Volume 37 (05) August 2025

Industry 4.0 is one of the foremost that drives the modern industrial revolution with the latest technologies, which comprises smart equipment, machinery and control systems. In this, digital manufacturing integrates the traditional manufacturing systems with the latest digital technologies for modelling, analysis, optimization and automation of manufacturing processes. This study analyses the application of various advanced technological tools for implementation of digital manufacturing. Based on content analysis, combined applications of digital manufacturing tools and advanced manufacturing technologies are framed and various technological trends identified. Integrated and automated machines can do repetitive works and humans can do complicated works through creativity and problem-solving capacity through technological tools. This paper support to identify the digital manufacturing obstacles in implementation and also helps to understand the digital manufacturing within the framework of the nine technological tools and it also recognizes that correlation with other technologies. In digital manufacturing, a huge manufacturing data can be analyzed with the big data analysis and stored in cloud for future use and secured with the cybersecurity technology. Simulation carried out to test digital model before implementation and monitoring with the digital twin technology. Virtual reality integrates the real-world with virtual environments and taking decisions through Internet of things. Robotic system enables to automate works and additive manufacturing technology develops the components from the stored data

Keywords: Digital manufacturing; Industry 4.0; Internet of things; cloud computing; augment and virtual reality

The geometric design of the building’s exterior, particularly using glass as the main building material, was found to be able to reduce the cooling load of a building, especially in the tropical climate of Malaysia. This study aims to measure the thermal performance of inclined glass walls with various angles and orientations in minimizing solar heat absorption in a building. This study uses Integrated Environmental Solutions – Virtual Environment (IES-VE) simulation software with 32 models simulated using the ApacheSim module to investigate the most effective angle and orientation of inclined glass walls in achieving thermal comfort. The results revealed that the optimum angle for each orientation depended on the sun’s path during the four (4) design days – 21st March, 21st June, 21st September, and 21st December. Throughout all design dates, the 55° inclined glass wall provided the best thermal performance for all orientations except for those exposed to extreme solar radiation due to the sun’s path on the measured dates. The 75° inclined glass wall is more effective at each design date. To optimize the inclined glass wall’s performance, the design is recommended to consider low-albedo material on the surface around the building. This is to reduce heat reflection which can affect the thermal performance of the inclined glass wall surface. Also, landscape design and airflow around the building’s exterior surfaces can help with more effective thermal performance. Therefore, the application of inclined glass walls is proven to be effective in reducing building energy, however, the surrounding pavement material should also be considered at the early stage of design.

Keywords: Inclined glass wall; solar heat gain; simulation; thermal performance; tropical climate

The integration of lignin-coated membranes and ultraviolet (UV) inactivation provides an effective approach for enhanced water filtration and pathogen removal. In this study, a hydrophobic polyvinylidene fluoride (PVDF) hollow fiber membrane was coated with lignin and calcium carbonate nanoparticles to improve hydrophilicity and antifouling properties. The modified membrane demonstrated significantly enhanced performance, achieving an oil emulsion rejection rate of 95.97% and a flux of 63.87 L·m⁻²·h⁻¹, which was approximately twice that of the unmodified PVDF membrane. Additionally, calcium carbonate nanoparticles further improved the oil removal efficiency. To complement membrane filtration, UV treatment was employed for pathogen deactivation, targeting the nucleic acids of waterborne pathogens to inhibit their replication. The UV-treated water was evaluated in term of bacterial regrowth, revealing distinct differences between bacterial species. Escherichia coli exhibited higher photoreactivation rates (10.66%) under fluorescent light compared to dark conditions, whereas total coliforms showed even greater photoreactivation susceptibility (21%) under the same UV dosage (237 mJ/cm²). The integrated membrane-UV filtration system effectively eliminated up to 95% of E. coli from river water and significantly reduced turbidity. These findings highlight the potential of calcium carbonate-enhanced lignin coatings for membrane modification and underline the importance of understanding bacterial responses to UV disinfection in integrated water treatment systems.

Keywords: Membrane; lignin; deinfection; UV

Strength properties of Malaysian timbers are given in MS 544: Part 2. However, the strength properties specified in MS 544: Part 2 was derived from the assessment of small clear specimen. In recent decades, there has been a significant and swift progression in the evolution of engineered timber products (ETP), accompanied by a substantial increase in their utilization within the construction industry. Therefore, to provide a reliable design for engineered timber product, it is essential to have the access to the strength properties derived from structural timber size. Thus, the aim of this study was to assess the compressive properties (parallel and perpendicular to the grain) of Malaysian tropical hardwoods utilising structurally sized specimens. The timber species tested ranged in strength group (SG) 1 until 7 in accordance with MS 544: Part 2 namely Balau, Kempas, Kelat, Resak, Kapur, Keruing, Mengkulang, Light Red Meranti and Geronggang. Based on EN 408:2012, the desired properties (compressive strength and modulus of elasticity) were determined for the timber specimens. According to the results of this study, Kempas (SG2) and Resak (SG4) have the highest compressive strength parallel (62.9 N/mm2) and perpendicular (17.7 N/mm2) to the grain, respectively. In contrast, Geronggang (SG7) and Light Red Meranti (SG6) shows the weakest compressive strength parallelly (26.9 N/mm2) and perpendicularly (3.3 N/mm2) respectively. Based on the findings obtained in this study, the experimental grade stresses are generally much higher than the values given in MS 544: Part 3. In conclusion, the grade stresses of Malaysian tropical hardwoods stated in MS 544: Part 3 are discordant with their genuine value.

Keywords: Compressive strength; Malaysian timber; large size; strength properties; MS544

In manufacturing, joining dissimilar metals is a crucial challenge. This study looked into the lap joining of galvanised steel to aluminum sheets (AA5052) using different materials by using a spraying technique to introduce a zinc conductive interlayer. Therefore an experimental investigation into the effects of alkaline treatment was conducted using potassium permanganate (KMnO4) and hydrogen peroxide (H₂O₂) at different immersion times (0, 10, 20 and 30 minutes). Zinc-rich layer was applied to the treated samples and single lap joints were welded using resistance spot welding (RSW) at 7 kA for 5 seconds. The resulting treated samples underwent comprehensive characterisation using several techniques, including macroscopic observation, surface roughness, nugget size measurement, tensile test, Vicker microhardness, pH measurement, and conductivity test. The results indicated that KMnO4 exhibited a higher etching rate and greater material removal during the chemical treatment than to H2O2. This resulted in the breakdown of the oxide layer on the metal surface, yielding increased surface roughness and improved conductivity for galvanised steel and AA5052 treated with KMnO4. The pH of the treated sample rose with prolonged immersion time, with KMnO4 exhibiting a higher pH value (9.48) compared to H₂O₂ (3.80) at the optimal immersion duration of 30 minutes. This condition resulted in an increased heat input, potentially enlarging the weld nugget diameter and thereby significantly enhancing both weld strength and hardness.

