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This study presents a case study of project-based learning (PBL) and the impact of PBL implementation on community issues in terms of the level of awareness and action of engineering students. It involves 30 undergraduate mechanical engineering students enrolled in the Manufacturing Ergonomics course for the 2024/2025 session. These students need to complete individual assignments and group projects in ten weeks. The purpose of this study is to assess the level of awareness, satisfaction and understanding of students through PBL. The relationship between the assessment of the level of awareness, satisfaction and understanding with academic performance was also conducted. A questionnaire instrument was used to determine the level of awareness and action through the assessment of the level of awareness, satisfaction and understanding based on the experience in PBL involvement. In these assignments and projects through PBL, student performance was measured through individual assignments, group progress reports, and also the final group report. The survey results showed that there was an increase in the level of awareness, satisfaction and understanding of students regarding the issues raised. There was a weak relationship (r<0.30) between the perception of the level of awareness, satisfaction and understanding of students with student performance, especially those involving group reports. However, there is a moderate positive relationship (r>0.50) for the level of awareness with individual assignment grade performance in this study. The results of this study will provide guidance to relevant parties on the appropriate methods in using this approach in the context of formal education. In general, the implementation of PBL in engineering can promise an increase in student skills and channel the added value of a holistic pedagogical model.

Community; engineering; learning; ergonomics; student

Design thinking is fundamental to engineering education, yet first-year students often face challenges, including design fixation, which limits creative problem-solving. This study investigates the perception of design thinking traits among first-year mechanical engineering students enrolled in the Introduction to Design course at a public university in Malaysia, aiming to identify factors influencing design thinking capabilities and strategies to overcome design fixation. A quantitative approach was employed using a questionnaire adapted from the Approaches and Study Skills Inventory for Students (ASSIST) and Design Thinking Traits (DTT) instruments. The survey, comprising 31 items with a 5-point Likert scale, was administered to 101 randomly selected first-year mechanical engineering undergraduates from 350 enrolled students across thirteen course sections. The instrument assessed five key constructs: Empathetic Engagement, Problem Sensing, Creative Risk Taking, Constructive Action, and Reflective Evaluation. Descriptive statistics were conducted using IBM SPSS Statistics 29 to examine relationships between design thinking perceptions and variables, including gender, former educational background, and technical proficiency in operating machines and tools. Results revealed generally positive perceptions across all constructs, with Reflective Evaluation scoring highest and Empathetic Engagement scoring lowest. These findings underscore the need for differentiated instructional strategies that cater to diverse educational backgrounds, incorporating defixation techniques, empathy-building activities, and hands-on skills development to cultivate adaptive, creative engineering problem solvers. This ongoing research agenda will continue to advance engineering education, ensuring that programs produce graduates equipped with both the technical competence and creative thinking capabilities necessary for innovative design practice.

Engineering education; design thinking; design fixation; perception

This study explores the impact of Open-Ended Laboratories (OEL) on enhancing problem-solving and critical thinking skills among second-year Chemical Process Engineering undergraduates at MJIIT, Universiti Teknologi Malaysia. A mixed-method approach was employed, combining quantitative data from a structured Likert-scale questionnaire and qualitative feedback from student reflections and instructor observations. The survey comprised of seven key items, assessed cognitive, psychomotor, and affective domains aligned with the course learning outcomes (CLOs). Descriptive statistical analysis revealed high levels of agreement across all dimensions, with mean scores ranging from 4.13 to 4.38 (on a 5-point scales) and standard deviations between 0.96 and 1.17, indicating positive student perceptions toward problem identification, theoretical application, and collaboration. Further analysis incorporating Cohen’s d and partial η² demonstrated moderate-to-large effect sizes (d = 0.0232–0.2732; η² = 0.0115–0.1202), confirming significant learning gains, particularly in CLO2 and CLO4. Qualitative findings corroborated the quantitative outcomes, with students reporting improved experimental design skills, increased independence, and greater confidence in navigating complex engineering tasks. Comparisons with global OEL initiatives underscore the international relevance of the findings, with performance improvements aligning with reported global deltas of 0.4 standard deviations in problem-solving and engagement metrics. This study supports the integration of OEL in engineering curricula to foster higher-order thinking, teamwork, and self-directed learning, as advocated by Malaysia’s MySTIE 10-10 Framework and SDG4/SDG9 imperatives. The findings offer empirical evidence to guide broader adoption of OEL pedagogies in outcome-based engineering education frameworks.

Open-ended labs; chemical engineering students; critical thinking and problem-solving skills

Effective student engagement and learning in engineering education depend on the alignment of teaching and assessment practices with Course Objectives and Course Learning Outcomes (CLOs). This study examines the implementation of various pedagogical and evaluative approaches in engineering modules, with particular emphasis on project-based and research-based learning. These approaches engage students in solving Complex Engineering Problems (CEPs) and participating in Complex Engineering Activities (CEAs), providing authentic exposure to real-world engineering challenges. Through these activities, students develop critical thinking, problem-solving, creativity, and innovation skills, alongside improved communication and teamwork competencies essential for professional practice. The study employs two case studies: an undergraduate module in Highway and Traffic Engineering (CE4623) involving 36 final year bachelor’s students, and a graduate module in Drainage and Irrigation Engineering (CE5153) course involving 10 master’s students, both implemented during the 2022-2023 academic year. In the undergraduate case study, rubric-based project assessments were employed to quantitatively evaluate student attainment of CLOs related to problem analysis and engineering design. In contrast, the postgraduate case study adopted a qualitative evaluation approach based on instructor observations, project presentations, and the overall quality of technical reports, reflecting the research-oriented nature of the course. The findings indicate that explicit alignment between project structure, CLOs, and assessment mechanisms enhances student engagement, analytical thinking, and communication skills. The use of rubrics further supports continuous improvement by clarifying performance expectations and facilitating constructive feedback. Overall, the results suggest that these teaching and assessment practices contribute to the development of lifelong learning skills and better prepare students to address evolving engineering challenges.

Complex engineering problems; problem-based learning; research-oriented; case studies

In the context of Malaysia’s strategic push toward a digital and innovation-driven economy, traditional engineering education models must evolve to equip graduates with both technical expertise and entrepreneurial acumen. This paper presents the outcomes of a decade-long implementation of the Program Pemerkasaan Kompetensi Akademik Siswa (PKAS), initiated in 2016 at Universiti Kebangsaan Malaysia (ÿ�մ���app). The program focuses on bridging technical and entrepreneurial competencies among engineering students, aligning with Malaysia’s strategic national agendas. Through experiential learning, industry collaboration, and startup incubation, PKAS has transformed postgraduate research into real-world applications. This case study evaluates the framework, outcomes, and potential for national replication. Originating as a postgraduate competency initiative, PKAS has expanded across multiple education levels, integrating hands-on innovation projects, industry collaboration, and entrepreneurship development. The study highlights key outcomes such as student start-ups, interdisciplinary innovation outputs, and institutional capacity-building, while drawing lessons on curriculum reform, policy alignment, and sustainable educational design. The paper concludes with policy implications for embedding technopreneurship in national education strategies and calls for broader adoption of experiential, innovation-focused learning in engineering education.

