Volume 38 (04) July 2026

This study examines workplace injuries in the Testing, Inspection, and Certification (TIC) sector to promote evidencebased improvements in occupational safety. Fifty-two incident reports from a TIC firm were analyzed using correlation methods, univariate and cross-tabulation analysis, and Failure Modes and Effects Analysis (FMEA) accompanied by the Hierarchy of Controls. Research indicates that hazardous situations, particularly chemical exposure during laboratory testing, were the primary cause of accidents (53.8%). Male employees experienced more severe injuries, particularly among those aged 20 to 29. The FMEA identified 10 failure modes, with the highest Risk Priority Numbers associated with exposure to caustic liquids, slips, trips, falls, and chemical ingestion. The findings highlight the imperative to improve control mechanisms, bolster safety training, and reinforce compliance with personal protective equipment (PPE) laws. This research offers practical insights for risk reduction and the development of proactive safety measures to improve the safety culture in the TIC sector.

Testing; Inspection; and Certification (TIC) Industry; failure modes and effects analysis; occupational safety; laboratory safety; hierarchy of controls

Prolonged sitting in office environments can cause health issues such as back pain. As an alternative, active sitting chair encourages movement and better posture, providing a potential solution. This study focuses on the design and analysis of an active sitting chair for Malaysian office workspaces, ensuring ergonomic benefits, user comfort and structural strength of the design. The specific objectives are (a) to redesign an active sitting chair tailored for Malaysian office workers, and (b) to conduct a preliminary ergonomic screening of the prototype. A questionnaire survey was distributed to gather user preferences, followed by stakeholder analysis and product design specifications. A House of Quality (HoQ) framework was utilized to ensure user needs are aligned with technical requirements. During conceptual design, various ideas were explored through 3 benchmarking, 14 sketching of ideas, and anthropometric study. A prototype was created through iterative improvements to an existing product design and assessed for usability and comfort through ergonomic screening and dimensional compliance based on NIOSH Malaysia anthropometric data. Failure Modes and Effects Analysis (FMEA) suggested that engineering analysis should focus on the wobbly mechanism. However Finite Elements Analysis (FEA) performed in SolidWorks under a static load of 100kg indicated that the leg rest was the most critical component exhibiting a factor of safety of 1.2. Postural comparison between sitting on conventional static office chairs and the active sitting chair was administered using Rapid Upper Limb Assessment tool (RULA). The preliminary ergonomic screening using the RULA across seven Malaysian office workers interacting with the prototype with predefined tasks observed that the Active Sitting Chair supports ergonomic posture, with the assessment score decreasing from 3 pre-design refinement to 2 post-refinement, indicating lower postural risk.

Prevention through Design (PtD); ergonomics; anthropometry; postural analysis; workstation design

The adoption of mobile applications for occupational health and safety is growing in higher education, especially in engineering environments where students face elevated physical risks. This study explores the Perceived Usability of a health and safety mobile app among students at a Philippine engineering university. Grounded in the Unified Theory of Acceptance and Use of Technology, safety culture dimensions, and the System Usability Scale, a structured questionnaire with 48 items covering 10 variables was distributed to 224 purposively selected respondents. After data cleaning and class balancing using SMOTE, five machine learning models such as Multilayer Perceptron, Support Vector Classifier, XGBoost, LightGBM, and Random Forest Classifier were trained and tuned using grid search. RFC achieved the best performance with 90.9% accuracy and a weighted F1-score of 0.91. Validation through cross-validation, log-loss tracking, and out-of-bag error estimation confirmed the model’s robustness. SHAP analysis and tree-based interpretation revealed Social Influence, Safety Training, and Safety Participation as the strongest predictors of PUS. Other contributing factors included Effort Expectancy, Performance Expectancy, Facilitating Conditions, Intention of Use, and Use Behavior. Safety Planning was excluded due to low correlation. Unlike traditional Structural Equation Modeling approaches, this machine learning framework captures complex, non-linear relationships in behavioral data and generates logic-based structures that can be embedded into rule-based systems. This study contributes to engineering systems design by providing a predictive modeling framework that supports planning and app optimization in academic environments.

Perceived usability; health and safety; machine learning; random forest classifier; SHAP; UTAUT; SUS; mobile application; SMOTE

Drowsiness is one of the contributing reasons to road accidents. So far, researchers and practitioners have undertaken many preliminary investigations to reduce drowsiness. Most of this research has mainly concentrated on evaluating drowsiness rather than preventing it. However, previous studies frequently neglected to analyze a diverse range of vibrotactile elements, such as frequency, waveform, and amplitude. As a result, there is a lack of knowledge regarding the most efficient vibration characteristics for preventing drowsiness. This creates an opportunity for more research to determine and enhance these elements for practical use. Hence, the purpose of this study is to determine the optimal vibration parameters for the vibrotactile system to stimulate drivers. This study developed four sets of different vibrotactile sensation systems to be tested on the participants. The results showed that most participants chose System 1 which offered vibration characteristics with the frequency at 125-140 Hz, 255 PWM amplitude, and sinusoidal signal type as the optimal vibrotactile system. It is expected that this study will benefit the road safety agency, vehicle manufacturer and help make cities safer and improve road safety.

Microsleep; driving; vibration

Motorcyclists in Malaysia face disproportionately high fatality risks, reflecting cognitive and behavioral gaps not adequately addressed by conventional training or enforcement. This study examined hazard perception and situational awareness (SA) as critical contributors to crash risk through two controlled experiments. Experiment 1 assessed 31 courier motorcyclists using a Motorcycle Riding Road Test (MRRT), a hazard perception test (HPT), and a theory test. Results revealed systemic weaknesses in hazard appraisal, with HPT scores significantly lower than MRRT and theory performance (F(1.66, 49.71) = 14.74, p < .0001, η² = 0.19). Riders frequently accelerated through intersections without sufficient hazard checks, underscoring lapses in risk recognition that elevate crash likelihood. Experiment 2 evaluated 264 riders using the Motorcycle Riding Situation Awareness Assessment (MRSAA), a videobased instrument anchored in Endsley’s three-level SA model: Level 1 (perception), Level 2 (comprehension) and Level 3 (projection). Results showed striking deficits, with mean total scores at 23.2% and Level 1 perception at just 27.5%. Older riders scored 17.4% higher than younger riders (t_111.12 = 5.93, p < .001), and monthly riding exposure correlated positively with total SA and Level 3 performance. Age was consistently associated with better outcomes across all SA levels. Together, these findings confirm that hazard perception and situational awareness are distinct, higher-order competencies with foundational deficits among Malaysian motorcyclists. The evidence provides a data-driven justification for adoption of collision warning technologies in motorcycle design to assist riders in hazard detection and collision avoidance. In parallel, the study proposes the Integrated Motorcycle Safety Empowerment Framework for Malaysia (IMSEF-MY) as a long-term strategy.

