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Virtual learning environments have been recognized as an area of particular importance by which educators can use to improve desirable learning behaviours. Investigating the impact of different virtual environments on learners' behaviours has become the centre of attention of researchers, especially during COVID-19. The homophily effect of avatar-identity on individuals' perceptions of an environment can be a key for understanding their learning behaviours. This study examined the relationship between key constructs related to avatar homophily (background and attitude) and learners' flow and exploratory behaviour. An online survey was distributed to 157 students (93 males and 64 females with age ranging from 19 to 21 years) who took part in an online learning activity using an avatar-mediated environment (Second Life). The results showed that users' flow experience can be influenced by the function of perceived background and attitude homophily in an avatar-mediated environment. Flow experience was found to mediate the relationship between avatar homophily and learners' exploratory behaviour. This study offers a conceptual understanding of the relationship between homophily and individual's flow state.

The application of different virtual reality interventions has been recently gaining interest from researchers and practitioners. Due to the current COVID-19 pandemic, higher education institutions increased the use of these technologies to help create much value for educators and students. The relationship between individual experience and engagement in avatar-mediated environments has garnered empirical support, but still little is known about the mediating mechanisms underlying this relation. This study investigated the mediating role of interpersonal attraction (e.g., social, physical, and task attraction) in the link between individual experience and engagement in an avatar-mediated learning environment. A total of 112 participants (21-23 years; 73 male and 39 female) participated in online peer learning sessions in Second Life. After controlling for demographic covariates, the results showed that (1) individual experience was positively associated with students' engagement; (2) social, physical, and task attraction was found to positively mediate the link between experience and engagement. The outcomes from this study provide valuable implications for promoting online learners' engagement in avatar-mediated environments.

Viral and bacterial infection diseases are the most common things caused by microbes. Infection diseases are serious issues because of the growth of COVID‐19. Because of the current living situation, clinical pathogens are difficult to identify. Therefore, biosensors have been widely utilized to sense the biomolecules relevant to viruses and bacteria. The biosensors observe the nanoparticles from the pathogens and help improve the infection analysis. The sensor information is processed using machine learning techniques because it consists of several learning patterns. However, the existing methods have multi‐objective optimization problems while analysing the changes in the nanoparticles. This work utilizes a mayfly optimized convoluted neural network (MOCNN) to overcome this research issue. The grid uses the fully convolution layer that processes the extracted biosensor features to determine the infections. The network performance is optimized by applying the exploitation and exploration properties of nuptial dance that help to escape from the local optima solutions. The effective utilization of the optimized training patterns improves the convergence speed and convergence rate compared to traditional methods. From the results, MOCNN ensures 98.97% accuracy, 0.388 error rate, and 0.322833 convergence rate on various iterations with different learning rates. [ABSTRACT FROM AUTHOR]

The application of different virtual reality interventions has been recently gaining interest from researchers and practitioners. Due to the current COVID-19 pandemic, higher education institutions increased the use of these technologies to help create much value for educators and students. The relationship between individual experience and engagement in avatar-mediated environments has garnered empirical support, but still little is known about the mediating mechanisms underlying this relation. This study investigated the mediating role of interpersonal attraction (e.g., social, physical, and task attraction) in the link between individual experience and engagement in an avatar-mediated learning environment. A total of 112 participants (21–23 years; 73 male and 39 female) participated in online peer learning sessions in Second Life. After controlling for demographic covariates, the results showed that (1) individual experience was positively associated with students' engagement; (2) social, physical, and task attraction was found to positively mediate the link between experience and engagement. The outcomes from this study provide valuable implications for promoting online learners' engagement in avatar-mediated environments. [ABSTRACT FROM AUTHOR]

The real-time obstacle avoidance strategy remains an important technical demand in robotic navigation. Although some progress has been achieved in recent years, existing works cannot handle unknown background inference in complex contexts. To deal with this challenge, this paper proposes a dynamic obstacle avoidance method in robotic navigation control through YOLOv5-based object detection. Specifically, an attention mechanism-based YOLOv5 algorithm is introduced to enhance the expression capability of small target channel features and spatial features. A P2 small target detection layer is added to strengthen the fusion of feature information between high and low layers. This is expected to address the problem that some detailed features of small targets are lost due to multiple down-sampling operations. For the navigation control part, an abstract potential field is introduced to guide the robots to move toward the target position while avoiding obstacles. Finally, we conduct some experiments on a real-world simulation scenario to make a comparative analysis. The obtained results show that the proposal can improve the obstacle avoidance effect compared with some baseline methods. [ABSTRACT FROM AUTHOR]