Technopreneurship education; engineering curriculum reform; experiential learning; innovation ecosystem; interdisciplinary learning

Government housing initiatives play a vital role in improving affordability, accessibility, and promoting sustainable urban development. This study investigates the key factors contributing to the success of public housing projects and examines how prospective property buyers perceive these factors. Data was collected through a questionnaire survey of 100 randomly selected prospective buyers in Shah Alam. Using quantitative methods, Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to analyze the relationships between various components. The analysis revealed that infrastructure, demographic, social, and environmental factors significantly influence the success of public housing projects. In contrast, other examined variables were found to have no significant effect. The findings highlight the importance of prioritizing these critical factors in housing policies and programs to better address the needs of future homeowners.

Public housing; house buyers; real estate; PLS-SEM; quantitative analysis

The implementation of Building Information Modelling (BIM) in the construction sector has significantly contributed to the cultivation of soft skills in quantity surveying professions. Nonetheless, challenges persist in developing these skills, necessitating focused advancements. This study explores the soft skills cultivated by BIM, examines related obstacles, and establishes strategies for enhancement. A quantitative approach was utilized through a questionnaire survey targeting Quantity Surveyors within the Malaysian construction sector, with data analysed using the Relative Importance Index (RII) method. Findings indicate that communication is the most substantially enhanced soft skill through BIM, followed by leadership and teamwork. BIM improves Quantity Surveyors’ capacity to communicate intricate information, oversee integrated project teams, and cooperate efficiently across several disciplines. Nevertheless, challenges remain, particularly insufficient leadership knowledge within organizations and communication hurdles encountered by introverted individuals, which can impede collaboration and project efficacy. The study advocates for the integration of soft skills training within BIM technical programs, the promotion of leadership involvement, the encouragement of transparent communication, and the cultivation of individual accountability within team environments to address these barriers. Self-directed social contact is recognized as an excellent method for enhancing interpersonal skills. This study emphasizes the essential importance of soft skills—specifically communication, leadership, and teamwork—in optimizing the advantages of BIM adoption. By overcoming identified challenges and executing focused initiatives, organizations may strengthen collaborative performance, leadership capabilities, and communication efficacy among Quantity Surveyors, thus enhancing project delivery outcomes in the Malaysian construction industry.

Soft skills; building information modelling; quantity surveyors; construction industry

Walking is the only way for many people in developing nations to get around because of the high rates of urban growth, large, underprivileged populations, and high densities of metropolitan areas. Travel conditions for walkers and non-motorised vehicles may be dangerous, in poor shape, or non-existent. Many emerging cities are seeing a gradual loss of pedestrian space. The core of a sustainable city is the Transit-Oriented Development (TOD) concept, which includes a robust transit network and fresh insights into better integrating less automobile-dependent land use into planning and design. To revitalise a small city, TOD is a procedure that reintegrates land use and transportation by developing a new, alternative transit system that complies with its tenets. This paper examines the application of the TOD concept for a small town in embracing pedestrians and non-motorised vehicles. Using a simple random sampling technique, a Case Study method was applied using Rawang City for site observations and a questionnaire survey with 230 samples. The findings showed that walkers and non-motorised vehicles lack adequate amenities and connectivity, which compromises user comfort and safety. Bicyclists are discouraged from riding to Rawang City’s railway station because it lacks amenities for them. The study concluded with recommendations for future enhancements that might be useful not only for Rawang City but also for other similar small cities. The recommendations from this study may serve as a guide for municipal governments looking to reduce carbon emissions by encouraging walkability and non-motorised transportation.

TOD concept; small city; pedestrian; walkability; non-motorised vehicle

Urban parks play a crucial role as green spaces amidst the bustling urban environment. Rapid development in urban areas seeks to accommodate the growing demand for residential, commercial, and institutional spaces, which has led to the formation of urban heat islands. The urban heat island effect affects densely populated cities, and urban parks are essential for regulating microclimate conditions. This study examines three microclimatic parameters—temperature, relative humidity, and wind speed—to determine how Titiwangsa Lake Garden and Shah Alam Lake Garden affect the neighbouring metropolitan regions. The impact of vegetation and water bodies on microclimatic control is evaluated in this study using in-situ field data and ENVI-met simulation software. Data was gathered over the course of two 24-hour periods from eight key locations inside each park. Model accuracy was guaranteed by validation approaches such as RMSE, MAPE, and MAD. The findings demonstrate that both parks considerably reduce urban heat, but Titiwangsa exhibits a larger temperature drop because of its closer proximity to water bodies and denser tree canopy. Heat accumulation was higher in Shah Alam, which is encircled by tall buildings. The results back contemporary urban planning techniques that promote climatesensitive urban design and maximize green infrastructure. These findings will aid in future microclimate planning for urban settings in Klang Valley cities area, while provide valuable insights for developing microclimate-responsive urban designs for urban planners and designers.

Urban park; urban heat island; vegetation; microclimate; ENVI-met

Estimating the axial capacity of piles using the direct method is still a challenging task. This estimation is primarily carried out after the completion of construction, using field methods such as static load tests or Pile Driving Analyzer (PDA) to calculate the axial capacity of piles in actual field settings. This study will focus on the estimating process of pile capacity using data from 5 building projects in Klang Valley. The data includes SPT test data which becomes the input for indirect calculations using the Meyerhof method and field measurement using the PDA test. The primary goal is to establish a correlation between the data obtained from direct and field methods. The Meyerhof method, known for its reliability and widely used by consulting geotechnical engineers, is the direct method employed in this study. The calculation findings indicate a strong correlation (R2 = 0.9434) between (Max Resistance) RMX values from the PDA test and Meyerhof pile capacities, validating the reliability of the Meyerhof method and its potential for accurate pile capacity estimation. The slope of 1.0925 indicates that Meyerhof is slightly higher than RMX values for bored piles. For the spun pile, the R2 value is 0.936, demonstrating a strong connection between RMX values and calculated pile capacities. As a recommendation, it is suggested that relying solely on traditional testing like Standard Penetration Tests (SPT) and Pile Driving Analyzer (PDA) tests may overlook critical insights into soil behaviour. Therefore, exploring advanced soil investigation techniques is essential. Techniques such as Cone Penetration Tests (CPT) provide continuous soil profiles and detailed stratigraphy, allowing for a more accurate assessment of soil strength and layering. Additionally, shear wave velocity measurements offer valuable data on soil stiffness and can enhance dynamic analysis capabilities.

