@article{60194,
author = {{Peeters, Hendrik and Hansel, Jan-Luca and Graute, André and Fischer, Matthias and Weinberger, Christian and Neiske, Iris and Fechner, Sabine}},
journal = {{Laborpraxis}},
number = {{5-6}},
pages = {{22--25}},
title = {{{Virtual Reality trifft Künstliche Intelligenz. KI unterstützt bei virtueller Praktikumsvorbereitung}}},
year = {{2025}},
}
@book{63180,
editor = {{Höink, Dominik and Werbick, Regina and Memering, Robert}},
title = {{{Friedrich Schneider, Christus der Meister. Oratorium in drei Teilen. Partitur}}},
doi = {{10.17879/41009673985}},
year = {{2025}},
}
@inbook{61239,
abstract = {{In diesem Beitrag wird ein Überblick gegeben, welche Forschung zu Digitalisierung und künstliche Intelligenz (KI) in den Bereichen Verwaltung, Forschung, Studium und Lehre sowie Governance von Hochschulen besteht. Der Fokus liegt hierbei auf aktuellen Forschungsergebnissen seit der Coronapandemie. Zentral sind Fragen nach Effektivität und Effizienz durch Digitalisierung und KI und danach, wie Verbesserungen angestoßen werden können.}},
author = {{Steinhardt, Isabel}},
booktitle = {{Hochschulforschung}},
editor = {{Pasternach, Peer and Reinmann, Gabi and Schneijderberg, Christian}},
isbn = {{9783748943334}},
keywords = {{Digitalisierung, Künstliche Intelligenz, Forschung, Lehre, Governance, Verwaltung}},
pages = {{197--206}},
publisher = {{Nomos}},
title = {{{Digitalisierung und Künstliche Intelligenz}}},
doi = {{10.5771/9783748943334-197}},
year = {{2025}},
}
@inbook{61237,
abstract = {{In diesem Beitrag wird zunächst die historische Entstehung von Open Science kurz skizziert und definiert, was unter diesem Begriff zu verstehen ist. Daran anschließend werden die Open-Science-Praktiken Open Data, Open Access, Open Source, Open Methodology und Open Peer Review dargestellt und diskutiert, welche Forschungserkenntnisse zu Open Science vorhanden sind. Im Schluss werden Forschungsdesiderate aufgegriffen und die Implikationen von Open Science für die Wissenschaft erläutert.}},
author = {{Steinhardt, Isabel and Röwert, Ronny}},
booktitle = {{Hochschulforschung}},
editor = {{Pasternack, Peer and Reinmann, Gabi and Schneijderberg, Christian }},
isbn = {{9783748943334}},
keywords = {{Open Data, Open Access, Open Source, Open Methodology, Open Peer Review}},
pages = {{487--496}},
publisher = {{Nomos}},
title = {{{Open Science}}},
doi = {{10.5771/9783748943334-487}},
year = {{2025}},
}
@misc{63185,
author = {{Schmidt, Rebecca}},
booktitle = {{ Sozialwissenschaftliche Methodenberatung}},
title = {{{KI als Herausforderungen für die qualitative Methodenlehre – ein Diskussionsaufruf!}}},
year = {{2025}},
}
@article{63192,
abstract = {{Lithium niobate (LiNbO3) is a widely used material with several desirable physical properties, such as high second-order nonlinear optical and strong electro-optical effects. Thus LiNbO3 is used for various applications such as electro-optic modulation or nonlinear frequency conversion and mixing. But LiNbO3 also exhibits a strong photorefractive effect, which limits the intensity of the optical fields involved. Various approaches to reduce the photorefractive effect have been investigated, such as increasing the temperature, doping the crystal or using different waveguide designs in LiNbO3. Here, we present an analysis of the approach to increase the photorefractive damage threshold by using different waveguide designs. Contrary to previous claims and investigations, our SHG measurements revealed no significant difference in resistance to photorefractive damage when comparing conventional Ti-doped channel waveguides and Ti-doped diced ridge waveguides in LiNbO3. Furthermore, we have investigated the effect of photorefractive cleaning and curing using a light field at 532 nm. Here, we observe a reduction in the photorefractive effect at room temperature during and after SHG measurements, which is an easy alternative to conventional approaches.}},
author = {{Kirsch, Michelle and Kießler, Christian and Lengeling, Sebastian and Stefszky, Michael and Eigner, Christof and Herrmann, Harald and Silberhorn, Christine}},
issn = {{0030-3992}},
journal = {{Optics & Laser Technology}},
publisher = {{Elsevier BV}},
title = {{{Photorefraction and in-situ optical cleaning in various types of LiNbO3 waveguides}}},
doi = {{10.1016/j.optlastec.2025.114260}},
volume = {{193}},
year = {{2025}},
}
@phdthesis{62766,
