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This study aims to enhance the assessment of green governance in energy projects along the Belt and Road, reduce the influence of fuzzy judgment, and construct a grey network analysis model from the perspective of Environmental, Social, and Governance (ESG).

The ESG concept is used to establish an evaluation indicator system. The Analytic Network Process (ANP) and the Grey System Theory are applied sequentially to determine the green governance grade of energy projects, exemplified by an evaluation of five projects.

The Karot hydropower project has the best green governance status among the five projects and is of excellent grade. This is followed by the Hongfeng photovoltaic project, the De Aar wind power project, and the Yamal liquefied natural gas project, which are of good grade. The Lamu coal power station project has the worst green governance and is at a medium level.

This study can assist Belt and Road energy projects in identifying their deficiencies and promoting sustainable development by providing a robust framework for green governance evaluation.

The indicator system developed in this study includes social and project governance aspects in addition to environmental performance, reflecting the comprehensive green governance status of projects. The combined use of ANP and grey system theory fully considers the mutual influence relationship between indicators and improves the objectivity of green governance grade judgment.

The purpose of the present research is to address the issue by conceptualizing artificial intelligence (AI) experience quality and its dimensions, and furthermore, to empirically test the relationships among AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention.

The data were collected from an AI community canteen in Shanghai. They were also analyzed using exploratory and confirmatory factor analyses (EFA and CFA) and structural equation modeling (SEM).

Four primary dimensions and 15 sub-dimensions of AI experience quality for community canteens were identified. The hypothesized paths between the higher-order constructs – AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention – were confirmed as well.

This is the first study to synthesize AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention in an AI restaurant setting.

Based on role theory, from the perspective of workplace behaviors (proactive behavior, in-role behavior and organizational citizenship behavior), this paper provides a perspective of matching process on the importance of fit in personnel selection.

Using a sample of 231 leader–employee dyadic in a two-wave survey, the hypotheses were demonstrated with hierarchical regression analyses.

The results presented that: (1) Employees' perceptions of implicit leadership prototype fit and leaders' perceptions of implicit followership prototype fit were positively related to employees' workplace behaviors; (2) Employees' perceptions of implicit leadership prototype fit and leaders' perceptions of implicit followership prototype fit increased person-supervisor fit; (3) The influence of the interaction between employees' perceptions of implicit leadership prototype fit and leaders' perceptions of implicit followership prototype fit on employees' workplace behaviors will be mediated, first by person-supervisor fit and then by work engagement.

This study introduces the perspective of matching process that reflects the relative importance of fit in personnel selection. The results also enriched role theory from the perspective of implicit prototype fit, which provides an important basis for managers to effectively use managerial cognition and inspire employees' positive workplace behaviors.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

This paper aims to investigate the actual impact, mechanism and internal and external environmental adjustment effect of ESG performance on the persistence of green innovation using data from China’s A-share manufacturing listed companies from 2009 to 2021.

Panel data regression models are used to explore the effect of ESG performance of manufacturing enterprises on the persistence of green innovation. To examine the mechanism of ESG performance affecting the persistence of green innovation of manufacturing enterprises, this paper refers to the research of Wen and Ye (2014) and constructs an analysis framework of intermediary effect.

This research was funded by Shandong Provincial Natural Science Foundation, grant number ZR2023MG075 & ZR2024QE171.

There are a few more limitations to this study that might be discussed from the following angles: first, due to data availability, this paper examines the persistence of green innovation from the output perspective. The authors can expand the data sources in the future and investigate the input-output combinations in green innovation as a means of understanding its sustainability. Second, the mechanism studied in this paper includes management costs, entry of green investors and risk-taking ability. In fact, it is possible that ESG performance influences green innovation persistence in other ways as well; these can be investigated more in the future.

First, it concentrates on the persistence of green innovation in manufacturing enterprises, surpassing the quantitative aspect and thereby broadening the research scope. Second, by including the “management expense ratio,” “green investor entry” and “risk-taking” as mediating factors, the study delves deeper into the mechanisms through which ESG performance impacts the persistence of green innovation in manufacturing enterprises, further broadening the research scope. Third, this research incorporates the internal and external environments encountered by manufacturing enterprises into the analytical framework to investigate their adjustment effects in the process of ESG performance influencing persistent green innovation, thus widening the research perspective. Fourth, this study introduces the subdimensions of ESG performance, specifically environmental responsibility, social responsibility and corporate governance, and assesses their impacts on the persistence of green innovation in manufacturing enterprises, thus enriching the research narrative.

Three-way decision (3WD) and probabilistic rough sets (PRSs) are theoretical tools capable of simulating humans' multi-level and multi-perspective thinking modes in the field of decision-making. They are proposed to assist decision-makers in better managing incomplete or imprecise information under conditions of uncertainty or fuzziness. However, it is easy to cause decision losses and the personal thresholds of decision-makers cannot be taken into account. To solve this problem, this paper combines picture fuzzy (PF) multi-granularity (MG) with 3WD and establishes the notion of PF MG 3WD.

