Universiti Teknologi MARA System

This research explores how international students in Malaysian higher education develop digital literacy, the barriers that impede such development, and strategies that may enhance institutional support. To understand their experiences using digital tools within an academic context, semistructured interviews were conducted with international students in a qualitative design. The results showed that confidence develops through repeated use and structured support, and barriers include complexities of software applications, integration challenges across different platforms, and limited chances for hands-on training. Participants called for targeted workshops, peer mentorship, improved resource access, and early integration into the curriculum. This paper offers practical suggestions useful to universities seeking to foster international students' adjustment to digitally mediated learning environments.

Neglected tropical diseases (NTDs) disproportionately affect marginalised populations, yet the distribution of research attention across pathogens, populations, and settings remains uneven. Malaysia, a middle-income country with strong research capacity and a diverse NTD burden, provides an important case for examining research equity. This review aimed to systematically map NTD research in Malaysia and identify equity gaps across pathogens, population groups, study designs, and geographic settings. We conducted a systematic review in accordance with PRISMA guidelines. The Web of Science Core Collection was searched for peer-reviewed studies published between 1 January 2014 and 31 December 2024. Eligible studies reported human NTD data from Malaysia and addressed infectious NTDs of viral, bacterial, helminthic, or protozoal aetiology. Data were extracted on publication characteristics, study design, population age groups, settings, geographic location, and pathogen category. Descriptive analyses, visualisation techniques, and chi-square tests were used to assess equity patterns. A total of 336 studies were included. Viral and bacterial infections accounted for more than two-thirds of publications, while helminthic and protozoal infections were consistently underrepresented. Among studies reporting age information, adults and adolescents were most frequently studied, whereas children were comparatively underrepresented; however, age was not specified in 35% of studies. Most research employed observational or cross-sectional designs, with few interventional or outbreak-focused studies. Research output was geographically concentrated in urban and high-density states, with limited coverage of rural and indigenous settings. Population–pathogen inequities were statistically significant (χ² = 51.22, df = 9, p < 0.0001). NTD research in Malaysia is characterised by substantial research equity gaps, including pathogen bias towards outbreak-driven diseases, inadequate demographic reporting, limited interventional evidence, and urban-centred research visibility. These inequities in knowledge production may reinforce downstream health inequities by constraining evidence-informed and equity-sensitive policy responses. Addressing these gaps will require rebalancing research priorities towards neglected pathogens and populations, strengthening reporting standards, and aligning national research agendas with the equity principles of the WHO NTD Roadmap 2021–2030.

ABSTRACT The rapid expansion of electrified energy systems has transformed batteries from passive storage components into critical infrastructure assets. This shift has intensified persistent challenges related to degradation, safety and lifecycle uncertainty. A growing body of literature examines the use of artificial intelligence (AI) and machine learning (ML) in battery modelling and management. However, most extant studies emphasise individual algorithms or isolated prediction tasks. The reliable deployment of intelligence on operational battery system receives limited attention. This perspective addresses this limitation by examining intelligent battery systems from a system‐level standpoint. Intelligence is approached as an outcome of coordinated integration across sensing, learning, optimisation and management functions, rather than as an isolated algorithmic feature. Recent advances in data acquisition, performance optimisation, lifecycle modelling and predictive management are examined within a unified battery management architecture. Attention is given to practical deployment constraints, including sensing fidelity, data representativeness, interpretability, scalability and compatibility with embedded control systems. Evidence from fast charging studies and grid‐scale deployments highlights both the potential of AI‐driven approaches and the structural barriers that restrict translation from laboratory settings to real‐world operation. The analysis highlights the role of early and diverse sensing, physics‐informed learning, uncertainty‐aware decision processes and deployment‐oriented model design in shaping dependable system‐level intelligence. The paper concludes by identifying research priorities that align algorithmic development with safety, sustainability and operational feasibility, positioning intelligent battery systems as adaptive assets within future energy infrastructures.

