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The quality of the unstructured mesh has a considerable impact on the stability and accuracy of aerodynamic simulation in computational fluid dynamics (CFD). Typically, engineers spend a significant portion of their time on mesh quality evaluation to ensure a valid, high-quality mesh. The extensive manual interaction and a priori knowledge required to undertake an accurate and timely evaluation process have become a bottleneck in the idealized efficient CFD workflow. This paper aims to introduce a neural network-based quality evaluation approach for unstructured meshes to enable higher efficiency and the level of automation.

The paper investigates the capability of deep neural networks for the quality evaluation of unstructured meshes. For training the network, we build a training dataset for mesh quality learning algorithms. The dataset contains a rich variety of unstructured aircraft meshes with different mesh sizes, densities, cell distribution, growth ratios and cell numbers to ensure its diversity and availability. We also design a neural network, AircraftNet, to learn the effect of mesh quality on the convergent properties of the numerical solutions. The proposed network directly manipulates raw point data in mesh source files rather than passing it to an intermediate data representation. During training, AircraftNet extracts non-linear quality features from high-dimensional data spaces and then automatically predicts the overall quality of the input unstructured mesh.

The paper provides a series of experimental results on GPUs. It shows that AircraftNet is able to effectively analyze the quality-related features like mesh density and distribution from the extracted features and achieve high prediction accuracy on the proposed dataset with even a small number of training runs.

Because of the limited training dataset, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further.

The paper publishes a benchmarking dataset for mesh quality learning algorithms and designs a novel neural network approach for unstructured mesh quality evaluation.

This study conducts a comparative analysis of the diversification effects of China's national carbon market (CEA) and the EU ETS Phase IV (EUA) within major commodity markets.

The study employs the TVP-VAR extension of the spillover index framework to scrutinize the information spillovers among the energy, agriculture, metal, and carbon markets. Subsequently, the study explores practical applications of these findings, emphasizing how investors can harness insights from information spillovers to refine their investment strategies.

First, the CEA provide ample opportunities for portfolio diversification between the energy, agriculture, and metal markets, a desirable feature that the EUA does not possess. Second, a portfolio comprising exclusively energy and carbon assets often exhibits the highest Sharpe ratio. Nevertheless, the inclusion of agricultural and metal commodities in a carbon-oriented portfolio may potentially compromise its performance. Finally, our results underscore the pronounced advantage of minimum spillover portfolios; particularly those that designed minimize net pairwise volatility spillover, in the context of China's national carbon market.

This study addresses the previously unexplored intersection of information spillovers and portfolio diversification in major commodity markets, with an emphasis on the role of CEA.

Drawing on the knowledge-based view, this study investigates how IT–business alignment influences business model design via organizational learning and examines the moderating role of data-driven culture in the relationship between IT–business alignment and business model design via organizational learning.

Using multi-respondent survey data collected from 597 Chinese firms, mediation and moderated mediation analyses were used to examine this study's hypotheses.

The mediation test results revealed organizational learning served as a mediator between IT–business alignment and two types of business model design (i.e. novelty- and efficiency-centered). In addition, data-driven culture strengthened the indirect effects of IT–business alignment on these two types of business model design via organizational learning.

This study extends current understandings of the relationship between IT–business alignment and business model design by revealing the mediating role of organizational learning and investigating its indirect effects under various degrees of data-driven culture. As such, it contributes to the literature on the business model and IT–business alignment and provides insights for managers seeking to achieve the expected business model design.

This study aims to explore the cross-level effect of the top management team (TMT) on group ambidextrous innovation and to analyze the mediating role of group behavioral integration and the moderating effect of group expertise heterogeneity.

We conducted a multi-source and multi-stage survey. We collected valid data from 43 companies in China, resulting in 141 samples from 43 TMTs and 462 valid responses from 111 organizational groups. The proposed theoretical model and hypotheses were tested using structural equation modeling.

