The metaverse has gained popularity recently in many areas including social media, healthcare, education and manufacturing. This work explores the use of the metaverse concept for cultural heritage applications. The motivation is to develop a systematic approach for the construction of a cultural heritage metaverse and to offer, potentially, more effective solutions for tourism guidance, site maintenance, heritage object conservation, etc. We propose a framework for this cultural heritage metaverse with an emphasis on fundamental elements and on characterization of the mapping between the physical and virtual cultural heritage worlds. Efforts are made to analyze the dimensional structures of the cultural heritage metaverse. Specifically, five different dimensions, linearity, planarity, space, time and context, are discussed to better understand this metaverse. The proposed framework and methodology are novel and can be applied to the digitalization of cultural heritage via its metaverse development. This is followed by a detailed case study to illustrate the tangible procedure, constructing a cultural heritage metaverse with a complex and dynamic nature which can be used for different applications, including heritage conservation.

An up-and-coming concept that seeks to transform how students learn about and study complex systems, as well as how industrial workers are trained, metaverse technology is characterized in this context by its use in virtual simulation and analysis. In this work, a virtual environment is created that duplicates real-world situations and enables immersive and interactive learning in the educational metaverse. For this purpose, we built a digital twin of the Nanyang Technological University (NTU) campus as a foundation, called NTUniverse. It is designed as an educational metaverse in which various academic and analytical applications are digitized as 3D content embedded within this virtual campus. The approach to digitally twinning educational systems and embedding them within virtual campuses enables remote and collaborative learning as well as professional technical skills training. It also makes feasible the analysis of abstract concepts, complicated structures, dynamic processes, and sensitive industrial procedures virtually, which is otherwise challenging if not impossible to perform in the real world. The work offers important insights into the behaviors and interactions of systems in the metaverse by evaluating design choices and user interests. NTUniverse is an attempt to explore a novel approach that addresses remote education and training challenges. Three efforts with NTUniverse will be discussed in this work, including (1) digitalization of the NTU campus; (2) campus train modelling and simulation; and (3) science, technology, engineering and mathematics education.

Virtual reality (VR) has good potential to promote technology-enhanced learning. Students can benefit from immersive visualization and intuitive interaction in their learning of abstract concepts, complex structures, and dynamic processes. This paper is interested in evaluating the effects of VR learning games in a Virtual and Augmented Reality Technology-Enhanced Learning (VARTeL) environment within an engineering education setting. A VARTeL flipped classroom is established in the HIVE learning hub at Nanyang Technological University (NTU) Singapore for the immersive and interactive learning. Experiments are designed for the university students conducting the learning, with three interactive and immersive VR games related to science, technology, engineering and mathematics (STEM), i.e., virtual cells, a virtual F1 racing car, and vector geometry. These VR games are a part of the VARTeL apps designed in-house at NTU for STEM education. Quantitative and qualitative analyses are performed. A total of 156 students from Mechanical Engineering participated in the experiment. There are 15 participants selected for an interview after the experiment. Pre-tests and post-tests are performed using two different models, the developed VARTeL and the modified Technology-Rich Outcome-Focused Learning Environment Inventory (TROFLEI), in order to measure the efficiency of the VARTeL environment in Higher Education. Significant improvements of about 24.8% are observed for the post-tests over the pre-tests, which illustrate the effectiveness of the VARTeL for Engineering education. Details of the VR simulation games, methods of data collection, data analyses, as well as the experiment results are discussed. It is observed from the results that all the underlying scales of the modified TROFLEI are above the threshold for the ‘Good’ category, indicating that a very reliable questionnaire is designed in this research. The mean ‘Ideal’ values are about 0.7–2.6% higher than the mean ‘Actual’ values. The limitations of the experiment and future works with recommendations are also presented in this paper.

Autism spectrum disorder (ASD) is a developmental disability that can impair communication, social skills, living skills, and learning capabilities. Learning approaches usually differ between mainstream schools and special needs schools, to cater for the different learning processes of children with ASD. Besides the traditional classroom-based education, alternative technology and methods are explored for special needs education. One method is to train children with ASD using Virtual Reality (VR) technologies. Many prior works show the effectiveness of VR-based learning with varying degrees of success. Some children with ASD may face challenges to gain independent living skills. Their parents or guardians have to expend a significant amount of effort in taking care of children with ASD. It will be very helpful if they can have a learning opportunity to gain such living skills. In this research, we develop a VR serious game to train children with ASD one of the basic living skills for road crossing safely. The VR serious game operates on multiple types of platforms, with various user interaction inputs including the Microsoft Kinect sensor, keyboard, mouse, and touch screen. The game design and methodology will be described in this paper. Experiments have been conducted to evaluate the learning effectiveness of the road crossing game, with very positive results achieved in the quiz and survey questionnaire after the gameplay.

Medical imaging is crucial for the detection and diagnosis of breast cancer. Artificial intelligence and computer vision have rapidly become popular in medical image analyses thanks to technological advancements. To improve the effectiveness and efficiency of medical diagnosis and treatment, significant efforts have been made in the literature on medical image processing, segmentation, volumetric analysis, and prediction. This paper is interested in the development of a prediction pipeline for breast cancer studies based on 3D computed tomography (CT) scans. Several algorithms were designed and integrated to classify the suitability of the CT slices. The selected slices from patients were then further processed in the pipeline. This was followed by data generalization and volume segmentation to reduce the computation complexity. The selected input data were fed into a 3D U-Net architecture in the pipeline for analysis and volumetric predictions of cancer tumors. Three types of U-Net models were designed and compared. The experimental results show that Model 1 of U-Net obtained the highest accuracy at 91.44% with the highest memory usage; Model 2 had the lowest memory usage with the lowest accuracy at 85.18%; and Model 3 achieved a balanced performance in accuracy and memory usage, which is a more suitable configuration for the developed pipeline.