The current state of energy insecurity in Maiduguri calls for immediate action to explore and implement alternative energy sources, especially off-grid sources such as solar power, wind power etc. which could provide reliable, cost-effective, and sustainable energy solutions to the town and neighbourhoods. The University of Maiduguri is not an exception, equally faced with electricity disruptions that almost ground down the academic and administrative activities. This study aims at modeling and simulation of a solar farm using HOMER PRO software for University of Maiduguri senate complex to enhance power supply. It involves conducting an energy audit to determine the complex's electrical energy consumption, Prediction of future energy demand using HOMER PRO software, designing a solar farm model based on the energy audit and the predicted energy demand of the Senate Complex using same software. Data collected were analysed and optimised for system sizing. Based on the optimization results, model 1682 CanadianSolar Max Power CS6U-340M, 340 W per panels, 144 lead acid generic batteries rated at 1KW each (totalling 4603Ah) and 2 inverters with a combined capacity of 318kW were designed, which met the annual load demand of the complex (1.03 GWh). This study, if implemented will address the power challenges faced by the Senate complex building, thereby reducing the administrative and academic bottle neck faced by the University

This paper outlines an evaluation and comparison of three different You Only Look Once (YOLO) object detection models - YOLOv3, YOLOv8, and YOLOv10 for use in automated license plate recognition (ALPR) systems. To analyze these models, a total of 261 license plate images were collected from the car park of an auditorium inside the University of Lagos, Nigeria. Metrics of each model (accuracy, precision, recall, F1 score, and training efficiency) were used to measure the performance of the models. Results indicates that YOLOv8 (accuracy of 86.9%, precision score 100%, recall of 87%, and an F1 score of 0.93) significantly outperformed the other models, demonstrating its potential as a robust solution for object detection. In contrast, YOLOv3 had an accuracy of 62.1%, precision of 75%, a recall of 78.3%, and an F1 score of 0.766, reflecting balanced performance but slower training times. YOLOv10, despite being the latest version, showed mixed results, achieving an accuracy of 43.2%, a precision of 47.5%, a recall of 82.6%, and an F1 score of 0.603. This study highlights the critical importance of model selection based on specific application needs and suggests that further optimization may enhance the capabilities of YOLOv10 for future developments in ALPR systems.

Unmanned Aerial Vehicles (UAVs) have gained significant traction for real-time surveillance applications such as object tracking and search-and-rescue missions. A key enabler of these capabilities is the integration of a real-time, highly accurate object detection system. While available two-stage detectors like R-CNN, Fast R-CNN, Faster R-CNN, and Mask R-CNN achieve high detection accuracy and precise localization by dividing the image into regions and classifying each region, they are often slow, complex, and resource-intensive, making them unsuitable for real-time applications. To address this limitation, this research utilizes a Darknet-based YOLOv3 (You Only Look Once, Version 3), a state-of-the-art algorithm for real-time object detection in videos, live feeds, and images. Leveraging a deep Convolutional Neural Network (CNN), YOLOv3 efficiently learns features and predicts object locations and class probabilities in a single pass, ensuring high-speed and reliable detection. A real-time aerial surveillance system for quadcopter application using machine vision is proposed. The training dataset, obtained from the internet and self-taken images from a camera, was manually annotated into three critical categories: suspect, bandit, and weapon. The dataset was subsequently divided into training and testing subsets. Experimental results demonstrate that the proposed system achieves outstanding detection accuracy, with an overall mean average precision (mAP@0.5) of 93.82%, precision of 94%, and recall of 83%. Compared to a ResNet-50-based Faster R-CNN model, the YOLOv3-based approach outperformed, achieving success rates of 0.81 for the Bandit class and 1.00 for both Suspect and Weapon classes. This research improves drone-based AI for real-time object detection in security systems, enhancing efficiency and adaptability. It also creates specialized datasets, including a tailored bandit dataset, to support future UAV security research.