The study findings demonstrated that TMT behavioral integration was positively related to group behavioral integration. Group behavioral integration mediates the relationship between TMT behavioral integration and these two types of innovations. Furthermore, we found that group expertise heterogeneity magnified the positive effect of group behavioral integration on exploratory innovation.

This study reveals the cross-level effects of TMT behavioral integration on other organizational groups and enriches the existing literature on TMT behavioral integration and ambidextrous innovation.

This paper examines the pre-factors of college students’ entrepreneurial behaviors and how their background characteristics affect corporate financial performance in high-tech businesses.

About 67 high-tech businesses in China focusing on technical innovation from the Guotai’an database are selected to carry out empirical analysis.

It is observed that the age, educational and professional backgrounds of college entrepreneurs profoundly influence their ventures geared toward high-tech innovation. Moreover, the transformation abilities, managerial proficiency and growth capabilities, which characterize these ventures, notably affect business performance. They further serve as a moderator in the relationship between the entrepreneurial backgrounds of college students and the overall business performance of their enterprises.

It insinuates novel strategic avenues for collegiate entrepreneurs’ entrepreneurial mindset and industrial positioning. Moreover, our findings will not only augment the practical research in the realm of collegiate entrepreneurship but also enhance the study of technological innovation theories, thereby offering further insight and guidance for collegiate entrepreneurs’ innovative endeavors and entrepreneurial pursuits.

The objective of this study is to utilize empirical research and analysis to examine the coupling coordination relationship between new quality productivity and higher vocational education sustainable development.

To this end, an evaluation index system for the new quality productivity and higher vocational education sustainable development was constructed. The panel data of 30 Chinese provinces from 2016 to 2022 were then analyzed using the entropy method, the coupling coordination degree model, the Tobit regression model and Dagum’s Gini coefficient.

The findings indicate that the coupling coordination degree of new quality productivity and higher vocational education sustainable development exhibited an upward trend, though significant regional disparities were observed, with the highest coupling coordination degree recorded in the eastern region and the lowest in the northeastern region.

The study’s findings further suggest that the three factors of technological innovation level, rationalization of industrial structure and advanced industrial structure have a significant positive influence on the coupling coordination degree, while the level of government intervention has a significant negative influence on the Coupling Coordination Degree. The study posits that augmenting policy support, optimizing the government’s role, reinforcing the drive for technological innovation, and enhancing regional cooperation and exchange are imperative to foster high-quality development of the integration of industry and education between new quality productivity and higher vocational education.

When recommending products to consumers, it is important to be able to accurately predict how consumers will rate them. However, existing collaborative filtering models are difficult to achieve a balance between rating prediction accuracy and complexity. Therefore, the purpose of this paper is to propose an accurate and effective model to predict users’ ratings of products for the accurate recommendation of products to users.

First, we introduce an attention mechanism that dynamically assigns weights to user preferences, highlighting key interaction information and enhancing the model’s understanding of user behavior. Second, a fold embedding strategy is employed to segment user interaction data, increasing the information density of each subset while reducing the complexity of the attention mechanism. Finally, a masking strategy is integrated to mitigate overfitting by concealing portions of user-item interactions, thereby improving the model’s generalization ability.

The experimental results demonstrate that the proposed model significantly minimizes prediction error across five real-world datasets. On average, the evaluation metrics root mean square error (RMSE) and mean absolute error (MAE) are reduced by 9.11 and 13.3%, respectively. Additionally, the Friedman test results confirm that these improvements are statistically significant. Consequently, the proposed model more accurately captures the intrinsic correlation between users and products, leading to a substantial reduction in prediction error.

We propose a novel collaborative filtering model to learn the user-item interaction matrix effectively. Additionally, we introduce a fold embedding strategy to reduce the computational resource consumption of the attention mechanism. Finally, we implement a masking strategy to encourage the model to focus on key features and patterns, thereby mitigating overfitting.

