Volume 37 (04) July 2025

This study presents a numerical investigation into the thermal performance of TiO₂ nanomaterials dispersed in pure paraffin phase change material (PCM) within straight tubes of circular, triangular, and elliptical cross-sections, serving as thermal energy storage systems. A constant heat flux ranging from 1000 to 10,000 W/m² was applied as boundary conditions, with TiO₂ nanoparticles having an average diameter of 20 nm and a volume fraction of 1%. Numerical simulations were conducted using ANSYS FLUENT 18 to evaluate the thermal behavior of these geometries. The results reveal significant thermal enhancements when using the Nano-PCM blend compared to pure PCM. Specifically, the Nano-PCM exhibited temperature increases of 22.8%, 20.9%, and 15.9% for circular, triangular, and elliptical cross-sections, respectively. Furthermore, the elliptical system demonstrated superior thermal performance compared to the triangular and circular systems. The PCM temperature in the elliptical system surpassed the triangular and circular systems by 46% and 52.45%, respectively, while the Nano-PCM temperature was higher by 42.8% and 48.3%, respectively Entropy analysis showed similar trends, with the PCM entropy in the elliptical system exceeding that of the triangular and circular systems by 75.2% and 91.6%, respectively, and the Nano-PCM entropy increasing by 75.8% and 91.7%, respectively. The findings were compared with prior studies on cylindrical storage systems, where the maximum storage temperature was improved by 37% and 42.3% for PCM and Nano-PCM, respectively, highlighting the superior performance of the present approach. This work underscores the potential of elliptical systems and Nano-PCM blends to enhance thermal energy storage efficiency.

Keywords: Phase change materials (PCM); thermal storage efficiency; nanofluid; Computational Fluid Dynamics (CFD)

This study assesses the academic achievement of students in the Water and Wastewater Engineering course at the Centre of Studies, Civil Engineering, UiTM Cawangan Pulau Pinang. The survey was distributed to a cohort of Civil Engineering students, and data from 100 respondents were analysed. The data indicates that the Expeditionary Learning approach, specifically through field trips or site visits, received the highest score from 43 respondents. This suggests that it is the most effective teaching and learning strategy for the present generation of students. Conversely, the least preferred teaching and learning approach is online collaborative teaching, as it offers limited opportunities for contact and engagement between students and instructors and lacks hands-on activities and experiential learning. The result showed that the failure sudden decreased from 47.4% (72 students) drop to 2.7% (3 students) after implementation of various innovative strategies in Water and Wastewater Engineering course. The implementation of the innovative teaching and learning (T&L) approach has resulted in improved academic performance among students in the current semester (Oct 2023-Feb 2024). It has also led to a more positively skewed distribution in the grading score and a decrease in the percentage of students who failed, as compared to the conventional method used in the previous semester (Oct 2023-Feb 2023). Therefore, the suggested innovative approaches can be recommended as a powerful pedagogical approach to developing an interactive, attractive, and effective environment for the quality of education.

Keywords: Expeditionary Learning; Innovative T & L; Civil Engineering Students’; pedagogical approach

Inverse Perspective Mapping (IPM) is a technique used in Intelligent Transportation Systems to generate virtual bird’s eye view (BEV) images, enabling accurate obstacle detection and free space estimation. The IPM approach used before exclusively prioritized straight and flat road surface. If the IPM approach is used on an uneven road, this becomes exceedingly challenging. As a result, the IPM approach does not fully use the Inertial Measurement Unit (IMU) sensor. As an outcome, there will be a temporal delay in the data when image and IMU data are combined. The goal of this study is to present an IPM-based technique for stabilizing the exact position of the vehicle using angular orientation parameters derived from images and IMU data that are synchronized and filtered using the Kalman and complementary filters. Experimental results show that IPM can determine the actual angle when passing the uneven roads at 94% accuracy, surpassing previous IPM techniques. It is obvious that the proposed method can generate reliable and accurate IPM images. The image data and IMU data that have been recorded can be utilized for analysis to choose the parameters that are most appropriate to be employed throughout the IPM process, despite the fact that the road structure is continually changing.

Keywords: Inverse Perspective Mapping (IPM); uneven roads; Inertial Measurement Unit (IMU); angular parameter; Kalman filter

Papua New Guinea is preparing to embrace the transformative impact of 5G technology on its telecommunications sector. As network expansion progresses, it becomes more susceptible to various challenges that can potentially disrupt data transfer and degrade service quality. A comprehensive analysis of the existing issues affecting current mobile network services is essential to facilitate an effective transition to 5G. This study investigates the traffic patterns of GSM, 3G and 4G mobile services offered by the Mobile Network Operators (MNOs) in selected areas of Lae, Papua New Guinea specifically the Lae Central Business District, Eriku Shopping Centre and Lae Downtown. The study utilizes a PR100 portable receiver and MS2T07 spectrum analyzer with HE300 and ESLP9145 log periodic antennas to measure the spectrum receiver (Rx) levels and base station power levels. Emissions were detected within the guard bands of MNO traffic channels and the Universal Mobile Telecommunications System (UMTS) carrier, with signal degradation being attributed to factors such as multipath effects, including spectral broadening, Doppler spread, and Rayleigh small-scale fading. These issues were also found to be caused by backhaul limitations, complexities in multipath propagation, network power outages, and various types of interference. To address the issues, strategies such as reducing base station transmission power and mitigating external interference were implemented. As a result, a substantial reduction in received total wideband power (RTWP) levels and an improvement in overall spectrum quality were achieved. The findings of this study provide valuable insights for stakeholders such as the National Information and Communication Technology Authority (NICTA) and MNOs, serving as a technical reference for optimizing network performance and facilitating the deployment of 5G infrastructure.

Keywords: Interference; network optimization; out-of-band emissions; spectrum; guard band; multipath effects; spectral broadening and Doppler spread

Ultra-High Frequency Radio-Frequency Identification (UHF RFID) tags have been studied as one of the potential alternatives for Wireless Body Area Network (WBAN), owing to their versatility and capacity to enable wireless communication across a range of applications. However, one of the major challenges associated with the tag is the electromagnetic interaction between the tag and the human body, which affects the tag’s read range. Therefore, this study was conducted to propose and design a reduced-size Artificial Magnetic Conductor (AMC) structure to improve the read range. An AMC unit cell was proposed and optimized to operate at 920 MHz using the CST Microwave Studio. A 3 x 3 array AMC structure was fabricated and its performance was examined and validated in terms of read range by experimentally testing it on the human body. When compared to the conventional square-patch-based AMC, the suggested AMC with a unit cell size of 74 mm achieved a size reduction of 24.4%. Based on the experimental findings, the AMC improved the on-body read range by 367% with an increase of 1.1 m. In addition, the suggested AMC has a simple configuration and is easy to fabricate. Therefore, it is anticipated that the proposed design applied in wearable UHF RFID applications.

