Phytoremediation of oil pollution using free-floating aquatic plants is a promising method for water body cleaning. In this study, the influence of Eichhornia crassipes and Pistia stratiotes on the degradation of oil pollution was investigated. The loss of oil alkanes and the rheological characteristics of water were evaluated, and an analysis of the emerging rhizospheric microbial communities was carried out using high-throughput sequencing. The presence of E. crassipes and P. stratiotes plants in oil-contaminated tanks had no effect on the degradation of oil alkanes. However, the presence of plants promoted the development of rhizospheric bacteria capable of growing in oil-contaminated environments. Alpha diversity of microbial communities in oil-contaminated samples was higher in the presence of plants. Additionally, plants significantly reduced the water/oil interfacial tension, which facilitated the availability of hydrocarbons for biodegradation. A difference was noted in the microbiome between E. crassipes and P. stratiotes. Changes in the composition of microbial communities highlight the potential of E. crassipes and P. stratiotes as rhizospheric hosts for microorganisms in the phytoremediation of water bodies.

Given the critical role of true triaxial strength assessment in underground rock and soil engineering design and construction, this study explores sandstone true triaxial strength using data-driven machine learning approaches. Fourteen distinct sandstone true triaxial test datasets were collected from the existing literature and randomly divided into training (70%) and testing (30%) sets. A Multilayer Perceptron (MLP) model was developed with uniaxial compressive strength (UCS, sigma c), intermediate principal stress (sigma 2), and minimum principal stress (sigma 3) as inputs and maximum principal stress (sigma 1) at failure as the output. The model was optimized using the Harris hawks optimization (HHO) algorithm to fine-tune hyperparameters. By adjusting the model structure and activation function characteristics, the final model was made continuously differentiable, enhancing its potential for numerical analysis applications. Four HHO-MLP models with different activation functions were trained and validated on the training set. Based on the comparison of prediction accuracy and meridian plane analysis, an HHO-MLP model with high predictive accuracy and meridional behavior consistent with theoretical trends was selected. Compared to five traditional strength criteria (Drucker-Prager, Hoek-Brown, Mogi-Coulomb, modified Lade, and modified Weibols-Cook), the optimized HHO-MLP model demonstrated superior predictive performance on both training and testing datasets. It successfully captured the complete strength variation in principal stress space, showing smooth and continuous failure envelopes on the meridian and deviatoric planes. These results underscore the model's ability to generalize across different stress conditions, highlighting its potential as a powerful tool for predicting the true triaxial strength of sandstone in geotechnical engineering applications.

This research focuses on enhancing water quality for concrete construction by utilizing treated wastewater from wetlands. The study employs a dual -stage treatment process involving charcoal and aggregate layers for primary treatment, followed by water hyacinths for secondary treatment. Investigating water hyacinths' ability to absorb nutrients and contaminants from wastewater is a unique aspect of the study, offering a potential solution for soil and water remediation. Water hyacinths, especially stems and leaves, act as natural filters, effectively indicating heavy-metal pollution in tropical regions. The primary goal is heavy-metal removal from wastewater, allowing treated -water use in concrete production at varying proportions (20 %, 40 %, 60 %, 80 %, and 100 %). Silica fume at 15 % concentration is incorporated to enhance the concrete's durability. Concrete specimens undergo thorough preparation and mechanical property evaluations, compared to conventional M20 -grade concrete. The results reveal improvements in mechanical properties, particularly with 80 % treated wastewater in the mix. The dual -stage treatment process removes heavy metals, and the inclusion of silica fume enhances the concrete's durability and resistance.