Assessment of wastewater effluent quality in terms of physicochemical and microbial parameters is a difficult task; therefore, an online method which combines the variables and represents a final value as the quality index could be used as a useful management tool for decision makers. However, conventional measurement methods often have limitations, such as time-consuming processes and high associated costs, which hinder efficient and practical monitoring. Therefore, this study presents an approach that underscores the importance of using both short- and long-term memory networks (LSTM) to enhance monitoring capabilities within wastewater treatment plants (WWTPs). The use of LSTM networks for soft sensor design is presented as a promising solution for accurate variable estimation to quantify effluent quality using the total chemical oxygen demand (TCOD) quality index. For the realization of this work, we first generated a dataset that describes the behavior of the activated sludge system in discrete time. Then, we developed a deep LSTM network structure as a basis for formulating the LSTM-based soft sensor model. The results demonstrate that this structure produces high-precision predictions for the concentrations of soluble $X_1$ and solid $X_2$ substrates in the wastewater treatment system. After hyperparameter optimization, the predictive capacity of the proposed model is optimized, with average values of performance metrics, mean square error (MSE), coefficient of determination (R²), and mean absolute percentage error (MAPE), of 23.38, 0.97, and 1.31 for $X_1$, and 9.74, 0.93, and 1.89 for $X_2$, respectively. According to the results, the proposed LSTM-based soft sensor can be a valuable tool for determining effluent quality index in wastewater treatment systems.

In this paper, we propose an inverse optimal neural control strategy for uncertain nonlinear systems subject to external disturbances. This control strategy is developed based on a neural observer for the estimation of unmeasured states and inverse optimal control theory for trajectory tracking. The stabilization of states along the desired trajectory is ensured via a control Lyapunov function. The optimal parameters of the control law are identified by different nature-inspired metaheuristic algorithms, namely: Ant Lion Optimizer, Grey Wolf Optimizer, Harris Hawks Optimization, and Whale Optimization Algorithm. Finally, a highly nonlinear biological system subject to parameter uncertainties and external disturbances (Activated Sludge Model) is proposed to validate the control strategy. Simulation results demonstrate that the control law with Ant Lion Optimizer outperforms the other optimization methods in terms of trajectory tracking in the presence of disturbances. The control law with Harris Hawks Optimization shows a better convergence of the neural states in presence of parameter uncertainty.

Green-synthesized nanomaterials have emerged as promising candidates for environmentally sustainable remediation; however, experimental evidence describing their synthesis routes, physicochemical properties, remediation performance, and sustainability-relevant attributes remains fragmented, inconsistently reported, and difficult to integrate across studies. This work addresses this challenge by proposing an ontology-based semantic framework for the interoperable integration of green-synthesized nanomaterials, contaminants, and remediation processes, incorporating explicit provenance metadata and structured sustainability descriptors. The ontology was developed using the Linked Open Terms (LOT) methodology and implemented in OWL 2 DL, with selective alignment to established vocabularies including eNanoMapper, ChEBI, ENVO, and PROV-O. Adsorption and photocatalysis were instantiated as representative remediation mechanisms to evaluate the framework’s capacity to accommodate structurally distinct processes. Logical reasoning and SHACL-based validation were applied to assess semantic consistency, provenance traceability, and data completeness. The results demonstrate that the proposed ontology effectively integrates heterogeneous experimental data within a unified, FAIR-compliant semantic framework, supports conservative and provenance-aware inference, and enables comparative analysis across mechanistically diverse remediation systems without structural modification. This ontology-based approach provides a robust foundation for sustainability-aware knowledge integration in environmental nanotechnology and establishes the basis for future extensions involving data quality assessment and explainable AI-driven analysis.

Charal (Chirostoma spp.) is a small pelagic fish of high nutritional and economic importance in central Mexico. However, its high moisture content and rapid post-harvest deterioration result in substantial losses in artisanal fisheries. Solar drying represents a sustainable preservation alternative, particularly in regions with limited access to refrigeration. This study investigates the drying kinetics of whole charal under field-realistic mild-to-moderate solar drying scenarios, including forced convection, natural convection, and open-air exposure. Experimental drying curves were modeled using classical thin-layer formulations, and neural network models were evaluated as complementary one-step-ahead predictors of experimental moisture ratio. Among the evaluated thin-layer models, the Modified Page formulation consistently provided the most reliable empirical description of the drying curves, with coefficients of determination greater than 0.97. An ablation-style comparison of ANN, CNN, LSTM, and CNN-LSTM architectures showed that the CNN model achieved the highest global predictive accuracy in the present dataset, with R² = 0.987 and MSE = 4.3 × 10⁻⁴. Because the dataset contained a limited number of independent drying curves, the deep-learning results are interpreted as exploratory and complementary to thin-layer modeling rather than as a replacement for classical empirical models. The proposed framework may support future drying-endpoint estimation and decision-support tools for artisanal fish processing, provided that additional validation is performed with standardized sample masses, environmental covariates, and product-quality indicators.

Background: Research on green-synthesized nanomaterials (GSNs) for environmental remediation is growing rapidly, yet data remains fragmented in non-interoperable formats. Methods: We present OntoNanoMat, a comprehensive semantic resource consisting of a modular OWL 2 DL ontology and a curated dataset of case studies. The data was structured into five thematic modules: Identification, Synthesis, Mechanism, Performance, and Provenance. Results: The dataset is provided in three interoperable formats: CSV for tabular analysis, JSON for web applications, and Turtle (RDF) for Semantic Web integration. Technical validation was performed using SHACL shapes and SPARQL query libraries to ensure logical consistency and data integrity. Conclusions: OntoNanoMat provides a FAIR-compliant (Findable, Accessible, Interoperable, and Reusable) foundation for future machine learning applications and knowledge graph integration in sustainable nanotechnology.

Elucidating the complex structure–property–performance relationships in multifunctional polymer matrix composites (PMCs) remains a formidable challenge. This difficulty stems from the intricate coupling between formulation variables, porous morphology, physicochemical attributes, and functional outcomes, particularly under the “small-data” constraints inherent to experimental materials research. This study introduces a robust, interpretable machine learning (ML) framework tailored for the analysis of macroporous polyHIPE-based magnetic composites. All analyses were conducted exclusively on curated experimental data reported in the literature. By leveraging a curated dataset synthesized from multiple independent studies with harmonized characterization protocols, we integrated processing parameters and quantitative morphological descriptors to predict two critical engineering outputs: dye removal efficiency (%) and saturation magnetization (M_s). Nonlinear ensemble ML models were rigorously trained and evaluated using repeated cross-validation and cross-study validation strategies to ensure predictive robustness and domain transferability. The superior performance of nonlinear models over linear baselines underscores that composite functionality is governed by synergistic, non-additive interactions. Model-agnostic interpretability analyses further revealed that pore interconnectivity and accessible surface area are the primary determinants of adsorption performance. Conversely, while increased magnetic nanoparticle loading enhances magnetic responsiveness, it induces a significant trade-off with adsorption efficiency. These findings demonstrate that uncertainty-aware ML can extract generalizable, physically grounded design insights from heterogeneous experimental literature, providing a streamlined, data-driven pathway for the rational design and screening of multifunctional porous materials without necessitating additional experimental overhead.