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Based on several important environmental protection and information disclosure policies that have been issued in China, the purpose of this paper is to test the relationship between characteristics and the environmental information disclosure quality of sample companies.

The OLS regression analysis is selected for this research which takes China’s heavy pollution companies listed on the Shanghai Stock Exchange from 2015 to 2016 as samples.

The quality of these environmental information disclosures needs to be strengthened, and while the quality of the disclosures among the companies examined improved significantly in 2016 compared with 2015, there are still high variations in quality from industry to industry. In addition, the scale of company is most closely correlated to the quality of environmental information disclosure and the economic situation of the enterprises is the next. Other factors affecting the disclosure quality include in order the degree of local economic development the scale of the state-owned shares and the independent directors. Listed years and equity restriction show a positive correlation but not significant in statistics.

The research will assist administrative organizations to allocate governance sources effectively, plan governance investment as a whole, and improve the overall level of the disclosure of environmental information while strengthening the governance efficiency and effectiveness, according to the correlation and degree between the company characteristics and environmental information disclosure quality.

Public-private partnership (PPP) projects for construction waste recycling have become the main approach to construction waste treatment in China. Risk sharing and income distribution of PPP projects play a vital role in achieving project success. This paper is aimed at building a practical and effective risk sharing and income distribution model to achieve win–win situation among different stakeholders, thereby providing a systematic framework for governments to promote construction waste recycling.

Stakeholders of construction waste recycling PPP projects were reclassified according to the stakeholder theory. Best-worst multi–criteria decision-making method and comprehensive fuzzy evaluation method (BWM–FCE) risk assessment model was constructed to optimize the risk assessment of core stakeholders in construction waste recycling PPP projects. Based on the proposed risk evaluation model for construction waste recycling PPP projects, the Shapley value income distribution model was modified in combination with capital investment, contribution and project participation to obtain a more equitable and reasonable income distribution system.

The income distribution model showed that PPP Project Companies gained more transaction benefits, which proved that PPP Project Companies played an important role in the actual operation of PPP projects. The policy change risk, investment and financing risk and income risk were the most important risks and key factors for project success. Therefore, it is of great significance to strengthen the management of PPP Project Companies, and in the process of PPP implementation, the government should focus on preventing the risk of policy changes, investment and financing risks and income risks.

The findings from this study have advanced the application methods of risk sharing and income distribution for PPP projects and further improved PPP project-related theories. It helps to promote and rationalize fairness in construction waste recycling PPP projects and to achieve mutual benefits and win–win situation in risk sharing. It has also provided a reference for resource management of construction waste and laid a solid foundation for long-term development of construction waste resources.

PPP mode is an effective tool for construction waste recycling. How to allocate risks and distribute benefits has become the most important issue of waste recycling PPP projects, and also the key to project success. The originality of this study resides in its provision of a holistic approach of risk allocation and benefit distribution on construction waste PPP projects in China as a developing country. Accordingly, this study adds its value by promoting resource development of construction waste, extending an innovative risk allocation and benefit distribution method in PPP projects, and providing a valuable reference for policymakers and private investors who are planning to invest in PPP projects in China.

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

The purpose of this paper is to propose a Chebyshev collocation method (CCM) for Hallén’s equation of thin wire antennas.

Since the current induced on the thin wire antennas behaves like the square root of the distance from the end, a smoothed current is used to annihilate this end effect. Then the CCM adopts Chebyshev polynomials to approximate the smoothed current from which the actual current can be quickly recovered. To handle the difficulty of the kernel singularity and to realize fast computation, a decomposition is adopted by separating the singularity from the exact kernel. The integrals including the singularity in the linear system can be given in an explicit formula while the others can be evaluated efficiently by the fast cosine transform or the fast Fourier transform.

The CCM convergence rate is fast and this method is more efficient than the other existing methods. Specially, it can attain less than 1 percent relative errors by using 32 basis functions when a/h is bigger than 2×10−5 where h is the half length of wire antenna and a is the radius of antenna. Besides, a new efficient scheme to evaluate the exact kernel has been proposed by comparing with most of the literature methods.

