Progress in Engineering Application and Technology
Progress in Engineering Application and Technology (PEAT) is an Open Access Periodical that focuses on publishing high quality article from conferences or research seminars in Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia. PEAT will publish article from all important topics in the field of Engineering Application and Technology. The topic includes but not limited to the recent research trend and its technological advancement in Chemical, Civil, Electrical, Mechanical, and Transportation Engineering Technology.
Formulation Optimisation of Erythrina variegata Wound-Healing Cream
This study focuses on the design and optimisation of the cosmetic cream formulated with bioactive extracts from Erythrina variegata for usage in wound-healing application, which utilised a two-step Design of Experiment (DOE) methodology via Design-Expert. In the first phase (DOE-1) the base cream was refined for maximum stability, identifying a formulation of 10 wt% virgin olive oil, 90 wt % of deionised water, and 2.75 phc of Aquagel35 as the most stable foundation. The second phase (DOE-2) focused on the successful integration of Erythrina variegata extract into the base cream at an optimal concentration of 0.5 phc. An optimised mixing speed of 5000 rpm for a duration of 2 minutes was employed to ensure uniform and stable distribution. Comprehensive physicochemical analyses, including accelerated stability test, viscosity determination, and droplet size measurements, confirmed that the optimised cream maintained sufficient consistency. Numerical optimisation performed via Design-Expert confirmed that the formulations selected for both phases demonstrated the highest structural integrity among the experimental sets. Rather than formulating an entirely new mixture based on the theoretical numerical solution, the specific runs from the DOE matrix that most closely aligned with the optimised parameters and highest desirability scores were identified for assessment. These findings demonstrate that the systematic DOE approach effectively produces a stable, high-quality topical formulation suitable for further medicinal application.
Chemical, Biotechnology, Sustainable Materials and Industrial Safety
Chemical Engineering Technology (Biotechnology)
Copyright (c) 2026 Progress in Engineering Application and Technology
Utilization of Fungal-Derived Chitosan from Flammulina Filiformis (Enoki Mushroom) and Pleurotus Ostreatus (Oyster Mushroom) as a Natural Coagulant in Wastewater Treatment
This research works on the use of chitosan derived from fungi, specifically enoki mushrooms (Flammulina filiformis) and oyster mushrooms (Pleurotus ostreatus), as a natural coagulant in treating wastewater. The purpose of this research is to derive chitosan from enoki and oyster mushrooms, determine its physicochemical property, and test its coagulation efficacy as an alternative to conventional alum. Chitosan is derived through various steps, including deproteinization, treatment with acid, decolorization, and deacetylation. The derived chitosan was analyzed using Fourier Transform Infrared Spectroscopy (FTIR), Zeta potential, rheology test, and yield test. Coagulation efficacy was determined using jar test experiments at differing pH levels, wherein turbidity levels served as indicators. The findings show that chitosan derived from enoki mushrooms had chitin yields of 6.50%, having much higher positive surface charges, with zeta potential measurements above +70 mV, portraying high electrostatic attraction forces. Zeta potential measurements presented much smaller values, merely +50 mV. Rheological tests revealed that all samples had high shear rates, reducing viscosity values ranging from 100 Pa.s to 10-1-10-3 Pa.s when increased. Jar test experiments revealed chitosan derived from fungi having high turbidity removal rates, about 90-96%, almost comparable to those derived using alums. In general, this research concludes that chitosan derived from enoki mushrooms possessed high surface charges, superior coagulation rates, and is thus viable and promising as sustainable, eco-friendly natural coagulants.
Development of a Digital PPE Request and Approval System for Industrial Safety Using Microsoft Power Platform
Personal Protective Equipment (PPE) plays a critical role in safeguarding workers from potential hazards in industrial settings. However, traditional methods of PPE request and approval are often cumbersome, paper-based, and inefficient. This study presents the development of a digital PPE request and approval system for industrial safety, implemented using the Microsoft Power Platform, including Power Apps, Power Automate, and Microsoft Lists. The objective of the system was to replace manual processes with a streamlined digital approach to improve efficiency, traceability, and communication. The system allows employees to submit PPE requests, track their status, and obtain approval without delays. It also provides real-time updates and centralizes data for better record-keeping. Results from a two-week pilot trial at Synthomer Sdn. Bhd. indicated that the digital system significantly reduced approval times, minimized delays in PPE issuance, and enhanced operational transparency. Feedback from users highlighted the system's ease of use, faster response times, and overall positive impact on PPE management, proving that digital transformation can improve safety compliance and operational efficiency.
Bachelor of Technology in Occupational Safety and Health with Honours program. This program is designed to equip students with the necessary knowledge and skills to manage and improve workplace safety and health standards.
