@article{63950,
  abstract     = {{Sodium-ion batteries are at the forefront of new, sustainable energy systems required for the global energy transition. 23Na in situ solid-state nuclear magnetic resonance spectroscopy is capable of unraveling structures in working electrochemical cells during the charging and discharging processes. To evaluate its suitability for long-term studies, local sodium environments in sodium/sodium ion cells based on silicon carbonitride and hard carbon materials are tracked for up to 49 cycles (228.5?h). The formation of dendrites as well as the decay of a secondary metallic sodium species is observed, and local structures are analyzed up to the point of capacity degradation and cell failure. Initial points of cell breakdown are reflected in the NMR data by characteristic changes in signal intensities, whereas the degradation of the cells is reflected by a cease to periodic signal intensity fluctuations. Meanwhile, ex situ 23Na NMR spectra of the deactivated cells reveal a complex range of environments for sodium ions.}},
  author       = {{Egert, Sonja and Remesh, Renuka and Jusdi, Agatha Clarissa and Sugawara, Yushi and Schutjajew, Konstantin and Oschatz, Martin and Buntkowsky, Gerd and Gutmann, Torsten}},
  journal      = {{Batteries & Supercaps}},
  keywords     = {{solid-state nmr, hard carbon, in-situ, SiCN, sodium ion batteries}},
  number       = {{n/a}},
  pages        = {{e202500516}},
  publisher    = {{John Wiley & Sons, Ltd}},
  title        = {{{Long-Term Cycling Stability of Sodium/Sodium Ion Cells Probed by In Situ Solid-State NMR Spectroscopy}}},
  doi          = {{10.1002/batt.202500516}},
  volume       = {{n/a}},
  year         = {{2025}},
}

@article{63945,
  abstract     = {{para-Hydrogen induced polarisation (PHIP) is an excellent tool for extracting mechanistic information in catalysis since it circumvents the intrinsic low sensitivity of nuclear magnetic resonance (NMR) spectroscopy. We report a class of iron complexes that are highly active in olefin hydrogenation catalysis and act as PHIP catalysts at 1.4 Tesla. Moreover, hyperpolarisation transfer to 19F is observed.}},
  author       = {{Doll, Julianna S. and Kergassner, Jan and Zhang, Bingyu and Thiele, Christina M. and Buntkowsky, Gerd and Enders, Markus and Gutmann, Torsten and Roşca, Dragoş-Adrian}},
  journal      = {{Chemical Communications}},
  number       = {{61}},
  pages        = {{11421–11424}},
  publisher    = {{The Royal Society of Chemistry}},
  title        = {{{Highly active iron catalysts for olefin hydrogenation enable para-hydrogen induced hyperpolarisation of 1H and 19F NMR resonances at 1.4 Tesla}}},
  doi          = {{10.1039/D5CC02409A}},
  volume       = {{61}},
  year         = {{2025}},
}

@article{63921,
  abstract     = {{Redox flow batteries (RFBs) are promising solutions for large-scale stationary energy storage due to their scalability and long cycle life. The efficient operation of RFBs requires a thorough understanding of the complex electrochemical processes occurring during charging and discharging. This review provides an overview and perspective of in situ and in operando analytical techniques to monitor RFBs. In more detail, these advanced techniques allow for real-time observation of redox reactions, ion transport, and electrode–electrolyte interactions under working conditions, offering insights into formation of intermediate species and mechanisms of electrolyte degradation, State-of-Charge (SoC), and ion crossover. By discussing the principles, capabilities, and limitations of techniques such as nuclear magnetic resonance (NMR), electron paramagnetic resonance (EPR), ultraviolet-visible (UV-vis) spectroscopy, Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), X-ray absorption spectroscopy (XAS), electrochemical impedance spectroscopy (EIS), tomography and radiography, mass spectrometry (MS), and fluorescence microscopy this review highlights the essential role of in situ and in operando approaches in advancing RFB technology.}},
  author       = {{Alem, Ahmad and Poormehrabi, Pooria and Lins, Jonas and Pachernegg-Mair, Lukas and Bandl, Christine and Ruiz, Virginia and Ventosa, Edgar and Spirk, Stefan and Gutmann, Torsten}},
  journal      = {{Energy & Environmental Science}},
  number       = {{15}},
  pages        = {{7373–7401}},
  publisher    = {{The Royal Society of Chemistry}},
  title        = {{{Monitoring chemical processes in redox flow batteries employing in situ and in operando analyses}}},
  doi          = {{10.1039/D5EE01311A}},
  volume       = {{18}},
  year         = {{2025}},
}

