In this paper, γ-aminopropyl triethoxysilane (APTES) was first used to covalent functionalised graphene oxide (GO). The resulting APTES functionalised GO (APTES-f-GO) was then incorporated into the bismaleimide (BMI) resin to improve its mechanical properties. Fourier transform infrared spectroscopy, X-ray photoelectron spectra and Raman spectroscopy were used to characterise APTES-f-GO. The study showed that the APTES-f-GO/BMI nanocomposites presented higher impact strength, flexural strength, tensile strength and elastic modulus than the neat BMI resin. The maximum increment of impact strength, flexural strength, tensile strength and elastic modulus of APTES-f-GO/BMI nanocomposites were 84.3, 54.5, 31.4 and 35.6%, respectively. The scanning electron microscopy photographs show that the fracture morphology of APTES-f-GO/BMI composite is entirely different from that of BMI resin, which exhibits typical ductile fracture features.
In this study, we investigate the role of mid-air gesture-based interaction technologies in cultural heritage learning. In an experiment, a mid-air gesture-based interactive media for Chinese Song Dynasty traditional painting-Ruihetu was developed and validated. Participants tested three experimental conditions: video learning only; interactive experience first, then video learning; and video learning first, then interactive experience. According to the research results, the outcomes of participants' learning of this cultural heritage differed significantly across all three experimental conditions. This study's findings offer insights into cultural learning of Chinese traditional painting in museums using mid-air gesture-based technology, specifically that video learning exhibits should be combined with and preceded by multimedia interactive exhibits for improved memory and understanding.
Existing research on wheel wear prediction uses either data-driven or model-based methods. However, due to the high reliability and limited sample characteristics of metro wheel wear, data-driven methods are not accurate enough and require relatively high data costs, and model-based methods mainly lack verification with measured data and generalization ability. To address the shortcomings of the two types of methods, a new approach combining model-based and data-driven methods is used to predict wheel wear in this paper. First, the least-squares algorithm is used to analyze and calculate the difference between the wear measurement for a specific running mileage and the corresponding simulated wear, with the minimum difference taken as an objective function. By means of optimization algorithms including Genetic Algorithm, Particle Swarm Optimization, Tabu Search and Simulated Annealing, the wear coefficient k in Jendel wear model is optimized, thereby obtaining an optimized Jendel wear model. Later, metro wheel wear for additional running mileage is simulated and predicted through combined application of the vehicle system dynamics, wheel-rail contact, and optimized Jendel wear models. Finally, the paper analyzes the wear prediction results obtained by the integrated data-model-driven approach and compares them with the results of traditional methods and measured data. The results suggest that the integrated data-model-driven approach effectively reduces the uncertainty in selecting the wear coefficient by experience, lowers the experimental data costs, and improves the wear prediction accuracy. Therefore, it is a promising approach to wheel wear prediction. INDEX TERMS Metro wheel, wear prediction, data-model driven, optimization algorithms, Jendel wear model.
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