Global population explosions coupled with increase in industrial activities have resulted in a corresponding rise in effluent discharge which end up in streams and rivers. Re-aeration rate plays a vital role in the self-purification of these rivers and streams. This study was aimed at investigating the re-aeration of Ekulu Stream in Enugu metropolis and the effects of hydraulic factors and water quality parameters on its re-aeration coefficient (k2). Water and sediment samples were obtained from a 2.56 km stretch of the Stream and subjected to physico-chemical characterization. Water samples were tested for temperature, pH, total dissolved solids, total solids, electrical conductivity, dissolved oxygen and biochemical oxygen demand. Hydraulic parameters of the channel namely, slope, velocity, flow depth and width were also measured and then used to estimate the value of k2 at each sampling point based on ten selected empirical re-aeration models designated as E1 to E10. Dissolved oxygen values ranged from 2.4 to 5.4 mg/L indicating pronounced oxygen sag. The re-aeration coefficient of Ekulu Stream as computed using the various models ranged from 1.42 to 57.11 day-1 . The models of Churchill (E4), Owens et al (E8 & E9) and Bennett and Rathbun (E1 & E2) gave the highest k2 values ranging 57.11 to 18.90, 49.46 to 16.56, 47.95 to 15.40, 45.96 to 23.56 and 41.54 to 14.74 day-1 respectively. Apart from E1 and E3, all the models used in this study produced k2 values that have positive relationship with pH of water. Between 55 and 91% of the variation in the value of K2 can be explained by a linear combination of TDS and the interaction of electrical conductivity and temperature (EC*Temp). Re-aeration models incorporating only velocity and flow depth (E2, E4, E7, E8 & E9) exhibited the best linear relationship with water quality parameters with p-values of 0.012, 0.001, 0.000, 0.005 and 0.014 and R2 values of 0.72, 0.88, 0.9, 0.78 and 0.71 respectively. Water quality parameters affect the re-aeration rates of Ekulu Stream. This finding will open a new research frontier with respect to stream re-aeration and is expected to help improve re-aeration models by integrating water quality parameters or an index thereof.

The integration of computers and artificial intelligence (AI) improves SG management and monitors were considered significant. More reliance on technological devices renders one more susceptible to dangerous assaults. The confidentiality of data has been put at risk due to the growing vulnerability of the supervisory control and data acquisition (SCADA) framework towards False Data Injection (FDI) assaults. The behavioral traits of FDI assaults are identified in this work by using historical measurement data and deep learning (DL) techniques. The z-score normalization approach was used for cleansing the historical data and converting continuous-time signals towards discrete form and the discrete wavelet transform (DWT) technique was utilized for data extraction. To find the best FDI assaults in the SG, cuttlefish optimization is combined with enriched recurrent neural network (CO-ERNN) technology. By efficiently reducing assumptions about possible attack scenarios, the suggested cyber-attack detection system exhibits outstanding dependability. Furthermore, an optimization model is put out to describe the behavior of a certain kind of FDI functioning, specifically in situations when accessible restricted positioning measures for power theft in energy systems are at risk. Based on the IEEE 118- bus test system, the versatility of the CO-ERNN proposed attack recognition mechanism is assessed, and the results of the simulation demonstrate the effectiveness of the proposed strategy.

This study uses a mathematical modelling technique to present the transient stretch behaviour synthesis of Polyvinyl Chloride (PVC) belts in medium-duty conveyor systems. The mechanical properties (stretch behaviour) of PVC belts, a vital component of industrial material transport, are evaluated using modern dimensional analysis methods of Buckingham Pi on different parameters. The research analyzes conveyor design and operational parameters using mathematical modelling and Response Surface Methodology (RSM) to improve efficiency and dependability. The objective is to enhance operational perception and forecasting performance under diverse load circumstances. RSM explores variable interactions and finds optimal conditions for minimising downtime and maximising throughput through organised experiments and statistical analysis. Medium-duty belt conveyors from several industries were surveyed for field data. It covers operational output and input variables. The mathematical model emphasises transitory behaviours, involving fluctuations in output parameters. The conveyor design and operation model optimises energy efficiency, component life, and maintenance intervals. Field measurements verify model outputs, confirming their effectiveness and confidence. Results show that the maximum belt stretch reached 12.5 mm under full load conditions, and the proposed dynamic model predicts stretch with an accuracy of 94.3% compared to empirical measurements. The goodness of fit obtained by the analysis is SSE-24.64, R2-0.8587, Adjusted R2-0.801, and RMSE-0.7091. The mathematical model obtained by this interdisciplinary work provides a base to connect biopolymers and resin-epoxy-based hybrid composites to conveyor belt materials, helping develop high-performance medium-duty conveyor systems.

