Journals of Universiti Tun Hussein Onn Malaysia (UTHM)
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Real-Time Glyphosate Detection Using a Colorimetric Optical Sensor
Get PDFThe availability of clean water is a growing global concern, exacerbated by pollutants such as glyphosate, a common herbicide. Conventional detection methods are often time-consuming and require laboratory analysis, which risks sample degradation. This study introduces the development of an in-situ colorimetric optical sensor for glyphosate detection, providing an integrated, real-time solution. The sensor utilizes a reaction between glyphosate and 2,4-dinitrofluorobenzene (DNFB), producing a yellow-colored compound as an indicator. A regression model was developed to estimate glyphosate concentration, and the sensor\u27s performance was compared with a spectrophotometer, yielding coefficients of determination of 0.9079 and 0.9715, respectively. While the spectrophotometer showed higher accuracy, the colorimetric sensor offers a cost-effective, portable, and reliable method for on-site monitoring, aligning with integrated engineering approaches for environmental management
Corrosion Behaviours of Carbon Steels Coated by Graphene Epoxy in Different Solutions
Get PDFCorrosion of carbon steel pipelines is a significant challenge in industrial applications, particularly in acidic and saline environments. This study investigates the corrosion resistance of ASTM A53 Grade B carbon steel coated with a graphene-epoxy composite. A 2%-wt graphene-epoxy coating was applied to the substrates using the bath method. Corrosion performance was assessed through potentiodynamic polarization in CH₃COOH 0.1 M, H₂SO₄ 0.1 M, HCl 0.1 M, and NaCl 3.5% solutions at room temperature. Scanning Electron Microscopy (SEM) analysis provided insights into coating thickness and elemental distribution. Results indicate significant improvements in corrosion resistance, with inhibition efficiencies exceeding 97% in HCl and H₂SO₄ solutions. A notable reduction in corrosion rate and current density was observed across all coated samples, with the graphene-epoxy layer forming a robust barrier against aggressive ions. SEM analysis revealed uniform graphene dispersion within the epoxy matrix and a consistent coating thickness of ±149.9 µm, supporting the enhanced corrosion resistance. However, the coatings exhibited reduced efficacy in CH₃COOH, attributed to potential degradation of the epoxy matrix in organic acid conditions. These results demonstrate the potential of graphene-epoxy coatings as potent anticorrosion agents for a variety of industrial applications, especially those that are acidic and chloride-rich. Future research should focus on optimizing formulations for organic acids to expand the applicability of this technology
Improving Coffee Roast Yield Consistency with Self-Tuned Fuzzy-PID Controller
Get PDFPrecise temperature control is essential in industrial processes like coffee roasting, where consistent quality is crucial. While traditional Proportional-Integral-Derivative (PID) controllers are widely used for temperature regulation due to their simplicity, they often struggle with dynamic process changes, leading to variability in product quality. This research investigates the Self-Tuning Fuzzy PID (STFPID) controller, which uses fuzzy logic to dynamically adjust its parameters, improving performance. The study aims to compare the effectiveness of conventional PID controllers and STFPID controllers in maintaining consistent roasting conditions and product quality. Experiments were conducted with both controllers to regulate the temperature during coffee roasting, focusing on indicators such as weight loss percentage, Agtron values (indicating roast level), and peak wavelength (reflecting color properties). Time-temperature profiles for multiple batches were analyzed to assess consistency and stability. Results show that the STFPID controller significantly outperforms the PID controller, achieving a 76.5% improvement in weight loss consistency, a 71.2% reduction in Agtron value variability, and a 64.4% enhancement in peak wavelength stability. These findings demonstrate the STFPID controller\u27s superior ability to adapt to process variations, maintaining uniform conditions and enhancing overall product quality in coffee roasting applications. The significance of this study lies in its potential to enhance the consistency and quality of coffee roasting, providing valuable insights for industrial applications and contributing to the advancement of process control technologies in this field
Investigating Measures, Challenges and Possible Solutions for Protecting Endangered Birds Species Around Lake Chilwa Ramsar Site in Malawi
Get PDFThis study was conducted to investigate measures, challenges and possible solutions established when protecting the endangered bird species around Lake Chilwa Ramsar site. Lake Chirwa is a shallow basin lake located in the south eastern region of Malawi. It is the second largest lake in Malawi and the twelfth largest in Africa. Lake Chilwa was designated as a wetland of international importance in November 1997 (Ramsar site No. 869). Its wetland is approximately 2310 km2 and it is a home for various bird species whose lives are endangered because of the high demand for the wetland’s resources. The drying out of the lake forced inhabitants who depended on it for fish to hunting birds. In order to protect the endangered bird species, Leadership for Environment and Development (LEAD) of Malawi in collaboration with communities around intervened with measures to protect endangered bird species. This study aimed at investigating the effectiveness of these measures. Data was collected through in-depth interviews and focus group discussions. Findings show that the effectiveness of the measures to protect endangered birds species depend on collaborative efforts of various stakeholders and involvement of the local inhabitants of the area. Interventions such as civic education, natural habitat restoration, creation of sanctuaries, annual closed season and patrolling of sanctuaries by game rangers have proven to be the most effective. However, the study found that these measures were effective when LEAD project was in place and became less effective when the project pulled out. The study further found that conflict of interest, encroachment and poaching, lack of resources and resistance to change by some inhabitants negatively affect implementation of the interventions in the wetland