1-D Consolidation Characteristics of Stabilised Dredged Marine Soils as Engineered Backfills
The Dredged Marine Soils (DMS) products that are by-products of navigational maintenance are usually considered to be geo-waste products due to their high moisture content, extreme compressibility and low shear strength. The use of such sediments as engineered backfill that could be used sustainably is becoming a more urgent requirement in order to reduce the negative environmental impact by offshore disposal and to deal with the alarming loss of inland sources of sand. Having a good feel of the one dimensional (1-D) behavior of consolidation is important to such materials, because the long-term behaviour in settlement is what directly determines serviceability to structures and adherence to high standards of infrastructure. This paper explores the possibility of regional DMS stabilization basing on bulk samples of four strategic points in Malaysian localities, namely, Kuala Kedah, Kuala Perlis, Lumut, and Endau. After physical characterization, samples were stabilized with 5% Ordinary Portland Cement (OPC) and a novel Polymer Modified Cement (PMC) 4% and then subjected to the oedometer testing, as per BS 1377: Part 5. The results obtained indicate that untreated DMS has extreme compressibility (Cc = 0.37 -0.47) and slow drainage. Although 5% OPC enhanced stiffness, it created a pore-clogging effect at high stress inhibiting consolidation. The 4% PMC treatment in contrast, demonstrated a technical breakthrough in which Cc was reduced up to 99.3% and a rigid skeleton of soil with increased consolidation rates was formed. Using simulations of performance, a 5-meter reclamation layer of PMC-treated DMS would settle within less than 10 days, with a comfortable margin of 100 mm which is the allowable settlement of the Public Works Department (JKR) Malaysia. The findings show that optimized PMC stabilization makes problematic geo-waste a high-performance, time-efficient, and sustainable resource in construction.
Civil, Building, Infrastructure, and Environmental Management
Wearable Knee Support Sensor for Real-Time Knee Flexion Monitoring
Wearable rehabilitation technologies are increasingly used to support continuous and objective monitoring of joint movement during recovery. This project presents the development of a wearable knee support sensor system for real-time knee flexion monitoring in rehabilitation applications. A flexible resistive sensor fabricated using conductive threads composed of polyester, silver, and stainless-steel fibres embedded within a flexible rubber substrate is integrated into a knee support to detect knee bending through resistance variation. An ESP32-S3 microcontroller acquires the sensor data and transmits the measurements wirelessly to a cloud based ThingsBoard dashboard for real-time visualization and analysis. Experimental results demonstrate a strong and consistent correlation between knee flexion angle and resistance values, confirming stable and repeatable sensor performance. Based on predefined resistance thresholds, the system automatically classifies knee condition into normal movement, moderate stiffness, or conditions requiring physiotherapy attention, which are displayed on the dashboard in real time. The experiment indicate that the developed system provides reliable knee flexion measurement, reduces reliance on manual assessment, and offers a practical solution for both clinical and home-based knee rehabiing.
Effects of Manual Extrusion on Density and Texture of Churros-Inspired Kuih Keria: A Comparative Quality Analysis
Traditional Malaysian kuih keria, a sweet potato-based fried doughnut, faces quality inconsistencies due to variable manual shaping. Inspired by the churros extrusion method, this study investigates whether manual extrusion can standardize shape and improve product consistency while preserving authentic sensory qualities. The objectives were to measure density, analyze extrusion's influence on dough properties, and compare these with commercial samples. Using a piston-based manual extruder, dough from a traditional vendor was shaped into cylinders, fried under controlled conditions, and evaluated via oil displacement for density and a structured manual protocol for texture. Results indicated that extruded samples had a higher average density (0.803 g/cm³) than traditional samples (0.757 g/cm³) but were perceived as softer. The extrusion process yielded more uniform shapes, suggesting improved production consistency. The study concludes that manual extrusion can enhance shape regularity and offer economic benefits through reduced waste and simplified labour, presenting a viable, low-tech option for small-scale producers seeking to modernize without compromising cultural authenticity.
Mechanical, Manufacturing, and Process Technology
Implementation of Prototype Solar-Powered Wireless Electric Vehicle Charging Station
The rapid adoption of electric vehicles (EVs) has increased the demand for efficient, safe, and sustainable charging infrastructure. Conventional conductive EV charging systems rely on physical connectors, which are prone to corrosion, safety risks, and high maintenance requirements, particularly in tropical climates. In addition, grid-dependent charging contributes to peak load stress and continued reliance on fossil fuel-based electricity generation. This study presents the development of a prototype solar-powered wireless electric vehicle charging station integrated with an intelligent control and monitoring system. The proposed system combines photovoltaic energy harvesting, resonant inductive wireless power transfer, and an Arduino-based control unit with Internet of Things (IoT) monitoring capabilities. Vehicle detection and authorization are achieved using infrared and RFID sensors, while a servo-assisted alignment mechanism ensures efficient energy transfer between transmitter and receiver coils. Real-time monitoring of voltage, current, temperature, and battery state-of-charge is implemented through a cloud-based interface. Experimental results demonstrate that the system can achieve wireless power transfer efficiency of up to 71.6% under optimal alignment conditions while operating independently from the electrical grid. The integration of solar energy reduces grid dependency and supports sustainable mobility initiatives. Overall, the prototype validates the feasibility of a compact, renewable-powered, wireless EV charging solution suitable for small-scale applications and future expansion toward smart charging infrastructure.