Disease has remained a threat to the existence of every living thing on earth. Particularly in agriculture, disease has remained a major constraint to the success of crop yield and demands urgent solutions, starting with early detection. While many studies have been presented on plant disease detection and control, despite their success, the research gap resides in creating a balance between plant disease detection and farm disease detection. This is because detecting disease in a plant does not necessarily imply that the farm is infected with diseases, and this has resulted in issue of false alarm in the existing system. To address this challenge, YOLOV-5 model was trained with a plant disease dataset considering diverse classes of plants such as corn, waterleaf, tomato, pepper, and cassava. The plant disease model generated was used to develop a farm disease monitoring, detection and notification algorithm, which was converted into mobile application software using Python programming for real-time monitoring notification of farm diseases. This multi-purpose system when tested, reported an average precision mean of 0.95. In addition, experimental validation of the model in maize and watermelon farms reported real-time disease detection and notification which facilitates rapid response and control of the disease by the farmer.

This study analyses current advances and challenges of artificial intelligence (AI) development through a systematic literature review. The literature search was conducted across major databases including IEEE, ScienceDirect and Scopus. Peer-reviewed articles published between 2016-2023 were included based on relevance to the research topic. Out of 152 initial search results, 48 articles were selected for in-depth review using PRISMA guidelines. The results were analyzed to identify key focus areas, techniques and applications of AI over the selected period. The study found a significant increase in AI-related publications in 2022-2023 (62% of selected articles), indicating growing research interest. Key application areas identified are smart cities, education, image processing and medicine. Machine learning methods like neural networks and deep learning were frequently applied for tasks like classification, prediction and pattern recognition. Along with opportunities, ethical concerns like privacy, security, transparency and bias were major AI challenges discussed. Developing standards, regulations and testing mechanisms to ensure reliability and fairness of AI systems was highlighted. In summary, the systematic review demonstrates the rising significance of AI across industries, while underscoring the need to proactively address risks for its safe and ethical development. Overall, the study confirms that AI has great potential in various industries, but its implementation requires the development of ethical standards and data security to maximise benefits and minimise risks. The practical significance of the study is to deepen knowledge about AI and its impact on modern society.

Soil hydrology plays a crucial role in many fields of study, including agriculture, due to the variation in the length of the rainy season brought about by erratic weather patterns. To mitigate crop failure, sustainable and drought-resistant agricultural techniques must be developed. This can be achieved by comprehending the transformative effects of nanoparticles on soil structure, water availability, and nutrient dynamics. Hence, this study aims to explore the impact of biochar-based nanoparticles on the hydrological properties of agricultural soil. Six (6) levels of biochar-based nanoparticles were used as treatments at 0 g, 100 g, 200 g, 300 g, 400 g, and 0 g with constant water supply as control applied to 20 g of soil. The hydrological properties considered are clay flocculation, dispersion ratio, structural stability, void ratio, sodium percentage, and water retention ability, among others, are relevant to agriculture. Biochar addition initially increased soil moisture retention and soil aggregation, but impaired stability at excessive levels due to nanoparticle toxicity. Clay flocculation dramatically improved with 100 g of biochar nanoparticle, yet severely declined beyond 200 g due to toxicity inhibiting natural aggregation. Lower biochar application rates increased aggregate stability compared to control samples, but stability did not increase proportionately as biochar application increased. Compared to higher amounts, the void ratio significantly changed with 100 g biochar addition, .patterns in stability and sodium content indicate biochar nanoparticles profoundly altered soil structure, highlighting the narrow threshold between benefits and ecosystem damage from excessive application. It was concluded that as much as biochar-based nanoparticles can help improve the hydrological properties of the agricultural soil, application beyond 200 g could have a counter effect and hence has to be monitored.

Taking a country into consideration where agriculture remains the primary occupation and farming still happens using conventional methods, the farmers are not able to produce anticipated yields. Modern farming strategies called precision farming play a vital role in improving crop yield and generating more profit for the farmers. This includes recommendations of crops that are suitable for specific fields based on soil conditions, temperature, rainfall, and humidity. To solve this problem, crop recommendation systems play an important role. In this research work, a crop recommendation system (CRS) was implemented using various machine learning algorithms that include random forest, decision trees, extreme gradient boosting (XG boost), and K-nearest neighbors (KNN). Experimental analysis was performed on the dataset collected from Kaggle. The Random Forest algorithm outperforms XG Boost, Decision Tree, and KNN with high accuracy and F1 score of 99.3% and 99.01% respectively. Hyperparameter tuning is additionally performed on XG Boost and Random Forest algorithms to improve accuracy. After hyperparameter tuning, the Random Forest algorithm outperforms XG Boost with an accuracy of 99.5%.