Since the kernel evaluation is vital to the solution of Hallén’s and Pocklington’s equations, the proposed scheme to evaluate the exact kernel may be helpful in improving the efficiency of existing methods in the study of wire antennas. Due to the good convergence and efficiency, the CCM may be a competitive method in the analysis of radiation properties of thin wire antennas. Several numerical experiments are presented to validate the proposed method.

The global popularity of short video platforms has surged with the rapid development of mobile internet and 5G technology. DOUYIN, among other platforms, has amassed a massive user base in China. This study presents a theoretical framework based on media dependency theory and user stickiness perspectives. It identifies three key factors that affect user stickiness: platform algorithms, content resources and user interaction. An interpretive philosophy and inductive qualitative approach were adopted to conduct an in-depth case study of DOUYIN. Thematic analysis of secondary data from various sources was used. The findings demonstrate DOUYIN’s innovative approach to utilising advanced algorithms, diverse content and social interactions to enhance user engagement. DOUYIN utilises machine learning techniques to create user profiles and comprehend video content. It subsequently provides real-time personalised recommendations and optimises the algorithms based on user feedback. DOUYIN also incorporates PGC-, UGC- and PUGC-generated content, supported by a creator incentive system. Moreover, DOUYIN enables interactions between users, creators and the platform through commenting, sharing and live streaming features.

Breakthrough technological innovation is of vital significance for firms to acquire and maintain sustainable competitive advantages. The construction of an innovation ecosystem and the interaction with heterogeneous participants have emerged as a new dominant model for driving sustained breakthrough technological innovation in firms. This study aims to explore the effects of collaborative modes within the innovation ecosystem on firms’ breakthrough technological innovation and the ecological legitimacy mechanisms involved.

The research employs data from 212 innovative firms and conducts empirical research using a two-stage structural equation modeling (SEM) and artificial neural network (ANN) analysis.

The results indicate that firm-firm collaboration (FF), firm-user collaboration (FU), firm-government collaboration (FG), firm-university-institute collaboration (FUI) and firm-intermediary collaboration (FI) all have significant positive effects on breakthrough technological innovation (BTI), with FU being particularly crucial. Furthermore, the results confirm the positive moderating effects of ecological legitimacy (EL) on the relationships between FF and BTI, as well as between FU and BTI. Conversely, EL has a negative moderating effect on the relationship between FUI and BTI, as well as between FI and breakthrough technological innovation. Additionally, EL does not have a significant influence on the relationship between FG and BTI.

Through resource dependence theory (RDT), this study unveils the black box of how collaboration modes within innovation ecosystems impact breakthrough technological innovation. By introducing ecological legitimacy as a contextual factor, a new research perspective is provided for collaboration innovation within innovation ecosystems. The study employs a combination of SEM and ANN for modeling, complementing nonlinear relationships and obtaining robust results in complex mechanisms.

This paper aims to explore the relationship between ethical self-fashioning and citizenship practices in the ongoing revival of “Chinese Traditional Culture” pursued in tandem by the party-state and by private actors in present-day China.

Adopting an anthropological approach, the author draws from three sets of resources: (1) research literature on China’s political history and key texts of early Chinese thought, (2) contemporary state discourses on citizen formation, and (3) participant observation notes and interviews with organizers and followers of the Wu-Wei School (a pseudonym). The author conducts a textual analysis of primary and secondary literature and a critical discourse analysis of the ethnographic data and examines emerging themes.

Firstly, the author identifies a crucial dimension in the historical and cultural roots of Chinese citizenship practices: an enduring conception that binds individual ethical self-improvement with socio-political flourishing. Secondly, examining contemporary state discourses on “citizen quality” and “reviving China’s outstanding traditional culture”, the author showcases how party-state authorities call on individuals to self-reform for national rejuvenation. Thirdly, the author investigates how members of the Wu-Wei School construe their individual pursuits of ethical self-improvement as significant for societal progress.

