BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY
Bayero Journal of Engineering and Technology (BJET) is a publication of the Faculty of Engineering, Bayero University, Kano - Nigeria. BJET publishes original articles in English Language from all fields of Engineering and Technology. The journal receives articles for publication at all times, however publishes three issues per year (January, May and September). The journal uses a double-blind review system for the received manuscripts. Desk evaluation and plagiarism check are applied in BJET before continuing to the next review process to make sure all manuscripts submitted to BJET are original and not duplication from others. References writing tools were also applied in this BJET to make sure a correct citation index such as Mendeley. The article writing template is attached and can be downloaded on the BJET website.
APPRAISNG THE PROSPECTS AND SCENARIO OF LOW CARBON ENERGY TRANSITION FOR ROAD TRANSPORT SECTOR IN MAIDUGURI METROPOLIS
Climate change mitigation requires a transition to clean and sustainable transport system. This paper evaluates the potentials for the adoption of low carbon vehicles (LCVs) in the road transport sector in Nigeria, with Maiduguri Metropolis as the case study. Five regular LCV options were chosen, namely; Biofuel, Battery Electric, Compressed Natural Gas (CNG), Liquefied Petroleum Gas (LPG) and Hydrogen fuel cell (HFC) vehicles. Primary data were collected through interviews with various relevant actors (government, industry, markets, etc.) in the road transport sector. The questions were structured in such a way that different possible economic conditions of LCVs existed in terms of cost, in relation to the conventional ICE Fossil Fuel Vehicles (FFVs). The LCVs’ operating costs were indexed against gasoline FFV, using different cost combinations. The result shows that the lower the initial purchase costs of LCVs in relation to the FFVs, the higher the tendency for their adoption and vice versa. With the right policy in place, the likely LCV alternative to emerge is either CNG or LPG vehicles through a de-alignment/re-alignment scenario.
Climate change; GHG Emissions; Low carbon vehicles; Transition pathways; Transport fuels.
Copyright (c) 2026 BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY
EFFECT OF OPEN WASTE DISPOSAL ON THE PHYSICO-CHEMICAL QUALITY OF GROUNDWATER IN SABON GARI AREA OF KANO STATE- NIGERIA
Water is one of the most essential resources that define the socio-economic wellbeing of man. Despite the abundance of water, access to safe drinking water is still a challenge to water resource managers due to increasing human activities which lead to water pollution. Open disposal of wastes in urban areas has potential forcreating serious ecological, environmental and health problems around the globe. In Nigeria, the effects could lead to pollution of groundwater due to leachates, which could affect the well-being of the populace. This paper is aimed at assessing thephysico-chemical quality of groundwater within SabonGari area of Kano State-Nigeria, with a view to establish correlation between leachates from the waste dumpsites at various locations in relation to the positions of the open wells and boreholes within the study area. Leachates and sewage as well as open wells and boreholessamples were collected and assessed using standard laboratory methods. Reconnaissance survey was conducted to assess the closeness of groundwater to possible sources of pollution.A total of ninety (90) samples were obtained to examine their physical and chemical parameters using gravimetric, titrimetric, colorimetric and atomic absorption spectrophotometer in accordance with APHA procedures were employed.The study established an average distance of the waste dumps to water sources of 10 meters within the study area. Further findings revealedthat water from boreholes and wells in the study area have temperature within the ambient range of 28.80C,with unobjectionable taste, odourless, and have low mean values (3.2 Hazen) for colour, electrical conductivityand other physico-chemical parameters.. Mean values ranging from (630 – 2410 mg/L) for total dissolved solids were recorded for wells and boreholes. High mean levels of heavy metals such as chromium (ranging from 1.5 mg/L to 4.0 mg/L for well and borehole samples respectively), lead (ranging from 0.5 mg/L to 4.9 mg/L for well and borehole samples respectively) and cadmium (ranging from 1.0 mg/L to 2.8 mg/L for well and borehole samples respectively) were obtained. High level of hardness (ranging from 356.0mg/L to 612.3mg/L for well and borehole samples) was also recorded as opposed to WHO limit of 500mg/L. Theseare indicative of pollution of the groundwater resulting largely from the proximity of the groundwater sources to waste dumps within the study area.
MODELLING KINETICS OF SELECTED ORGANIC AMENDMENTS ON BIOSTIMULATION OF CRUDE OIL CONTAMINATED SOIL
Crude oil polluted soils are very common in the Niger Delta region of Nigeria due to oil spillage, theft and activities of artisanal refiners. Though, the Nigerian government have put in place different measures to checkmate further contaminations, but the already contaminated soils are adversely affecting the livelihood of residents. The Niger Delta region being known for its numerous creeks and rivers is also affected by the presence of aquatic weed, water hyacinths in many ways. Hence, the research investigated the possibility of utilizing water hyacinth (WH) blended with poultry droppings (PD) as biostimulants for the remediation of a non-recent crude oil contaminated site. This was achieved by treating the contaminated soil for a period of 91 days in different bioreactors labelled A to F with varied proportions of WH and PD. Results showed that the selected site had a concentration of 1309 mg/kg of total petroleum hydrocarbons (TPH) prior to the experiment. However, the best amendment among the six bioreactors recorded a half-life of 44 days with kinetics rate constant of 0.0157 day-1 as well as percentage degradation and biostimulant efficiency of 76.17% and 65.09% respectively, after 91 days. Reliable predictive model equations were developed for the various amendments and it was concluded that both water hyacinth and poultry droppings could be effectively used in remediating crude oil contaminated soils especially when blended at ratio 3:1 of WH:PD, thereafter, necessary recommendations were made.