abstract = {{raditional assessment formats in university-based EFL teacher education programs usually focus on cognitive dispositions rather than on the actual performance of pre-service EFL teachers in everyday teaching situations. This assessment gap is addressed in this thesis by developing and validating a role-play-based simulation (RobS) designed for the summative assessment of pre-service EFL teachers' feedback competence on writing. Drawing on theories from multiple disciplines, such as higher education, medical education, teacher education, educational psychology, and EFL-specific didactics, the RobS is developed as a performance-oriented assessment format. In the RobS, pre-service EFL teachers engage in a feedback conversation with trained actors who portray a standardized learner. Following an argument-based approach to validation, the extent to which the RobS can be considered valid is investigated. Data from multiple studies, focusing on aspects such as authenticity, fairness, reliability, and external validity, are presented. The discussion in the validity argument indicates that the RobS can elicit and assess the performative facet of feedback competence on writing with sufficient confidence. This work contributes a novel, empirically supported assessment framework to teacher education research. Moreover, it presents an approach to address the assessment gap, enabling pre-service EFL teachers to show how they provide feedback, rather than just tell their lecturers about it.}},
author = {{Janzen, Thomas}},
publisher = {{Logos Verlag}},
title = {{{Show, don’t tell - Developing and Validating a Role-Play-Based Simulation (RobS) for the Assessment of Pre-Service EFL Teachers’ Feedback Competence on Writing}}},
doi = {{https://doi.org/10.30819/5994}},
year = {{2025}},
}
@inproceedings{63397,
abstract = {{Decarbonizing industrial process heat is a crucial step in mitigating climate change. While Process Mining (PM) has gained traction in sustainability research—such as optimizing production scheduling to reduce energy use or accounting for carbon footprints—it has largely overlooked the challenges and opportunities related to thermal energy, accounting for 66% of total energy demand in industrial processes. At the same time, Heat Integration (HI) is an established engineering discipline focused on maximizing the efficiency of thermal energy systems. However, HI traditionally relies on static or incomplete data about energy demands, limiting its effectiveness and accuracy. In this paper, we propose a novel framework that combines PM and HI to enable data-driven, process- and product-centric modeling of industrial energy demands. By integrating event logs and thermal energy data, our approach allows for a fine-grained analysis of heat demand patterns corresponding to specific process activities and product variants. We demonstrate the applicability and advantages of the framework by simulating a pharmaceutical manufacturing process and evaluating energy demands and heat recovery potentials. Our findings show that our PM-enabled HI framework provides more accurate and actionable insights into the temporal and product-specific variation of thermal energy demands. By capturing the causal relationships between process activities, product characteristics, and energy consumption, our approach enables improved analysis, planning, and optimization for heat recovery and process decarbonization. This integration of PM and HI expands the analytical tools for both disciplines and contributes to advancing the sustainable transformation of industrial processes.}},
author = {{Zapata Gonzalez, David Ricardo and Brennig, Katharina and Benkert, Kay and Schlosser, Florian and Müller, Oliver}},
booktitle = {{ACM SIGEnergy Energy Informatics Review}},
issn = {{2770-5331}},
number = {{3}},
pages = {{19--31}},
publisher = {{Association for Computing Machinery (ACM)}},
title = {{{Process Mining for Robust Heat Integration through Process- and Product-Centric Energy Demand Modeling}}},
doi = {{10.1145/3777518.3777520}},
volume = {{5}},
year = {{2025}},
}
@inproceedings{63400,
