UNIVERSITI TEKNIKAL MALAYSIA MELAKA INSTITUTIONAL REPOSITORY : No conditions. Results ordered -Date Deposited.

Heat transport and adsorption mechanism of linear alkane liquids on solid surfaces

2026-03-17T07:16:55Z

The interaction between solid surfaces and liquid hydrocarbons known as solid-liquid (S-L)interfaces plays a crucial role in various engineering applications that determine the behaviour of liquid orientations which are referred to as adsorption mechanisms. Such interaction is commonly paired with heat transfer at S-L interfaces which are referred to as thermal transport mechanisms. These two important characters are the main contributors to the performances of the S-L interfaces system. The problem arises from the limited understanding of how liquid hydrocarbons behave on solid surfaces during operational conditions, which directly affects their adsorption mechanisms and subsequently their heat transport mechanisms efficiency. Existing studies have primarily focused on experimental methods that do not adequately capture the molecular orientations near the solid surfaces. To address this gap, this study employs nonequilibrium molecular dynamics (NEMD)simulations to analyse the adsorption mechanisms of linear liquid alkane of butane (C4H10) and pentane (C5H12) as they interact with crystal planes of Face-centered cubic (FCC) lattice of (100), (110) and (111). The methodology involves simulating the model that consists of three layers of solid - liquid – solid, with a temperature difference applied across the system to evaluate the heat flux across the systems. To address the adsorption mechanisms the structural properties namely density distributions, orientation order parameter and radius of gyration are evaluated. On the other hand the thermal transport is evaluated based on the temperature distributions and the generated heat flux as the temperature difference is applied across the systems. Molecular dynamics simulations were used to investigate how FCC crystal orientations affect the thermal and adsorption behaviour of C₄H₁₀ and C₅H₁₂ at solid– liquid interfaces. The (111) surface exhibited the highest first adsorption peak at 966.94 kg/m³ for C₅H₁₂, followed by (100) (801.79 kg/m³) and (110) (784.76 kg/m³), corresponding to the number of atoms per layer (224, 200, and 192, respectively). The thermal conductivity for C5H12 is 6.62 × 106 for (100), 6.07 × 106 for (110) and 6.51 × 106 for (111). For C4H10 the thermal conductivity is 5.92 × 106 for (100), 6.08 × 106 for (110) and 5.69 × 106 for (111). In conclusion, C5H10 has a larger adsorption mechanism and higher thermal conductivity as compared to C4H10. The findings contribute to a deeper understanding of interfacial thermal resistance and lay the groundwork for future research aimed at optimising heat transfer in engineering applications involving liquid hydrocarbons.

Estimation of elbow flexion torque from NMES MMG signals and anthropometric variables using GLEO-RFR

2026-03-17T07:16:29Z

This thesis presents the development of an elbow joint flexion torque (TQ) estimation model that integrates neuromuscular electrical stimulation (NMES) induced mechanomyography (MMG) signals from the biceps brachii (BB) muscle across three forearm postures and four elbow flexion angles, together with anthropometric variables of the arm. 36 healthy male participants received NMES at 30Hz frequency, 110µs pulse width, 30mA current amplitude, 1s ramp time, and a 6s on over 2s off duty cycle, which induced TQ levels below 15% of maximum voluntary isometric contraction (MVIC). 30s recordings of TQ and MMG signals were collected at forearm positions of neutral, pronation, and supination, and at elbow flexion angles of 10°, 30°, 60°, and 90°. In addition, each participant performed voluntary contractions in 3 randomly selected combinations of elbow angle and forearm posture to provide data for torque estimation model validation. MMG, TQ and anthropometric measurements were recorded into a computer through a data acquisition device for offline analysis. 12 MMG features were extracted and assessed for reliability using Two-Way Random Effects, Single Measurement and Absolute Agreement Intraclass Correlation Coefficient ICC (2,1), at 95% confidence interval. Also, 7 anthropometric variables were validated via intra-test percentage reliability (%R) and technical error of measurement (TEM). Further, Grey Relational Degree (GRD) analysis quantified the correlation of MMG and anthropometric++ features with TQ output. These features were subsequently employed to develop a random forest regression (RFR) based TQ estimation model, optimized via the general learning equilibrium optimizer (GLEO) for feature selection and hyperparameter tuning. Test–retest ICC (2,1) values for TQ and MMG ranged from 0.6880 to 0.8230, indicating moderate to high reliability. Forearm posture and elbow angle significantly affected TQ RMS (p < 0.05), with notable variations in MMG RMS, MMG MPF and MMG MDF. MMG RMS and TQ RMS increased from 10° to 60° and then declined at 90° (p < 0.05), whereas MMG MPF and MMG MDF progressively decreased with increasing joint angle (p < 0.05) along the lateral and transverse muscle axes. Since the behaviour of the transverse axis was statistically significant across a majority of postures and angles, data from it was used for model development. GRD analysis showed TQ and MMG correlation coefficients from 0.5734 to 0.8173. The optimized RFR model achieved 33% of feature reduction (from 12 to 8), yielding 6.25% of improvement in the R2 values (from 0.7228 to 0.7853) and 0.5232 on the testing and validation datasets respectively. Similarly, anthropometric variables exhibited TEM values between 0.0079 and 0.2417, with %R ranging from 97.9294 to 99.9567. GRD analysis showed TQ and anthropometric++ features correlation coefficients ranging from 0.5808 to 0.8708. Anthropometric++ features based RFR model also achieved 33% of feature reduction (from 9 to 6), 6.10% improvement in the R2 values (from 0.6560 to 0.7170) and R2 of 0.4437 on the testing and validation datasets respectively. These findings support the integration of MMG and anthropometric features towards enhanced TQ prediction accuracy, with MMG features demonstrating superior performance. This hybrid approach holds significant implications for ergonomic design, assistive technologies, and sports rehabilitation strategies.