Motorcycle safety; hazard perception, situational awareness; collision warning technologies; policy

Optical fiber is crucial for modern communication, overcoming limitations of traditional systems. Wi-Fi 6 addresses network congestion but still faces signal and security issues. Polymer Optical Fiber (POF) is a flexible alternative to copper wire or glass fiber. To implement WDM-POF systems, POF splitters can be fabricated using various techniques such as twisting, moulding or lithography. For this study, POF splitters were developed using a waveguide technique that was engraved on an acrylic block (PMMA) which is called as polymer optical waveguide (POW). The objectives of the study are to design a Y-branched optical splitter device, fabricate the POF splitter design and perform a performance analysis of the device that has various cores with different refractive indexes. Equations are provided to calculate splitters specification such as Y branch angle and taper length. CAD software is used for the design process, then fabrication is done using an engraving machine. OpticStudio software used for simulations and power measurements was carried out. Y-branch 1x2 optical splitters device has been successfully produced and fabricated with a branch angle of 17.5°. The average value of insertion loss with only air as core material for Port 1 is 17.3 dB and Port 2 is 17.1 dB. Based on simulation results with index values from Epo-Tek OG603 and SU-8 epoxy, the device has an Insertion Loss of 3.77 dB and 3.67 dB. Excess Loss is 0.78 dB with a Split/Coupling Ratio near 50:50. However, the fabricated POW devices using Epo-Tek OG603 are having insertion loss from 7dB to 14 dB. This shows that the Y-branch splitter is a successful 3 dB power splitter and can be incorporated in the WDM-POF system network.

Polymer optical fiber; optical splitter; ray tracing

Construction safety has gained a lot of attention, especially in high-rise residential buildings. In this study, an investigation was carried out on the safety risk perceptions among different demographics. 419 participants were involved in this investigation into the safety risk factors, whereby a Kruskal–Wallis test was conducted based on responses categorised by distinct demographic characteristics. To identify and quantify safety risks in highrise building construction, this study proposes a novel risk assessment method- Risk Severity (RS). The results indicate that insufficient safety knowledge among high-rise builders, inadequate subcontractor safety control, and lack of safety training are the most critical sub-factors influencing construction safety, with RS values of 0.590, 0.588, and 0.587, respectively. At the broader level, high-rise builders with an RS value of 0.572 represent the highest risk category, underscoring the need to prioritise this group. Meanwhile, the Kruskal–Wallis test result reveals strong consensus in safety risk perceptions across distinct job positions and educational backgrounds, but significant variation across different levels of work experience. Extending the psychometric paradigm, the findings demonstrate that work experience plays a significant role in shaping perceived safety risks an aspect that has been underexplored in the existing literature. The findings suggest that the risks linked to high-rise builders represent the most critical threat to overall construction safety.

High-rise residential building construction; safety risk factors; Risk Severity(RS); Relative Importance Index (RII); Kruskal-Wallis Test

Artificial intelligence (AI) has emerged as a transformative technology capable of addressing complex engineering challenges through advanced data-driven modeling, prediction, and optimization techniques. In the context of sustainable energy systems, hydrogen production via water electrolysis has attracted considerable attention as a promising pathway toward carbon-neutral energy generation. However, the widespread deployment of electrolyzer technologies remains constrained by challenges related to energy efficiency, operational stability, system degradation, and dynamic process control. In response, machine learning (ML) techniques have increasingly been integrated into electrolyzer systems to enhance performance prediction, adaptive control, predictive maintenance, and operational optimization. This review presents a comprehensive and engineering-oriented analysis of recent advancements in ML applications for hydrogen production via electrolysis. The study first outlines the fundamental principles of ML, including major learning paradigms, data processing approaches, and commonly adopted algorithms for electrochemical systems. Subsequently, the review critically examines ML-driven strategies for hydrogen production optimization, hyperparameter tuning, intelligent process control, system design enhancement, and degradation monitoring in electrolyzer technologies such as Proton Exchange Membrane Water Electrolyzers (PEMWE) and Anion Exchange Membrane Water Electrolyzers (AEMWE). Comparative analysis of algorithms including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Reinforcement Learning (RL), and Physics-Informed Neural Networks (PINN) is also discussed from both electrochemical and engineering perspectives. Furthermore, current challenges involving data quality, model interpretability, computational complexity, and industrial deployment are critically evaluated. Overall, this review provides strategic insights into future intelligent electrolyzer systems and highlights critical research directions for scalable and sustainable green hydrogen production.

Artificial Intelligence (AI); Machine Learning (ML); Control systems; Proton Exchange Membrane Water Electrolyzers (PEMWE); Anion Exchange Membrane Water Electrolyzer (AEMWE)

Industry 4.0 brings significant productivity gains and competitive advantages through the integration of the Internet of Things (IoT) and Smart Manufacturing. In Malaysia, micro, small and medium enterprises (MSMEs) account for approximately 97.4% of total business establishments, contributing substantially to national employment and GDP. However, many MSMEs still face many challenges and lack of direction toward industry 4.0. Existing frameworks, such as the Ministry of International Trade and Industry’s (MITI) Industry4WRD are not specifically designed based on the challenges of MSMEs. To address this gap, this study develops the Industry 4.0 Readiness Assessment Model tailored for Malaysian MSMEs. The model organises readiness into five thrusts: Planning and Strategy, Finance, Technology, Skills, and Data Digitalisation and encompassing by thirteen specific dimensions. The Analytical Hierarchy Process (AHP) is used to determine the priority weight for each thrust and dimension, while the Weighted Sum Model (WSM) aggregates the self-assessment scores into an Industry 4.0 readiness score. The model was implemented and verified through a case study involving a Malaysian F&B manufacturing MSME. The analysis revealed that ‘Planning and Strategy’ and ‘Finance’ emerged as the most critical thrusts for MSME readiness. The case company achieved an overall readiness score of 2.16, corresponding to Level 2 (Beginner). This result demonstrates the model’s ability to pinpoint specific gaps, particularly in automated infrastructure and data integration, providing a structured basis for prioritizing transformation initiatives. The developed assessment model offers a practical and systematic approach to help Malaysian MSMEs plan and implement Industry 4.0 transformation more effectively.

Industry 4.0 Readiness; MSMEs; AHP; WSM; digital transformation; readiness assessment tool; Malaysia

Earthquakes originating from Indonesia pose a major threat to Malaysia, which can cause infrastructure damage and loss of life. Traditional earthquake detection methods often lack sufficient accuracy for timely warning. This study introduces a cost-effective real-time earthquake detection system by integrating geophone sensors with Support Vector Machine (SVM) algorithms. The system development involves Raspberry Pi, Geophone, ADS1115 as the main building blocks, Micromix 5 Shaker as the laboratory vibration source, and the data obtained are analysed using Microsoft Excel for graph construction and MATLAB for vibration data analysis using the SVM algorithm. Six types of kernels are tested in machine learning training for classification of vibration types using SVM. Laboratory tests of geophone vibration detection at five different vibration amplitudes reveal different sensitivity of geophones to positive and negative amplitudes, demonstrating their potential for early warning systems. Data processed with the SVM model in MATLAB using the confusion matrix as a statistical tool shows that the Quadratic and Fine Gaussian kernels achieve the highest classification accuracy (96%), effectively distinguishing seismic events from background noise. Further studies are suggested by testing the effectiveness of this system in the field, as well as the integration of multiple sensors in an integrated system. The successful development and testing of this system offer a promising step towards improving Malaysia’s earthquake preparedness through reliable, machine learning-driven early warning capabilities.