Analysis on Performance Comparison Between Empirical and Machine Learning Path Loss Models
Deployment of mobile networks requires accurate Link Budget to determine the heights of antennas, transmit power, and cell radius. An efficient Link Budget is only possible when path loss has been determined accurately. Empirical models are widely used for prediction of path loss but their accuracy tends to be low. Hence, they are sometimes tuned to fit the environment under consideration if path loss measurements are available. Machine Learning (ML) Models have evolved with time and their performances have surpassed that of Empirical Models. Although the accuracy of Tuned Empirical Models is better than the existing Empirical Models, performance comparison between the accuracy of Tuned Empirical Models and ML models is not reported in literature. This paper identified researches in which Comparisons were made between Empirical models and Tuned Empirical Models and determined an average performance improvement of the tuning process as 56.12%. It also identified researches in which the performances of ML and existing Empirical Models were observed. Predictions were made on how the accuracy of these Empirical Models that have been compared with ML Models will be in case they have been tuned. This was achieved using the average improvement in performance introduced by tuning process (56.12%) computed earlier. Results showed that the performance of the Tuned Empirical Models can be as good or sometimes better than that of ML Models. It is therefore recommended that comparisons should be made between Tuned Empirical Models and ML Models for determination of a suitable model especially when considering the fact that the Tuned Empirical Models are Glass Box Models such that their explanation is clear.
Transient Stability Enhancement and Power Oscillation Damping in a SMIB System using a Dual Lead-Lag Controlled TCSC.
This paper investigates the transient stability enhancement in a Single Machine Infinite Bus (SMIB) system using a Thyristor Controlled Series Capacitor (TCSC) with a dual lead-lag Power Oscillation Damping (POD) controller. The dynamic model of the SMIB system was subjected to a three-phase fault disturbance. Three operating conditions were considered: without TCSC, with fixed TCSC compensation, and with TCSC and a dual lead-lag POD controller. The controller was designed to improve damping of electromechanical oscillations through phase compensation and gain tuning. The simulations were carried out in a Python-based environment using the SciPy and NumPy libraries. The system dynamics were solved using the solve_ivp numerical integrator with a Runge-Kutta (RK45) method and plots were generated using Matplotlib. The results show that fixed TCSC compensation improves synchronizing performance but does not adequately damp oscillations, whereas the proposed dual lead–lag POD controller reduces peak speed deviation, lowers overshoot, and restores stable operation with a settling time of about 4.2 s.
Deep Learning-Based Classification of Maize Grain Defects Using Convolutional Neural Network
The automated classification of post-harvest maize kernels is critical for ensuring agricultural economic value and mitigating food safety risks, particularly mycotoxin contamination from fungal infections. This study evaluates the efficacy of a deep Convolutional Neural Network, specifically the ResNet-50 architecture, for categorizing standard RGB images of maize kernels into three primary quality tiers: pure, broken, and fungal. A publicly available dataset was curated and refined to exclude minor physiological anomalies (silk cut), focusing the model entirely on the most critical structural and pathological defects. To evaluate deployability on standard, accessible hardware, the model was trained locally on a central processing unit (CPU) using a 60:20:20 data split for training, validation, and testing. The ResNet-50 model achieved a robust overall accuracy of 88% on an independent test set of 3,396 images. It demonstrated exceptional diagnostic performance for intact, healthy grain with a recall of 92.7%. Despite the well-documented computer vision challenge of morphological overlap between mechanically fractured and intact kernels, the network maintained competitive detection rates for broken (82.8% recall) and fungal-infected (81.2% recall) grain. These results indicate that standard residual architectures, operating without computationally heavy pre-segmentation algorithms or specialized hardware, offer a highly efficient and scalable baseline for real-time agricultural quality control .
Determinants of Agile Manufacturing Systems Adoption and its Impact on Operational Performance Among Small and Medium-Scale Enterprises (SMEs) in Ogun State, Nigeria
Manufacturing firms today face demand volatility, shorter product lifecycles, and increasing global competition, making traditional production systems inefficient. Agile Manufacturing Systems offer a suitable response, allowing organisations to adjust their production capacity fast in light of changes in the market and maintaining their performance across all aspects. The study uses a positivist cross-sectional survey method based on the Technology-Organization-Environment (TOE) Framework, Dynamic Capabilities Theory, and the Resource-Based View. A closed-ended questionnaire on a Likert scale (α range: 0.803–0.889) was conducted among 365 manufacturing small and medium-scale enterprises (SMEs) in Ogun State, Nigeria, with 239 valid responses collected (response rate = 65.5%). Multiple ordinary least squares regression [F(3, 235) = 80.993, p < 0.001], five simple linear models, and one-way ANOVA tests for moderating firm size were used. A three-factor TOE model accounted for 50.8% of the variance in adoption (R = 0.713, R² = 0.508). Organisational (β = 0.415, t = 9.062, p < 0.001) and environmental factors (β = 0.413) exerted comparably strong effects, both substantially exceeding the effect of technological factors (β = 0.345). The use of Agile Manufacturing Systems positively influenced all five performance criteria measured: Delivery Reliability (R² = 0.474), Product Quality (R² = 0.466), Manufacturing Lead Time (R² = 0.463), Production Flexibility (R² = 0.450), and Cost Efficiency (R² = 0.426). Firm size had no moderating effect on adoption [F = 0.190, p = 0.663, η² = 0.001]. The study shows the adoption of AMS is basically an organisational strategic process and has little relation to firm size. An excess emphasis on organisational factors raises concerns about the technology-driven perspective of the concept and implies far-reaching consequences for SME policy and strategy
Evaluation of Tensile and Fatigue Behaviours of Novel A356/Melon Husk Ash Nanocomposite