PDA test; meyerhof method; pile capacity; RMX

This study addresses the challenges associated with short fibres in composite materials, particularly their reduced stiffness, lower tensile strength, and limited reinforcing ability compared to long fibres. These limitations significantly influence the mechanical performance of composites. To overcome these issues, the research explores the relationship between fibre length and tensile properties, aiming to identify the optimal fibre size to enhance mechanical performance. The study uses fixed fibre lengths (1 cm, 2 cm, and 3 cm) with a constant 3% weight fraction in an epoxy/resin matrix. Tensile tests are conducted on individual short silk and bamboo fibre composites to determine the optimal fibre length for each material. These optimal fibre sizes are then combined in varying weight ratios to create hybrid composites, which are also subjected to tensile testing. Results demonstrate that fibre size plays a critical role in mechanical performance. Silk fibre composites exhibit varying tensile strengths depending on fibre length, while bamboo fibre composites show size-dependent variations in tensile properties. Notably, hybrid composites with a 2% silk fibre and 1% bamboo fibre weight ratio achieve superior tensile strength. This research provides valuable insights into the design of hybrid fibre composites, emphasizing the importance of optimizing fibre size and weight ratios to achieve enhanced tensile properties in composite materials.

Silk fibre; bamboo fibre; short fibre; hybrid composite; epoxy; tensile strength

Concrete is a brittle material with low tensile strain and strength capacities. Steel fiber reinforced concrete (SFRC) has gained prominence as an innovative construction material, offering improved tensile and flexural properties over conventional concrete. Singapore Standards, “SS 674: Fiber concrete- Design of fiber concrete structures” just launched recently to offers a comprehensive structure for enhancing comprehension of Fiber-Reinforced Concrete. This study presents a comprehensive characterization of SFRC, focusing on both its mechanical properties particularly residual strength. Following this, a series of experimental works are conducted to assess the residual tensile strength under flexural loading behavior of SFRC specimens complying to SS674. Examining the tensile strength and residual strength of SFRC to assess its material strength and behaviour after being subjected to a load will help engineers comprehend the material’s strengths and weaknesses. At 40 kg/m3 fibers content shows the highest residual strength of 4.6 MPa with a strain hardening behaviour observed as compared to the 20 kg/m3 and 30 kg/m3 fibers content which is 3.0 MPa and 2.1 MPa. In conclusion, this study offers a holistic characterisation of SFRC, encompassing mechanical properties with higher residual strength. The outcomes provide scientific understanding and practical insights of SFRC for engineers and practitioners seeking to optimise the use of SFRC to enhance structural longevity and reduce maintenance costs.

Flexural bending; steel fibers; first crack; residual strength; resilient and sustainable

The Traditional Malay House (RTM) is a significant architectural heritage showcasing local wisdom through its refined design, rooted in traditional principles, socio-cultural practices, and local community behaviour. Constructed with careful consideration of climate, tidal patterns, and the natural environment, RTM provide essential shelter and foster strong family systems. Their design harmonizes with nature and the user needs, incorporating features that ensure excellent ventilation for comfort. Despite their unique attributes, the prevalence of RTM has declined, leading to the marginalization of their design values and their reduced role in modern architectural preferences. Consequently, many modern houses lack sufficient natural ventilation and indoor environmental quality. This study investigates the potential of RTM designs in enhancing natural indoor ventilation, focusing on window opening designs. The research examines the ratio of window openings to interior space size to determine ventilation quality and user comfort. Using literature review, interviews, and observations of two RTMs as case studies, the findings indicate that the window opening-to-interior space ratio in RTM far exceeds the minimum standards outlined in the Uniform Building By-Laws (UBBL) with readings recorded between 12.6% to 26.4%. This study underscores the importance of RTM in promoting sustainability through better ventilation performance and improved indoor environmental quality, contributing to user comfort and the preservation of traditional architectural values.

Traditional Malay House (RTM); ventilation; window openings; room size; user comfort

The imperative to diminish emissions has propelled the automotive sector towards the use of lightweight materials, thereby augmenting interest in aluminium metal matrix composites for applications where strength and wear resistance are essential. Aluminium–silicon alloys, such as A356, remain important light alloys in the automotive field due to their low density and castability. Hence, the influence of the combined stir-casting and thixoforming process on the microstructure, hardness, and wear properties of graphene-reinforced A356 aluminium metal matrix composites was investigated. The microstructure results indicate that the stirring process transforms dendritic 𝛼-Al to a non-dendritic and rosette-like structure. The thixoforming process generates the coarse equiaxed α-Al and refined eutectic silicon. This morphological transformation, combined with the addition of GNPs, improves the mechanical strength and wear resistance of the composites. The findings demonstrated significant improvements in material performance, including a hardness test that reveals a 36% improvement in the thixoformed A356/GNP composite compared with the unreinforced alloy. The wear rate of the thixoformed A356/GNPs composite decreased by 23.4% and 19% under 10 N and 50 N normal loads, respectively, compared to that of the A356 alloy. The enhancement in hardness and wear resistance of the composites was attributed to both the efficient dispersion of GNPs with appropriate content in the composites and the presence of graphene as a self-lubricant on the composite’s sliding surface. This study contributes to the development of stir casting and thixoforming as semi-solid processing methods for composite materials with enhanced wear resistance for engineering applications.

Aluminium composites; graphene; thixoforming; wear; microstructure

The hitch joint plays a critical role in heavy vehicles, serving as the primary linkage between the tractor and the semitrailer. To replicate accurately the behavior of an actual tractor–semitrailer system, it is necessary to consider the full range of kinematic and dynamic interactions acting on this joint. Previous hitch joint modeling approaches often relied on the Pacejka Magic Formula, which, although accurate, requires extensive parameter tuning and imposes high computational demand, making it inconvenient for real-time applications. This research addresses the limitation by developing a novel hitch joint model based on the Dugoff tire formulation, a wellestablished technique in vehicle dynamics for simulating nonlinear tire behavior. Compared to the widely used Pacejka Magic Formula, the Dugoff model offers simpler implementation while maintaining competitive accuracy. The proposed hitch joint model was combined with a 12-degree-of-freedom (DOF) tractor-semitrailer handling model, where maneuverability predictions were evaluated using Double Lane Change (DLC) and Step Steer Cornering (SSC) tests, and verified using the validated simulation software, TruckSim. Simulation results demonstrated strong agreement between the proposed model and the validated TruckSim model, with average percentage differences in root mean square (RMS) values of 3.141% and 1.830% for the DLC and SSC tests, respectively. These findings confirm that the proposed hitch joint model can serve as a reliable dynamic representation for future applications in heavy-vehicle design optimization, handling control development, and rollover stability evaluation.