Geophone, Earthquake Detection, Support Vector Machine (SVM), Real-time Monitoring

Water quality monitoring in the Permanent Reserve Forest (PRF), especially the Soil Protection Forest (SPF), is essential to safeguarding ecological integrity, sustaining biodiversity, and supporting human activities such as ecotourism and clean water supply. SPF is a gazetted area specifically intended to protect land from erosion and lanslides. The HPT located within the HSK area is important in maintaining the function of the forest as a slope protector and water supply. This study assessed the water quality of the rivers located in the PRF area and also getted as HPT, namely Kincin and Kernam Rivers in Rompin State Park (TNR), Pahang, using physico-chemical parameters and the Water Quality Index (WQI) method prescribed by the Malaysian Department of Environment (DOE). Water samples were collected from 10 locations and analyzed for pH, temperature, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (SS), and ammoniacal nitrogen (AN). The average values for all parameters were pH 6.5, Temperature 25.9°C, DO 8.05 mg/L, BOD 6.1 mg/L, COD 7.6 mg/L, SS 9.6 mg/L and AN 0.0415 mg/L. Next, the WQI results indicated that the Kernam River was classified under Class I (WQI 94.28) while the Kincin River fell into Class II (WQI 89.92), both reflecting clean water status in the catchment area. Statistically significant differences (p <0.05) were observed in certain parameters, particularly BOD, SS, and turbidity, between the two rivers. The findings highlight the importance of continuous monitoring and conservation management to preserve water quality in protected forest watersheds like TNR.

Water quality; Taman Negeri Rompin; water quality index; permanent reserve forest; ecotourism

Inconel 718 is widely used in machining applications due to its exceptional mechanical properties, especially in high-temperature environments, making it the material of choice in industries such as aerospace and automotive. Machining Inconel 718 poses significant challenges, particularly in managing tool wear under dry cutting conditions in a CNC turning machine, as inappropriate cutting parameters can rapidly increase tool wear rates. This study addresses the critical issue of improper cutting parameters involving cutting speed (Vc) and feed rate (fz), which can affect the flank wear (VB) of carbide-cutting tools. This study aims to determine the optimal cutting parameters that minimise VB while machining on Inconel 718 under dry cutting conditions. A systematic methodology was used, where Vc ranged from 30 to 100 m/min, fz was adjusted from 0.03 to 0.07 mm/rev, and the constant depth of cut (ap) was 0.5 mm. Response Surface Methodology (RSM) uses central composite design (CCD) to analyse the effect of these parameters on VB, allowing for the identification of optimal machining conditions. The results demonstrate that optimal cutting parameters of 100 m/min Vc and 0.696 mm/rev fz yield the minimum VB of 0.137 mm. The validation tests confirm these findings with over 92% reliability, indicating strong predictive accuracy of the developed model. This study contributes to the knowledge of CNC lathe machining on Inconel 718 in dry-cutting conditions. It provides valuable insights for machine operators aiming to improve machining efficiency in industrial applications.

CNC Turning; Inconel 718; rry cutting; RSM; tool wear

A dual condenser heat pump dryer (HPD) was utilised for herb drying, demonstrating its capability to provide a cost-effective drying solution via economical energy consumption, expedited drying processes, and improved system coefficient of performance (COP). The drying properties, exergy energy, and techno-economic feasibility were comprehensively evaluated. The HPD employed refrigerant R-32, operating within a temperature range of 6 °C for the evaporator and 47 °C for the condenser to dry kesum and pandan leaves. The experimental results demonstrate that the dryer required a modest amount of electricity, 0.012 and 0.143 kW/kg of fresh kesum and pandan, respectively. The dryer effectively removed moisture during its passage through the dual condensers and evaporator, yielding 16.85 kg and 3.39 kg of condensate water from the initial mass of 50 kg and 5.5 kg, respectively. The specific moisture extraction rate (SMER) for the HPD mode was calculated at 11.23 and 2.26 kg/ kWh, and the average COP was 5.7 and 5.34 for kesum and pandan, respectively. The dual condenser improved the COP by approximately 11%. A maximum dryer productivity of about 0.75 kg/m²h is obtained at the air with a temperature of 47°C, velocity of 2.32 m/s and dryer surface load of 3.39 kg/m². The exergy efficiency of the thorough drying period varied from 56.85% to 96.33%. Moreover, the economic analysis determined a payback period of 3.24 months and 6.09 months for drying 24,000 kg and 2,640 kg of kesum and pandan leaves annually, with calculated annual incomes of 147,822.48 and 27,046.80 USD, respectively.

Heat pump dryer; dual condenser; R32; COP; SMER; exergetic; techno-economic; herbs

A Waiting Time Predictor System (WTPS) is designed in web-based system using Queuing Theory method to predict and provide average of waiting time that the patient was going to spend waiting, given the arrival rate, service rate and traffic intensity. This study addresses the uncertainty waiting times in outpatient clinics in Unit Kesihatan UiTM Campus Seremban. The system incorporates a real-time update for a patient to monitor their position in registration and triage, consultation, and pharmacy process. The system is built using Visual Studio Code and phpMyAdmin to enhance user experience and enables them to enter their data easily. A group of twenty-three students were encouraged to use the system and answered the questionnaire. A feedback survey has been conducted using System Usability Scale (SUS) resulting 83.45 that classify into excellent system meanwhile, the User Experience Questionnaire (UEQ) feedback forwarded high attractiveness, efficiency, and reliability but low novelty. The system can imply further improvement due to limitations such as introducing real-time notifications and supporting mobile application development that can approach bigger potential for broader applications.

Queuing Theory; waiting time; system development; patient management

Bipolar plates constitute a critical component of fuel cells, accounting for a substantial portion of the stack’s volume and significantly influencing overall system efficiency. This study investigates the fabrication of bipolar plate using epoxy/graphite (EP/G) composites reinforced with chopped carbon fiber (CCF) through a one-step compression molding technique. By comparing electrical conductivity and structural compactness using high-aspect-ratio graphite materials expanded graphite (EG) and carbon black (CB) the results reveal that EG provided superior conductivity and compactness due to its higher density, lower surface area, and minimized void formation. The CCF-reinforced EP/G/EG composite achieved the highest conductivity of 11.2 S cm⁻¹ at 7.5 wt% EG, while CB-filled composites recorded 8.5 S cm⁻¹ at 2.5 wt% CB. The CCF-reinforced EP/G/EG composite exhibited a significantly lower corrosion rate compared to the CB-filled counterpart, a performance attributed to its superior compactness, which effectively minimizes water absorption through the bipolar plate. The optimized CCF-reinforced EP/G/EG composite demonstrates excellent potential for energy conversion in fuel cell applications, contributing directly to Sustainable Development Goal (SDG) 7: Affordable and Clean Energy through improved material efficiency and system performance. Moreover, by promoting the use of sustainable materials and scalable fabrication techniques, this work also supports SDG 13: Climate Action, paving the way for greener, more efficient energy technologies that reduce reliance on fossil fuels and minimize carbon footprints.

