Volume 37 (07) October 2025

Taguchi Method is a valuable tool that has been introduced to optimize the quality of products or processes. It effectively identifies the optimal combination with a minimal number of experiments. In the case of parts printed with Fused Deposition Modeling printers, the outcomes vary depending on changes in the printer process parameters, such as build orientation, layer thickness, and raster angles. This study focused on samples produced using Stratasys FDM 400 FDM 3D printer, evaluating their build time and surface roughness performance. To achieve optimal performance, the Taguchi Method was applied. For this project, an L9 (33) orthogonal array was employed, involving 9 sample pieces printed with ABS-M30i material to assess three parameters, namely build orientation, layer thickness, and raster angles, each at three different levels. The samples (mounting bracket) were designed with SolidWorks. They were then evaluated for build time and tested for surface roughness using the Mitutoyo Surftest SJ 301. The resulting data was analyzed, revealing that the samples with an XY orientation, a layer thickness of 0.33 mm, and a raster angle of 90° achieved the shortest build time. Conversely, the samples with an XY orientation, a layer thickness of 0.18 mm, and a raster angle of 30° demonstrated the lowest average surface roughness (Ra value). These findings indicate that the properties of samples produced with the Stratasys FDM 400 MC printer vary depending on the combination of process parameters.

Fused Deposition Modeling; Taguchi Method; ABS-M30i; process parameters; build time; surface roughness

Although the Iraqi government is seeking to advance the networks of the e-government project, many institutions in Iraq still rely on Wi-Fi technology for their internal networks due to its ease of installation. However, Wi-Fi networks suffer from low capacity and performance, instability, and difficulty scaling, which is due to poor design and failure to follow established standards. In response to the growing demand for higher data rates and more reliable services, many organizations have begun studying how to update or replace their internal networks. In this work, the internal communication network of the Informatics and Telecommunications Public Company (ITPC) was studied and analyzed as an example of one of the networks of the Iraqi institutions that adopt wireless local area networks (WLANs). This research presents a comparative study between an existing real-world network and a simulated model developed using OPNET. The institution’s internal network was evaluated and redesigned based on the actual infrastructure, services, applications, and subscriber numbers. Multiple scenarios measured key parameters such as received traffic, throughput, load, and delay. Networks designed and evaluated through simulation yielded significantly better performance. For example, one proposed scenario recorded traffic received of 9 MB with a delay of only 0.0000019 seconds, compared to 5.8 MB in the original network with a delay of 0.00020 seconds. The OPNET-based model enabled thorough examination of various design scenarios, providing insights into optimal hardware selection, transmission media, and topology configuration, reducing implementation costs and improving application performance.

E-government; ITPC; Iraq, OPNET; delay; load; traffic received; throughput

The growing emphasis on sustainable development has led researchers to investigate alternative materials aimed at mitigating the environmental impact associated with conventional bitumen production. A promising strategy involves the use of waste materials from the palm oil industry, specifically Oil Palm Decanter Cake (OPDC), a biomass by-product produced in substantial quantities. OPDC is characterized by its high moisture content, organic composition, and nutrient richness, alongside its significant biodegradability, rendering it a viable candidate for bitumen modification. This study examines the feasibility of employing OPDC and polyurethane (PU) as modifiers in 60/70 penetration grade bitumen. Experimental blends were formulated with varying OPDC contents (0%, 5%, 10%, 15%, and 20% by weight of bitumen), maintaining a constant proportion of PU, to evaluate their impact on the physical and chemical properties of the modified bitumen. Physical performance was assessed through penetration, softening point, ductility, viscosity, and storage stability tests, while chemical analysis was conducted using Fourier Transform Infrared (FTIR) spectroscopy. The findings indicated that OPDC enhances the stiffness and thermal stability of bitumen, with the 10% OPDC blend exhibiting the most favourable balance of physical and chemical performance. These results suggest that OPDC, in conjunction with PU, can serve as an effective and sustainable bitumen modifier, contributing to the advancement of pavement materials and promoting the valorisation of agricultural waste.

OPDC; oil palm waste; modified bitumen; physical; chemical

The constant current method is commonly utilized in concrete durability studies to accelerate corrosion in steel reinforcement. This study investigates the effects of different current densities, concrete cover thickness and type of chloride exposure on the corrosion rate of steel reinforcement. A total of eleven small-scale concrete slabs were tested, each reinforced with 10 mm diameter steel bars. Corrosion was induced using electrical power supplies, with 5% NaCl electrolyte solution. The current (A) and crack response were continuously monitored on all slab’s surfaces to assess the impact of expansive corrosion products. Gravimetric analysis was used to measure actual mass loss of steel reinforcement and was compared against theoretical values. Results showed that specimens with 5% NaCl admixture exhibited higher mass loss despite no visible surface cracks, due to enhanced early-age strength and stronger concrete bond. Submerged specimens in electrolyte pond showed greater corrosion than those with electrolyte tank on top of slab. Higher current densities also resulted in higher corrosion rate but produced nonuniform results at 500 µA/cm². However, specimens subjected to 200 µA/cm² displayed more consistent corrosion, with better correlation to theoretical predictions. Thicker concrete covers showed minor improvement in delaying corrosion by reducing chloride penetration. The analysis shows that, the best combination of parameters for simulating faster corrosion rate with realistic damage is through NaCl admixture, immersion exposure, thinner concrete cover and a current density of 400 µA/cm². These findings offer valuable guidance for improving accelerated corrosion testing and assessing reinforced concrete durability in chloride environments.

Induced corrosion; mass loss; concrete cover; chloride exposure

Vibration-assisted cutting (VAC) has transitioned from industrial applications to orthopedic bone cutting. However, the anisotropic nature of bone complicates the process due to its intrinsic toughening mechanisms and susceptibility to surface damage. Finite element analysis (FEA) is widely used to evaluate cutting efficiency, but simulating toolbone contact remains challenging due to large deformations causing convergence issues. To simplify modeling, this study develops a Python-based micro-modeling framework incorporating user-defined bone microstructure and porosity. Two models, representing young and aged bones, are validated by comparing stress intensity factors with analytical results for a single-edge notch bending (SENB) specimen, yielding a 4.5% deviation. Using the extended finite element method (XFEM), these models are used to examine the effects of amplitude and cutting depth on VAC performance. Results indicate that VAC reduces cutting force by up to 30.6% in aged bone compared to conventional cutting, with force reductions being more pronounced at higher amplitudes. Cutting stress is also reduced, particularly at 40 μm depth, where VAC decreases von Mises stress by 40.6% in aged bone and 9.63% in young bone. Crack propagation patterns show smoother and more controlled paths under VAC, particularly at shallow depths. Although VAC results in higher local cutting temperatures, the affected area is more confined, potentially leading to reduced chip sizes. These findings highlight VAC’s potential to enhance orthopedic bone cutting by reducing force and stress while improving crack control and minimizing thermal damage.