Detection of Free Obstacle Region using Distance Transform and Image Subtraction Method for Monocular Camera Sensor
Get PDFAn area that is free of any barriers is known as a free region. Prior studies have demonstrated that when textureless impediments are used, the pixel volume expansion approach has limitations in identifying free spaces. To address the shortcoming, an approach that combines distance transform and image subtraction techniques is suggested. This algorithm incorporates three primary image processing processes: distance transform, image subtraction, and k-means segmentation. An object that is 170 cm and another that is 200 cm from the camera serve as the algorithm\u27s inputs. Following image processing, the output is separated into 12 sections, one of which is designated as a free region. Two distinct scenarios—a congested atmosphere and an uncluttered environment—were tested by the program. Four different types of obstacles are tested for each scenario. In this case, there are four types of obstacles: texture, textureless, textureless with one obstacle on the left, and textureless with one obstacle on the right. The outcome demonstrates that the technique can accurately identify free zones. Most importantly, it has a 100% success rate in detecting obstructions that lack texture. This demonstrates that the approach can get around the prior method\u27s drawback. Additionally, each obstacle\u27s success rate is compiled and shown in a table. Furthermore, a comparison is made between the proposed method and the pixel volume expansion method. Combining the image subtraction and distance transform method with the volume pixel expansion method yields the same success rate (100%) in texturing barriers for crowded situations
Development of Engine Oil Maintenance and Battery Health Monitoring through Short Messaging Service (SMS)
Get PDFWith proper care and proper maintenance, vehicle can be maintained to work as it supposed to and has longer life cycle. Vehicle maintenance for new vehicle monitored using service booklet while for some of older vehicle, the maintenance totally depends on owner awareness and most cases, they are using service note provided by service centre which are sticked on the windshield. Prior to this situation, the owner may not be notified because sometimes the vehicle may exceed the recommended mileage and date for next service. The aim of the project is to develop the vehicle maintenance monitoring through short messaging system (SMS) using Arduino Uno and GSM SIM900A module. The proposed project helps the vehicle user to monitor on the engine oil mileage and battery voltage on mobile phone. The vehicle owner can check and notified their vehicle next service using phone message. The project is developed using simple programming software and hardware. Arduino Uno as microcontroller to receive data from voltage sensor and send battery voltage through SMS. The push buttons used to represent the input from vehicle owner about the type of oil used during vehicle service to check the vehicle condition. The GSM module act as a message transmitter and receiver for user communication. The task was programmed into Arduino Uno using Arduino Integrated Development Environment (IDE). The development of this project helps the user to monitor vehicle condition especially for their maintenance schedule. The maximum percentage error was 3.2725% and lowest percentage error of 1.9787% of vot meter reading is measured. This project can be concluded that the device was successfully being developed and the functionality was observed
Dimensional Stability of Oil Palm Empty Fruit Bunch Fibre (OPEFB) Cement Board Over The Curing Period
Get PDFOil palm fruit bunch fibre bunch (OPEFB) is a byproduct of palm oil manufacturing. Empty fruit bunch (EFB) can be recycled into compost, renewable energy, and nutrients, reducing waste and making industry more environmentally friendly and cost-effective. Asbestos and synthetic fibre exposures pose health risks, particularly mesothelioma, due to their carcinogenic effects. Natural fibres offer a sustainable alternative with comparable mechanical properties, fire resistance, and crashworthiness. They can be used in cement composite materials for biodegradability, design flexibility, and sustainability. The ratio of mixed cement- fibre is set at 3.5:1, aiming for targeted density of 1300 kg/m³. The total number of samples is 9 samples empty fruit bunch cement board (EFBCB) with the dimensions size of 350×350×12 mm. To enhance the material’s properties, hot water treatment being used at 100°C for a duration of 2 hours for treating the EFB fibre. The testing was conducted at intervals of 7, 14, and 28 days of curing process. The results demonstrated notable declines in dimensional stability and mechanical performance over the curing period. The analysis of the dimensional stability of OPEFB fibre cement board (EFBCB) during the curing period revealed significant changes in both physical and mechanical properties. The results demonstrated how the reduction in thickness, density, and other physical changes impacted the dimensional stability and mechanical performance of the boards
Finite Element Modelling on The Tensile Performances of Boxed-Flange Cold-Formed Steel C- Section Connections
Get PDFCold-formed steel (CFS) has gained significant attention due to its high strength-to-weight ratio, cost-effectiveness, and ease of fabrication. This study focuses on finite element modeling (FEM) of boxed-flange connections in CFS C-sections under tensile loading, using WELSIM software to simulate structural behavior. The research validates FEM results against experimental data, performs a parametric study on steel thickness and number of screws, and compares failure modes to optimize connection design. The findings indicate that increasing steel thickness and screws enhances load-bearing capacity and delays failure onset. The FEM model predicted a yield load of 24.23 kN for 1.00 mm thick connections with 4 screws, closely matching the experimental result of 23.50 kN. For 0.75 mm thick connections with 2 screws, the FEM model predicted 6.87 kN, compared to the experimental 7.00 kN. These results validate the efficiency of numerical modeling for structural optimization.