Composite bipolar plate; electrical conductivity; fuel cell; lightweight bipolar plate; proton exchange membrane fuel cell (PEMFC)

Phosphorus is a major contributor to water pollution, causing harmful algal blooms, oxygen depletion, hence eutrophication. Studies have shown that seashells can effectively remove phosphorus from water due to their calcium carbonate content. While seafood is widely consumed, the shells are typically discarded as waste. Previous studies have explored the use of seashells for phosphorus removal, but none have focused on using raw Lala clam shells. Hence, this study addresses this gap by exploring the potential of Lala clam shells as an adsorbent for phosphorus removal from water at different dosages (2g, 4g, 6g, 8g and 10 g). The adsorbents were characterized using XRD, SEM, EDXRF and FTIR analyses. Batch adsorption experiments assessed the effects of adsorbent mass and contact time on phosphorus removal efficiency. Kinetic analysis revealed that the pseudo-second order model best described the adsorption kinetics (R2 = 0.9964), indicating that the adsorption followed a chemisorption mechanism. Isotherm analysis showed that, although all models had low correlation coefficients, the Langmuir model provided a relatively better fit (R2 = 0.0122), suggesting that phosphorus adsorption occurred on a homogeneous surface, forming a monolayer of phosphorus molecules. The raw Lala clam shells could remove up to 64.2 % of phosphorus with the highest adsorption capacity of 0.0818 mg/g observed at 8 g adsorbent mass. Overall, these findings confirm that raw Lala clam shells exhibit moderate phosphorus removal performance and demonstrate potential as a low-cost and eco-friendly adsorbent, due to their abundance and minimal processing requirements, although further modification is required to improve their adsorption performance.

Lala clam shell, phosphorus adsorption, batch experiment, kinetic models, isotherm models

The application of quadcopters continues to grow across various industries to execute duties requiring competent flight control and tracking capabilities. Due to the under-actuation, non-linearity, and instability of the system, which are the nature of the quadcopter, challenges are encountered in designing a proper controller. One of the most used controllers is the PID controller. A PID controller demands precise tuning for accurate and steady operation. However, certain modeling techniques of the quadcopter cause Ziegler-Nichols, the commonly used tuning method, to become inapplicable. Consequently, a manual tuning approach, which consumes a long adjustment time and is prone to contribute desirable responses, is used. To overcome the disadvantages that resulted from the manual tuning process, an attempt to optimize the performance of the system by designing an auto-tuning tool for the PID parameters using an adaptive neuro-fuzzy inference system (ANFIS). Based on the simulations of the manually tuned PID controller, the training data for the ANFIS system is collected. The trained data are utilized as parameters to generate the auto-tuning process in the designed ANFIS-PID. The ANFIS-PID controller is simulated with five different trajectories, and the results are analyzed by comparison with the PID controller. The analysis found that several small improvements are achieved with the ANFIS-PID. Hence, further adjustments and finetuning are suggested to be applied during the training sessions. In the future, the disturbances and obstacle factors should be taken into consideration in designing and modeling the quadcopter and its system to make it more applicable in real time.

ANFIS; neuro-fuzzy; PID controller; quadcopter; trajectory tracking; UAV

This study investigates erosion behaviour within a double-bend piping configuration subjected to sand-laden multiphase flow using Computational Fluid Dynamics (CFD). Three erosion prediction models E/CRC, Finnie, and DNV were applied to quantify erosion rate under varying particle diameters, inlet velocities, and sand concentrations. Simulations were conducted for particle diameters ranging from 100-250 µm, inlet velocities between 2-5 m/s, and sand mass flow rates of 30-90 g/s. The results show a strong dependence of erosion rate on particle inertia, impact angle distribution, and secondary flow structures (Dean vortices) generated within the bends. Statistical analyses based on standard deviation and coefficient of variation were employed to quantify moderateto-strong variability in predicted erosion rates across operating conditions, with the E/CRC model producing a mean erosion rate of 0.00252 kg/m²·s. The first bend consistently exhibits 1.46 times higher erosion due to concentrated particle impingement at shallow to moderate angles. The study highlights optimal erosion inducing conditions at medium particle sizes and moderate-to-high inlet velocities. These findings provide a mechanistic and quantitative basis for improved pipeline design and erosion mitigation strategies.

Sand erosion; multiphase flow; computational fluid dynamics; Eulerian-Lagrangian modeling; oil and gas pipelines; pipeline elbow

Transformer oil is a vital insulating medium for maintaining the dielectric strength of power transformers due to its thermal stability and electrical insulating properties. However, conventional gas-based monitoring techniques often fail to detect early cellulose bridging, which can result in insulation failure and transformer breakdown if not addressed through timely maintenance. To overcome this limitation, this study proposes a deep learning (DL)–based approach for predicting transformer oil health condition. Transformer oil condition is assessed by analysing images captured from laboratory experiments that simulate cellulose bridging formation. The visual patterns are categorised into three stages: pre-bridging, bridging, and post-bridging, where pre-bridging and bridging represent prebreakdown conditions, and post-bridging indicates the after-breakdown state. Image classification is performed using a pretrained AlexNet model and a modified version, TR-Net. A dataset of 1,800 cellulose bridging images, equally distributed across the three classes, is used for training and evaluation. The dataset includes both original and augmented images to improve robustness. The models are trained using optimised hyperparameters, including Stochastic Gradient Descent with Momentum (SGDM), with carefully selected epoch numbers, batch sizes, learning rates, and convolutional layer configurations. Results show that TR-Net achieves a higher confidence level of 0.97 with a shorter training time of 72 minutes and 50 seconds, compared to the pretrained AlexNet, which attains a confidence level of 0.90 with a training time of 86 minutes and 45 seconds. These findings demonstrate that TR-Net provides improved accuracy and efficiency for automated transformer oil condition assessment without human intervention.

Deep learning; cellulose bridging; image classification; transformer breakdown; transformer oil

This study addresses the need for resilient, off-grid food production on tropical islands by integrating renewable energy management with outdoor hydroponic cultivation. The objective of the research was to design and fieldvalidate an adaptive energy balance controller that coordinates energy availability and water management using real-time measurements of battery state of charge (SoC) and rainfall–runoff signals. The proposed system comprised a photovoltaic array, a battery energy storage unit, low-cost environmental and electrical sensors, and an embedded microcontroller executing a supervisory control policy. SoC was estimated from voltage and current measurements with signal conditioning to stabilize noisy data, enabling rain-aware irrigation prioritization during daylight hours and SoC-dependent duty cycling during nighttime operation. Using 5-minute averaged data, the SoC estimation achieved a mean absolute error of 1.207% and a root mean square error of 1.689%, corresponding to estimation accuracies of 98.79% and 98.31%, respectively. Field trials on a nutrient film technique hydroponic system in East Lombok, Indonesia, under sunny, cloudy, and rainy conditions demonstrated that SoC was maintained above a conservative safety margin, recovering during daylight periods and declining in a controlled manner overnight. Rainfall–runoff detection suspended irrigation for extended durations, with brief circulation intervals to maintain root oxygenation, thereby reducing water consumption and limiting nutrient dilution. Nighttime duty cycling aligned pump operation with available stored energy, avoiding deep discharge and supporting battery health. Time-series analysis confirmed autonomous responses without manual intervention.

