Alternatives to costly fossil fuel have witnessed rekindled interest because of the depletion of fossil reserves and environmental concerns. In this study, non-edible vegetable oil from Khaya Senegalensis (KS), which grows well in semi-arid zones was converted to biodiesel via transesterification under the reaction condition of 1.25 % catalyst and 1:6 Oil-methanol molar ratio. FTIR, GC-MS and NMR techniques were concurrently used to monitor common or similar peaks in the conversion process in an extensive cross comparison to validate other techniques' results. Furthermore, the composition of conventional diesel with the vegetable biodiesel produced were simultaneously analyzed. The results showed that conversion peaks were aligned, and successful delivered 82.02 % yield. The results from FTIR, GC-MS and NMR investigations all agreed, on the conversion of triglyceride molecules in the crude KS oil to fatty methyl esters in the biodiesel. Both produced biodiesel and conventional diesel were observed to contain alkanes and alkenes functional groups, that impart good fuel properties.

Nutrient losses from agricultural fields are significantly influenced by fertilizer management practices and rainfall patterns. Gaining insight into these effects supports the development of effective mitigation strategies. This study investigated the effect of rainfall and fertilizer application timing on nutrient export from a maize field. Daily rainfall data were obtained from Ogun-Osun River Basin Development Authority (OORBDA) meteorological station at Igboora. The field experiment included early, split, and late single fertilizer application treatments, specifically assessing their impact on nitrate loss. Runoff from each treatment plot was collected using plastic gutters leading to wooden tanks. Samples were manually drawn at the onset, midpoint, and conclusion of each runoff event, provided rainfall exceeded 10 mm. Runoff samples were analyzed for sediment concentration using gravimetric techniques, while nitrate, ammonium, total dissolved nitrogen, and total dissolved phosphorus were measured using standard colorimetric methods. Rainfall events ranged from 29.7 mm to 393.8 mm. The analysis revealed that nitrate concentrations in runoff averaged 35.3±20.55 mg/L, ranging from 5.67 to 78.41 mg/L. Early fertilizer application resulted in an increased average nitrate concentration of 41.4 mg/L. In terms of sediment-bound nutrients, particulate phosphorus concentrations averaged 10.09±7.17 μg/g, with values between 0.37 and 24.09 μg/g. Particulate nitrogen concentrations averaged 13.15 ± 7.87 μg/g, ranging from 3.07 to 31.04 μg/g. Also, it was observed that fertilizer treatments led to an increase in particulate phosphorus concentrations, averaging 10.83 μg/g after the initial application. The observed influence of rainfall and fertilizer timing on multiple nutrient pathways highlights the need for integrated nutrient management strategies, especially in tropical agro-ecosystems.

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.

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.

The concept of smart homes has gained more interest in recent years, as the unwavering advancement in IoT technologies enable devices to operate and communicate autonomously. As urbanization accelerate and energy demand increase, on-grid energy system faces significant challenges, including high carbon emissions, energy inefficiency, and reliance on nonrenewable energy sources. The incorporation of solar power into this system not only reduces dependence on traditional energy sources but also promotes sustainability by utilizing clean, renewable energy. The research Present a comprehensive model that addresses these challenges, focusing on energy efficiency, seamless IoT integration, user interaction, scalability. A potable model called SMHome system is powered by solar energy, developed with an algorithm for monitoring and controlling the home autonomously over the internet. The Arduino Nano collects data from various sensors (temperature, motion) and communicates with the ESP8266 for Wi-Fi connectivity. The ESP8266 sends this sensor data to a cloud platform via TCP, allowing remote monitoring and control through a mobile app. Integration with the IFTTT app enables automation, where specific triggers from the sensors can initiate actions, such as sending notifications or controlling devices based on user-defined conditions. The proposed SMHome system is structured in a form that a potable box powered by solar energy can only be easily and efficiently control appliances over the Internet and support home safety with autonomous operation. By integrating solar energy with IoT technologies, the research demonstrates the potential for improved energy management, reduced environmental impact and enhanced quality of life for residents.

The increasing environmental concerns associated with petroleum-based lubricants and growing demand for environmentally friendly solutions have spurred research into non-edible green oil-based lubricants, particularly for their application in two-stroke engines. Two-stroke engines, while offering advantages in power-to-weight ratio, pose environmental challenges due to their inherent design of the oil-fuel mixture. By utilizing non-edible sources, these lubricants help avoid competition with food crops, ensuring that edible oils remain available for human consumption, while non-edible oils present a promising solution. Study have shown green oil enhances mechanical and thermal braking efficiency by 13% and 27%, while it reduces emission of CO, CO2 and HC approximately by 62%, 54% and 44% respectively compared to conventional lubricants. This review explores the properties of various non-edible green oils, including their physicochemical characteristics and potential as base stocks for lubricants production. Furthermore, it explores the challenges and opportunities associated with these non-edible green oil-based lubricants. Furthermore, it analyses previous research on the application of green oil-based lubricants in two-stroke engines, focusing on their impact on engine performance, emissions, and wear. The review also discusses the role of additives and blending in improving the overall performance of these lubricants and highlights the need for further research to optimise their application in two-stroke engines. Finally, the review identifies key research gaps and future directions, emphasizing the need for systematic investigation of blend ratios, exploration of environmentally friendly nano-additives, and rigorous engine testing to unlock the full potential of blended non-edible green oil-based lubricants for sustainable two-stroke engine applications. This review underscores the potential of blended non-edible green oil-based lubricants as viable solutions for enhancing performance in two-stroke engines and encourages further research into their long-term effects and commercial viability.

This study assesses the water resources potential within the Maiduguri catchment of the Lake Chad Basin by analyzing a 20-year using rainfall data between 1999 to 2018 and streamflow data between 1981 to 2000 (each over a 20 year period). The results indicates that monthly rainfall was generally higher between 1999 and 2011, followed by a significant decline from 2012 to 2018. This shift suggests a progressive extension of aridity from the Lake Chad region into the Sudan Savannah, likely influenced by the effects of climate change. Flooding events were recorded in 16 of the 20 years analyzed. Streamflow analysis of River Ngadda, which traverses Maiduguri, revealed an average discharge of 6,201.2 m³/s over the study period, with a 7-year span of flooding. Additionally, storage apportionment data were evaluated to inform water resource management in the region.

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.

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 (𝑀𝑖) 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 𝑀𝑖 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 (R2) 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.

Any machining operation that generates heat must adequately dissipate it to minimize thermal stresses on the tool-workpiece contact. This inescapable heat production phenomenon, caused by using suboptimal machining parameters and a lack of long-lasting machining lubricant, adversely impacts the finished surface quality, tool destruction rate, and workpiece structure. Therefore, this review focuses on optimizing machining parameters and nano-lubricant effects on the various response parameters such as surface finishing, materials removal rate, tool wear rate, and cutting forces under drilling, grinding, and turning machining. The study reviewed reputable articles from Elsevier, Springer, and other quality outlets. This review cut across the impact of machining parameters and nano-lubricants on the drilling, grinding and turning process. The study also discussed the challenges of optimizing machining parameters during the drilling, grinding, and turning.