This investigation analyzes hydroxyapatite (HAp) derived from tilapia fish scales as a green and sustainable corrosion inhibitor for copper in 1M HCl. While fish scales are an abundant biowaste source, the use of the scales' in assessing its corrosion inhibition capability will provide an eco-compatible option for the protection of industrial metals. The hydroxyapatite was purified using a multi-step process consisting of deproteinization, alkaline treatment, and high-temperature calcination under 1000°C to yield a purified nano-HAp powder. Open Circuit Potential (OCP), Linear Sweep Voltammetry (LSV), and Tafel polarization were used to determine the corrosion inhibition efficiency under various temperatures (30°C, 40°C, and 50°C) and different concentrations (0.2 g, 0.4 g, and 0.6 g). The results showed an extensive reduction in the current density of the corrosion and the rate of the corrosion, with a maximum inhibition efficiency of (~95%) occurring under the 0.6 g concentration. The analysis of the adsorption showed that the inhibitor conformed to the Freundlich and Temkin isotherms, inferring multilayer adsorption and strong surface interactions. Optical micrographs corroborated the protective capability of the inhibitor through reduced roughness of the surface and reduced pitting. This study established that hydroxyapatite obtained from fish scales is an effective alternative, and eco-compatible corrosion inhibitor, contributing to waste reduction and green chemistry.

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.

Integrating intermittent renewable energy sources into the power grid poses significant challenges to grid stability and reliability. This study examines the integration of Energy Storage Systems (ESS) into power grids to enhance stability and performance. A simulation framework was developed to analyze the technical and economic viability of Battery Energy Storage Systems (BESS) and Pumped Hydro Storage (PHS) systems. The results demonstrate the effectiveness of ESS in alleviating voltage fluctuations, frequency deviations, and grid disturbances. A diversified energy storage portfolio with optimized siting and innovative market mechanisms maximizes the benefits of ESS integration. The study reveals that BESS excel in rapid response times and scalability, while PHS systems offer superior economic benefits. This study pioneers a comprehensive simulation framework integrating technical, economic, and regulatory aspects to optimize Energy Storage Systems (ESS) integration, providing novel insights into maximizing grid stability and performance amidst escalating renewable energy integration. The findings provide valuable insights for policymakers, industry stakeholders, and researchers seeking to optimize energy storage strategies for resilient, sustainable, and efficient power systems.

This study investigates the impact of three key factors - irrigation deficit percentage, NPK application rate, and tillage - on soil moisture content, a crucial parameter in agricultural productivity. To achieve this, a field experiment was conducted at Nnamdi Azikiwe University's Department of Agricultural and Bioresources Engineering Experimental Site/Farm Workshop, Awka. The experiment utilized a central composite design in response surface methodology, incorporating three factors: irrigation deficit percentage, NPK application rate, and tillage. This design enabled the researchers to examine the individual and interactive effects of these factors on soil moisture content. The results of the study revealed that the model was highly significant, with an R-squared value of 0.9084. This indicates that approximately 91% of the variation in soil moisture content could be explained by the model. Furthermore, the analysis showed that irrigation deficit percentage, NPK application rate, and tillage were all significant factors influencing soil moisture content. However, the interaction terms between these factors were found to be non-significant. A key outcome of this study was the development of predictive models for soil moisture content under different tillage conditions, namely No Tillage, Conservative Tillage, and Conventional Tillage. These models could be employed to forecast soil moisture content for specified levels of irrigation deficit percentage and NPK application rate under each tillage condition. The findings of this research provide valuable insights into the effects of irrigation deficit percentage, NPK application rate, and tillage on soil moisture content. These insights could be applied to inform agricultural practices in similar regions, ultimately contributing to improved crop yields and sustainable agricultural management.

Bio-oil production from Luffa fibre, a plentiful agricultural byproduct, has attracted considerable interest as a sustainable and renewable energy source. In this study, response surface methodology (RSM) and artificial neural network (ANN) modelling were used to optimize operating conditions for bio-oil produced by pyrolysis from luffa cylindrica fibre. Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) are used to model and improve operational parameters like temperature, particle size diameter, and inert gas flow rate. This is done to boost bio-oil production and quality. We develop a predictive model for bio-oil characteristics using ANN modelling, which effectively optimizes pyrolysis conditions. This study offers significant knowledge on the production and characteristics of bio-oil derived from luffa cylindrica fibres. By employing both models, we leveraged Response Surface Methodology (RSM) flexibility to provide statistical measures of individual models and their interaction impact on the process output while benefiting from Artificial Neural Networks (ANN) efficiency in processing data and acquiring complex patterns. It offers a method to improve the production process methodically. Comparing the prediction findings of the ANN with those of the RSM, it was shown that the former were superior. Different models have been trained using various transfer functions and varying numbers of neurons with 0.99797, 1.0, and 0.9989 R² values for the training, validation, and test stages, respectively. The proposed network had an overall R² factor of 0.99869. The results were deemed satisfactory based on the overall R² value being near 1.0. The optimization of operational parameters enhances the effective transformation of luffa cylindrica fibre into bio-oil, therefore encouraging the utilization of this sustainable resource for the generation of renewable energy. This strategy aligns with the increasing focus on decreasing the environmental consequences of conventional fossil fuels and promoting alternative and eco-friendly energy supplies.

Biochar is attractive mainly due to its diverse surface functionality bringing the possibility of multipurpose utilization. The purpose of this study was to evaluate the effect of biomass cellulosic content on evolution of molecular structures of biochar. Commercial cellulose, oil palm front, and palm kernel shell were pyrolyzed at 630 °C, and their biochar structures were analyzed using ultimate and proximate compositions, pH point of zero charge, FTIR and XRD. Evaluation of biochar nanotexture based on cellulosic content (100% for commercial cellulose, 39.5% for oil palm front and 20.5% for palm kernel shell) revealed that commercial cellulose decomposed rapidly into non-graphitizing large size crystallites (65 nm) with substantial defects within their graphene sheets. The thermal decomposition mechanism for cellulose and lignin differs: cellulose decomposed rapidly to form O heterocyclic rings before final phase transition to graphene sheets at a much lower temperature, whereas lignin decomposed slowly and begins forming graphene sheets after series of condensation reactions at higher temperatures. Amorphous chars derived from lignin were thermally stable, slowing down the rapid formation of crystallites in biochar from palm kernel shell. Conclusively, highly cellulosic biomass is highly thermally unstable and tends to decomposed into biochar with lower surface functionality compared to biochar derived from highly lignified biomass.

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.