Search results

This research aimed to determine the differences and similarities in each pilot project to understand the primary design forms and concepts of sponge city concept (SCC) projects in China. It also aimed to examine ten pilot projects in Shanghai to extrapolate their main characteristics and the processes necessary for implementing SCC projects effectively.

A literature review and field survey case study were employed. Data were mostly collected through a field survey in Shanghai, focusing on both the projects and the surrounding environment. Based on these projects' examination, a comparative method was used to determine the characteristics of the ten pilot SCC projects and programs in Shanghai.

Six main types of SCC projects among 30 pilot cities were classified in this research to find differences and similarities among the pilot cities. Four sponge design methods were classified into ten pilot projects. After comparing each project size using the same geographical size, three geometrical types were categorized into both existing and new city areas. SCC project characteristics could be identified by combining four methods and three geometrical types and those of the SCC programs by comparing the change in land-use and the surrounding environment in ten pilot projects.

The results are valuable for implementing SCC projects in China and elsewhere and future research on the impact of SCC projects.

Human behavior recognition poses a pivotal challenge in intelligent computing and cybernetics, significantly impacting engineering and management systems. With the rapid advancement of autonomous systems and intelligent manufacturing, there is an increasing demand for precise and efficient human behavior recognition technologies. However, traditional methods often suffer from insufficient accuracy and limited generalization ability when dealing with complex and diverse human actions. Therefore, this study aims to enhance the precision of human behavior recognition by proposing an innovative framework, dynamic graph convolutional networks with multi-scale position attention (DGCN-MPA) to sup.

The primary applications are in autonomous systems and intelligent manufacturing. The main objective of this study is to develop an efficient human behavior recognition framework that leverages advanced techniques to improve the prediction and interpretation of human actions. This framework aims to address the shortcomings of existing methods in handling the complexity and variability of human actions, providing more reliable and precise solutions for practical applications. The proposed DGCN-MPA framework integrates the strengths of convolutional neural networks and graph-based models. It innovatively incorporates wavelet packet transform to extract time-frequency characteristics and a MPA module to enhance the representation of skeletal node positions. The core innovation lies in the fusion of dynamic graph convolution with hierarchical attention mechanisms, which selectively attend to relevant features and spatial relationships, adjusting their importance across scales to address the variability in human actions.

To validate the effectiveness of the DGCN-MPA framework, rigorous evaluations were conducted on benchmark datasets such as NTU-RGB + D and Kinetics-Skeleton. The results demonstrate that the framework achieves an F1 score of 62.18% and an accuracy of 75.93% on NTU-RGB + D and an F1 score of 69.34% and an accuracy of 76.86% on Kinetics-Skeleton, outperforming existing models. These findings underscore the framework’s capability to capture complex behavior patterns with high precision.

By introducing a dynamic graph convolutional approach combined with multi-scale position attention mechanisms, this study represents a significant advancement in human behavior recognition technologies. The innovative design and superior performance of the DGCN-MPA framework contribute to its potential for real-world applications, particularly in integrating behavior recognition into engineering and autonomous systems. In the future, this framework has the potential to further propel the development of intelligent computing, cybernetics and related fields.

With the help of a quasi-natural experiment on Chinese policies, this study aims to understand the actual contribution of Smart City (SC) policies to the development of information and communications technology (ICT) in different cities. It also discusses the social and digital differences that such policies may generate, with a particular focus on the potential for exacerbating urban inequalities.

To achieve this, the study employs a principal component analysis (PCA) to develop an ICT development indicator system. It then employs a difference-in-differences (DID) model to analyze panel data from 209 Chinese cities over the period from 2007 to 2019, examining the impact of SC policies on ICT development across various urban settings.

Our findings show that SC policies have significantly contributed to the enhancement of ICT development, especially in ICT usage. However, SC policies may inadvertently reinforce developmental disparities among cities. Compared to less developed areas, the benefits of SC policies are more pronounced in economically booming cities. This is likely due to the agglomeration of the ICT industry and the strong allure of developed urban centers for high-caliber talent.

This study contributes to the related literature by explaining the role of SC policies in driving ICT development and by focusing on the often-overlooked impact of SC policies on urban inequality. These findings can provide guidance to policymakers on the need to recognize and address existing urban inequalities.

The purpose of this paper is to provide an understanding of principal preparation and training in China by providing a background discussion of principal preparation in a number of countries. As an illustration, it provides an overview of the curriculum used in the initial preparation of school principals at Beijing Normal University.

The paper draws mainly on writing and research from China, Australia and the USA to explore principal preparation and training in China.

In addition to providing a rich description of principal preparation in China, the paper's main findings comprise seven key challenges that confront China as it endeavours to provide quality principal preparation. These challenges include China's diversity and uneven social, cultural and educational development; limited resources in some regions throughout China; the place and importance of study tours for principal preparation; the teaching approach used to train principals; the process used for assessing principal learning during their training programs; the limited transfer of learning from the classroom to the school environment; and the timing of training for principals.

Each of the challenges arising here raises important practical implications for developers of principal training programs.

The paper paints a picture of principal preparation in China and raises a number of issues and challenges with which it continues to grapple. Of note is that China is not alone in facing some of these ongoing concerns.