Design and Development of a Solar and Hand-Cranked Dynamo-Based with Battery Storage for Low Power Device Charging
This project addresses the dual challenges of urban sedentary behaviour and the demand for sustainable public energy infrastructure through the design and development of a Hybrid Solar-Kinetic Charging Station at Tanjung Emas Park, Muar. Unlike traditional charging kiosks, this system implements a "Smart Exercise Clause," requiring users to maintain a physical effort of 50 RPM to unlock the 5V USB charging output. The hardware architecture integrates a 10W Solar Photovoltaic panel with a high torque (1.5Nm) NEMA 23 Bipolar Stepper Motor used as a kinetic harvester. A critical technical challenge of converting low-RPM mechanical energy into stable DC power was solved using a Greinacher Voltage Doubler circuit optimized with Schottky diodes. Experimental results demonstrated that the use of Schottky diodes minimized forward voltage loss, achieving a stable output at the target speed, providing sufficient headroom for a 5V regulator The system is governed by an Arduino Nano microcontroller, which utilizes a Hall Effect sensor to monitor rotational speed and calculates real-time calorie expenditure. Data analysis from the prototype confirms that the "Exercise Clause" successfully disrupts sedentary behaviour by gamifying energy production, while the hybrid design ensures power availability in the shaded riverside environment of Tanjung Emas. The findings suggest that this "Play-to-Charge" model provides a viable, off-grid solution for promoting public health and renewable energy awareness within the Malaysian municipal landscape.
BNE - BACHELOR OF ELECTRICAL ENGINEERING TECHNOLOGY (ELECTRICAL POWER) WITH HONOURS
Real-Time Phishing Detection Using Microcontroller and Machine Learning
Phishing attacks pose a significant threat to online security by tricking users into revealing sensitive information through deceptive website. This project proposes a real-time phishing detection system that integrates machine learning with a microcontroller-based platform to provide a fast, low cost and automated solution. The system uses an ESP32 microcontroller to capture URLs in real-time and transmit them to a machine learning model hosted on a cloud-based Flask server. The model analyzes each URL to classify it as either phishing or legitimate based on learned patterns and features. Experiment results demonstrate the system’s effectiveness in detecting phishing websites with high accuracy and responsiveness, using visual alerts via an OLED display and physical warning through LEDs and buzzers. The proposed approach offers a portable and scalable cybersecurity solution especially useful for environments with limited computing resources. Future enhancements may include the integration of deep learning models and mobile interfaces for wider usability.
Electrical Power Generation from Refrigerator Waste Heat Using Thermoelectric Generator for Small Device Charging
This project aims to explore the potential of recovering waste heat from domestic appliances, specifically refrigerator condenser coils, to generate electrical power for low-power applications. Energy harvesting is achieved by utilizing the Seebeck effect through six thermoelectric modules (TEC1-12706) connected in series to maximize the total voltage output. The methodology involves the construction of a thermal sandwich structure consisting of an aluminum heat spreader plate, the Peltier modules and a finned heat sink equipped with an active cooling system (DC fan). A primary challenge in thermal transfer bridging the gap between the curved surface of the condenser coils and the flat surface of the modules was addressed through the application of high-conductivity thermal grease and a robust mechanical clamping system to minimize thermal resistance. The harvested electrical energy is processed via a DC-DC boost converter (XL6009) to stabilize the output at 5V, which is then used to charge 18650 Lithium-ion cells through a USB power bank module. Preliminary results indicate that the system is capable of generating sufficient voltage to charge mobile devices sustainably, relying solely on the temperature gradient between the refrigerator's waste heat and the ambient air. This project demonstrates the feasibility of green energy harvesting concepts in improving energy efficiency within a residential environment.
Development and Optimization of an Urban-Scale Omnidirectional Wind Turbine with Integrated Solar Power System
Renewable energy sources are very important for the preparation for the shift from the fossil fuel. This study introduces the design and optimization of city wide hrbrid renewable energy system between wind turbine and solar system [1]. The design aims at efficiently the turbulent and multidirectional urban wind, and supplementing energy generation with solar power on low wind conditions to ensure stable and continous output. Much emphasis has been given to design optimization, selection of materials, durability, cost-effectiveness, and easy installation in densely packed urban cities. Its compact hybrid design demonstrates strong potential for deployment in smart cities and sustainable urban energy infrastructure.