abstract = {{Data centers (DCs) form the backbone of our growing digital economy, but their rising energy demands pose challenges to our environment. At the same time, reusing waste heat from DCs also represents an opportunity, for example, for more sustainable heating of residential buildings. Modeling and optimizing these coupled and dynamic systems of heat generation and reuse is complex. On the one hand, physical simulations can be used to model these systems, but they are time-consuming to develop and run. Machine learning (ML), on the other hand, allows efficient data-driven modeling, but conventional correlation-based approaches struggle with the prediction of interventions and out-of-distribution generalization. Recent advances in causal ML, which combine principles from causal inference with flexible ML methods, are a promising approach for more robust predictions. Due to their focus on modeling interventions and cause-and-effect relationships, it is difficult to evaluate causal ML approaches rigorously. To address this challenge, we built a testbed of a miniature DC with an integrated waste heat network, equipped with sensors and actuators. This testbed allows conducting controlled experiments and automatic collection of realistic data, which can then be used to benchmark conventional and causal ML methods. Our experimental results highlight the strengths and weaknesses of each modeling approach, providing valuable insights on how to appropriately apply different types of machine learning to optimize data center operations and enhance their sustainability.}},
author = {{Zapata Gonzalez, David Ricardo and Meyer, Marcel and Müller, Oliver}},
booktitle = {{ACM SIGEnergy Energy Informatics Review}},
issn = {{2770-5331}},
number = {{2}},
pages = {{4--10}},
publisher = {{Association for Computing Machinery (ACM)}},
title = {{{Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study}}},
doi = {{10.1145/3757892.3757893}},
volume = {{5}},
year = {{2025}},
}
@inproceedings{63399,
abstract = {{Data centers (DCs) form the backbone of our growing digital economy, but their rising energy demands pose challenges to our environment. At the same time, reusing waste heat from DCs also represents an opportunity, for example, for more sustainable heating of residential buildings. Modeling and optimizing these coupled and dynamic systems of heat generation and reuse is complex. On the one hand, physical simulations can be used to model these systems, but they are time-consuming to develop and run. Machine learning (ML), on the other hand, allows efficient data-driven modeling, but conventional correlation-based approaches struggle with the prediction of interventions and out-of-distribution generalization. Recent advances in causal ML, which combine principles from causal inference with flexible ML methods, are a promising approach for more robust predictions. Due to their focus on modeling interventions and cause-and-effect relationships, it is difficult to evaluate causal ML approaches rigorously. To address this challenge, we built a testbed of a miniature DC with an integrated waste heat network, equipped with sensors and actuators. This testbed allows conducting controlled experiments and automatic collection of realistic data, which can then be used to benchmark conventional and causal ML methods. Our experimental results highlight the strengths and weaknesses of each modeling approach, providing valuable insights on how to appropriately apply different types of machine learning to optimize data center operations and enhance their sustainability.}},
author = {{Gonzalez, David Zapata and Meyer, Marcel and Müller, Oliver}},
booktitle = {{ACM SIGEnergy Energy Informatics Review}},
issn = {{2770-5331}},
number = {{2}},
pages = {{4--10}},
publisher = {{Association for Computing Machinery (ACM)}},
title = {{{Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study}}},
doi = {{10.1145/3757892.3757893}},
volume = {{5}},
year = {{2025}},
}
@unpublished{63403,
abstract = {{Stateful signatures like the NIST standardized signature schemes LMS and XMSS provide an efficient and mature realization of post-quantum secure signature schemes. They are recommended for long-term use cases like e.g. firmware signing. However, stateful signature schemes require to properly manage a so-called state. In stateful signature schemes like LMS and XMSS, signing keys consist of a set of keys of a one-time signature scheme and it has to be guaranteed that each one-time key is used only once. This is done by updating a state in each signature computation, basically recording which one-time keys have already been used. While this is straightforward in centralized systems, in distributed systems like secure enclaves consisting of e.g. multiple hardware security modules (HSMs) with limited communication keeping a distributed state that at any point in time is consistent among all parties involved presents a challenge. This challenge is not addressed by the current standardization processes.