Keywords: Spot weld; alkaline treatment; dissimilar metals; zinc interlayer

Traffic jams is a typical urban problem caused by a variety of reasons such as high traffic volume, construction, roadwork, incidents, and accidents. With the development of urbanization, daily traffic has become a problem, especially in some key areas of heavy road or parking can cause serious traffic problems. There are many method has been suggest to reduce a traffic such as using public transport, carpooling, ridesharing, traffic management system, road infrastructure improvements, flexible work hours and promoting non motorized transport. One of the approach suggest is to improve a time consume by vehicle on the road. Abnormal pattern can be define by using plate number detection and the information will send to autorized organisation to alert or support for road management system. In this project, a system has been developed using Python and YOLO model to monitor a specified area with an external camera, identify vehicles within the area by recognize license plates. Based on the experiment conducted, 88.23% of number car plate has been indentified. The system aims to solve the problem of unauthorized road or parking by tracking the time or how long a vehicle stays in the monitored area. If a vehicle exceeds a predetermined time limit, the system triggers an alert to authorize people. The solution is designed to reduce traffic jam or parking area and ensure well-organized vehicle management in restricted areas.

Keywords: Traffic jam; number plate extraction; road management System; YOLO algorithm; Phyton; license plate recognition

Ensuring the safety and compliance of electrical systems is critical in maintaining the integrity of public infrastructure, particularly in government project buildings. Using a descriptive-evaluative research design, this study evaluated the electrical wiring systems of public infrastructure buildings in the Province Surigao del Sur, Philippines, describing their current state and assessing their compliance with Philippine Electrical Code (PEC) standards. The Megger test results showed varying levels of insulation resistance across the buildings, with New Building 1 and Old Building 1 exhibiting generally good readings, but some circuits fell into the “Fair” category. New Building 2 mostly had high readings, but a few circuits showed lower resistance, indicating the need for maintenance, while Old Building 2 displayed robust insulation with minor areas needing attention. The evaluation of wiring installations against Philippine Electrical Code (PEC) standards revealed compliance in New Building 1 but identified undersized wires and incorrect breaker ratings in New Building 2, Old Building 1, and Old Building 2, posing safety risks. The study also reviewed the requirements for building energization by SURSECO II, emphasizing the importance of following a structured process involving zoning permits, electrical plans, fire safety certifications, and personal documentation. Regular maintenance and insulation testing, wiring installations reviewed and upgraded to meet PEC standards, and strict adherence to SURSECO II’s requirements are all recommended to ensure the safety, reliability, and compliance of the electrical systems in public infrastructure buildings.

Keywords: Compliance, building wiring installation; Philippine Electrical Code; assessment; electrical safety; energization

Copper oxide (p-type CuO) and zinc oxide (n-type ZnO) thin film semiconductors were synthesized and evaluated as low-cost, environmentally friendly materials for a proof-of-concept thermoelectric device. The fabrication process involved alkaline vapor oxidation for CuO and a combination of electrodeposition and chemical bath deposition for ZnO, offering scalable and energy-efficient routes. The influence of NaOH concentration (3M,4M,5M) on CuO film properties was systematically studied. X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses revealed that films synthesized in 5M NaOH exhibited enhanced crystallinity and surface morphology. Alkaline vapor oxidation was conducted for 24 and 48 hours, and both CuO and ZnO films were subjected to thermoelectric characterization. Using a custom-built setup, Seebeck coefficient, electrical conductivity, and thermoelectric power factor (PF) were measured. The highest PF values were observed for ZnO (2.310 nW/K²·m) and CuO oxidized for 48 hours (2.746 nW/K²·m). These values align with reported ranges in recent literature and demonstrate the potential of CuO–ZnO systems in all-oxide thermoelectric generators. The results confirm that simple chemical processing routes can produce oxide thin films with viable thermoelectric performance, supporting sustainable and cost-efficient device fabrication. Furthermore, the morphological tuning through oxidation duration and concentration offers a pathway for material property engineering without requiring highvacuum or high-temperature conditions. This study highlights the feasibility of integrating CuO and ZnO as a p–n junction pair in micro-scale or flexible energy recovery modules, particularly for wearable electronics or selfpowered sensors operating in ambient or low-heat environments. The favorable material compatibility, low toxicity, and mechanical stability of the films also make them attractive candidates for next-generation energy harvesting technologies in distributed and low-power applications.

Keywords: p-type CuO; n-type ZnO; Seebeck coefficients; power factor (PF); electrodeposition

The effectiveness of chemical coagulation-flocculation for treating coffee processing mill effluent (CPME) was evaluated in this study using various commercial coagulants, such as ferric chloride, aluminium sulphate (alum), polyaluminum chloride, ferrous sulphate, chitosan, alum-lime, and ferric chloride-lime. Untreated CPME initially had a pH of 4.5, which increased to between 5 and 9 after pH adjustment with 0.25 M sodium hydroxide. Alum demonstrated the best performance, achieving 98% turbidity removal, 30% COD reduction, 85% NH₃-N removal, and complete iron removal at an optimal dosage of 1000 mg/L. The effect of pH on coagulation-flocculation effectiveness was also investigated, with alum being most effective at pH 7–8. While turbidity and iron were effectively removed using alum, its performance in reducing COD and color was limited. Additionally, NH₃-N removal efficiency varied with alum dosage, but residual nitrogen was consistently present in the effluent. As none of the treated samples met the Environmental Quality (Industrial Effluent) Regulation 2009 Standard B discharge limits, particularly for COD and color, post-treatment is necessary. Adsorption is suggested as a viable post-treatment technique to improve CPME treatment quality and ensure compliance with regulatory standards. Integrating coagulation-flocculation with adsorption may offer a more effective and environmentally friendly approach for CPME treatment.

Keywords: Coffee effluent; coagulation-flocculation; commercial coagulants; alum; color removal

The recent advancements of graphene in industry and research have garnered considerable attention from researchers due to its perceived benefits across various fields. However, graphene in a single or pure structure has several issues, notably its low conductivity and fewer atoms. Thus, the objective of this study is to comprehend its functionality as a catalyst support material, particularly in applications related to fuel cells. This investigation examines the electronic properties of single-layer, holey graphene and multilayer holey, specifically in terms of surface area and conductivity, utilizing the Density Functional Theory (DFT) Method. The software employed for this purpose is Materials Studio 2016, which is used to develop chosen graphene structures then geometry optimization simulation and analyses electronic properties. The study’s outcomes encompass the determination of energy band structure is 0.0 eV for all structures, higher density of states level for holey multilayer graphene (~8.0 electrons/eV at 0.0 eV), Higher Mulliken charge distribution among atoms at -0.103e around the pore, and HOMOLUMO energy gap (0.30 eV for Graphene, 0.29 eV for holey Graphene, and 0.13 eV for holey multilayer graphene) for all examined structures. The findings reveal that holey multilayer graphene with multiple layers exhibits superior electronic properties and attains a notably high conductivity value. Consequently, this research makes a substantial contribution, particularly in advancing the development of structures for better electronic conductivity.

