Agile software development companies considered small entities (VSE) face a new reality of remote development. However, remote communication has generated many videos because video calls are often recorded for later reference. The architectural knowledge contained in videos, derived from virtual meetings, is essential for companies facing the knowledge vaporization problem. However, only some proposals in the literature can potentially manage AK in videos. The present article proposes a solution to recover this architectural knowledge contained in videos, using an ontology as a classification scheme. We based our proposal on the concept of architectural knowledge condensation and proposed a condensation cycle from it. Finally, we validated our ontology to manage architectural knowledge following the Methontology guidelines. Implementing an ontology as a classification scheme represents a step forward to achieving the condensation of architectural knowledge in an agile development environment for VSE.

Generative AI has revolutionized numerous fields, including software engineering education. Tools like GitHub Copilot, ChatGPT, or Llama have reshaped learning by offering real-time suggestions, automating code generation, and enhancing debugging. However, software engineering encompasses far more than writing code—it spans requirements analysis, specification, design, implementation, testing, deployment, maintenance, configuration management, etc. The rise of generative AI raises critical questions about how to train software engineers who can master these principles in an era where AI automates many traditionally manual tasks. This chapter explores strategies for integrating AI into education while reinforcing fundamental software engineering principles, ensuring students develop critical thinking, ethical awareness, and a comprehensive understanding of the entire software development lifecycle.

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.