LAUTECH Journal of Engineering and Technology (LAUJET)
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Influence of elastobar and zycoprime nanochemicals on concrete properties with coarse aggregate partially replaced by periwinkle shell
No full textSolid waste management, particularly of periwinkle shells, poses a persistent environmental challenge in Nigeria's riverine regions. While past studies have explored the use of periwinkle shells in concrete, limited attention has been given to the potential benefits of nano-chemical additives. This study investigated the properties of concrete mixed with periwinkle shells and nano-chemicals (Elastobar and Zycoprime) enhancing its performance. Periwinkle shells were obtained from Ilaje, Lagos State, and subjected to particle size distribution analysis to determine their coefficient of curvature (Cc) and uniformity coefficient (Cu). Their porosity, permeability, specific gravity, aggregate impact value (AIV), and aggregate crushing value (ACV) were determined according to standard methods. Concrete of grade M30 was prepared with partial periwinkle shell replacement at 0, 20, 40, 60, 80, and 100% aggregate. Tests included slump, bulk density, compressive strength, tensile strength, and water absorption at 28 days. The microstructure of the optimal mix was analyzed using Scanning Electron Microscopy (SEM). The periwinkle shell had Cu (1.31), Cc (1.80), porosity (45%), permeability (1.2 × 10?² cm/s), specific gravity (2.68), AIV (40.2%) and ACV (37.6%). The slump, bulk density, compressive strength for slumps above 20 mm, tensile strength, and water absorption ranged from 51.56 to 76.12 mm, 2345.78 to 2360.25 kg/m³, 10.91 to 42.10 MPa, 1.49 to 4.50 MPa, and 3.6 to 26% respectively. SEM images revealed a denser microstructure with fewer voids in the optimal mix. Incorporating Elastobar and Zycoprime with periwinkle shells in concrete enhances its performance, making it suitable for mass concrete and load-bearing applications
The Assessment of Groundwater Quality for Drinking and Construction uses in Odo-Oba Town,Southwestern Nigeria.
No full textAssessment of Groundwater Quality for Drinking and Construction
uses in Odo-Oba Town, Southwestern Nigeria.
1Temilade Oladeji and 2Olufunmilayo Adewoye
: [email protected],
[email protected]
Abstract
Groundwater is a crucial natural resource for human survival, serving as the primary source of freshwater worldwide. However, a lack of data on water quality can have detrimental effects on human health and resource management. This study aims investigates the chemical composition of groundwater in Odo-Oba, Oyo State, Nigeria, to assess its quality for drinking and construction. The objective is to evaluate the potential for possible contamination, to determine the water facies type and assess its suitabilityfor sustainable development using water quality indices, plotsand statistical analysis for potable water supply and construction purposes.Twenty groundwater samples were analyzed for physical and chemical parameters and were compared to international standards. Results showed slightly acidic water with moderate temperatures and elevated mineral content. Potassium was the most abundant cation, while chloride was the dominant anion. Most parameters were within acceptable limits, but some samples were unsuitable for drinking. Rock-water interactions significantly influence the water chemistry. While groundwater in Odo-Oba is unsuitable for drinking without treatment, it is suitable for construction. However, further microbiological analysis and proper waste management are crucial to protect groundwater resources. Testing concrete samples for pH changes is recommended to assess potential impacts on structural integrity.
Keywords: Assessment, Groundwater, Quality, Construction, Drinking, Suitabilit
Development of an ai-driven secure safe box biometric authentication system using speech and face recognition technique: Development of an ai-driven secure safe box biometric authentication system using speech and face recognition technique
No full textThe rapid advancement of Artificial Intelligence (AI) has enabled multimodal biometric systems that integrate speech and face recognition for more secure and reliable authentication. Unlike Unimodal systems that depend on a single trait and are prone to noise, lighting variations, spoofing, and user variability, multimodal systems combine complementary modalities to enhance robustness. In this study, Convolutional Neural Networks (CNN) were applied for face recognition and Recurrent Neural Networks (RNN) for speech recognition, with feature extraction using Mel-Frequency Cepstral Coefficients (MFCC), Linear Predictive Coding (LPC), and Perceptual Linear Prediction (PLP). A custom dataset was collected using ESP32-CAM for facial images and a CV-02 speech module for voice samples under varying environmental conditions. The CNN model achieved 93.5% accuracy, while the RNN gave 92.7% accuracy. When integrated to the app, the multimodal system significantly outperformed unimodal approaches with reduced False Acceptance Rate of 1.5% and the False Rejection Rate of 3.5%. These show that combining CNN and RNN models with advanced speech features provides robust, accurate, and secure real-time authentication, resilient to environmental and user-based variability