This research conducts a comparative analysis of the vertical accuracies of satellite-derived Digital Elevation Models (DEMs) in the Akure South Local Government Area, Ondo State, Nigeria. The DEMs assessed were obtained from three open-source satellite missions: Sentinel-1A, ALOS PALSAR, and TanDEM-X. The reference Ground Control Points (GCPs) used in the evaluations comprised 133 GCPs of 2nd and 3rd-order accuracy acquired from the Office of the Surveyor General, Ondo State, and through field surveys using the South Galaxy-1 Differential GPS. These obtained GCPs provided extensive coverage of the study area. The evaluation was undertaken based on the land cover, which was established by dividing the study area into open (built-up and bare land) and vegetation-covered (agricultural and forested) areas. The vertical accuracies of the satellite-based DEM collected within the study area were then compared and analyzed. The results revealed that Sentinel-1A and TanDEM-X were the most suitable in both open and vegetation-covered parts of the study area, with Sentinel-1A exhibiting the best performance. The effect of vegetation cover was mostly felt by ALOS PALSAR. Sentinel-1A and TanDEM-X displayed commendable vertical accuracies, deemed highly suitable for applications in topographic mapping, as shown by an RMS of less than 2m. This research recommends the open-source satellite DEMs Sentinel-1A and TanDEM-X for planning and engineering applications because they offer the highest accuracy among their counterparts within Akure and its environs.

There is a continuous need for effective corrosion control materials and the recent advances in nanotechnology show that nanomaterials can play significant roles in corrosion control. Graphene nanoparticles are a well-known nanomaterial and have been employed in various functions including corrosion control. The electrochemical investigation was conducted to ascertain the inhibition efficiency of cobalt graphene nanocomposite. Coupon preparation involved marking out different 30 x 30 mm2 mild steel coupons that were subjected to corrosion in an acidic environment. Three different compositions of the composite used for the study includes 0.70 weight (wt)% cobalt /0.30 weight (wt)%, graphene , 0.55 weight (wt)% cobalt /0.45 weight (wt)%, graphene , 0.85 weight (wt)% cobalt /0.15 weight (wt)%, grapheme respectively. The corrosion tests were open circuit potential and linear sweep Voltammetry. Characterization was done using Scanning Electron Microscope (SEM), The chemical characteristics of the inhibitor were achieved using Energy dispersive X-ray,SpectroscopyEDX, Parameters used for the corrosion analysis are corrosion rate, polarization resistance, and electric potentials analysis. It was observed that the composite with 0.85 weight% cobalt and 0.15 weigth% graphene exhibited the best anti-corrosion property with the best inhibitor efficiency at a concentration of 0.4grams. The addition of graphene nanoparticles improved the mechanical properties of the composite's microstructure." The synthesized nanocomposite can be applied for corrosion control either as an inhibitor or as a coating. The nanocomposite, cobalt- graphene was therefore proven to be a good anti-corrosion material at a low concentration of graphene. This is an improvement in corrosion resistance and it is believed to serve as a good coating due to its adsorption properties.

Adoption of Precision Agriculture (PA) technologies has apparent benefits for agricultural production management, cost savings, and environmental sustainability. However, these technologies are also extremely complicated and influenced by various social and economic forces, which impact their adoption rate, particularly in developing nations. Demographic and agricultural characteristics also play an important role in determining willingness to adopt PA. This study is a survey aimed at establishing the willingness indices of farmers and other relevant personnel to embrace precision farming technologies in Gombe and Bauchi states of Nigeria. The survey included individuals of varied ages; but an average age of 28.5 years for Gombe and 36.5 years for Bauchi, educational levels, and employment statuses, as well as agricultural and operational features. The willingness to adopt PA is high in both states, with 92% in Gombe and 96% in Bauchi. The findings suggest that, while high costs and a lack of technical know-how had a negative impact on readiness to adopt PA in both states, the level of complexity was rated higher by the older generation in Bauchi state, which had a comparatively high mean farmer age. The size of the farm and the number of employees had no direct influence on the adoption of PA technology. Other criteria, such as age and number of years of operation, indicate overlap between the various stages of internet and digital technology adoption, mechanization, and PA. The high willingness rating indicates that farmers are willing to adopt PA technologies if made accessible. It is therefore advised that legislative tools, training programs, and necessary access to financing facilities be employed to make these technologies available and cheap for farmers.