Tractor-semitrailer; Hitch joint model; double lane change; step steer cornering; Dugoff tire model

This study investigated the use of red mud, an industrial waste material, as a sustainable alternative to conventional limestone fillers in asphalt mixtures, combined with the application of a novel predictive model. Utilizing a Radial Basis Function Neural Network optimized by the Horse Herd Optimization Algorithm (RBFNNHOA), the research predicts key mechanical (Marshall stability, and Marshall flow) and volumetric properties of asphalt mixtures, including bulk density, air voids, voids in mineral aggregate, and voids filled with asphalt binder. Experimental work involved preparing and testing 30 asphalt mixture samples with varying red mud contents (0%, 25%, 50%, 75%, and 100%) and different asphalt contents (5.0, 5.5, and 6.0) under the Marshall mix design. The RBFNN-HOAmodel demonstrated exceptional predictive accuracy 99.97%, R² values exceeding 0.999 and minimal errors (e.g., RMSE < 0.0055, MAPE < 0.025% and SI < 0.00029), while variable importance analysis highlighted asphalt binder and red mud as critical input factors. This research highlights the dual advantages of employing advanced machine learning techniques and sustainable materials in asphalt design. The RBFNN-HOA model provides a reliable tool for predicting and optimizing asphalt mixture properties and reduces the need for extensive experimental work, thereby saving time and resources. Furthermore, the study supports the transition to a circular economy by demonstrating the effective use of red mud as a filler, achieving enhanced mechanical performance and environmental benefits.

Industrial waste; red mud; Marshall mix design; sustainable pavement, artificial neural network

Digital technologies in educational environments have brought disruptive changes to the teaching and learning process due to their excessive use. This has resulted in new challenges to teachers’ well-being in the form of technostress. Technostress arises from the inability to adapt to or manage technological demands. This research examines how technostress creators, inhibitors, and technology-enabled performance (TEP) are related. Data were gathered using a structured questionnaire from 146 educators in India. The study used structural equation modeling via SmartPLS 4.0, to analyise the data. The results showed that techno-insecurity, a contributor to technostress, adversely affects performance, whereas facilitators such as digital literacy and technical support are essential in minimising these impacts and improving performance. Technostress inhibitors reduced techno-complexity and techno-invasion significantly, but were less effective at lowering technooverload and techno-insecurity. These findings suggest competency-based training programs to reduce complexity-related stress; structured policies limiting after-hours digital communication to reduce invasions; technical support infrastructure to reduce overload; and organizational mechanisms to optimize technology and technology-driven environments to meet future curriculum design needs.

Technostress; future curriculum; technology-enabled performance; Indian education; technostress inhibitors; Structural Equation Modeling

Due to the fact that secondary school and high school level schools are not available in every settlement in Osmaniye Province, it was not until the 1990s that school-aged individuals were sent to the city center to study. This situation has led to accommodation problems for students coming to the city for educational purposes, resulting in the proliferation of rental properties consisting of a single main room. The research includes the evaluation of the Veli Bağ house, which is the most well-known of these properties and one of the only surviving examples, in the context of a studio apartment. In this context, a literature review was conducted to identify the primary characteristics of the studio apartment typology. In the next stage, oral history methods were employed to determine the reasons for the emergence of rental housing in the city and the period during which it appeared, specifically in the case of the Veli Bağ house, and to reveal the spatial characteristics of this type of housing. Semi-structured interviews were conducted with 10 people who had lived in the building. Based on the findings, the similarities and differences between the Veli Bağ House and the studio apartment typology were discussed.

Studio apartment; oral history; Osmaniye; Veli Bağ House; housing

Recent express bus crashes in Malaysia have highlighted the urgent need to strengthen safety in bus operations. To address this concern, this study explores how demographic attributes (age, education level) and occupational related factors (driving experience, number of working days per week, and daily driving hours) influence risky driving behaviours among express bus drivers in Malaysia. Risky driving was assessed using two proxies: history of traffic summons and crash involvement. A total of 280 drivers participated through face-to-face structured interviews using a self-developed questionnaire. Spearman’s rank correlation and binary logistic regression were employed to examine associations between these variables and the two risk indicators. Findings revealed that most demographic and occupational related factors had no statistically significant association with either traffic summons or crash involvement. However, the number of working days per week was weakly but significantly associated with crash involvement, suggesting that longer workweeks may increase crash risk, likely due to fatigue accumulation. These results indicate that commonly examined demographic and exposure variables alone offer limited insights into the complex nature of risky driving behaviour. The study underscores the need for a more holistic approach in future investigations. Integrating organisational culture, behavioural tendencies, and psychological stressors may provide a more comprehensive understanding of risk factors, ultimately contributing to more effective interventions aimed at improving the safety of commercial drivers on Malaysian roads.

Bus express; demographic; driving related predictors; risky driving; Malaysia

Weathered sedimentary rock slopes must be in stable condition to reduce the risks and disasters, especially when there are critical infrastructures in proximity. Risk assessment, exposure analysis, vulnerability analysis, hazard identification, mapping, and zoning are the factors that are impacted by the instability of the rock slope in the geohazard vulnerability assessment. This study aims to determine the weathering grade, build high-density 3D models using unmanned aerial vehicle (UAV)-based photogrammetry, identify anisotropic planes based on kinematic analysis and analyse geohazard risk and vulnerability index. The results demonstrated that unfavourable discontinuity orientations and weathering lead to wedge failure at Location A and planar failure at Locations B and C as the primary mechanisms on the weathering grade III sedimentary rock. Infrastructure risks are categorised as low to moderate by vulnerability assessments, where roads and buildings benefit from moderate slope angles and the presence of the drainage systemsalong the retaining structures, with the index value ranging from 0.24 to 0.29. Additionally, forecasts from Agisoft Metashape, CloudCompare, and Stereonet software are aligned with observed failures in the field, such as wedge failure forecasted by kinematic analysis and was confirmed upon observation at Location A. By providing information through vulnerability and risk assessments to support infrastructure development and catastrophe mitigation, the integration with UAV also provides a comprehensive alternative to conventional slope stability analysis techniques for data acquisition in the field.