Vibration-assisted cutting; cortical bone; microstructure; crack propagation; extended finite element analysis

The fracture mechanics of quasi-brittle materials, such as concrete, ceramics, and rocks, pose significant challenges due to their nonlinear stress-strain response, microstructural heterogeneity, and complex failure mechanisms. Traditional numerical and analytical methods often fall short in capturing the full intricacies of fracture propagation and damage evolution in such materials. However, recent advances in machine learning (ML) offer promising solutions to these limitations by enabling data-driven insights and enhanced computational performance. In this review paper, we explore the growing role of ML techniques in the fracture analysis of quasi-brittle materials. By leveraging large and diverse datasets from experiments and numerical simulations, ML models not only complement traditional fracture mechanics approaches but also introduce novel capabilities such as real-time damage prediction, adaptive modelling, and improved generalization across varying material conditions. The integration of data-driven models with physics-based frameworks, especially through hybrid techniques like physics-informed neural networks (PINNs), marks a significant shift in how fracture phenomena are modelled and understood. Key ML methods discussed include artificial neural networks (ANNs), convolutional neural networks (CNNs), and PINNs, with a focus on their respective advantages and implementation strategies. The review highlights how these approaches can enhance safety, efficiency, and predictive accuracy in engineering applications, ultimately making machine learning a transformative tool in the study of quasi-brittle fracture behaviour.

Machine learning; fracture mechanics; quasi-brittle materials; crack characterization

Natural asphalt is a very important material that has been in use by man for various purposes for centuries. There are numerous advantages, such as being more environmentally friendly as opposed to artificial alternatives, great versatility, and cost-effectiveness, that make it an important natural resource. The use of asphalt mixtures should be tailored within target limits to improve the serviceability of flexible pavements by enhancing their ability to withstand rutting, waviness, fatigue cracking, or moisture damage, which in turn can present opportunities for cost savings and pollution reduction. Most of the work has focused on the application of NAs in asphaltic formulations, considering their good performance in these systems and the binding properties of the mixtures. The primary goals of this research are to examine previous related studies about the use of natural asphalt in the manufacturing of hot-mixed asphalt for the construction of flexible highway pavement and to use some types of natural asphalt in the modification of petroleum asphalt cement properties. This study deals with the description of the resources and uses of natural asphalt in the production of hot-mix asphalt to construct flexible pavements. According to past related studies, the use of natural asphalt as a modifier with petroleum asphalt cement showed improvement of asphalt and hot mixture properties.

Natural asphalt; lake asphalt; rock asphalt; gilsonite; Marshall test; penetration

Bridges serve as indispensable lifelines in modern transportation infrastructure, linking highways, railways, and geographically complex regions. This study investigates the static and dynamic behavior of concrete highway bridges to enhance structural resilience, with a focus on damage assessment methodologies and advanced rehabilitation strategies. The results show that dynamic loads, like heavy traffic, accelerate the production of cracks by causing stress concentrations to be 15-20% larger than under static conditions. Concrete’s compressive strength can be lowered by up to 30% after prolonged exposure to moisture and freeze-thaw cycles. Furthermore, prolonged exposure to moisture and freeze-thaw cycles reduces concrete compressive strength by up to 30%, significantly compromising structural integrity. Retrofitting damaged regions with carbon fiber-reinforced polymer (CFRP) is highly successful; it restores performance while increasing load-bearing capacity by 25-35%. Field investigations identify bridge joints and midspan sections as significant damage hotspots requiring priority maintenance. These findings underscore the need to reduce risk through proactive maintenance procedures and integrated monitoring systems. The proposed framework provides practical recommendations for optimizing sustainable infrastructure management, ensuring safety, and extending bridge service life. This study directly improves the durability and reliability of concrete highway bridges in the face of shifting demands by linking theoretical analysis to practical solutions.

Bridge performance; static; dynamic; damage assessment; finite element method

Magnetic reconnection is an essential mechanism in the collisionless plasmas that enables the transformation of magnetic energy into plasma kinetic energy and thermal energy. The efficiency of energy conversion during magnetic reconnection is measured by the rate of reconnection. This article focuses on investigating the relationships between reconnection characteristics and the reconnection rate, via in-situ observations (including measurements of magnetic field, electric field, ion and electron plasma moments) of magnetic reconnection in the Earth’s magnetotail, as recorded by the Cluster spacecrafts from 2001 to 2005. Thirteen reconnection x-line events were selected for examining correlation coefficients (r) between the reconnection rate and reconnection characteristics (e.g., current sheet thickness, current density, lobe magnetic field, inflow speed, Alfvén speed, reconnection electric field, and converging electric field). The correlations of the reconnection characteristics were classified into three categories, i.e., |r| > 0.6 (good), 0.3 < |r| ≤ 0.6 (ambiguous), and 0 < |r| ≤ 0.3 (no correlation). The results show that a good correlation is obtained for lobe magnetic field, inflow speed, and converging electric field, while an ambiguous correlation is observed for reconnection electric field and current density. No correlation is found for Alfvén speed and current sheet thickness. This study provides insights into key physical parameters that influence fast magnetic reconnection (lobe magnetic field, inflow speed and converging electric field) and highlights the complexity of reconnection processes that may cause unresolved correlation in space plasma dynamics.

Magnetic reconnection; reconnection rate; magnetotail; current sheet; collisionless plasmas

The singgora roof is an important element in traditional house architecture in Malaysia, especially in Terengganu and Kelantan. The process of making singgora roofs since the 1800s without modern technology has proven the production’s uniqueness. The special thing is that the singgora roofs that have been used can be removed, resold and reused in new building structures. Despite being increasingly extinct since the 1940s due to modernization, costs and labor constraints, the reuse of singgora roofs offers great potential for sustainability and environmentally friendly building materials. This study aims to highlight the relevance of singgora roof reuse in architectural structures in Malaysia using qualitative methods involving primary and secondary sources. The results show that the reuse of singgora roofs not only saves costs and time but also reduces waste of building materials, preserving heritage elements in Malay architecture. Apart from that, the reuse of singgora roofs which involves the process of inspection, removal, arrangement and installation on a new structure applies the principle of Design for Deconstruction and Reuse (DfD). This sustainability aligns with the Sustainable Development Goals (SDGs) 2030, which focus on industry, innovation and infrastructure (SDG 9), sustainable communities and cities (SDG 11) and responsible consumption and production (SDG 12). In conclusion, this study can enhance the reuse of singgora roofs from a sustainability perspective and efforts to implement aesthetic values and preserve the heritage of traditional Malay architecture.