Keywords: Artificial magnetic conductor; read range; UHF RFID; wearable device

The study investigates the relationship and descriptive tabulation of meteorological parameters and air pollutant variables in air quality monitoring in Peninsular Malaysia from 2017 to 2022. The main objective is to understand how the different weather conditions and pollutant variables are distributed across all monitoring stations in Peninsular Malaysia to provide insights for environmental monitoring and public health policy. Approximately 111,345 daily averages were collected from continuous air quality monitoring stations in Peninsular Malaysia (CAQMS) under the supervision of the Department of Environment (DOE), Malaysia. Key meteorological parameters such as wind direction (WD), wind speed (WS), relative humidity (RH), solar radiation (SR) and ambient temperature were analysed together with air pollutant variables such as nitrogen oxide (NOx), nitric oxide (NO), nitrogen dioxide (NO₂), ozone (O₃), carbon monoxide (CO) and particulate matter (PM10 & PM2.5). Statistical analysis was performed using Spearman’s correlation to identify significant predictors of air quality monitoring. In the descriptive analysis, all CAQMS
were compared with each other, including the mean. Boxplots were shown to describe the meteorological parameters, and correlation amongst the parameters and variables were done to investigate relationships. Results showed that PM_2.5 mean value exceeded the WHO guideline of 15μg/m³ and some CAQMS showed higher mean values especially in the central region (Selangor and Nilai, Negeri Sembilan). Carbon monoxide exhibits moderate correlation to certain air pollutant variables including PM_2.5 which is a significant air pollution indicator in peninsular of Malaysia.

Keywords: Correlation; descriptive analysis; air pollutant variable; meteorological parameters; air pollution trends

This study aims to identify the mathematical competency of first-year students in engineering mathematics courses at Universiti Kebangsaan Malaysia (UKM). A pre-test was conducted prior to the Vector Calculus class. The test consists of 28 questions on topics such as functions, limits, differentiation, vectors, geometry, and trigonometry. A total of thirty-five students participated in the study. Correlation analysis revealed a weak positive relationship between SPM Additional Mathematics scores and pre-test results (r = 0.215) and a moderate correlation with pre-university grades (r = 0.350), indicating the limited predictive power of high school achievements for university-level readiness. The findings highlight strengths in vector operations and integration but significant weaknesses in trigonometric functions and multivariable calculus. These gaps underscore the need for targeted interventions to support students in bridging the transition from pre-university to advanced engineering mathematics. Early detection of mathematical deficiencies through diagnostic testing can guide the design of remedial programs, enabling educators to address specific areas of difficulty. This study highlights the need to align pre-university curricula with the mathematical requirements of engineering education to help students succeed in their courses. Future research should explore adaptive diagnostic tools for individual learning strategies.

Keywords: Early detection; mathematics competency; engineering mathematics courses; vector calculus; pre-test

Knowledge about the properties of materials is essential, especially in engineering and technology. By understanding the potential and performance of a material, the lifespan, reliability, and safety of the structure can be enhanced. Moreover, it can prevent undesirable engineering failures such as roof collapses or tool damage during manufacturing processes. Higuchi’s fractal analysis is employed in this study to investigate the fractal dimensions of square, rectangular, and circular specimens of stainless steel, mild steel, brass, and copper. The specimens were subjected with varying impact forces. The vibration data from those set of specimens was acquired using a piezoelectric film sensor system, where each specimen was subjected to impact forces ranging from 300 N to 800 N through an impact hammer. Fractal dimension values were extracted from the vibration signals using MATLAB software to quantify the complexity of the responses across different materials. The results indicate a consistent increase in fractal dimension with rising impact forces for all materials. These suggest that higher impact forces lead to more complex vibrational patterns. These findings highlight the potential of fractal dimension analysis as a tool for understanding the dynamic behaviour of materials under mechanical impacts. Further studies could explore the real-time fractal analysis with artificial intelligent (AI) for predictive modelling of material failure in static and dynamic environments.

Keywords: Fractal analysis; impact force; mechanical properties; Higuchi Method; vibration signal

Forecasting air pollutant concentrations is crucial for managing pollution levels that pose significant risks to human health and the environment. Among the various pollutants, ground-level ozone (O₃) is a secondary pollutant of particular concern due to its harmful health effects and complex formation dynamics involving nitrogen oxides (NOₓ)and volatile organic compounds (VOCs) under sunlight. This study focuses on predicting ozone levels using the autoregressive fractionally integrated moving average (ARFIMA) model, which effectively captures long memory behavior in time series data. The research begins by analyzing ozone concentration data from three monitoring stations across Peninsular Malaysia to identify long memory behavior. Using the Aggregated Variance (V/S) method, the presence of long memory is confirmed through the estimated differencing parameter d, which falls between 0 and 0.5 based on the Geweke and Porter-Hudak (GPH) estimator. The Augmented Dickey-Fuller (ADF) test was employed to assess the stationarity of the time series. Optimal ARFIMA models were selected based on the Akaike Information Criterion (AIC), and model performance was evaluated using the Root Mean Square Error (RMSE). The selected ARFIMA models demonstrate the lowest RMSE values, confirming their efficacy in forecasting ozone concentrations. The findings suggest the effectiveness of the ARFIMA model in forecasting ozone concentrations, which can significantly aid in air pollution assessment and management strategies. It contributes to understanding ozone pollution in Peninsular Malaysia, facilitating informed decision-making for environmental policies. Future research should explore additional time series models to strengthen the understanding of ozone dynamics and enhance forecasting capabilities in the context of long memory properties.

Keywords: Air pollution; long memory; forecasting

This study focuses on analyzing shoreline changes along the Pangandaran coastline, located in West Java Province, Indonesia, by utilizing the Digital Shoreline Analysis System (DSAS), a specialized extension developed for ArcGIS. The primary objective of this research is to investigate the patterns and rates of shoreline movement, specifically erosion and accretion over a ten-year period, from 2014 to 2024. By examining temporal and spatial shoreline data, the study seeks to assess how the coastline has evolved during this period and to project its potential future changes over the next 10 and 20 years. The DSAS tool was employed to calculate statistical rates of change, including Linear Regression Rate (LRR), End Point Rate (EPR), and Net Shoreline Movement (NSM), allowing for precise quantification of shoreline displacement across various segments. The analysis revealed considerable variability in shoreline dynamics across the Pangandaran coast. Several segments of the coastline experienced substantial erosion, with an average shoreline retreat of approximately 36.99 meters, corresponding to an erosion rate of about 3.7 meters per year. Conversely, other areas demonstrated shoreline advancement due to accretion, with an average increase of 34.39 meters and a corresponding accretion rate of 3.4 meters per year. This variation reflects the complex interplay between natural coastal processes, such as wave action and sediment transport, and anthropogenic influences like tourism development, infrastructure construction, and land use changes. These findings underscore the importance of developing adaptive and site-specific coastal management strategies. The predictive capability of DSAS proves highly beneficial in supporting proactive decision-making, risk reduction planning, and sustainable coastal development. Ultimately, this study contributes valuable data and insights that can guide local authorities, planners, and policymakers in managing and protecting the Pangandaran coastline amidst ongoing environmental and climatic changes.

