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Fuzzy Evaluation for Infectious Disease (FEID) for Prevention and Control of COVID-19: A Case Study of Pasai Siong Cluster
No full textThe COVID-19 outbreak has raised global concern due to its high infection rates and substantial number of deaths worldwide. This study introduces the Fuzzy Evaluation for Infectious Disease (FEID) to assess COVID-19's transmission potentials, effects, and causes, specifically within the longhouses of Pasai Siong. The notorious Pasai Siong Cluster affected 2,693 individuals across Sibu and ten other districts in Sarawak, Malaysia. The Failure Mode Effect Analysis (FMEA) method was applied by considering the severity, occurrence, and detection rating of each failure mode through the Risk Priority Number (RPN) value. However, conventional FMEA alone may not yield precise risk evaluations, as the generated RPN can be unreliable in real-world applications. To address this, a fuzzy logic system was integrated with FMEA to obtain a fuzzy RPN (FRPN) value. In this project, RPN values were gathered from site assessments in Pasai Siong, while FRPN values were derived through MATLAB simulations of expert rules. The results indicated that the FRPN values provided a more consistent prioritization of risk factors compared to conventional RPN. Specifically, close social interaction in living rooms recorded the highest FRPN value of 836, followed by poor ventilation in confined areas at 807, highlighting them as the most critical contributors to SARS-CoV-2 transmission. Practising social distancing and improving airflow were shown to significantly reduce the infection risk. Furthermore, a monotonicity test validated the input parameters before their integration into the fuzzy system in MATLAB
Manuscript Preparation and Format for Borneo International Journal of Medicine and Health (BIJMH)
No full textDo migrant workers introduce asymmetries in the Phillips curve?
No full textThis study hypothesizes that an increase in migrant workers may introduce asymmetric effects into the Phillips curve relationship in dynamic and open economies. As the share of migrant workers increases, the traditional price level–unemployment–output trade-off could become asymmetric, potentially reshaping price dynamics through labor market imbalances. Malaysia and Singapore have been used as case studies to explore this evolving structural asymmetry. Time series analysis has been employed to carefully analyze the sample study over the period 1980 to 2021. The findings reveal that gross domestic product (GDP), has a positive effect on the consumer price index (CPI), and that this effect is significantly amplified by higher levels of migrant workers. Conversely, while rising unemployment typically exerts downward pressure on price level, this negative effect is weakened in the presence of a larger migrant workforce. These results suggest that migrant workers play a key role in reshaping price dynamics. Given the role of migrant workers in amplifying price pressures, policymakers must carefully balance price level and income growth in order to preserve real gains and support sustainable economic development
Design and optimization of a test case generation algorithm for real-time embedded systems based on adaptive Q-Learning
No full textTesting real-time embedded systems requires intelligent strategies that balance test coverage, timing constraints, and resource limitations. The traditional test case generation methods, such as random testing and conventional Q-learning, often fail to adapt to dynamic workloads and maintain real-time responsiveness. To address these limitations, an automated test case generation method based on adaptive Qlearning (AQL) is proposed in this study; the method is specifically designed for real-time embedded software. The proposed method introduces dynamic parameter
adjustment and adaptive time-window control schemes to optimize multiple objectives including test coverage, resource utilization, and empirical real-time performance under varying workloads. Experiments were conducted on an ATV dashboard-embedded platform, and AQL was compared with random testing (RT) and traditional Q-learning (QL). The results demonstrated that AQL achieved significant
performance improvements: the statement coverage level reached 92%, the average CPU utilization rate decreased to 63%, and under experimental loads, the deadline miss rate remained below 2% across all scenarios (e.g., 1.2% under high CPU load), while faster response times were achieved. A statistical analysis (ANOVA, p < 0.01) confirmed the significance of these improvements. In summary, the proposed AQL method provides an efficient and scalable intelligent solution for testing embedded systems in real time. Its feedback-driven adaptive structure effectively overcomes
the static limitations of the conventional reinforcement learning approaches, offering both academic innovation and practical potential for testing intelligent software
in resource-constrained real-time environments
The Impact of Public Service Announcement (PSA) on Cyber Wellness and Digital Citizenship Among Students of Faculty of Education, Language and Communication, University Malaysia Sarawak, Malaysia.