Weathered sedimentary rock slope; unmanned aerial vehicle (UAV); photogrammetry; discontinuity characterisation; and geohazard vulnerability and risk analysis

The increasing demand for construction accuracy, accountability, and digital integration has highlighted the limitations of traditional quality control (QC) systems, which often rely on manual measurements and subjective assessments. This study introduces a novel conceptual framework for automating dimensional quality control by integrating 3D laser scanning, Building Information Modeling (BIM), and a rule-based workflow to optimize deviation identification. The proposed system enables a methodical workflow that records as-built conditions of real physical structures using 3D laser scanning, aligns this data with the digital design model, and implements established criteria to automate dimensional quality control for model-based comparisons by encoding rulebased and project tolerances based on compliance standards and criteria, and to evaluate their implementability in real-world construction. A mixed-methods approach was employed, involving a synthesis assessment of theories and concepts from previous studies, and expert validation was implemented to refine the framework and operational definitions. The conceptual framework includes: data acquisition, data processing and registration, model integration and alignment, and deviation detection. Theoretically, the framework enhances model-driven construction theory by formalizing continuous, rule-based quality control workflows that are dynamically aligned with real-world conditions. It offers a scalable, auditable solution for deviation detection, minimizes rework, and accelerates acceptance decisions. Future work will focus on empirical validation within active projects, development of standardized rule libraries in accordance with project specifications, and integration of predictive analytics to facilitate intelligent, real-time decision-making. This research provides a novel theoretical framework and a practical approach for automated, data-driven quality control in modern construction.

Dimensional quality control; Building Information Modeling (BIM); 3D laser scanning; automation

Energy Management Systems (EMS) are pivotal in increasing the efficiency of Hybrid Renewable Energy Systems (HRES) and decreasing diesel generator usage. This study aims to perform a Systematic Literature Review (SLR) on advanced EMS strategies, providing insights into the impact of EMS on reducing diesel consumption, costeffectiveness, and operational efficiency of off-grid hybrid solar-diesel systems. Recent progress on predictive control, real-time observations, and optimization algorithms has been reviewed along with battery storage integration for better energy dispatch. Using a systematic search method across multiple databases, 28 relevant articles published from 2022 to 2025 were identified and analyzed based on strict inclusion criteria. The findings show that rule-based and algorithm-driven EMS approaches, including Fuzzy Logic Control (FLC), Marine Predator Algorithm (MPA), and Deep Deterministic Policy Gradient (DDPG), contribute to a substantial decrease in reliance on diesel while enhancing renewable energy utilization. Battery storage optimizes EMS performance by shifting energy and minimizing operating costs. Nevertheless, challenges such as high upfront costs, complexities in system integration, and the need for standardization remain. This review emphasizes the importance of EMS as a key component in sustainable energy transitions for off-grid applications and proposes an optimization framework for battery energy storage. Future research should emphasize real-world implementation, scalability, and policy frameworks to facilitate broader adoption and long-term economic viability of HRES.

Energy Management Systems; battery storage; diesel generator reduction; Solar Photovoltaic Systems (PV); rural electrification, optimization algorithm.

Wide Area Network (WAN) play a vital role in contemporary network systems, facilitating communication among devices across broad geographic regions. Despite its importance, WAN infrastructure often comes with high complexity. To address this issue, Software-Defined Networking (SDN) has evolved as a means to decouple the control logic from its data forwarding components, simplifying the overall network architecture and facilitating more streamlined management. Combining SDN and WAN results in SD-WAN, a more efficient and cost-conscious approach that delivers enhanced Quality of Service (QoS). However, as technology continues to advance, the volume of network traffic grows significantly that leads to congestion because each packet is processed individually. Furthermore, Customer Premise Edge (CPE) in SD-WAN becomes the bottleneck of the network makes the congestion worse. Most of recent studies rely on complex control mechanisms and process packets individually. These limitations highlight the need for a lightweight, proactive strategy that reduces congestion by decreasing packet-processing frequency. Hence, this paper proposes a novel congestion control algorithm based on the bucket sort technique that aggregates packets with identical destination network IPs into a single bucket, then, sending them together as one packet to reduce processing load. We simulated the proposed algorithm using Python and compared it against a standard loss-based congestion control method in GNS3. The results demonstrate that our proposed algorithm achieves higher throughputs which are 13,882 kbps in 10 Gbps link and 27,764 kbps in 20 Gbps link. Hence, validating its effectiveness and showing its potential to enhance network performance under heavy traffic conditions.

SDN, SD-WAN, TCP, congestion control, bucket sort.

This research study presents on both experiments on double pass solar air heater (DPSAH) for fins only (DPSAHWF) and fins with a composite phase change material porous medial (DPSAHWFPCMSW). The objective of this study is on the thermal performance evaluation. The addition of fins, paraffin wax and steel wool has been found to improve the increase of heat transfer, energy storage period and heat efficiency. Forty diamond-shaped fins, 4kg of RT-44 paraffin wax (phase change material) and one roll of steel wool (porous media) were used in the experiment. For the first experiment, only diamond-shaped fins were used while for the second experiment, there was an addition of paraffin wax and steel placed at the bottom. There are four mass flow rates (0.0104 kg/s, 0.0157 kg/s, 0.0209 kg/s, 0.0261 kg/s) and four variations of solar radiation (400 W/m2, 600 W/m2, 800 W/m2 and 1000 W/m2) used. The effect of mass flow rate, solar radiation and outlet temperature has had an impact on the overall performance for DPSAH. The highest thermal efficiency obtained for DPSAHWF and DPSAHWFPCMSW were 59.34% and 97.91%, at 0.0261kg/s and 1000 W/m2, respectively. The lowest thermal efficiency obtained for DPSAHWF and DPSAHWFPCMSW was 43.51% and 60.33%, at 0.0104kg/s and 400 W/m2 respectively. The second experiment showed a huge improvement in energy efficiency due to the added energy storage and better heat transfer. This confirms that DPSAHWFPCMSW was more thermally efficient than DPSAHWF.

DPSAHWF; DPSAHWFPCMSW; fins; paraffin wax; steel wool; heat efficiency

The global transition toward renewable energy has positioned solar photovoltaic (PV) systems as a cornerstone of sustainable power generation. This transition is creating an urgent need for a robust and intelligent monitoring solutions. This scoping review systematically examines peer-reviewed studies published between 2020 and 2025, employing the PRISMA-ScR framework to ensure a transparent and structured process of identifying, screening, and synthesizing relevant literature. The analysis highlights progress across four key dimensions. Techniques such as machine learning (ML), deep learning (DL), signal processing, and statistical modelling demonstrate high accuracy in fault detection, predictive maintenance, and performance forecasting. Technologies including Internet of Things (IoT), unmanned aerial vehicles (UAVs), edge computing, and blockchain enable real-time monitoring, remote automation, and secure data handling. Usability considerations such as interface design, scalability, costeffectiveness, and responsiveness remain underexplored, with few systems developed through user-centered approaches. Security emerges as the weakest area, with most systems offering little protection against critical threats such as false data injection, spoofing, or unauthorized access. The review identified six persistent research gaps; inadequate security frameworks, insufficient user-centered design, scalability versus cost trade-offs, lack of standardization, limited intelligent automation, and underutilized prescriptive analytics. These gaps highlight the need for integrated approaches that go beyond technical performance improvement to ensure resilience, affordability, and user adoption. By addressing these challenges, next-generation PV monitoring systems can become more secure, user-centric, scalable, and capable of autonomous operation and optimization, thereby enhancing the reliability, affordability, and long-term sustainability of global solar energy infrastructures.

