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This paper aims to explore the impact of green finance on the heterogeneity of enterprise green technology innovation and the underlying mechanism between them.

Using the data of China's A-share listed enterprises from 2008 to 2020 and the fixed effect model, the authors empirically explore the relationship and mechanism between green finance and green technology innovation by constructing the green finance index while considering both the quality and quantity of innovation.

The study suggests that green finance is positively related to the quality and quantity of enterprise green technology innovation, while green finance is more effective in stimulating the quality of green technology innovation than quantity. In addition, alleviating financial mismatch and improving the quality of environmental information disclosure are core mechanisms during the process of green finance facilitating green technology innovation. Furthermore, green finance exerts a more positive effect on the quality and quantity of green technology innovation with large-size enterprises, heavily polluting industries and enterprises in the eastern region.

This paper enriches the literature on green finance and green technology innovation and provides practical significance for green finance implementation.

The present study aims to ascertain the relationships between subordinate moqi and leader behaviors, by primarily discussing how and when subordinate moqi is associated with leadership empowerment.

A self-report study was conducted by recruiting 334 employees from 13 firms. All concepts were rated on a seven-point Likert-type response scale. Linear regression analysis (conducted in MPLUS 7) was conducted to verify the hypotheses.

First, subordinate moqi showed positive association with empowerment. Second, trust-in-supervisor mediated the relationships between subordinate moqi and empowerment. Third, subordinates' power distance orientation (PDO) could moderate the subordinate moqi – leader empowerment relationship. When subordinates reported higher PDO, the relationships between subordinate moqi and empowerment were more robust; likewise, subordinate moqi would have more significantly indirectly impacted empowerment via trust-in-supervisor.

Though researchers have discussed the impacts of subordinate moqi on subordinates' outcomes, the impact of subordinate moqi on supervisors' attitudes or behaviors remains unclear. The relationships between subordinate moqi and supervisor empowerment behaviors are empirically ascertained by emphasizing the leader-subordinate dyadic process. The findings here suggested that subordinate moqi boosted subordinates' trust-in-supervisor, and moqi would also predict the behaviors of leader empowerment. This study extended the PDO literature by identifying the moderating role of PDO in the subordinate moqi – leader empowerment behavior relationship.

Ocean exploration is of importance and in great demand throughout the world. This results in a huge challenge in tribology in the marine environment. Moreover, polymeric materials with large molecules or macromolecules play an important role in marine equipment.

The tribological performance of sea-water-eroded polyether polyurethane (PU) was systematically studied by using a multi-specimen test machine for different durations from 0 to 60 days. Surface characterization technologies, such as scanning electric microscopy energy dispersive spectroscopy, were used to analyze the PU samples.

It can be found that the COF measured against 316 steel increases with the testing load because of the change of contact areas for the original PU samples. The effect of hydrodynamic lubrication and heat resulted in the decline of the COF with the increase in testing speed. The COF of PU sample immersed for 20 days was the lowest compared with other samples. With the immersion time increased to 60 days, the COF increased first and then decreased. The reduced COF of PU resulted in improved anti-wear performance of the PU sample.

These results enhanced the comprehension of the tribological performances of PU immersed in sea-water.

This study analyzes the causal effect of education on consumers' cognition and attitudes toward genetically modified (GM) foods.

The authors propose an analytical framework to clarify the role of education levels and education content in the formation of attitudes toward GM foods and utilize education reforms in China as natural experiments to test the theoretical predictions empirically. For education levels, the authors use Compulsory Education Law's implementation to construct the instrument variable. For education content, the authors utilize the revision of the biology textbook in the Eighth Curriculum Reform to implement staggered difference-in-difference estimation. The authors use two national household surveys, the China Genuine Progress indicator Survey (CGPiS) and the China Household Finance Survey (CHFS) of 2017, combined with provincial-level data of education reforms.

The education level, instrumented by the Compulsory Education Law's implementation, has an insignificant effect on consumers' cognition and attitudes toward GM foods, whereas the acquisition of formal education on genetic science, introduced by the Eighth Curriculum Reform, has a statistically significant and positive influence.

This is the first study to investigate the causal effects of education level and content on consumers' cognition and attitude toward GM foods using national representative data. It is also the first to evaluate the long-term effects of the biology textbook reform in China. The findings help open the black box of how education shapes people's preferences and attitudes and highlight the significance of formal biology education in formulating consumers' willingness to accept GM foods.

The unitized curtain wall system (UCWS), a symbol of modern architecture, is gaining popularity among prefabricated components. Previous studies have focused on both construction technology advances and material selection strategies to facilitate the UCWS. However, the topic of client satisfaction, which drives industry development by targeting clients' demands, has gone unnoticed. Therefore, the current study aims to investigate client satisfaction with UCWS products in Hong Kong by finding its influential factors.

A systematic review was employed to first identify the influential factors. A semi-structured interview was employed to validate the reliability of the extracted factors. The machine learning algorithm Extreme Gradient Boosting (XGBoost) and the Pearson correlation were then employed to rank the importance and correlation of factors based on the 1–5 Likert scale scores obtained through a questionnaire survey.

The findings revealed that “reduction in construction time” and “reduction in construction waste” are the most important factors and have a strong positive influence on client satisfaction.