Keywords: Graphene; Electronic properties; Band Structure; Density of States; Mulliken Charge

Depth–damage curves (DDC) are widely used in flood risk analysis to represent the range of losses when exposed to a range of flood depth. However, variations in DDCs, whether from international, national, or combined sources, pose challenges for selecting the most accurate curve for local applications, particularly in developing regions. The availability of these varied functions enables scientists and practitioners to perform monetary flood risk evaluations, aiding better investment decisions. However, ensuring that these models are locally validated is crucial, as unverified models can lead to significant inaccuracies. This study aims to compare the performance of international damage curve (IDC), national dam age curve (NDC), and unified damage curve (UDC) in local floodprone areas of Malaysia where the monetary damages for each models were analysed. Comparisons were made at a community scale, with verification against site-specific damage curves (SDC), which include uncertainty bounds that established using boxplot based on empirical data. Results show that the internationally derived DDC overestimates community-scale aggregated building-level damage by 30 times compared to the SDC, revealing a significant overestimation. Conversely, the aggregated damages using NDC and UDC fall within the SDC’s uncertainty bounds. This demonstrates that integrating national data with international models significantly improves accuracy and reduces overestimation. Ignoring pre-treatment of IDC in flood risk studies could result in an alarming overestimation of damages. This study highlights the indispensable role of local data in ensuring accurate DDC representation and emphasizes the need for coordinated efforts in flood damage data collection and inventory
management.

Keywords: Building-level damage; flood depth–damage function; flood risk; stage–damage

The increasing demand for sustainable and eco-friendly materials in the construction industry has led to the exploration of agricultural waste as an alternative modifier in hot mix asphalt (HMA). Agricultural waste is one of the potential materials that can be used as bitumen modifier due to the abundance of waste been dumped without being utilized properly. This review aims to evaluate the effects of incorporating various types of agricultural waste on the rheological, mechanical, and microstructural performance of HMA. Specifically, it focuses on the influence of materials such as rice husk ash, sugarcane bagasse, Palm Oil Fuel Ash (POFA), coconut and other agricultural byproducts on the physical properties of asphalt binder and mix. Additionally, the review highlights changes in the microstructure of the asphalt mixture, observed through scanning electron microscopy (SEM) and other characterization techniques. The findings suggest that agricultural waste materials have the potential to enhance the performance of HMA by improving its mechanical properties, increasing rutting resistance, and promoting sustainability in road construction. However, challenges such as optimal dosage, compatibility with conventional asphalt components, and long-term performance stability are also identified. Therefore, chemical and physical treatment need to be done to promote the compatibility between agricultural waste and bitumen. This paper provides a comprehensive overview of current research, offering valuable insights for future studies and practical applications in the integration of agricultural waste in asphalt pavements.

Keywords: Hot Mix Asphalt (HMA); agricultural waste; rheological; mechanical; microstructure

Underwater Optical Wireless Communication (UOWC) is a promising technology for short-range communication
in marine environments, offering significant advantages in terms of high data transfer rates and low latency. However, its performance is highly dependent on environmental factors such as absorption, scattering, and turbulence of light in seawater. This study investigates the effects of absorption and scattering on UOWC systems in the South China Sea (SCS), a region with unique marine conditions and limited research on UOWC, by estimating the attenuation coefficient, simulating the UOWC channel models, and analysing system performance in terms of path loss, impulse response, and frequency response. A key contribution of this work is the estimation of the attenuation coefficient for SCS, which is essential for accurately modelling UOWC channels in this region. Based on theoretical calculations and empirical data, the attenuation coefficient is estimated to be 0.8748 m-¹, indicating moderate optical signal loss compared to other water types. Then, using Monte Carlo numerical simulations, this estimated coefficient is incorporated to analyse path loss, impulse response, and frequency response by varying the water depth. The obtained results show that both absorption and scattering considerably increase path loss and delay spread compared to the coastal water type, with laser and LED beams experiencing delays of up to 2.92 × 10-¹¹ and 1.13 × 10-9 seconds, respectively. Additionally, frequency-dependent attenuation leads to signal distortion. These findings offer practical implications for optimising UOWC systems in the South China Sea. The study highlights the need for further research to address regional variations in optical properties and improve system design for more reliable communication.

Keywords: Underwater optical wireless communication; South China Sea; Monte Carlo method; absorption, scattering

As the service life of sewer pipelines increases, various types of defects inevitably occur, posing significant risks to urban infrastructure and public safety. Timely detection and assessment of these defects are crucial to ensuring the structural integrity and functionality of sewer systems. Traditional manual inspections are time-consuming, labor intensive, and prone to human errors. Object detection technology based on deep learning provides an efficient and automated alternative by training models to accurately identify the type and location of pipeline defects. However, the complex internal environment of pipelines, including low-light conditions, noise interference, and varying defect appearances, presents significant challenges for detection accuracy. To address these issues, we conducted an in-depth study on common sewer pipeline defects and applied image preprocessing techniques such as grayscale conversion and denoising to enhance dataset quality. Furthermore, we improved the YOLOv8 model by integrating the CPA Enhancer module into its Backbone structure, optimizing feature extraction and defect recognition. Based on this enhancement, we developed a deep active learning framework, YOLOv8-CPA, which leverages a chain-thinking prompt mechanism to refine detection performance iteratively. Experimental results demonstrate that the YOLOv8-CPA pipeline inspection system achieves high accuracy in detecting and classifying pipeline defects. By improving detection efficiency, ensuring consistency, and accelerating validation processes, our system significantly enhances the sewer inspection workflow. The proposed method contributes to effective defect management, aiding in the timely implementation of appropriate maintenance and rehabilitation strategies for sewer infrastructure.

Keywords: Sewer pipeline detection; YOLOv8; deep learning; CPA- Enhancer

Abnormal activities like oil pipeline vandalism need to be identified promptly. Manual surveillance systems for oil pipelines use ground team surveys, while CCTV Cameras are employed in semi-automated surveillance to detect those abnormal behaviours. Oil pipeline failure resulting from vandalism has detrimental effects on both humans and the environment. Despite the availability of the current technologies, escalating incidences of vandalism occur, prompting the necessity for computerized monitoring techniques. Computerized solutions that use deep learning networks require an enormous quantity of information for their implementation. The popular UCF Crime dataset is meant to detect generic vandalism and other anomalies of a similar or divergent nature. Hence, a dataset explicitly designed to complement such a model and assist in pipeline monitoring is needed. This work aims to investigate and develop a behaviour recognition model and a new dataset named Vandalism Detection Dataset 2024(VDD 24) for detecting and classifying abnormal behaviours along oil pipelines. A Modified pre-trained ResNet18 is used for feature extraction, and a Bi-directional long-short-term memory (Bi-LSTM) is employed to detect and categorize those human actions as normal or abnormal (vandalism). Digging, sawing, hammering, and stone impact are regarded abnormal, while activities such as walking, running, and cycling are defined as normal. Despite the similarity in the abnormal actions in the dataset, the model was able to detect and classify the anomaly. Experimental results reveal that our model’s performance on VDD 24 is significant, with an accuracy of 82.5%. The model is further validated on the UCF-Crime dataset with an impressive performance.