Online health question-and-answer (Q&A) forums have developed a new business model whereby listeners (peer patients) can pay to read health information derived from consultations between askers (focal patients) and answerers (physicians). However, research exploring the mechanism behind peer patients' purchase decisions and the specific nature of the information driving these decisions has remained limited. This study aims to develop a theoretical model for understanding how peer patients make such decisions based on limited information, i.e. the first question displayed in each focal patient-physician interaction record, considering argument quality (interrogative form and information details) and source credibility (patient experience of focal patients), including the contingent role of urgency.

The model was tested by text mining 1,960 consultation records from a popular Chinese online health Q&A forum on the Yilu App. These records involved interactions between focal patients and physicians and were purchased by 447,718 peer patients seeking health-related information until this research.

Patient experience embedded in focal patients' questions plays a significant role in inducing peer patients to purchase previous consultation records featuring exchanges between focal patients and physicians; in particular, increasingly detailed information is associated with a reduced probability of making a purchase. When focal patients demonstrate a high level of urgency, the effect of information details is weakened, while the interrogative form is strengthened.

The originality of this study lies in its exploration of the monetization mechanism forming the trilateral relationship between askers (focal patients), answerers (physicians) and listeners (peer patients) in the business model “paying to view others' answers” in the online health Q&A forum and the moderating role of urgency in explaining the mechanism of how first questions influence peer patients' purchasing behavior.

During the cooperation process between prefabricated building construction enterprises (PBCEs) and Internet platforms (IPs), the sentiments of both parties influence their behavioral strategies. They are the key to improving the informatization and operational efficiency of the prefabricated building industry chain (PBIC).

This paper introduces mental accounting theory and rank-dependent expected utility theory to construct the MA-RDEU game model, exploring the evolutionary mechanism between sentiment and behavioral strategies of PBCEs and IPs.

The study indicates that (1) a mixed strategy equilibrium can be achieved when both parties have no sentiments. (2) PBCEs and IPs are more likely to achieve an optimal equilibrium for cooperation if the latter is optimistic. In contrast, pessimism may lead both parties to prioritize self-interest when only one party has a sentiment. (3) The combined impact of sentiments and behavioral strategies on decision-making is significant: the influence of sentiments from PBCEs or IPs on the optimal strategy for achieving cooperation is contingent upon the behavioral strategies of the other party; different behavioral strategies of IPs or PBCEs can have varying effects on sentiments when both parties have sentiments. (4) The influence of external factors on the sentiments and behavior strategies of PBCEs and IPs is apparent. PBCEs and IPs should concurrently consider the combined influence of external factors and sentiments to contribute to the realization of cooperation between the two parties. Additionally, government supervision is an effective means to restrain “free-riding” behavior.

Finally, based on the above conclusions, the paper proposes measures to improve the construction of service-oriented IPs and establish a mechanism for monitoring and adjusting risk sentiments. Meanwhile, this paper also indicates that under the combined effect of the government, PBCEs and IPs, the influence of external factors on sentiments can be maintained within a controllable scope and the risks of uncertainty can be mitigated.

This study aims to identify the primary research areas of modern methods of construction (MMC) along with its current trends and developments.

A combination of bibliometric and qualitative analysis is adopted to examine 1,957 MMC articles in the Scopus database. With the support of CiteSpace 6.1.R6, the clusters, leading authors, journals, institutions and countries in the field of MMC are examined.

Offsite construction, inter-modular connections, augmenting output, prefabricated concrete beams and earthquake-resilient prefabricated beam–column steel joints are the top five research areas in MMC. Among them, offsite construction and inter-modular connections are significantly focused, with many research articles. The potential for collaboration, among prominent authors such as Wang, J., Liu, Y. and Wang, Y., explains the recent rapid growth of the MMC field of research. With a total of 225 articles, Engineering Structures is the journal that has published the most articles on MMC. China is the leading country in this field, and the Ministry of Education China is the top institution in MMC.

The findings of this study bear significant implications for stakeholders in academia and industry alike. In academia, these insights allow researchers to identify research gaps and foster collaboration, steering efforts toward innovative and impactful outcomes. For industries using MMC practices, the clarity provided on MMC techniques facilitates the efficient adoption of best practices, thereby promoting collaboration, innovation and global problem-solving within the construction field.