@article{55459,
  author       = {{Bullerjahn, Nils and Kovács, Balázs}},
  journal      = {{IMA Journal of Numerical Analysis}},
  title        = {{{Error estimates for full discretization of Cahn--Hilliard equation with dynamic boundary conditions}}},
  doi          = {{10.1093/imanum/draf009}},
  year         = {{2025}},
}

@article{53141,
  author       = {{Edelmann, Dominik and Kovács, Balázs and Lubich, Christian}},
  journal      = {{IMA Journal of Numerical Analysis}},
  number       = {{5}},
  pages        = {{2581----2627}},
  title        = {{{Numerical analysis of an evolving bulk--surface model of tumour growth}}},
  doi          = {{10.1093/imanum/drae077}},
  volume       = {{45}},
  year         = {{2025}},
}

@article{55781,
  abstract     = {{In this paper, we prove that spatially semi-discrete evolving finite element
method for parabolic equations on a given evolving hypersurface of arbitrary
dimensions preserves the maximal $L^p$-regularity at the discrete level. We
first establish the results on a stationary surface and then extend them, via a
perturbation argument, to the case where the underlying surface is evolving
under a prescribed velocity field. The proof combines techniques in evolving
finite element method, properties of Green's functions on (discretised) closed
surfaces, and local energy estimates for finite element methods}},
  author       = {{Bai, Genming and Kovács, Balázs and Li, Buyang}},
  journal      = {{IMA Journal of Numerical Analysis}},
  title        = {{{Maximal regularity of evolving FEMs for parabolic equations on an  evolving surface}}},
  doi          = {{10.1093/imanum/draf082.}},
  year         = {{2025}},
}

@inbook{64201,
  author       = {{DeAndres-Tame, Ivan and Faisal, Muhammad and Tolosana, Ruben and Al-Refai, Rouqaiah and Vera-Rodriguez, Ruben and Terhörst, Philipp}},
  booktitle    = {{Lecture Notes in Computer Science}},
  isbn         = {{9783031876561}},
  issn         = {{0302-9743}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{From Pixels to Words: Leveraging Explainability in Face Recognition Through Interactive Natural Language Processing}}},
  doi          = {{10.1007/978-3-031-87657-8_22}},
  year         = {{2025}},
}

@inproceedings{64204,
  author       = {{Voth, Diana and Dane, Leonidas and Grebe, Jonas and Peitz, Sebastian and Terhörst, Philipp}},
  booktitle    = {{2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}},
  publisher    = {{IEEE}},
  title        = {{{Effective Backdoor Learning on Open-Set Face Recognition Systems}}},
  doi          = {{10.1109/wacv61041.2025.00109}},
  year         = {{2025}},
}

@inproceedings{64203,
  author       = {{Al-Refai, Rouqaiah and Hempel, Philipp and Biagi, Clara and Terhörst, Philipp}},
  booktitle    = {{2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}},
  publisher    = {{IEEE}},
  title        = {{{FALCON: Fair Face Recognition via Local Optimal Feature Normalization}}},
  doi          = {{10.1109/wacv61041.2025.00337}},
  year         = {{2025}},
}