In this paper we present a security model for the distributed key management of post-quantum secure stateful signatures like XMSS and LMS. We also present a simple, efficient, and easy to implement protocol proven secure in this security model, i.e. the protocol guarantees at any point in time a consistent state among the parties in a distributed system, like a distributed security enclave. The security model is defined in the universal composabilty (UC) framework by Ran Canetti by providing an ideal functionality for the distributed key management for stateful signatures. Hence our protocol remains secure even if arbitrarily composed with other instances of the same or other protocols, a necessity for the security of distributed key management protocols. Our main application are security enclaves consisting of HSMs, but the model and the protocol can easily be adapted to other scenarios of distributed key management of stateful signature schemes.}},
author = {{Blömer, Johannes and Bröcher, Henrik and Krummel, Volker and Porzenheim, Laurens Alexander}},
keywords = {{distributed state, hash-based signature, stateful hash-based signature, universal composability, secure enclave}},
pages = {{22}},
title = {{{Secure Distributed State Management for Stateful Signatures with a Practical and Universally Composable Protocol}}},
year = {{2025}},
}
@inproceedings{59091,
abstract = {{Abstract. Liquid Metal Embrittlement (LME) cracking is a well-documented issue encountered during resistance spot welding (RSW) of zinc-coated advanced high-strength steels (AHSS) in automotive manufacturing. Given that existing research has predominantly focused on laboratory-scale samples and lacks investigation into the load-bearing capacity of joints under crash conditions, this study aims to fill these gaps by analyzing third-generation zinc-coated AHSS. S-Rail components were produced through stamping to replicate real-world manufacturing conditions and geometries of automotive parts. To account for the disturbances typically encountered in production, samples with LME cracks were intentionally fabricated. Subsequently, a modified three-point bending test, assisted by numerical simulations, was developed to effectively apply loads to the weld spots of the S-Rail components. Results from crash tests demonstrated that observed light crack severity does not significantly compromise the joint's load-bearing capacity or lead to earlier joint failure.}},
author = {{Yang, Keke and Biegler, Max and Happe, Linus and Striewe, Marius and Olfert, Viktoria and Hein, David and Rethmeier, Michael and Meschut, Gerson}},
booktitle = {{Materials Research Proceedings}},
issn = {{2474-395X}},
publisher = {{Materials Research Forum LLC}},
title = {{{Influence of Liquid metal embrittlement on load-bearing capacity of resistance spot welds under crash loads: A study based on S-Rail components}}},
doi = {{10.21741/9781644903551-42}},
volume = {{52}},
year = {{2025}},
}
@inproceedings{60604,
abstract = {{Abstract. In the field of online condition monitoring, non-destructive testing methods using active acoustic testing [1] emerged as innovative tools. These techniques are particularly effective because damage in joined structures leads to significant changes in their vibrational characteristics. However, the consistent use of online condition monitoring through active acoustic testing combined with complex pattern recognition for early crack detection in joined components has not yet been fully established. This research aims to develop an online crack detection system employing pattern recognition techniques under cyclic loading during fatigue tests, utilizing non-contact active acoustic testing with laser vibrometry. Due to the wide range of materials that can be joined, mechanical joining processes can be used in many different industry branches. Self-pierce riveting (SPR), in particular, is a well-established joining process. Therefore, the investigations for online crack detection initially focus on SPR joints. To achieve this, the fatigue behavior of SPR joints in a lap-shear configuration was characterized. Experimental fatigue testing demonstrated that SPR joint failure occurs either through cracks propagating in the sheet material away from the rivet or in the rivet foot, depending on the material combination. Laser vibrometry has been successfully used as a crack detection system and has proven to be effective in detecting crack initiation in SPR joints. Cracks can be detected without contact regardless