Keywords: Abnormal activities; Oil pipeline; ResNet18+Bi-LSTM; deep learning; VDD 24

Personnel safety in offshore environments is a critical concern due to the high-risk operations involving oil and gas extraction. Quantitative Risk Assessment (QRA) provides a systematic approach to identify, analyse, and mitigate potential hazards in these challenging settings. This paper explores the application of QRA in assessing offshore safety risks, focusing on evaluating accident scenarios such as hydrocarbon leaks, explosions, fires, occupational hazards, and structural failures. Incorporating statistical models and historical incident data into the analysis, QRA quantifies the likelihood and consequences of hazardous events, offering a clear perspective on individual risk per annum (IRPA) and potential loss of life (PLL) for offshore personnel. Integrating drones into offshore operations significantly enhances safety and risk management, especially for Remote, Normally Unmanned Offshore Production Platforms (RNUOPP). Using drones is theoretically foreseen to reduce the PLL and lower the IRPA. Drones provide real-time, high-resolution data that improve the accuracy of QRA models by facilitating frequent and detailed inspections of critical infrastructure, such as pipelines, flare stacks, and other hard-to-reach areas, without exposing personnel to hazardous conditions. By enabling early detection of equipment deterioration, corrosion, or structural issues, drones help prevent accidents before they escalate, significantly reducing the likelihood of catastrophic events like explosions or spills. This proactive approach minimises the potential for accidents and decreases PLL by reducing worker exposure to high-risk tasks. Furthermore, by automating inspections and eliminating the need for personnel to conduct dangerous manual tasks, drone usage directly lowers IRPA, enhancing overall offshore safety.

Keywords: Quantitative Risk Assessment (QRA); Individual Risk Per Annum (IRPA); Potential Loss of Life (PLL); unmanned offshore platform; drone

Developing strong mathematical proficiency is paramount for engineering students’ success. This study evaluates students’ perception of learning mathematics and examines how entry qualifications and previous math achievement (SPM mathematics grades) influence students’ perceptions of cognitive and affective learning in a Signals and Systems course. A survey questionnaire that collects perceptions of cognitive and affective learning was administered to 113 students enrolled in the course. Descriptive statistics revealed that over 80% of students reported confidence in organizing course materials (C1) and self-directed learning (C4). However, only around 28.5% felt confident creating a study guide (C2), and 31.4% expressed confidence in demonstrating their knowledge to others (C3). Notably, over 80% of students reported positive changes in attitude (A1), increased self-reliance (A2), and active engagement in learning (A3). The Kruskal-Wallis test indicated a statistically significant influence of entry qualifications on students’ perceptions (p<.05). Students with UiTM diplomas displayed a stronger self-perception in knowledge organizing skills than those with Matriculation or other qualifications. This effect was more pervasive for affective responses (p < .05), with UiTM diploma holders reporting significantly more positive changes in attitude, self-reliance, and engagement compared to their peers. Conversely, Spearman’s rank correlation coefficient did not reveal any significant associations between SPM mathematics grades and perceived learning abilities (all p >.05). These findings emphasize the importance of considering students’ entry qualifications in educational design and practice, as these backgrounds can influence students’ learning experiences and outcomes.

Keywords: Perceived cognitive; perceived affective; demographics; mathematics course; Spearman’s rank correlation; entrance characteristics

Outcome-Based Education (OBE) has been widely implemented in Malaysia’s tertiary education system as part of initiatives to improve the quality and relevance of the curriculum approach. The Engineering Technology Accreditation Council (ETAC) accreditation is an essential criterion for assuring program quality and linking curricular outcomes with industry demands and standards. The issue is that there is not a dependable and systematic framework for evaluating and assessing these 12 Program Outcomes (PO) attainment in a way that meets accreditation and educational requirements. Additionally, curriculum design, instructional strategies, and evaluations need to be better aligned with the changing needs of the industry’s evolving demands. In this study, we examine the implementation and evaluation of an OBE model that is adapted to the specific requirements of the Diploma in Electrical Engineering (Electronic) (CEEE111). The main objective of this paper is to investigate the successful implementation of OBE as a strategic framework for measuring, monitoring, and evaluating the POs’ attainment for ETAC accreditation purposes. In this paper, the overall level of attainment of the POs Average and POs Density of the Diploma in Electrical Engineering (Electronic) program in Electrical Engineering Studies, College of Engineering in UiTM Pasir Gudang (PKE UiTMPG) was analyzed and conducted by OBE-ANAs v15.0 tool. The findings of this study demonstrate that successful OBE implementation plays a pivotal role in assisting programs to attain ETAC accreditation and providing structured strategies for ensuring that curriculum design, teaching techniques, and assessment align with these accrediting standards.

Keywords: Outcome based education; program outcomes; ETAC; engineering education; OBE-ANAS

The Westernization Movement in the Ottoman Period caused various changes in the field of arts and crafts. One of these changes was the hand-drawn decorations applied on cloth, wood and plaster. This change in architecture also brings with it the question of whether the architectural features of buildings with hand-drawn decorations are similar. The question of to what extent mosques built in different periods and regions in Anatolia are similar or different from each other in terms of architectural elements and decorative details plays a critical role in understanding the development of Ottoman architecture. The study particularly focused on mosques in Çankırı without minaret and with intensive hand-drawn decorations, and investigated whether these mosques had a common architectural style. The research used methods based on literature review, on-site observation and documentation. First of all, previous studies on Ottoman-era mosque architecture were examined and evaluation criteria were established. Then, in line with the observations made in the field, the plan and architectural features, material and technique, and decorative features of Hacı Muradı Veli Mosque, Karadayı Mosque and Hacı Mustafa Efendi Mosque were determined and evaluated in line with these criteria. As a result, it can be said that although the mosques were built in different periods, they are quite similar in terms of plan and architectural features, but they differ in terms of materials and construction techniques, and it is necessary to avoid making a clear statement about whether they have a common architectural style in terms of decoration.

Keywords: Wall paintings; Ottoman Empire; mosque architecture; Çankırı

Buildings have played a crucial role in shaping the environmental space of sustainability. In Malaysia, the introduction of the Green Building Index (GBI) represents a significant effort to advocate for sustainable building designs, with a focus on energy efficiency and technological advancement. Against this backdrop, this paper explores the potential of emerging drone technology in assessing and enhancing the environmental sustainability of building designs. It utilizes drone-assisted imagery modelling to analyse thermal performance in two GBI-certified buildings in Putrajaya: the Z10 Tower and the Ministry of Science, Technology, and Innovation (MOSTI) building. The study is conducted with the objective of utilizing drone technology for thermal pattern inspections. In response to this objective, four methodological procedures are employed: site selection, drone equipment identification, mapping technique planning, and aerial imaging documentation. The findings reveal varied responses in terms of the buildings’ compositions and materiality to sun heat exposure, illustrating the intricate interplay between architectural design and environmental spaces. For instance, the Z10 Tower’s metal roof reaches 53°C by noon, whereas its concrete roof attains 45°C, and its north-facing glass window cools significantly by 4 p.m. Similarly, the MOSTI building exhibits temperature variations based on orientation, with the roof peaking at 51°C and the solar panel at 49°C. These temperature variations inform the targeted design interventions proposed in this study, specifically the installation of louvres to enhance the building’s sustainable design. As an exploratory investigation, this study calls for more interdisciplinary collaboration between design thinking, architecture, and engineering studies.

