The Chinese government has long attached great importance to rural revitalization. The harmony and stability in rural areas are the fundamental guarantees of rural sustainable economic growth and farmers’ prosperity, and they rely on effective rural governance. Taking the Fu’an City in Fujian Province of China as an example, this paper analyzes the rural governance problems arising from the implementation of the rural revitalization strategy with the method of dynamic games with incomplete information after data collection by on-the-spot investigation and file inquiry, etc. The results show that the “solicitation” behavior of village B to increase its own income does not maximize the income of village B and village N; Even under the optimal state of income distribution is derived through the game model, the income distribution between the two villages is still unfair to village N, the investor of the “Waterlands Resort”. Therefore, in order to solve the rural governance problems caused by the distribution of benefits between village B and village N, government subsidies, property rights protection, village rules and regulations are required. Besides, the leading role of rural grassroots organizations should be given full play, and villagers’ self-governing system needs to be improved.
At present,the social waterlogging disasters are frequent and there are many uncertain factors in the analysis of waterlogging. In this study, we use dynamic Bayesian model to analyze the probability of uncertain disasters, and achieve the wargame technology of waterlogging disasters in Harbin Daoli District. The experimental results show that Bayesian network is feasible for analyzing the total probability of waterlogging disaster under wargame technology model. It is great significance for further research the effects of uncertain disasters.
In view of the fact that the traditional graph model method which only considers statistical features or general semantic features when extracting keywords from existing massive educational resources, lacks the function of mining and utilizing multi-factor semantic features, this paper proposes an improved TextRank-based algorithm for keyword extraction of educational resources. According to the characteristics of Chinese text and the shortcomings of traditional TextRank algorithm, the improved algorithm featuring multi-feature fusion is developed using the importance of words in the corpus, the location information in the text and the attributes of words. Experimental results show that this method has higher accuracy, recall rate, and F-measure value than traditional algorithms in the process of keyword extraction of educational resources, which improves the quality of keyword extraction and is beneficial to better utilization and management of educational resources.
Urban waterlogging disaster poses a great threat to the safety of residents' life and property, so the study on the residents' safety of urban waterlogging disaster has been paid more and more attention.Taking the daoli
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