Adaptive state-of-charge control; solar-powered hydroponics; rain-responsive irrigation; off-grid energy management; tropical island agriculture

Planting grape trees is very familiar in Palestine, particularly in the southern area of the West Bank, as this tree represents the second largest share of horticultural trees in Palestine after olive trees (PCBS, 2022). The total cultivated area of grapes trees in the West Bank and Gaza Strip is estimated to be 29.1 thousand dunums, i.e. 4.3% of the total area of horticultural trees in 2021. Hebron and Bethlehem Governorates south of West Bank represents the highest cultivated area planted with grapes. According to the Palestinian Ministry of Agriculture (MoA), the village of Al- Khader is the most productive area which cultivated with grapes tree in Bethlehem Governorate. This village is one of the most sensitive areas that is surrounded with Israeli colonies and military activities due to its location near Gush Etzion Israeli Settlement Bloc. The question of the real area of grapes cultivation within this village was a debate between the official organizations in Bethlehem Governorate for planning and economic purposes, as there is no scientific reference for this figure since they rely on estimations by questioning the farmers. The Applied Research Institute – Jerusalem (ARIJ) has accepted the implementation of this study to assist local organizations in the census of grapes trees in Al-Khader. After several focus group meetings and a week of fieldwork in a sensitive military area, the team was able to visit the whole targeted agricultural area. All the field data was combined with geospatial data and stored in a geo-database for the purpose of analysis and mapping. A previously developed landuse-landcover analysis for the targeted area shows that the total agricultural land in the village constitutes 12.63 thousand dunums which represents 61% of the total village area. The task analysis of the fieldwork results showed that the total planted land with grapes trees counts nearly 3,500 dunums which represents 27% of the total agricultural land of the village. Furthermore, the analysis showed that the total estimated grapes production of the village is nearly 2,500-3,000 tons from more than 250,000 grapes trees. The results of the study reveal the significance of geospatial technologies in fulfilling real world needs, which can help decision-makers to make evidence-based decisions and policies to develop the agricultural sector. Additionally, such studies can also contribute to the sustainable agricultural development and food security in Palestine and can contribute to the support of the resilience of Palestinian farmers on their lands.

Geospatial; agricultural census; grapes trees; Palestine; colonization

Approximately 60% of the country’s coconut plantations comprise various coconut species, which play a vital role in the national economy. However, infestations by the Red Palm Weevil (RPW) pose a significant threat, adversely affecting both the quality and yield of coconut production due to extensive damage to the trees. This study aims to detect RPW infestations in coconut plantations through the utilization of SPOT-6 satellite imagery, employing multiple classification techniques. Unsupervised classification methods were initially applied to derive the Normalized Difference Vegetation Index (NDVI) values, facilitating the differentiation of tree health status. Subsequently, supervised classification methods, namely Maximum Likelihood (ML) and Support Vector Machine (SVM), were implemented to evaluate their efficacy in identifying healthy and unhealthy coconut trees. NDVI values ranging from 0.33 to 1 were classified as indicative of healthy trees, whereas values below 0.33 were categorized as unhealthy. The results demonstrated that the SVM classifier outperformed the Maximum Likelihood approach, achieving an overall accuracy of 93% and a Kappa coefficient of 0.8543. These findings highlight the effectiveness of integrating GIS and remote sensing technologies in agricultural research and suggest significant potential for further development and commercialization of such approaches in precision agriculture.

Driver; maximum likelihood; support vector machine; classification; red palm weevil; coconut trees; NDVI.

The construction industry plays an important role in national development by supporting economic growth, especially through large-scale projects such as rail construction. However, rail projects are highly complex and frequently involve change orders (COs), which may result in delays, cost overruns, and administrative challenges if not managed properly. This study examines the key challenges in Change Order Management (COM) practices in Malaysian rail construction projects across the COM framework. A qualitative approach was adopted using semistructured interviews with eight industry professionals from organisations identified through the Malaysian Rail Supporting Industry Roadmap 2030 (MIGHT). Purposive and snowball sampling techniques were employed to obtain relevant respondents with experience in rail construction projects. The findings identified 15 key challenges across the three phases of the COM framework. Building on these findings, the study analyses how the identified COM challenges can be addressed through specific elements of the Last Planner System (LPS). The analysis focuses on Master Planning, Phase Planning, Lookahead Planning, Weekly Work Plan, and Measure and Learning to explore their potential in improving current COM practices. This study provides practical insights into strengthening COM implementation in Malaysian rail construction projects.

Rail construction; change order management; Last Planner System (LPS)

Landslides are a signi icant geological hazard, occurring across various spatial and temporal scales and resulting in severe environmental damage and loss of human life. This study presents a rapid framework for landslide detection by using a high-density airborne LiDAR data deep learning approach. The study area is located in the tectonically active area of Kundasang, Sabah, Malaysia. High-density airborne LiDAR data was acquired over the active, dormant, and relict landslides. The airborne LiDAR data was used to generate high resolution of two key inputs: Digital Terrain Model (DTM), hillshade and slope, and Digital Terrain Model (DTM), slope and aspect. The detection process was based on the Mask Region-based Convolutional Neural Network (Mask R-CNN) with the architecture of ResNet-101 and ResNet-152. The R-CNN model was trained and evaluated using a landslide inventory developed by landslide boundary that has been manually digitized on the DTM. The reference data was prepared by the experts, which has been randomly divided into two groups of training (70%) and validation (30%) datasets. The performance of the R-CNN method was evaluated based on the resolutions of input parameters, different landslide states of activities, and architecture of R-CNN. The results show that R-CNN can detect the active landslide more effectively than dormant and relict landslides with the integration of DTM, hillshade, and slope map at 1 m resolution. The R-CNN with 152 layers showed the best F-measure of 50.7% value for active landslide detection. However, the detection results for dormant and relict landslides remain low. The remote sensing-based landslide detection framework showed promising results, though more effort should be focused on detecting historical landslides.

Landslide detection; Light Detection and Ranging (LiDAR); Digital Terrain Model (DTM); Deep Learning (DL)

Particularly in low-temperature fuel cell applications, graphitic carbon nitride (g- C₃N₄) has attracted substantial interest due to its distinctive properties, including its exceptional thermal and chemical stability, adaptable electrical structure, and simplicity of synthesis. In this investigation, the function of g-C₃N₄ is investigated in a variety of lowtemperature fuel cell systems, such as proton exchange membrane fuel cells (PEMFCs) and direct methanol fuel cells (DMFCs). Potential remedies to the constraints of conventional materials, including cost, durability, and activity, are provided by the incorporation of g-C₃N₄ as a catalyst support, co- catalyst, or membrane additive. g-C₃N₄ is a promising alternative for improving fuel cell efficacy due to its nitrogen-dense structure. Doping elements such as sulfur, phosphorus, and transition metals has significantly enhanced the performance of fuel cells. The following are included: improved power density, improved electrochemical stability, and increased resistance to fuel crossover. The potential of g-C₃N₄ to address cost and efficiency concerns in low-temperature fuel cells is investigated within the context of sustainable energy applications. The United Nations Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), are in alignment with these enhancements, as they underscore the development of energy conversion technologies that are both clean and efficient. Nevertheless, even with the promising results, additional research is required to fully understand the mechanisms that control the catalytic properties of g-C₃N₄ and to improve the synthesis of g- C₃N₄-based materials for commercial applications. In the development of next-generation low- temperature fuel cells, the recent progress and obstacles associated with g-C₃N₄ in fuel cell technology are analyzed in detail.