Lightweight materials such as aluminum matrix composites are gaining prominence in automobile manufacturing due to their competitive properties, with further improvement in properties is achievable by exploiting nanotechnology and developing nanocomposites. The present study explores the tensile and fatigue behaviours of a novel lightweight aluminium-based nanocomposite—A356 reinforced with melon husk ash nanoparticles (MHAnp). Melon husk ash nanoparticles (MHAnp) produced from an abundant Nigerian agro-waste, Egusi melon (Citrullus colocynthis L.) husks, were used to fabricate an A356/MHAnp composite through a modified stir-casting route, i.e., a low-frequency vibration-assisted stir casting. Samples cast were evaluated for tensile and fatigue performance using standard test procedures. X-ray diffraction (XRD) and optical microscopy (OPM) and scanning electron microscopy (SEM) were used to study the microstructure of the fabricated samples and morphology of the fatigue-fractured surfaces, respectively. XRD patterns of the nanocomposite revealed presence of α-Al, Si, β-Al5FeSi and SiO2, while optical micrographs of the samples showed a refined microstructure of primary α-Al dendrites, acicular Si and a relatively even distribution of nanoparticles. Results of tensile tests showed that the nanocomposite exhibited improved properties over the as-cast aluminium alloy. The improvements are by 22.85, 3.09 and 8.00 % for tensile strength, yield strength and ductility, respectively. Compared to the as-cast aluminium alloy sample, the nanocomposite sample exhibited a 3.12% improvement in fatigue strength and 10.60% improvement in fatigue life. Fractography of fatigue-fractured samples using SEM indicated that, despite low-frequency vibration during processing, some nanoparticle agglomeration persisted, contributing to crack initiation. In summary, this study demonstrates that melon husk ash nanoparticles are a viable and effective reinforcement for aluminium matrix composites, offering a sustainable pathway to enhance mechanical and fatigue properties for lightweight automotive applications
Spatial Distribution and Pollution Assessment of Heavy Metals in the Sediments of River Gwagwalada
Sediments serve as long-term sinks for heavy metals in river systems, reflecting the cumulative effects of natural and anthropogenic inputs. This study assessed the concentrations, spatial distribution, seasonal variation, and contamination status of seven heavy metals (Cd, Co, Cr, Fe, Mn, Ni, and Zn) in sediments from River Gwagwalada, Nigeria. Sediment samples were collected from twelve stations during the wet and dry seasons and analyzed using Atomic Absorption Spectrophotometry (AAS). Mean metal concentrations (mg/kg) were Cd (0.52), Co (17.46), Cr (11.43), Fe (1643.67), Mn (350.03), Ni (11.16), and Zn (23.26), with generally higher concentrations observed at downstream locations. Analysis of variance (ANOVA) revealed significant seasonal variations for Cd (p = 0.032), Fe (p < 0.001), and Ni (p = 0.002), whereas Co, Cr, Mn, and Zn showed no significant seasonal differences (p > 0.05). Contamination factor (CF) and pollution load index (PLI) were used to evaluate sediment quality. Cadmium exhibited moderate contamination (CF = 1.73), while the remaining metals showed low contamination levels (CF < 1). The overall PLI (0.25) indicated that the sediments were generally unpolluted. These findings provide baseline information for environmental monitoring and sustainable management of River Gwagwalada.
Heavy metals; River sediments; Spatial distribution; Seasonal variation; River Gwagwalada
Performance Comparison of Fuzzy-PSO and Fuzzy-GA for Double Girder Crane Control
Double-girder overhead cranes are essential in industries for material handling, but their nonlinear dynamics, under actuation, and payload swing complicate control. This paper presents two Mamdani-type fuzzy logic controllers (FLC) using expert operator knowledge for trolley positioning and sway control. The crane’s nonlinearity was modelled via Euler-Lagrange and simulated in MATLAB/Simulink. Particle swarm optimization (PSO-FLC) and genetic algorithm (GA-FLC) tuned the FLC membership functions to minimize a composite cost function based on integral of time-squared error (ITSE). Simulation results showed PSO-FLC outperforms standalone FLC and GA-FLC. For position control, PSO-FLC achieves fastest settling time (3.8 sec), lowest overshoot (0.039%), and smallest error metrics. ITSE and ISE are reduced by 70% compared to standalone FLC, and by 43% (ITSE) and 40% (ISE) compared to GA-FLC. For sway control, PSO-FLC delivers 3.3 sec settling time, minimal overshoot (0.0049%), and negligible undershoot. IAE is reduced by 36% versus GA-FLC and 40% versus standalone FLC. Larger improvements in ITAE (44% and 49%) compared to IAE (36% and 40%) are significant, as ITAE penalizes steady-state error more heavily over time, indicating superior long-term error reduction and faster settling. Overall, PSO-FLC demonstrates the most effective strategy with superior precision, faster stabilization, and improved sway suppression for double-girder crane systems.
Deep Learning Techniques for Quadcopter-Based UAV Systems: Applications and Challenges
Unmanned aerial vehicles (UAVs), particularly quadcopters, integrated with deep learning techniques have transformed aerial monitoring across diverse domains, including security surveillance, target tracking, traffic monitoring, precision agriculture, and disaster management. This review examines the evolution of object detection methods from traditional handcrafted feature-based approaches to modern deep learning frameworks, highlighting the development and application of one-stage and two-stage detectors in UAV imagery. Key challenges associated with deploying deep learning on UAV platforms, including limited onboard computational resources, energy constraints, communication latency, complex backgrounds, small-object detection, class imbalance, and arbitrary object orientations, are critically discussed. The review further explores recent applications of UAV-based deep learning systems in traffic surveillance, crop health assessment, flood and wildfire monitoring, and post-disaster damage evaluation. In addition, operational considerations such as obstacle avoidance, battery management, adverse weather conditions, flight control, noise suppression, and communication range are examined. Overall, the reviewed literature demonstrates that the combination of quadcopters and deep learning provides accurate, scalable, and cost-effective solutions for intelligent aerial monitoring. Future advances in lightweight models, edge computing, multisensor fusion, and autonomous decision-making are expected to further enhance the performance, reliability, and real-world deployment of UAV-based monitoring systems.