Automated Classification of Refined, Bleached and Deodorised (RBD) Palm Olein Grades CP6 and CP8 Using Deep Learning and Computer Vision
This study investigates the use of deep learning and computer vision to automate the classification of Refined, Bleached, and Deodorised (RBD) palm olein grades, specifically for the CP6 and CP8 categories. The primary challenge in the current industry is the reliance on manual inspection, which is prone to human error, and chemical analysis, which is both destructive and costly. Therefore, the main objective of this research is to develop a multi-class Convolutional Neural Network (CNN) model to automatically differentiate between these oil grades. The methodology involves developing a CNN model using the TensorFlow framework within a Google Colab environment, with training limited to five epochs to ensure compatibility with industrial edge devices. Furthermore, Fourier-Transform Infrared (FTIR) spectroscopy was utilised as a validation method to identify molecular differences between the two grades. The findings demonstrate that the CNN model achieved promising performance, with precision exceeding 0.70 and recall values above 0.62. FTIR analysis confirmed significant differences in carbonyl (C=O) stretching, which scientifically supports the visual-based classification. In conclusion, the integration of Artificial Intelligence into palm oil grading provides a rapid, non-destructive, and efficient alternative, supporting digital transformation in the palm oil sector in alignment with the goals of Industrial Revolution 4.0.
Geofencing-Based Real-Time Monitoring System for Endangered Animal Tracking and Protection
This project is to develop a real-time animal tracking and monitoring system to enhance the conservation of endangered species. By integrating Internet of Things (IoT) technology with geofencing and data analytics, the project aims to address the limitations of traditional wildlife monitoring methods that often lack real-time data and structured analysis. The system utilizes a Raspberry Pi 4 microcontroller equipped with a UPS HAT module to ensure uninterrupted operation, a GPS module for location tracking, and an MPU6050 accelerometer and gyroscope sensor for motion detection and behavioural pattern analysis. The systme use Telegram for delivering real-time notifications and monitoring updates, providing a cost-effective and accessible solution that enables conservationists to receive immediate alerts on standard mobile devices without requiring specialized infrastructure. With the implementation of geofencing, automated alerts are triggered via Telegram when animals move beyond designated safe zones or into high-risk areas, enabling rapid response from conservation teams to prevent poaching incidents, human-wildlife conflicts, or other threats. Ultimately, the project seeks to support data-driven decision-making and improve the effectiveness of protection strategies for endangered wildlife through continuous real-time monitoring.
Development of a Structured Machinery MaintenanceInspection Checklist in a New High-Capacity Production Line
The implementation of new high-capacity production lines often emphasizes efficiency and output, while maintenance-related safety risks may receive less attention, particularly during early operational stages. At Kilang Makanan Mamee Sdn. Bhd., Subang, the absence of a structured and machine-specific maintenance inspection checklist for critical equipment highlighted the need for a systematic safety-based approach. This study aims to develop and propose a structured machinery maintenance safety inspection checklist to enhance worker safety and support consistent hazard identification. Hazard Identification, Risk Assessment, and Risk Control (HIRARC) analysis was conducted to identify critical equipment with higher maintenance-related risks. The methodology involved site walkthrough observations, document reviews, and informal discussions with safety, maintenance, and production personnel to understand actual maintenance practices and hazard exposure. Based on the findings, a safety-focused maintenance inspection checklist was developed, emphasizing practical applicability rather than machine performance evaluation. The results indicate that the developed checklist addresses key maintenance safety concerns, including mechanical hazards, thermal exposure, electrical risks, lockout–tagout (LOTO) readiness, safe access, and post-maintenance verification. Feedback from relevant departments confirmed that the checklist is clear, practical, and suitable for routine maintenance use. It is recommended that the checklist be formally adopted into maintenance procedures, periodically reviewed, and expanded to other critical machines to strengthen long-term maintenance safety practices.
Development Of A Structured Machinery Maintenance Inspection Checklist In A New High-Capacity Production Line
Mobile Application for Vehicle Maintenance and Scheduling System
Vehicle maintenance is essential to ensure reliable performance, reduce the risk of breakdowns and extend the lifespan of the vehicle. Traditional maintenance practices often rely on manual tracking and delayed diagnostic, which can lead to missed service intervals and unexpected issues. This project proposes the enhanced of an integrated system that combines real-time battery and mileage monitoring with intelligent maintenance scheduling through a mobile application. The system utilizes an ESP32 microcontroller and an OBD-II adapter to continuously collect vehicle battery and mileage data. The data is transmitted to a user-friendly android application and developed using MIT App Inventor via Bluetooth. The app tracks odometer readings and calculates a maintenance schedule based on the type of engine oil used and driving patterns. In addition, the application provides automatic service alerts also the system is designs to be low cost, efficient and scalable offering vehicle owners a smart accessible tool for proactive vehicle health management. Performance evaluation confirms the accuracy of the sensor data and the reliability of service reminders, making this solution both practical and effective for everyday use.