Sustainability; singgora roof, architecture; traditional house; Malaysia

The global semiconductor industry is expected to reach $650 billion by 2024, spurred by the adoption of 5th Generation (5G), artificial intelligence (AI), and Internet of Things (IoT) technologies, making the electronics and semiconductor sector a key component of technological innovation worldwide. With 13% of the global market for semiconductor assembly, packaging, and testing, Malaysia is a key player. The electrical and electronics (E&E) sector contributes 5.8% of the nation’s GDP and 40% of its exports. By improving fault detection accuracy to over 90% and lowering maintenance costs by up to 25%, the incorporation of AI into semiconductor production has revolutionized processes. The $5.3 billion National Semiconductor Strategy and Malaysia’s Industry 4.0 goals align with AI-powered solutions that maximize productivity, anticipate equipment faults, and encourage sustainable practices. The review focuses on the integration and effectiveness of AI in the electronics and semiconductor industry including the utilization of AI in quality control and inspection, and inventory management. Emphasizing its impact on productivity, innovation, and global competitiveness particularly for fault detection, predictive maintenance, sustainable manufacturing practices, productivity enhancement, and economic contribution of semiconductors. The challenges of high energy consumption associated with AI infrastructure are also discussed. However, it is realized that the application of AI has greatly increased productivity, quality, and efficiency by reducing waste and managing to build robust and adaptable manufacturing ecosystems. Future advancements in AI, including digital twins and robotics, could create strong and flexible manufacturing systems, leading to a more innovative and resilient semiconductor industry.

5G, artificial intelligence (AI), Internet of Things (IoT), electronics and semiconductor industries

Gridded datasets are essential for climate monitoring in regions with sparse observational data, but they often face challenges in capturing local climatic variability and accuracy. This study evaluates three widely used gridded precipitation datasets (TerraClimate, CHIRPS, TAMSAT) and three potential evapotranspiration (PET) datasets (TerraClimate, MODIS, ERA5-Land) against observations from seven stations in Nineveh, Iraq. The primary objective is to assess the accuracy of these gridded products in replicating observed precipitation and PET values using key statistical metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Normalized RMSE (NRMSE), Percent Bias (PBIAS), Nash-Sutcliffe Efficiency (NSE), coefficient of determination (R2), and Kling-Gupta Efficiency (KGE). Compromise Programming (CP), a decision-making tool that integrates multiple statistical metrics into a unified composite score, was applied to rank the performance of the gridded datasets. The findings reveal that TerraClimate outperforms other products for precipitation and PET, with an average MAE of 30.54 for precipitation and 39.48 for PET, and KGE of 0.79 for precipitation and 0.84 for PET. CHIRPS and TAMSAT rank second and third for precipitation, while MODIS and ERA5-Land follow TerraClimate for PET. This study emphasizes the importance of evaluating gridded data to ensure its accuracy and reliability, particularly in regions with limited ground-based observations. The study’s contribution lies in employing CP to aggregate multiple statistical metrics into a composite score, providing an effective framework for resolving discrepancies in ranking gridded datasets. This supports the climate impact assessments and environmental management in regions like Iraq with limited meteorological data.

Climate monitoring; Compromise Programming; Gridded datasets; TerraClimate; CHIRPS

Aluminum-doped zinc oxide (AZO) thin films for thermoelectric solar energy conversion are investigated in this work. It examines how different substrates and sputtering powers affect the morphology and thermoelectric characteristics of AZO thin films. Polyimide, quartz, and fused silica were selected as substrates to explore this interaction. Utilizing radiofrequency (RF) magnetron sputtering, AZO thin films were deposited onto the substrates. The impact of the sputtering power on the films was examined by varying it between 200 W, 250 W, and 300 W. The distance between the target and substrate was kept at 5 cm, the argon gas flow rate was kept at 10 sccm, and the sputtering period was fixed at one hour. All other deposition parameters were kept constant. The SEM study showed that thicker AZO thin films and a rougher surface texture were the outcomes of increasing the sputtering power. On the other hand, the EDS analysis confirmed that all predicted components were consistently present in the AZO composition for every substrate that was examined. In addition, the Seebeck coefficient, electrical conductivity, and thermoelectric power factor (PF) were measured using a specially designed experimental setup to characterize the thermoelectric properties. The results revealed that the substrate material significantly influenced the morphology of the AZO thin film, which subsequently impacted their thermoelectric properties. Fused silica yielded the most promising results, achieving a power factor of 9.362 nW/mK², conductivity of 2.817 S/m, and Seebeck coefficient of -56.58 μV/K at 150° C and a sputtering power of 300 W. The study highlights the critical role that substrate selection and deposition parameters play in optimizing the thermoelectric performance of oxide-based thin films. Higher sputtering power generally improved film thickness and conductivity but also introduced surface roughness that varied depending on substrate smoothness. Polyimide, due to its naturally rougher surface, exhibited more irregular AZO film growth compared to quartz and fused silica. These structural differences translated into noticeable variations in the thermoelectric behavior. The findings suggest that optimizing both deposition power and substrate type can enhance the efficiency of AZO thin films in solar-thermal energy harvesting applications. This work contributes to ongoing efforts to develop cost-effective, non-toxic, and stable thermoelectric materials suitable for integration in flexible and transparent energy systems.

Aluminum Zinc Oxide thin film; fused silica; quartz; polyimide; power factor (PF)

This study investigates the microstructural and mechanical property changes in P91 steel used in the main steam pipe of a 700 MW sub-critical power plant after 130,000 hours of operation at 548°C and 19.4 MPa. P91 steel, valued for its high-temperature strength and corrosion resistance, is a critical material in thermal power plants. This research evaluates the effects of prolonged high-temperature exposure on the material’s microstructure, hardness, and tensile strength by comparing samples of the original (virgin) P91 material with those subjected to longterm operation. Tensile and hardness tests, following ASTM E8M and E21 standards, and microstructural analysis using optical microscopy were conducted. Results indicate a reduction in hardness from 224 HV in the virgin sample to 214 HV in the exposed sample, which remains within the acceptable range for P91 (196 HV to 265 HV) but signifies some softening due to microstructural changes. Tensile testing revealed a decrease in maximum tensile strength from 705.9 MPa in the virgin sample to 671.6 MPa in the exposed sample, alongside a slight increase in elongation at break (21.3 mm in the exposed sample vs. 20.5 mm in the virgin sample). Microstructural analysis showed coarser tempered martensitic grains and increased precipitate size and density in the exposed material, consistent with thermal aging effects. These findings highlight the gradual degradation of mechanical properties, including reduced hardness and plasticity, underscoring the importance of regular monitoring to ensure the long-term reliability of power plant components. This study contributes to a better understanding of P91 steel’s durability and structural integrity under prolonged operational stress, supporting predictive maintenance and material performance assessment in high-stress environments.

P91 alloy; microstructure stability; mechanical properties; hardness; power plant

Flywheel Energy Storage System (FESS) plays a crucial role in enhancing power quality in renewable energy systems, particularly in isolated micro-grids. However, challenges such as power supply consistency, lack of inertia from power electronics converters, and high starting currents in induction motors necessitate further improvements. This study aims to improve FESS performance by introducing a speed-controlled double star-delta configuration with low-power double drives for flywheel motor-generator (FMG) synchronization. The proposed methodology involves developing a prototype system with a double three-phase star-delta configuration, employing a low-power induction motor for FMG speed synchronization. A Programmable Logic Controller (PLC) with a Human-Machine Interface (HMI) is integrated for control and monitoring, utilizing a PID-based closed-loop control system. The system is tested in a laboratory environment with various flywheel weights to evaluate its performance. Experimental results demonstrate that the double star-delta configuration effectively synchronizes FMG speed while reducing power consumption. The star connection provides stable frequency and lower starting torque, while the delta connection offers higher torque, ensuring efficient flywheel acceleration. The experimental results showed that this system of hybrid star-delta with a double low-power three-phase induction motor can drive a high-inertia flywheel of up to 100 kg and synchronise the speed of the system to the nominal speed of the generator up to 1500 rpm. The proposed system minimizes energy losses, optimizes starting conditions, and reduces overall development and maintenance costs. These findings indicate that the integration of a low-power double motor and a controlled switching mechanism significantly enhances FESS efficiency, making it a viable solution for sustainable energy applications.