The goal of this research is to enhance road safety by delivering an affordable, non-intrusive, and effective drowsiness detection solution suitable for integration into mainstream vehicle systems. Driver drowsiness is a critical factor contributing to road accidents, often resulting in severe injury or death. In Malaysia alone, it is estimated that drowsiness causes between 2,000 and 3,000 traffic accidents annually. Conventional vehicle-based drowsiness detection systems, which rely on steering behavior or lane departure, often fail to detect early physiological signs of fatigue. Recent advancements in artificial intelligence (AI), particularly in computer vision and machine learning, offer new opportunities for developing low-cost, real-time drowsiness monitoring systems. This research proposes a driver drowsiness monitoring system (DDMS) that utilizes deep learning and visual behavior analysis to detect signs of fatigue through real-time monitoring of eye and mouth activity. A customized Convolutional Neural Network (CNN) is developed to classify eye states (open vs. closed), while yawning is detected using facial landmark analysis and Mouth Aspect Ratio (MAR) computations. The system is trained and evaluated using the MRL Eye Dataset, which consists of 4,000 annotated images, with data preprocessing and augmentation applied to enhance robustness. Through systematic experimentation and hyperparameter tuning, the model achieves a peak accuracy of 98%, with equally high precision, recall, and F1 Scores, using the Adam optimizer, a learning rate of 0.001, and 50 training epochs. The system also demonstrated strong real-time performance across varied lighting conditions, though challenges remain in scenarios involving occlusion (e.g., sunglasses) and extreme head positions. The results indicate that the proposed method is not only feasible but highly accurate, marking a significant advancement in proactive traffic accident prevention technologies.

Power distribution systems face increasing threats from high-impact, low-probability (HILP) events caused by extreme weather conditions such as floods, typhoons, droughts, and heatwaves. These events often lead to power outages worldwide, highlighting the need for effective strategies to mitigate their impact. This work proposes an emergency response strategy that integrates network reconfiguration (NR) with the deployment of mobile emergency generators (MEGs) to enhance system resilience. The objective is to maximize power supply availability following HILP events. To achieve this, mixed-integer quadratic constraint programming (MIQCP) is used to optimize MEG deployment and network restoration. Additionally, improved quantitative resilience metrics are introduced to assess system degradation, pre-recovery, and recovery phases, enabling continuous resilience measurement and informed decision-making. Furthermore, an optimal capacity deployment strategy (OCDS) is proposed to ensure that MEGs are deployed with suitable capacities based on the specific needs of outage-affected areas. The effectiveness of the proposed strategy is demonstrated through tests on the IEEE 118 bus system. The results show a significant improvement of up to 100% in system resilience, reducing power outages and accelerating restoration. The findings confirm that integrating NR with optimized MEG deployment enhances service restoration, providing an effective approach for utilities to manage power disruptions.

This research examines the influence of excessive material thickness on the microwave absorption efficiency of SrFe12O19/MWCNT composite materials, emphasizing their electromagnetic properties within the X-band frequency range (8–12 GHz). The synthesis of the composites was performed using high-energy ball milling (HEBM) and chemical vapor deposition (CVD) techniques, followed by comprehensive characterization of their structural, magnetic, and dielectric attributes. The study identified an optimal material thickness of 4 mm, achieving maximum reflection loss (RL) efficiency, with a minimum RL of − 1.16 dB was observed for Sample S3 at a thickness of 6 mm in the X-band (8–12 GHz) in MWCNT-integrated samples. Incremental increases in thickness beyond 4 mm led to reductions in microwave absorption performance, attributed to absorption peak shifts and reduced electromagnetic matching efficiency. Magnetic characterization revealed significant reductions in saturation magnetization (Ms) and retentivity (Mr) in MWCNT-incorporated samples due to structural alterations, while coercivity (Hc) was primarily influenced by grain size and synthesis parameters. Dielectric analysis highlighted the enhancement of dielectric loss (ε″) driven by interfacial polarization and MWCNT distribution, particularly at lower frequencies. These findings show the critical interdependence of material thickness, magnetic, and dielectric properties in optimizing radar-absorbing materials, offering key insights for the advancement of composite nanostructures in electromagnetic wave absorption applications.

