2

This study investigates the application of 8% and 10% manganese-doped copper (II) oxide nanoparticles (MDCN) for treatment of contaminated water infested with Salmonella specie, Staphylococcus aureus, lead and cadmium ions. 8% and 10% Mn-doped CuO nanoparticles were synthesized using the co-precipitation method, and their physicochemical properties were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier Transform Infrared Spectroscopy (FTIR). The antibacterial activity of both nanoparticles was evaluated using a turbidimetric assay.

If an oil reservoir has significant height and is bounded laterally by sealing boundaries, optimizing oil production using a horizontal well requires varying the well location along the open vertical axis, especially in highly anisotropic reservoirs. This work analyzes early, transitional, and late-time dimensionless pressure and its derivative responses for a horizontal well in an anisotropic reservoir that is vertically unbounded but laterally sealed.

When a reservoir is bounded by one or more constant pressure boundaries, such as aquifer or gas cap, the main expectation of the production engineer is to delay the arrival of either water or gas into the well completed in the reservoir to optimize oil production. To achieve this expectation, it is important to understand fluid flow pattern through the reservoir system when oil is produced through the well.

Accurate crop recommendation systems are essential for optimizing agriculturalproductivity and sustainability, yet existing approaches often fail to integrate diverse environmental factors and adapt to location-specific conditions. This study proposes a hybrid machine learning model that leverages soil and climate parameters through a threestage pipeline: Random Forest for feature selection, Extreme Gradient Boosting for robust prediction, and a lightweight Feed forward Neural Network for final decision-making.

Growing interest in the creation of biodegradable composites is a result of the growing demand for sustainable substitutes for synthetic polymers. The main aim of this study is to characterize and contrast biodegradable composites composed of unripe banana starch reinforced with pineapple leaf fiber. Two composite formulations were created: Sample B (10 g of unripe banana starch, 5 mL of glycerol, 100 mL of distilled water, and 5 g of fiber) and Sample A (10 g) of unripe banana starch, 5 mL of glycerol, 100 mL of distilled water, and no added fiber). 

The spreading behavior of automobile oil in soils is a critical factor influencing hydrocarbon contamination, soil degradation, and remediation strategies. This study examined the effects of oil concentration (1500–3000 mg/kg) and time (6–12 hr) on oil spreading rate using a central composite design (CCD) within response surface methodology (RSM). Thirteen experimental runs were conducted, and spreading rates were measured in centimeters. Statistical analysis revealed that the quadratic model provided the best fit for the experimental data (R² = 0.9714; Adj. R² = 0.9510; Pred.

This study presents a comparative analysis of two prominent time series forecasting models, Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN), in predicting monthly rainfall in Nigeria. Utilizing a quantitative methodology, the research analyzes average monthly rainfall data from January 1980 to April 2025, obtained from the Humanitarian Data Exchange. The dataset, known for its linear and nonlinear patterns, was preprocessed through linear interpolation and normalization to ensure compatibility with each modeling technique.

Multivariate Analysis of Variance (MANOVA) is a foundational statistical method used to detect group differences across multiple correlated outcome variables. Classical MANOVA test statistics, including Wilk’s Lambda, Pillai’s Trace, and Roy’s Root, are optimal under multivariate normality and homogeneity of covariance matrices. However, their performances can deteriorate under non-normality or small sample sizes. This study builds upon the truncated MANOVA statistics (W3, P3, R3) which demonstrated improved robustness under specific non-Gaussian conditions.

Capture-recapture methods are essential for estimating hidden populations in fields such as public health and the social sciences. Traditional estimators based on the Poisson distribution often underestimate population sizes when the data exhibit overdispersion. To address this limitation, this study introduced the Zelterman-type and MantelHaenszel-type estimators within the zero-truncated discrete Lindley distribution. The conditional technique was utilized for variance estimation.

The development of biodegradable composites has gained significant attention due to the environmental concerns associated with synthetic polymers. Unripe banana starch, characterized by its high amylose content, presents excellent film-forming properties and biodegradability, making it a promising matrix for composite fabrication. Despite these advantages, its poor mechanical properties restrict its standalone use in structural applications. To improve its performance, reinforcement with natural fibres such as pineapple leaf fibres has been explored.