Flywheel Energy Storage System; Flywheel motor generator system synchronisation; power consumption reduction; voltage and frequency stability.

The rapid expansion of palm oil production has significantly increased the generation of biomass residues, notably palm oil mill effluent (POME). As the palm oil industry shifts towards environmental sustainability and competitiveness, it is essential to address the ecological challenges posed by POME. This study investigates the membrane fouling propensity and mitigation strategies during the concentration of anaerobic palm oil mill effluent (AnPOME) using Fertilizer-drawn forward osmosis (FDFO). Utilizing monoammonium phosphate (MAP) as the draw solution (DS), the study evaluates the fouling behaviour and cleaning efficacy of hydraulic flushing (HF) and osmotic backwashing (OB). Experiments revealed that initial water flux declined sharply due to the deposition of foulants on the membrane’s active layer, followed by a steady decrease. Physical cleaning effectively recovered the flux, with OB demonstrating superior performance in restoring membrane function. While the combination of HF and OB managed to remove the foulants, but they did not completely restore the water flux. The initial cleaning by HF resulted in an 88.3% flux recovery. In comparison, the subsequent cleaning by OB achieved a 103.9% flux recovery, indicating that OB effectively restored the flux and improved the membrane condition. Although both HF and OB can partially alleviate fouling, further optimization and potential combinations of these methods are necessary to enhance cleaning efficiency and sustain the long-term operational performance of the membrane.

Fertilizer-drawn forward osmosis; fouling; physical cleaning; hydraulic flushing; osmotic backwashing

The widespread occurrence of residual soils in Malaysia presents a significant challenge to the construction industry due to their highly unpredictable and variable nature. Residual soils, which include types such as granite residual soil and sedimentary residual soil, are formed through the weathering of parent rock, and they are commonly found across various regions in Malaysia. This study specifically focused on saturated sedimentary residual soil from Jengka area in Pahang, which has been less explored compared to other types of residual soils. Given the lack of prior research on this soil type, it was chosen for detailed investigation to better understand its physical, mechanical and critical properties. A total of 12 remolded soil samples, each with dimensions 2 in 1 which is 100 mm in height and 50 mm in diameter, were tested using consolidated drained triaxial testing methods to determine key parameters. The results classified the saturated sedimentary residual soil as silty sand with medium plasticity. The mechanical strength values recorded for the soil include an average effective cohesion (c’) of 27.7 kPa and an effective friction angle (ϕ’) of 30°. In addition to these mechanical properties, four average critical soil parameters were documented which are M 1.4637, λ 0.102, κ 0.018, and Г 2.019. These values are essential for accurately predicting the behavior of sedimentary residual soils, providing engineers with the data needed for safer geotechnical structure design, including retaining walls and building foundations.

Residual sedimentary soil; physical; mechanical; critical state; remolded

The structural activity of any structure under horizontal seismic stress will determine its classification for design reasons. Response spectra relevant to the site location, such as proximity to significant active faults, site subsurface conditions, the design earthquake loadings are defined by the ductility modification factor, the structural performance factor, and the determined yearly likelihood of surpassing the design earthquake. After every earthquake, a structural safety assessment must be conducted. The significance of life and material losses underscores the relevance of efforts in earthquake engineering. The primary aims of this work are to examine previous research concerning the seismic resistance assessment of highway bridges subjected to lateral earthquake forces and to analyze and elucidate the seismic design methodologies. The subjects mentioned in this study were bridge structural components which were affected by earthquake action such as piles, piles cap, piers, piers cap and abutments, evaluation of seismic design of bridge supports, damages of bridges due to earthquake load, evaluation methods of seismic resistance such as non-linear static analysis, modal analysis, and demand to capacity ratio.

Bridge; pier; abutment; earthquake; pushover method; modal

This paper examines the impact of Virtual Learning Spaces (VLS) on green building education and to augment architectural pedagogy as well as sustainability awareness among undergraduate learners. Through a scoping review methodology, the literature database was searched for relevant concepts, which were retrieved and analyzed from the year 2014 onwards to identify common VLS strategies. In this paper, the three aspects of the major methods of implementation of VLS are investigated along with the position of VLS to clarify student comprehension of green building principles and its influence on student outcomes. The findings suggest that leveraging digital tools, including virtual reality (VR), gamified simulations, and interactive learning platforms, significantly enhances student engagement, conceptual understanding, and application of sustainability principles. Moreover, this study highlights the relevance of using VLS in the curriculum design process since it creates a connection between what is learnt and what is done. While VLS possesses significant benefits for green building pedagogy, effectiveness is contingent on well-structured implementation in conjunction with contemporary architectural theory and educational practices. Further research should focus on strategies for long-term adoption, usability enhancements, and integration with AI-supported learning platforms to provide a more optimal solution to sustainability education. These research backgrounds contributed to the debate on digital education and augmented focus on sustainability- aligned global goals and built environmental scenarios for architecture students prepping them for the real world environmental problems.

Virtual learning spaces; green building pedagogy; architecture students; scoping review

The emergence of 5G technology highlights millimeter-wave (mmWave) communication as a critical enabler due to its ability to handle vast data demands, addressing the exponential growth in wireless services. Massive MultipleInput Multiple-Output (MIMO) antenna arrays play a pivotal role in mitigating mmWave propagation challenges by compensating for severe path loss, ensuring high capacity, enhanced coverage, and superior spectral efficiency for 5G base stations. This paper provides a comprehensive review of enhancement techniques applied to Massive MIMO antenna arrays in 5G mmWave base stations. It begins with an overview of Massive MIMO and mmWave technologies, discussing their advantages and challenges in practical deployments. Key challenges include significant path loss, beamforming complexities due to beam misalignment, and hardware intricacies arising from the large number of antennas required. The study then explores advanced enhancement techniques designed to address these challenges, detailing their contributions to signal quality, coverage enhancement, and interference mitigation. It also highlights innovative antenna array designs and architectures that optimize gain and radiation patterns for improved base station performance. This survey offers valuable insights into state-of-the-art solutions for Massive MIMO antenna arrays in 5G mmWave systems and underscores their role in advancing wireless communication technologies. The findings aim to inspire further research and innovation to meet the evolving demands of modern wireless networks.

