The research landscape in the specified area is witnessing a significant shift towards more integrated, automated, and dynamic systems. A notable trend is the development of meta operating systems tailored for scientific and engineering domains, which promise enhanced flexibility and efficiency in managing complex models and data. These systems leverage web-based architectures and domain-specific languages to create dynamic workspaces that facilitate both interactive and programmatic modifications. Additionally, there is a growing emphasis on automating knowledge extraction and creation of knowledge graphs from structured data, addressing the limitations of traditional methods that rely on unstructured data. This advancement is particularly impactful in industrial contexts, where it streamlines the process of deriving context and populating ontology-based knowledge graphs. Another key area of progress is the acceleration of manufacturing scale-up through autonomous frameworks that automate the design of process engineering schematics. These frameworks utilize retrieval-augmented AI and ontological knowledge graphs to generate regulation-compliant diagrams with minimal human intervention. Furthermore, the field is seeing a push towards improving schema evolution in interactive programming systems, aiming to shorten feedback loops and enhance collaboration. This involves developing standardized approaches to handle data and code modifications in various programming contexts. Lastly, there is a focus on coordinating ubiquitous devices using Service-Oriented Architecture (SOA) standards, which aims to create more transparent and interoperable environments for pervasive devices.