Keywords: Buildings; design interventions; drone technology; energy efficiency; environmental sustainability; Green Building Index (GBI).

This study investigates the potential health effects of electrical and magnetic fields generated by high voltage power
transmission lines (HVTLs), with a particular focus on farmers who are exposed to these fields in different weather conditions. HVTLs emit low frequency electromagnetic fields that have been shown to be harmful to human health. Therefore, it is important to raise awareness about the risks associated with living in close proximity to HVTLs. The main objective of this study is to develop a GUI-based software system that enables the communication of electromagnetic field exposures using various parameters, including different climate conditions affecting the electric field, the effects of electromagnetic fields on people of different ages, and the identification of areas that require additional safety measures through the development of an appropriate mathematical model. To achieve this goal, the study integrates the GUI with various sensors and user-defined functionality. The developed software system provides valuable insights into the potential health effects of HVTLs by generating output electromagnetic field data that can be used to identify likely disorder statistics. The study utilizes data from reputable sources such as the WHO and ICNIRP to inform the analysis of disorder statistics. The findings of this study contribute to the ongoing discussion surrounding the potential health effects of HVTLs and highlight the importance of developing effective tools for monitoring and communicating electromagnetic field exposures. The developed GUI-based software system represents a significant step forward in this direction, providing a valuable resource for researchers, policymakers, and the general public.

Keywords: High Voltage Transmission Lines; GUI software; diseases; health

The extensive generation of Polyethylene Terephthalate (PET) waste, exceeding 50 million tons annually, presents both an environmental challenge and an opportunity for sustainable geotechnical applications, particularly in soil stabilization. While various forms of PET materials, including shredded materials, fibers, sheets, and strips, have shown promise in soil reinforcement, there are inconsistent findings regarding their effectiveness across different soil types and PET configurations, necessitating further investigation into the optimal use of PET sheets for soil stabilization. The purpose of this study is to determine the physical properties of polyethylene terephthalate (PET) stabilized soil. Amount of 0.3%, 0.6% and 0.9% of PET from the dry weight of soil with dimension of 20mm length and 10mm width were mixed with soil. The compaction test results show that the optimum moisture content (OMC) of the soil sample increased to 15.3% at 0.3% and 0.6% PET content, but decreased to 14.3% at 0.9% PET content. A similar trend was observed for the maximum dry density (MDD) of the soil, which increased to 1.79 Mg/cm³ at 0.3 and 0.6% PET content but declined to 1.75 Mg/cm³ at 0.9% PET content. From the permeability test, 0.9% PET content was found to be optimal for reducing the water flow rate within the soil sample, yielding a hydraulic conductivity of 1.033 cm/s. Meanwhile, 0.3% PET content proved optimal for increasing the CBR value of the soil sample to 21.81%. These findings indicate that PET waste exhibits considerable potential as a soil
stabilizer, wherein the optimum content is governed by the desired enhancement in specific engineering characteristics.

Keywords: Geotechnical properties; soil stabilization; polyethylene terephthalate sheet

Sludge is associated with high concentrations of organic contaminants, unstable and difficult to manage. One of the methods to overcome this is by using the Solidification and Stabilization (S/S) method. However, this method is generally ineffective in treating organic compounds contained in sludge and requires additional treatment. Thus, this study evaluates the performance of the sequence treatment method of bioaugmentation and the S/S method to treat municipal sewage sludge. The fungi strain Aspergillus brasiliensis ATCC© 16404 was used in bioaugmentation treatment while Portland Cement (PC) was used as a binder for the sequence treatment. A series of laboratory tests for different ratios of S/S matrices are conducted which are a compressive strength test and a leaching test (pH, COD,and BOD test). As a result, sample C, with a ratio of 50% sludge and 50% cement, is the best ratio in this sequence treatment due to adequate compressive strength for safe disposal (above 0.35 Mpa) and also generates low COD and BOD levels. Results BOD/COD ratio indicate that all the samples after treatment show high stability. The findings improve the utilization of sludge waste for safe disposal and beneficial use, especially in the production of interlocking bricks. Partnering with manufacturers and waste companies can help integrate this method into existing facilities.

Keywords: Bioaugmentation; interlocking brick; sewage sludge; sequence treatment; solidification and stabilization method

Based on offshore operations for remote unmanned platform philosophy, an emergency shutdown (ESD) can only be manually and locally reset by human intervention. Personnel shall be dispatched from the nearest manned facilities to verify that the impacted offshore facilities are safe before manually resetting the fire and gas system and resuming hydrocarbon production. However, sending personnel via boats to remote unmanned platforms can be challenging and pose an increased risk to humans, especially during adverse weather events such as a monsoon, which may prolong to two weeks until weather conditions subside. Furthermore, the fire and gas system at a remote unmanned offshore platform is prone to failure, giving wrong signals and causing the platforms to shut down spuriously, causing unnecessary delays in oil and gas production. This research aims to demonstrate the feasibility of using drones instead of sending personnel to ESD-affected remote unmanned platforms to address spurious ESDs caused by faulty fire and gas detectors. The design references international industry standards of the International Society of Automation ISA TR84.00.07-2018 and the National Fire Protection Association NFPA 72, which resulted in a 5-meter grid track size for the drones to travel and perform the verification process. Economic analysis also demonstrates this research project is viable. Despite the high discount factor of 25%, the net present value remains positive at USD 1.5 million after 15 years, with a healthy profit-investment ratio value at 2.93 for 15 years of operation and a positive internal rate of return of 46%.

Keywords: Emergency shutdown; remote reset; unmanned offshore platform; drone

The Malaysian construction sector demonstrated significant recovery in 2022, driven by the revival of major infrastructure projects like the Mass Rapid Transit 3 (MRT3) and increased residential project approvals. The sector is projected to grow by 7.1%, reaching MYR 181,599 million. However, this growth is overshadowed by a persistently high rate of occupational injuries, with the construction industry accounting for 59.8% of total workplace fatalities from January to November 2022. This study investigates the impact of safety management practices on construction workers’ familiarity with standard operating procedures (SOPs) and their engagement with safety measures. The research adopts a quantitative approach, utilizing a structured questionnaire distributed to experienced construction professionals. The study evaluates the relationship between key safety management practices including management commitment, safety training, worker involvement, and the adequacy of SOPs and workers’ safety behavior. The theoretical framework is rooted in safety management literature, focusing on the link between safety protocols and behavioral outcomes. Preliminary findings suggest that insufficient management commitment and inadequate safety training significantly hinder workers’ understanding and adherence to safety procedures, leading to elevated accident rates. This research highlights the importance of robust safety management practices to reduce workplace fatalities and improve operational efficiency in the Malaysian construction industry. The insights derived from this study will inform policymakers and industry stakeholders, providing actionable recommendations to foster safer working environments and enhance the sustainability of the sector.