MIMO antenna; fifth generation; mm-Wave communication; Massive MIMO Antenna; Millimeter Wave; metamaterial; base station

Biodiesel production from microalgae is often hindered by inefficiencies inherent in traditional optimization methods, which rely heavily on empirical data and frequently fail to achieve optimal efficiency and economic viability. This study introduces a novel approach that integrates Artificial Neural Network (ANN) modelling with experimental optimization to address these challenges, aiming to enhance biodiesel yield, optimize cost-effectiveness, and improve sustainability. Extensive experimentation was conducted to optimize key parameters in the biodiesel production process. Using sulfuric acid (H2 SO4 ) as a catalyst, a maximum biodiesel yield of 97.46% was achieved under specific conditions: 2 hours reaction time, 40°C temperature, 0.5 M concentration, 400 RPM stirring speed, a methanol-to-oil ratio of 12:1, and 50 g/L Chlorella vulgaris biomass. The process involved lipid extraction, centrifugation at 3500 RPM for 15 minutes, transesterification at 55°C and 600 RPM for 20 minutes, followed by phase separation. ANN modelling played a crucial role in optimizing these experimental conditions. The Feed Forward ANN model successfully predicted the optimal conditions as 2.175 hours, 86.28°C, 347.15 RPM, and 0.5 M acid concentration, with a projected yield of 99.53%. Implementing these predicted parameters resulted in a biodiesel yield of 98.12%, surpassing the experimental yield and closely aligning with the forecasted yield. These results underscore the precision and effectiveness of ANN modelling in optimizing biodiesel production, significantly enhancing efficiency, reducing costs, and shortening experimental durations. This study represents a significant advancement in sustainable energy solutions, demonstrating the transformative potential of ANN modelling in the efficient production of biodiesel from microalgae.

Microalgae; biodiesel production; Artificial Neural Network (ANN) optimization; acidic hydrolysis; transesterification reaction

Global health studies forecast that 61 million people will be suffering from blindness by 2050. Blind people’s guidance devices are important for blind people to move in an unknown environment because they cannot obtain surrounding information through vision which causes their mobility and independence to be hampered significantly. However, the existing blind people’s guidance devices could not provide accurate and reliable obstacle detection data to blind users in different environments and conditions. This is because, in common, they do not have a stabilizer system to maintain the viewing angles of obstacle detection sensors when they are tilted to different angles, and they do not have a sensor layout that can detect obstacles at different levels and directions simultaneously. In this paper, wearable blind people’s guidance devices (WBPGD) were developed to provide accurate and reliable obstacle detection data in different environments and conditions to improve blind users’ mobility and independence effectively based on a stabilizer, object detection, and human detection systems. The performance of the stabilizer and object detection systems had a low percentage of errors up to 10.376%. The human detection system has a measuring range of at least 90° cone to detect humans, while the vibration motors and light-emitting diodes (LEDs) on wristbands responded correctly according to the collected obstacle detection data. Hence, the WBPGD can provide accurate and reliable obstacle detection data in different environments and conditions to prevent users from being hurt by obstacles and allow them to approach humans around them for help independently when necessary.

Wearable blind people guidance services; obstacle detection system; multiple obstacle detection sensors; stabilizer system; wireless communication protocol

In the fast-paced food industry, ensuring consistent fruit quality is paramount for customer satisfaction and compliance with health standards. Traditional manual grading methods are labor-intensive, subjective, and prone to inconsistencies, leading to inefficiencies and potential financial losses. This study presents an automated fruit grading system utilizing the You Only Look Once (YOLO) algorithm, advanced image processing, and real-time processing on a Raspberry Pi 4. The system evaluates visual attributes such as size, color, texture, and internal defects to classify and grade fruits with high precision. Experimental results demonstrate that the YOLO-based system achieves a mean Average Precision (mAP) of 93.87%, surpassing YOLOv3 (82.36%), CornerNet (75.31%), and Faster RCNN (73.45%) by significant margins. Implemented on a Raspberry Pi 4, the system processes images at an average of 0.12 seconds per frame, enabling real-time grading with a throughput of 8 frames per second (FPS). The integration of a cost-effective, portable Raspberry Pi enhances its applicability for small to medium-sized enterprises. Despite sensitivity to environmental factors like lighting, the system significantly improves grading speed (up to 10 times faster than manual methods), consistency, and objectivity. This innovative solution leverages artificial intelligence and affordable hardware to address fruit quality control challenges, enhancing operational efficiency and customer satisfaction. Future work aims to refine the training dataset and adapt the system to diverse environmental conditions for broader applicability in agricultural settings.

Automated fruit grading; YOLO algorithm; image processing; real-time processing; Raspberry Pi; artificial intelligence

Timber is increasingly recognized as a sustainable construction material due to its renewability, carbon sequestration properties, and lower environmental impact compared to conventional concrete building materials. Engineered wood products such as cross-laminated timber, glulam, and laminated veneer lumber offer enhanced structural performance and durability, making them viable for modern construction. However, fire safety remains a critical concern due to timber’s combustibility and potential for rapid flame spread. The widespread adoption of timber structures in both urban and rural areas present significant fire safety challenges, highlighting the need for a comprehensive understanding of fire behavior, existing fire prevention measures, and the role of building design in mitigating fire risks. While various fire protection strategies have been reported in the current literature, fireretardant coatings have gained particular attention for their effectiveness in enhancing timber’s fire resistance. This review examines past fire incidents in timber buildings and evaluates the role of fire-retardant coatings as a key fire protection strategy. By addressing fire safety concerns, timber buildings can be further optimized as a sustainable and resilient option for future developments. In conclusion, implementing fire safety measures, especially fireretardant coatings, is crucial for advancing timber buildings as a safe, eco-friendly, and durable choice for future construction.

Timber buildings; fire accidents; safety measurements; fire-retardant coating; sustainable construction

Superconductivity is a fascinating phenomenon where certain materials exhibit zero electrical resistance and expel magnetic fields through diamagnetism when cooled below a specific critical temperature (Tc). This unique behavior, characterized by the Meissner effect, has the potential to transform various industries, including energy, healthcare, transportation, and communications. This comprehensive analysis explores both low-temperature superconductors (LTS) and high-temperature superconductors (HTS), highlighting their distinct properties, significant applications, and the challenges associated with their implementation. LTS materials, such as NbTi and Nb3 Sn, are essential for high-field applications like MRI machines and particle accelerators, but they require costly liquid helium for cooling. On the other hand, HTS materials like YBCO and BSCCO can be cooled with liquid nitrogen, making them more affordable and expanding their applications in power transmission, magnetic levitation systems, and advanced electronics.Recent research is also focused on achieving room-temperature superconductivity and increasing critical temperatures, which could make superconducting materials more accessible and efficient, potentially revolutionizing key technologies and infrastructure. This overview summarizes the current state of research and outlines future directions, emphasizing the need for a multidisciplinary approach to fully realize the potential of superconducting materials.