No full textThis study looks at how Public Service Announcements (PSAs) affect students at University Malaysia Sarawak's
Faculty of Education, Language and Communication (FELC) in terms of cyber wellness and digital citizenship (UNIMAS). An online questionnaire was used to gather data from 82 undergraduate students as part of a quantitative study design that used a descriptive survey approach. Students' exposure to PSAs, attitudes, social norms, and digital activity were all examined using descriptive statistics and multiple regression analysis. The results indicate that students exhibit good digital citizenship behaviors and a high degree of awareness of cyber wellbeing. Students' digital conduct was found to be strongly influenced by attitudes about social norms and cyber health. However, exposure to PSAs by itself did not significantly alter behavior, suggesting that PSAs have an indirect impact on behavior through social influence and internal views. The most popular platform for coming across PSAs was found to be TikTok. This study offers information for creating more successful initiatives to promote cyber wellness among college students
Creating an Immersive Learning Environment for Teaching Agile Scrum and Team Software Process : A Framework for Software Engineering Education
Full text linkMost of the principles and concepts that need to be taught in Software Engineering courses are hard to share the realistic experiences because it is difficult to give the student practical exposure to the insight and processes involved. This paper presents an innovative framework tailored for the establishment of an immersive learning environment within the context of a Software Engineering Project course. The overarching objective is to effectively tackle the inherent challenges associated with teaching intricate software engineering concepts, notably Agile Scrum and Team Software Process (TSPi). Conventional pedagogical approaches often prove inadequate in providing a comprehensive and engaging learning experience for students, educatorsandstakeholders. In response, our study introduces a pioneering immersive learning approach, offering a robust solution to this educational gap. To gauge the framework's efficacy and pertinence, we conducted online surveys, specifically targeting third-year students enrolled in the Software Engineering Laboratory course and the project stakeholders involved. These surveys were instrumental in collecting valuable feedback on the practicality and impact of our approach in enhancing the teaching and learning processes. This study presents a thorough exposition encompassing the framework's conceptualization, implementationanditerative evolution. Our research outcomes reveal that our immersive learning approach has successfully met the predefined course objectives, effectively addressing the intrinsic challenge of imparting hands-on experiences associated with software engineering principles and concepts. As a significant contribution to the ongoing initiatives aimed at elevating software engineering education, our study underscores the importance of providing students with tangible exposure to vital concepts such as Agile Scrum and TSPi. Moreover, this paper delineates the collaborativejourney involved in the creation, executionandrefinement of the course framework. Ultimately, our research endeavoursto evaluate the degree to which our innovative framework aligns with the objectives established by both students and stakeholders
An overview of current technological developments in apparel fit customization
No full textAbstract
Purpose – This study reviews the literature that discusses the technologies that will influence fit customization (FC). The purpose of this study is to clearly understand the current progress of such technology and the potential future development path. Design/methodology/approach – Research papers, publications and websites of well-known apparel
companies using FC are reviewed to explore the latest technological advancements in apparel FC.
Findings – Given the advances in body measurement, pattern and fit assessment technologies, FC represents
a sustainable production approach for the apparel industry.
Originality/value – This review examines the state of the field, looks at possible trends in the study and
highlights future directions
Leveraging Artificial Intelligence to Transform Leadership Development
No full textArtificial intelligence (AI) is transforming the way learning and teaching are implemented in schools (Adams & Chuah, 2022). In particular, the emer-gence of Generative AI (GenAI) technologies, such as ChatGPT, has signifi-cantly impacted the field of education in recent years (Chiu, 2024). These AI tools support personalised learning by analysing its users’ performance data and tailoring educational content to individual needs (Hwang et al., 2020). GenAI also enables the creation of original content such as text, images, and videos based on specific prompts (Adams et al., 2024). One of its unique fea-tures is its ability to produce humanlike conversational dialogue (Chiu, 2024), and it can simulate various roles, such as teachers, administrators, students, or school leaders (Adams & Thompson, 2025).
While the implications of AI for teaching and learning have been widely examined (Adams & Chuah, 2022; Levine et al., 2025), its potential to support leadership development has received far less attention in the lit-erature. This gap is particularly noteworthy given that effective leadership is essential for navigating the increasingly complex challenges facing edu-cational organisations (Mincu, 2022). To address this gap, this chapter ex-plores how AI, particularly GenAI can be leveraged to enhance leadership competencies in educational settings, an area that is critically important yet underexplored.
This chapter begins by tracing the development of AI and its evolution into today’s transformative force in education. It then examines the intersection of AI and educational leadership, highlighting how aspiring school leaders can harness GenAI technologies as virtual coaching tools. These tools have the po-tential to supplement traditional leadership training by offering personalised, on-demand guidance and simulation-based learning. The chapter concludes by discussing the broader implications of AI for leadership development and offers recommendations for integrating AI into leadership preparation and practice
Mapping the Intersection of Organizational Justice and Sustainability Development in Asian Higher Education: A Bibliometric Perspective
No full textThe pursuit of sustainability has become a defining priority in higher education institutions across Asia. Within this shift, organizational justice, covering fairness in decision-making, resource distribution, and interpersonal treatment, emerges as a critical yet often overlooked factor in advancing sustainable practices. This chapter explores the intersection of sustainability and organizational justice in Asian higher education through a bibliometric analysis of academic literature published between 2014 and 2024. Using Harzing’s Publish or Perish and VOSviewer, 92 relevant publications were systematically selected following PRISMA guidelines. Findings reveal a rapidly growing field, primarily driven by research from China, India, and Indonesia, rooted in social sciences and management disciplines. The bibliometric analysis highlights regional and disciplinary fragmentation, with limited cross-national collaboration. Meanwhile, thematic mapping identifies equity as a central bridge linking micro-level justice issues with macro-level sustainability agendas. This chapter highlights the crucial role of equity as an integrative framework, positioning Asian higher education as both an innovative scholarly space and a catalyst for social and environmental transformation. The insights provided aim to inform future research, policymaking, and institutional practices by clarifying the current knowledge landscape and identifying areas for further exploration
A multi-criteria recommendation system for personalised tourism experiences with user query analysis
No full textRecommendation systems play a crucial role in assisting users with decision-making by suggesting relevant items. Multi-criteria recommender systems (MCRS) enhance this process by incorporating user preferences for various aspects, leading to more personalised and effective recommendations. However, MCRS faces challenges such as high computational complexity and limited consideration of user
context, including user preferences for relaxation, which may differ between solo trips and trips with friends. This paper addresses these limitations by proposing a
novel MCRS approach for tourism recommendation systems. Our proposed system combines matrix factorisation with a deep residual network (ResNetMF), demonstrating substantial performance improvements in terms of RMSE, MAE and lower
training time compared to a wide range of baselines. Additionally, a user query analysis component allows users to express their dynamic preferences through queries, catering to the context-specific nature of travel decisions. The evaluation demonstrates that our ResNetMF model outperforms baseline and deep learning methods in most of the tested evaluation metrics while having the lowest training time. This work contributes to the field of tourism recommendation systems by proposing a user-centred approach that addresses both accuracy and user interaction
for effective travel recommendations