This paper analyses wherever top executives were born and wherever they attended university to reveal regional groupings of the executives that form company culture and strategy in China and the mechanisms by which they affect corporate performance. It was found that the personal histories of top executives affect their decision-making orientation, and, in turn, company culture. The personal histories of executives and intra-regional, intra-provincial and intra-city links of corporate headquarters were obvious factors for executive selection. Distances were higher, and percentages of intra-regional links were lower for higher profit and growth companies. This shows that more competitive companies are more likely to hire executives who have lived in different regions or institutions in their lifetimes and university educations. The study concludes that Chinese firms’ key choices are influenced, in part, by external geographic factors way more advanced than the self-operation of individual enterprises.

By referring to relevant literature, we will deeply study the development of school sports in the early Republic of China, the rise of the New Culture Movement, the advocates and advocates of new culture, actively promote "new sports", and strive to overthrow the dregs of military nationalism education ideas and change the impact of Japanese gymnastics on school sports. With the outbreak of the "May Fourth Movement", the New Culture Movement not only had a greater impact on education and culture, but also accelerated the domestic political game to a certain extent, which ultimately led to the transformation of school physical education and the realization of Pragmatism education. In view of this, this article will start from an overview of the characteristics of school sports in the early Republic of China, focus on the analysis of the impact of the May Fourth New Culture Movement on the transformation of school sports in the early Republic of China, and then explore the incentives for the transformation of school sports under the May Fourth New Culture Movement, hoping to play a certain reference role.

In agriculture, crop yield and quality are critical for global food supply and human survival. Challenges such as plant leaf diseases necessitate a fast, automatic, economical, and accurate method. This paper utilizes deep learning, transfer learning, and specific feature learning modules (CBAM, Inception-ResNet) for their outstanding performance in image processing and classification. The ResNet model, pretrained on ImageNet, serves as the cornerstone, with introduced feature learning modules in our IRCResNet model. Experimental results show our model achieves an average prediction accuracy of 96.8574% on public datasets, thoroughly validating our approach and significantly enhancing plant leaf disease identification.

Since the proposal of the low-carbon economy plan, all countries have deeply realized that the economic model of high energy and high emission poses a threat to human life. Therefore, in order to enable the economy to have a longer-term development and comply with international low-carbon policies, enterprises need to speed up the transformation from a high-carbon to a low-carbon economy. Unfortunately, due to the massive volume of data, developing a low-carbon economic enterprise management model might be challenging, and there is no way to get more precise forecast data. This study tackles the challenge of developing a low-carbon enterprise management mode based on the grey digital paradigm, with the aim of finding solutions to these issues. This paper adopts the method of grey digital model, analyzes the strategy of the enterprise to build the model, and makes a comparative experiment on the accuracy and performance of the model in this paper. The results show that the values of MAPE, MSE and MAE of the model in this paper are the lowest. And the r^2 of the model in this paper is also the highest. The MAPE value of the model in this paper is 0.275, the MSE is 0.001, and the MAE is 0.003. These three indicators are much lower than other models, indicating that the model has high prediction accuracy. r² is 0.9997, which is much higher than other models, indicating that the performance of this model is superior. With the support of this model, the efficiency of building an enterprise model has been effectively improved. As a result, developing an enterprise management model for the low-carbon economy based on the gray numerical model can offer businesses new perspectives into how to quicken the shift to the low-carbon economy.

This study systemically examines the numerous impacts of climate change on agriculture in Tunisia. In this study, we establish an empirical and comprehensive methodology to assess the effects of climate changes on Tunisian agriculture by investigating current climatic patterns using crop yields and socioeconomic variables. The study also assesses the types of adaptation strategies agriculture uses in Tunisia and explores their effectiveness in coping with climate-related adversities. We also consider some resilience factors, namely the ecological aspect and economic and social camouflage pursued by the (very) men in Tunisian agriculture. We also extensively discuss the complex interconnected relationship between policy interventions and community-based adaptations, a crucial part of the ongoing debate on climate change adaptation and resilience in agriculture. The findings of this study contribute to this important conversation, particularly for areas facing similar challenges.