Superconducting materials; high-temperature superconductors; low-temperature superconductors; roomtemperature superconductivity; cryogenic cooling

Zirconia-toughened alumina (ZTA) is an advanced ceramic material used in various applications, including cutting inserts, wear parts, and biomedical applications, due to its improved fracture toughness and suitable hardness. However, further improvement of mechanical properties is still a key consideration for expanding its applicability in such demanding applications. Hence, this study examines the effects of nitinol (NiTi) additions (0–5 wt %) on the physical and mechanical properties of ZTA composites. NiTi was incorporated through wet mixing and the samples were sintered at 1600°C, facilitating liquid-phase sintering at 1300°C. At 2 wt% NiTi, optimal densification (4.197 g/cm³), minimal porosity (0.135%), and maximum hardness (1684.15 HV) were achieved, primarily due to microstructural refinement and the Hall-Petch effect. At higher NiTi content (5 wt%), enhanced fracture toughness (5.966 MPa·m¹/²) was observed, resulting from crack-bridging and stress dissipation enabled by NiTi’s ductility and the presence of the retained B2 austenite phase. X-ray diffraction (XRD) analysis confirmed phase stability with no evidence of secondary phase formation. These findings highlight a trade-off between hardness and fracture toughness, demonstrating NiTi’s dual role as a sintering aid and toughening agent. The results offer significant potential for tailoring ZTA composites for advanced mechanical applications, particularly in the development of high-performance cutting tools and wear-resistant components.

ZTA composites; Nitinol additive; liquid-phase sintering; fracture toughness; hardness

Metal-organic framework (MOF) is a crystalline structure which has high porosity and surface area. MOFs’ design and functionality are versatile and stem from limitless combination of metals and organic linkers, which has proven stability in processes operated at high pH and temperature and resistant to various chemicals. These features are effective in biocatalysis as enzymes need protection from harsh environments. Nevertheless, during the synthesis of Enzyme-Metal organic complexes (E-MOF), the process must be designed in a delicate way such as avoiding harsh solvent (methanol) and room temperature synthesis, to preserve protein microenvironment. The study is aimed at analyzing the effect of different solvents; ultrapure water (which consists only H+ and OH- ions) and methanol towards ZIF-67 morphology. ZIF-67(W) shows leaf-like morphology with crystal size in the range of 10-16μm while ZIF-67(M) shows rhombic dodecahedron morphology with crystal size in the range of 200-400nm. It was found that ZIF-67(M) has better thermal stability compared to ZIF-67(W). The relationship between the different solvents used to synthesize ZIF-67 and in-situ enzyme encapsulation is discussed by considering the feasibility of ZIF-67 synthesis with water as an eco-friendly solvent with good characteristics. Preliminary result showed that more than 90% of methylene blue can be degraded by laccase@ZIF-67(W).

ZIF-67; biocatalysis; laccase; metal organic framework; enzyme immobilization

Concrete, a primary construction material, is typically composed of cement, sand, gravel, and water, which combine to produce a durable and robust material. In traditional concrete, natural resources, such as fine and coarse aggregates, are often sourced from quarries. Quarrying involves blasting rock into smaller fragments, especially to produce coarse aggregates for industrial construction. However, this extraction process has significant environmental repercussions, including habitat disruption and resource depletion. To address these issues, materials like Lightweight Expanded Clay Aggregate (LECA) have been explored as sustainable alternatives to conventional aggregates. LECA can reduce the environmental impact associated with quarrying by decreasing the reliance on natural resources. However, previous studies have shown that using LECA as a replacement for coarse aggregate tends to increase water absorption, which can negatively impact the compressive strength of concrete. This study aimed to improve the performance of lightweight concrete by substituting coarse aggregates with LECA and enhancing the mixture with sodium silicate (SS). By varying the proportions of sodium silicate alongside LECA, this research evaluated the impact on concrete properties through lab tests, including slump, compressive strength, water absorption, and density assessments. Results revealed that the inclusion of sodium silicate in the LECA-based mix yielded a 42.5% increase in compressive strength, a 20% reduction in slump, a 97.3% reduction in water absorption, and a 46.3% reduction in density compared to control samples. These findings contribute valuable insights into concrete technology, supporting SDG 9 by promoting sustainable, innovative practices in the Malaysian construction industry.

Lightweight concrete; LECA balls; sodium silicate; SDG2030; sustainable construction

A key component of asphalt road pavement is well known for its strength and adaptability. The basic ingredients of asphalt, such as aggregates and binder, are examined in this overview along with how they interact to greatly affect pavement performance. A systematic literature review was undertaken using peer-reviewed journals, technical reports, and industry publications, focusing on advancements in asphalt materials and sustainable pavement technologies from the past two decades. The study investigates asphalt classifications according to aggregate structure, manufacturing temperature, and the use of additives or modifications to enhance performance under various traffic and environmental conditions. Advanced modifications, such as the incorporation of polymers, crumb rubber, and recycled asphalt pavement, are explored for their ability to enhance rheological properties while addressing sustainability concerns. The review also highlights innovative production techniques, which reduce energy consumption and emissions, making it a more sustainable alternative to conventional methods. Furthermore, factors influencing asphalt selection, such as climate adaptability, load-bearing capacity, fatigue resistance, and maintenance requirements, are analyzed to provide a comprehensive understanding of material behavior in realworld applications. The importance of proper mix design, compaction techniques, and quality control measures is emphasized to ensure optimal pavement longevity and performance. By integrating advanced material science and engineering practices, modern asphalt technologies aim to enhance road durability, reduce lifecycle costs, and minimize environmental footprint. This review underscores the critical role of material composition, innovative additives, and sustainable production techniques in optimizing asphalt performance for future road infrastructure developments.

Asphalt performance; mix design; sustainability; modifiers and additives; road pavement

When steel is exposed to its surroundings and reacts with them, corrosion becomes one of the problems that steel bridges face. As it weakens and increases the likelihood of failure, corrosion is a concern to steel structures. Bridge fatigue life may be severely impacted by the resulting stress, which might end in catastrophic failure. The appearance and spread of fatigue cracks can be accelerated by corrosion, which causes the steel structure to deteriorate more quickly. Visual examinations frequently identify corrosion quickly, but a thorough assessment and study of corrosion damage are crucial. This research focuses on analysing the behaviour of a steel arch bridge under the influence of corrosion that occurs at the bridge. The study aims to determine the different stress levels resulting from corrosion. By employing SAP2000 software, three corrosion levels with 10%, 20%, and 50% of the selected section area at the bridge’s centre span and abutment are considered by reducing the structure’s cross-sectional area. Stress values are obtained by considering the most probable location of corrosion. The results and discussion reveal that steel arch bridges exhibit excellent flexibility and deformation. Segment of the bridge that located at the centre, experienced the highest stress range and potential concentrations. These finding underscore the importance of effective corrosion prevention and maintenance strategies. It is crucial to ensure their long-term durability and structural integrity.

