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This study investigates the roles and interactions of various actors of one-stop digital culture platforms (ODCPs) by applying actor-network theory (ANT).

Data were collected from focus group interviews with 19 participants and over 20 policy texts related to the digitization of cultural services in China. Utilizing ANT as the analytical framework, we employed the grounded theory to analyze the dataset.

The empirical results indicate that: (1) governments, cultural institutions, enterprises and users each play dual roles, contributing to the functionality and development of ODCPs; (2) introduces the theory of commons to reconceptualize ODCPs as commons, highlighting the need for collective management and shared responsibility in the governance of ODCPs.

This study identifies and elaborates on the different roles of stakeholders and addresses conceptual ambiguities within ODCP projects. By introducing the theory of commons into the governance of ODCPs, this research provides new theoretical guidance for decision-makers and practitioners involved in ODCP projects.

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.

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.

The purpose of this paper with pork as a case is to analyze Chinese consumer preference and demand for traceable food attributes, in order to provide a useful reference for Chinese Government in developing the safe food market and preventing food safety risks.

This research surveyed 1,380 consumers in seven pilot cities that designated by the Chinese Ministry of Commerce for the construction of a meat and vegetable circulation traceability system. A choice-based conjoint analysis and multinomial logit model were used to study consumer preferences and demand for traceable pork attributes.

The results demonstrated that certification of traceable information was the most important characteristic, followed by appearance and traceable information. Significant heterogeneity was obtained in consumer preferences for the attributes of traceable pork. Also, consumers’ preferences for traceable attributes were memorably influenced by age, education level, and income level.

Based on these results, the government should encourage and support the production of traceable food with different certification types and different traceability levels. Meanwhile, the development of food traceability systems should be combined with a quality certification labeling system.

This study extends the applicability of the setting of traceable food attributes and levels in China, and it will improve Chinese food traceability systems through multilateral cooperation.

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.

This paper aims to obtain optimal specialization mode and level for complex network or system structures. In the e-commerce system, this paper studies the changes of each transaction subject in the process of ecological structure based on the income level of each transaction subject.

This paper aims to research the change of transaction efficiency evolution process of intermediaries. With the improvement of transaction efficiency, intermediaries interact with other transaction subjects at given modes in e-commerce systems. This paper analyzes the relationship between the factors of production and trade and explains the quantitative relationship between them in the form of mathematical modeling. An evolution simulation framework is established to elaborate the simulation process and method of crowd network in e-commerce ecosystem and then sets up the simulation experiment.

During simulation processes, the changes of data are observed and analyzed to obtain the optimal evolution paths and specialization modes. Furthermore, this paper provides solid supports for the research of the quantitative analysis of ecological structure evolutions.

Evolution simulation of ecological structure is first proposed in the topic of crowd network. It is with the aid of the concept of ecology, the theory and method, simulation of complex network structure and system structure. This paper analyses and researches the evolution process of optimal specialization modes and intelligent level of crowd networks with transaction efficiency changing. The ecological structure optimal evolution paths can be obtained by trend of simulations.

The purpose of this paper is to evaluate the performance of 3D printed materials for use as rapid tooling (RT) molds in low volume thermoforming processes such as in manufacturing custom prosthetics and orthotics.

3D printed specimens of different materials were produced using the Z‐Corp process. The parts were post processed using both standard and alternative methods. Material properties relevant to the 3D printed parts such as pneumatic permeability, flexural strength and wear rate were measured and compared to standard plaster compositions commonly used.

Three‐dimensional printing (3DP) can replicate the performance of the plaster materials traditionally used in prosthetic/orthotic applications by using modified post process techniques. The resulting 3D printed molds can still be modified and adjusted using traditional methods. The results show that 3D printed molds are feasible for thermoforming prosthetic and orthotic devices such as prosthetic sockets while providing new flexibility.

The proposed method for RT of a mold for prosthetic/orthotic manufacturing provides great flexibility in the manufacturing and fitting process while maintaining proven materials in the final device provided to patients. This flexibility increases the value of digital medical records and efforts to develop model‐based approaches to prosthetic/orthotic device design by providing a readily available process for recreating molds. Depending on the needs of the practitioners and patients, 3DP can be incorporated at a variety of points in the manufacturing process.