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An increasing number of restaurants are building mobile instant messaging-based online restaurant communities to enhance connections with customers, but the mechanisms of customers’ behavioral intentions toward these communities remain unclear. To address this gap, this study aims to investigate the factors affecting customers’ acceptance of mobile instant messaging-based online restaurant communities from the perspectives of social support theory and technology acceptance model.

Partial least squares structural equation modeling was used to analyze data collected from 360 customers.

The results indicate that informational support and social presence influence perceived ease of use and usefulness. By contrast, emotional support significantly affects only perceived usefulness. Additionally, perceived ease of use and usefulness can positively predict customer attitudes and foster behavioral intentions.

This research broadens existing research on online restaurant communities by constructing an integrated model that considers social support-related and technology use-related factors to understand consumers’ participation in a new type of restaurant community, namely, the mobile instant messaging-based online restaurant community. The findings also offer guidance on how restaurants can encourage customers to embrace mobile instant messaging-based online restaurant communities.

This study aims to design a novel seasonal discrete grey model for forecasting monthly natural gas consumption by incorporating damping accumulation and time-polynomial term.

Considering the principle of new information priority and nonlinear patterns in the original series of monthly natural gas consumption, we establish a novel discrete seasonal grey model by adding the damping accumulation and time-polynomial term into the existing model. In addition, the order of damping accumulation and the coefficient of time-power term can be determined by the moth flame optimization (MFO) algorithm.

The empirical cases show that the proposed model has a better prediction performance when compared with other benchmark models, including six seasonal grey models, one statistical model and one artificial intelligent model. Based on forecasts, the proposed model can be considered a promising tool for monthly natural gas consumption (NGC) in US.

By combining the damping accumulation and the time-polynomial term, a new discrete seasonal grey model for improving the prediction performance of the existing grey model is proposed. The properties of the proposed model are given, and the newly-designed model is initially applied to predict monthly NGC in US.

The purpose of this study is to advance the computational modeling of liquid splashing dynamics, while balancing simulation accuracy and computational efficiency, a duality often compromised in high-fidelity fluid dynamics simulations.

This study introduces the fluid efficient graph neural network simulator (FEGNS), an innovative framework that integrates an adaptive filtering layer and aggregator fusion strategy within a graph neural network architecture. FEGNS is designed to directly learn from extensive liquid splash data sets, capturing the intricate dynamics and intrinsically complex interactions.

FEGNS achieves a remarkable 30.3% improvement in simulation accuracy over traditional methods, coupled with a 51.6% enhancement in computational speed. It exhibits robust generalization capabilities across diverse materials, enabling realistic simulations of droplet effects. Comparative analyses and empirical validations demonstrate FEGNS’s superior performance against existing benchmark models.

The originality of FEGNS lies in its adaptive filtering layer, which independently adjusts filtering weights per node, and a novel aggregator fusion strategy that enriches the network’s expressive power by combining multiple aggregation functions. To facilitate further research and practical deployment, the FEGNS model has been made accessible on GitHub (https://github.com/nanjinyao/FEGNS/tree/main).

The purpose of this paper is to study the essential characteristics of grey relational degree of proximity, to analyse the abstract meaning of grey relational degree of similarity and fully consider the two different relational degree models.

The paper constructed the grey proximity relational degree by using the weighted mean distance. To analyse the motivation of the development of things, this paper constructed the grey similarity degree by using the concept of induced strength. Finally, the two correlation models are weighted by reliability weighting.

The research finding shows that the distance is the essence of the grey relational degree of proximity, and the induced strength is a good explanation of the similarities in the development of things.

The analyses imply that the total amount of water consumption in China has the greatest correlation with the consumption of agricultural water resources, followed by the consumption of industrial water resources, and the least correlation with the consumption of domestic water resources.

The paper succeeds in realizing the essential characteristics of grey relational degree of proximity and the abstract meaning of grey relational degree of similarity. Besides, the resolution of the correlation degree can be greatly improved by reliability weighting.

The construction industry is experiencing digital transformation, which is also defined as intelligent construction. With the rise of intelligent construction, job characteristics are changing rapidly. Current knowledge about job competencies required by intelligent construction is lacking. Therefore, the aim of this paper is to explore job competencies related to intelligent construction by text mining recruitment information. It is expected to reveal the trend of talent development for the intelligent construction industry.

A total of 375 job advertisements regarding the demanding professionals and industrial workers related to intelligent construction were collected and analyzed to reveal the demands of the current labor market. Different job posts related to intelligent construction were classified into 11 categories. Job competencies were extracted and analyzed using the latent Dirichlet allocation (LDA) model, frequency–inverse document frequency (TF-IDF) algorithm and k-means cluster analysis method. The text mining results identified 10 job competencies.