Unlike previous studies, the present study focused on a novel research topic and introduces an objective analysis process using machine learning algorithms. The findings contribute to narrowing the knowledge gap regarding client preference for UCWS products from both individual and collaborative perspectives, providing decision-makers with an objective, quantitative and thorough reference before making investments in the curtain wall management development.

The purpose of this paper is to explore the effect of ZnO on the structure and properties of micro-arc oxidation (MAO) coating on rare earth magnesium alloy under large concentration gradient.

The macroscopic and microscopic morphology, thickness, surface roughness, chemical composition and structure of the coating were characterized by different characterization methods. The corrosion resistance of the film was studied by electrochemical and scanning Kelvin probe force microscopy. The results show that the addition of ZnO can significantly improve the compactness and corrosion resistance of the MAO coating, but the high concentration of ZnO will cause microcracks, which will reduce the corrosion resistance to a certain extent.

When the concentration of zinc oxide is 8 g/L, the compactness and corrosion resistance of the coating are the best, and the thickness of the coating is positively correlated with the concentration of ZnO.

Too high concentration of ZnO reduces the performance of MAO coating.

The MAO coating prepared by adding ZnO has good corrosion resistance. Combined with organic coatings, it can be applied in corrosive marine environments, such as ship parts and hulls. To a certain extent, it can reduce the economic loss caused by corrosion.

The effect of ZnO on the corrosion resistance of MAO coating in electrolyte solution was studied systematically, and the conclusion was new to the common knowledge.

Digital technology, which is regarded as a prominent and transformational force in modern society, encompasses a wide variety of technology that utilize digital data to process, store and transfer various types of information. Digital technologies have continually been introduced as cutting-edge information tools in order to achieve effective management of vast information that arises from the prefabrication supply chain. However, without a sufficient performance evaluation, drawbacks of technology investment, such as financial losses and ineffective resource allocation, keep occurring, which hinders the widespread implementation of digital technologies. This study demonstrates a comprehensive evaluation of digital technologies’ effects on the prefabrication supply chain based on multi-criteria decision analysis (MCDA) theory.

Specifically, the targeted digital technologies and project constraints were first identified through a systematic literature review. The effects of the digital technologies were then scored using a questionnaire survey. The TOPSIS model was established to quantitatively rank the effectiveness of selected digital technologies.

Overall, BIM technology shone out in the rankings and is regarded as the most beneficial digital solution by multi-stakeholders to the existing constraints, such as working efficiency. Collaboration patterns between different stakeholders and technology integration trend were also indicated.

Compared with existing outcomes, this study specifically focused on examining the effects of digital technologies on the prefabrication supply chain, the most significant link in the process for prefabricated structures. New findings indicate the overall performance that considered both multi-stakeholders’ preferences and project constraints. The quantitative evaluation presents a comprehensive understanding of digital technologies’ effects, enabling industrial participants to reach well-informed, strategic and profitable investment decisions.

Recent years have witnessed an unexpected and astonishing rise of AI-generated (AIGC), thanks to the rapid advancement of technology and the omnipresence of social media. AIGCs created to mislead are more commonly known as DeepFakes, which erode our trust in online information and have already caused real damage. Thus, countermeasures must be developed to limit the negative impacts of AIGC. This position paper aims to provide a conceptual analysis of the impact of DeepFakes considering the production cost and overview counter technologies to fight DeepFakes. We will also discuss future perspectives of AIGC and their counter technology.

We summarize recent developments in generative AI and AIGC, as well as technical developments to mitigate the harmful impacts of DeepFakes. We also provide an analysis of the cost-effect tradeoff of DeepFakes.

The mitigation of DeepFakes call for multi-disciplinary research across the traditional disciplinary boundaries.

Government and business sectors need to work together to provide sustainable solutions to the DeepFake problem.

The research and development in counter-technologies and other mitigation measures of DeepFakes are important components for the health of future information ecosystem and democracy.

Unlike existing reviews in this topic, our position paper focuses on the insights and perspective of this vexing sociotechnical problem of our time, providing a more global picture of the solutions landscape.

The unitized curtain wall system (UCWS), one of the prefabricated technologies, is increasingly attracting attention in the Hong Kong construction industry. However, this innovative technology still lacks on-site implementation in high-rise residential buildings. To promote its development, this study aims at identifying the influential factors of UCWS adoption in Hong Kong's high-rise residential buildings from a multi-stakeholder perspective.

Factors were first selected through an in-depth literature review and a semi-structured interview. Then the factors were validated through a questionnaire survey using Cronbach's Alpha Reliability Test. Next, the factors were ranked regarding their importance using mean-score ranking and standard deviation. Meanwhile, different stakeholders were clustered using an experimental factor analysis (EFA) model to find the shared preferences (namely common factors).

The result shows that reduction of construction time (B1) and insufficient site storage area (C1) are the most important factors. The six stakeholder groups were clustered into two segments. B1 and improved quality control are the shared interests. While C1 and the need of specification change are the common concerns.

There are two major breakthroughs in this study. First is the novelty of research objects. UCWS, particularly its application preference in high-rise residential buildings, has rarely been studied, yet it is urgently required. Second is the novel research perspective. The influential factors were studied from a multi-stakeholder perspective. Not only the significant factors for six specific stakeholders but also the shared preference for stakeholder groups was identified. The findings contribute to promoting UCWS more targeted, efficient and comprehensive, as well as demonstrating the collaborative possibilities of multi-stakeholders.

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.