Recruitment practices effect on organisational performance through employee commitment

2026-03-17T07:16:08Z

Recruitment significantly affects organizations, as the selection of new employees directly influences the skills, talents, and overall performance of the workforce, ultimately affecting firm outcomes. In short, the company's selection system has an effect on the bottom line, such as productivity and financial results. Besides, Employee Commitment is a key factor that is often mediates the Recruitment factors and Organisational Performance as found in literature. In addition, commitment is often being associated with employeesâ€™ loyalty which concerns most todayâ€™s organisations. The objectives of this thesis were fourfold; to investigate the effect of Recruitment Practices on Organisational Performance; to examine the relationship between Employee Commitment and Recruitment Practices; to examine the relationship between Employee Commitment and Organisational Performance; and to analyze the roles of Employee Commitment in mediating the effect between Recruitment Practices and Organisational performance. The main analysis which analysed the relationship among the research constructs and the research framework was validated by employing the Partial Least Squares Structural Equation Modeling (PLS-SEM) Software. PLS-SEM is a tool deemed fit for the measurement of Likert-type measurement models. The current research was guided by Resource Based-view Theory for the theory behind it is on how internal corporate resources are developed. Companies do this by acquiring a variety of resources and developing distinctive capabilities. The basis for a firm's competitive advantage is this disparity in resources and capabilities. Quantitative research method was employed through survey utilizing questionnaires for primary data collection. The samples in cluded 377 managerial personnel of firms in the energy industry such as Abu Dhabi National Oil Company, Emirates National Oil Company, Abu Dhabi National Energy Company, Sharjah National Oil Corporation. Energy industry is one of the significant contributors for the UAEâ€™s economy, hence, highlighting the importance of hiring competent personnel for firm performance and sustenance. Simple random sampling method was used in achieving the total number of samples through the provided sampling formula. The results indicated a significant relationship between Recruitment Practices and Organisational Performance, a significant relationship between Recruitment Practices and Employee Commitment, a highly significant relationship between Employee Commitment and Organisational Performance and the Employee Commit ment partially mediates the relationship between Recruitment Practice and Organisational Per formance. The findings from the study were in line with existing literatures which offer invaluable contribution to the literature on human resource management practices in the UAE in general and in public organisations in particular. Specifically, the results shall serve to high light the relationship between Recruitment Practices and Organisational Performance through Employee Commitment.

Enhanced incremental dynamic community detection for improving the stability of community structure in network analysis