Currently, there was a high demand for high-tech talents in the labor market related to intelligent construction. Those high-tech job posts, such as software engineers and R&D staff, required digital technology, R&D skills, electrical automation knowledge and programming capability. Current employees demanding for intelligent construction are expected to be capable of both using information technology and having a general knowledge of the construction industry.

Through text mining of current job advertisements, the overall demand for compound talents in the labor market of intelligent construction were explored. The results provide empirical reference for personnel training and talent cultivation in the development of intelligent construction. Higher educational institutions, human resources professionals, as well as experts that are already employed or aspire to be employed in intelligent construction companies, would benefit from the results of our analysis.

This study aims to explore the relationship between fraud triangle theory (FTT) and the accounting fraud phenomenon in all listed companies in China.

The CSMAR database is used as the sample, including 16,063 data of all listed companies in Shanghai and Shenzhen markets for the 2010–2020 period. The authors also use quantitative methods, such as regression analysis, to investigate the relationship between five variables (cover three elements of FTT) and fraud occurrence.

Results show that leverage and liquidity ratios positively affect fraud detection, whereas return on net equity, audit size and independent director percentage negatively affect fraud detection.

This study enriches theoretical research on the causes of accounting fraud in China and is of great significance to the sound development of China’s capital market.

Recently, the constrained surface projection stereolithography (SL) technology is gaining wider attention and has been widely used in the 3D printing industry. In constrained surface projection SL systems, the separation of a newly cured layer from the constrained surface is a historical technical barrier. It greatly limits printable size, process reliability and print speed. Moreover, over-large separation force leads to adhesion failures in manufacturing processes, causing broken constrained surface and part defects. Against this background, this paper investigates the formation of separation forces and various factors that affect the separation process in constrained surface projection SL systems.

A bottom-up projection SL testbed, integrated with an in-situ separation force measurement unit, is developed for experimental study. Separation forces under various manufacturing process settings and constrained surface conditions are measured in situ. Additionally, physical models are constructed by considering the liquid resin filling process. Experiments are conducted to investigate influences of manufacturing process settings, constrained surface condition and print geometry on separation forces.

Separation forces increase linearly with the separation speed. The deformation and the oxygen inhibition layer near the constrained surface greatly reduce separation forces. The printing area, area/perimeter ratio and the degree of porousness of print geometries have a combined effect on determining separation forces.

This paper studied factors that influence separation force in constrained surface SL processes. Constrained surface conditions including oxygen inhibition layer thickness, deformation and oxygen permeation capability were investigated, and their influences on separation forces were revealed. Moreover, geometric factors of printing layers that are significant on determining separation forces have been identified and quantified. This study on separation forces provides a solid base for future work on adaptive control of constrained surface projection SL processes.

Tour guides often use humor to entertain tourists, but the process of tour guide humor (TGH) affecting tourists’ positive word of mouth (PWOM) remains unclear. To fill the gap, this study aims to investigate how TGH enhances tourists’ PWOM through perceived relationship investment, perceived wellness value and trust in tour guides.

A comprehensive mediation model was proposed based on social exchange theory (SET). Data were obtained from 335 tour group tourists and analyzed using structural equation modeling.

Results present that TGH positively predicts tourists’ PWOM. Perceived relationship investment, perceived wellness value and trust in tour guides not only play mediating roles between TGH and tourists’ PWOM, respectively, but also jointly provide two sequential mediation paths (TGH → perceived relationship investment → trust in tour guides → tourists’ PWOM and TGH → perceived wellness value → trust in tour guides → tourists’ PWOM).

The findings have practical value for tour guides and travel agencies to use TGH to improve tourists’ PWOM.

The major contribution is that a reciprocity-based framework rooted in SET was proposed to parse the complex process of TGH promoting tourists’ PWOM. Furthermore, this study enriches current knowledge by confirming that perceived wellness value is not only available in wellness tourism but can be experienced from TGH in mass tourism.

The purpose of this paper is to investigate the self‐assembled monolayers (SAMs) of phytic acid on cupronickel B30 surface of anticorrosion and inhibiting mechanisms.

Electrochemical and photocurrent response methods were performed to determine the effect of phytic acid SAMs on cupronickel B30.

The results indicated that phytic acid was liable to interact with B30 as a result of formation of complexes on B30 surface for anti‐rust and anti‐corrosion. The SAMs changed the structure of the electrochemical double layer and made the value of double layer capacitance decrease significantly. The B30 electrode showed p‐type photoresponse, which came from Cu₂O layer on its surface. The photoresponse decreased greatly due to the SAMs of phytic acid as the corrosion resisting property was enhanced. This finding was in good agreement with the results obtained from EIS and polarization curves. Adsorption of phytic acid was found to follow the Langmuir adsorption isotherm and the adsorption mechanism was typical of chemisorption.

The SAMs of phytic acid on cupronickel B30 was gained for the first time. The photo‐electrochemical method was an in situ method, which was effective for characterizing optical and electronic properties of passive films.