of the material combination, the damage location, the size of the damage, or the type of damage. The optimization of the crack detection system involved several key enhancements, including adjusting data acquisition to improve crack detection, incorporating principal component analysis (PCA) to reduce dimensionality, and implementing a classification model based on a global training dataset. An intuitive, problem-specific software demonstrator for analyzing the crack initiation behavior of SPR joints under cyclic loading was developed and iteratively optimized. Future work will focus on the implementation of an autoencoder network to further enhance crack detection capabilities.}},
author = {{Olfert, Viktoria and Yang, Keke and Gollnick, Maik and Krause, Jacob and Hein, David and Meschut, Gerson}},
booktitle = {{Materials Research Proceedings}},
issn = {{2474-395X}},
publisher = {{Materials Research Forum LLC}},
title = {{{Analysis of fatigue behaviour of self-piercing riveted joints under cyclic loading using laser vibrometry}}},
doi = {{10.21741/9781644903599-154}},
volume = {{54}},
year = {{2025}},
}
@inproceedings{63434,
author = {{Hoffmann, Max}},
booktitle = {{Proceedings of the Fourteenth Congress of the European Society for Research in Mathematics Education (CERME14)}},
editor = {{Bosch, Marianna and Bolondi, Giorgio and Carreira, Susana and Michael, Gaidoschik and Camilla, Spagnolo}},
keywords = {{hoffmann, reviewed, proceedings}},
title = {{{Using scriptwriting as a response format for interface tasks: Exemplary analyses in the context of symmetry}}},
year = {{2025}},
}
@article{62000,
author = {{Claes, Leander and Koch, Kevin and Friesen, Olga and Meihost, Lars}},
issn = {{2681-4617}},
journal = {{Acta Acustica}},
number = {{65}},
publisher = {{EDP Sciences}},
title = {{{Machine Learning-Supported Inverse Measurement Procedure for Broadband, Temperature Dependent Piezoelectric Material Parameters}}},
doi = {{10.1051/aacus/2025044}},
volume = {{9}},
year = {{2025}},
}
@article{54837,
author = {{Claes, Leander and Lankeit, Johannes and Winkler, Michael}},
issn = {{1793-6314}},
journal = {{Mathematical Models and Methods in Applied Sciences}},
number = {{11}},
pages = {{2465--2512}},
publisher = {{World Scientific Pub Co Pte Ltd}},
title = {{{A model for heat generation by acoustic waves in piezoelectric materials: Global large-data solutions}}},
doi = {{10.1142/s0218202525500447}},
volume = {{35}},
year = {{2025}},
}
@inproceedings{59689,
author = {{Friesen, Olga and Meihost, Lars and Koch, Kevin and Claes, Leander and Henning, Bernd}},
location = {{Copenhagen}},
title = {{{Estimation of piezoelectric material parameters under varying electric field conditions}}},
doi = {{10.71568/DASDAGA2025.078}},
year = {{2025}},
}
@article{59056,
author = {{Seeger, Karl and Genovese, Matteo and Schlüter, Alexander and Kockel, Christina and Corigliano, Orlando and Díaz Canales, Edith Benjamina and Praktiknjo, Aaron and Fragiacomo, Petronilla}},
issn = {{0360-3199}},
journal = {{International Journal of Hydrogen Energy}},
pages = {{558--576}},
publisher = {{Elsevier BV}},
title = {{{Techno-economic analysis of hydrogen and green fuels supply scenarios assessing three import routes: Canada, Chile, and Algeria to Germany}}},
doi = {{10.1016/j.ijhydene.2025.02.379}},
volume = {{116}},
year = {{2025}},
}
@article{60837,
abstract = {{In light of growing demands for resource efficiency and sustainability in vehicle engineering, the environmentally compatible separation of structural adhesive joints is gaining increasing relevance. This study presents a comparative analysis of two physically based debonding methods: the established hot-air process and a cryogenic cold process based on liquid nitrogen (LN2). The primary objective is to assess the ecological impact and process-related sustainability of both approaches.
Experimental investigations were conducted on a component-representative triple-sheet structure that simulates common automotive flange joints. Thermal input was applied either by convective heating using a hot air gun or by direct cooling through a contact-based LN2 tool. The resulting temperature profiles were recorded using spatially distributed thermocouples. Subsequently, the outer panel was selectively debonded to replicate a repair scenario, and the mechanical integrity of the remaining adhesive joint was evaluated through Mode I testing of L-shaped specimens. Process data served as input for an Life Cycle Assessment (LCA) according to DIN EN ISO 14040.