2026-03-17T07:15:38Z

Network analysis plays a crucial role in detecting communities within complex networks, which are prevalent across diverse domains and exhibit intricate structures and interactions. Dynamic community detection (DCD) is essential for understanding evolving complex networks, where vertices and edges continuously change over time. However, maintaining stability and continuity in dynamic networks remains a significant challenge, as communities experience birth, death, splitting, or merging. Existing techniques often struggle with these evolving dynamics, causing inconsistent tracking and reduced stability. To address these limitations, this thesis proposes an enhanced incremental dynamic community detection called Dynamic Community Detection based on the Bird Flock Effect (DCDBFE). This technique was inspired by the behavior of bird flocks and utilizes the principles of separation, alignment, and cohesion. The proposed technique incorporates a Resource Allocation similarity measure and third-level module attraction function to dynamically update community structures. This incremental approach effectively captures temporal changes and ensures continuous community structure tracking, unlike traditional static models. To ensure rigorous evaluation, synthetic networks were generated using an extended Lancichinetti- Fortunato-Radicchi benchmark, specifically designed in three scales, low, medium, and large with controlled parameters for mixing coefficient, edge density, and probability of vertex switching. These network datasets enabled systematic assessment of module stability across different network complexities. The experimental results were further validated using real-world network datasets to confirm the stability of the proposed technique. Stability was quantified based on inter-snapshot similarity, using metrics such as the Normalized Mutual Information and Adjusted Rand Index. Statistical validation was conducted using the Friedman test with a 95% confidence level to confirm the significance of performance improvements among competing techniques. The results demonstrated that DCDBFE achieved up to 15-25% higher stability compared to state-of-the-art DCD techniques. This research contributes to the field of dynamic community detection by providing a stable solution through an innovative methodology inspired by natural phenomena. Future work will extend DCDBFE to heterogeneous and weighted networks, and integrate deep learning- based adaptive similarity functions to further enhance temporal prediction and real-time community detection analysis.

Pre-clinical evaluation of early osteoarthritis via biomechanical properties derived from low field MRI

2026-03-17T07:14:36Z

Osteoarthritis is a degenerative disease which is associated with the articular cartilage in the synovial joint. As the disease progresses, it changes the macromolecular structure inside the cartilage tissue and alters the biomechanical properties. Hence, the ability to detect the disease at its earliest grade is crucial for early intervention of the disease. The osteoarthritis is clinically diagnosed using Magnetic Resonance Imaging (MRI) by examining the morphology of the articular cartilage and the geometrical data of the synovial joint. However, most of the diagnoses were performed when this disease is already progressed into advanced grade. At early grade of osteoarthritis, the biomechanical properties start to show noticeable change. Although studies were conducted to correlate the biomechanical properties of cartilage with MRI image, high-field MRI was utilized to produce the cartilage image. Therefore, this study aimed to investigate the potential use of low-field MRI to monitor the biomechanical properties of articular cartilage. This includes to determine the greyscale of low-field MRI image, characterize the biomechanical properties of articular cartilage and finally to observe the correlation between the greyscale and biomechanical properties of articular cartilage from fibrillated cartilage sample. Cartilages from hip joints of bovine were scanned using 0.18 T MRI. The images of cartilage were characterized based on the intensity of the greyscale. Creep indentation test was then conducted on the cartilage specimens and subsequently the indentation test was simulated using finite element method. The biomechanical properties of Elastic Modulus and Permeability of cartilage were characterized by incorporating the experimental data from the indentation test with the computational finite element model. The mean Elastic Modulus was found to be 0.26 ± 0.07 while the Permeability was 5.26 ± 1.72×10⁻¹⁵ m⁴/Ns. Further correlation analyses were performed to examine the relationship between the greyscale of MRI image and biomechanical properties of Elastic Modulus and Permeability of the cartilage. Good correlation was found between the cartilage greyscale and cartilage biphasic Elastic Modulus (r= 0.74) and a strong correlation with negative value was observed between the cartilage greyscale and cartilage Permeability (r= -0.86). Hence, present results indicate that the low-field MRI has potential for non-invasive assessment of the condition of articular cartilage. It could be further developed to serve as an early intervention of osteoarthritis disease. However further validation using human samples is necessary before clinical application.

Optimization in cooling variation of core and cavity temperatures for HDPE in injection moulding process