UAV; Quadcopter; Deep-learning; Algorithm; Object Detection
Proceedings of the 5th Faculty of Engineering Conference 2026
The 5th Faculty of Engineering Conference of Bayero University, Kano provided a dynamic platform for researchers, policymakers, industry practitioners, and innovators to exchange ideas and present research addressing contemporary engineering challenges. Held from 9th to 10th February 2026, the conference was organized under the theme “Engineering Innovation and Economic Policies: Driving Sustainable Industrial Growth in Nigeria.” The conference brought together participants from academia, industry, and government institutions, fostering interdisciplinary dialogue on the role of engineering in supporting national economic transformation, technological advancement, and sustainable development. The technical sessions featured peer-reviewed papers covering diverse areas of engineering, including industrial development, energy systems, infrastructure innovation, agricultural mechanization, digital technologies, artificial intelligence, engineering entrepreneurship, and sustainable development solutions. This volume of proceedings contains selected peer-reviewed papers presented during the conference. The contributions highlight emerging research trends, practical engineering solutions, and policy-relevant insights aimed at strengthening Nigeria’s industrial capacity and technological competitiveness. The proceedings serve as an important knowledge resource for academics, practitioners, policymakers, and students interested in advancing engineering-driven development. The Faculty of Engineering, Bayero University, Kano, remains committed to promoting research dissemination, innovation, and collaboration among stakeholders. It is hoped that the ideas presented in this volume will stimulate further research, strengthen academia–industry–government partnerships, and contribute to sustainable industrial growth in Nigeria and beyond.
Predictive Maintenance for Equipment Failure Analysis Using Machine Learning Approach
Predictive maintenance has emerged as an effective strategy for enhancing equipment reliability, minimizing unplanned downtime, and optimizing maintenance resources in modern industrial systems. Conventional maintenance approaches, including reactive and preventive maintenance, remain insufficient for anticipating failures in complex machinery such as hydraulic press equipment. This study proposes a data‑driven predictive maintenance framework based on a Long Short‑Term Memory (LSTM) neural network for equipment failure prediction using sensor‑based operational data. A subset of 1,000 records comprising temperature, vibration, and usage‑related features was extracted from a publicly available hydraulic system dataset and pre-processed to ensure suitability for deep learning modelling. The proposed LSTM model was trained and optimized using two optimization algorithms, Adam and RMSprop, to examine their impact on convergence behaviour and predictive performance. Model evaluation was conducted using five widely accepted classification metrics: accuracy, precision, recall, F1‑score, and the area under the receiver operating characteristic curve (ROC‑AUC). Experimental results demonstrate that the LSTM model optimized with Adam achieved superior overall performance, recording an accuracy of 97%, precision of 97%, recall of 97%, F1‑score of 96%, and ROC‑AUC of 99%. The RMSprop‑optimized model exhibited comparable performance, achieving a perfect ROC‑AUC of 100%. Comparative analysis against benchmark machine learning models reported in prior studies including Random Forest, Gradient Boosting, and Support Vector Machine shows that the proposed LSTM‑based approach consistently outperforms conventional techniques. These findings demonstrate strong effectiveness of LSTM networks for predictive maintenance within the evaluated experimental setting in hydraulic press systems and highlight the role of optimizer selection in enhancing model performance.
ML (Machine Learning), DL (Deep Learning), LSTM (Long Short Term Memory); SVM (Support Vector Machine), Random Forest (RF), Gradient Boosting Machines (GBM,) AUC (Area Under the Curve), Predictive maintenance (PdM).