Development of Flood Risk Prediction Model Using Water Level and Rainfall Volume Using Multiple Regression Analysis
Flash floods are a very serious natural risks that usually happened in the tropical areas like Malaysia whereby heavy rainfall causes a quick rise in the surface water levels. Traditional predictive models often rely on single variable indicators, which easily leads to slow or inaccurate hazard assessment. The current research developed a flood risk model, the implementation of involving Multiple Linear Regression (MLR) models along with the use of real-time Internet of Things (IoT) data collection. Tipping bucket rainfall sensor and ultrasonic sensor were used to collect sensor data thus send them using LoRaWAN, and Wi-Fi to a central cloud-based MySQL database. The data streams were treated by a Python based analytical pipeline to make predictions of the flood risk index and classification. The results were sent to the final users over Telegram notification bot and a real-time web dashboard named “FloodWatchAI”. The model was validated on twenty consecutive days empirical observations in Panchor, Johor in Malaysia, collaborating with SENA Traffic Sdn. Bhd and obtained an R^2 coefficient of 0.67 on the test set. The system proved to be associated not only with the practically immediate processing of data and spreading of alerts but also with significantly increased chances of the timely evacuation and proper management of the disaster.
Community Readiness for Plastic Waste Reduction Through Recycled Craft-Making: A Case Study in Parit Jelutung, Batu Pahat, Johor
Plastic waste management remains a critical environmental challenge in Malaysia due to increasing plastic consumption, rising waste generation, and limited sustained public participation in sustainable practices. This study examines community readiness for plastic waste reduction through recycled craft-making activities as a practical, community-based approach in Parit Jelutong, Batu Pahat, Johor. A recycled plastic craft-making demonstration programme was conducted to provide hands-on exposure, followed by a questionnaire survey involving students, teachers, and members of the Parent–Teacher Association. A quantitative approach was adopted, and the data were analysed using descriptive analysis, reliability test, and the Mann–Whitney U test to examine differences in awareness based on respondents’ demographic characteristics and programme participation. The results indicate that respondents generally demonstrated positive perceptions towards recycled craft-making activities, particularly in relation to plastic waste reduction and potential economic value. However, the analysis revealed no significant differences in awareness levels based on respondents’ status, gender, or attendance at the demonstration programme. Feedback from respondents showed strong support for expanding recycled craft-making initiatives to a wider community, although willingness to invest financially in recycled products or paid training programmes remained moderate. Overall, the findings suggest that while recycled craft-making activities are well accepted, sustained engagement and continuous practical exposure are essential to strengthen long-term awareness and promote behavioural change in plastic waste management.
Civil Engineering Technology (Environment)
Smart Rooftop Cooling and Ventilation System
The heat that is gathered by the roofs of the residential buildings in tropical areas like Malaysia is usually overwhelming which results in extreme thermal discomfort indoors. Traditional cooling methods, including air conditioning and passive ventilation are often inefficient in energy consumption and are not flexible enough to react well to the dynamic climatic conditions. To solve this problem, the paper presents an Artificial Intelligence (AI) Smart Rooftop Cooling and Ventilation System to optimize the airflow and temperature regulation in the house by predictively automating these processes. The key point is to develop a smart system with the help of an ESP32 microcontroller, exhaust fans, mist sprayers, and environmental sensors, combined with a machine learning model, to predict the temperature trends and activate time-based cooling. The strategy uses a hybrid control system involving real-time sensor feed with AI predictions on the basis of a Multiple Linear Regression (MLR) model. The findings indicate that the system will effectively cool rooftops and adjust ambient temperatures to 36˚C and above in a few minutes. Besides, the system has a smart energy control where the fans are slowed down to the required temperature and has a rain detection safety interrupt. To sum up, the system manages to combine embedded engineering, AI, and IoT in the creation of a relatively inexpensive sustainable system to control the thermal comfort of residences.
Conversion of Oil Palm Empty Fruit Bunch (OPEFB) and Mesocarp (OPM) Fibers into Liquid Natural Coagulants and Performance Evaluation for Palm Oil Mill Secondary Effluent (POMSE)
The palm oil industry is one of the major agricultural sectors in Malaysia and Southeast Asia, contributing significantly to economic growth while generating large volumes of liquid and solid waste during the milling process. Among these wastes, palm oil mill effluent (POME) represents the largest fractions with approximately 0.5 – 2.5 tonnes generated for every tonne of crude palm oil produced. Palm oil mill secondary effluent (POMSE) may still contain residual organic matter and suspended particles even after biological treatment, which can affect the quality of wastewater discharged into the environment. Therefore, additional treatment is required to further improve effluent quality before discharge. This study aimed to evaluate the effectiveness of liquid oil palm empty fruit bunch (OPEFB) and oil palm mesocarp (OPM) fibers as natural bio-coagulants for POMSE treatment. Liquid bio-coagulants were prepared using an alkaline extraction method with sodium hydroxide (NaOH). Fourier Transform Infrared Spectroscopy (FTIR) was used to identify the functional groups present in the liquid extracts. Jar test experiments were conducted using 200 mL of POMSE per beaker, with bio-coagulant dosages varied from 0 to 600 mL. Water quality parameters including pH, turbidity, dissolved oxygen (DO), biochemical oxygen demand (BOD5) and total suspended solids (TSS) were analysed. FTIR analysis confirmed the presence of dominant hydroxyl (-OH) functional groups in both liquid extracts indicating their lignocellulosic nature. At the optimum bio-coagulant dosage of 300 mL added to 200 mL of POMSE, liquid OPEFB achieved a noticeable reduction in BOD5 with removal efficiencies ranging from approximately 49% to 56% whereas liquid OPM showed limited improvement with BOD5 removal below 10%. Turbidity reduction was modest, with removals of 11.9% for OPEFB and 4.8% for OPM, while changes in TSS were minimal (<5%). The effluent pH remained stable at pH 8 and a slight decrease in DO. Although the treated POMSE did not fully comply with the Department of Environment (DOE) discharge standards, particularly for BOD5 and TSS, the observed reductions indicate that liquid OPEFB and OPM bio-coagulants are potentially suitable as supplementary or polishing treatments. Further optimisations of bio-coagulant concentration and dosage is required to enhance treatment efficiency and achieve regulatory compliance.
