@inproceedings{67303,
  author       = {{Amakor, Augustina Chidinma and Sonntag, Konstantin and Peitz, Sebastian}},
  booktitle    = {{European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)}},
  keywords     = {{own, own-conference}},
  pages        = {{652--668}},
  title        = {{{Interactive Pareto navigation for deep multi-task learning}}},
  doi          = {{10.1007/978-3-032-37667-1_37}},
  year         = {{2026}},
}

@inproceedings{67305,
  author       = {{Becktepe, Jannis and Franz, Aleksandra and Thuerey, Nils and Peitz, Sebastian}},
  booktitle    = {{International Conference on Machine Learning (ICML)}},
  keywords     = {{own, own-conference, erc}},
  title        = {{{Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control}}},
  doi          = {{10.48550/arXiv.2601.15015}},
  year         = {{2026}},
}

@article{67301,
  author       = {{Wohlleben, Meike Claudia and Schütte, Jan and Berkemeier, Manuel and Sextro, Walter and Peitz, Sebastian}},
  journal      = {{Multibody System Dynamics}},
  keywords     = {{own, own-journal}},
  title        = {{{Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings}}},
  doi          = {{10.1007/s11044-026-10146-9}},
  year         = {{2026}},
}

@unpublished{67298,
  author       = {{Mugisho Zagabe, Christian and Peitz, Sebastian}},
  booktitle    = {{arXiv:2603.26464}},
  keywords     = {{own, own-preprint, erc}},
  title        = {{{Automatic feature identification in least-squares policy iteration using the Koopman operator framework}}},
  year         = {{2026}},
}

@unpublished{67304,
  author       = {{Plotzki, Tim and Peitz, Sebastian}},
  booktitle    = {{arXiv:2603.28074}},
  keywords     = {{own, own-preprint, erc}},
  title        = {{{Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection}}},
  year         = {{2026}},
}

@article{62816,
  abstract     = {{The increasing demand for advanced sensing technologies drives the development of chemical sensors using innovative materials. In gas sensing, optical sensors are often used to detect gases such as CO, NOx, and O2. Oxygen sensors typically incorporate dyes into oxygen-permeable matrices like polymers, silica, or zeolites. Alternatively, semiconductor surface chemistry can enable O2 detection. However, these approaches are often limited by slow response and recovery times and low selectivity, restricting their practical applications. The metal-organic framework MOF-76(Eu) and its yttrium-modified variant, MOF-76(Eu/Y) are reported to exhibit highly reversible and fast optical responses to varying O2 concentrations. Time-resolved emission measurements are performed over short (seconds) and long (hours) timescales using N2 and synthetic air mixtures. Cross-sensitivity to humidity is analyzed. Multichannel scaling photon-counting experiments confirm quenching at the linker level, as the emission lifetime remains nearly constant. Yttrium significantly improves stability and performance at room temperature. Structural and optical changes induced by yttrium are investigated. Additionally, MIL-78(Eu), another Eu-BTC-based MOF with a different coordination environment, is synthesized. Unlike MOF-76(Eu), MIL-78(Eu) exhibits distinct optical properties but lacks a reversible response to O2. These results highlight the potential of MOF-76-based materials for high-performance O2 sensing.}},
  author       = {{Zhao, Zhenyu and Weinberger, Christian and Steube, Jakob and Bauer, Matthias and Brehm, Martin and Tiemann, Michael}},
  issn         = {{1616-301X}},
  journal      = {{Advanced Functional Materials}},
  publisher    = {{Wiley}},
  title        = {{{Fast‐Responding O2 Gas Sensor Based on Luminescent Europium Metal‐Organic Frameworks (MOF‐76)}}},
  doi          = {{10.1002/adfm.202511190}},
  year         = {{2026}},
}

@inproceedings{67339,
  abstract     = {{Explainable artificial intelligence (XAI) is essential for critical domains such as healthcare and autonomous systems to build trust and confidence in real-world deployment. In this context, description logic knowledge bases (KBs) provide structured and semantically rich representations that support reasoning and informed decision-making. A core task in applying KBs to XAI is class expression learning (CEL), which generates explainable logical descriptions for classifying instances within KBs. Unlike black-box models with opaque internal mechanisms, CEL provides global explainability and ease of integration with domain knowledge. However, current approaches to CEL face significant limitations such as poor scalability, failure to capture rare patterns, and limited exploration of the vast class expression search space. To overcome these limitations, we introduce LYRA, a novel multi-agent deep reinforcement learning framework that formulates CEL as a collaborative planning task under uncertainty. The integration of the Dempster–Shafer theory enables agents to effectively reason under ambiguity and manage conflicting or inconsistent information. Our experiments show that LYRA outperforms state-of-the-art methods on seven out of eight datasets, demonstrating robust and scalable CEL. Additionally, LYRA offers interpretable decisions and employs advanced search strategies, enabling the discovery of more precise and expressive class expressions than existing approaches.}},
  author       = {{Abdulmaqsod, Amgad and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed Ahmed}},
  booktitle    = {{The Semantic Web – ISWC 2026}},
  keywords     = {{amgad dice enexa fairomics mahmood ngonga sailproject sherif simba whale}},
  title        = {{{LYRA: Belief-Driven Scalable Class Expression Learning in Description Logics}}},
  year         = {{2026}},
}