Development and Implementation of an IOT-Enabled Biometric System for Automated Examination Management
The need to meet the ever-increasing requirements of safe, transparent, and automated examination centres in educational institutions has led to the development of intelligent systems that can help in their enhancement. This project presents the development and implementation of an IoT-based biometric authentication system using fingerprint recognition and ESP32 microcontroller architecture to manage the examination centres in real-time. There was the integration of a low-cost fingerprint module, Organic Light Emitting Diode (OLED), LED, buzzer, and web interface to provide dynamic feedback to the users and automate the record-keeping process. The system has been tested under diverse conditions such as dry, wet, oily, and partial fingerprints and has achieved an average recognition accuracy of 95.7% with an average response time of 1.5 secs. Conducting comparative analyses with existing models showed the superiority of this model in terms of speed, data integrity, and accessibility within the context of the semi-urban academic environment. Conclusively, the study established the viability and efficacy of the proposed model in developing a scalable and cost-effective solution to integrate biometric and IoT technologies to improve digital examination security
Ternary Blended Sustainable Concrete Incorporating Rice Husk Ash, Wild Vine Ash and Polycarboxylate Ether Superplasticizer
This study examines the synergistic influence of rice husk ash (RHA) and wild vine ash (WVA) as supplementary cementitious materials (SCMs), combined with a polycarboxylate ether superplasticizer (PCE-SP), on the fresh, mechanical, and durability performance of Grade 25 concrete. Binary systems (RHA+SP, WVA+SP) and a ternary system (RHA+WVA+SP) were evaluated through slump, compressive strength, splitting tensile strengths, and water absorption tests.. X-ray fluorescence (XRF) analysis confirmed RHA’s high silica content (73.8%) and WVA’s significant calcium (16.2%) and magnesium (9.3%) contents, indicating their complementary pozzolanic–hydraulic potential. The Ternary blend consistently outperformed the control, achieving ≈34–35 MPa compressive strength, ≈3.5–3.6 MPa splitting tensile strength, and ≈2.6–3.9% water absorption at 28 days, representing 10–25% improvements over the control. Durability performance followed an inverse relationship with strength, confirming enhanced matrix densification in blended systems. Overall, the results demonstrate that RHA–WVA–PCE-SP ternary blends offer a viable, low-carbon alternative to conventional concrete, delivering improved performance while contributing to cement reduction and sustainability objectives
Durability; Mechanical Performance; Wild Vine Ash; Sustainable Concrete; Rice Husk Ash
Evaluating the Effects of Road Bumps on Passenger Comfort in Ilorin Metropolis: A Case Study of Odota-Airport Road and Adewole-Oloje Road, Ilorin West
This study investigates the effects of road bumps on passenger comfort along two selected routes in Ilorin West, Nigeria: Odota-Airport Road and Adewole-Oloje Road. Field surveys, physical measurements of bump geometry, and in-vehicle acceleration data were used to determine vertical acceleration and root mean square (RMS) values for different vehicle seats and road segments. Passenger comfort was also assessed through structured questionnaires, and results were compared with ISO 2631 comfort thresholds. The findings reveal that larger and more frequent bumps, as well as higher vehicle speeds, are associated with increased vertical acceleration and discomfort, particularly for passengers in smaller vehicles. Survey responses indicated that road bumps contribute to body pain, fatigue, and reduced travel satisfaction, with the Adewole-Oloje Road showing a higher incidence of discomfort. The study recommends standardizing bump dimensions, implementing appropriate signage, and conducting regular maintenance to improve both safety and comfort. These results offer practical guidance for urban planners and transport authorities seeking to balance traffic calming and passenger well-being in Nigerian urban settings.
Ilorin West, Passenger comfort, Road bumps, Traffic calming, Vibration dose value.
Engr. Dr. Ibiwoye Emmanuel Olatunde IBIWOYE
Monopole Antenna with Ultra-Wideband Characteristics for Broadband Communication Systems
This study presents the design, simulation and performance evaluation of a transparent Ultra-wideband (UWB) monopole antenna employing a shrinked-trapezoidal defected ground structure (DGS) fabricated on a quartz substrate. The antenna was modeled and optimized using CST Studio Suite. A transparent quartz glass substrate was selected due to its low loss tangent and optical clarity, while a silver mesh was used as the conductive layer to preserve transparency and enhance conductivity. The design supports deployment on see-through surfaces such as glass panels, windshields, and display screens. Simulation results show an impedance bandwidth of 157%, spanning 1.52–12.58 GHz, which meets UWB specifications and enables applications in high-data-rate wireless communication, radar sensing, and Internet of Things (IoT) systems. Radiation characteristics including gain, efficiency, and polarization were analyzed, confirming stable omnidirectional patterns
A Methodological Comparison of Linear and Non-Linear Regression for Kinetic Analysis of Hydrogen Peroxide Decomposition by Catalase
TThis study presents a comprehensive kinetic analysis of hydrogen peroxide decomposition by bovine liver catalase, with particular emphasis on methodological evaluation of parameter estimation techniques. Kinetic analysis revealed that under the experimental conditions employed, the decomposition exhibited apparent second-order behavior (R²>0.993), with rate constants of 0.13 L·mmol⁻¹·min⁻¹ at 24°C and 0.064 L·mmol⁻¹·min⁻¹ at 6°C. The apparent activation energy was estimated as 28.5 kJ/mol from Arrhenius behavior. A critical comparison of regression methods revealed striking discrepancies: linear transformation approaches (Lineweaver-Burk, Eadie-Hofstee, and Hanes-Woolf methods) produced statistically problematic results, including physiologically implausible negative values for Michaelis-Menten kinetic parameters, Vm and Km. In contrast, non-linear regression, which minimizes the residual sum of squares (RSS), yielded robust, meaningful parameters (Vm = 659 and 25.6 mmol/L.min; Km = 962 and 69.9 mmol/L at 24°C and 6°C, respectively) and a significantly better fit to the experimental data. However, the analysis also highlights the necessity for cautious interpretation of regression-derived parameters due to inherent correlation between Vm and Km, experimental design limitations, and model simplification constraints. This work demonstrates that while non-linear regression is indispensable for accurate kinetic analysis, its outputs must be interpreted within appropriate statistical and experimental contexts to ensure biologically relevant conclusions.