2026-03-17T07:14:09Z

Plastic injection moulding is a widely used process for producing thermoplastic parts, yet warpage and shrinkage remain major challenges that compromise quality. This study employed a Box-Behnken Design with 17 experimental runs under the Response Surface Methodology (RSM) framework in Minitab to investigate the effects of core temperature, cavity temperature, and cooling time on five critical responses: warpage, volumetric shrinkage, ultimate tensile strength (UTS), Young’s modulus, and percentage elongation. The Analysis of Variance (ANOVA) confirmed that all three parameters significantly influenced the responses, with cavity temperature having the strongest effect on warpage, while core temperature dominated volumetric shrinkage, UTS, Young’s modulus, and elongation. Optimization results revealed that warpage could be reduced below 2.48 mm with lower core and cavity temperatures; volumetric shrinkage minimized to below 20.38% at a core temperature of 15 °C and cooling time of 12.5 s; UTS maximized above 19.52 N/mm² at 129.9 °C core temperature and 12.5 s cooling time; Young’s modulus improved beyond 4.91 N/mm² at 15 °C core temperature and 17.55 s cooling time; and % elongation enhanced above 91.79 mm at 15 °C for both core and cavity temperatures. Validation showed minimal percentage errors between experimental and model predictions where 2.40% (warpage), 0.02% (shrinkage), 0.85% (UTS), 0.48% (Young’s modulus), and 1.34% (elongation), confirming the robustness of the regression models. The real-world experimental validation using the optimal predicted value of 44°C, 15°C dan 37.5s, demonstrated a strong agreement between the predicted and experimental results, confirming that the developed quadratic regression models were statistically significant and capable of reliably representing the relationships between process parameters and responses. The minor deviations observed can be attributed to natural variations in machine performance, material behaviour, or environmental conditions during moulding. The importance of this study lies in its ability to tackle two of the most persistent defects in injection moulding of warpage and shrinkage while simultaneously improving the mechanical performance of HDPE parts. Beyond addressing these challenges, the study contributes to knowledge by establishing a robust multi-response optimization framework that highlights not only the main effects but also the interactions of core temperature, cavity temperature, and cooling time. This framework provides manufacturers with a practical tool for improving dimensional accuracy, enhancing mechanical reliability, reducing material waste, and increasing production efficiency, thereby bridging the gap between theoretical modelling and real-world manufacturing applications.

An integrated support system with internet of things for lean manufacturing

2026-03-17T07:13:41Z

Lean Manufacturing (LM) has been widely recognised as a systematic approach to improving operational efficiency and eliminating waste across industries. However, in the fast-evolving context of the Fourth Industrial Revolution (IR4.0), traditional LM approaches face significant limitations due to their lack of real-time responsiveness, data-driven adaptability, and intelligent decision-making capability. Current research remains largely conceptual, with many studies relying on simulated or theoretical models rather than live industrial data, which limits their reliability and industrial relevance. Moreover, there is still no comprehensive framework that effectively integrates LM, Decision Support Systems (DSS), and Internet of Things (IoT) technologies to support continuous improvement. Existing applications of data analytics in LM are often restricted to monitoring functions instead of enabling real-time optimisation, while the connection between LM tools such as Line Balancing, Single-Minute Exchange of Die (SMED), and Kanban with IoT-based systems remains insufficiently developed. To address these theoretical and empirical gaps, this study developed iDSS-ProLean, an integrated, sensor-driven DSS designed to align LM principles with IoT-based real-time data analytics for adaptive manufacturing environments. Implemented on a semiconductor backend line comprising seven sequential stages, the system utilised ESP32 microcontrollers and multiple sensors (HC-SR04, SW-420, DHT11, BMP280) connected to a Firebase database and Android interface for live monitoring. Results from sixty trial runs demonstrated significant performance improvements: Line Balancing Efficiency value increased from 0.82 to 1.00, SMED ratios improved from inefficient changeovers, greater than 1.0, to optimised setups in less than 1.0, and Kanban inventory levels were stabilised within the target range of 500 ± 5 units based on the best kanban card available on the production line. Statistical validation confirmed reliable data transmission (p > 0.05) and a significant correlation between the parameters and each LM Tools indicators. Expert evaluation further showed high user acceptance, confirming iDSS ProLean as a reliable, economical, and scalable framework for smart manufacturing aligned with IR4.0 principles. The significance of this study lies in establishing the first empirically validated framework that unifies LM, DSS, and IoT within a single, scalable, and adaptive model tailored to semiconductor manufacturing. This study extends the boundaries of LM by embedding sensor-driven intelligence and real-time analytics into traditional continuous improvement systems. Methodologically, it demonstrates a novel approach for integrating sensor-based data acquisition with DSS modelling, thereby offering a replicable and reliable validation process for industrial-scale experimentation. Practically, iDSS-ProLean provides an economical, accessible, and scalable digital solution capable of transforming static LM practices into adaptive, self-optimising systems aligned with IR4.0 principles. By addressing long-standing theoretical and empirical deficiencies, this research contributes to the advancement of smart manufacturing and positions iDSS-ProLean as a viable model for sustainable, data-driven industrial excellence.