Steel bridge; arch bridge; corrosion; moving load; SAP2000

A high voltage transformer is one of the most critical components in power systems, responsible for regulating voltage levels and ensuring stable power delivery. Its main structure comprises the transformer core, voltage windings, insulation, metallic enclosure, and mechanical support. Over time, these components deteriorate due to thermal stress, mechanical strain, chemical aging, and especially voltage stress, which is a major contributor to insulation failure. Such failures can trigger partial discharges, inter winding faults, and eventual breakdown of the transformer windings. This paper presents a method for analyzing voltage distribution along transformer windings using partial differential equations (PDEs), providing greater insight into the effects of high-frequency transients. The methodology begins with a cross-sectional analysis of a single-phase transformer to extract physical and geometric parameters. An equivalent lumped parameter model comprising resistance (R), inductance (L), and capacitance (C) is developed to re lect the winding’s electrical behaviour. The model is extended into a timedomain PDE framework, yielding two directional voltage wave solutions: one propagating toward the bushing, and the other toward the neutral terminal. Experimental validation was carried out by injecting a rectangular voltage wave and measuring the voltage response at several winding points. The results show strong correlation between theoretical predictions and experimental data, con irming the model’s accuracy. The study highlights how voltage stress is unevenly distributed during fast transients, with certain winding sections experiencing elevated stress levels. This PDE-based approach enhances diagnostic accuracy and provides a valuable tool for assessing insulation performance, aiding in transformer design and predictive maintenance strategies.

Partial differential solutions; lumped parameter model; transformer windings; Hybrid PDE

Urban areas are increasingly vulnerable to severe flooding due to rapid urbanization and the growing impacts of climate change. Accurate flood modelling is essential for disaster preparedness, land-use planning, and infrastructure resilience. However, a key gap in current flood models is the influence of the Digital Terrain Model (DTM) bit depth on prediction accuracy. While spatial resolution has been the focus of many studies, the role of DTM bit depth, especially in signed versus unsigned formats, remains underexplored. This study addresses this gap by systematically investigating the effects of varying DTM bit depths (8-bit, 16-bit, and 32-bit) on flood prediction accuracy in 3D urban models. By comparing signed and unsigned formats, the study quantifies how these configurations influence water depth predictions, variance, and maximum deviation in flood simulations. The findings show that higher bit depths, particularly 16-bit signed DTMs, provide improved precision in capturing complex urban topographies, significantly enhancing the accuracy of flood risk assessments. The study proposes recommendations for selecting optimal DTM bit depths that balance computational efficiency with prediction accuracy. These insights contribute to the development of more resilient urban planning strategies and flood mitigation efforts, crucial for adapting to the increasing frequency and severity of urban flooding events caused by climate change.

DTM; rainfall modelling; 3D city model; spatial resolution; bit depths

Spiral bevel gears are important components in many mechanical systems. Their complex geometry, characterized by curved teeth following a spiral path, allows for smooth operation under high loads but presents significant challenges in inspection, and maintenance. Traditional methods for detecting gear deformations often rely on manual inspection, resulting in system downtime and high costs. This study focuses on detection of geometrical deviations in the three-dimensional geometry of spiral bevel gears. The need for high-resolution data capture and efficient large-scale data processing makes the problem more challenging. The objective of this work is to propose an algorithm using depth data from mobile phone-based depth sensing cameras to detect deformations in spiral bevel gears in a non-invasive way and compare the results of these algorithms with state-of-art methods. The methodology uses an iPhone 13 Pro’s TrueDepth camera, capable of capturing depth maps at 640x360 resolution and a computational framework involving frame synchronization, NaN-handling, and differential analysis to construct a digital mold of ideal gear geometry. For deformation detection, an algorithm was created to compare individual depth frames against the created mold, utilizing threshold-based filtering and statistical analysis. The algorithm achieved perfect classification performance, correctly identifying all 94 non-deformed and 14 deformed gears without any errors. In conclusion, the deformation detection algorithm demonstrated high accuracy in identifying gear anomalies, with the confusion matrix confirming excellent classification performance between deformed and non-deformed frames. The findings of this paper contribute potential applications extending beyond gear systems to other critical mechanical components.

Spiral bevel gear; TrueDepth camera system; computational framework; additive manufacturing; remanufacturing

The bubble deck slab system, formally recognised as the reinforced bubble deck slab, presents a distinctive structural solution contributing to the optimisation of building functionality and design especially in modern construction. Compared to traditional solid slabs, this study examines the initial properties of bubble deck slabs under vertical point loads, with particular attention to bending effects, deflection behaviour, and failure causes under controlled experimental conditions. The primary objectives of this study are to evaluate the deflection of both slab types, determine failure conditions, and examine the response to point loads applied at their edges with special focus on the stiffness characteristics of the slabs. This inventive slab, made of reinforced concrete, incorporates hollow plastic bubble balls made of high-density polyethylene (HDPE), which reduces the overall concrete volume compared to conventional reinforced concrete slabs. Under the shear point load applied at the centre of the slab, the bubble deck slabs exhibited superior elastic behaviour compared to their conventional counterparts. Consequently, the bubble deck slab in this study significantly reduces concrete usage. Despite the reduced concrete volume, the strength and performance characteristics of bubble deck slabs are maintained. A detailed examination of the applied loads explores their effects on flexural strength, bending stiffness, and load-deflection behaviour, providing a comprehensive understanding of the system’s structural performance and potential application in sustainable building design.

Bubble Deck Slab; Cracking pattern; Voided slab; Compressive Strength Test; Flexural Test

In this study, the characteristics of landfill leachate, the potential mechanism, and the performance of the nanoadsorbent Zirconium-Based Metal Organic Frameworks double ligands with amine and carboxylic acid (UiO-66-NH2D), were investigated for humic acid (HA) removal. The landfill leachate characteristic analysis revealed that the leachate is classified as stabilised landfill leachate, as the biodegradability ratio (BOD5 /COD) was recorded at 0.046. Most of the measured parameters exceeded the standard discharge limitations, including Chemical Oxygen Demand (COD), Ammoniacal Nitrogen (NH3-N), and HA, which were recorded at 3681 mg/L, 2250 mg/L, and 186.4 mg/L, respectively. Characterisation analysis of UiO-66-NH2-D confirmed that this adsorbent possesses a high degree of crystallinity and abundant active sites, which significantly contributes to its strong adsorption performance through electrostatic forces (between negatively charged HA and positively charged adsorbent, or vice versa), hydrogen bonding (involving N-H, O-H, or F-H bonds and lone pairs), and π-π interactions (non-covalent stacking of aromatic rings). The mesoporous structure and active sites of UiO-66-NH2-D were optimised by varying the molar concentration of ligands from 2 mmol to 6 mmol during synthesis to enhance its adsorption capabilities. The sample with a 4 mmol ligand concentration exhibited the highest HA removal efficiency, achieving 44.4% at a dosage of 1 g and a reaction time of 30 minutes. This concentration was found optimal due to a balance of surface functionality and minimal pore blockage. These findings emphasise UiO-66’s potential as an effective adsorbent for removing HA in landfill leachate, contributing to improvements in existing conventional treatment plants.