A LoRa Based Distributed Environmental Sensing Network for a Digital One Health Surveillance System
This paper presents the design, development, and validation of a low-cost, energy-efficient Internet of Things (IoT) temperature monitoring node developed as a proof-of-concept for Digital One Health Surveillance Systems (DOHS). The system is designed to address microclimatic variability in resource-constrained environments, where real-time environmental data are critical for understanding and mitigating health risks at the human–animal–environment interface. The proposed architecture integrates low-power sensing, data logging, and wireless communication to enable continuous temperature monitoring while maintaining minimal energy consumption. Field evaluations conducted using a standalone IoT node demonstrate reliable data acquisition, stable operation, and cost-effective performance under real-world conditions. Although the current implementation focuses on a single-node deployment to validate system functionality and energy efficiency, the design is inherently scalable and intended to support future multi-node, networked deployments for distributed environmental surveillance. The results confirm the feasibility of the proposed node as a foundational component for scalable DOHS frameworks, particularly in low-resource settings where affordability, energy efficiency, and deployment flexibility are essential. Field testing over 14 days demonstrated ±0.6°C measurement accuracy, robust communication up to 820 m with <5% packet loss, and calculated battery autonomy of 32.9 days via ESP32 deep-sleep power management. Total component cost (₦65,000 ≈ $40–$50 USD) represents <13% of commercial alternatives. The system provides a scalable, replicable framework for distributed environmental sensing in academic campuses, agricultural zones, and wildlife–human interface areas, enabling high-resolution microclimate mapping to support One Health oriented research, smart campus development, and evidence-based public health decision-making. It demonstrates that low-cost, locally maintainable, solar-powered LoRa IoT nodes can deliver robust, long-range environmental monitoring in tropical, resource-limited contexts
Assessment of Physico-Chemical Water Quality Standard of Bayero University
Safe drinking water is essential for public health, yet water quality deterioration remains a concern in many developing regions. This study assessed the physico-chemical and selected heavy metal quality of drinking water within Bayero University Kano (BUK), Nigeria, and compared it with the Nigerian Standard for Drinking Water Quality (NSDWQ) and World Health Organisation (WHO) guidelines. Water samples were collected from boreholes, underground reservoirs, and overhead storage tanks across both the Old and New Campuses of the university. Standard analytical methods recommended by the American Public Health Association (APHA) were used to analyse the parameters. The results revealed variations in water quality along the distribution system. Most parameters, including turbidity, electrical conductivity, sulphate, chloride, calcium, magnesium, manganese, and dissolved oxygen, were within the permissible limits of WHO and NSDWQ. However, all samples showed acidic pH values ranging between 5.32 – 5.38 which is below the recommended range. The concentrations of nitrate (ranging from 56.04 to 434.31 mg/L) and ammonia (ranging from 1.40 to 2.94 mg/L) in all samples surpass the recommended guideline limits, indicating potential contamination from agricultural practices and other human-related sources. High nitrate content in drinking water can cause methemoglobinemia in infants. Additionally, isolated instances of elevated iron and potassium levels were detected. The study underscores the need for continuous monitoring, improved water treatment, and effective management strategies to ensure safe and sustainable drinking water supply within the BUK community.
Physico-chemical parameters; Groundwater; Nitrate contamination; Drinking water standard
Development of FLC-Based Antilock Braking System Wheel Slip Control for Half And Quarter Vehicle Models
An antilock braking system (ABS) is an essential mechanism that improves the braking system of a vehicle. Different methods were used to design a better controller in order to improve the system. Literature studies had shown that the major technique used in modelling the system is the quarter-vehicle model for uncomplicatedness in the controller design. A half-vehicle model was adapted and will have more dynamic parameters of the overall system as compared to the quarter-vehicle model. In this work a fuzzy logic controller (FLC) for each wheel is designed separately; this is because the frictional force for the front wheel is higher than that of the rear wheel. Using the same vehicle parameters for the half model, another FLC is designed for the quarter model. The performance of the two models was evaluated by comparing the stopping distance of the two models. Simulation results show that the FLC-controlled half-vehicle model performed better than the quarter-vehicle model in terms of stopping distance, which is the critical factor in ABS. Using the same vehicle parameters for both models, simulation results have shown that the percentage reduction in terms of stopping distance has improved by 16.3% for the FLC-controlled half-vehicle model as against that of the FLC-controlled quarter-vehicle model, as well as a 13.8% improvement with the proposed FLC as against the sliding mode controller with disturbance observer. Evaluation results show that the stopping distance of a vehicle with ABS is better with the half-vehicle model and FLC than with the commonly used quarter-vehicle model; this improves safety and can save more lives, as the vehicle has more tendency to stop as compared with the quarter-model design
Dynamics; Frictional; Antilock; Spontaneously; Quarter; Half
Assessing Hospital Air-Conditioning System Management at the National Hospital, Abuja in Relation to Total Quality Management Principles
Effective management of hospital air-conditioning systems is critical to patient comfort, staff productivity, as well as infection control in healthcare facilities. This study assessed air-conditioning management practices at h Total Quality Management (TQM) principles. A quantitative survey design was adopted, and data were collected from clinical staff, patients on admission, and maintenance personnel. A total of 284 valid responses were obtained, representing a response rate of 90.7%. Descriptive statistics and chi-square tests were employed for data analysis using IBM SPSS. The findings reveal substantial deficiencies in system reliability, maintenance responsiveness, and preventive servicing. Only 12.5% of respondents strongly agreed that air-conditioning units are consistently functional, while over 50% expressed dissatisfaction with temperature regulation and indoor air quality. Maintenance inefficiencies were evident, as 43.7% of respondents disagreed that faults were addressed promptly. Chi-square analysis indicated a statistically significant disparity between existing management practices and TQM-aligned benchmarks (p < 0.05), suggesting weak adherence to key TQM principles such as preventive maintenance, continuous improvement, and data-driven decision-making. Furthermore, 89.9% of respondents supported the adoption of IoT-based real-time monitoring systems as a proposed improvement strategy. The study concludes that current air-conditioning management practices exhibit limited conformity with TQM principles and highlights the need for structured, quality-driven, and technology-supported maintenance frameworks to improve system reliability and service delivery in public healthcare facilities.
