Train drivers must balance sustained attention and attention shifts for safe monitoring, but environmental complexity and limited attention make this challenging. This study designed three auditory Monitoring Requests (MR) to aid attention shifts: abstract MR (a beeping sound), warning MR (containing directional information with traditional alertness-related emotions), and consultation MR (directional information with positive emotions). Thirty-two drivers completed a simulated driving experiment comparing baseline and MR conditions in attention performance, physiological responses, and subjective perceptions. Results showed that abstract MR elicited the highest arousal, while warning and consultation MRs improved attention shifts due to directional cues. Pupil diameter analysis indicated that consultation MR stabilized cognitive processing, optimizing sustained attention and task workload, trust, and acceptability, though slightly reducing vigilance. These findings suggest that integrating positive emotion and directional cues in MRs enhances attention support, With potential for adaptive designs based on physical feedback.

This paper investigates whether different task difficulties, color complexity, and choice of instructional application mode affect cognitive load, usability, visual discomfort, and job performance in subway overhaul training tasks and compares the findings under AR learning modes with those under traditional paper-based learning manuals. This study had sixteen participants and used eight experimental conditions. Tests were completed for environmental color complexity, task difficulty, and instructional application mode. The experimental variables were analyzed using three-way analysis of variance. The results showed that the color complexity of the carriage environment did not affect the participants’ cognitive load and performance, but the use of AR glasses in subway maintenance was influenced by task difficulty and instructional application mode. For example, the AR image format reduced task completion time by an average of 25.9% for easy tasks, while the AR real-time format reduced head deflections by an average of 83.9%, suggesting that there is reliable potential for applying AR to subway overhaul training. This paper can provide a theoretical basis for developing training strategies for the different environments and tasks difficulties of subway maintenance.

The maintenance and overhaul of rail transit vehicle frames are vital for ensuring the safe and reliable operation of equipment. AR-based maintenance has been proven to be cost-reducing and effective. Few studies explored the application of AR-based maintenance on subway overhaul. In this study, 74 participants were selected to conduct the underground maintenance AR recognition and obstacle avoidance experiment. And two-factor ANOVA, independent samples t-test, and non-parametric test were used for data statistics. The results show that a high-opacity AR interface has better recognition but affects obstacle avoidance. Facing different types of obstacles, AR interfaces with different layouts have significant differences in terms of recognizability and obstacle avoidance effects. Compared with the list layout, the matrix layout AR interface will make more effort for workers. The findings provide useful references for designing the visual information layout of AR interfaces and conducting usability testing of AR interfaces in complex scenarios such as underground maintenance.

Remote maintenance refers to the maintenance tasks performed by off-site personnel using communication devices. The quality of collaboration among off-site personnel has been a focus of research on remote maintenance. With the rapid development of virtual technology, remote collaboration has gradually transformed into a three-dimensional (3D) collaborative virtual environment (CVEs) based on mixed reality technology. 3D CVEs has improved the efficiency of the staff using real-time screen sharing and fusion of virtual and real information. The interaction modes provided by 3D CVEs also have deepened the sense of task participation of off-site personnel. Nevertheless, the addition of new technologies has had a complex impact on trust, communication, cohesion, and task performance among off-site personnel. In this study, we investigated the combined effects of intimacy between collaborators and the avatar interaction mode provided by a 3D CVEs on social presence, interpersonal trust, task performance, and task load during collaboration. The results showed that acquaintances were able to quickly establish good interpersonal trust among themselves and had better task performance even in a CVEs. For strangers, performing tasks through CVEs enhanced their social presence. In addition, the avatar had a more complex effect on the performance of members with different levels of intimacy in 3D CVEs.

Single image reflection removal is a challenging task because important features will be completely obstructed by strong reflections. During removing strong reflections, existing methods focus on mining global contextual features, which will lose some useful local features and fail to remove strong reflections completely. To solve this problem, we propose a feature-integrated patch-hierarchical network (FIPHN), which progressively removes reflections by focusing on integrating global and local features. Specifically, we design a layer-wise feature integration module (LFIM) to integrate global and local features layer by layer across multi-scale stages, effectively enhancing the representation ability of features. Moreover, we design a patch-wise feature integration module (PFIM) to extract contextual features between adjacent patches, avoiding the loss of important features. Meanwhile, with the guidance of ground-truth images, PFIM provides intermediate supervision signals to promote the subnetwork training at each stage. Experimental results show the superiority of our method compared with existing methods.