Palm oil mill secondary effluent (POMSE)
Department of Civil Engineering Technology
The Effects of Water to Cement Ratio on Polyurethane Concrete Towards SDG Goals
The growing demand for sustainable and lightweight construction materials by investigating the optimization of the water-to-cement (w/c) ratio in polyurethane lightweight concrete (PU-LWC). The study examines the effects of different w/c ratios on the mechanical, physical, and durability properties of PU-LWC, aiming to enhance its performance for non-structural applications. Laboratory testing of PU-LWC samples with two w/c ratios (0.60 and 0.70), incorporating epoxy moulding compound (EMC) as a filler, reveals significant insights into the material’s compressive strength, density, and water absorption. The results demonstrate that a higher w/c ratio improves early-stage strength and enhances polymer dispersion but also increases porosity and water absorption. The study concludes that a w/c ratio of 0.70 provides an optimal balance between strength and lightweight characteristics, making it a promising material for sustainable construction practices.
Polyurethane Lightweight Concrete (PU-LWC),
Smart Watch for Elderly Using Microcontroller with Voice Recognition
This study focuses on the development of a simple health monitoring and alert system for basic vital signs. The main problem is that many people do not receive early warnings when heart rate or body temperature reaches unsafe levels. This issue can cause late action and increase health risks. The objective of this research is to design and test a system that can monitor heart rate and body temperature in real time and send alerts when values exceed set limits. The methodology uses sensors to measure heart rate and temperature, a microcontroller to process data, and the Blynk platform to display data and alerts on a mobile device. The system is tested under normal and abnormal conditions to check accuracy, response time, and stability. The results show that the system can detect changes in vital signs and send alerts clearly to the mobile application. The local display updates faster than the cloud display, but a small delay appears during internet transmission. Some issues such as sensor instability and connection loss occur, but troubleshooting improves performance. The study recommends improving sensor quality, network stability, and system calibration for better reliability in future use.
Geotechnical Improvement of Dredged Marine Soil using Blended Cement and Rice Husk Ash for Subgrade : Strength and Mix Evaluation
Dredged Marine Soil (DMS) is a waste material produced from dredging activities in marine waters. Although recent studies have shown its potential use in construction, untreated DMS is generally unsuitable for direct application, especially in road construction. Therefore, DMS must be mixed with stabilizers, such as cementitious or pozzolanic materials, to enhance its strength, durability, and load-bearing capacity prior to use in road construction The characteristics of DMS are high moisture content, low strength, and high compressibility. This study aims to evaluate the effectiveness of DMS stabilization using a mixture of Ordinary Portland Cement (OPC) and Rice Husk Ash (RHA) for use as a subgrade layer in road construction. To identify DMS characteristics and chemical composition of RHA, laboratory tests such as hydrometer, Atterberg limit, compaction characteristics, Unconfined Compressive Strength (UCS), California Bearing Ratio (CBR) and Scanning Electron Microscopy (SEM). The mixing ratio of cement (4%, 6%, and 8%) and RHA (5% and 10%) were tested at curing periods of 7, 14, and 28 days. The results showed that untreated DMS had low strength and did not meet the requirements of the JKR (2020) specification for subgrade applications. The addition of cement and RHA was proven to significantly improve the mechanical performance of DMS soil, where the strength increased in line with the curing period. The optimum mixture was identified at a ratio of 5% RHA and 6% cement, which met the minimum strength requirements of road subgrade with more efficient cement usage. SEM analysis confirmed the presence of amorphous silica and the formation of binding products in RHA contributed to the increase in soil strength. Overall, this study proved that the use of cement and RHA mixture is an effective and sustainable method to improve the engineering properties of marine dredged soil for road construction applications.