The cryogenic method achieved a 40% reduction in carbon footprint compared to the hot-air process (0.337 kg vs. 0.559 kg CO2-equivalents), primarily due to its shorter process time and more efficient heat transfer. While the hot-air method’s impact is mainly driven by electrical energy use, that of the cold method stems from cryogenic media consumption. Notwithstanding certain disadvantages in specific impact categories, the LN2-based process exhibits a superior overall ecological performance and signifies a promising solution for repair- and recycling-oriented adhesive separation in structural vehicle applications.}},
author = {{Jordan, Alex and Hermelingmeier, Lucas and Gilich, Julian and Meschut, Gerson and De Santis, Marco Sebastian and Schlüter, Alexander}},
issn = {{2666-3309}},
journal = {{Journal of Advanced Joining Processes}},
keywords = {{Sustainable debonding, Structural adhesives, Sustainable joining technologies, Life Cycle Assessment (LCA), Automotive repair process, Economically efficient debonding}},
publisher = {{Elsevier}},
title = {{{Comparison of the economic efficiency and sustainability of two debonding processes for structurally bonded sills}}},
doi = {{10.1016/j.jajp.2025.100332}},
volume = {{12}},
year = {{2025}},
}
@techreport{63209,
abstract = {{Die DFG-Projekte AddFeRo-PM (406108415) und AddFeRo-SR (465089065) untersuchten die Potenziale des LB-PBF/M-Verfahrens zur Herstellung von Rotoren für unterschiedliche elektrische Maschinen. Im interdisziplinären Ansatz wurden Materialentwicklung und mechanische sowie elektromagnetische Optimierung verbunden. Im Projekt „AddFeRo-PM“ wurde der Rotor einer permanentmagneterregten Synchron- maschine (PMSM) untersucht. FeSi erwies sich als geeignete Legierung, konnte aber wegen Spannungsrissen nur bis zu 3 % Siliziumanteil (kurz: FeSi3) verarbeitet werden. Mechanische und elektromagnetische Untersuchungen ermöglichten eine 3D-Optimierung der Rotorgeometrie und -struktur. Der Demonstrator wurde additiv gefertigt und zeigt Leicht-baupotenziale sowie reduzierte Drehmomentwelligkeit. Im Folgeprojekt „AddFeRo-SR“ kam eine Hochtemperatur-Bauraumheizung (HTBH) zum Einsatz, die FeSi mit 6,5 % Siliziumanteil verarbeitbar machte, welches bessere elektro- magnetische Eigenschaften bietet. Sie wurde bei einer Synchron-Reluktanzmaschine (SynRM) getestet. Eine hybride Rotorfertigung erwies sich jedoch aufgrund von HTBH-Einschränkungen als ungeeignet, weshalb eine einteilige Fertigung mit FeSi3 umgesetzt wurde. Experimente bestätigten vergleichbare Betriebsergebnisse zur konventionellen Fertigung bei reduzierter Rotormasse. Zusätzlich wurde eine Methodik entwickelt, um additive Verfahren als Ergänzung zur konventionellen Fertigung zu integrieren. Beide Projekte zeigen das Potenzial additiver Fertigung für Leichtbau und Wirkungsgradsteigerung im Elektromaschinenbau und bieten wertvolle Grundlagen für industrielle Anwendungen.}},
author = {{Haase, Michael and Behrendt, Marius and Hengsbach, Florian and Kunnathully Sathees Kumar, Vinay and Magerkohl, Sebastian and Magyar, Balázs and Ponick, Bernd and Schaper, Mirko and Zimmer, Detmar}},
keywords = {{Additive Fertigung, Elektromotor, Leichtbau, Synchronmotor, DFG}},
publisher = {{Technische Informationsbibliothek}},
title = {{{Additive Fertigung im Elektromaschinenbau: Erforschung von Potentialen der additiven Fertigung in Rotoren permanentmagneterregter Synchronmaschinen}}},
doi = {{10.34657/26753}},
year = {{2025}},
}