Business Intelligence System and innovative work behaviour in UAE SME and the mediating roles of knowledge sharing and SME innovation

2026-03-17T07:13:16Z

In today’s increasingly dynamic and competitive business environment, Business Intelligence Sys- tems (BIS) serve as critical enablers of innovation and data-driven decision-making, especially for Small and Medium Enterprises (SMEs). In the United Arab Emirates (UAE), SMEs account for ap- proximately 94% of all registered businesses, contribute over 60% to the non-oil GDP, and employ nearly 86% of the private sector workforce. Despite their strategic economic role, many UAE SMEs struggle to foster sustainable innovation, often due to limited adoption of BIS and underdeveloped knowledge-sharing cultures. This study investigates the impact of BIS on Innovative Work Behavior (IWB) and explores the mediating roles of Knowledge Sharing (KS) and SMEs Innovation (SMEI). Grounded in the Technology-Organization-Environment (TOE) framework and using a cross-sec- tional quantitative design, data were collected from 386 SME owners, managers, and shareholders across the UAE who have adopted or are in the process of adopting BIS. Partial Least Squares Struc- tural Equation Modeling (PLS-SEM) was applied using SmartPLS 4 to analyze the relationships among variables. The findings confirmed that several BIS components specifically data integration, information access quality, information content quality, use of information in business processes, and analytical decision-making culture significantly enhance IWB. However, analytical capabilities alone did not demonstrate a significant direct effect on IWB. Knowledge sharing was not found to directly influence IWB significantly; nonetheless, it exhibited a statistically significant mediating effect in the relationships between analytical capabilities, use of information in business processes, and analytical decision-making culture with IWB. Similarly, SME innovation significantly mediated the relationships between analytical capabilities and information content quality with IWB. These findings suggest that while knowledge sharing and SME innovation may not universally mediate all BIS components, they play pivotal roles in shaping how specific BIS functions influence innovative behaviors. This study makes a notable theoretical contribution by refining the TOE framework through the dual mediation model involving KS and SMEI. It also provides practical implications for SME leaders and policymakers, urging them to prioritize BIS implementation alongside the cul- tivation of innovation-friendly and knowledge-sharing cultures. Strengthening these organizational capabilities can substantially enhance individual-level innovation, contributing to overall SME com- petitiveness and economic sustainability in the UAE.

The effect of density and ionisation ring parameter on the performance of Taylor cone characteristics based on electrospray technique

2026-03-17T07:12:46Z

This study investigates the influence of fluid density and ionisation ring parameters on the formation and stability of the Taylor cone in electrospray systems that are utilised in precision combustion, biomedical, and microfabrication applications. Despite the intense research on electrospray, achieving a consistent Taylor cone remains a challenge due to the complex interplay between electrostatic forces and fluid dynamics. To overcome the problem, this work aims to optimise electrospray parameters, including ionisation ring diameter (IDIR), ionisation ring (IR) positioning or nozzle-to-target distance, and applied voltage. In addition, the effects of electrospray settings on the density of liquid and Taylor cone formation are comprehensively investigated. An experimental approach was taken to examine the influences of fluid densities using ethanol and n-heptane mixtures and ionisation ring configuration effects on cone angle, jet length, and droplet dispersion. Key parameters evaluated include applied voltage (2â€“10â€¯kV), ionisation ring inner diameters, and axial distances from the nozzle (10.3â€¯mm, 15.3â€¯mm, and 21.2â€¯mm), under controlled flow rates using a precision syringe pump. Imaging was employed to observe transitions between dripping, spindle, cone-jet, and multijet modes. Results reveal that lower-density fluids, like ethanol, require less voltage for cone-jet initiation but exhibit greater instability at higher voltages, while denser fluids, such as water, demand higher voltages and closer ionisation ring proximity for stable operation. The combination of low-density fluid and optimised ionisation ring positioning yielded the most uniform spray and droplet sizes. Furthermore, the study identifies significant synergistic effects between fluid density and electric field shaping via the ionisation ring, with field concentration found to strongly dictate cone formation quality. In conclusion, the results suggest that a larger IDIR requires a higher voltage to initiate the formation of the Taylor cone but provides a wider range of stable cone-jet operation. In contrast, low-density liquids require a lower voltage to generate the Taylor cone; however, they are more susceptible to transitioning into a spray or flying current at higher voltages.

