Special food safety supervision by means of intelligent models and methods is of great significance for the health of local people and tourists. Models like BP neural network have the problems of low accuracy and poor robustness in food safety prediction. So, firstly, the principal component analysis was used to extract the key factors that influenced the amount of coliform communities, which was applied to reduce the dimension of this model as the input variable of BP neural network. Secondly, both the particle swarm optimization (PSO) and BP neural network were implemented to optimize initial weights and threshold to obtain the optimal parameter, and a model was constructed to predict the amount of coliform bacteria in Dai Special Snacks, Sa pie, based on PSO-BP neural network model. Finally, the predicted value of the model is verified. The results show that MSE is 0.0097, MAPE is 0.3198 and MAE is 0.0079, respectively. It was clear that PSO-BP model was better accuracy and robustness. That means, this model can effectively predict the amount of coliform. The research has important guiding significance for the quality and the production of Sa pie.

This study explores how the perceived co-creation values (PCVs) from tourists’ perspectives are applied in the customized tour arrangement service setting. The sequential qualitative and quantitative methods are adopted for this study. The initial qualitative method in terms of the proactive semi-structured interview is conducted to identify and explore the dimension of the PCV construct and to develop its measurement scale. The quantitative method by the structure equation model is employed for the proposed conceptual model fitness assessment and consolidation. Our work contributes to the progression of value co-creation research in a customized tourism context and provides a valid and reliable PCV instrument to tourism practitioners for a better service platform designing. The mediating role of customer satisfaction (CS) between PCV and customer loyalty (CL) offers service providers a deeper understanding of customer psychology and behavior, and thus, the loyal customer cultivation strategy.

Privacy and information protection are important issues in the era of big data. At present, China’s elderly care industry is gradually adopting the supply model of smart elderly care to alleviate the contradiction between supply and demand. However, the low level of regulation of smart aging platforms may lead to a low level of privacy protection on the platforms. Therefore, in this paper, based on the evolutionary game and Lyapunov theory, we discuss the willingness of elderly people to participate in regulation, the privacy protection status of platform service providers, and the degree of government regulation, as well as the key factors affecting the equilibrium of the three-party game system, and conduct simulation analysis and game system optimization using MATLAB. The simulation results show that A1(0,0,1) and A5(0,0,0) can be transformed to A8(1,1,0) by adjusting the parameters, i.e., the optimal ESS is participation, high-quality protection, and low investment supervision; the service income of the elderly, the loss of privacy leakage, the investment cost of service providers, and the amount of government rewards and punishments are the key factors affecting the tripartite game system. By analyzing the impact of factors, such as benefits and costs, on privacy protection and the regulation of smart senior care platforms, the level of privacy protection of smart senior care platforms can be improved and the process of the comprehensive regulation of domestic senior care services can be promoted.

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Abstract-The effectiveness and deterrence of food safety punitive damage system in China have been widely questioned in academic circle. Based on the Prospect Theory, this paper gives an analysis of the causes of frequent food safety incidents with the current punitive damage system in China. Simulation analysis is adopted to acquire the best range of punitive damage coefficient. And it proposes a flexible punitive damage calculation method with more discretionary space. Furthermore, it presents suggestions for improving the existing food safety punitive damage system.