Development of Eco-Friendly Polyester Matrix Composite Reinforced with Doum Palm Kernel Powder for Advanced Motorcycle Leg Shield Design
This study investigates the mechanical properties of polyester matrix composites reinforced with varying weight percentages of doum palm kernel powder (DPKP) aimed at motorcycle leg shield design. Four composite formulations with varying polyester-to-doum palm kernel powder ratios (90/10, 80/20, 70/30, and 60/40) were fabricated and subjected to mechanical and thermal testing to evaluate their suitability for structural applications. The mechanical properties were evaluated through tensile, compression, flexural, and impact tests, while the thermal performance was analyzed using dynamic mechanical analysis (DMA). The results indicated that the 90/10 composition exhibited the highest stiffness, strength, and energy dissipation capacity, making it the most suitable for applications requiring rigidity and impact resistance. In contrast, the 60/40 composition demonstrated increased flexibility and water absorption at the rate of 8.30% after four days, along with reduced mechanical strength (12.119 MPa) and thermal stability. DMA results further revealed that the 90/10 formulation retained its stiffness up to higher temperatures of 55.5°C indicating the onset of the material's transition from a glassy to a rubbery state. Overall, the work successfully demonstrated the potential of doum palm kernel powder as reinforcing filler in polyester composites for high-performance motorcycle leg shields due to its superior mechanical and thermal properties.
Doum palm kernel, composites, polyester matrix and Dynamic Mechanical Analysis
Assessment of an Improved Wiener II Path Loss Model for Signal Propagation on Concrete Walls
Path loss analysis of Wi-Fi (IEEE 802.11x) signal propagation plays significant role in the plan and function of wireless local area networks (WLAN) applications. Path loss models are crucial in the planning of wireless network as they facilitate survey and mapping of a site for RF installation via simulation; thus, avoiding tedious physical measurements and serve as a guide to a network designer in determining the best location for network devices. Among others, path loss depends upon signal frequency, distance, antenna height and other environmental (medium) characteristics. Frequency and distance are the most important parameters for path loss calculation. This paper develops a modified Wiener II path loss model suitable for assessing Wi-Fi signal propagation through concrete wall. It analyses the effect of distance and frequency on the received signal quality. Empirically, a computer system running the inSSIDer software (a Wi-Fi network scanner application) was used to measure the received signal strength from a Wi-Fi signal source (access point); the measured results were recorded at every 10 m interval between the source (Wi-Fi Access point) and the destination (a personal computer). For the theoretical results, Wiener II model was simulated in Matlab and the result was also recorded. A comparison of both the empirical and theoretical results revealed that the original Wiener II model consistently deviates from the measured results by an average of 1.11%. The Wiener II model was modified by tuning its parameters based on the medium characteristics. The improved Wiener II model estimates the measured path loss with an average deviation of only 0.38% from the measured results.
Copyright (c) 2025 BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY
Artificial Neural Network-Based Model of Ajaokuta Steel Thermal Power Plant
This paper develops the identification of thermal power plant using artificial neural network (ANN) technique taking Ajaokuta steel power plant as a case study. The thermal power plant, built in 1987, has a full capacity of 60MW at a frequency of 50Hz. Data was collected from the Ajaokuta steel power plant for the input/output parameters of the main components for the identification process. This include governor valve position, superheated steam flow rate to the turbine, turbine blades torque as well as the frequency of the generated electrical power. Firstly, identification models for the governor, turbine and generator were separately developed. To simplify the process, each model is treated as a single input single output (SISO) system. Then, the thermal power plant was modelled as a whole taking frequency as the input and generated electrical power as the output. The obtained data is used to train a feedforward neural network with one hidden layer in Matlab environment. An exhaustive search routine was developed to control the choice of the architecture (training function and number of layers of neurons) for the feedforward ANN models during the identification process. This optimizes the developed ANN model for every component of the power plant. A correlation analysis on the inputs/output dependencies reveals 95% confidence. To validate the developed ANN models, dynamic linear models were estimated using ARMAX method. Similarly, the order of each ARMAX model was chosen to minimize the mean square error. Compared to the ARMAX models, the accuracy of the proposed ANN models outweigh that of their numerical counterparts by 6.32, 0, 99.82, and 3.09 percent for the governor, turbine, generator and the complete plant respectively. Thus, the identified ANN models provide better representation of the thermal power plant.
Abdulrahman Rufai YusufAbdulrahman Rufai
A Gantry Crane Control Scheme Using Hybrid Input Shaper and PID Controller
Gantry crane is a critical system used for point-to-point transportation of a payload in industries, ports, nuclear plants etc. Speed, accuracy and safety are of paramount importance in gantry cranes (GCs) operation. However, operating GCs as fast as possible generally results in payload sway coupled with inaccurate positioning of the load which degrades the speed, accuracy and safety. This work proposed a hybrid control system for sway suppression and precise trolley positioning in gantry crane systems. The hybrid control system combines Zero-vibration (ZV) and Zero-Vibration Derivative (ZVD) input shapers for sway suppression. Then a Proportional- Integral Derivative (PID) controller is designed for trolley position control. The effectiveness of the hybrid controller was studied and investi-gated by simulating the proposed hybrid ZV-ZVD shaper for sway suppression and the PID controller for precise position control using MATLAB/Simulink. Experimental results show that hybrid ZV-ZVD was able to suppress the pay load sway to 99.69% and the combination of hybrid ZV-ZVD and PID controllers offer precise trolley position control.
