Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and routes. The current strategies for observation of ornamental plant booming periods are mainly based on manpower and experience, which have problems such as inaccurate recognition time, time-consuming and energy sapping. Therefore, this paper proposes a neural network-based method for predicting the flowering phase of pear tree. Firstly, based on the meteorological observation data of Shijiazhuang Meteorological Station from 2000 to 2019, three principal components (the temperature factor, weather factor, and humidity factor) with high correlation coefficient with the flowering phase of pear tree are obtained by using the principal component analysis method. Then, the three components are used as input factors for the BP neural network. A BP neural network prediction model is constructed based on genetic algorithm optimization. The crossover operator and mutation operator in the adaptive genetic algorithm are improved. Finally, the meteorological sample data from 2013 to 2019 are used to test and verify the algorithm in this paper. The results demonstrate that, the model can solve the local optimization problem of the BP neural network model. The prediction results of the flowering phase of pear tree are evaluated in terms of relevance and prediction accuracy. Both are superior to the traditional effective accumulated temperature and the prediction results of the stepwise regression method. This method can provide more reliable forecast information for the blooming period, which can provide decision-making reference for improving the development of tourism industry.
Taking 34 science and technology policy documents issued by Hubei Provincial Government in 2013-2020 as analysis samples, the qualitative research method was used to analyze the theme clustering of science and technology policies. Then, the science and technology policy system was sorted out based on the policy text, and the theme clustering model of science and technology policy was obtained.It is concluded that science and technology policies can be understood from function dimension and effect dimension, function dimension mainly reflects the function of science and technology policies, and effect dimension mainly reflects the supply, demand and environmental effect of science and technology policies.
Taking 34 science and technology policy documents issued by Hubei Provincial Government in 2013-2020 as analysis samples, the qualitative research method was used to analyze the theme clustering of science and technology policies. Then, the science and technology policy system was sorted out based on the policy text, and the theme clustering model of science and technology policy was obtained.It is concluded that science and technology policies can be understood from function dimension and effect dimension, function dimension mainly reflects the function of science and technology policies, and effect dimension mainly reflects the supply, demand and environmental effect of science and technology policies.
The article first sorts out, summarizes and comments the literature of sustainable growth, then points out that the relevance of sustainable growth of CSR should be viewed from the long-term time dimension, and based on the long-term perspective, the relationship between stakeholders will produce different credit capital, and they can produce synergies, reduce the development resistance, improve the competitive advantage, increase the long-term value of the enterprise, and finally realize the sustainable growth of enterprises. This paper constructs the circular evolution model of corporate social responsibility and sustainable growth, and puts forward suggestions on the emphasis on social responsibility.
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