@inproceedings{64207,
  author       = {{Bora, Revoti Prasad and Terhörst, Philipp and Veldhuis, Raymond N. J. and Ramachandra, Raghavendra and Raja, Kiran Bylappa}},
  booktitle    = {{Conference on Uncertainty in Artificial Intelligence, Rio Othon Palace, Rio de Janeiro, Brazil, 21-25 July 2025}},
  editor       = {{Chiappa, Silvia and Magliacane, Sara}},
  pages        = {{332–354}},
  publisher    = {{PMLR}},
  title        = {{{BELIEF - Bayesian Sign Entropy Regularization for LIME Framework}}},
  volume       = {{286}},
  year         = {{2025}},
}

@inproceedings{64219,
  author       = {{Graunke, Jannis and Bita, Isaac Mpidi and Hermelingmeier, Dominik and Humpert, Lynn and Schierbaum, Anja and Dumitrescu, Roman}},
  booktitle    = {{2025 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE)}},
  publisher    = {{IEEE}},
  title        = {{{Evolving Modularity: Rethinking Architecture in Product and Organizational Systems}}},
  doi          = {{10.1109/rasse64831.2025.11315315}},
  year         = {{2025}},
}

@inproceedings{64205,
  author       = {{Xu, Ying and Terhörst, Philipp and Pedersen, Marius and Raja, Kiran}},
  booktitle    = {{2025 IEEE 19th International Conference on Automatic Face and Gesture Recognition (FG)}},
  publisher    = {{IEEE}},
  title        = {{{Chasing Shadows: Solving Deepfake Detection Benchmarks Using Irrelevant Features Only}}},
  doi          = {{10.1109/fg61629.2025.11099163}},
  year         = {{2025}},
}

@article{64206,
  author       = {{Groß, Sebastian and Heindorf, Stefan and Terhörst, Philipp}},
  journal      = {{CoRR}},
  title        = {{{A Responsible Face Recognition Approach for Small and Mid-Scale Systems Through Personalized Neural Networks}}},
  doi          = {{10.48550/ARXIV.2505.19920}},
  volume       = {{abs/2505.19920}},
  year         = {{2025}},
}

@article{64210,
  author       = {{Al-Refai, Rouqaiah and Ramasamy, Pankaja Priya and Ramesh, Ragini and Arias-Cabarcos, Patricia and Terhörst, Philipp}},
  journal      = {{arXiv preprint arXiv:2508.13874}},
  title        = {{{A Comprehensive Re-Evaluation of Biometric Modality Properties in the Modern Era}}},
  year         = {{2025}},
}

@inproceedings{64220,
  author       = {{Disselkamp, Jan-Philipp and Seidenberg, Tobias and Lick, Jonas and Dumitrescu, Roman}},
  booktitle    = {{2025 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE)}},
  publisher    = {{IEEE}},
  title        = {{{24 Reasons for the Failure of Integrative and Integrated Product and Production System Development Methods}}},
  doi          = {{10.1109/rasse64831.2025.11315397}},
  year         = {{2025}},
}

@article{64222,
  author       = {{Ködding, Patrick and Koldewey, Christian and Dumitrescu, Roman}},
  issn         = {{0360-8581}},
  journal      = {{IEEE Engineering Management Review}},
  pages        = {{1--9}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Tailoring scenario projects with method and tool building blocks}}},
  doi          = {{10.1109/emr.2025.3646110}},
  year         = {{2025}},
}

@inproceedings{64229,
  abstract     = {{<jats:title>ABSTRACT:</jats:title><jats:p>This paper presents the MBSE-Graph-RAG framework to address key challenges in Model-Based Systems Engineering (MBSE). Traditional MBSE tools suffer from usability barriers, limited accessibility, and integration challenges. By combining knowledge graphs with Retrieval-Augmented Generation (RAG), the proposed framework enables AI-Augmented engineering through natural language interactions and automated system architecture generation. A systematic literature review establishes a solid research foundation, identifying gaps in AI-assisted MBSE. Key contributions include a structured MBSE-Graph interface, improved usability via Large Language Models (LLMs), and automated graph construction aligned with SysML. A proof-of-concept demonstrates the potential of this approach to enhance MBSE by reducing complexity, improving data accessibility, and supporting engineering collaboration.</jats:p>}},
  author       = {{Hanke, Fabian and Mpidi Bita, Isaac  and von Heißen, Oliver  and Weller, Julian and Hovemann, Aschot and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the Design Society}},
  issn         = {{2732-527X}},
  pages        = {{439--448}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{AI-augmented systems engineering: conceptual application of retrieval-augmented generation for model-based systems engineering graph}}},
  doi          = {{10.1017/pds.2025.10058}},
  volume       = {{5}},
  year         = {{2025}},
}

