The influence of service performance in China's sci-tech commissioner system: Using social network analysis and interpretable machine learning.

The Sci-Tech Commissioner System (SCS) is a result of exploratory efforts by the Chinese government to use science and technology to strengthen the agricultural sector. Social network analysis (SNA) and machine learning (ML) techniques make it feasible to assess the service performance in China's SCS by using indicators such as group types and structure features. In this study, SNA and a clustering algorithm were employed to categorize service group types of sci-tech commissioners. By comparing the accuracy of different classification algorithms in predicting the clustering results, LightGBM algorithm was finally select to determine the clustering features of sci-tech commissioners and establish an interpretable ML model. Then, the SHAP was used to algorithm to analyze influences affecting service performance. Results show that the service forms of sci-tech commissioners are group-oriented, and that group types include small groups of young commissioners with close cooperation, larger groups of young and middle-aged commissioners, small groups of middle-aged and old commissioners with close cooperation, and isolated points of highly-influential commissioners. Furthermore, while group size is not the determinant of a commissioner's average performance, group structure and coordination ability were found to be more critical. Moreover, while differences in distinct types of service performance are caused by various factors, but good group structures and extensive social contacts are essential for high service performance.

Effects of Synthesis Conditions of Na0.44MnO2 Precursor on the Electrochemical Performance of Reduced Li2MnO3 Cathode Materials for Lithium-Ion Batteries.

Li2MnO3 nanobelts have been synthesized via the molten salt method that used the Na0.44MnO2 nanobelts as both the manganese source and precursor template in LiNO3-LiCl eutectic molten salt. The electrochemical properties of Li2MnO3 reduced via a low-temperature reduction process as cathode materials for lithium-ion batteries have been measured and compared. Particularly investigated in this work are the effects of the synthesis conditions, such as reaction temperature, molten salt contents, and reaction time on the morphology and particle size of the synthesized Na0.44MnO2 precursor. Through repeated synthesis characterizations of the Na0.44MnO2 precursor, and comparing the electrochemical properties of the reduced Li2MnO3 nanobelts, the optimum conditions for the best electrochemical performance of the reduced Li2MnO3 are determined to be a molten salt reaction temperature of 850 °C and a molten salt amount of 25 g. When charge-discharged at 0.1 C (1 C = 200 mAh g-1) with a voltage window between 2.0 and 4.8 V, the reduced Li2MnO3 synthesized with reaction temperature of Na0.44MnO2 precursor at 850 °C and molten salt amounts of 25 g exhibits the best rate performance and cycling performance. This work develops a new strategy to prepare manganese-based cathode materials with special morphology.