Ergonomic Risk Assessment of Manual Material Handling in a Logistic Warehouse
Manual Material Handling (MMH) is a critical component of logistics operations, yet it remains a primary driver of work-related musculoskeletal disorders (WMSDs) due to high physical demands. This study investigated the ergonomic risk factors associated with MMH tasks at the Ninja Van Malaysia central warehouse. The primary objectives were to identify the prevalence of musculoskeletal discomfort among workers, classify ergonomic risk levels using the Department of Occupational Safety and Health (DOSH) Initial Ergonomic Risk Assessment (IERA) Guidelines (2017), and propose feasible control measures based on the findings. A mixed-methods research design was employed, utilizing the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) for subjective discomfort reporting and direct site observations for task analysis. A purposive sample of 40 experienced workers was assessed. Results from the CMDQ revealed a high prevalence of discomfort, particularly in the lower extremities, with 60% of respondents reporting right-knee pain and 55% reporting right-foot discomfort. Additionally, significant strain was noted in the neck (45%) and upper back (37.5%). The IERA identified several critical risk factors: 70% of workers were exposed to prolonged static standing, 40% performed frequent reaching above shoulder height, and 35% engaged in torso twisting during lifting tasks. Environmental constraints, including clutter (57.5%) and limited workspace (45%), further exacerbated awkward postures. Furthermore, 72.5% of workers operated without mechanical assistance, and 60% reported a lack of formal ergonomic training. Based on these findings, key MMH tasks were classified as Medium to High Risk, necessitating immediate intervention. Proposed control measures include engineering solutions such as height-adjustable conveyors and anti-fatigue matting, alongside administrative strategies like formal job rotation and the implementation of a 5S housekeeping system. This research provides a proactive framework for warehouse managers to enhance worker safety, reduce injury-related absenteeism, and improve overall operational efficiency in the logistics sector.
Engineering Technology (Occupational Safety and Health)
Study on Seawater Intrusion Detection and Investigation in the Muar River Basin.
Seawater intrusion is detrimental for agricultural activities, water supply, and the health of river ecosystems, such as that affecting Malaysia's Muar River. This paper looks at an affordable, IoT system made to spot seawater intrusion as it happens.The system has a salinity sensor, an ESP32 Wi-Fi chip, and GPS for finding locations. It watches salinity and location, sending info to a web platform. The Blynk app shows the info and sends out alerts.Observations from the river mouth show salinity is reduced upstream, supporting the idea that seawater intrusion happens when the river's discharge decreases. This system allows fast data collection and cheap early warnings, which helps with tracking the environment and handling water resources. Later studies might watch water quality and study long-term trends in the data.
Application of Dass-21 to Assess the Mental Health Status of Office Employees at Oil and Gas Company
This study explores the mental health status of office employees at Petra Energy Berhad, focusing on depression, anxiety, and stress within the corporate oil and gas sector. While research in this industry predominantly targets offshore workers, the mental well-being of office-based employees remains underexplored. Using the Depression, Anxiety, and Stress Scale (DASS-21), the study aims to assess the severity of these mental health issues and identify correlations with sociodemographic and occupational factors. A cross-sectional design was used, with 132 office employees participating in the survey. The data were analyzed through SPSS, applying descriptive and inferential statistics. The results indicate that a significant portion of respondents reported high levels of depression (79.5%) and anxiety (67.4%), while stress was most categorized as moderate (47.0%). Sociodemographic factors such as age, gender, and education were found to influence mental health outcomes. Additionally, workload and coping strategies were identified as important factors affecting employees’ mental well-being. In conclusion, mental health challenges are prevalent among office employees in the oil and gas sector. The findings suggest the need for targeted mental health interventions, improved coping resources, and organizational support to address these issues. Organizations like Petra Energy Berhad should prioritize mental health initiatives to foster a healthier work environment and improve employee
Extraction of Lutein from Banana Peel via Ultrasound-Assisted Extraction Method for the Formulation of Antioxidant Soap
Banana peels are normally discarded, although they possess precious bioactive compounds like lutein, which is a xanthophyll-carotenoid pigment that has been found to possess a high antioxidant capacity. The goal of this work is to extract lutein from yellow and green banana peels using the Ultrasound-Assisted Extraction (UAE) technique and to determine the effect of ethanol concentrations (50% and 96%) on lutein extraction using UV-Visible spectroscopy at 450nm. Additionally, the lutein extracts from the UAE process will be analyzed with the use of Fourier Transform Infrared Spectroscopy (FTIR) to identify functional groups that are characteristic of carotenoids. The antioxidant capacity was determined using the DPPH assay at a wavelength of 517nm for all four lutein extracts (Y96, G96, Y50, and G50). The concentrated lutein extract was used to prepare an antioxidant soap using the cold saponification process, and the product was analyzed for its physical and chemical properties, which include pH (8-9), foamability (80%), foam stability (77.95%), moisture content (8.31%), and organoleptic tests, in addition to analyzing its DPPH radical scavenging activity (7.31%). The results of the work show that banana peels are a good source of lutein, and the UAE process is effective in the extraction of heat-sensitive compounds, in addition to contributing to the design of an environmentally friendly cosmetic material from agro-industrial waste.