@article{64253,
  abstract     = {{<jats:p>This paper presents a comprehensive design framework for Enterprise Architecture aimed at facilitating decision-driven analytics in smart factories. The motivation behind this research lies in challenges faced by manufacturing companies, such as skilled labor shortages and increasing global competition, alongside the imperative for sustainable production. This journal provides a novel approach for designing and documenting prescriptive analytics use cases in manufacturing environments. The framework addresses the need for effective integration of advanced data analytics and prescriptive analytics solutions within existing production environments, thereby enhancing operational efficiency and decision-making processes. A Design Science Research approach is used to iteratively derive a framework based on stakeholder needs and activities along the prescriptive analytics use case development cycle. The resulting framework is demonstrated and evaluated in an IoT Factory setup in a research facility. From a practical perspective, the framework supports manufacturing companies in systematically designing prescriptive analytics use cases. From a research perspective, it contributes to the body of knowledge on Enterprise Architecture Management (EAM) by operationalizing the design of prescriptive analytics use cases in manufacturing contexts. The main contributions of this study include the development of a framework that supports the planning, design, and integration of prescriptive analytics use cases. This framework fosters interdisciplinary collaboration and aids in managing the complexity of data-driven projects.</jats:p>}},
  author       = {{Weller, Julian and Dumitrescu, Roman}},
  issn         = {{2079-9292}},
  journal      = {{Electronics}},
  number       = {{21}},
  publisher    = {{MDPI AG}},
  title        = {{{Decision-Driven Analytics in Smart Factories: Enterprise Architecture Framework for Use Case Specification and Engineering (FUSE)}}},
  doi          = {{10.3390/electronics14214271}},
  volume       = {{14}},
  year         = {{2025}},
}

@inproceedings{64252,
  abstract     = {{<jats:title>ABSTRACT:</jats:title><jats:p>The increasing complexity of modern product and production system development, driven by dynamic market demands, supply chain disruptions and economic pressures, poses significant challenges for companies. Existing methodologies often fall short due to their domain-specific focus, inconsistent terminology and lack of integration. To address these challenges, this paper presents a taxonomy for integrative product and production system development. The taxonomy systematically structures key elements, dependencies and processes to improve collaboration, decision-making and communication within organisations. Developed iteratively the taxonomy identifies ten core artefacts. It enables organisations to better plan improvements, synchronise development processes, and select appropriate methods and tools.</jats:p>}},
  author       = {{Disselkamp, Jan-Philipp and Seidenberg, Tobias and Westphal, Svenja and Lick, Jonas and Ptock, Lukas and Wyrwich, Fabian and Hovemann, Aschot and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the Design Society}},
  issn         = {{2732-527X}},
  pages        = {{2121--2130}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Integrative and Integrated product and production system development: a taxonomy for managing dependencies and processes}}},
  doi          = {{10.1017/pds.2025.10226}},
  volume       = {{5}},
  year         = {{2025}},
}

@inproceedings{59517,
  author       = {{Lick, Jonas and Kattenstroth, Fiona and Trienens, Malte and Disselkamp, Jan-Philipp and Kühn, Arno and Dumitrescu, Roman}},
  booktitle    = {{2024 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)}},
  publisher    = {{IEEE}},
  title        = {{{Guidance on a Digital Factory Twin: Proposal for a Reference Architecture}}},
  doi          = {{10.1109/ictmod63116.2024.10878221}},
  year         = {{2025}},
}