Heuristic scheduling strategies for the airport check-in counter allocation problem

2026-03-17T06:20:27Z

The post-pandemic resurgence in global air travel has placed renewed strain on airport infrastructure, establishing the check-in hall as a critical bottleneck for operational efficiency and passenger satisfaction. This study addresses the Airport Check-in Counter Allocation Problem (CCAP) within the specific context of Malaysian airports, proposing a robust heuristic scheduling framework to mitigate resource congestion. By integrating rule-of-thumb heuristics with fundamental dispatching algorithms, specifically First-Come-FirstServe (FCFS), Earliest Deadline First (EDF), and Shortest Job First (SJF). The research employs a discrete simulation to evaluate performance under two contrasting regulatory environments: a flexible Mixed Counter strategy and a stringent Preferred Counter policy. The comparative analysis reveals that the Heuristic-FCFS combination under flexible allocation rules yields the optimal outcome, achieving a peak resource utilization rate of 45.3% while minimizing idle dormancy. Conversely, the enforcement of airline-specific constraints resulted in significant resource fragmentation, necessitating a 35% increase in active counters and depressing utilization rates to approximately 33.5% across all algorithmic variants. These findings provide empirical evidence that while algorithmic optimization contributes to efficiency, the structural removal of categorical resource barriers offers the most significant potential for economic and operational improvement in airport management.

Performance evaluation of a nearest level control-based TCHB multilevel inverter for PMSM motors in electric vehicle systems

2026-03-17T06:20:03Z

Transistor Clamped H-Bridge (TCHB) multilevel inverter (MLI) is receiving considerable research interest because it can provide excellent outputs with a reduced number of switches and DC voltage sources compared to traditional MLI topologies. This study investigates the application of a nearest level control (NLC) technique in a three-phase TCHB inverter fed permanent magnet synchronous motor (PMSM) with a focus on improving power quality and minimizing total harmonic distortion (THD). The proposed system is modeled in MATLAB/Simulink, incorporating a field-oriented control (FOC) strategy with a proportional-integral (PI) controller for precise speed regulation of the PMSM. The NLC technique is selected for its ability to reduce switching losses and improve inverter efficiency. Key performance metrics, including THD, efficiency, and dynamic response, are analyzed under various operating conditions. Results indicate that the NLC technique reduces THD to 4.73 %, achieving smoother output waveforms and enhanced power quality. Additionally, the system demonstrates over 97 % efficiency, reliable dynamic performance, and accurate reference speed tracking during real-life driving cycle testing. These findings highlight the potential of the NLC-based TCHB inverter as a robust solution for high-performance motor drives in electric vehicle (EV) applications.

Global research trends in digital technology and financial inclusion: A bibliometric approach

2026-03-17T06:19:33Z

This study examines how research on digital technology and financial inclusion has evolved between 2005 and 2022 through a bibliometric analysis of 291 Scopus-indexed publications. Using Microsoft Excel and VOSviewer, it evaluates publication trends, influential authors and institutions, and the intellectual structure of the field. The findings show that contributions were modest in the early years but expanded rapidly after 2017, supported by fintech adoption, global development agendas, and the acceleration of digital financial services during the COVID-19 pandemic. Three main streams of research are identified: financial technology and innovation, including mobile banking and blockchain; technology adoption and institutional factors; and social inclusion and financial literacy, emphasising trust, equity, and cultural acceptance. These streams demonstrate that digital financial inclusion is shaped not only by technological and economic factors but also by social and cultural dimensions. Despite substantial progress, challenges such as digital divides, limited skills, and regulatory gaps constrain equitable access, while emerging frontiers link digital finance with sustainability and green innovation. By mapping these developments, the study contributes a roadmap for future research. It offers practical insights for policymakers, financial institutions, and development agencies working to harness digital technologies for inclusive and sustainable growth.

The factors influencing working Gen Z intentions to purchase green cosmetics products in Malaysia

2026-03-17T06:19:11Z

The rising demand for green and sustainable products has positioned green cosmetics as a growing segment of the Malaysian beauty industry. Despite increasing awareness, purchase intentions toward green cosmetics among working generation Z remain inconsistent and underexplored compared to student populations. Therefore, the research aims to examine the influence of attitude, subjective norm, perceived behavioral control an environmental concern on working Gen Z’s intentions to purchase green cosmetics in Malaysia. A quantitative, cross-sectional survey design was distributed, targeting 120 working Gen Z respondents selected through purposive sampling. Data were collected using an online structured questionnaire and analyzed through SPSS, incorporating descriptive statistics, reliability tests, normality checks and spearman’s rank order correlation. The results revealed that all four independent variables, attitude (r = 0.647, p < 0.01), subjective norm (r = 0.657, p < 0.01), perceived behavior control (r = 0.757, p < 0.01) and environmental concern (r = 0.657, p < 0.01) showed strong and positive correlations with purchase intentions. These findings indicate that working Gen Z consumers are more likely to adopt green cosmetics when they hold favorable attitudes, experience positive social influence, feel confident in their purchasing ability, possess strong environmental values. Academically, this research contributes by extending the theory of Planned Behavior (TPB) to a working Gen Z context, which has been less examined in previous research. From an industry perspective, the results provide practical guidance for cosmetic brands to strengthen green marketing strategies by enhancing eco-friendly branding, social engagement, affordability and accessibility. By addressing these key factors, businesses can effectively encourage sustainable purchasing behavior and build long term loyalty among Malaysia’s working Generation Z consumers.