Contributing Factors to Non-Compliance of PPE Usage among Construction Workers
The construction field is among the industries that are most dangerous to work in, and where workers often face risks like falling, being struck by objects and machine related accidents. Construction workers and the use of PPE despite stipulation in accordance to the Personal Protective Equipment (PPE) for Construction Works Regulation 1993 under Occupational Safety and Health Act (OSHA) 1994, low compliance towards the PPE among construction workers is still sensible issues for safety. The objective of the study is to explore the reasons for not compliance with PPE and its association with workers demographic factors. A cross sectional study was conducted among 132 construction workers in Kepong Kuala Lumpur using a structured bilingual questionnaire. Data were collected and analyzed by SPSS based on descriptive and inferential statistics. The results show that, although workers have good knowledge of PPE and know the importance of using it, non-compliance is still present due mainly to discomfort, heat exposure, perceived difficulty in accomplishing work tasks and some practices particularly to themselves. Availability of PPE and years of work experience were the significant correlates with PPE compliance; demographic factors had no or not significantly relationship. In summary, efforts to improve PPE use not only need to provide PPE itself but also focus on promoting comfort by issuing well- fitted and adjustable PPE, intensifying safety supervision, enhancing workers safety training and a good safety climate at construction sites.
A Study of Human Traffic for Emergency Evacuation at Universiti Tun Hussein Onn Malaysia (UTHM) Pagoh Residential College
High density residential buildings like university hostels need effective evacuation in case of emergencies to save lives. The objectives of this study is to evaluates and enhances the emergency evacuation efficiency of Block A10 of the Universiti Tun Hussein Onn Malaysia (UTHM) Pagoh Residential College. The study simulated the emergency situation by models of occupant movement to determine the congestion points using Pathfinder program to model the building environment based on site survey and architectural plans. The simulation involved the comparison between existing evacuation routes with better approaches such as low occupancy density, route assignment according to the nearest exits and walking speed. Findings showed that the bottlenecks were narrow walkways and lack of enough exits with the width of the staircases being the main structural limitation. The best approach was to reduce occupant density (load management) to the lowest possible evacuation and the time was at 276 seconds in comparison to the current baseline of 420 seconds. These results indicate that structural constraints cannot be surmounted only through raising the velocity of movement and imply that additional signage, clearing corridors and zoning measures are all necessary to increase safety.
Natural Weathering Effects on Mechanical and Thermal Properties of Bamboo Cotton Textile Waste Reinforced Recycled Polypropylene Composites for Construction Applications
The increasing generation of plastic and textile waste has become a serious environmental issue and has encouraged the development of more sustainable materials. In this study, bamboo cotton textile waste (BCTW) was used as a natural reinforcement in recycled polypropylene (rPP) composites to study the effects of natural weathering on mechanical and thermal properties. The composites were prepared with different BCTW loadings using an internal mixing and compression moulding process, with maleic anhydride grafted polypropylene (MAPP) added as compatibilizer. Natural weathering exposure carried out under outdoor conditions. Prior to weathering, the BCTW were characterized using Fourier Transform Infrared Spectroscopy (FTIR) and Field emission scanning electron microscopy (FESEM) followed by composites testing using differential scanning calorimetric (DSC) to evaluate the initial chemical, morphological and thermal properties. The effects of natural weathering were analyzed based on changes observed in tensile and thermogravimetric (TGA) results. The results show that the addition of BCTW increased the stiffness of the composites while tensile strength showed better performance at moderate fiber contents. Thermal analysis indicated a slight reduction in thermal stability after weathering exposure. Among all formulations, the composite with 15 wt% BCTW shows the most balanced overall performance after weathering. Overall, the findings suggest that bamboo cotton textile waste reinforced recycled polypropylene composites have good potential as sustainable materials for outdoor applications.
IoT-Based Water Quality Sensor Node with Cloud Storage and Iterative Dashboard
This paper presents the development of an IoT-based river water quality monitoring system integrated with cloud storage and an interactive dashboard for real-time analysis. In this context, a Heltec WiFi LoRa32-V3 microcontroller was applied with various water quality sensors such as pH, turbidity, temperature, dissolved oxygen and total dissolved solids. Sensor data transmitted by LoRaWAN to The Things Network are processed using Node-RED, stored in a cloud-based MySQL database and then visualized on a Grafana dashboard. Field testing carried out at Sungai Panchor, Muar, Johor attested to the reliable transmission of data, effective cloud integration and clear visualization in real time. The proposed system offers a scalable, cost-effective and efficient solution for the continuous monitoring of the quality of river water.
BACHELOR OF ELECTRONIC ENGINEERING TECHNOLOGY (COMMUNICATION AND COMPUTER) WITH HONOURS