Copyright (c) 2021 BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY
Physicochemical Evaluation of Sludge from a Wastewater Treatment Plant for its Potential Application as a Non-Chemical Fertilizer
Sewage sludge which poses a serious disposal problem and it’s application for fertilizers production leads to reduction in the amount of waste and improves usage of natural resources. This study is aimed at developing a non-chemical fertilizer using sewage sludge from a water treatment plant. The sludge was modified with sawdust and plantain peels and characterized. Properties determined include chemical composition, functional groups, water holding capacity, bulk density, pH, moisture content and organic matter. The moisture content (21.8-88.3%), pH (6.61 - 7.2), water holding capacity (51.88-59.67%), bulk density (5.8g/cm2- 6.1g/cm2) and total organic matters (68.54-81.65%). he FT-IR revealed the presence of alkane, alkene, alkyne, carboxylic acid and aldehyde functional groups. Macro-nutrients detected were P2O5 (21–32%) and K2O (0.470–53.1%) while micro-nutrients are ZnO (1.09-4.74%), CaO (2.79-7.57%), CuO (0.091-0.48%), MgO (0.16-2.43%) and Fe2O3 (1.14-7.36%). The presence of heavy metal oxides present in trace amount are MnO (0.296-1.27%) and TiO2 (0.009-1.32%). The result shows that sewage sludge from Cross River State Water Treatment Plant is suitable for the production of non-chemical fertilizer.
Assessment of the Feasibility and Potential Impact of IoT-Based Predictive Maintenance for HVAC Systems in Public Hospitals in Nigeria
Effective heating, ventilation, and air-conditioning (HVAC) performance is critical to thermal comfort, operational efficiency and better performance of installed equipment in healthcare facilities. However, air-conditioning systems in many Nigerian public hospitals are managed using reactive maintenance practices, resulting in frequent breakdowns, energy waste, and service disruption. This study presents an engineering feasibility assessment of an Internet of Things (IoT)-based predictive maintenance framework for hospital HVAC systems using the National Hospital, Abuja, as a case study. A low-cost distributed monitoring architecture incorporating temperature, current, voltage, and humidity sensors interfaced with an ESP32 edge gateway was designed to enable real-time equipment condition monitoring and automated fault detection. Analytical models were developed to estimate system power consumption, sensor performance, and preventive maintenance thresholds for early fault identification. A cost–benefit analysis indicates that predictive monitoring could reduce HVAC downtime by approximately 25–35% and decrease energy consumption by 20–30%, with a projected payback period of less than two years. Stakeholder surveys (n = 284) further validated operational deficiencies and confirmed institutional readiness for technology-assisted maintenance. The results demonstrate that IoT-enabled predictive maintenance is technically feasible and economically viable for improving HVAC reliability and service delivery in resource-constrained public hospitals. The proposed framework provides a scalable pathway for modernizing healthcare facility management in Nigeria and similar contexts.
Hydraulic Performance and Optimal Siting of Service Reservoirs in the Ilorin Water Distribution System
This paper presents the evaluation of the hydraulic performance of Ilorin metropolitan water distribution system with the aim to determine the optimal locations of service reservoirs and nodal pressures for improve water supply. In an attempt to improve the water services in Ilorin, the Kwara State Government proposed the construction of additional service reservoirs at different locations within the metropolis. The GIS was used to provide georeferenced locations of pipes, nodes, reservoirs, while the Hydraulic conditions were performed with EPANET 2.2 software in order to determine the output pressure at demand nodes for various scenarios under consideration. Ten cases were considered using the location of the service reservoirs and the quantity of water distribution to the study area. The pressure distribution at the nodes were determined for the ten cases. The minimum and the maximum nodal pressures obtained were 10 m and 84 m respectively based on case IV. These are favorably comparable with the recommended values for satisfactory performance. Case IV has the highest percentage number of nodes pressure that fall between 25 m and 84 m. About 20% of the nodes have pressure below 25 m and none above 84 m. This will rule out the possibility of pipe bursting as a result of excessive pressure in the pipeline. Hence Case IV has the optimal nodal pressure distribution and can be concluded that the study provide a suitable model for appropriate location of the service reservoirs based on pressure requirement at demand nodes.
An In-Depth Comparison of Plain CNN, Fine-Tune VGG16, and Vision Transformer Models in Object Detection
Object detection is one of the most essential yet challenging tasks in computer vision, playing a crucial role in applications such as tumour detection, quality control and inventory management, security and surveillance, autonomous systems and robotics, crop monitoring and disease detection in agriculture, defect detection in the construction industry and items detection for home robots. With the rise of deep learning techniques, significant progress has been made through models like convolutional neural networks (CNNs) and, more recently, vision transformers (ViTs). In this research, we investigate how three object detection architectures- plain CNN, transfer learning, and ViTs- perform in detecting multiple household objects under controlled conditions. The findings show that ViTs consistently outperformed plain CNN and transfer learning in key performance areas. While the plain CNN achieved a peak IoU of 97.10%, VGG16 reached 98.01%, and the ViTs model attained the highest IoU of 98. 42%, with a smoother and more stable learning curve. Additionally, the mean square error (MSE) was lower for ViTs at 5. 5.197%, compared to the plain CNN and the fine-tune VGG 16, which settled at 11. 40% and 10. 21%, indicating better prediction precision. Loss metrics for ViTs were also consistently lower, decreasing to 5. 88% compared to plain CNN and VGG16, which settled at 11.23% and 10. 01%, demonstrating more efficient learning with less fluctuation during training. However, this came at the expense of increased training time. The VGG16 required approximately 1, 326 seconds per epoch, compared to about 147 and 150 seconds per epoch for plain CNN and the ViTs. By comparing the three models in terms of IoU, MSE, loss behaviour, and execution time, this research highlights the growing strength of transformer-based models in object detection tasks. These results not only reinforce the potential of ViTs but also offer valuable insights for researchers and practitioners aiming to balance performance with computational costs in real-world detection tasks.
Key words: Computer Vision (CV); Deep Learning (DL), CNNs (Convolutional Neural Networks); You Only Look Once (YOLO); Vision Transformers (ViTs); VGG (Visual Geometry Group); MSE (Mean Squared Error)