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Online learning is increasingly popular, and educational platforms provide a wealth of courses. Improving course sales is the key to promoting sustainable development of online course platforms. However, limited research has explored the marketing of online courses. We study how to drive online course sales by leveraging teacher information.

We performed an empirical study. We collected data through a crawler and image recognition from Tencent classroom.

Our results show that providing teacher information and profile images helps promote online course sales. However, detailed course descriptions weaken the positive impact of teachers' profile images on online course sales. Furthermore, our study shows an inverted U-shaped relationship between the intensity of smiling in teacher profile photos and online course sales, and teacher descriptions negatively moderate this relationship.

Our study contributes to the research on online course sales and extends the context of the research on smiling as well as the studies of visual and textual information.

The results have practical implications for online course sellers and platforms.

Existing scholarly efforts have explored online courses mainly from an education perspective. More research is needed to advance the understanding of online course sales. Our study advances research in the marketing of online courses.

This study aims to provide a critical reflection of the application of image recognition techniques in visual information mining in hospitality and tourism.

This study begins by reviewing the progress of image recognition and advantages of convolutional neural network-based image recognition models. Next, this study explains and exemplifies the mechanisms and functions of two relevant image recognition applications: object recognition and facial recognition. This study concludes by providing theoretical and practical implications and potential directions for future research.

After this study presents different potential applications and compares the use of image recognition with traditional manual methods, the main findings of this critical reflection revolve around the feasibility of the described techniques.

Knowledge on how to extract valuable visual information from large-scale user-generated photos to infer the online behavior of consumers and service providers and its influence on purchase decisions and firm performance is crucial to business practices in hospitality and tourism.

Visual information plays a crucial role in online travel agencies and peer-to-peer accommodation platforms from the side of sellers and buyers. However, extant studies relied heavily on traditional manual identification with small samples and subjective judgment. With the development of deep learning and computer vision techniques, current studies were able to extract various types of visual information from large-scale datasets with high accuracy and efficiency. To the best of the authors’ knowledge, this study is the first to offer an outlook of image recognition techniques for mining visual information in hospitality and tourism.

The purpose of this paper is to improve opening performance of bi-directional rotation gas face seals by investigating the hydrodynamic effect of non-closed elliptical grooves.

A model of non-closed elliptical groove bi-directional rotation gas face seal is developed. The distribution of lubricating film pressure is obtained by solving gas Reynolds equations with the finite difference method. The program iterates repeatedly until the convergence criterion on the opening force is satisfied, and the sealing performance is finally obtained.

Non-closed elliptical groove presents much stronger hydrodynamic effect than the closed groove because of drop of the gas resistance flowing into grooves. Besides, the non-closed elliptical groove presents significant hydrodynamic effect under bi-directional rotation conditions, and an increase of over 40 per cent is obtained for the opening force at seal pressure 4.5 MPa, as same level as the unidirectional spiral groove gas seal. In the case of bi-directional rotation, the value of the inclination angle is recommended to set as 90° presenting a structure symmetry so as to keep best opening performance for both positive and reverse rotation.

A model of non-closed elliptical groove bi-directional rotation gas face seal is established. The hydrodynamic mechanism of this gas seal is illustrated. Parametric investigation of inclination angle and integrity rate is presented for the non-closed elliptical groove bi-directional rotation gas face seal.

The purpose of this paper is to study the insulation structure optimization method of multiwinding high-frequency transformer (HFT).

This paper takes 100 kW, 10 kHz multiwinding HFT as the research object. First, the distribution of electric field strength within the core window of multiwinding HFT with different winding configurations is simulated by the electrostatic field finite element method. The symmetrical hybrid winding structure with minimum electric field strength is selected as the insulation design. To reduce the electric field strength at the end region of the winding, the electrostatic ring and angle ring are designed based on the response surface method.

The optimal results show that the maximum electric field strength can be reduced by 15.4%, and the low voltage stress can be achieved.

The above research provides guidance and basis for the optimal design of insulation structure of multiwinding HFT.