@inproceedings{67344,
  author       = {{Becker, Alexander and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed}},
  booktitle    = {{The Semantic Web – 23rd European Semantic Web Conference, ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings}},
  keywords     = {{becker dice enexa kiowl ngonga sailproject sherif trr318_inf whale}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{TIM: Tiered Iterative Knowledge Graph Matching}}},
  year         = {{2026}},
}

@inproceedings{67341,
  author       = {{Ekinci Birol, Duygu and KOUAGOU, N'Dah Jean and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{IEEE International Conference on Data Mining (ICDM) 2026}},
  keywords     = {{dice duygu fairomics kouagou ngonga sail sherif trr318 whale}},
  title        = {{{ATLAS: Adaptive Attribute-Aware Post-Hoc Alignment of Knowledge Graph Embeddings}}},
  year         = {{2026}},
}

@inproceedings{67340,
  author       = {{Abdulmaqsod, Amgad and Ihtassine, Reda and Pénicaud, Caroline and Saint-Eve, Anne and Brisset, Lucia and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed Ahmed}},
  booktitle    = {{Proceedings of FOODSIM’2026}},
  keywords     = {{amgad dice fairomics ihtassine ngonga sail sherif whale}},
  title        = {{{PBFF-KG: A FAIR Knowledge Graph Towards Sustainable Design of Plant-Based Fermented Foods}}},
  year         = {{2026}},
}

@inproceedings{67342,
  author       = {{Ekinci Birol, Duygu and Kouagou, N’Dah Jean and Sharma, Shivam and Khomich, Albert and Sherif, Mohamed Ahmed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{ECML PKDD 2026}},
  keywords     = {{dice duygu fairomics khomich kouagou ngonga sail sharma sherif trr318 whale}},
  title        = {{{ASTRA: Adaptive Structure-Aware Post-Hoc Alignment of Knowledge Graph Embeddings}}},
  year         = {{2026}},
}

@inproceedings{67345,
  author       = {{Bobe, Julia and Eckert, Marcus and Scherenberg, Viviane and Klingsieck, Katrin B.}},
  location     = {{Hannover}},
  title        = {{{Heute schon an morgen denken - Gruppenunterschiede in der Prokrastination klimafreundlichen Verhaltens}}},
  year         = {{2026}},
}

@inproceedings{67346,
  author       = {{Bobe, Julia and Eckert, Marcus and Scherenberg, Viviane and Klingsieck, Katrin B.}},
  location     = {{Luxembourg}},
  title        = {{{Stress, Compulsive Internet Use, and Procrastination - A Mediation Model of Procrastination in High School Students}}},
  year         = {{2026}},
}

@phdthesis{67348,
  author       = {{Bobe, Julia}},
  title        = {{{Prokrastination und die Bedeutung des subjektiven Unwohlseins}}},
  year         = {{2026}},
}

@article{67357,
  abstract     = {{<jats:p>The complexity and interconnectivity of modern automotive systems are rapidly increasing, particularly with the rise of distributed and cooperative driving functions. These developments increase exposure to a range of disruptions, from technical failures to cyberattacks, and demand a shift towards resilience-by-design. This study addresses the early integration of resilience into the automotive design process by proposing a structured method for identifying gaps and eliciting resilience requirements. Building upon the concept of resilience scenarios, the approach extends traditional hazard and threat analyses as defined in ISO 26262, ISO 21448 and ISO/SAE 21434. Using a structured, graph-based modeling method, these scenarios enable the anticipation of functional degradation and its impact on driving scenarios. The methodology helps developers to specify resilience requirements at an early stage, enabling the integration of resilience properties throughout the system lifecycle. Its practical applicability is demonstrated through an example in the field of automotive cybersecurity. This study advances the field of resilience engineering by providing a concrete approach for operationalizing resilience within automotive systems engineering.</jats:p>}},
  author       = {{Mpidi Bita, Isaac and Ugur, Elif and Hovemann, Aschot and Dumitrescu, Roman}},
  issn         = {{1999-5903}},
  journal      = {{Future Internet}},
  number       = {{1}},
  publisher    = {{MDPI AG}},
  title        = {{{Resilience-by-Design: Extracting Resilience Requirements Using the Resilience Graph in the Automotive Concept Phase}}},
  doi          = {{10.3390/fi18010051}},
  volume       = {{18}},
  year         = {{2026}},
}