Factors influencing students' satisfaction towards campus bus services at UTeM

2026-03-17T06:18:41Z

Transportation services within university campuses are essential for ensuring smooth academic, social, and extracurricular engagement. As student populations grow, campus bus services play a central role in facilitating mobility, yet issues such as unreliable schedules, overcrowding, and inadequate facilities remain common. The research objective about the factors influencing students’ satisfaction towards campus bus services at Universiti Teknikal Malaysia Melaka (UTeM), focusing on two service quality dimensions derived from the SERVQUAL model: facilities of bus and responsiveness. A quantitative descriptive research design was adopted, with data collected from 120 UTeM students through stratified random sampling to ensure representation across faculties and study years. The survey instrument, structured on a 5-point Likert scale, measured student perceptions of bus facilities, responsiveness, and overall satisfaction. Data analysis was conducted using SPSS, incorporating descriptive statistics, reliability tests, normality tests, and Spearman’s rank correlation analysis. The results demonstrated that both facilities and responsiveness significantly affect students’ satisfaction, with facilities showing a very strong relationship correlation (r = 0.708, p < 0.01) and responsiveness reflecting a strong relationship correlation (r = 0.598, p < 0.01). These findings indicate that tangible aspects such as cleanliness, seating comfort, and safety features, alongside intangible aspects like timely communication and attentive driver behavior, are critical to shaping positive student commuting experiences. The study contributes academically by filling the research gap on UTeM’s campus transport system and practically by offering actionable insights for university administrators and transport service managers. Enhancements in bus facilities and improvements in service responsiveness are expected to increase student trust, reduce reliance on private vehicles, and support UTeM’s sustainability objectives.

The role of entrepreneurship education in shaping self-employment intentions: A TPB-based study of Malaysian TVET students

2026-03-17T06:18:10Z

Entrepreneurship education prepares technical and vocational education and training (TVET) students with the competencies and entrepreneurial mindset required for future business endeavors. Guided by the theory of planned behavior (TPB), this study examines how such education shapes students’ self-employment intentions (SEI). A quantitative survey was administered to 300 undergraduates at Universiti Teknikal Malaysia Melaka (UTeM), and the data were analyzed using correlation and regression methods. The results show that entrepreneurial intention is positively linked to attitude toward behavior (r=0.474), entrepreneurship education (r=0.416), subjective norms (r=0.374), and perceived behavioral control (r=0.346), with attitude identified as the most influential predictor, accounting for 22.4% of the variance. These outcomes emphasize the centrality of individual motivation and the enabling role of entrepreneurship education in cultivating entrepreneurial aspirations. The study highlights the strategic importance of embedding stronger entrepreneurship curricula in TVET institutions to enhance self-employment readiness and support national goals for workforce development and economic resilience. The findings resonate with Malaysia’s National Entrepreneurship Policy 2030 and the TVET empowerment agenda, pointing to the value of initiatives such as campus incubators, seed funding, and mentorship in transforming entrepreneurial intention into tangible venture creation.

Democratic leadership as a driver of creativity, engagement, and performance in UAE universities

2026-03-17T06:17:22Z

Higher education institutions (HEIs) in the United Arab Emirates (UAE) are under growing pressure to improve organisational performance in line with government policies that prioritise quality, innovation, and the development of human capital. Despite these goals, hierarchical leadership styles are still common at many UAE colleges, which restricts employee involvement, participation, and creativity. To address the lack of attention given to democratic leadership in the context of higher education in the United Arab Emirates, this review paper aims to investigate the role of democratic leadership on job performance through mediating roles of creativity, engagement, and creative conduct. To create a conceptual framework that explains the links between leadership and performance, the review study synthesises current theoretical and empirical studies, drawing on participatory and behavioural leadership views. The results show that democratic leadership improves academic staff members' job performance by encouraging psychological safety, teamwork, creativity, and innovation. The study suggests integrating participatory leadership techniques into leadership development programs and institutional governance. To better understand democratic leadership mechanisms and investigate hybrid leadership approaches in various higher education contexts, future research should use empirical and longitudinal techniques.