Solar photovoltaic monitoring; fault detection techniques and technologies; predictive maintenance; machine learning; usability and security

A significant percentage of road pavements suffer from cracking and rutting, primarily owing to poor asphalt mixture characteristics and heightened traffic loads. Subsequently, they acquire defects such as creep, fatigue, and rutting. To address these issues, this study explores the use of crumb rubber (recycled tyres) and silica fume (by-product of silicon alloy production) as modifiers for enhancing asphalt performance. Asphalt mixes were prepared with varying amounts of crumb rubber (5–10% by weight of bitumen) and silica fume (5–10% by weight of fine aggregate). Aggregate tests (Aggregate Impact Value Test, Aggregate Crushing Value Test, Los Angeles Abrasion Value Test, and water absorption), bitumen tests (penetration, softening point, and ductility), and mechanical performance tests (resilient modulus, dynamic creep, and Scanning Electron Microscopy analysis) were performed. Results revealed that the mixture containing 10% crumb rubber and 5% silica fume exhibited a 25% increase in resilient modulus and a 30% reduction in permanent strain compared to the conventional mixture. This combination modification method is unique in integrating both crumb rubber and silica fume at optimized levels to enhance the performance for both mechanical and morphological properties simultaneously. It can be concluded that the utilizing of these materials significantly improves pavement lifespan and reduces environmental impact while also improving sustainability and durability.

Crumb rubber; silica fume; hot mix asphalt; modified asphalt; green pavement

Solar stills offer a decentralized solution for water purification, but conventional designs face limitations such as low efficiency (~3 L/day) and a lack of real-time monitoring, hindering widespread adoption. This study presents an innovative hybrid solar still enhanced with a thermoelectric Peltier system and IoT-based monitoring to address these challenges. By integrating active thermoelectric heating (using three TEC1-12706 modules) with passive solar evaporation, the system achieves a 59.22% improvement in efficiency compared to traditional stills. Real-time performance monitoring is enabled through DS18B20 temperature sensors and water-level sensors, connected via an ESP8266 microcontroller for cloud-based data logging. This IoT integration allows for dynamic optimization under varying environmental conditions, ensuring consistent performance. Experimental results demonstrate that the enhanced system increases peak water temperature by 38.87% and produces 1.2 L of clean water —four times more than passive designs. The dual-energy input (solar and thermoelectric) ensures reliable operation even in suboptimal sunlight conditions, making it suitable for off-grid and resource-limited settings. The key novelty of this work lies in the synergistic combination of active thermoelectric heating, passive solar distillation, and IoT-enabled monitoring, addressing critical gaps in efficiency and adaptability for decentralized water purification. This study provides a scalable and sustainable solution for clean water production in challenging environments, with potential applications in remote and disaster-affected areas.

Solar still; thermoelectric; IoT; evaporation; clean water

The world demand dictates that alternative eco-friendly and sustainable materials are very much coveted that many researchers are drawn to composites materials made of natural fibres and biodegradable materials that are cheap, boast off good mechanical properties and biodegradability. This paper reviews relevant literature which deals with the natural plant known as Luffa cylindrica (LC) or Luffa sponge (LS). An overview is given by numerous research works for commonly used fibre surface treatments for Luffa fibre (LF). Furthermore, this study discusses the compositions and the mechanical properties of Luffa plant, also how water immersion affects compression behavior in addition the remarkable shape memory effects and mechanical recovery features are also common features of Luffa sponge material. A comprehensive understanding behavior of LS as energy absorbing structure under different compression loads that were investigated on it, such as (quasi-static and dynamic load) and the mechanical-properties of the luffa plant. As a result, from an energy-absorbing point of view, the Luffa sponge material is evidenced to display striking stiffness, strength and energy absorption capacities as opposed to the metallic cellular counterparts. In addition, Luffa sponge is an ultra-light cellular material, it also has an environmentally friendly feature, sustainable, renewable, ease of production and low cost.

Energy absorption; luffa sponge; lightweight; compression; natural fiber

This study reviews the challenges and prospects for low-cost Hybrid Solar Systems (HSS) for off-grid applications, focusing on energy optimization and storage technology. Accordingly, HSS can deliver clean energy and reduce operating costs. However, it is highly dependent on diesel-generated power and has high energy storage costs. This study aims to identify solutions to mitigate diesel power generation dependency and to evaluate challenges and opportunities for enhancing the cost minimization and efficiency of Energy Storage Systems (ESS). In the approach known as Systematic Literature Review (SLR), we retrieved 54 articles published from 2019 to 2024 on optimization technologies, energy storage, and reducing diesel consumption in Hybrid Energy Systems (HES). The main findings reveal that using stochastic programming and Social Spider Optimization (SSO) approaches has improved the performance of energy consumption management. In contrast, Hybrid Energy Storage Systems (HESS) and hydrogen energy storage technologies offer promising potential for reducing storage costs and enhancing the system’s efficiency. Although diesel generators are still necessary in some cases, the study indicates that innovative energy management technologies reduce dependence on diesel consumption, ultimately cutting costs and carbon emissions. Moreover, the proposed HSS Optimization Framework integrates data acquisition, optimization algorithms, and decision-making layers to holistically manage power generation, storage, and dispatch. Through this framework, the hybrid system achieves an optimal balance between energy production, storage utilization, and reliability. Ultimately, the findings could serve as a reference model to enhance the operation of HSS and minimize environmental impacts through technological innovation and enhanced optimization strategies.

Diesel generator; energy optimization; energy storage; green technology; solar hybrid system; rural electrification

This study aims to identify a suitable core design for sandwich panels to enhance the delamination resistance. Three out of the four main types of core design (foam, honeycomb, and lattice) feature an open and/or closed low-density cavity structures. These compromise the structural integrity between panel layers when subjected to high loading. A three-dimensional geometry of the sandwich panel, incorporating high-strength AR500 steel face sheets and an AZ31B magnesium alloy core, was developed using finite element modelling of four-point bending according to ASTM C393 under both static and cyclic loading. The results indicate that Mode II failure, due to high interfacial shear forces between panel layers, leads to Mode I failure. Stress contour distribution under four-point bending condition shows that the honeycomb core is more prone to core crushing, exceeding 50%, compared to the dimpled surface core. Additionally, fatigue life modelling shows a significant difference in fatigue life for the honeycomb core at 50%–70%, whereas the dimpled surface core is nearly equivalent to the solid plate core at 6%12%. Validation through testing shows that small-sized dimples (6.0 mm in diameter and 3.0 mm in depth) provide better delamination resistance, with a difference percentage of up to 50%. The fatigue life cycle data from both finite element modelling and experiment were consistent and fall within the 90% confidence interval. This study contributes to the development of a novel core configuration that is more resistant to delamination under high loading.