g-C₃N₄; low-temperature fuel cells; proton exchange membrane fuel cells (PEMFCs); Sustainable Development Goals (SDGs); catalyst support

Water infrastructure decay and facility maintenance failures represent a silent crisis across Africa, yet systematic analysis of why infrastructure deteriorates and why maintenance systems persistently fail remains limited. This study critically reviews water infrastructure decay and facility maintenance across five African countries Nigeria, Ethiopia, Democratic Republic of Congo, South Africa, and Morocco using desk research methodology that synthesizes academic literature, institutional reports, utility audits, and NGO evaluations. The findings reveal that maintenance failure is systemic, not accidental, driven by predictable patterns of underfunding (maintenance budgets at 2-12% of capital value versus 15-20% needed), institutional fragmentation, corruption, spare parts supply chain failures, and perverse political incentives favoring new construction over asset preservation. Consequences include intermittent water supply, waterborne diseases (cholera, typhoid, diarrheal disease causing ~200,000 annual child deaths across Sub-Saharan countries), high lifecycle costs (replacement costing 3-5 times maintenance), and disproportionate burdens on poor and rural communities. The study contributes a root cause analysis framework distinguishing immediate, underlying, and systemic causes, a comparative assessment of five diverse nations, and actionable recommendations for governments (dedicated maintenance budgets, asset management systems), donors (shift funding from new construction to maintenance), utilities (preventative maintenance, staff training), and communities (strengthened maintenance committees, local technician training). Addressing infrastructure decay requires simultaneous intervention at all three causal levels not just repairing broken pumps but reforming budgets, strengthening institutions, and changing donor and political incentives.

Water infrastructure; facility maintenance; infrastructure decay; Sub-Saharan Africa; management; root cause analysis

Cultural heritage places worldwide are more exposed to natural disasters and climate change, threatening their survival. These disasters damage heritage sites physically and devalue them historically, culturally, and significantly. Losing cultural and historical connections affects present and future generations, which is distressing. Despite awareness of the risks, risk mitigation initiatives for cultural heritage assets in natural disaster-prone areas are insufficient. Comprehensive heritage management strategies are needed to safeguard heritage assets. Disaster risk indicators are often neglected in heritage management strategies. The Conservation Management Plan (CMP) in Malaysia guides heritage management; however, its monitoring and risk assessment guidelines for disaster risk mitigation are ambiguous. This study evaluates Malaysia’s heritage management plans’ risk assessment criteria to remedy the gap. Content analysis is utilised to analyse the CMP’s disaster risk management plan in this qualitative study. The result suggested that the CMP’s structure prioritises disaster risk mitigation inadequately. The lack of policies and methods for preserving Malaysia’s cultural, historical, and socially significant heritage sites, despite changing environmental issues, must be addressed. This will ensure the current enjoyment and the future protection of these cultural heritage sites.

Disaster risk reduction; cultural heritage; conservation; resilience preservation; climate change adaptation

Public Facilities Maintenance Contracts (FMC) are critical for ensuring the functionality, cost-effectiveness, and longterm performance of government buildings. In recent years, integrated contract structures have gained prominence as they provide a single point of responsibility and promote better service coordination. However, inefficiencies remain, largely stemming from inadequate resource allocation and insufficient performance oversight within these arrangements. Despite their growing importance, limited research has investigated how contractual and physical characteristics influence maintenance contract efficiency. This study addresses this gap by evaluating the efficiency of integrated FMC in Malaysia using data from 45 government contracts. Each FMC efficiency was measured using a normalized per annum Present Value (PV) method and correlation analysis of key variables, including contractual characteristics (contract value and period) and physical characteristics (building size, number of storeys, number of blocks, and building age). The analysis reveals that efficiency decreases with higher contract values, extended contract periods, and buildings with multiple blocks, while taller buildings demonstrate marginal efficiency gains. Interestingly, building age does not significantly influence contract performance, highlighting the role of contract design in driving efficiency outcomes. The study emphasizes the importance of careful contract planning and well-structured agreements with explicit provisions for performance-based incentives to enhance maintenance efficiency. Aligning contractual frameworks with building attributes is, therefore, critical for optimizing resource allocation and improving public infrastructure maintenance practices.

Facilities Maintenance Contracts (FMC); maintenance efficiency; contractual characteristics; physical attributes

School buildings in Malaysia, particularly those constructed between the 1970s and early 2000s, were developed with limited consideration for energy efficiency (EE) and climate-responsive design, resulting in high operational energy consumption and suboptimal indoor environmental conditions. In hot and humid tropical climates, cooling and lighting systems make up a substantial portion of the electricity demand in educational facilities. Although green technology retrofitting is widely recognised as an effective strategy for improving building performance, the decision to retrofit public schools remains constrained by the lack of context-specific prioritisation approaches. Therefore, this study aimed to identify and prioritise suitable green technology interventions for retrofitting Malaysian school buildings. A structured literature review was conducted to identify potential retrofit measures, which were subsequently categorised into three domains, namely, energy efficiency (EE), indoor environmental quality (IE), and smart operational controls (SO). A questionnaire survey involving 52 certified energy auditors in Malaysia was then conducted to evaluate the relative importance of each intervention. The findings identified heating, ventilation, and air conditioning (HVAC) system improvements, energy-efficient lighting upgrades, reflective roof treatments, window sealing enhancements, shading applications, and energy monitoring practices as being among the most significant retrofit strategies for Malaysian school buildings. The study contributes by providing a climate-responsive prioritisation basis, integrating technical feasibility, economic considerations, and operational practicality. The findings from this research may serve as a useful foundation for the future development of retrofit decision-making frameworks and strategic planning approaches for public school buildings in resource-constrained environments.

