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The current trend towards living in big cities contributes to an increased demand for efficient and sustainable space and resource allocation in urban environments. This leads to enormous pressure for resource minimization in city planning. One pillar of efficient city management is a smart intermodal traffic system. Planning and organizing the various kinds of modes of transport in a complex and dynamically adaptive system such as a city is inherently challenging. By deliberately simplifying reality, models can help decision-makers shape the traffic systems of tomorrow. Meanwhile, Smart City initiatives are investing in sensors to observe and manage many kinds of urban resources, making up a part of the Internet of Things (IoT) that produces massive amounts of data relevant for urban planning and monitoring. We use these new data sources of smart cities by integrating real-time data of IoT sensors in an ongoing simulation. In this sense, the model is a digital twin of its real-world counterpart, being augmented with real-world data. To our knowledge, this is a novel instance of real-time correction during simulation of an agent-based model. The process of creating a valid mapping between model components and real-world objects posed several challenges and offered valuable insights, particularly when studying the interaction between humans and their environment. As a proof-of-concept for our implementation, we designed a showcase with bike rental stations in Hamburg-Harburg, a southern district of Hamburg, Germany. Our objective was to investigate the concept of real-time data correction in agent-based modeling, which we consider to hold great potential for improving the predictive capabilities of models. In particular, we hope that the chosen proof-of-concept informs the ongoing politically supported trends in mobility—away from individual and private transport and towards—in Hamburg.

Model-Driven Engineering (MDE) relies on explicit architecture models to document and evolve systems across abstraction levels. For ROS~2, subsystem structure is often encoded implicitly in distributed configuration artifacts -- most notably launch files -- making hierarchical structural decomposition hard to capture and maintain. Existing ROS~2 modeling approaches cover node-level entities and wiring, but do not make hierarchical structural (de-)composition a first-class architectural view independent of launch artifacts. We contribute (1) a UML-based modeling concept for hierarchical structural architectures of ROS~2 systems and (2) a blueprint-guided automated recovery pipeline that reconstructs such models from code and configuration artifacts by combining deterministic extraction with LLM-based agents. The ROS~2 architectural blueprint (nodes, topics, interfaces, launch-induced wiring) is encoded as structural contracts to constrain synthesis and enable deterministic validation, improving reliability. We evaluate the approach on three ROS~2 repositories, including an industrial-scale code subset. Results show high precision across abstraction levels, while subsystem-level recall drops with repository complexity due to implicit launch semantics, making high-level recovery the remaining challenge.

Dissolved organic matter (DOM) is the main substrate and energy source for heterotrophic bacterioplankton. To understand the interactions between DOM and the bacterial community (BC), it is important to identify the key factors on both sides in detail, chemically distinct moieties in DOM and the various bacterial taxa. Next-generation sequencing facilitates the classification of millions of reads of environmental DNA and RNA amplicons and ultrahigh-resolution mass spectrometry yields up to 10 000 DOM molecular formulae in a marine water sample. Linking this detailed biological and chemical information is a crucial first step toward a mechanistic understanding of the role of microorganisms in the marine carbon cycle. In this study, we interpreted the complex microbiological and molecular information via a novel combination of multivariate statistics. We were able to reveal distinct relationships between the key factors of organic matter cycling along a latitudinal transect across the North Sea. Total BC and DOM composition were mainly driven by mixing of distinct water masses and presumably retain their respective terrigenous imprint on similar timescales on their way through the North Sea. The active microbial community, however, was rather influenced by local events and correlated with specific DOM molecular formulae indicative of compounds that are easily degradable. These trends were most pronounced on the highest resolved level, that is, operationally defined ‘species’, reflecting the functional diversity of microorganisms at high taxonomic resolution.