@inproceedings{63011,
  author       = {{Wedekind, Lisa and Pollmeier, Pascal and Fechner, Sabine}},
  booktitle    = {{Entdecken, lehren und forschen im Schülerlabor}},
  editor       = {{van Vorst, Helena}},
  location     = {{Bochum}},
  pages        = {{145--148}},
  title        = {{{Analogiebildung in kontextorientierten Lernumgebungen}}},
  volume       = {{45}},
  year         = {{2025}},
}

@article{62269,
  abstract     = {{The titanium in-diffused lithium niobate waveguide platform is well-established for reliable prototyping and packaging of many quantum photonic components at room temperature. Nevertheless, compatibility with certain quantum light sources and superconducting detectors requires operation under cryogenic conditions. We characterize alterations in phase-matching and mode guiding of a non-degenerate spontaneous parametric down-conversion process emitting around 1556 nm and 950 nm, under cryogenic conditions. Despite the effects of pyroelectricity and photorefraction, the spectral properties match our theoretical model. Nevertheless, these effects cause small but significant variations within and between cooling cycles. These measurements provide a first benchmark against which other nonlinear photonic integration platforms, such as thin-film lithium niobate, can be compared.}},
  author       = {{Lange, Nina Amelie and Lengeling, Sebastian and Mues, Philipp and Quiring, Viktor and Ridder, Werner and Eigner, Christof and Herrmann, Harald and Silberhorn, Christine and Bartley, Tim}},
  issn         = {{1094-4087}},
  journal      = {{Optics Express}},
  number       = {{24}},
  publisher    = {{Optica Publishing Group}},
  title        = {{{Widely non-degenerate nonlinear frequency conversion in cryogenic titanium in-diffused lithium niobate waveguides}}},
  doi          = {{10.1364/oe.578108}},
  volume       = {{33}},
  year         = {{2025}},
}

@inproceedings{63034,
  author       = {{Stallmeister, Lea and Rezat, Sebastian}},
  booktitle    = {{Proceedings of the Fifth International Conference on Mathematics Textbook Research and Development}},
  editor       = {{Pepin, Birgit and Kohanová, Iveta and Langfeldt, Marit Buset}},
  isbn         = {{978-82-691902-2-9}},
  location     = {{Trondheim, Norway}},
  publisher    = {{Norwegian University of Science and Technology}},
  title        = {{{Students’ use of different material resources for specific purposes in the process of learning mathematics}}},
  year         = {{2025}},
}

@article{63072,
  abstract     = {{<jats:p>Titanium alloys are widely employed for biomedical implants due to their high strength, biocompatibility, and corrosion resistance, yet their lack of intrinsic antibacterial activity remains a major limitation. Incorporating copper, an antibacterial and β-stabilising element, offers a promising strategy to enhance implant performance. This study investigates Ti-6Al-7Nb modified with 1–9 wt.% Cu via in situ alloying during metal-based laser powder bed fusion (PBF-LB/M), with the aim of assessing processability, microstructural evolution, and mechanical properties. Highly dense samples (&gt;99.9%) were produced across all Cu levels, though chemical homogeneity strongly depended on processing parameters. Increasing Cu content promoted β-phase stabilisation, Ti2Cu precipitation, and pronounced grain refinement. Hardness and yield strength increased nearly linearly with Cu addition, while ductility decreased sharply at ≥5 wt.% Cu due to intermetallic formation, hot cracking, and brittle fracture. These results illustrate both the opportunities and constraints of rapid alloy screening via PBF-LB/M. Overall, moderate Cu additions of 1–3 wt.% provide the most favourable balance between mechanical performance, manufacturability, and potential antibacterial functionality. These findings provide a clear guideline for the design of Cu-functionalised titanium implants and demonstrate the efficiency of in situ alloy screening for accelerated materials development.</jats:p>}},
  author       = {{Steinmeier, Paul and Hoyer, Kay-Peter and Lopes Dias, Nelson Filipe and Zielke, Reiner and Tillmann, Wolfgang and Schaper, Mirko}},
  issn         = {{2073-4352}},
  journal      = {{Crystals}},
  keywords     = {{Biomaterial, In Situ Alloying, Titanium, Additive Manufacturing}},
  number       = {{12}},
  publisher    = {{MDPI AG}},
  title        = {{{In Situ Alloying of Ti-6Al-7Nb with Copper Using Laser Powder Bed Fusion}}},
  doi          = {{10.3390/cryst15121053}},
  volume       = {{15}},
  year         = {{2025}},
}

@inproceedings{61427,
  abstract     = {{The carbon footprint of modern vehicles and their mechatronic systems is more
important than ever. Research by the publicly funded Nalyses project and the HELLA
company shows that the headlamps use phase makes a significant contribution to the life
cycle footprint taking into account the current electricity mix [1]. Today, functionalities
such as adaptive curve light or glare-free high beam ensure comfort and safety by
assessing the state of the vehicle and evaluating the driving scenario ahead. In future,
this evaluation will be expanded and used to adapt the headlamp to the driving scenario
in such a way that as little light as possible is emitted, but as much light as necessary. In
order to achieve this goal, an overall evaluation of the regulatory compliant energy
saving potential is crucial in a first step and leads to constraints for a dynamic adaption
while driving. In this paper, the potential is illustrated by evaluating UNECE Regulation
No. 149 and optimizing luminous intensity distributions. Depending on the different
resolutions of matrix LED headlamps, this approach can result in a significantly lower
luminous flux. On the other hand, the results are point-like distributions that raise the
question of whether the regulation still provides for sensible minimum requirements for
modern matrix LED headlamps. The results are further presented in a simulated virtual
environment with regard to the resulting luminance in different driving scenarios. We
then present an approach to integrate regulatory requirements into a control algorithm by
setting optimization constraints and saturating the control. Finally, we classify the found
luminous intensity distributions qualitatively according to common lighting criteria. In summary, although the investigated minimum distributions are by no means desirable
for drivers themselves, they form the basis on which energy-saving distributions for
illuminated areas and twilight scenarios could be adaptively controlled in the future.}},
  author       = {{Fittkau, Niklas and Bußemas, Leon and Malena, Kevin and Gausemeier, Sandra and Trächtler, Ansgar}},
  booktitle    = {{Proceedings of the 16th International Symposium on Automotive Lighting 2025}},
  location     = {{Darmstadt}},
  title        = {{{Regulatory-compliant energy-saving potential for the passing beam of matrix LED headlamps}}},
  doi          = {{10.26083/tuprints-00030840}},
  year         = {{2025}},
}

@article{60466,
  author       = {{Brockmeier, Julian and Schapeler, Timon and Lange, Nina Amelie and Höpker, Jan Philipp and Herrmann, Harald and Silberhorn, Christine and Bartley, Tim}},
  journal      = {{New Journal of Physics}},
  title        = {{{Harnessing temporal dispersion for integrated pump filtering in spontaneous heralded single-photon generation processes}}},
  doi          = {{10.1088/1367-2630/ade46c}},
  year         = {{2025}},
}

@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     = {{<jats:p>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.</jats:p>}},
  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     = {{<jats:p>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.</jats:p>}},
  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}},
}