This research investigates bio-oil production from soursop seed through pyrolysis, with process modelling and optimization conducted using RSM via Box-Benkhen design. Soursop seeds were oven-dried, ground, and sieved into various particle sizes within 1.0 to 6 mm range before undergoing pyrolysis at temperatures between 400 to 600 °C under inert nitrogen gas flow rates between 1.0 to 1.5 L/min. A Box-Behnken design under Response Surface Methodology (RSM) was used to model and optimize the effects of temperature, particle size, and inert gas flow on bio-oil yield. Proximate and ultimate analyses characterized the feedstock, while SEM revealed a porous structure favorable for pyrolysis. Bio-oil was characterized using FTIR and GC-MS to identify key functional groups and fatty acid composition. Proximate analysis showed the seeds had high volatile matter and fixed carbon, indicating good potential for pyrolysis. Ultimate analysis revealed carbon, hydrogen, nitrogen, oxygen, and sulphur contents of 51.29%, 5.90%, 0.50%, 42.30%, and 0.01%, respectively. Scanning Electron Microscopy (SEM) showed a rough, porous structure with oil-like droplets on the surface, which enhanced pyrolysis efficiency by providing a larger reactive surface area and improving devolatilization rates. The experimental design considered temperature, particle size, and gas flow rate combinations, with the bio-oil yield as the response. Results showed that increases in these parameters significantly affected bio-oil production. The maximum observed yield of 33.1% was achieved at 500°C, 6 mm particle size, and 1.0 L/min gas flow. The RSM model showed a high degree of fit with an R² value of 0.9875, adjusted R² of 0.9715, and predicted R² of 0.8007. Optimization predicted a maximum yield of 31.43% under conditions of 461.84°C, 3.84 mm particle size, and 1.02 L/min flow rate, with a desirability of 1.0. Experimental results closely matched these predictions, validating the model. Similarly, FTIR analysis indicates that the predominant monounsaturated fatty acid made up 45.55% of the total fatty acid content, which depicts that the oil belongs to the linoleic acid group. Furthermore, the FTIR analysis reveals that the alkene group contributes to increased reactivity and combustion efficiency, boosts the octane number of the bio-oil, and decreases the boiling point of the oil. Therefore, the FTIR and GC-MS analysis findings confirm that the bio-oil was within ASTM specifications.

Increase in the cost of fossil fuels as a source of energy, coupled with environmental pollution associated with their consumption, calls for the search for alternative energy sources. Therefore, this study focused on the construction and performance evaluation of a biodigester to produce biogas as a renewable energy source for domestic use. A 24-litre biodigester was constructed and used in this study. The biogas was produced using a mixture of cassava peels and avocado seeds with cow dung as inoculum under mesophilic conditions. The cassava peels and avocado seeds were mixed in the ratio 5:1. The experiment was carried out under mesophilic conditions, while the mass and composition of the produced biogas were measured. The findings showed the production of 256 g of biogas corresponding to 0.438 m3. The biogas produced was found to contain 64.4% of methane, 5% of water vapour, 0.9% of O2 content, 560 ppm of CO, and 478 ppm of H2S. interestingly, a relatively higher methane content was observed in this study than in previous studies. Thus, implying the influence of the choice of the agricultural wastes (cassava peels and avocado seeds in this case) and inoculum used as substrate which enhanced anaerobic digestion process in connection with the improved biodigester. These results revealed that biogas generation could be achieved using locally sourced agricultural wastes and thus promoting green and renewable energy in agreement with the Sustainable Development Goals. Therefore, Biogas production would not only provide an alternative source of energy but would also aid in farmland and marketplaces' waste management.

This study investigated the effects of drying temperature on the nutritional and phytochemical components of Gongronema latifolium (Bush Buck) leaves; a widely spread edible indigenous leafy vegetable of West Africa.The leaves were dried at room temperature (RTD), in the open sun (OSD) and at 30, 40, 50, 60 and 70 C in a convective oven (OD).Proximate and phytochemical analyses were done on the products of each OD temperatures, while for RTD and OS drying, the tests were done at their equilibrium moisture content ( ) of the ambient.Statistical analysis was done using MATLAB.The parameters of the fresh leaves with 82.95% moisture content served as the control.Drying decreased the product to 12.95%, 7.65%, 6.95%, 5.79% and 5.03% in the listed order of the drying temperatures.The proximate and phytochemical contents increased with drying temperature.70 C drying temperature gave the highest contents of ash (16.35%), fibre (27.85%), protein (22.70%), carbohydrate (32.84%) and fat (2.42%) in the dried leaves.Same went for the phytochemicals: alkaloid (4.50%), flavonoid (3.92%), saponin (1.46%), tannin (1.39%) and terpenoids (2.72%).Second order polynomial models fitted the data well and gave coefficient of determination (R 2 ) of 0.789 for fibre, 0.869 for fat, 0.895 for protein, 0.921 for ash, 0.923 for terpenoids, 0.941 for moisture, 0.943 for flavonoids, 0.982 for saponnins, 0.987 for alkaloids and 0.992 for tannins content.These results show that there are strong relationships between the proximate and phytochemical composition and the drying temperatures.At their corresponding temperatures, OSD yielded the highest value of saponins (1.45%), tannins (0.64%) and terpenoids (2.45%), RTD gave highest value of alkaloids, flavonoids, carbohydrate (58.38%), protein (17.07%) and fat (1.10%), while OD yielded the highest fibre content (28.32%).The results show that drying methods and conditions should be chosen based on the intended use of the products.