Keywords: Safety management practice; safety participation; awareness level; Standard Operating Procedure (SOP)

The increasing demand for efficient renewable heat transfer fluids in solar system applications has intensified interest in exploring bio-oil as a renewable and sustainable alternative to conventional thermal fluids. Bio-oils, derived from biomass, offer a promising alternative for industrial applications. However, their relatively low efficiency has prompted the incorporation of nanoparticles to create bio-oil-based nanofluids. Despite the notable improvement in heat transfer effectiveness, the full-scale implementation of bio-oi-based nanofluids is constrained by several critical challenges. These include the tendency of the nanoparticles to aggregate over time, compromising long-term stability, and inherently high viscosity of the bio-oil fluid, which hinders the fluid flow and energy efficiency. Additionally, thermal stability under varying operating conditions and bio-oil scalability for industrial applications remains a significant concern. The present review comprehensively examines the potential of bio-oil-based nanofluids as an improved heat transfer fluid, focusing on their environmental and sustainable benefits over conventional fluids. It also presents recent advanced studies on bio-oil-based nanofluids, addressing critical issues related to long-term stability, thermal stability, and high viscosity through different preparation and characterization techniques to improve the thermophysical properties to optimize their performance in solar thermal systems. The review identifies key challenges and prospects for full-scale implementation of bio-oil-based nanofluids in solar thermal technologies. It aims to provide researchers in this field with valuable insight into advancing an improved heat transfer fluid that is environmentally friendly and suitable for solar thermal applications that contribute to the global transition toward a greener future.

Keywords: Bio-oil; nanofluid; stability; heat transfer fluid; sustainability; enhancement

During the assembly phase of cylindrical lithium batteries, the beading process is essential for establishing a support platform required for the installation and sealing of the top cap assembly. This process involves rolling grooves on the battery shell. Despite this, the beading process often results in the formation of cracks in the cylindrical battery can. To this end, endeavors were hereby made to analyze the reasons for the cracks of the beading portion of the 18650-lithium battery can shell. Through the analysis of the microstructure of the can shell and finite element simulation, an in-depth study of the key parameters and series of experiments in the beading process was conducted, summarizing the fundamental causes of cracks in the beading process. The regression analysis result reveals obvious regularity of each influencing factor. The influencing factors in the order of most to least significant are as follows: i. beading knife feeding speed; ii. can thickness; iii. swaging part type; iv. beading holder revolutions speed; and v. beading knife radius (R) and beading knife shape. The feeding speed of the beading knife and the thickness of the can have been identified as significant factors in this study. Optimal process parameters include a beading knife feeding speed of 4.98 mm/s, a can thickness of 0.3 mm at the beading section, a 3-jaw swaging part with a size of 18.2 mm, a holder revolution speed of 1000 RPM, and a beading knife radius & shape of 0.5 (previous specification).

Keywords: Cylindrical lithium batteries; cracks; can shell; beading process; finite element analysis

State-of-charge (SoC) balancing control is essential in a Battery management system (BMS) of an Electric vehicle (EV) since it aims to maximize the accessible SoC of each cell, which in turn enhances the overall capacity of the battery system. Cell imbalance can have a negative impact on the battery system, without SoC balancing control, some cells might suffer overcharge or deeply discharge than others, affecting the overall performance of an EV. This work presents a comparative study of three emerging DC-DC converters, notably Zeta, SEPIC, and Ćuk converters as well as three controllers namely Proportional integral (PI), Artificial neural network (ANN) and Reinforcement learning (RL) to select the best converter and controller. The comparative study demonstrated that a Zeta converter
with an RL controller is the most efficient in terms of output voltage ripple, voltage stress on output voltage, and settling time. A simulation model is developed in MATLAB/Simulink using twenty Lithium-ion battery (Li-ion) cells where this integration intelligently selects active cell combinations to meet the load, aiming to perform rotation among the cells so that they are not overcharged or deeply discharged. A hybrid SoC balancing control incorporating voltage-based using RL controller is designed to perform cell balancing within the battery packs of an EV. The simulation results demonstrated that SoC convergence among twenty Li-ion cells (with SoC difference as little as 0.5 percent) occurs within 10,000 seconds using the proposed hybrid novel integration.

Keywords: Battery management system; electric vehicle; neural network; reinforcement learning; state-of-charge; Zeta converter

The optimal pitch value for a fixed-tilted bifacial grid-connected photovoltaic system is crucial to maximize technoeconomic benefits. The insufficient studies on the optimal pitch value for bifacial photovoltaic (bPV) systems in tropical climates, with self-shading and electrical mismatch caused by design errors of tilt angle and pitch coupling, present a significant challenge for maximizing system efficiency and lead to financial losses. This study investigates the optimal pitch value for a 45 MW fixed-tilted bifacial grid-connected photovoltaic system for solar farms in Sungai Petani, Kedah via simulation using PVsyst software. The study simulated three cases which were one one module in height (Case A), two modules in height (Case B), and three modules in height (Case C) without space constraints. The bifacial design key parameters of albedo, module height, pitch, and tilt angle were incorporated into the simulations. The simulations revealed that increasing the pitch enhances specific yield but reaches a saturation point. The saturation point represents the optimal pitch values, which were 4.1 m, 5.5 m, and 7.2 m, for Cases A, Case B, and Case C. Moreover, the corresponding GCRs and specific yields for Case A, Case B, and Case C were 41.4%, 62.1%, and 71.3%, and 1568 kWh/kWp/yr, 1522 kWh/kWp/yr, and 1501 kWh/kWp/yr, respectively. It is worth highlighting that this study provides insight on the relationship of optimum pitch value with energy yield for cases of one, two, and three modules in height under scenario of no space constraint for tropical regions.

Keywords: Bifacial photovoltaic system; pitch; specific yield; ground coverage ratio; tropical climate

This paper examines the impact of delays in issuing Certificates of Completion and Compliance (CCC) on sustainable development. Such delays represent a significant obstacle to achieving sustainability goals, as they can hinder economic productivity and contribute to environmental harm. The primary aim of this research is to identify the major effects of delayed CCC issuance on sustainable development in Malaysia. A quantitative survey was conducted involving four respondent groups: architects, engineers, local authorities, and Green Building Index (GBI) facilitators. The study employs the Kruskal-Wallis statistical analysis method and Importance Index Calculation to analyze the data. The research reveals three key findings: the effects of ‘escalated costs,’ ‘revenue loss,’ and ‘pollution’ are deemed highly significant. Additionally, the impacts of ‘community disruption’ and ‘reduced investor confidence’ are considered important. These insights offer valuable guidance to policymakers, government agencies, regulatory bodies, and construction industry stakeholders, enabling them to make informed decisions and implement targeted strategies to address these challenges

Keywords: Impactss; certificate of completion and compliance; sustainable development; Malaysia