Keywords: Abrasion; End Point Rate (EPR); erosion; Least Regression Rate (LRR); Net Shoreline Movement (NSM).

This research presents the development of an optimized octocopter that emphasizes structural efficiency, reduced weight, and enhanced sustainability. The primary objective was to redesign the drone’s structure using additive manufacturing and topology optimization to minimize material usage while maintaining structural integrity. Wood-PLA filament, a composite of polylactic acid and wood fibres known for its environmental benefits and weight reduction was employed throughout the study. Extensive material testing, flight trials, and a comprehensive analysis of the original design were conducted. Topology optimization techniques led to a 29.9% reduction in weight, and the use of Wood-PLA enabled a 31.25% increase in payload capacity, resulting in an overall weight reduction of 32.7%. Despite challenges associated with the hygroscopic nature of Wood-PLA, these findings demonstrate that advanced manufacturing techniques combined with innovative materials can significantly improve the performance and sustainability of unmanned aerial vehicles (UAVs). This initiative sets a new benchmark for sustainable drone technology in the rapidly evolving UAV sector.

Keywords: Octocopter; topology optimisation; additive manufacturing; wood-PLA filament; sustainability

The rising prevalence of mental health challenges among engineering students highlights the necessity for improved methods in recognising and managing stress. Conventional self-evaluation techniques seem to be subject to interpretation when it comes to assessing stress levels. This research examines the identification of electroencephalography (EEG) patterns that could serve as markers for mental stress, along with the classification of stress and relaxation mental states using machine learning methods. Earlier studies applied machine learning methods, particularly clustering algorithms, to analyse EEG data; nonetheless, a more targeted approach is required for improved accuracy in stress detection. This study employs EEG data that has undergone processing through feature extraction methods to develop a machine learning model aimed at distinguishing between states of stress and relaxation in the brain. EEG signals were collected using OpenBCI software, and feature extraction was conducted employing Fast Fourier Transform (FFT). Subsequently, four distinct machine learning algorithms were employed to categorise the brainwave signals: K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Wide Neural Network (WNN), and Naïve Bayes (NB). The WNN achieved an impressive accuracy rate of 90%, with a Receiver Operating Characteristic (ROC) area analysis revealing a value of 0.88, which suggests favourable outcomes in distinguishing between stress and relaxation states. The findings contribute to the domain of mental health research by offering a dependable method for distinguishing and categorising stressed versus relaxed students, possibly improving future evaluation instruments for mental well-being in engineering students.

Keywords: Electroencephalography (EEG); OpenBCI; feature extraction; Fast Fourier Transform (FFT); KNearest Neighbor (KNN); Support Vector Machine (SVM); Wide Neural Network (WNN); Naïve Bayes (NB); mental stress; relaxation

Conventional EMG sensor designs, typically fabricated using Ag/AgCl materials, often suffer from drawbacks, such as discomfort, limited reusability, and skin irritation. In contrast, textile-based electrodes are viable alternatives, offering enhanced comfort and durability. This paper presents an experimental evaluation and performance analysis of an 8-channels nickel fabric sEMG sensor with three different dimensions. This study aims to assess how different materials besides conventional silver electrodes, such as nickel fabric, and variations in the sensor’s dimensions affect the EMG signal quality. Nickel was chosen because of its flexibility, durability, and superior conductivity. Sensors of varying dimensions (1 cm × 1 cm, 2 cm × 1 cm, and 2 cm × 2 cm) were fabricated and tested on twenty-five participants. The nickel fabric sEMG sensor was used to measure the muscle activity of an arm while performing certain movements. The sensors’ signal amplitude and RMS values, compared to conventional Ag/AgCl electrodes, were the primary metrics used to evaluate their performance. The results demonstrated that the sensor dimensions affected the quality of the recorded EMG signals. All sensor dimensions showed very little variation compared to conventional electrodes, with dimensions of 1 cm × 1 cm was closest to the Ag/AgCl electrode readings with average of 15% difference. Statistical analysis showed 1 cm × 2 cm sensors consistently gives higher peak-to-peak and root mean square values compared to conventional wet electrodes.

Keywords: Surface EMG; muscles; nickel fabric electrodes; data acquisition; signal processing

The use of agricultural by-product fibers as reinforcements in natural-based composites presents a promising approach to addressing the limited availability, high cost, and inconsistent quality of traditional natural fibers such as jute, flax, and hemp. This study evaluates the mechanical properties of Nata de Coco-derived bacterial cellulose reinforced with an epoxy polymer at different loading fractions. The bacterial cellulose was extracted through a fermentation process using Acetobactr xylinum, followed by chemical treatment and incorporation into an epoxy matrix at weight fractions of 1%, 2%, and 3%. The composite samples were subjected to tensile, impact, and hardness testing to assess the effects of bacterial cellulose reinforcement. Experimental results revealed that the inclusion of bacterial cellulose significantly influenced the mechanical performance of the epoxy composites. The highest tensile strength was observed in the 1wt% bacterial cellulose /epoxy composite, surpassing virgin epoxy by 22.73%. However, as the bacterial cellulose content increased beyond 1wt%, tensile strength declined due to poor dispersion and void formation, which weakened the material structure. Young’s modulus was highest for the 2wt% bacterial cellulose /epoxy sample, demonstrating a 77.85% increase compared to virgin epoxy, indicating improved stiffness. Conversely, an increase in BC content led to reduced hardness and impact resistance, making the composite more brittle because the bacterial cellulose particles, likely in the 70-100 nm range, were not fully dispersed in the matrix.

Keywords: Nata de coco; bacterial cellulose; acetobacter xylinum; tensile

This study evaluates the feasibility of a hybrid renewable energy system (HRES) for rural Malaysian regions, specifically Pulau Carey and Pulau Ketam. This research employs a three-pronged approach: Multi-Criteria Decision Making (MCDM): This method selects the most suitable HRES configuration for the target islands. Criteria Analysis: Economic, environmental, energy security and technical aspects are considered. PSO (Particle Swarm Optimization) Validation: This technique validates the accuracy of HRES sizing simulations. The MCDM analysis identifies wind and solar energy as the most suitable choices, with scores of 0.8138 and 0.7877, respectively.The study identifies Configuration 5, which includes solar PV, wind turbines, a diesel generator, and battery storage, as the most effective HRES. It balances cost-efficiency, energy security, and renewable integration, achieving the lowest Levelized Cost of Energy (LCOE) and Net Present Cost (NPC) with a 99.99% renewable energy fraction. MATLAB-based PSO optimization closely matches results from Homer Pro software, showing minimal errors. This configuration is particularly beneficial for remote communities like Kampung Orang Asli Sungai Bumbun, offering significant cost savings over current energy solutions.