Finite element analysis; fatigue life; bending; delamination; dimple core

High spatial resolution and the possibility for distant and noncontact measurements have led to substantial research into laser ultrasonic techniques. Pulse lasers produce heat when the surface of a target object absorbs its electromagnetic energy. When a material undergoes a temperature shift, it generates stress and strain fields, which in turn cause an elastic wave known as ultrasound. This research simulated the effects of an ultrasonic wave produced by a laser striking an aluminium plate. Using extremely fine meshes, the pulse laser stimulation is modelled as a localised heat flux imposed to the structure’s surface. The 2D simulation data for intact specimen is collected and then compared to an existing experimental data using contactless laser ultrasonic measuring equipment . The model is then subjected to surface damage and identified using a threshold to retrieve the peak count from the laser ultrasonic data while analysing the correlation between damage effects found in various locations and peak counts. The total number of peak counts is measured, and the damage is successfully detected. Based on the identified peak counts, different damage locations can be determined and analysed.

Laser ultrasonics; finite element method; damage localization; peak count

Reinforcing ageing infrastructure with carbon fibre-reinforced polymer (CFRP) plates is a viable alternative to standard steel plate bonding, but it has drawbacks like heavier weight and corrosion exposure. This study assesses the structural efficiency, energy absorption, and failure mechanisms of concrete beams that have been reinforced with CFRP plates, with four-point bending tests to measure the flexural capacity, deflection, and bond strength. The experimental results showed that the load-bearing capacity and deflection of CFRP-reinforced beams were much higher than those of control beams and that larger CFRP plates provided the optimal performance. Results showed significant improvements in structural performance when compared to control beams (CB series) and notched beams (NB series) reinforced with CFRP plates from the CFRP250 and CFRP500 series. The CFRP500-3 beam boasted the maximum load-bearing capacity and flexural strength, which highlights the benefits of larger CFRP plates. A thorough assessment of the bond strength showed strong adhesion, and the CFRP-reinforced beams exhibited noticeably higher ultimate load and shear stress. These results corroborate that the better performance found in CFRP-reinforced beams is contingent upon effective bonding. Overall, CFRP plates are a lasting and effective way to strengthen structures by increasing the bond strength and flexural strength of concrete beams. This study advocates for the expanded use of CFRP materials in infrastructure rehabilitation, providing practical recommendations for optimising CFRP application and fostering more resilient, secure structures.

Carbon fiber reinforced polymer plate; fiber reinforced polymer; flexural strength; repairing; energy absorption

Cement production’s rising emissions of greenhouse gases, especially CO₂, are a major cause of environmental alarm. Concerning that, this study examines the effects of carbonic acid on Lightweight Foamed Concrete Blocks (LFCB), with an emphasis on its density and thermal properties. It aims to investigate the impact of different carbonic acid concentrations on the density and thermal conductivity of LFCB, as well as the relationship between these variables. Modest levels of carbonic acid appear to promote the growth and connection of carbonate crystals in the concrete matrix, which, in effect, creates a less porous material and insulating ability. Higher concentrations result in larger pore sizes and structural changes that can generate higher thermal conductive materials. The carbonic acid shows as a derivation of density and thermal conductivity (principally till 50%) when there is an optimal density for normal LFCB blocks. Meanwhile, at this stage of concentration, it can lead to an increase in density and thermal conductivity. The results aim to fill the knowledge gap for the construction behaviour of LFCB as a material and provide a reference for future research on carbon neutrality in buildings.

Lightweight concrete; foamed concrete; concrete block; carbonic acid; thermal conductivity

Understanding the compressive behaviour and properties of fibre-reinforced polymer (FRP) composites is essential for designing effective structures and mechanical components. However, there is limited knowledge about the effect of nanosilica on the compressive properties of natural fibre composites. This gap in understanding hinders the optimization of these materials for real-world structural applications. The aim of this study is to investigate the effect of nanosilica on the compressive properties of Unidirectional (UD) continuous Arenga Pinnata Fibre Reinforced Epoxy Composites (APREC). By integrating 5 wt%, 13 wt%, and 25 wt% nanosilica into the epoxy matrix, the research evaluates how varying nanosilica content influences the compressive stressstrain behaviour. AP fibres, a lightweight, plant-based material, were utilized to develop sustainable FRP composites. The nanosilica-modified epoxy resin was mixed at 4000 rpm and 80ºC for 1 hour, and compressive tests were performed following ASTM D3410 standards. Results showed that adding nanosilica enhances the compressive strength and modulus of APREC. The 5 wt% nanosilica-modified APREC exhibited the highest compressive strength (102.053 MPa) and modulus (2.142 GPa), indicating optimal interfacial bonding. At 13 wt%, compressive strength and modulus decreased to 93.336 MPa and 1.945 GPa, respectively, while 25 wt% nanosilica further reduced performance to 70.786 MPa and 1.874 GPa. These findings suggested that 5 wt% nanosilica achieves the best balance between particle dispersion and resin wetting, while higher concentrations lead to particle aggregation and reduced mechanical properties. This study provides valuable insights into optimizing nanosilica content in APREC to enhance compressive performance in structural applications.

Compression; arenga pinnata; nanomaterials; nanosilica

It is acknowledged that microplastic contamination is a worldwide problem that jeopardise both human and environmental health. Rapid industrial and urbanisation in many tropical nations, along with weak ecological regulation, may lead to an upward trend in microplastics entering rivers; yet there is a shortage of fundamental data on contamination levels. The purpose of this study was to catalogue the physical features of microplastics found in the Klang River and to examine their characteristics in relation to the river basin in the Malacca Strait. It was selected for its commercial, residential, and industrial regions, which are situated along its length. The study also measured the water quality parameters, including DO, pH, salinity, turbidity, and FTIR analysis. A total of 171 microplastic pieces were obtained from the six stations. Microplastics have been discovered in many shapes and sizes, including films, fibres, and fragments. The FTIR analysis revealed that the microplastics most found were HDPE, LDPE, polypropylene, and polyamide/nylon. The findings indicate that the Klang River is heavily polluted with microplastics. The outcomes enable future research to further the investigations of microplastics along the Klang River as it is one of the main water sources in Peninsular Malaysia and provide new insights into understanding of microplastics.

