Shri Rai

Hyper-Realistic Virtual Reality (Hyper-Real VR) environments have the potential to elicit profound emotional responses by leveraging high-fidelity visual elements. While previous research has extensively examined the role of Virtual Reality (VR) in evoking negative emotions, there is limited understanding of how Hyper-Real VR can be systematically designed to induce positive emotions, such as awe and calm. This paper introduces a conceptual framework that examines the impact of four key visual factors, i.e. geometry, material surfaces, lighting, and colour, and their sub-factors in shaping emotional experiences. Geometry, including scale and proportion, influences spatial perception and depth, which are crucial for inducing awe. Material surfaces, such as reflections and textures, enhance realism and presence, reinforcing emotional engagement. Lighting, particularly global illumination and shadows, modulates mood and spatial perception, creating immersive experiences that promote awe and calm. Colour, through physically based rendering (PBR), values, and tones, shapes emotional responses by enhancing realism and aesthetic harmony. This framework integrates theories from presence research, perceptual psychology, and environmental design to establish a structured approach for designing emotionally engaging Hyper-Real VR environments. By mapping visual factors to emotional outcomes, it provides a foundation for optimising VR experiences to elicit awe and calm. The proposed framework has implications for fields such as digital therapy, mental well-being, and immersive entertainment. Future research will validate this model through empirical studies, further refining the role of hyper-realistic visual elements in emotional engagement within VR.

Vaccine hesitancy is still a significant barrier to achieving widespread immunity in many communities. In this paper, we evaluated a serious game focusing on vaccination against COVID-19. This study investigates the potential of virtual reality (VR) as an innovative educational tool to address this issue. Focusing on the serious game “Spike Force”, which simulates the mechanisms of the mRNA COVID-19 vaccine, this research evaluates the game’s effectiveness in enhancing participants’ understanding, altering attitudes, and influencing behaviours related to vaccination. Participants engaged with “Spike Force,” and their knowledge, attitudes, and behaviours were assessed through pre- and post-gameplay questionnaires. The findings show that immersive VR experiences can significantly improve vaccine literacy, increase confidence in vaccine-related discussions, and promote positive behavioural changes toward vaccination. These results suggest that VR could play an effective advocacy role for public health education, particularly in combating vaccine hesitancy.

Murdoch University was one of several institutions and consortia funded to investigate the educational use of the Collaborative Online Learning and Information Services (COLIS) system developed in 2002 by a consortium based at Macquarie University. This project set out to investigate the use and usability of learning objects across three aspects of the COLIS system. The major focus of this research was on the experience of the teacher in using learning objects within the COLIS framework. Several activities took place. Librarians catalogued learning objects into the intraLibrary Learning Object Repository. Academic teaching staff searched for learning objects in intraLibrary and inserted them into WebCT. We investigated how easy it was for these stakeholders to use the suite of systems and identified ways in which both the systems and the processes around the systems might be improved.

In this paper, a method for pitch independent musical instrument recognition using artificial neural networks is presented. Spectral features including FFT coefficients, harmonic envelopes and cepstral coefficients are used to represent the musical instrument sounds for classification. The effectiveness of these features are compared by testing the performance of ANNs trained with each feature. Multi-layer perceptrons are also compared with Time-delay neural networks. The testing and training sets both consist of fifteen note samples per musical instrument within the chromatic scale from C3 to C6. Both sets consist of nine instruments from the string, brass and woodwind families. Best results were achieved with cepstrum coefficients with a classification accuracy of 88 percent using a time-delay neural network, which is on par with recent results using several different features.