Chunxiao Jiao, Jianghai Xu, Donglin Zou, Na Ta and Zhushi Rao
The purpose of this paper is to study the flow field characteristics of the micro-scale textured bearing surfaces using the lattice Boltzmann method based on the microscopic…
Abstract
Purpose
The purpose of this paper is to study the flow field characteristics of the micro-scale textured bearing surfaces using the lattice Boltzmann method based on the microscopic dynamics of the fluid.
Design/methodology/approach
Considering the inertia effects and the micro-scale effects, the models of a single micro-scale texture unit cell with different shapes and different film thickness ratios are established. The influence of pressure difference between inlet and outlet of the unit cell on flow characteristics is studied.
Findings
The surface pressure distribution, flow patterns and pressure contours in the flow field are obtained. The results reveal that the pressure difference has a significant influence on the characteristics of the micro-textured flow field.
Originality/value
The results have certain guiding significance for further step investigation on microscopic lubrication mechanism of the water-lubricated polymer bearings.
Details
Keywords
This study aims to examine the integration of AI in student engagement and its benefits in the learning environment.
Abstract
Purpose
This study aims to examine the integration of AI in student engagement and its benefits in the learning environment.
Design/methodology/approach
The study employed a quantitative research method, analyzing data from a sample of 720 students. The econometric data analysis used the structural equation modeling (SEM) technique.
Findings
The results show that facilitating conditions and performance expectations positively affect students’ attitudes toward AI, whereas the negative impact of perceived risk is statistically weak and only marginally significant at the 90% level. On the other hand, the main findings are that attitudes significantly influence the use of AI, which consequently increases students’ productivity, performance, and self-efficacy.
Research limitations/implications
Through the integration of new variables in the TAM and UTAUT models, steps are suggested for institutions to take to increase the acceptance and efficiency of AI.
Originality/value
This study introduces a novel approach to AI integration within higher education, presenting an innovative model that significantly enhances the discourse on AI’s tangible impacts on educational processes.