Keywords: MCDM; TOPSIS; Particle swarm optimization; hybrid renewable energy system

Copper Aluminium Manganese (CuAlMn) alloy exhibit the unique ability of shape memory alloy (SMA) which can recover their original shape when exposed to external stimulus typically thermal heat or mechanical stress making them highly desirable for advanced applications. Shape memory alloys based on copper (Cu) exhibit the highest potential for practical applications owing to their robust recovery force, affordability, and suitability for large-scale production. The increasing demand for micro parts in the global market has driven the development of SMA using powder injection moulding (PIM) which is a prominent manufacturing technique used to create intricate parts. This study investigates the PIM process for Cu-Al-Mn alloys using a binder system of polyethylene glycol (Peg), polymethyl methacrylate (Pmma), and stearic acid (Sa). A key focus is on optimizing feedstock properties to ensure uniform powder and binder mixing, minimizing segregation during injection moulding. The ideal powder loading was determined to be 59%, resulting in defect-free moulded parts under optimized parameters: injection temperature of 125°C, pressure of 1.2 MPa, and injection time of 8 seconds. Then the green parts were proceeded with debinding process and sintering conducted under argon atmosphere at 950°C, 1000°C, and 1050°C for three hours. Morphological analyses using SEM revealed that sintering at 950°C and 1000°C did not produce adequate densification, potentially leading to inferior mechanical properties. This study underscores the importance of achieving optimal sintering conditions to enhance microstructure and mechanical performance, providing valuable insights for scalable production of Cu-Al-Mn SMAs through PIM method.

Keywords: Copper; aluminium; manganese; powder injection moulding; binder system; sintering

Discovering substitutes for the natural resources is crucial for achieving a more sustainable future and is vital for extensive construction and infrastructure endeavours. One of these substitutions is utilizing coffee waste as a soil stabilizer. An industrial or agricultural setting’s leftover byproduct of processing coffee beans is called coffee husk ash. This study’s main goal is to evaluate the geotechnical effectiveness of clay soil treated with coffee husk ash in order to provide a more affordable and environmentally friendly composition. The ideal conditions and various ratios of coffee husk ash were tested in the lab to see how clay soil’s mechanical and physical characteristics were affected. The concentration of coffee husk ash was modified in increments of 5% by the dry weight of the soil, ranging from 5% to 25%. Overall, the inclusion of coffee husk ash led to a significant boost in the physical and mechanical characteristics. The inclusion of the coffee husk ash results in a coarsening of the soil classification of the combination. The ultimate compressive strength (UCS), which was 89.17 kN/m2 in the untreated soil, increased significantly to 130.83 kN/m2 after a 25% increase in coffee husk ash was achieved. Furthermore, the cohesion and internal friction angles of the soil increased from 80.1 kN/m2 to 148.7 kN/m2 and from 16.1o to 25.8o, respectively, as the coffee husk ash content increased from 0% to 25%.

Keywords: Clay; coffee husk ash; environment; stabilization; sustainability

Hazards in the field of engineering need to be emphasized because this field has various risks in terms of safety and health, especially those involving practical tasks in workshops and engineering laboratories. Users, particularly engineering students, can be exposed to various types of chemicals, ergonomic, electrical and mechanical hazards that require strict regulations regarding laboratory and workshop safety. The aims of this study are to determine the level of safety and health hazards based on the Guidelines for Hazard Identification, Risk Assessment and Risk Control (HIRARC) and Simple Risk Assessment and Control (SiRAC) in laboratories and workshops and suggest improvement strategies and safety measures against safety and health risks in Department of Electrical Engineering (JKE) laboratories and workshops. The questionnaire forms, HIRARC and SiRARC are used for the purpose of collecting KAP data and determining the level of risk. The respondents consisted of 154 students who followed the Diploma in Electrical Engineering program from the Department of Electrical Engineering who used laboratories and workshops. The data obtained through a quantitative questionnaire method including demographic aspects and KAP will be analyzed using the Statistical Package for Social Science Software edition 28 (IBM SPSS 28). The results of the study show that the mean for level of knowledge, attitude and practice (KAP) is above 4.0. The correlation results, KAP has a significant linear relationship for all variables on risk management. This study can help the institution to improve the level of safety and health among students particularly in the Technical and Vocational Educational Training Program (TVET) in addition to assessing and taking the necessary follow-up actions to ensure the comfort and excellence of students in the future.

Keywords: Ergonomics; engineering practical; laboratory; workshop; performance; student

Hydrogel is a material rich in water and can help maintain moisture in the wound area, so it can speed up the wound healing process. In this study, sodium alginate-based hydrogels were synthesized using natural plants, namely aloe vera and garlic extracts, for wound healing applications where this material can be linked to the Sustainable Development Goals, SDG 12, which is responsible consumption and production. This study aims to investigate the physicochemical properties of different types of hydrogels and the efficiency of hydrogels in terms of swelling ratio and antibacterial properties. This study distinguishes four types of hydrogels with ratios of alginate to aloe vera and garlic at a ratio of 95:5 and 90:10 respectively. The samples were characterized by 95% alginate + 5% aloe vera (AGA5), 95% alginate + 5% garlic (AGG5), 90% alginate + 10% aloe vera (AGA10), and 90% alginate + 10% garlic (AGG10).The functional groups in this sample will be confirmed through a fourier transform infrared spectroscopy (FTIR). A hydrogel swelling test will be conducted to compare the water uptake capacity. In addition, the disc absorption test will be carried out on gram negative bacteria, namely Escherichia coli, to study the potential of the material as an antimicrobial agent. Based on the results of the study, the concentration of aloe vera and garlic affects the rate of water absorption in the hydrogel. The AGA5 hydrogel has a higher swelling percentage and better stability compared to the other three hydrogel samples: AGA10, AGG5, and AGG10. For the antimicrobial test, no zone of inhibition was observed for all four hydrogels. In conclusion, the production of this hydrogel synthesis has the potential to provide benefits in the medical industry, especially in treating wounds.

Keywords: Alginate; hydrogel; aloe vera; garlic; antibacterial activity

Evaporative cooling refers to a passive cooling method that utilizes the principle of water evaporation to cool the surrounding environment. This is a crucial concept in naturally cooled buildings, where water pools are used to maintain thermal comfort. This study explores and discusses the concept of using water pools as an evaporative cooling element within the context of sustainable architecture and environmental design. The issue addressed in this study is the continuous use of HVAC systems in urban areas, which contributes to heat accumulation, as hot air from air conditioning absorbs heat, increasing local temperatures and exacerbating urban warming. The high temperatures in Malaysia, which has a tropical climate, result in a significant reliance on air conditioning for building cooling. The energy consumption of HVAC systems during peak demand presents a challenge in achieving sustainability by balancing comfort needs and energy efficiency. The objective of this study is to identify the impact of using water pools as an evaporative cooling element in buildings. This research was conducted at Pusat Kreatif Kanak-Kanak Tuanku Bainun through field studies. Environmental parameters, including air temperature, relative humidity, and air velocity, were recorded using a data logger. The findings indicate that water pools significantly stabilize indoor temperatures and moderate humidity levels, contributing to a comfortable 1814 indoor environment. The data also highlight the importance of strategic placement and integration of water pools with natural ventilation systems to maximize cooling efficiency. This study contributes to the advancement of knowledge on sustainable building practices, promoting the integration of passive cooling strategies in the design of future educational facilities.