Green retrofit prioritisation; school building retrofitting; tropical climate buildings; Relative Importance Index (RII); energy efficiency strategies

Malaysia is currently experiencing rapid urbanisation that led to a more efficient and equitable public transportation systems. Though Light Rail Transit (LRT) systems in Kuala Lumpur did play a key role for urban mobility, spatial variations of Level of Services (LOS) across stations remain insufficiently understood. Hence, this study developed spatial framework that integrated Analytical Hierarchical Process (AHP) and GIS for measuring LOS of rail-transit stations by using four main criteria; accessibility, land use, population and services’ quality as the basis. The criteria were weighted using experts’ judgement and GIS index modelling was used to derive a composite index map that classify the LOS across the study area. Then, spatial statistical analysis including Average Nearest Neighbor, Global Moran I and Getis-Ord Gi* were used to examine the clustering patterns of the LOS. Results revealed a pronounced spatial variation of the LOS where high LOS are concentrated in central urban area. AHP revealed that accessibility is the most dominant factor with 0.571 of weightage indicating the need of considering both operational efficiency and accessibility in measuring LOS. Significant spatial autocorrelation with Moran I of 0.438 shows that the LOS distribution is structurally embedded within the urban form rather that it is randomly distributed. Findings suggested that the disparities in the LOS are driven more by landuse integration and connectivity in addition to the rail-line characteristics. This study provides a replicable GIS-MCDA framework for a spatially-explicit LOS assessment for rail-transit services. It highlights the importance of addressing accessibility gaps to achieve a more sustainable Transit Oriented Development (TOD).

ahp; gis; mcda; Level of Service; accessibility; rail transit

Corrosion, a pervasive issue across industries, necessitates the exploration of eco-friendly and cost-effective corrosion inhibitors. Focus of this study to delves into the corrosion behaviour of mild steel and the potential of natural corrosion inhibitors in acidic environments. Therefore, eggshell contain of high calcium carbonate was proposed as corrosion inhibitor to determine the effectiveness on the mild steel. The methodology started with the extraction of eggshell in sulfuric acid medium to produce 5% extract of natural corrosion inhibitor. The performance of corrosion inhibitor was then verified through electrochemical measurements and weight loss methods. The experimental data revealed a notable reduction in corrosion rates when mild steel specimens were treated with eggshell extract, indicating the potential of this natural material as a corrosion inhibitor. The adsorption mechanism of organic and inorganic compounds onto the metal surface, forming a protective layer, is explored, along with the identification of specific compounds responsible for corrosion inhibition. The study embraces sustainability by considering the scalability, cost-effectiveness, and potential environmental benefits of utilizing eggshell waste as a corrosion inhibitor and contributes to a greener and more sustainable approach to corrosion prevention.

Corrosion inhibitor; eggshell; mild steel; potentiodynamic polarization; weight loss

Hydrogen is a promising clean energy source, but impurities like carbon dioxide can negatively impact its fuel quality and performance. This problem also has gained attention due to the global concern over rising carbon dioxide emissions contributing to global warming. Consequently, purifying hydrogen through pressure swing adsorption has become crucial. However, a significant challenge in hydrogen purification is the trade-off between purity and recovery. To address this challenge, an artificial neural network (ANN) is used to optimize the hydrogen purification process, balancing the compromise between purity and recovery. A feedforward ANN (FANN) model was developed with one hidden layer containing 14 neurons. The model included three input factors and two output responses. The model successfully identified adsorption pressure and blowdown time as the key operating conditions affecting hydrogen purity with a very low mean square error (MSE) of 0.00109. The regression coefficients (R) for training, validation, and testing were exceptionally high at 0.99998, 0.99996, and 0.99996, respectively. Optimal operating parameters were found to be 2 bar pressure, 5 minutes of adsorption time, and 5 minutes of blowdown time, resulting in 99.99% hydrogen purity. This approach offers a renewable and efficient solution with significant potential for the energy sector, supporting efforts to achieve a cleaner and more sustainable future.

Artificial neural network; hydrogen gas; pressure swing adsorption; PKS-AC

One kind of pollution that presents serious catastrophic risks to marine ecosystems is oil spills from petroleum industry. Although there are many methods that have been applied to clean the oil spill, nerveless the most popular method is using adsorbent. The objective of this study is to investigate the capability of kenaf fiber and rice husk mixture in the form of fuel briquettes as an adsorbent. Various proportions of these substances were utilised to create fuel briquettes, ranging from a ratio of 20:80 to 80:20, in order to determine the most effective ratio for an oil adsorbent. Besides oil sorption capacity, the briquette sample has also been tested for its reusability and durability. Results indicated that sample with a ratio of 20% kenaf fiber and 80% rice husk display the highest oil sorption capacity with a value of 0.7817g of oil. Meanwhile, sample with a ratio of 80% kenaf fiber and 20% rice husk exhibits exceptional durability, it had the highest compressive strength, reaching up to 68 N/mm2. All samples that are produced in this study can retain over 70% of the sorption capacity after 15 consecutive sorption-squeezing cycles. In term of cost, application of kenaf and rice husk was found to be 45% more economical for each ton acquired as compared to pine sawdust as reported from other research. Therefore, the best sample from this study is sample A with a combination of 20% kenaf fiber and 80% rice husk.

Oil spill; oil sorbent; kenaf fibre; rice husk; natural sorbent

Kaempferia galanga Linn. (KGL) is an herbaceous plant renowned for its antimicrobial, anti-inflammatory, antioxidant, nematicidal, and larvicidal properties. However, research on its mosquitocidal potential is limited. With the increasing global mosquito population, there is an urgent need for effective and eco-friendly mosquito control methods. This study aims to evaluate the larvicidal activity of KGL essential oil against Aedes aegypti larvae and its toxicity effect on the non-target organism Epipremnum aureum. The essential oil was extracted using solvent-free microwave extraction (SFME) at 450 W for 120 minutes and analyzed using Gas Chromatography-Mass Spectrometry (GC-MS), identifying ethyl p-methoxycinnamate as the major constituent. Toxicity assessment on Aedes aegypti larvae revealed LC₅₀ and LC₉₀ values of 53.97 ppm and 90.50 ppm, respectively, at 24 hours of exposure. The 50 ppm concentration provides a balanced effect, supporting significant stem elongation and moderate leaf area growth of Epipremnum aureum. These findings highlight the effectiveness of KGL essential oil as a natural larvicide and raise concerns regarding its impact on non-target plant species. Thus, while KGL essential oil shows promise as an alternative to synthetic pesticides, future studies should optimize the concentration for targeted applications, minimize non-target effects, and evaluate its environmental sustainability for large-scale implementation.

Larvicidal activity; essential oil; kaempferia galanga linn; epipremnum aureum; aedes aegypti

This study was conducted to discover a new palm shell charcoals (PSC) filter medium, for safe drinking water treatment to remove pathogenic microorganism. To determine the PSC performance, a granular medium filtration in dual- and tri-media filters were designed and constructed to assess the physical and biological contaminants. The PSC morphology, porosity, specific gravity, and ball-pan hardness were studied. The physical characteristics of PSC, as evaluated by ball-pan hardness at 97.30%, demonstrate that it is potentially comparable to or greater than commercially available granular filter media. This study also found that, the granular medium filter in dual-media filtration was performed better respect to physical and biological contaminations removal as compared to tri-media filtration. Notably, the PSC/sand filter media with Effective Size (ES) 1.0/0.5 mm dualmedia filtration success to remove 73.41%, 69.36%, 57.04% and 0.37 log of turbidity, suspended solid, colour and: Escherichia coli (E. coli) bacteriological respectively. For instance, the tri-media filters of PSC/Granular Activated Carbon (GAC)/sand show the highest bacteriological removal of 0.33 log for total coliform. Besides, the types of media filters to the turbidity, suspended solid, colour and E. coli were in significance interaction with p < 0.05 under one-way anova. Additionally, there was statistically significant difference at p < 0.05 between the types of media filters and flow rate to the physical removal under two-factor analysis of variance. Therefore, this study offers practical insights for adopting PSC as these materials are available, abundant, easy to maintain and economical for water treatment towards the environmental sustainability enhancement.

