Oryza sativa japonica (ofada rice) is largely grown in Aramoko, Abakaliki and Ofada are communities and consumed by both the poor and rich in Nigeria. A total of twenty ofada rice farmlands were identified in each study area and rice samples were randomly collected, thoroughly mixed to make a representative sample from each farmland. Soil samples were collected in each farm to a depth of 5-15cm from at least eight different points and thoroughly mixed together to form a representative sample. The samples were thereafter taken to the laboratory for preparation and spectroscopic analysis. A well-calibrated NaI(Tl) gamma-ray detector was used in spectrometric analysis of the samples and descriptive statistics was used to analyze th

Thin films ZrO₂: MgO nanostructure have been synthesized by a radio frequency magnetron plasma sputtering technique at different ratios of MgO (0,6, 8 and  10)% percentage to be used as the gas sensor for nitrogen dioxide NO₂. The samples were investigated by X-ray diffraction (XRD), atomic force microscopy (AFM), scanning electron microscopy (SEM), energy-dispersive X-ray (EDX) and sensing properties were also investigated. The average particle size of all prepared samples was found lower than 33.22nm and the structure was a monoclinic phase. The distribution of grain size was found lower than36.3 nm and uninformed particles on the surface. Finally, the data of sensing properties have been discussed, where the

Environmental pollution is experiencing an alarming surge within the global ecosystem, warranting urgent attention. Among the significant challenges that demand immediate resolution, effective treatment of industrial pollutants stands out prominently, which for decades has been the focus of most researchers for sustainable industrial development aiming to remove those pollutants and recover some of them. The liquid membrane (LM) method, specifically electromembrane extraction (EME), offers promise. EME deploys an electric field, reducing extraction time and energy use while staying eco-friendly. However, there's a crucial knowledge gap. Despite strides in understanding and applying EME, optimizing it for diverse industrial pollutant

This study proposes a hybrid predictive maintenance framework that integrates the Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) for intelligent fault diagnosis in industrial rotating machinery. The method is designed to address challenges posed by non-linear and non-stationary vibration signals under varying operational conditions. Experimental validation using the FALEX multispecimen test bench demonstrated a high classification accuracy of 97.5%, outperforming traditional models such as SVM, Random Forest, and XGBoost. The approach maintained robust performance across dynamic load scenarios and noisy environments, with precision and recall exceeding 95%. Key contributions include a hardware-accelerated K