Earthquakes pose a significant threat to structural integrity and human safety, necessitating effective seismic mitigation strategies. This review paper explores the effectiveness of Fluid Viscous Dampers (FVDs) in mitigating seismic responses in high-rise structures. FVDs, as passive energy dissipation devices, enhance structural resilience by reducing inter-story drift, base shear, and overall structural accelerations without significantly altering stiffness. The study compares FVDs with alternative damping systems, including Tuned Mass Dampers (TMDs), Hysteretic Dampers (HDs), Friction Dampers (FDs), and Lead Rubber Bearings (LRBs), highlighting the superior adaptability and efficiency of FVDs in high-rise applications. Placement strategies for FVDs, their integration with other damping systems, and their effectiveness under varying seismic intensities are examined. Findings indicate that uniform distribution across all stories ensures balanced energy dissipation, while targeted placement at lower levels enhances efficiency in specific structural configurations. Also, hybrid approaches, such as combining FVDs with BRBs or base isolation, show promising outcomes in optimizing seismic resilience. The review suggests the importance of further research into computational optimization and practical implementation, torsional irregularities, uniform load distribution in high-rise structures on FVD performance particularly in developing regions like Nigeria, where seismic risks are evolving.

Underwater Wireless Sensor Networks (UWSNs) rely on small, energy-constrained sensors deployed at varying sea depths for applications such as surveillance, environmental monitoring, and data collection. However, high energy consumption and end-to-end delay, exacerbated by dynamic depth and turbidity variations, significantly impact communication efficiency. Existing protocols, such as the Neighboring-Based Energy-Efficient Routing Protocol (NBEER), attempt to optimize energy usage but fail to adapt to these environmental changes, leading to reduced network performance. To address this, an Improved Energy-Based Efficient Routing Protocol (IEBERP) was developed, integrating a Distributed Underwater Clustering Scheme (DUCS) for efficient cluster formation and a Strata Adaptation Scheme (SAS) to dynamically reassign displaced nodes to the nearest cluster head (CH) after depth or turbidity changes. The protocol was simulated in MATLAB (R2024b), and results showed significant performance improvements over NBEER, achieving 9.68 TEC, 81.45 PDR, 46.42 E2ED, 234 NAN, and 35,191 NPR, compared to NBEER’s 12.60 TEC, 78.50 PDR, 53.00 E2ED, 198.30 NAN, and 22,500 NPR. These results highlight IEBERP’s enhanced energy management and adaptive clustering, leading to improved routing efficiency, reduced latency, and higher packet delivery rates. This makes IEBERP a promising solution for reliable and energy-efficient communication in dynamic underwater environments.

In modern power systems particularly those incorporating renewable energy sources like solar and wind, batteries are essential for balancing supply and demand, stabilizing the grid, and enabling energy storage during low-demand periods. This research introduces a comprehensive modeling and simulation framework aimed at predicting how long batteries will last by examining various degradation mechanisms, including capacity fade, temperature effects, internal resistance growth, and state of charge cycling. A hybrid approach that combines electrochemical, thermal, and mechanical degradation models was used to simulate how batteries age under different operating conditions. The simulation results showed that lithium-ion batteries experienced a 12.5% capacity fade after 500 charge-discharge cycles under normal operating conditions, and the degradation rate increased to 20% in high-temperature environments (45°C). Additionally, it was observed that an 8.7% increase in internal resistance significantly affected efficiency. Furthermore, the result revealed that the adopted predictive models achieved an impressive accuracy of 94.2%, allowing for a reliable estimation of the remaining useful life (RUL) of the batteries. These findings highlight how advanced modeling techniques can really enhance battery management strategies with reduced maintenance costs and boost the reliability of power systems. Therefore, the hybrid degradation models showed an impressive predictive accuracy, surpassing recent benchmarks in the field, where older methods showed accuracies of between 91% and 93% using machine learning and physics-informed neural networks.

Urban vegetation is one of the key essential components that contributes to ecological balance and environmental sustainability. However, due to rapid urbanization in the last few decades, there has been cases of vegetation loss which has increased the land surface temperature (LST) and affected the environmental sustainability. Thus, the aim of this study is assessing the impact of vegetation loss on Land Surface Temperature of Bwari Area Council during the periods of 1990-2021, using geospatial techniques. The support vector machine (SVM) supervised classification algorithm was used to classify Satellite imageries into land use land cover (LULC) maps according into buildup area, water body, vegetation and rock/bare ground using ArcGIS Pro. All the classified LULC maps had an overall accuracy of more than 90% with the overall Kappa coefficient also more than 0.9. The analysis of LULC estimation suggests a significant increase in Built-up areas (+ 255.45%) and a reduction in Vegetated areas (-75.17 %) from 1990 to 2021. Brightness temperature, Land Surface Emissivity (LSE) and Normalized Difference Vegetation Index (NDVI) were computed to estimate land surface temperature. The results show that the LST was higher in the regions of built-up areas and rock/bare ground but lower in vegetated areas with the maximum temperature of the study area increasing from 37.11 °C in 1990 to 58.87 °C in 2021. Correlation between land surface temperature and NDVI/NDBI for the study periods was carried out. The results show that correlations between NDVI and LST are rather weak negative, but there is a strong positive correlation between NDBI and LST. These results call for implementation of policies to control rapid urban growth in Bwari Area Council and preserve vegetal covers and as well an extension of the implementation of the Abuja Master Plan to Satellite Towns around Abuja.