The central government of China has intensively guided regional integration and policy coordination towards the development of digital governance in the last ten years. The Guangdong-Hong Kong-Macao Greater Bay was one of the most important regions of China expected to accelerate regional development through policy coordination and establishment of digital infrastructures. This article adopted the method of content analysis to explore the policy transitions of digital governance in the Greater Bay including policy contents (in terms of policy objectives and instruments) and policy networks. Based on our empirical analysis, we found that top-down guidance from the central government did not necessarily generate regional coordination. Different governments of the same region could start policy coordination from shared policy objectives and policy instruments and establish innovative governance frameworks to achieve consensus. Therefore, regional coordination could be fulfilled.

With the continuous development of science and technology, network technology has been applied to various fields, and the education model of universities has also made innovations with the application of network technology. In ideological and political education in universities, influenced by traditional educational models and other factors, the quality of education is uneven, and the learning effectiveness of students needs to be improved. Therefore, integrating network technology and innovating teaching methods in ideological and political education in universities is very important. Conducting online ideological and political education in universities can enhance students' interest in learning, while also helping them develop good moral qualities and providing assistance for their future development. This article focuses on the research goal of ideological and political education models in universities, exploring the importance and methods of integrating online ideological and political education in universities, hoping to provide some help for relevant universities.

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.