Accurate electricity demand forecasting is crucial for maintaining grid stability and optimizing energy resources. Traditional methods often fail to capture power load demand’s complex and variable nature. Hence, artificial intelligence techniques, such as Support Vector Regression, have emerged as promising tools for improving forecasting accuracy. Even though SVR has been used for electricity demand forecasting, most of the research has concentrated on input variables such as temperature and weather. This study aims to explore the potential of SVR in predicting electricity demand based on other characteristics such as land size, floor area ratio, build-up area, load density, diversity factor, and group diversity factor. An SVR model was built with data collected from industrial consumers in Malaysia Vision Valley. Hyperparameter tuning, including the selection of the kernel function and regularization parameters, was employed to enhance the model’s accuracy. The SVR model accurately predicted electricity demand, achieving high R² values, low MAE and MAPE across different training and testing scenarios. The best performance was recorded at a 70/30 training/testing split, yielding a mean absolute error (MAE) of 0.0059, a mean absolute percentage error (MAPE) of 0.82%, and an R-squared value of 99.97%. These findings highlight the effectiveness of SVR in capturing complex relationships between input variables and electricity demand, which can aid utilities in effective planning and resource allocation, ultimately enhancing grid reliability and efficiency, reducing operational costs, and making informed decisions regarding energy resources.

Keywords: Support vector regression; low-voltage distribution network; electricity demand; hyperparameter tuning.

Although most international green building rating tools emphasise green features to enhance indoor environmental quality (IEQ), several post-occupancy studies have reported occupant dissatisfaction with specific IEQ parameters during the operational stage of a building. Therefore, a post-occupancy evaluation (POE) was carried out in this study to assess the occupant perceptions and satisfaction with IEQ concerning thermal, visual, and acoustic comfort in a Malaysian green building. A total of 151 participants responded to a questionnaire survey employing the seven-point sensation, tolerance, preference, and acceptance scales to evaluate their experiences and satisfaction with their workspace environment. Even though the respondents predominantly expressed satisfaction with the relative humidity (86.1%), air movement (85.4%), and noise level (94.7%) conditions, their perceptions of temperature and lighting conditiong demonstrated certain variability. These results confirmed that the examined green building possessed comfort with well-designed workspaces, effective maintenance, and sustainable building practices. The findings highlighted potential areas for further improving the IEQ conditions based on the perceptions of the occupants. Future studies should consider a comparative analysis of IEQ data across similar building types while incorporating objective measurements with occupant surveys to yield a more precise assessment of IEQ.

Keywords: Green building; indoor environmental quality; occupant perception; post-occupancy evaluation; questionnaire

Analysing the literature related to construction delays, 74 papers that were published from 2018 to 2023 werereviewed. A comprehensive search was conducted thru numerous databases, including Scopus, Web of Science, and Google Scholar, using keywords such as “ Buildings construction delay, “This key word was used to select the linked papers. The review concludes that past construction delay studies centred on 3 main causes of delay factors, namely Input Delay Factors (4 factors), Internal Delay Factors (18 factors), and External Factors (5 factors), and these studies have proposed improvement on techniques and frameworks. The methodology involved a systematic review procedure, concentrating on input, internal and external factors such as project management inefficiencies, financial constraints, labor lacks, material procurement problems, etc. The study noticed that construction internal and external delay factors issues were the most frequently cited cause of delays. This review provides valuable awareness into delay mitigation strategies for construction authorities and experts. Overall, this article contributes to the existing body of understanding on building construction delay by synthesising and studying the latest research from selected journals. The identified causes of delay and the improvement techniques and frameworks outlined in this study provide valuable insights for researchers, experts, and decision-makers involved in the construction industry, simplify informed decision-making and improve construction project to mitigate delays in building construction projects.

Keywords: Delay causes; construction delays; office buildings; systematic review; buildings construction

Blockchain technology is a forefront innovation that drives multi-user data accessibility with patient ownership, traceability, transparency, security, and privacy protection. However, optimizing blockchain latency and its relationship with security remains underexplored in health data management. This study aims to optimize security and latency in healthcare data management through a novel Blockchain-based framework. The objectives include: (1) developing an innovative EMR management architecture that integrates key stakeholders using Blockchain and smart contracts, (2) creating a multi-objective optimization model for Hyperledger Fabric Blockchain in medical health management, and (3) implementing and evaluating advanced optimization techniques using block Size and consensusLevels as key decision variables. This study bridges the gap by proposing a novel methodology that integrates the Hyperledger Fabric framework with smart contracts to optimize security and storage in health data systems. A key contribution of this research is the development of a multi-objective optimization model employing two advanced non-dominated sorting genetic algorithms, NSGA-II and NSGA-III, to configure system parameters such as blockSize and consensusLevels. Evaluation results show that NSGA-II outperforms NSGA-III, achieving 50% higher Hyper-volume and five distinct non-dominated solutions compared to two, demonstrating its superior ability to balance conflicting objectives and provide optimal configurations. The proposed framework tackles critical challenges in health data interoperability, privacy, and security while ensuring robust, patient-centric data management. By enabling multi-user access, enhanced security, and optimized storage, this work advances Blockchain’s role in healthcare and positions the industry for the Digital Health era. Future work will expand objectives and enable real-time deployment.

Keywords: Electronic medical record; blockchain technology; NSGA-II; NSGA-III; hyper-volume

The swift and widespread adoption of electric vehicles (EVs) has resulted in a dramatic surge in the number of batteries being produced and utilized globally. However, this growth has also led to a concerning trend: a significant portion of these batteries is discarded annually, often through improper or environmentally harmful disposal methods. Such practices pose serious environmental risks, including pollution and the release of toxic substances into ecosystems. This remaining energy capacity makes them highly suitable for repurposing and extended use in secondary applications, such as energy storage systems, renewable energy integration, or other non-automotive uses. By leveraging these batteries for secondary purposes, we can not only mitigate environmental harm but also maximize their value and utility, contributing to a more sustainable and circular economy. Second-life batteries (SLBs) present a sustainable alternative to direct disposal, helping to minimize environmental harm while maximizing the energy and resources invested in battery production. Implementing SLBs in energy storage systems (ESS) offers a practical solution to battery waste while enhancing energy efficiency. However, ensuring safety and reliability remains a critical challenge, particularly in accurately predicting the State of Health (SoH) to prevent battery-related incidents. This paper explores the implementation of SLBs in ESS, their potential benefits, and the challenges associated with their adoption. It also examines existing barriers and proposes solutions to advance research in this rapidly evolving field. By addressing these challenges, SLBs can serve as a transformative technology in energy storage, paving the way for a more sustainable and resource-efficient future.

