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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.

This study aims to modify the human factors analysis and classification system (HFACS) to make it suitable for collapse accident analysis in construction. Based upon the modified HFACS, distribution patterns of causal factors across multiple levels were discerned among causal factors of various stakeholders at construction sites. It explored the correlations between two causal factors from different levels and further determined causation paths from two perspectives of level and stakeholder.

The main research framework consisted of data collection, coding and analysis. Collapse accident reports were collected with adequate causation information. The modified HFACS was utilized for coding causal factors across all five levels in each case. A hybrid approach with two perspectives of level and stakeholder was proposed for frequency analysis, correlation analysis and path identification between causal factors.

Eight causal factors from external organizations at the fifth level were added to the original HFACS. Level-based correlation analyses and path identification provided safety managers with a holistic view of inter-connected causal factors across five levels. Stakeholder-based correlation analyses between causal factors from the fifth level and its non-adjacent levels were implemented based on client, government and third parties. These identified paths were useful for different stakeholders to develop specific safety plans for avoiding construction collapse accidents.

This paper fulfils an identified need to modify and utilize the HFACS model for correlation analysis and path identification of causal factors resulting in collapse accidents, which can provide opportunities for tailoring preventive and protective measures at construction sites.

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.

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.

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.

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.