reversal. The method is applied to classify 31 brownfield redevelopment projects based on available interval gray information. The case analysis verifies the rationality and feasibility of the model. This study proposes a robust method for ideal point choice when the decision information is limited or dynamic. This method can reduce the influence of ideal sequence changes in gray relational models on decision-making results considerably better than other approaches.