Advanced study of wavy dynamical behavior of suspended living organisms in generalized nanomaterials with magnetic and buoyancy effects

2026-03-17T06:16:48Z

This study presents a novel mathematical framework to investigate the undulating flow of a Cross fluid containing gyrotactic microorganisms under bioconvection. The problem is important because microorganism-induced density gradients can significantly influence momentum, heat, and mass transport in biological and engineering systems, such as microfluidic devices and nanofluid-based thermal management. The novelty of this work lies in simultaneously analyzing the effects of key parameters Peclet number, Rayleigh number, and magnetic field strength on drag force, heat and solute transport rates, and microorganism density, extending beyond previous studies that considered only partial effects. The transformed nonlinear boundary value problem is solved numerically using an improved bvp4c algorithm, which enhances computational stability and accuracy compared to conventional approaches. The results indicate that increasing the Péclet and Rayleigh numbers leads to a notable reduction in drag force and in the rates of heat and mass transfer. Moreover, higher values of the magnetic field parameter and Rayleigh number cause a significant decrease in the Sherwood number, demonstrating their strong influence on solute transport. The outcomes are presented through detailed graphical analyses and validated by comparison with previously published studies, confirming the reliability of the improved numerical method and the consistency of the physical model.

Towards low-noise tunable terahertz waves generation via photomixing

2026-03-17T06:16:15Z

We demonstrate a low-cost, stable, and tunable laser system by phase-locking a commercial telecom-grade tunable laser (ITLA) to an Optical Frequency Comb (OFC) within the C-band. Using minimal optical hardware and sub-mW OFC power, we achieved short-term integrated phase noise of 10 mrad and long-term frequency stability of ±0.01 Hz over 10 h. This system enables scalable, OFC-locked tunable lasers and holds promise for applications like tunable THz wave generation and comb-locked transmitters in DWDM systems, supporting scalable phase-locking of multiple lasers with OFC power in the nW regime, making it highly adaptable for various OFC generators.

Enhancing social experience in smart stadium with Wi-Fi 6 quality management

2026-03-17T06:15:30Z

Smart stadiums are revolutionizing live sports by creating digitally connected environments that turn spectators into active participants. However, when thousands of fans simultaneously use public Wi-Fi for streaming and sharing, network congestion often occurs. This overload degrades connectivity and disrupts live broadcasts for remote viewers, causing buffering, poor quality, and interruptions. Limited bandwidth and diverse user demands further challenge Wi-Fi performance, leading to inconsistent experiences. This study proposes a Wi-Fi 6-based Quality of Service (QoS) management framework to ensure uninterrupted, high-quality video streaming during live events. Developed using Python and NS-3 simulation tools, the framework employs the Priority Queuing algorithm to optimize traffic flow. Agile methodology guided iterative development for scalability and adaptability. Performance was evaluated under simulated high-density conditions using key QoS metrics: throughput, packet loss ratio, traffic volume, and bandwidth usage. Results show that Priority Queuing significantly reduces congestion, improves responsiveness, and supports real-time traffic optimization. the study highlights how reliable Wi-Fi 6 connectivity can enhance inclusivity, improve operational efficiency, and foster more immersive and equitable social experiences. Future work will explore advanced prioritization techniques, integration with emerging technologies, and personalization based on user feedback, which bridges technical precision with meaningful social impact in the Wi-Fi 6 era.

210 km long incoherent WDM spectrum-sliced system running at 10 Gb/S incorporating semiconductor optical amplifier (SOA) enhancements

2026-03-17T06:14:46Z

The deployment of more economical and cost-effective wavelength division multiplexing (WDM) solutions for access and metro networks still remains a key research focus. This paper reports on significant performance enhancement improvements of a highly economical, four channel totally incoherent spectrum-sliced WDM system with the incorporation of a Semiconductor Optical Amplifier (SOA) placed in one of its channels. The baseline system was shown to operate well at 10 Gb/s at a maximum link length of 210 km, demonstrating average Q-factor and signal-to-noise ratios (SNRS) over the four channels. However, the introduction of a single saturated SOA and a single filter placed on one channel yielded a sizable improvement in the Q-factor and exceptional improvement in the measured signal-to-noise ratio (SNR), effectively allowing a further 130 km link travel length whilst still yielding acceptable signal quality – making a total link length of 340 km for one channel.