Journals of Universiti Tun Hussein Onn Malaysia (UTHM)
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Characterization of Bottom Ash from the Combustion of Palm Oil Empty Fruit Bunches (EFB)
Full text linkThe palm oil industry generates large volumes of biomass waste, particularly Empty Fruit Bunches (EFB), which pose environmental disposal challenges but offer potential as a renewable energy source. This study focuses on the characterization and combustion analysis of bottom ash derived from pelletized EFB, with special attention to how combustion temperature affects ash quality at a fixed time duration. Pelletized EFB offers improved energy density and uniform combustion behavior compared to loose EFB. Combustion was conducted for 30 minutes at three different temperatures: 400°C, 600°C, and 800°C. The results showed that combustion temperature significantly influences ash yield and composition. Lower temperatures produced darker ash with higher residual carbon, while higher temperatures generated lighter ash with more fused and mineral-rich phases containing silicon, potassium, and calcium. XRD analysis confirmed a transition from simple crystalline phases at low temperatures to more complex silicate and glassy phases at higher temperatures, suggesting potential for construction-related applications. TGA revealed that major mass loss occurred between 200-375 °C due to decomposition of hemicellulose and cellulose, leaving about 33% inorganic residue forming the ash. Overall, this study highlights the critical role of combustion temperature in determining the physicochemical and mineralogical characteristics of EFB bottom ash and supports its potential for sustainable utilization within the palm oil industry.
Electrochemical Optimizing of SSC-SDCC Cathodes for LT-SOFCs: Synergistic Control of Composition, Phase Structure, Morphology, and Thermal Properties
Full text linkThe growing global demand for alternative energy sources has driven the development of solid oxide fuel cells (SOFCs), which offer efficient and eco-friendly energy conversion. However, conventional SOFCs high operating temperatures accelerate material degradation, necessitating the exploration of low-temperature SOFC (LT-SOFC) materials. This study investigates samarium strontium cobalt-samarium doped ceria carbonate (SSC-SDCC) composite cathodes with varying weight ratios (50:50, 60:40, and 70:30, denoted as SSCB55, SSCB64, and SSCB73) mixed via high-energy ball milling (HEBM). The powders were calcined at 750°C, pelletised using the uniaxial pressing method, and sintered at 600°C. X-ray diffraction (XRD) analysis confirmed the formation of the SrCO₃ secondary phase, despite of this phase formation, the cathode exhibited enhanced performance with reduce ASR values. The energy dispersive spectroscopy (EDS) mapping demonstrated uniform elemental distribution across all samples, ensuring compositional homogeneity. The field emission scanning electron microscopy (FESEM) revealed microstructural evolution, including increased agglomeration after calcination process. Porosity measurement (31-44%) aligned with optimal cathode material requirements, facilitating efficient gas diffusion and electrochemical reactions. Thermal expansion coefficient (TEC) analysis indicated that only SSCB55 exhibited acceptable compatibility with the SDCC electrolyte, whereas SSCB64 and SSCB73 exceeded the recommended thresholds, risking mechanical failure during thermal cycling. Electrochemical impedance spectroscopy (EIS) further revealed that the SSCB55 cathode achieved low area specific resistance (ASR) by 5.06 Wcm2 at 600°C, indicating superior oxygen reduction reaction (ORR) kinetics and highlighting its potential for LT-SOFC applications. These findings suggest that optimised SSC-SDCC composites, particularly SSCB55, are promising candidates for high-performance LT-SOFC cathodes.