Escherichia coli; mixed-media filter; palm shell charcoals (PSC); total coliform

The past few decades of electrification transformation in India probably mark the most important engineering and technological developments in the history of modern India. From the very first colonial forays into power generation and distribution to the present day of advancements in grid modernization, the entire industry has witnessed diversity and growth at a very rapid pace. As of 2024, India’s total installed electricity generation capacity will cross 430 GW, with renewable energy sources accounting for more than 44% of this mix, including more than 180 GW from solar, wind, biomass, and small hydro power. Transmission infrastructure has evolved over almost 470,000 circuit kilometres of high-voltage networks integrating diverse energy sources into the largest synchronized grid in the world. In parallel, developments in smart grid technologies, digital monitoring, and automation allow better energy management in real-time, improved grid stability, and loss reduction across the system. The electrification rate of only slightly above 56% in 2000 crossed almost 100% by 2019, ensuring reliable access to both rural and urban population and maintaining rapid industrial and digital development. Strong policy measures and regulatory reforms have supplemented technological advancement and aided the integration of renewables, demand-side management, and energy storage while addressing the challenges of balancing regional demand-supply variations. The government’s thrust on decarbonization and sustainable growth has rightly dovetailed with India’s aim of 500 GW of renewable capacity by 2030 and a 45%-reduction in carbon intensity of GDP from the 2005 levels.

Electrical energy; demand; generation; distribution; smart grid

Coreless windings are highly promising for both low and high-speed axial flux permanent magnet machines, offering distinct advantages over traditional cored windings. Their ability to be freely arranged within the airgap provides extensive opportunities for optimizing lightweight generator designs. Despite this potential, research on coreless windings remains limited. This study deviates from conventional winding and permanent magnet designs, demonstrating that generator performance can be significantly enhanced by refining winding geometry and electromagnet configuration. A tri-spiral coil (TR-SPC) with uniform turns was designed alongside two electromagnet configurations: a trapezoidal-shaped electromagnet (TR-ELMAG) and a dual spiral electromagnet (DR-ELMAG) to investigate these improvements. The analytical design methodology is first presented and subsequently verified through finite element analysis (FEA) using ALTAIR Flux® v2022.3. The electromagnetic performance was evaluated on a single-stator, double-rotor coreless axial flux electromagnet generator (SSDR CAFEMG) using both ferromagnetic and non-ferromagnetic materials at 500 rpm and 50 Hz. Notably, the results indicated that the DRELMAG configuration outperforms TR-ELMAG across most metrics, achieving an airgap flux density of 495.25 mT and a coil flux density of 478.65 mT, compared to 194.42 mT and 224.72 mT for TR-ELMAG. Further evaluation shows that DR-ELMAG delivers a higher average torque of 1.15 Nm, zero cogging torque, output power of 166 W, and efficiency of 92.2%, in contrast to TR-ELMAG, which produces 0.18 Nm of torque, 34 mNm of cogging torque, 164 W of output power, and 91.1% efficiency. Future work will focus on prototype fabrication, load-based experimental validation, and comprehensive thermal modeling to improve scalability.

Coreless Axial flux PM generators; electromagnet; finite element analysis; spiral coil design; power quality

Green synthesis of metal oxide nanoparticles using plant extracts has emerged as an environmentally friendly and sustainable alternative to conventional chemical methods. In the present study, ZnO and Fe2 O3 nanoparticles were synthesized via a plant-mediated route and systematically characterized using UV–Visible spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), High-Resolution Transmission Electron Microscopy (HRTEM), X-ray Diffraction (XRD), and X-ray Photoelectron Spectroscopy (XPS) to confirm their structural, morphological, and chemical properties. The removal efficiency of the synthesized nanoparticles was evaluated against two model dyes, crystal violet and bromophenol blue, with initial concentrations of 5mg/L and 10mg/L, respectively, under ambient conditions over a 24-hour reaction period. The results demonstrated distinct performance trends for the two nanomaterials. Fe2 O3 nanoparticles exhibited higher removal efficiency toward crystal violet, achieving a maximum removal efficiency of 84.32%. In contrast, ZnO nanoparticles showed enhanced removal capability for bromophenol blue, reaching up to 98.15% removal under identical experimental conditions. These findings indicate that removal efficiency is influenced by both the physicochemical properties of the nanoparticles and the nature of the dye molecules. The comparative evaluation highlights the selective applicability of ZnO and Fe2 O3 nanoparticles for targeted dye removal in wastewater treatment applications.

Zinc oxide nanoparticles; iron oxide nanoparticles; green synthesis; characterization; dye removal

Budget optimization for road maintenance is critical to ensure maximum production while minimising costs. In recent years, various technology-based approaches have been developed to optimize maintenance budgets. However, despite these advancements, there is still a lack of a structured approach to evaluate the acceptance and reliability of such technologies, particularly within the public sector. This study employed a Systematic Literature Review (SLR) to examine the extent to which technology acceptance and system reliability factors have been addressed in prior studies on road maintenance budget optimization. A total of twelve (12) technology acceptance factors and four (4) system reliability factors were identified from existing literature. Relevant publications were retrieved from major electronic databases using specific keywords related to road maintenance and budget optimization. After applying the inclusion and exclusion criteria, fifteen (15) relevant articles were selected and systematically reviewed. The findings reveal that only five (5) of the twelve identified technology acceptance factors were commonly addressed in the reviewed studies. Similarly, among the four (4) system reliability factors, only one (1) factor was consistently discussed across all publications. These results indicate that existing studies tend to focus more on technical optimization models while giving limited attention to behavioral and system reliability considerations. This study highlights the need to develop a comprehensive model that integrates technology acceptance and system reliability factors to evaluate technology-driven budget optimization methods. Such integration is essential to support more effective decision-making, enhance public-sector adoption of optimization technologies, and improve the sustainability of road-maintenance management.

Optimization method; technology; road maintenance; technology acceptance; reliability

This article presents a literature review on air-pollution-absorbing façade systems, focusing on their technological development and integration into architectural design. The study highlights the use of advanced materials such as photocatalytic coatings, which can neutralize airborne pollutants like nitrogen oxides and particulate matter, alongside the incorporation of vegetation through green façade systems. Conducted through a comprehensive desktop review, the research synthesizes findings from reputable academic databases including Web of Science, Scopus, Elsevier, and MDPI, as well as selected case studies of buildings that have implemented such systems. The review reveals that when these façade technologies are combined with greenery, they can significantly improve urban air quality by both absorbing and decomposing pollutants. Photocatalytic materials, activated by sunlight, facilitate chemical breakdown of pollutants, while vegetation contributes natural filtration and enhances thermal comfort and aesthetic appeal. The synergy between high-performance materials and biophilic design elements offers a promising approach to sustainable urban architecture. Beyond their environmental benefits, such façade systems also present architectural and socio-cultural value, especially in dense urban environments facing critical air quality challenges. The article concludes that air-purifying façades represent a viable and innovative solution for integrating environmental performance into architectural practice, thus contributing to broader goals of urban sustainability and public health.

Facade system; air pollution adsorption; green facade