Digital transformation of the agricultural industry: Behavioral decision-making, influencing factors, and simulation practices in the Yunnan highlands.

Motivating the agricultural industry to engage in digital transformation is a challenge academically and socially. It is of great significance to study the choice of digital transformation mode of agricultural industrial organization and analyze its driving factors for promoting the sustainable development of agricultural industrial organization. This study adopts a bilateral evolutionary game to construct a decision-making model for behavioral decision-making during the digital transformation of the agricultural industry. The contingent-actual logical framework and multiple case studies of Yunnan highland agriculture are used to explore the impact of various factors on behavioral decision-making during the digital transformation of the agricultural industry. Additionally, a simulation analysis is used to verify the validity of the bilateral evolutionary game model. The results demonstrate that: (1) When the agricultural industry chooses "active transformation," behavioral decision-making during the digital transformation of the agricultural industry reaches a Nash equilibrium; (2) transformation costs, industry revenue, and reward and penalty mechanisms are the main driving factors for whether or not the agricultural industry chooses to actively engage in digital transformation; and (3) the probability of active digital transformation increases when agricultural industry organizations obtain higher returns at lower costs. Simultaneously, the higher the government's incentives, the greater the enthusiasm. However, when the penalty is excessive, the digital transformation takes the shape of either passive transformation or forced active transformation. Subsequently, it is necessary to improve the digital transformation planning of the agricultural industry, strengthen this field's cooperation mechanism, and formulate a reasonable reward and penalty system for digital transformation.

Prediction of the Development Trend of the "Internet +" Logistics Industry under the "Belt and Road" Strategy.

In order to accurately predict the development trend of the "Internet +" logistics industry in the context of the new period and to understand the circulation of the Internet and logistics between countries and the development dynamics of the economy, this paper will take the "Belt and Road" initiative as the research background and elaborate the specific development mode of the "Internet +" logistics industry and the corresponding strategies. Meanwhile, the development trend is evaluated based on its development data using the method of combined forecasting. The results show that the combined prediction results fluctuate within the range of 1, indicating a high degree of prediction accuracy. This is important for predicting the trend of development forces and adjusting the strategic approach and development direction.

Development and evaluation of a holographic method for analysis of infant formula humanization based on the milk nutrient molecular dataset.

A comprehensive understanding of the qualitative and quantitative similarities and distinctions between the nutrient system of human milk and infant formula is critical in developing infant formulas. However, a holographic comparison method has not been intensively developed to measure the degree of humanization of infant formulas. Consequently, discriminative biomarkers affecting the degree of humanization of infant formulas have not been extensively investigated. This study compiled a milk nutrient molecular dataset, and then presented a new method to identify the degree of humanization of infant formula. The molecular information was converted into a matrix, and then the degree of humanization was elucidated according to the matrix correlation, PCA and OPLS-DA. Compared with infant formulas 2 and 3, infant formula 1 showed the highest degree of humanization at 0.9563. Furthermore, we reported many discriminative biomarkers, such as His, Leu, and Thr, which have not been found in other studies.