 
Effect of Calcination on The Bioactivity of Hydroxyapatite (HAp) from Black Tilapia Fish Scale
Full text linkHydroxyapatite, HAp is extensively used for orthopaedic and dental reconstruction as implant material due to their chemical and biological similarity to human hard tissue. Recently, vigorous research efforts made to obtain HAp from an animal bone in providing alternative feedstock materials for biomedical applications. Therefore, the extraction of natural HAp from the Black Tilapia (Oreochromis Niloticus) fish scales was produced via a conventional heat treatment (calcination) at 1000 °C. To produce HAp fine powder, the natural HAp from the tilapia fish scale went through a grinding process before characterization and testing. The sample was characterized using powder X- rays Diffraction (XRD), Field Emission Scanning Electron Microscopy (FESEM), and Energy Dispersive X-ray spectroscopy (EDX). The bioactivity of the samples was characterized using a Simulated Body Fluid (SBF) Test, Anti-Microbial and MTT-assay using a Human Fetal Osteoblast (hFOB) 1.19 cell line. XRD result shows the crystallinity of extracted HAp is similar to the standard HAp. The FESEM image shows the particles have different morphologies. The EDX analysis shows that the Ca/P ratio is 1.69 that slightly different from the standard HAp (1.67). The SBF result shows apatite deposition on top of the pellet sample surface after immersion for 7 days. Anti-Microbial shows that there are no anti-microbial properties on the extracted HAp and the MTT-assay analysis shows that the samples were not toxic to the cell. This work shows that studies on the extraction of fish scale into high value-added product are the promising alternative to produce natural HAp that is beneficial to medical applications. The bioactivities show that the natural HAp produced is bioactive and not toxic
Causes Of Cost Overrun and Solutions in Construction Projects in Somalia
Full text linkThe construction industry plays a critical role in Somalia’s socioeconomic development and has expanded significantly since the end of the civil war; yet, cost overruns remain a major challenge, as many projects exceed their initial budgets. Existing studies indicate that limited skilled personnel, inexperienced managers, and poor communication are contributing factors to this issue. This study aims to identify the causes, solutions, and impacts of cost overruns in Somali construction projects and to analyse the relationships between these variables. A quantitative approach, utilising a structured questionnaire, was employed, yielding 63 valid responses from Grade A contractors, including project managers, engineers, and architects. Descriptive statistics and correlation analysis were used to evaluate the data. The results indicate that unskilled labour, insecurity, lack of insurance, poor project management, and weak infrastructure are the primary causes of cost overruns, while effective cost management, time management, and quality management practices serve as key solutions. Major impacts identified include financial strain on clients, compromised project quality, reputational damage to contractors, and project delays. Correlation analysis revealed a moderate positive relationship between the causes and solutions and a strong positive relationship between the solutions and impacts, suggesting that addressing the root causes is moderately linked to solution effectiveness, whereas implementing the proposed solutions strongly contributes to mitigating the impacts. Overall, the study offers practical insights for improving construction performance and strengthening cost-control mechanisms in Somalia
Digital Twin for Dynamic Construction Site Monitoring and Control
Full text linkThe construction industry is inherently complex and poses significant safety challenges, requiring innovative solutions to enhance operational efficiency and risk management. Digital Twin (DT) technology has emerged as a transformative tool, offering real-time digital representations of physical construction sites to facilitate proactive decision-making and improve site monitoring. This study aims to explore the integration of Digital Twin technology into construction site management, focusing on its impact on safety, operational efficiency, and risk mitigation. By leveraging real-time data, predictive analytics, and Internet of Things (IoT) sensors, the research seeks to assess the effectiveness of DT in enhancing safety protocols and streamlining construction workflows. A mixed-methods approach was adopted, combining quantitative surveys with construction managers and safety officers, along with qualitative interviews with industry professionals. Statistical analysis was conducted to evaluate the correlation between DT adoption and safety performance indicators, while thematic analysis provided insights into practical challenges and benefits. Findings indicate a significant reduction in workplace accidents and near-miss incidents, with a 35% decrease in reported accidents following DT implementation. Additionally, participants highlighted improved situational awareness, proactive risk management, and a cultural shift towards safety-conscious practices. However, challenges such as initial costs and training requirements were identified as key barriers to widespread adoption. The study underscores the potential of Digital Twin technology to revolutionize construction site management by enhancing safety outcomes and optimizing operational efficiency. These findings offer valuable insights for industry practitioners, policymakers, and researchers, advocating for increased investment in DT systems to promote safer and more sustainable construction environments
Self-Sustaining Solar Panel Cleaning Solution with Integrated IoT Monitoring