Keywords: Electric vehicle; end-of-life; energy storage system; second-life battery; state of health

The retina, a transparent neural tissue lining the eye, is crucial for detecting both ocular and systemic diseases through imaging techniques such as fundus photography, OCT, and angiography. Recent advancements in artificial intelligence and machine learning have significantly improved retinal image analysis, enabling automated classification of arteries and veins to assists in diagnosing conditions like diabetic retinopathy, glaucoma, and systemic diseases such as hypertension and Chronic Kidney Disease (CKD). However, pixel-level analysis of retinal features, particularly colour features, remains underexplored, with challenges such as image variability and non-linear feature relationships hindering optimal classification. This study addresses these gaps by analyzing nine colour features—Red, Green, Blue, Hue, Saturation, Value, Y, Cb, and Cr—extracted pixel-wise from regions of interest (ROI) across three datasets: DRIVE, HRF, and VICAVR. A total of 3864, 9804, and 7466 normalized artery and vein pixel values underwent F-tests and two-sample T-tests, revealing statistically significant differences (p <0.05) for all features. The Minimum Redundancy Maximum Relevance (mRMR) algorithm ranked features by relevance, and combinations of top-ranked features were evaluated using nine machine learning classifiers. The Ensemble model, utilizing Random Forest Bagging with Decision Tree learners and optimized hyperparameters, achieved the highest AUC scores: 91% (DRIVE), 88.2% (HRF), and 90.1% (VICAVR). Incorporating all nine features yielded the best classification results, emphasizing their complementary roles. These findings demonstrate the potential of colour features in improving retinal vascular analysis, offering insights for non-invasive diagnostics and disease monitoring.

Keywords: Artery-vein classification, colour feature, image processing, machine learning, pixel-level analysis

Marine vessels experience motion in six degrees of freedom, particularly during adverse weather conditions such as storms, heavy rain, rough seas, and strong winds. Gyroscopic stabilizers offer a promising solution to mitigate these motions, as they are unaffected by hydrodynamic drag and external factors like seaweed. This study focuses on the development and optimization of the gyro plate, a critical component of the gyrostabilizer system. Five gyro plate models, inspired by a published design from prior research, were created and analyzed using Finite Element Analysis (FEA) in SOLIDWORKS. The methodology included a mesh curvature study to ensure the accuracy of stress, deformation, and strain predictions under static loads. The results demonstrated progressive improvements from Models 1 to 5, with Model 5 emerging as the optimal design. For PLA Pro material, Model 5 achieved the lowest stress (2.18 MPa), minimal deformation (0.618 mm), and reduced strain, enhancing structural efficiency by minimizing stress concentrations and evenly distributing loads. While Models 1 and 2 stood out for their simplicity and cost-effective manufacturability, Model 5 balanced superior performance with high compatibility for 3D printing, requiring minimal post-processing. This study highlights the structural efficiency, reduced displacement, and manufacturability of the optimized gyro plate, paving the way for improved gyroscopic stabilization systems. The findings contribute to more efficient and reliable marine transportation, with potential applications for larger vessels.

Keywords: Gyro plate; optimal design; Finite Element Analysis (FEA)

The rehabilitation of individuals who have suffered a stroke primarily relies on physical therapy directed towards enhancing the affected limb capabilities and mitigating lasting disability. Clinical assessment of a stroke
patient is a critical step in determining the appropriate treatment and management of the patient’s condition. The clinical assessment also helps to determine the severity of the stroke which can affect the patient’s prognosis and the type of care that is needed. The dilemma with current clinical assessment in rehabilitation is that they are subjective and rely heavily on therapist’s experience, which leads to inconsistency and do not directly quantify patients’ ability to perform activities of daily living (ADLs). In this context, hand pose recognition and recovery estimation present significant academic and technical challenges due to complex structure and dexterous movement of human hands. This paper thoroughly examines the current research status of hand pose recognition and hand recovery estimation when performing ADL tasks, focusing on various types of hardware deployed for data collection. It carefully analyzes the advantages and drawbacks of hardware technologies and reviews the performances of different machine learning algorithms deployed. Furthermore, the clinical assessment tools were categories into four groups based on the evaluation methods. The current clinical assessments tools deployed in studies and utilized in stroke rehabilitation were reviewed. The summary of the existing research limitation and initiates a discussion on future research are provided.

Keywords: Hand pose recognition; hand recovery estimation; clinical assessment; machine learning algorithms

Sewage treatment is a critical process for ensuring that wastewater is treated to meet regulatory discharge standards. However, further polishing of the treated effluent is often required for applications such as vehicle washing, where water quality must meet specific criteria to prevent harm to users and potential damage to vehicles. This study evaluates the quality of treated effluent from a dedicated Sewage Treatment Plant (STP) after undergoing various post-treatment processes. Key parameters for physical, chemical, and biological, were analyzed to assess the suitability of the polished water for vehicle washing, referencing national drinking water standards as a benchmark. Review showed that the quality of the treated water for the targeted application should meet the national water quality standards (NWQS) Class I at the minimum, however best practice to follow drinking water quality standards. The turbidity, biological oxygen demand (BOD), chemical oxygen demand (COD), Ammonia nitrogen, oil and grease as well as e-coli/total coliform are parameters that do not meet the requirements upon treatments other than reverse osmosis. It is also confirmed that the membrane-based treatments, particularly RO, significantly improve water quality compared to conventional biological processes. RO-treated water exhibited characteristics comparable to local potable tap water, with complete removal of bacterial contaminants, confirming its safety for direct contact applications. In conclusion, integrating advanced membrane technologies such as ultrafiltration (UF) and RO into STPs can produce high-quality effluent suitable for vehicle washing without adverse effects. Several samplings and water quality analyses of the RO treated water have verified the consistency of the water quality that meets the drinking water standards. This study could serve for sustainable development goal (SDG) specifically for SDG 6 on clean water and sanitation, which is essential in ensuring the availability and sustainable management of water and sanitation for all.

Keywords: Treated water; sewage; membrane treatment; vehicle wash water; water quality

The abundant biomass derived from oil palm exhibits valuable characteristics of lignin and biocellulose, which hold considerable promise for conversion into various products. However, this process necessitates a suitable pretreatment strategy that achieves a balance between effective lignin removal and gentle cellulose extraction, thereby maximizing the potential of these biopolymer components. This research highlights the successful application of lignin and βcellulose obtained from oil palm biomass in the creation of an innovative biofilm product, emphasizing the efficient use of lignin biocellulose. A promising technique involving bacterial cellulose, which is gaining attention for new applications in the food and medical sectors, is assessed for its effectiveness in biomass pretreatment. The study establishes optimal pretreatment conditions that enhance lignin purity, lignin recovery, and holocellulose yield from oil palm biomass. A lignin purity of 88.2% was achieved, surpassing the 68.9-71.0% purity levels reported in existing literature, while maintaining comparable recovery performance. Furthermore, this method demonstrates a commendable level of cellulosic recovery with minimal disruption to the crystalline structure of cellulose. This results in enhanced lignin purity and recovery, while preserving cellulose crystallinity, which is essential for subsequent product conversion. Nonetheless, it was noted that the current enzymatic method is not fully effective in penetrating the lignin protective barrier during biomass pretreatment. Opportunities for improvement in this approach are identified, particularly in the development of ligninolytic enzymes that can compete with emerging alkaline pretreatment techniques.

Keywords: Oil palm biomass; lignin; biocellulose; biofilm; bacterial cellulose