Lower-limb muscle injuries are common in football and are often linked to fluctuations in training load. Global Positioning System/Global Navigation Satellite System (GPS/GNSS) and Inertial Measurement Unit (IMU) based monitoring of external workload is now routine, yet evidence for its association with injury risk remains inconsistent. This review aimed to synthesize current evidence on the relationship between GPS/IMU-derived workload variables and lower-limb muscle injuries in football players. Following PRISMA guidelines, we searched PubMed and Scopus to September 2025. Eligible studies involved football players at any competitive level, reported GPS/IMU-based workload metrics, and included muscle-injury outcomes. Study quality was assessed with the Newcastle–Ottawa Scale. Twelve studies met the inclusion criteria for qualitative synthesis; however, only four studies provided extractable statistical data for quantitative pooling. These four studies contributed 21 independent effect-size comparisons, analysed as odds ratios (ORs) or relative risks (RRs). Workload definitions and thresholds varied substantially across studies—particularly speed zones, sprint metrics, and ACWR calculations—limiting direct comparability. The exploratory quantitative analysis of OR-based studies showed no significant association between high workload and injury risk (pooled OR=1.33, 95% CI 0.85–2.07; p=0.21; I²=93%). In contrast, the RR-based analysis indicated a significant association, with high workloads more than doubling injury risk (pooled RR=2.33, 95% CI 1.65–3.30; p&lt;0.00001; I²=0%). Given the small number of extractable studies and substantial heterogeneity in workload definitions, these findings should be interpreted as exploratory rather than confirmatory. GPS/IMU-monitored external workload may be associated with muscle-injury risk, but the direction and magnitude depend on analytic approach and how “high workload” is operationalized. Larger, multi-club prospective studies with standardized workload definitions are needed to strengthen the evidence base.

The integration of modern digital technologies into traditional crafts is widely promoted as a pathway to cultural revitalization and sustainable development, yet such interventions may generate unintended socio-cultural consequences. Focusing on Yazhou pottery, a nationally recognized form of intangible cultural heritage in Guizhou Province, China, this study examines the drawbacks of modern technology in the research and development of traditional ceramic crafts. Using a qualitative design, we conducted semi-structured interviews with ten stakeholders, including master potters, young practitioners, and design or technology professionals. Interview data were analyzed through grounded theory supported by NVivo, and descriptive counts were retained as minimal quantitative supplements to enhance transparency. Findings show that while computer-aided design, ceramic 3D printing, and AI-assisted design can improve efficiency and expand creative possibilities, they also introduce substantial challenges. Key concerns include high costs and uneven access to equipment, steep learning curves, technical instability in digital fabrication, intergenerational tensions in training and decision-making, and threats to cultural authenticity and handcraft identity. Participants cautioned that reliance on digital templates may encourage standardization and cultural homogenization if not carefully governed. The study concludes that technological integration in Yazhou pottery is a double-edged sword and argues for prudent integration, positioning technology as an assistive rather than substitutive tool guided by community participation, cultural integrity, and sustainable skill transmission. By foregrounding practitioners’ perspectives, this research contributes a practice-based understanding of how heritage crafts can navigate technological change while preserving cultural diversity and human creativity.

Using a sample of unlisted companies from the 2007–2020 stock market, this study validates the association between tax reduction, rate reduction, digitization, model modification, and total element productivity. The study found that (1) Tax cut and rate reduction policy greatly increases the company's total element productivity. (2) Conversion of digitization encourages high-quality development through the resource optimization effect and technological energy supply. (3) Digitization conversion had the strengthening effect on the tax cut and rate reduction policy, and the total element productivity increased by 7.75% by the synergistic effect of both. Heterogeneity analysis shows that synergistic effects of non national and high-tech industries and low competitive markets are more pronounced. According to the verification of the mechanism, the tax reduction and the rate reduction are promoting quality development through the relaxation of the loan constraint, the promotion of the innovative investment, and the expansion of the scale economy.

This study aims to analyze the political marketing strategies implemented by the Anies Rasyid Baswedan and Abdul Muhaimin Iskandar (AMIN) ticket in the 2024 Presidential Election through a phenomenological approach. The research focuses on interpreting the experiences of political actors—including the supporting parties, campaign teams, and volunteers—in building a political narrative based on change and social justice. The phenomenological approach was chosen to explore the subjective meaning of the political communication process and campaign strategies that shape the candidate pair's image in the public eye. The results of the study indicate that the strategy of Segmenting, Targeting, and Positioning (STP) and the implementation of the Marketing Mix (product, promotion, price, and place) became the main foundation in shaping the public perception of the AMIN ticket. Political products that emphasize moral values, integrity, and support for the common people were promoted through digital strategies and a massive volunteer movement. These findings confirm that political marketing is not only an electoral instrument, but also a social communication process that builds moral legitimacy and public participation. This research provides a theoretical contribution to the development of political marketing studies in Indonesia, particularly in the context of participatory democracy that emphasizes ethics, transparency, and public trust.