Keywords: Evaporative cooling; thermal comfort; water pool; passive design

This study aimed to investigate the effect of different electron beam irradiation doses on the degree of PVC crosslinking. The PVC nanocomposites were created by the melt blending of Trimethylolpropane triacrylate (TMPTA) and Aluminium Trihydrate (ATH) and then treated with electron beam irradiation doses of 0, 20, 40, and 60 kGy to achieve crosslinking. To measure the degree of crosslinking, a gel content test was carried out with 0.20 g taken from each of the samples. The mechanical properties of PVC, such as its tensile strength, elongation at break, and Young’s modulus, were then investigated using a Universal testing machine. Next, a measurement of the PVC breakdown voltage was performed using a breakdown voltage tester with the ASTM standard D1816. Based on the experiment carried out, the results of the gel content test showed that PVC-FR3 with 5 phr TMPTA, 5 phr ATH, and a 20 kGy irradiation dose provided the optimal conditions for desirable crosslinking. This result achieved the JASO D11 standard, which requires more than 40% degree of crosslinking. The ultimate tensile strength was higher at 60 kGy, reaching 31 MPa. Therefore, this study helps determine the suitable irradiation dose to enhance crosslinking and increase tensile strength as well as the breakdown voltage of irradiated PVC.

Keywords: PVC; electron beam radiation; insulation materials; breakdown

The assessment of aggregate quality depends on manual grading together with mechanical filtering through traditional methods. Aggregates need to pass through multiple mechanical and physical as well as chemical tests to verify their compliance with established standards. The evaluation procedures performed by hand prove to be inherently inefficient and subjective and take up too much time. A project seeks to create an image processing system which will classify aggregates into various categories. The classification system employs an artificial neural network (ANN) to analyse images for determining aggregate shapes. The study compares the performance of different training algorithms for the ANN. The study compares the performance of Levenberg Marquardt (LM) against Bayesian Regularization (BR) as training algorithms. The results show that BR training outperforms other methods since it provides better mean square error (MSE) values and enhanced regression outcomes. The combination between BR training method and MLP network delivers optimal performance levels regarding regression accuracy and MSE measurement. Through BR training the network obtained an MSE of 1.2042 and a regression of 0.9892 which confirms its successful ability to classify aggregates through image analysis. Through this alternative method researchers gain an efficient and objective solution to replace traditional manual classification approaches.

Keywords: Aggregate classification; MLP network; training algorithm; MSE; Regression

The white-collar vocational teaching is a profession at high risk for stress. This research attempted to predict and determine the effect and consequence associated with the stress among technical vocational teachers in Malaysia. A cross-sectional random sample was taken on seven (7) vocational college in Perak, which involve approximately 490 technical teaching staff. The questionnaire method by Depression, Anxiety and Stress Scale (DASS-42) and Job Content Questionnaire (JCQ) instrument used as a data to indicate stress level among teachers. These data consist of psychosocial factors contributing to stress, a simple and multiple linear regression analysis were carried out. The prediction element of Artificial Intelligence (AI) implements Support Vector Machine (SVM) method, ignites a few groups of stress teacher start to form among the others. As in the 2023 to 2025, expected a clear segregation between normal and stress teacher within the boundary. While in 2026-2029, expected a huge migration trends of normal to critical teacher dominate the chart. However, the AI system are still depending on several controlled variable, and the result are still expected to be, means there must be a room to improvise the situation of teacher mental health and etc.

Keywords: Stress analysis; artificial intelligence; machine learning

This paper presents the potential use of palm oil (PO) as a transformer insulator medium by exploring its dielectric properties. The study includes evaluating the ac breakdown, tan-δ test, and Raman measurement to assess the impact of doping various concentrations (0.01%, 0.1%, 0.2%, 0.4%, and 0.5%) of silicon (Si) and the influence of a magnetic field (0.01T) on PO’s electrical properties. The results indicate that the dielectric properties of PO can be enhanced to make it suitable for transformer oil applications with proper treatment such as filter and filtered dried process and proper tune the concentration of Si. This matter was proven when the doping of 0.1% Si particle with PO has the highest voltage breakdown value 45.73 kV/s compared to the mixture with other Si concentrations and pure PO. Furthermore, it has been observed that the voltage breakdown of all samples is higher when a magnetic field is present, in contrast to the samples without any magnetic field applied. For example, the doping of 0.1% Si particle with voltage breakdown value 45.73 kV/s increased to 50.92 kV/s when 0.01T magnetic field applied. It’s worth noting that the specific relationship between the magnetic field and voltage breakdown may vary depending on the material properties and experimental setup.

Keywords: Breakdown voltage; transformer; palm oil; semiconductor particles and insulation

Mechanical filtration and manual sorting have long been the standard methods for evaluating aggregate quality. While producing high-quality aggregates necessitates a variety of mechanical, chemical, and physical assessments, these tests are often conducted manually, leading to inefficiencies, subjectivity, and significant labour-demands. This research aims to develop an innovative image-based classification system to categorize aggregates more effectively. An artificial neural network (ANN) has been employed for the classification of the images captured in this process. In contrast to the Purelin activation function, the Logsig activation function shows improved performance, indicated by a decrease in mean square error (MSE) and better regression outcomes. Notably, the BR training algorithm utilizing a multilayer perceptron (MLP) network, aimed at reducing the MSE, provides the most effective regression results and the lowest MSE. The MSE achieved by the network trained with BR was 1.4235, accompanied by a regression coefficient of 0.9760. These findings suggest that implementing advanced computational techniques can significantly enhance the quality control processes in aggregate production, thereby promising improvements in efficiency and material performance standards.

Keywords: Aggregate classification; MLP network; Training algorithm; MSE; Regression

Frequent motorcycle crashes have prompted the Malaysian government to construct Exclusive Motorcycle Lanes (EMLs) to separate motorcyclists from mixed traffic. However, despite the availability of EMLs, many motorcyclists still choose to ride on Federal Highways, increasing crash risks and violating existing traffic regulations. Limited research has explored the underlying reasons behind this preference, representing a critical gap in current road safety literature. This study investigates the factors influencing motorcyclists’ preferences for using EMLs along Federal Highways in Shah Alam. A questionnaire survey was conducted on 310 motorcyclists, employing descriptive analysis, the Relative Importance Index, and binary logistic regression. The results revealed that 74.8% of respondents preferred using EMLs and identified safety as a primary benefit, particularly along the lanes and at entry and exit points. However, safety concerns were noted during rainy and night-time conditions. Motorcyclists acknowledge that EMLs reduce interactions with other vehicles, but poor lighting remains a significant issue, with respondents prioritizing the need for improved street lighting. Modelling results indicate that male, older, and frequent riders are more likely to choose EMLs. The main contribution of this study lies in identifying actionable factors to enhance EML usage, providing empirical evidence to guide infrastructure improvements and policy interventions aimed at improving motorcyclist safety.