Secretary bird optimised support vector machine model for adire fabric defect detection: Secretary bird optimised support vector machine model for adire fabric defect detection
No full textAdire fabric is a traditional hand-dyed fabric indigenous to the Yoruba people of Nigeria. It is produced using manual resist-dyeing techniques involving materials such as raffia, wax, starch, or stitching. These handcrafted processes while culturally significant, make the fabric highly susceptible to a variety of defects such as stains, tears, pattern misalignment, colour variation, colour crocking, sulphur-mark and, colour-smear. Traditional visual inspection methods are labour-intensive, time consuming and prone to error. Hence, this study developed an optimised Support Vector Machine (SVM) model using the Secretary Bird Optimisation Algorithm (SBOA) for automated detection of Adire fabric defects. 234 Adire fabric images were acquired using a Redmi 14C 50MP digital camera and augmented to 884. Among the data collected, 396 images represented various type of defects while 488 were non-defective (Normal). Preprocessing involved Gaussian filtering and Contrast Limited Adaptive Histogram Equalisation (CLAHE), while Gray-Level Co-occurrence Matrix (GLCM) was used for texture-based feature extraction. SBOA was applied to optimise SVM hyperparameters (penalty factor , kernel type, gamma ( ), and polynomial degree) yielding the SBOA-SVM model, implemented in MATLAB R2023a. The performance of the model was evaluated against standard SVM using accuracy, sensitivity, specificity, false positive rate as metrics. SBOA-SVM outperformed standard SVM across all defect categories. For colour crocking and colour smear, SBOA-SVM achieved accuracy of 95.81% and 96.04%, and sensitivity of 85.29% and 85.61%, respectively, while the corresponding values obtained for standard SVM were 93.55% and 94.23%. Challenging defects like colour variation, tear, and stain, SBOA-SVM improved sensitivity to 70.45%, 72.73%, and 72.50% at specificity above 98%. The developed SBOA-SVM model demonstrated superior accuracy and efficiency, establishing its potential for automated defect detection in Adire and other related textile applications
Evaluation of Pedestrian Safety on Highway Infrastructure in Ibadan
Get PDFPedestrian safety is becoming increasingly important in metropolitan settings, especially in developing nations like Nigeria. This study examines the pedestrian safety state in Ibadan, Nigeria, highlighting the main difficulties and hazards pedestrians confront. A mixed-methods approach was used, combining quantitative and qualitative data gathering and analysis techniques. Five hundred people shared their experiences. The study revealed that 48% of respondents rated the roads as fair, while 27% described them as poor and 6% as very poor, citing concerns such as poor road design, inadequate pedestrian facilities, and careless driver behaviour as major safety issues for walkers. Pedestrians are most worried about poor traffic control, a lack of pedestrian bridges, and inadequate lighting. The study identified particular Ibadan streets that desperately require improvements for pedestrian safety. The findings of this research emphasize the need for a comprehensive plan to tackle the challenging issues regarding pedestrian safety in Ibadan. Enhancing pedestrian safety should be regarded as a primary concern; this may include appropriate pedestrian infrastructure, better road design, and more effective traffic management, for policymakers, urban planners and other interested parties. The study contributes to the information already available on pedestrian safety in Nigeria and provides practical analysis for improving pedestrian safet
Performance evaluation of hit frequency modulation 95.7 megahertz radio signal strength along selected routes in Benin city, Edo State, Nigeria
Get PDFThe research work presents the evaluation of the performance of Hit FM 95.7Mhz radio signal strength along six selected routes in Benin City, Edo State, Nigeria. The methodology involved the use of Tiny Spectrum Analyser to measure the signal strength at intervals of 500 meters across the routes selected in Benin City.  