@article{67358,
  abstract     = {{<jats:title>ABSTRACT:</jats:title>
                  <jats:p>Small and medium-sized enterprises often lack the time, expertise, and tools for effective scenario management. This paper proposes a modular, AI-enabled scenario architecture integrating a guided wizard and expert environment on a shared knowledge backbone. The design aims to reduce effort and tool fragmentation while preserving human judgment, structural quality, explainability, and traceability. The proposed pattern outlines a provenance-aware foresight pipeline with human-in-the-loop capabilities that aims to transform one-off projects into reusable organizational knowledge.</jats:p>}},
  author       = {{Knepler, Jonas and Seidenberg, Tobias and Grigoryan, Khoren and Asmar, Laban and Dumitrescu, Roman}},
  issn         = {{2732-527X}},
  journal      = {{Proceedings of the Design Society}},
  pages        = {{2373--2382}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{AI-based scenario management for SMEs: the need for modular, explainable and reusable foresight pipelines}}},
  doi          = {{10.1017/pds.2026.10595}},
  volume       = {{6}},
  year         = {{2026}},
}

@article{67367,
  abstract     = {{<jats:title>ABSTRACT:</jats:title>
                  <jats:p>Connected and Autonomous Vehicles (CAV) are increasingly complex, making resilience hard to guarantee. Resilience means maintaining functionality and availability despite disruptions while ensuring safety. Designing a resilient system requires system-level analysis early in the concept phase to identify and mitigate risks, thereby securing reliability and availability. This paper introduces the Automotive Resilience Maturity Model (ARMM), which evaluates automotive systems’ resilience levels based on their system architecture.</jats:p>}},
  author       = {{Mpidi Bita, Isaac and Mellacheruvu, Venkatesh and Ugur, Elif and Hovemann, Aschot and Dumitrescu, Roman}},
  issn         = {{2732-527X}},
  journal      = {{Proceedings of the Design Society}},
  pages        = {{2821--2830}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Resilience-by-design: maturity model for assessing the resilience capabilities of automotive systems architecture in the concept phase}}},
  doi          = {{10.1017/pds.2026.10640}},
  volume       = {{6}},
  year         = {{2026}},
}

@article{67368,
  abstract     = {{<jats:title>ABSTRACT:</jats:title>
                  <jats:p>Sustainability is a central challenge in engineering. Early architectural design decisions strongly influence a product’s ecological footprint and long-term sustainability potential. Addressing these aspects in the concept phase is therefore essential. This paper analyses how architecture decisions - such as modularity, standardisation, redundancy, and updateability - affect ecological sustainability. The qualitative assessment helps practitioners to anticipate environmental impacts and foster sustainability awareness in product architecture development.</jats:p>}},
  author       = {{Grothe, Robin and Bita, Isaac Mpidi and Hovemann, Aschot and Dumitrescu, Roman}},
  issn         = {{2732-527X}},
  journal      = {{Proceedings of the Design Society}},
  pages        = {{1291--1300}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Sustainability by design: the impact of product architecture decisions on the sustainability of products – a practitioner qualitative assessment}}},
  doi          = {{10.1017/pds.2026.10487}},
  volume       = {{6}},
  year         = {{2026}},
}

@article{67360,
  author       = {{Twardzik, Jan Luca and Schreiner, Nick and Cichon, Gerrit and Kürpick, Christian and Dumitrescu, Roman}},
  issn         = {{0921-3449}},
  journal      = {{Resources, Conservation and Recycling}},
  publisher    = {{Elsevier BV}},
  title        = {{{Embedding environmental and circularity metrics in product development decisions: A structured decision-support approach for early-stage industrial concept evaluation}}},
  doi          = {{10.1016/j.resconrec.2026.109112}},
  volume       = {{235}},
  year         = {{2026}},
}

@book{67371,
  author       = {{Hein, Bernhard and Weller, Julian and Heuwinkel, Tim and Eckertz, Daniel and Gabriel, Stefan and Hovemann, Aschot and Dumitrescu, Roman}},
  publisher    = {{Hannover: Technische Informationsbibliothek}},
  title        = {{{Verbundprojekt: Validierung einer Prescriptive-Analytics-Plattform für die Smart Factory - VIP4PAPS; Teilvorhaben: Validierung des Konzepts und des Rahmenwerks der Prescriptive-Analytics-Plattform für die Smart Factory}}},
  doi          = {{https://doi.org/10.34657/42533}},
  year         = {{2026}},
}