Keywords: Highways; exclusive motorcycle lanes; motorcycle; motorcyclist behaviours; road safety

This study investigates the porosity and mineral composition of tropical weathered limestone using a comprehensive approach that integrates thin section and petrography analysis. This weathering process predominantly involves chemical dissolution by acidic rainwater, which gradually alters the mineral composition and physical structure of limestone. There are few testings that has been conducted to achieve the objective of this study such as Rebound Hammer Test, Uniaxial Compressive Strength (UCS), Xray Fluorescent (XRF), Mercury Intrusion Porosimetry (MIP) and thin section and petrographic analysis. The objective of this study is to determine the engineering properties of weathered limestone and chemical composition of the weathered limestone. This study also examines how porosity and mineral composition affect weathered limestone engineering qualities. Rock specimen strength and weathering grade were measured using the rebound hammer test to corroborate this analysis. The indings highlight the intricate interrelationship between the mechanical behaviour of the limestone, its degree of weathering, and underlying geological characteristics. These insights are essential for understanding the long-term performance and durability of weathered limestone in engineering applications, particularly in regions where tropical climates accelerate rock degradation.

Keywords: Weathered limestone; thin section; porosity; mineral composition

Exposure to fire could affect the structural behavior of the concrete, including Self-Compacting Concrete (SCC). Inclusion of polypropylene fibres (PPF) in SCC as fire protection may reduce the effect of thermal stress, however, there is still lack of information. Thus, the main purpose of this study is to conduct heat transfer analysis of SCC beam subjected to ISO834 standard fire curve for two hours with the fire protection method by Finite Element Method. Heat analysis was conducted using ABAQUS software to determine the temperature-time graph at various location in the cross section of beam (at midspan) and analyse the heat transfer pattern. Four samples developed from the previous experimental work with different proportions of PPF of 0%, 0.05%, 0.10% and 0.15% have been analysed in this study. Findings shows that temperature-time graph analysis across various position at cross section demonstrated that higher fibre content consistently resulted in lower peak temperatures, indicating enhanced fire-resistant properties. The study of temperature contours at the midspan confirmed uniform heat distribution along the beam. These findings support the conclusion that incorporating PPF enhances the fire resistance of concrete beams. This study is necessary since there is limited analysis on the SCC with fire protection measures. The study of heat transfer analysis in this research has significant implications for fire resistance analysis. By understanding how heat propagates through concrete beams with varying proportions of PPF, the thermal behaviour and structural integrity of these materials under fire conditions can be assessed.

Keywords: Self compacting concrete; heat transfer analysis; meshing sensitivity; polypropylene fibre; fire protection

Timber has been used for centuries as a construction material and is currently valued for its sustainability. In recent years, demand for high-strength timber species has grown, but they tend to be expensive and limited in supply. This situation encourages the use of lower-strength timber, especially in regions like Malaysia where such species are more accessible. However, before they can be reliably used, their performance—particularly under high-temperature conditions—needs to be better understood. This study focuses on the flexural strength of two Malaysian hardwoods, Keranji (SG3) and Keruing (SG5), by using Dynamic Mechanical Thermal Analysis (DMTA). Tests were conducted at 120°C and 160°C. At 120°C, both timbers maintained most of their original strength. However, at 160°C, Keranji showed a sharp decrease in strength, while Keruing became stronger. These findings suggest that Keruing may be more suitable for applications involving heat exposure, such as roof structures. The study offers new insights into how native Malaysian hardwoods behave in high-temperature environments, which is important for structural design and fire performance assessments.

Keywords: Malaysian timbers; hardwood timbers; flexural strength; strength group; DMTA

This study optimizes zinc ion removal from solutions using cation exchange resin, focusing on resin concentration, zinc solution concentration, and stirring speed as key variables. Utilizing Response Surface Methodology (RSM), optimal conditions for these variables were identified. Analysis of Variance (ANOVA) and mathematical modeling were employed to evaluate the effects of varying input conditions. The research also examined the impact of process variables on the pH of treated samples. A quadratic model, developed using the Central Composite Design method, correlated the independent variables. The results indicate that a cation exchange resin concentration of 25.17 g/L, a zinc solution concentration of 7 mg/L, and a stirring speed of 231 rpm achieve the lowest zinc concentration of 2.65 mg/L. The study highlights cation exchange resin concentration and stirring speed as the most effective parameters for zinc reduction, as indicated by higher F-statistics. The model demonstrates high accuracy, with a percentage difference of only 2.3% between predicted and actual values in the validation experiment. These findings underscore the potential of reversible ion exchange capabilities of the resin for sustainable and effective water treatment. This research advances wastewater treatment methodologies, providing crucial data for future studies aimed at reducing heavy metal contaminants in water.

Keywords: Zinc removal; cation exchange resin; stirring speed; RSM; optimum design

This study investigates the spatial distribution of rainfall in the Kelantan River Basin, a region prone to significant flooding and landslides during the monsoon season. The problem addressed is the recurrent and severe flooding in the basin, which poses substantial risks to the local population and infrastructure. The purpose of this study is to analyse the spatial patterns of rainfall to predict flood mitigation strategies and support sustainable development. This study employed four spatial interpolation methods, namely the Inverse Distance Weighting, Ordinary Kriging, Thiessen Polygon, and Universal Kriging, to estimate rainfall distribution using data from 10 rainfall stations over 30 years. The rainfall characteristics of this study showed that the Kelantan River Basin receives higher rainfall distribution at the end of the year due to the Northeast Monsoon (NEM) season. Spatial analysis results indicated that north region areas such as Pusat Ternakan Haiwan Tanah Merah and Mardi Jeram Pasu receive higher annual mean rainfall ranging from 2,996 mm to 3,519 mm, identifying these as critical zones for flood risk. This result is related to the location of the north regions of the Kelantan River Basin that the Tahan Ranges did not cover, making them receive direct wind during the monsoon season. Among the flood mitigation methods assessed, enhancing early warning systems, implementing sustainable land-use planning, and constructing resilient infrastructure were identified as Kelantan’s most effective strategies for mitigating flood impacts. The significance of this study lies in its ability to provide detailed spatial insights into rainfall patterns, which are crucial for water resource management, flood risk assessment, and infrastructure development.