UiO-66; humic acid; adsorption; landfill leachate treatment; wastewater treatment; nano adsorbent

This paper includes a comprehensive investigation into the electric stress behaviour of crosslinked polyethylene (XLPE) cables containing alumina nanoparticles. XLPE is commonly utilised in high-voltage applications because of its excellent dielectric characteristics, mechanical strength, and heat resistance. However, there are two main factors contribute to the critical issues that lead to dielectric breakdown because the XLPE insulation may age over time and high voltage applications create significant electrical stress within the insulator of XLPE cable. Therefore, this research studies electric field distribution in XLPE and XLPE containing alumina nanoparticles. Alumina was chosen in this research due to its high thermal conductivity to efficiently transfer heat and high resistant to corrosion, acids and bases. By adding the alumina nanoparticles in XLPE it will enhance the performance of the cable. This research also studies the various shapes of alumina nanoparticles (sphere, rod and triangle) with three different cases (3 layers, 5 layers and 7 layers) of alumina nanoparticles in XLPE cable. The research includes modeling XLPE and XLPE containing alumina nanoparticles with different shapes through three cases, followed by simulation and analysis by using COMSOL Multiphysics software. The outcome of this research, XLPE containing sphere alumina nanoparticles gives better results with the increment of 58% (2.37×107V/m) compared to pure XLPE (1.50×107V/m), increment about 8.44% from Case 1 (2.37×107V/m) to Case 2 (2.57×107V/m) and decrement about −2.33% from Case 2 (2.57×107V/m) to Case 3 (2.51×107V/m). to enhance the performance of cable in terms of higher electric field intensity around nanoparticles while reducing the electric field distribution and it will contribute to the higher breakdown strength.

XLPE; COMSOL; high voltage; shapes; breakdown strength

In electrical insulation systems, the selection of dielectric fluids is critical to ensuring the reliable and efficient operation of power transformers. Traditionally, mineral oil—also known as transformer oil—has been widely used due to its effective insulating and cooling properties. However, due to growing environmental concerns, researchers are increasingly exploring alternative oils. Natural ester oils, such as rice bran oil, have emerged as promising candidates for replacing mineral oil. This study investigates the potential of rice bran oil enhanced with titanium oxide (TiO₂) nanoparticles as a biodegradable insulating fluid. Nanotechnology is employed to improve the dielectric performance, with a focus on AC breakdown voltage. Additionally, Cetyl-Trimethyl Ammonium Bromide (CTAB) is used as a stabilizing agent to prevent nanoparticle agglomeration and enhance dispersion stability in the fluid. The effects of varying TiO₂ concentrations (0.005 wt%, 0.01 wt%, and 0.05 wt%) and the presence of CTAB (0.01 g) are evaluated in both rice bran oil and mineral oil. Experimental results indicate that the optimal concentration—rice bran oil with 0.01 wt% TiO₂ and 0.01 g CTAB—achieves a maximum AC breakdown voltage of 74.03 kV. This suggests that rice bran oil-based nanofluids offer a viable and environmentally sustainable alternative to conventional mineral oil in transformer insulation applications.

Insulation oil; AC breakdown voltage; alternative oil; rice bran oil; nanoparticle

This study explores the combination of 3D scanning and 3D printing technology for automotive retrofitting in the design and create prototypes of automotive retrofitting components. Designs were conceptualized in CAD using 3D scans of the artifacts and 3D prints of accurate physical prototypes were produced. The precision of creating new automotive accessory components, on the other hand, is severely hampered by mistakes made in the early phases of the design process. Such discrepancies result in incorrect fitting, increased production costs, and delayed deliveries, reflecting the necessity of accurate and reliable design methodologies. The combination of highresolution 3D scanning data with advanced 3D printing techniques demonstrates a high-quality prototype parts production, showcasing an efficient workflow. A comprehensive methodology involving data acquisition via 3D scan, CAD model conversions for the purpose of 3D print, as well as prototypes fabrication is provided. Deviation analysis was performed to assess fitting accuracy and ensure the best match between prototype and carryover components. Result shows that by using the deviation analysis, the prototype produced match with the surfaces of the carryover part within the acceptable range of 0mm to 0.5mm. It shows a significant improvement in design accuracy, manufacturing speed, and overall prototyping speed as compared to traditional processes. How 3D scanning and 3D printing technologies can be integrated was found to be the key to accelerating the automotive components developing, realize faster iteration cycle and higher-quality results. It is expected that future work would further optimize this embedding to explore the process on complex vehicle parts and assemblies.

Automotive retrofitting; automotive design; 3D scanning; additive manufacturing; 3D printing

In today’s rapidly evolving work environment, graduates in engineering technology must continuously improve their general skills to remain competitive and relevant. A significant gap exists between these graduates’ skills and the industry’s expectations, particularly in communication. Effective communication is widely recognized as a key competency that significantly influences employability and overall success in the workforce. This study aimed to assess the communication skills of Malaysian engineering technology graduates through a comprehensive survey comprising of 61 items. 37 graduates, aged between 22 to 30 years, participated in the study and shared their perceptions of their communication abilities. To ensure the validity and reliability of the survey instrument, Rasch analysis was employed. The analysis demonstrated strong person reliability and separation values, confirming the survey instrument’s robustness. Item reliability further affirmed the tool’s adequacy, although two items did not meet standard fit indices, the items were retained due to their relevance to 21st-century communication practices and their contribution to overall reliability. These skills are crucial for graduates to navigate digital communication landscapes and adapt to technology-driven industries. The study’s results provide significant insights into how these graduates’ communication competencies align with industry needs, emphasizing the necessity of integrating advanced communication training into educational curricula. By equipping students with these critical skills, institutions can better prepare graduates to meet workforce demands, enhancing their employability and career progression.

Communication skills; engineering technology graduates; survey instrument; employability; validity and reliability

Urbanization, climate change, and hydrological variability have significantly heightened flood risks in metropolitan areas, particularly in low-lying and highly developed regions. The increasing frequency and severity of urban floods necessitate advanced spatial decision-making techniques for effective flood vulnerability mapping. This study applies Multi-Criteria Decision Analysis (MCDA) integrated with Geographic Information Systems (GIS) to assess urban flood susceptibility in Taman Sri Muda, Shah Alam, Malaysia, an area frequently affected by flash floods. The study aims to identify key factors contributing to urban flooding, develop a spatial flood hazard model, and propose mitigation strategies. Six critical parameters including land use and land cover, distance from the main channel, drainage density, rainfall distribution, elevation and slope were reclassified and assigned weights. The weighted overlay technique was employed to generate the flood vulnerability map, which was validated using historical flood records. The results indicate that 37.8% of the study area is classified as high flood risk, while 24.8% falls under moderate risk, 27.6% under low risk, and 9.8% under very low risk. The findings provide valuable insights for urban planners, emergency response teams, and policymakers in developing flood mitigation strategies, improving drainage infrastructure, and enhancing flood resilience. This study underscores the necessity for integrating real-time hydrological data and machine learning models to further improve flood prediction accuracy in future research.

Urban flooding; MCDA; geospatial; vulnerability mapping