The presence of heavy metals in groundwater and wastewater has been a concern for health organizations. This study investigated the effectiveness of activated carbon derived from various natural precursors, including acorns from red oak trees (Quercus rubra), date seeds, and peach seeds, employing the thermal activation method for the removal of heavy metals from aqueous solutions. Batch adsorption tests investigated the effects of sorbent quantity, pH levels, disinfectant presence, and dissolved organic matter (DOM) on the removal efficiency of Pb and Cu. Characterization of the prepared activated carbon was conducted using scanning electron microscopy (SEM). Lead removal efficiency diminished at pH 7 relative to pH 3 and 5, but copper exhibited superior removal efficiencies at pH 7 compared to pH 5. The addition of monochloramine at 4 parts per million (ppm) effectively eliminated lead from the solution. A rise in free chlorine concentration from 2 to 4 mg/L led to a reduction in metal removal from water by 20 to 60%. DOM at concentrations of 1 and 6 mg/L reduced metal removal efficacy relative to DOM at 3 mg/L. Date seed-activated carbons underscore their distinctive potential, offering useful insights for the enhancement of water and wastewater treatment systems.

Resilient water distribution system is crucial for sustainable urban water management. Evaluating the inherent resilience of the buried water infrastructure is key to ensuring reliable water distribution. The water distribution network maintains water quality and supplies sufficient water to users. Evaluating the system’s resilience under varying failure conditions is crucial to guarantee continued service delivery. This study investigates the resilience of the water distribution network for the University City, Sharjah, United Arab Emirates subjected to failure conditions caused by pipe failure, water contamination, and water excess demand. This research quantifies the corresponding performance under these stressors and develops an innovative resilience index by using the global resilience analysis (GRA) approach. The corresponding strain is in the form of node failure, chlorine decay, and pressure failures among all the pipes throughout the network. A survey was conducted with the water company to identify recovery time for the designated water distribution network. Another survey was conducted among the experts to evaluate the relative significance of all the strains in contribution towards resilience. Based on the resilience index, four levels (high, moderate, low, and very low) of resilience were defined. The study revealed Sharjah water distribution network has up to 40% of its stress categorized as low resilience and 60% of its stress categorized as very low resilience. The study also presented a management plan for the improvement of the designated water distribution network.

The intensifying global challenges of environmental degradation, escalating energy demands, and unsustainable waste accumulation necessitate the exploration of alternative, high-value biomass resources. Algal biomass has emerged as a uniquely versatile and sustainable candidate, offering transformative potential across a broad range of industrial and environmental sectors. This review comprehensively evaluates the recent advancements and multidisciplinary applications of algae, with a strong emphasis on their distinctive chemical composition, bioactive compounds, and environmental adaptability. The novelty of this review lies in its integrative scope, which spans from algae cultivation, production, and trade to cutting-edge extraction technologies for bioactive constituents. Furthermore, the review presents a detailed exploration of algae’s functionality as a feedstock for biofuels, pharmaceuticals, sustainable agriculture, bioplastics, green chemistry, and water treatment, positioning it as a cornerstone in the development of the blue economy. By critically analyzing both conventional and innovative applications, this work contributes to a deeper understanding of algae’s strategic role in shaping a sustainable and resilient bioeconomy.

The process of dry reforming methane (DRM) is seen as a viable approach for producing hydrogen and lowering the atmospheric concentration of carbon dioxide. Recent times have witnessed notable advancements in the development of catalysts that enable this pathway. Numerous experiments have been conducted to investigate the use of nickel-based catalysts in the dry reforming of methane. All these reported experiments showed that variations in the catalyst property, namely pore size, pore volume, and surface area, affect the hydrogen production in DRM. None of the previous studies has modeled the surface nickel-incorporated catalyst activity based on its properties. In this research, DRM’s hydrogen yield is predicted using three different artificial neural network-learning algorithms as a function of the physical properties of Ni-based catalyst along with two reaction inputs. The geometric properties as an input set are a different approach to developing such empirical models. The best-fitting models are the artificial neural network model using the Levenberg–Marquardt algorithm and ten hidden neurons, which gave a coefficient of determination of 0.9931 and an MSE of 7.51, and the artificial neural network model using the scaled conjugate gradient algorithm and eight hidden layer neurons, which had a coefficient of determination of 0.9951 and an MSE of 4.29. This study offers useful knowledge on how to improve the DRM processes.

The rapid advancement of Machine Learning (ML) has significantly transformed polymer science by enabling efficient prediction and design of polymer properties through high‑throughput screening. However, current methods still struggle with nonlinear Structure–Property Relationships (SPRs), limited dataset standardization, and computational inefficiency, which restrict prediction accuracy and interpretability. This study proposes a comprehensive ML‑based framework for predicting polymer properties and identifying SPRs. The approach integrates data preprocessing, molecular descriptor and topological index–based feature extraction, iterative feature selection, and XGBoost predictive modeling. Model hyperparameters are optimized using the Starfish Optimization Algorithm (SOA) to enhance performance and efficiency. Model interpretability is achieved through SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), providing both global and local insights into the influence of molecular features on polymer properties. Experimental evaluation on the PolyOne dataset demonstrates strong predictive performance, with R² values exceeding 0.92, mean absolute error (MAE) below 0.08, and root mean square error (RMSE) under 0.12 for key physical and optical polymer properties. Overall, the proposed framework effectively balances accuracy, computational efficiency, and interpretability, offering a robust and practical tool for accelerating polymer design while enhancing understanding of molecular structure–property relationships.