Keywords: Spatial analysis; rainfall; Kelantan river basin; flood; interpolation

The increasing demand for ingredients like soybeans and corn for chicken feed can lead to higher global prices. Malaysia possesses abundance of agricultural biomass waste namely peanut shells, palm kernel cake, banana peels, and coconut husks, which remains largely untapped. This study aims to produce chicken feed from four types of biomass waste: coconut husks, soybean waste, banana peels, and palm kernel expeller, using four different waste ratios. Nutrient analysis were conducted on the produced chicken feed to determine protein, fiber, ash, moisture, fat, carbohydrate, and energy content. Subsequently, the four mixtures was compared to determine the optimal composition. Analysis shows notable differences from theoretical values; mixture A had the lowest ash (4.5g), D had the highest ash and moisture (5.4g and 9.3g), B had the lowest protein (12.5g), C had the highest protein (17.4g), thus the most balanced option for chicken feed from biomass wastes. FTIR analysis was performed to identify and characterize unknown substances in the produced chicken feed. The production of chicken feed on an industrial scale (production rate of 70 tons per day), was also studied. Economic analysis in this study shows, the estimated price for produced chicken feed was RM 435 per ton, with an IRR percentage of 28.26%, and a payback period for the factory of 3.97 years. The determined price of chicken feed was lower compared to the market price (RM 2100 to RM 4000 per ton). Therefore, biomass waste could be utilized as an alternative ingredient in chicken feed production.p>

Keywords: Livestock; biomass residue; by-products; mixing; chicken feed

Clean water is becoming an urgent need due to increased pollution and exposure to the environment. The requirement for chemical coagulants to treat water rises in proportion to the level of impurities, leading to many setbacks that negatively impact day-to-day consumer use. Despite the fact that natural-based coagulants offer an environmentally friendly alternative, they do not perform better than conventional chemical coagulants. To surpass this limitation, the combination of chemical and natural coagulants has been proposed as a viable option. In this study, the effectiveness of Aluminium Sulphate (Alum), Cassava Peel Starch (CPS), and a dual coagulant system (Alum:CPS) in improving turbidity removal is explored. The objectives are to (i) compare the turbidity removal efficiency of chemical and dual coagulants, (ii) determine the optimum coagulant dosage through the Jar Test, and (iii) conduct the cost-benefit analysis of the coagulant systems. Sungai Penchala water samples were analyzed based on Malaysia’s National Water Quality Standards (NWQS) in considering selected parameters for the coagulants effectiveness evaluation. The Jar Test was employed to compare the turbidity removal efficiency of Alum, CPS, and Alum:CPS. Results indicated that the optimal doses of Alum, CPS, and Alum:CPS were 25 mg/L, 100 mg/L, and 80%:20% (20 mg/L:20 mg/L), respectively. The highest turbidity removal efficiencies achieved were 80.22% for Alum, 54.25% for CPS, and 91.25% for the dual coagulant system. The Alum:CPS combination exhibited improved turbidity reduction compared to individual coagulants. Furthermore, in the water treatment plant being studied, Alum:CPS exhibited both higher cost and maximum effectiveness, leading to 1.2 times greater cost-benefit ratio than Alum alone. These findings suggest that dual coagulant systems represent an effective approach to enhancing water treatment efficiency without compromising economic viability.

Keywords: Chemical soagulant; Aluminium Sulphate (Alum); Cassava Peel Starch (CPS)

Green plants are known to improve indoor air quality by filtering harmful pollutants. This study evaluates the effectiveness of green plants in enhancing indoor environments, particularly in educational settings. The methodology includes air quality monitoring and student feedback through questionnaires. Two spaces were observed over four weeks: Space A (with plants) and Space B (without plants). Six parameters were measured: PM2.5, PM10, CO₂, Total Volatile Organic Compounds (TVOCs), temperature, and humidity. Most air quality indicators were within the Occupational Safety and Health Administration (OSHA) standards, except for temperature and humidity. The temperature in both spaces reached 29°C, exceeding the OSHA limit of 23–26°C. Humidity rose to 75%, surpassing the 40–70% recommended range. Although statistical analysis using an independent t-test in SPSS showed no significant difference between the two spaces, consistent trends in Space A suggested slight improvements in TVOCs (reduced by 4.2%), CO₂ (reduced by 3.7%), and particulate matter levels (PM2.5 reduced by 2.8%). Temperature and humidity readings in Space A were also marginally more stable. Furthermore, student questionnaires revealed that 66% of respondents in Space A reported satisfaction or high satisfaction with indoor air quality, compared to only 21.4% in Space B, reflecting a 44.6% difference in perceived comfort and air quality. These results suggest that while a single plant may not significantly alter measurable air quality, the presence of greenery can contribute to environmental stability and comfort. This study highlights the subtle but positive impact of indoor plants, supporting their use in biophilic design strategies and emphasizing their potential as a sustainable solution for healthier indoor spaces.

Keywords: Indoor air quality; air pollution; green plants; independent sample t-test; SPSS analysis

To ensure the continued advancement of the civil engineering industry, it is imperative to cultivate a highly skilled and competent workforce capable of addressing technological innovations and competing in a globalized environment. However, disparities in individual capabilities may influence economic, environmental, and social progress both positively and negatively. Capacity development—also referred to as capacity building or human capability development—serves as a transformative strategy aimed at enhancing individuals’ competencies within defined skill domains. This study explores the concept of capacity development among civil engineers within Malaysia’s construction industry. Three primary objectives guide the research: (1) to identify the key attributes of capacity development relevant to the construction sector, (2) to establish an evaluative framework for these attributes, and (3) to design an assessment tool to strengthen engineers’ capacity development. Employing a qualitative methodology, the study integrates systematic literature review, a pilot study, and semi-structured interviews with five and ten experts, respectively. Data were analysed using content analysis and Kendall’s coefficient of concordance. The findings reveal 25 critical attributes associated with capacity development, leading to the construction of an analytical rubric designed to assess these capacities. Expert feedback further refined the rubric to enhance its applicability and accuracy. Nonetheless, the study acknowledges certain limitations, particularly the restricted sample size and the manual computation of Kendall’s coefficient.

Keywords: capacity development; engineers; construction industry; attributes of capacity development; assessment rubric

Collision avoidance is one of the important safety systems for automated vehicle to avoid potential collision due to frontal collision or rear-end collision. One of the main systems used in automated vehicles are the autonomous emergency braking (AEB) system to halt the vehicle from rear end collision. However, during high-speed driving and low time-to-collision (TTC) conditions, capability of AEB for halting vehicle from motion is affected. This increases the probability for the rear end collisions with other traffic vehicle which leads to road accidents. Therefore, an active safety system namely autonomous emergency steering (AES) control using estimated lateral force feedback (EsLaF) system was developed and implemented in this study along with AEB for collision avoidance application. The proposed estimated lateral force feedback (EsLaF) system is designed based on the imaginary lateral skyhook damper mounted at both longitudinal position of vehicle. The fictitious lateral skyhook generates the lateral forces for the vehicle to perform lane changing profile once the TTC is low and the vehicle is at high-speed driving condition. The automated vehicle equipped with AES with EsLaF is tested at maximum speed of 60 km/h. The ego-vehicle results such as yaw rate, lateral acceleration and lateral displacement is analysed in this study. The performance of AES with EsLAF is evaluated where the proposed system is only activated during high speed conditions and low TTC with minimum lateral distance between CoG of traffic object and ego-vehicle is less than 2 meter and during high TTC, the ego-vehicle used AEB to halt the vehicle without steering intervention with longitudinal distance of less than 2 meter. Thus, it can be noted that the AES with EsLaF is able to overtake the traffic objects in longitudinal direction using steering input at critical driving conditions and maintain the usage of AEB for less critical driving condition to avoid instability condition for the ego-vehicle.