Based on these findings, the author demonstrates the ways in which autochthonous conceptions of Chinese citizenship give a central place to private acts of self-fashioning. The author argues that the entanglement between individual ethics and citizenship practices constitutes a crucial but largely understudied dimension of Chinese citizenship.

Generation Z makes up 20% of China’s population, and accounts for the highest share of household spend at 13% (vs. 3% for the United Kingdom and 4% for the United States). To advance marketers’ understanding about this group of consumers and capitalise on China’s booming market, this chapter uses rich statistics and information to show that China’s Generation Z has distinct behaviour patterns, which can be attributed to the unique background in which they grew up: (1) rigidity of social stratification, (2) abundance of materialism, (3) digital era, (4) limited (vs. extended) family, and (5) heavy schoolwork. Growing up in such a background, Generation Z’s lifestyle and consumption-related attitudes and behaviour are distinct from their predecessors. The chapter presents specific actions that marketers can take when targeting this distinct group of consumers in China, along with useful guidelines to HR managers for hiring them.

Knowledge transfer which refers to the communication of knowledge from a source so that it is learned and applied by a recipient has long been a challenge for knowledge management. The purpose of this study is to understand influencing factors of transactive memory system (TMS) and knowledge transfer.

Drawing on the theories of communication visibility, social distance and flow, this study develops a research model. Then, data are collected from users of the social media mobile App. Partial least squares-structural equation modeling (PLS-SEM) is employed to analyze data.

TMS is a valid second-order construct in the social media mobile app context, which is more reflected by credibility. Meanwhile, communication visibility and social distance each have positive effects on TMS which further has a positive effect on knowledge transfer. Flow has a positive effect on knowledge transfer.

Developers of the mobile App should carefully consider the role of information and communication technology (ICT) in supporting TMS and knowledge transfer. They should consider recommendation algorithm so that the benefit of communication visibility can be retained. They should design the feature to classify users based on similarity so as to stimulate users' feeling of close social distance. They should keep on improving features based on users' holistic experience.

This study incorporates the perspectives of communication visibility, social distance and flow to understand TMS and knowledge transfer, presenting a new lens for research.

The practice of artificial intelligence (AI) is increasingly being promoted by technology developers. However, its adoption rate is still reported as low in the construction industry due to a lack of expertise and the limited reliable applications for AI technology. Hence, this paper aims to present the detailed outcome of experimentations evaluating the applicability and the performance of AI object detection algorithms for construction modular object detection.

This paper provides a thorough evaluation of two deep learning algorithms for object detection, including the faster region-based convolutional neural network (faster RCNN) and single shot multi-box detector (SSD). Two types of metrics are also presented; first, the average recall and mean average precision by image pixels; second, the recall and precision by counting. To conduct the experiments using the selected algorithms, four infrastructure and building construction sites are chosen to collect the required data, including a total of 990 images of three different but common modular objects, including modular panels, safety barricades and site fences.

The results of the comprehensive evaluation of the algorithms show that the performance of faster RCNN and SSD depends on the context that detection occurs. Indeed, surrounding objects and the backgrounds of the objects affect the level of accuracy obtained from the AI analysis and may particularly effect precision and recall. The analysis of loss lines shows that the loss lines for selected objects depend on both their geometry and the image background. The results on selected objects show that faster RCNN offers higher accuracy than SSD for detection of selected objects.

The results show that modular object detection is crucial in construction for the achievement of the required information for project quality and safety objectives. The detection process can significantly improve monitoring object installation progress in an accurate and machine-based manner avoiding human errors. The results of this paper are limited to three construction sites, but future investigations can cover more tasks or objects from different construction sites in a fully automated manner.

This paper’s originality lies in offering new AI applications in modular construction, using a large first-hand data set collected from three construction sites. Furthermore, the paper presents the scientific evaluation results of implementing recent object detection algorithms across a set of extended metrics using the original training and validation data sets to improve the generalisability of the experimentation. This paper also provides the practitioners and scholars with a workflow on AI applications in the modular context and the first-hand referencing data.