An Enhanced Hybrid Binary Grey Wolf and Harris Hawk Optimization Algorithm Based on Cumulative Binomial Probability for Feature Selection in Classification
Get PDFFeature selection is a widely used approach for reducing dimensionality in datasets by eliminating irrelevant and redundant features. It significantly enhances the accuracy and efficacy of classification models. Hybrid binary grey wolf with Harris hawk optimization (HBGWOHHO) is a metaheuristic algorithm that has been effectively employed for feature selection in classification. However, the HBGWOHHO algorithm has a limitation in unbalanced exploration and exploitation in achieving the sub-optimal solution. This limitation refers to the linearly declining value of a balancing parameter, which lacks regulation between the exploration and exploitation phases. This paper presents an enhanced HBGWOHHO that employs an adaptive technique based on cumulative binomial probability (CBP) called hybrid grey wolf Harris hawk optimization-CBP (HBGWHHO_CBP) to fine-tune the balancing parameter. This adaptive adjustment technique ensures a more effective trade-off between exploration and exploitation, thus improving the algorithm\u27s search efficiency and solution quality. Dimension-wise diversity metric is used to quantitatively assess this balance during the optimization process. Eleven UCI benchmark datasets were utilized to assess the efficacy of the proposed HBGWHHO_CBP. The proposed algorithm demonstrated superior performance across the evaluated datasets, yielding an average accuracy of 0.94, a mean of 8.51 selected features, and a mean fitness value of 0.06, while requiring less computational time. The Wilcoxon signed-rank test results indicate that the proposed algorithm significantly outperforms the native HBGWOHHO and three other metaheuristic-based feature selection algorithms. The proposed metaheuristic can be applied for addressing the feature selection in classification
EffNetEye: A Multimodal Fusion Model for Multiclass Classification of Retinal Diseases
Get PDFThe Eye is the most sensitive human organ; if affected by any disease, it can hinder the individual’s quality of life. Some Retinal diseases, such as Epiretinal Membrane (ERM), Age-related Macular Degeneration (AMD), glaucoma, and Retinal Vein Occlusion (RVO), are major contributors to eyesight loss. Timely detection of such diseases is essential for successful treatment. A novel model called ‘EffNetEye has been proposed for the classification and diagnosis of retinal diseases by combining two modalities, fundus and OCT. The suggested model provides a simple but effective dual-modality feature-level fusion approach. It uses two EfficientNetB0 backbone networks to extract features from each modality and classifies four retinal diseases: AMD, ERM, RVO, and Normal. None of the studies on multimodal approaches included ERM disease, which distinguishes this model from the existing multimodal approaches. A total of 5,484 image datasets were constructed from three publicly available datasets: OIA-ODIR, RFMid, and OCTDL. Different preprocessing steps are applied to each modality image to address the domain differences among the three datasets. OCT images were preprocessed with Wiener filtering to reduce speckle noise and to improve local contrast in fundus images; the CLAHE technique was applied. Additionally, data augmentation was applied to address class imbalance in the dataset. The model was trained on a combined training and validation dataset and evaluated using 5-fold stratified cross-validation to ensure consistency and eliminate bias. The use of Grad-CAM demonstrated the model’s ability to highlight clinically relevant features in both fundus and OCT scans during prediction. Finally, the model was tested on an independent test set, which showed strong classification performance, achieving an accuracy of 94.2% and a high AUC of 99.99%