Keywords: Autonomous emergency braking; time-to-collision; lateral skyhook damper; collision avoidance; IPG carmaker

Automotive air conditioning systems play a vital role in ensuring comfort and safety, particularly in tropical climates where high temperatures and humidity demand reliable cooling. The compressor, as the main component, is essential for circulating refrigerant and maintaining system efficiency. Ensuring the compressor operates is crucial as it compromises the performance and could impact the overall functionality of the air conditioning system. The moving components inside the compressor, such as the bearing and piston, require lubrication with a specific quantity of oil. Therefore, maintaining optimal compressor oil levels is crucial to prevent system inefficiencies and potential mechanical failures. This study introduces a novel diagnostic approach using Z-freq 3D statistical analysis to monitor and predict insufficient or excessive oil levels in belt-driven air conditioning compressors. Triaxial vibration data were collected using wireless accelerometers across varying compressor speeds and oil quantities. The data were analyzed and formed into a novel Z-Chart, a control chart that correlates vibration characteristics with oil adequacy. Results reveal that compressors with 80 ml of oil exhibited the lowest vibration, while both insufficient (40 ml, 60 ml) and excessive (100 ml, 120 ml) oil levels led to higher vibration due to suboptimal performance. The Z-Chart, based on the ‘b/a’ coefficient derived from the Gauss Amp model, provides a new visual and quantitative tool for predictive maintenance, enabling early detection of oil-related issues. This method offers significant implications for the automotive industry by reducing operational costs and enhancing compressor reliability. Future research could integrate this technique with IoT-enabled real-time monitoring systems to further improve maintenance practices.

Keywords: Z-chart analysis; compressor oil diagnosis; vibration monitoring; predictive maintenance; automotive air conditioning compressor

Magnetic gears (MG) demonstrate substantial developments over the years and is seen as a potential direct alternative for conventional mechanical gears due to their contactless torque transmission. Although it offers increased reliability and reduced maintenance, the contactless nature of magnetic gears also makes it prone to pole slipping when the load torque exceeds the transmitted torque. While this prevents potential damage to the MG, it also causes a loss of output speed, which would ultimately reduce the driven rotor to a standstill. In conventional automotive vehicles, gearboxes with multiple transmission ratios are used to vary the transmitted torque and output speed according to operating conditions. Based on a similar concept, a prototype MG with two switchable transmission ratios has been proposed in an earlier study. In the present work, the study focuses on the development of a control system that regulates gear ratio switching to prevent slipping, thus aiming to address an inherent limitation of MGs. This paper presents a simulation study of the MG switching transmission ratio control system. Developed via Simulink and State flow, the system starts with high gear ratio (1:2). After two seconds, the load was increased until it exceeded the transmitted torque and initiated slipping. The system then switches to the lower gear ratio (4:1). It was observed that slipping stopped, and the driven rotor starts rotating steadily again. Simulation results demonstrate that driven rotor speed, which dropped sharply during slipping, regains stabilization at approximately 80rad/s after the gear ratio switch. In contrast, the non-changeable ratio system failed to recover from slipping with rotor speed reducing to zero. The simulation results provide evidence for the effectiveness of dynamic gear ratio switch control in maintaining stable and safe operation and preventing unwanted pole slipping from occurring in MG systems.

Keywords: Magnetic gear; changeable ratio; pole-slip; over-torque; control system; state flow

This paper presents an optimized and explainable stacked LSTM-GRU model for predicting PM2.5 and PM10 concentrations in a formal End-of-Life Vehicle (ELV) recycling zone using multivariate and multioutput dataset. The model was developed using sensor data of weather and pollution parameters collected through an in-house developed device, consisting of a set of highly precise sensors and enhanced the interpretability of the model with Explainable AI (XAI) techniques. The Bayesian optimization technique was employed to accurately fine-tune the hyperparameters of the model for greater accuracy. The results show that the model significantly outperforms baseline models. For PM2.5, the model shows performance improvements of 66.75% for MAE, 81.59% for MSE, and 66.68% for MAPE. For PM10, the model demonstrates performance improvements of 64.45% in MAE, 81.64% in MSE, and 63.65% in MAPE. XAI reveals the dominance of past values and decomposition components, along with meteorological parameters for PM2.5 and PM10 predictions. The results demonstrate the model’s superior accuracy and interpretability, offering valuable insights for air quality management. The integration of Explainable AI further contributes to the model’s practical utility by providing transparent insights into feature importance. This research contributes a novel approach to air quality prediction in industrial settings, offering both improved accuracy and actionable insights.

Keywords: Air Quality Prediction; PM2.5 and PM10 Forecasting; Stacked LSTM-GRU Model; Explainable AI (XAI); End-of-Life Vehicle (ELV) Recycling

As the incidence of house break-ins continues to rise, the demand for effective real-time intruder detection systems has become increasingly critical. Among various biometric systems, face recognition stands out for its contactless nature, high accuracy, and unique capability to identify individuals. However, current face recognition methods often necessitate substantial computational resources, including high-performance GPUs, which can result in significant costs and complex implementations. This study introduces a resource-efficient face recognition system for real-time intruder detection, leveraging a Raspberry Pi edge device and a face recognition module built upon Dlib’s pretrained deep learning model based on the ResNet architecture. Our methodology comprises two main phases: prototype development and system evaluation. The prototype development phase encompasses hardware configuration, software integration, dataset preparation, individual dataset learning, face matching, and the integration of Twilio API and buzzer systems for real-time responses. Upon detecting an unknown face, the system promptly sends WhatsApp alert messages and activates a buzzer. Comprehensive experiments were conducted to evaluate the system’s accuracy, recall, and precision across five distinct known subjects. Furthermore, we assessed the system’s performance in identifying known faces under varying conditions, including different illumination levels, facial angles, expressions, and occlusions. The implemented system achieved an impressive overall accuracy and precision of 96%, along with a recall rate of 100%. It effectively recognized known faces across diverse settings, significantly reducing the rate of false alarms. This study presents a low-cost yet reliable solution for real-time intruder detection, thereby enhancing accessibility for a broader range of users.

Keywords: Real-time; intruder detection; face recognition; deep learning; Dlib