Jackel Vui Lung Chew

Digital Storytelling (DST) is one of the innovative pedagogical approaches that enhances student engagement in the learning process. However, its adoption within the Malaysian university e-learning environment remains underexplored. This study investigates undergraduate students’ perceptions of DST adoption within Universiti Malaysia Sabah's learning management system called iTEL. This study uses the Unified Theory of Acceptance and Use of Technology (UTAUT) as the guiding framework. Throughout this study, a structured survey instrument was developed to examine the influence of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) on students’ Behavioral Intention (BI) to adopt DST. Data collection was conducted among undergraduate students in the information technology field to explore their perceptions of DST and its adoption in iTEL. The findings revealed that PE significantly influences students’ BI to adopt DST in iTEL. This implies that the usefulness of DST strongly determines students’ intention to use it. In contrast, EE, SI, and FC do not significantly affect students’ BI in adopting DST. These results imply that students’ willingness to adopt DST is primarily driven by their perception of its performance benefits rather than by ease of use, encouragement by peers or lecturers, or institutional and technical support. This study contributes to the understanding of DST adoption within the Malaysian university e-learning environment.

Efficient ambulance routing plays an important role in emergency medical services. However, solving the ambulance routing problem remains challenging. This study investigates the performance of ant colony optimization to solve the ambulance routing problem, aiming to improve the quality of route planning under constraints such as traffic, patient urgency, and ambulance capacity. To simulate realistic emergency scenarios, 27 benchmark instances from the classical vehicle routing problem were adapted to the ambulance routing context by mapping depots to ambulance stations, customer nodes to emergency sites, and incorporating patient urgency and ambulance capacity. The performance of ant colony optimization was compared with the genetic algorithm and particle swarm optimization. Each algorithm was independently applied to all instances, and route quality was evaluated based on best route cost, average cost, and standard deviations. The experimental results show that ant colony optimization consistently outperformed both genetic algorithm and particle swarm optimization across most instances. Specifically, ant colony optimization achieved shorter total route distances, which are measured as the cumulative route distance of ambulances required to serve the emergency sites. These improvements were accompanied by greater consistency in solution quality across multiple runs. These findings suggest that ant colony optimization is a robust and effective tool for ambulance routing optimization. This study contributes to the growing body of work on intelligent emergency logistics by demonstrating the practical advantages of ant colony optimization in critical decision-making. The findings are valuable for optimization researchers in enhancing ambulance routing efficiency.

The complex and invasive dynamics of brain tumours present significant clinical and computational challenges. This study aims to enhance the numerical efficiency of brain tumour growth simulation through an improved iterative solver. A two-dimensional diffusion–proliferation model of brain tumour growth is discretized using an implicit finite difference scheme to generate a large, sparse linear system. The resulting system is solved iteratively using the Kaudd Successive Over-Relaxation (KSOR) method and compared with the classical Gauss–Seidel (GS) approach in terms of convergence rate, iteration count, computation time, and numerical accuracy. Numerical experiments reveal that the KSOR method achieves up to 94.65% reduction in iterations and 86.33% faster computation compared to GS while maintaining high stability and accuracy. These findings demonstrate that integrating the implicit finite difference scheme with KSOR provides a robust and efficient numerical framework for modelling two-dimensional brain tumour dynamics. This approach offers practical implications for improving computational modelling of tumour progression, potentially supporting real-time prediction and treatment planning in biomedical and clinical applications.

Vehicular traffic flow anomaly detection is crucial for traffic management, public safety, and transportation efficiency. It assists experts in responding promptly to abnormal traffic conditions and making decisions to improve the traffic flow. This survey paper offers an overview of the application of machine learning to detect anomalies in the traffic flow. Through an extensive review of the literature from the Scopus database, this paper explores the technical aspects of traffic flow anomaly detection using machine learning, including data sources, data processing approaches, machine learning algorithms, and evaluation metrics. Additionally, the paper highlights the emerging research opportunities for researchers in enhancing traffic flow anomaly detection using machine learning.

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