An effective incomplete model based on PF MG 3WD is designed in this paper. First, the form of PF MG incomplete information systems (IISs) is established to reasonably record the uncertain information. On this basis, the PF conditional probability is established by using PF similarity relations, and the concept of adjustable PF MG PRSs is proposed by using the PF conditional probability to fuse data. Then, a comprehensive PF multi-attribute group decision-making (MAGDM) scheme is formed by the adjustable PF MG PRSs and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Finally, an actual breast cancer data set is used to reveal the validity of the constructed method.

The experimental results confirm the effectiveness of PF MG 3WD in predicting breast cancer. Compared with existing models, PF MG 3WD has better robustness and generalization performance. This is mainly due to the incomplete PF MG 3WD proposed in this paper, which effectively reduces the influence of unreasonable outliers and threshold settings.

The model employs the VIKOR method for optimal granularity selections, which takes into account both group utility maximization and individual regret minimization, while incorporating decision-makers' subjective preferences as well. This ensures that the experiment maintains higher exclusion stability and reliability, enhancing the robustness of the decision results.

The purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and sharing.

This paper proposes a hybrid approach to combining domain knowledge similarity and topic similarity to retrieve similar questions in online health communities. The domain knowledge similarity can evaluate the domain distance between different questions. And the topic similarity measures questions’ relationship base on the extracted latent topics.

The experiment results show that the proposed method outperforms the baseline methods.

This method conquers the problem of word mismatch and considers the named entities included in questions, which most of existing studies did not.

This paper aims to propose a classification method for steel strip surface defects based on a mixed attention mechanism to achieve fast and accurate classification performance. The traditional method of classifying surface defects of hot-rolled steel strips has the problems of low recognition accuracy and low efficiency in the industrial complex production environment.

The authors selected min–max scaling comparison method to filter the training results of multiple network models on the steel strip surface defect data set. Then, the best comprehensive performance model EfficientNet-B0 was refined. Based on this, the authors proposed two mixed attention addition methods, which include squeeze-excitation spatial mixed module and multilayer mixed attention mechanism (MMAM) module, respectively.

With these two methods, the authors achieved 96.72% and 97.70% recognition accuracy on the steel strip data set after data augmentation for adapting to the complex production environment, respectively. Using the transfer learning method, the EfficientNet-B0 based on MMAM obtained 100% recognition accuracy.

This study not only focuses on improving the recognition accuracy of the network model itself but also considers other performance indicators of the network, which are rarely considered by many researchers. The authors further improve the intelligent production technique and address this issue. Both methods proposed in this paper can be applied to embedded equipment, which can effectively improve steel strip factory production efficiency and reduce material and time loss.

This study aims to investigate the synergistic friction reduction and antiwear effects of lyophilized graphene loading nano-copper (RGO/Cu) as lubricating oil additives, compared with graphene.

The friction performance of freeze-drying graphene (RGO) and RGO/Cu particles was investigated at different addition concentrations and under different conditions.

Graphene plays a synergistic friction reduction and antiwear effect because of its large specific surface area, surface folds and loading capacity on the nanoparticles. The results showed that the average friction coefficients of RGO and RGO/Cu particles were 22.9% and 6.1% lower than that of base oil and RGO oil, respectively. In addition, the widths of wear scars were 62.3% and 55.3% lower than those of RGO/Cu particles, respectively.

The RGO single agent is suitable for medium-load and high-speed conditions, while the RGO/Cu particles can perform better in the conditions of heavy load and high speed.

Value co-creation is a new initiative for enterprises to form a competitive advantage, and user engagement is the basis for achieving value co-creation; nevertheless, few studies have discussed the influence mechanisms of user engagement on value co-creation behavior. In this study, the authors aim to reveal the influence mechanisms of online fitness user engagement on value co-creation behavior by considering emotional resonance and immersive experience as mediating variables.

The authors proposed and empirically tested a research model based on a survey involving 461 Chinese respondents through partial least squares structural equation modeling (PLS-SEM).

The results of this study confirm that consumer engagement, contributing engagement and social engagement are important drives of value co-creation behavior among online fitness users. Furthermore, emotional resonance and immersive experience have been revealed as important mediating mechanisms to explain why user engagement drives value co-creation behavior.

The results of this study suggest that practitioners need to focus on the social engagement and consumer engagement of users in online fitness communities and to provide the appropriate environment and conditions for online fitness user to achieve mutual value co-creation.

This study makes two main contributions. It examines user engagement in an online fitness community context and helps to understand its applicability in other contexts. It explains the influence mechanisms of online fitness user engagement on value co-creation behavior and enriches the studies related to the drivers of value co-creation behavior.