Speed control enhancement of a brushless direct current motor using transit search optimizer-based proportional integral derivatives controller
Get PDFBrushless Direct Current (BLDC) motors are capable of achieving accurate speed control tailored to the requirements of various applications, making them suitable for devices that need high-precision motion management, such as robots and medical devices. However, most motors perform poorly as a result of commutation problems where the switching of current is controlled by an electronics speed controller (ESC) thereby causing variation in speed. Linear Quadratic Regulator (LQR) and Proportional Integral Derivatives (PID) controllers have been used to control the speed but with little expected result due to maximum overshoot which leads to system instability. This research aimed to control the Speed of a Brushless DC Motor using a Transit Search Optimizer (TSO) based PID controller. The dynamic mathematical model for the DC motor and PID controller was formulated. Then, the TSO-PID model for DC speed motor control was developed. Simulation of the developed model was done using MATLAB R2021a. The performance evaluation and comparison of the developed model with Simulated Annealing (SA) and Nelder Mead-PID (SA-NM-PID) using rise time, settling time, and percentage overshoot as metrics. The outcome of the results showed that the developed TSO-PID outperformed other conventional methods in terms of rise time, settling time, and percentage overshoot. The application of this work can be considered for domestic and industrial control of drives
Solid Wastes and Greenhouse Gas Emissions Management for Energy Derivation: Case Study of LAUTECH Ogbomoso and Environs
Get PDFFinal disposal of solid wastes at Ladoke Akintola University of Technology (LAUTECH) Ogbomoso, and its environs is by scavenging, dumping sites and open-air burning. This research aimed at studying the solid waste generation and greenhouse gas emissions management for energy derivation at LAUTECH and environs. The university was divided into sixteen zones based on Faculties and other prevailing activities on campus. Waste samples were obtained from bins and dumping sites, for 5 days (Monday, Tuesday, Wednesday, Thursday and Friday) in three years (2021, 2023 and 2024) for waste composition data. Sorted waste samples were taken to the laboratory to carry out moisture and energy content analyses. Methane (CH?) and Carbon dioxide (CO?) emissions from dumping sites and farm areas within LAUTECH and its environs were also measured using gas detectors. The collected primary data was analyzed statistically and discussed. Estimated waste generation in LAUTECH was 6161.47 kg/day, resulting in a daily waste generation rate of about 187 g per head, considering a university population of 33,000. The Energy content of daily wastes was 107.19 MJ, implying an electricity generation up to 0.02977 MWh (approx. 29.77 kWh) from daily steam production. Methane (CH?) levels range from 75 ppm (Rabbit Unit) to 2,107 ppm (layer birds, Abogunde Farms) and CO? concentrations vary between 400 ppm and 470 ppm, across farms. However, methane levels recorded peak values e.g., 11,169 ppm at AA Rano, 8,763 ppm at college, and 6,900 ppm at ALICE. CO? is highest at college (1,171 ppm) and AA Rano (1169 ppm). TVOC and HCHO values remain low at farm sites, while elevated at dumpsites. Considering the high material recyclability, reusability and energy recovery potentials from solid wastes generated from LAUTECH Ogbomoso and environs, there is an urgent need for emissions control in high-risk dumpsites through methods such as methane capture and air quality filtration. These actions are critical for environmental protection and safeguarding public health
Development of a Machine Learning-Based Cyber Threat Intelligence Dashboard System for Strategic Operations Centre
Get PDFCyber Threat Intelligence (CTI) has become an essential element in the toolkit of Cybersecurity experts. In recent years, the significance of CTI has grown exponentially due to the increasing sophistication and frequency of cyber attacks. The incorporation of machine learning methodologies into CTI systems represents a substantial advancement in the domain. Conventional rule-based systems frequently fall short in identifying emerging threats and adjusting to the swiftly evolving strategies employed by cybercriminals. This paper presents a systematic appraisal of CTI dashboard systems that incorporate machine learning techniques to enhance strategic cybersecurity operations, which provide a user-friendly platform for real-time threat detection, analysis, and visualisation. At the core of this study is the utilisation of Gradient Boosting Trees (GBT) as the primary machine learning algorithm for threat detection and classification. The research only focused on the detection, analysis, and presentation of threat intelligence, leaving the specific response strategies at the discretion of the organisation implementing the system. The CTI dashboard system, which is the result of this work, showed strong performance, with a precision of 99.6%, a recall of 99.5%, and an F1-score of 99.97%. The system also showed an average response time of 3 minutes and 12 seconds, demonstrating its effectiveness in delivering timely and accurate threat intelligence
Hybridization of Ring and Mesh Topologies in Fifth Generation (5g) Small Cells for Energy Optimisation of Millimetre-Wave Backhaul
Get PDFSmall cells are positioned as a complementary solution to the existing cellular infrastructure, rather than a complete replacement. However, densification of cells leads to an exponential rise in power consumption, especially in the backhaul segment connecting the small cells. Adopting mm Wave spectrum with a vast bandwidth for wireless backhaul links can provide multi-gigabit capacity through intelligent network design. Hence, the paper presents a hybrid backhaul architecture combining the reliability of ring topologies with the flexibility of mesh interconnects using mmWave technology. The methodology developed the hybrid ring-mesh (HRM) topology adaptation spanning the physical link and network layers. The optimisation problem formulation used a bio-inspired firefly algorithm and was embedded in MATLAB Simulink environment. The result showed that HRM topology has the highest throughput of 900Mbps and is fastest compared to Ring and Star topologies of 300Mbps and 500 Mbps, respectively. It maximises energy efficiency from 30Mbps/W and 50Mbps/W of Ring and Star topologies to 90Mbps/W and a latency of approximately 0.5ms. The study provides an alternative means for mm Wave backhauling of small cells as it maximises energy efficiency while ensuring stringent quality of service (QoS) and also offers considerable throughput and latency where there is few number of small cell base stations