Full text linkDeposition of dust on photovoltaic panels significantly reduces their power output efficiency, posing a major hindrance to optimal solar energy generation. In this article, the development of a self-sustaining solar panel cleaning system with an inbuilt Internet of Things (IoT) platform for real-time monitoring and control is reported. The system design suggested herein combines a wiper mechanism and a centrifugal air blower powered by a monocrystalline solar panel for effective removal of dust and debris. The cleaning cycle is made automatic using a dust sensor and real-time clock (RTC) module with an override facility through the Blynk IoT interface. Environmental and electrical parameters such as dust density, panel output voltage, current, and power are also monitored by the system. Results indicated a considerable power output increase after cleaning operations, validating the effectiveness and feasibility of the system as a cost-effective, environmentally friendly means of domestic solar panel cleaning
Optimal Sizing and Location of Multi-Unit Photovoltaic System on Distribution Network Using Particle Swarm Optimization
Full text linkThis paper investigates the optimal sizing and location of multi-unit photovoltaic (PV) systems in a distribution network using Particle Swarm Optimization (PSO). The goal is to reduce power losses and improve voltage stability by installation PV as distributed generator (DG). The IEEE 33-bus and 69-bus radial distribution systems were used as test system. Power flow analysis was conducted using the Newton-Raphson method in MATLAB software. Simulation results revealed that integrating three PV units provided the best performance, reducing power losses by 65.52% and 69.16% for the IEEE 33-bus and 69-bus systems respectively. A scenario with a 20% increase in total load was also analyzed to simulate the impact of electric vehicle charging station (EVCS) to PV sizing and location. It found that, proper sizing and placement of PV integration can improve the network losses when EVCS connected to the distribution system
Detection and Classification of Emergency Vehicles from Audio and Video Inputs using Deep Learning Techniques
Full text linkWith the rapid advancement of autonomous vehicles, ensuring road safety is one of the biggest concerns of the automotive industry. One critical aspect of safety is the accurate and timely detection of emergency vehicles such as ambulances, and fire trucks and promptly switching lanes to ensure smooth passage. This paper proposed an efficient and straightforward method to locate and label emergency vehicles with the help of the most updated deep learning algorithms known as YOLOv8 and long short-term memory (LSTM). The accuracy and efficiency of emergency vehicle detection in terms of perfecting the models is the focus. Data augmentation methods are carried out to enhance day-night and low visibility performance of the model on the dataset. The system is capable of identifying and classifying emergency vehicles on the basis of audio and video data using several signal/image processing methods and accomplished with the means of explainable artificial intelligence (XAI) mechanisms providing detailed information. The given system can be used with self-driving and human-driven vehicles which can be fitted to advanced driver assistance systems (ADAS). It is found that the accuracy and performance of emergency vehicle detection have improved significantly with the 96.6% accuracy rate that will ameliorate the interaction safety of autonomous vehicles and emergency vehicles
Dual-Stage Deep Learning Framework for Prostate Cancer Grading Using Swin U-Net and Attention-Based CNNs
Full text linkAccurate grading of prostatic adenocarcinoma is essential in treatment planning. However, Gleason grading is time-consuming and clinically undependable. We presented a hybrid deep learning framework which comprises Swin U-Net for transformer-based segmentation network and attention-based CNNs for ISUP grade classification task. We incorporated Grad-CAM to aid in model interpretability and to visualize decision crucial areas. Quantitative evaluations on the PANDA, ISUP Grade-wise and transverse datasets achieve 100% accuracy on the smaller balanced Transverse dataset, 90.2 ± 0.7% performance in terms of ISUP with only 3.5M parameters, and a vicious Dice score equal to 0.99 ± 0.005 for segmentation. Notably, this cross-dataset generalization has not deteriorated below 92.3 ± 1.4% in any TIO experiment with no form of retraining applied to the transferred models. Inference time is less than 20 ms, deployment on the edge and mobile. The proposed model has achieved state-of-the-art performance for interpretability, accuracy, and computational complexity. The broadcast-then-categorize platform has been validated in ablation and optimization experiments, which demonstrate the potential for real-time diagnosis of prostate cancer
Multi-Class Flood Classification Model Using Spatial Topo-Hydrological Features and Interpretable Machine Learning
Full text linkRapid urban expansion of cities in many developing countries, such as Nigeria, is aggravating occurrences of devastating urban floods because of sudden changes in climate and uncontrolled land use. Methods based on traditional flood prediction are expensive, primarily binary-classification-based, and lacking generality, which hampers their capability for disaster preparedness purposes. In this paper, we propose a standardized multiclass flood classification framework with spatial, topographic, hydrological and meteorological covariates based on three ML classifiers, including Random Forest, Support Vector Machine and Logistic Regression. The model was evaluated strictly using stratified 5-fold cross-validation and a 20% held-out test set. From the right, the RF model recorded the highest performance accuracy, at 92%, indicating desirable generalisation and resistance to overfitting. SVM was successful with 87% and LR achieved 83%, both being relatively unstable at minor flood classification. The experimentation-based feature importance analysis indicated that the environmental data index is the most important predictor, increasing interpretation and transparency in modelling. The results introduce RF as a trustworthy multi-class urban flood classification tool in data-poor contexts, with potential applications for early warning systems, city management and climate-resilient policies in the Global South