Microplastic; Klang River; water; FTIR

Effective health management is essential, yet hindered by challenges in traditional healthcare systems and an uneven physician-to-population ratio. The integration of 5G networks improves communication in healthcare. This paper delves into integrating deep learning (DL) within network slicing to provide tailored solutions for the Hospital of the Future (HoF). To the best of our knowledge, this paper presents the first instance of classification techniques being used in network slicing using DL demonstrated via OMNeT simulations. We evaluate three scenarios namely, network slicing using DL, network slicing without DL, and unsliced network in terms of throughput and delay. Throughput result for URLLC network slicing using DL shows approximately a 33.33 times improvement compared to network slicing without DL and unsliced network, while eMBB network slicing using DL exhibits approximately a 10 times improvement. Additionally, mMTC network slicing using DL demonstrates a 53% improvement. Regarding delay, URLLC network slicing using DL exhibits the lowest delay compared to network slicing without DL and unsliced network, while in eMBB, network slicing using DL shows the second lowest delay. In mMTC slice, network slicing using DL shows an overlapping performance with unsliced networks, and network slicing without DL exhibits the lowest delay. It’s noteworthy that the differences in delay among eMBB, mMTC, and URLLC slices compared network slicing without DL and unsliced network slices are minimal, approximately less than 1%. The intelligent distribution of resources by DL makes it ideal for critical healthcare applications, surpassing alternatives in heterogeneous networks.

Deep learning; network slicing; OMNeT; hospital of the future; 5G

The characterisation of mechanical properties is essential to ensure the performance and structural integrity of components in engineering applications. This study employs the Impulse Excitation Technique (IET), a nondestructive dynamic testing method, to evaluate the elastic properties of materials. As an alternative to conventional analysis methods, this work utilises the Integrated Kurtosis-based Algorithm for Z-notch Filter (Ikaz™) and the Mesokurtosis Zonal Nonparametric (M-Z-N) technique to analyse the impulse responses. A simplified IET experiment setup, based on ASTM E1876, was implemented using an impact hammer as the exciter and a piezoelectric film sensor to capture the signals. Six different materials were selected as test specimens: 6582 alloy steel, bronze, T250 cast iron, copper, P20 plastic mould steel, and SKD-11 cold work tool steel. The recorded data included two signal types: impact force and vibrational response. Both I-kaz and M-Z-N methods were applied to analyse the vibrational data and extract signal features. These features were then correlated with the measured impact forces and theoretical Young’s modulus to develop mathematical modelling equations. The I-kaz method yielded a high average coefficient of determination (R² = 0.94908) with a relative error of 13.12%. In contrast, the M-Z-N method produced a significantly lower R² value of 0.34780 and a higher error of 49.82%, indicating its lower reliability. The results highlight that employing relevant signal features and establishing a strong correlation between impulse excitation and vibrational response enables effective estimation of a material’s Young’s modulus through statistical signal analysis.

Non-destructive test; impulse excitation technique; Young modulus; statistical signal analysis; I-kaz

The rapid increase in vehicle population has intensified rutting and fatigue cracking, two critical distresses affecting pavement service life under Malaysia’s tropical climate. To address these challenges, this study investigates the potential of bioresource modifiers waste cooking oil (WCO) and ground tire rubber (GTR) to improve bitumen stiffness and elasticity without increasing production cost or environmental footprint. The research was conducted in three phases: (1) characterization of modified bitumen, (2) optimization of mix design using the Marshall method, and (3) performance evaluation through resilient modulus and moisture susceptibility tests. Bitumen with a penetration grade of 80/100 was modified with varying WCO contents (0%, 1%, 2%, 3%, and 4%) and a fixed 20% GTR by bitumen weight. The physical and rheological properties were evaluated through penetration, softening point, and dynamic shear rheometer (DSR) tests, while Marshall Stability, resilient modulus, and tensile strength ratio (TSR) tests assessed mixture performance. The findings demonstrated that the addition of 1% WCO and 20% GTR significantly enhanced bitumen performance, as evidenced by a 42% reduction in penetration, a 31% increase in softening point, and a 79% improvement in resilient modulus at 40 °C (1000 ms). These improvements reflect substantial gains in stiffness and elasticity, confirming the superior rutting and fatigue resistance of the optimized WCO–GTR-modified asphalt bitumen. Overall, the results suggest that WCO and GTR-modified asphalt mixtures offer a cost-effective and sustainable solution for improving pavement durability while promoting waste recycling and environmental sustainability.

Asphalt mixture; waste cooking oil; ground tire rubber; permanent deformation; modified bitumen; sustainable pavement

Class attendance is vital in education, closely linked to student engagement and academic performance. However, traditional attendance-tracking methods such as roll calls and sign-in sheets are inefficient and error-prone. Modern alternatives such as RFID and QR code scanning improve efficiency but require specialized infrastructures and remain susceptible to proxy attendance. This paper presents a student attendance system that utilizes facial recognition to automate attendance-tracking and enhance accuracy. It identifies students in real-time and automatically updates attendance records. The system employs a low-power single-board computer with a webcam and ultrasonic sensor to detect student presence and capture facial images. These are processed on a consumergrade workstation. The system first detects faces using a HOG-based detector and then a deep neural network extracts unique facial features for identification. A web-based user interface was also developed to enable instructors to schedule their classes. The system was tested with 20 students, equally divided into two groups: one with their facial images pre-registered in the system, and another without, simulating unregistered users. Each student completed five trials, totalling 100 tests. The system achieved 92% accuracy, with a True Positive Rate of 100% and False Negative Rate of 0%, effectively identifying all registered students. Precision was 86%, while False Positive Rate was 16%, attributed to rare instances of unregistered users being misidentified as registered students. True Negative Rate was 84%, indicating capability to reject most unregistered users. The results demonstrate the system’s feasibility for automated, real-time attendance-tracking using facial recognition in classroom settings.

Artificial intelligence; computer vision; facial recognition; machine learning; student attendance

This study explores the mediating effect of employee payroll methods (EPMs) on the relationship between lean production (LP) and corporate performance (CP) in small and medium-sized enterprises (SMEs). Drawing on the Job Demands-Resources (JD-R) model and lean management theory, we developed a theoretical framework to test how LP practices influence CP directly and indirectly through three typical EPMs (hourly payroll system, piece-rate payroll system, and performance-based payroll system). A quantitative research design was adopted, with data collected from 328 production managers and front-line employees across 68 manufacturing SMEs in Shandong, China. Structural equation modeling (SEM) and bootstrap analysis were used to validate the hypotheses. The results show that: (1) LP has a significant positive impact on CP (β=0.587, p<0.001); (2) LP positively predicts the adoption of piece-rate payroll system (β=0.423, p<0.001) and performance-based payroll system (β=0.516, p<0.001), but negatively predicts hourly payroll system (β=-0.289, p<0.01); (3) Piece-rate payroll system (β=0.215, p<0.001) and performance-based payroll system (β=0.302, p<0.001) play partial mediating roles between LP and CP, while hourly payroll system shows no significant mediating effect (β=-0.064, p>0.05). These findings contribute to the literature by clarifying the mechanism through which LP affects CP in SMEs and provide practical implications for SME managers to optimize payroll systems while implementing lean practices.

Lean production; employee payroll methods; corporate performance; SMEs; mediating effect