This study investigates the drying kinetics of green tomatoes under controlled conditions, comparing 14 mathematical models including Newton, Page, Modified Page, Henderson and Pabis, Logarithmic, Two-Term, Wang and Singh, Thomson, Approximate Diffusion, Verma, Modified Henderson and Pabis, Two-Term Exponential, Simplified Fick’s Diffusion, and Midilli et al. The Verma, Two-Term Exponential, and Modified Page models demonstrated superior accuracy (R² > 0.998, RMSE 0.01268 - 0.01345). A novel approach integrating a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) optimized drying process prediction, significantly enhancing moisture content prediction over time. This hybrid CNN-LSTM model, coupled with the accurate mathematical models (especially the Two-Term Exponential), offers a robust framework for predicting and optimizing food dehydration.

Freshwater ecosystems are increasingly affected by eutrophication, sediment loading, and other anthropogenic pressures, creating a growing need for monitoring frameworks that are spatially extensive, temporally consistent, and methodologically robust. Although in situ sampling remains essential, its limited spatial coverage and operational constraints have accelerated the use of satellite remote sensing combined with artificial intelligence (AI) and machine learning (ML) for water quality assessment. This review critically examines recent studies published between 2020 and March 2026 on the estimation of physicochemical water quality parameters in lakes and rivers using remote sensing, with particular attention to the methodological structure of image processing workflows rather than performance metrics alone. The synthesis shows that predictive performance is strongly conditioned by three interrelated stages: atmospheric correction (AC), spectral feature construction, and validation design. Across the reviewed studies, substantial variation is observed in atmospheric correction processors, spectral engineering strategies, and model architectures, leading to differences in the spectral inputs and analytical conditions used for model development. Validation approaches remain highly heterogeneous and often rely on internal data splits without geographically independent testing, which weakens claims of model generalizability. In addition, few studies explicitly distinguish algorithmic, matchup, and preprocessing uncertainties, revealing a persistent gap in uncertainty reporting. Overall, the review suggests that improvements attributed to newer ML models may partly reflect upstream preprocessing choices rather than algorithmic superiority alone. Future research should prioritize transparent reporting of atmospheric correction pipelines, structured uncertainty decomposition, standardized validation protocols, and cross-site transferability assessments. By synthesizing these methodological patterns, this review provides a consolidated methodological synthesis that supports improved reproducibility, comparability, and operational reliability of remote-sensing-based freshwater quality monitoring.

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.

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.

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.