@inproceedings{48632, abstract = {{Digital Servitization is one of the significant trends affecting the manufacturing industry. Companies try to tackle challenges regarding their differentiation and profitability using digital services. One specific type of digital services are smart services, which are digital services built on data from smart products. Introducing these kinds of offerings into the portfolio of manufacturing companies is not trivial. Moreover, they require conscious action to align all relevant capabilities to realize the respective business goals. However, what capabilities are generally relevant for smart services remains opaque. We conducted a systematic literature review to identify them and extended the results through an interview study. Our analysis results in 78 capabilities clustered among 12 principles and six dimensions. These results provide significant support for the smart service transformation of manufacturing companies and for structuring the research field of smart services.}}, author = {{Koldewey, Christian and Fichtler, Timm and Scholtysik, Michel and Biehler, Jan and Schreiner, Nick and Sommer, Franziska and Schacht, Maximilian and Kaufmann, Jonas and Rabe, Martin and Sedlmeier, Joachim and Dumitrescu, Roman}}, keywords = {{Digital Servitization, Transformation, Capabilities, Maturity, Smart Services}}, location = {{Hawaii}}, title = {{{Exploring Capabilities for the Smart Service Transformation in Manufacturing: Insights from Theory and Practice}}}, year = {{2024}}, } @article{47275, author = {{Herbert, Franziska and Becker, Steffen and Buckmann, Annalina and Kowalewski, Marvin and Hielscher, Jonas and Acar, Yasemin and Dürmuth, Markus and Sasse, M. Angela and Zou, Yixin}}, journal = {{IEEE Symposium on Security and Privacy. IEEE, New York, NY, USA}}, title = {{{Digital Security -- A Question of Perspective. A Large-Scale Telephone Survey with Four At-Risk User Groups}}}, doi = {{10.48550/arXiv.2212.12964}}, year = {{2024}}, } @inproceedings{49354, author = {{Afroze, Lameya and Merkelbach, Silke and von Enzberg, Sebastian and Dumitrescu, Roman}}, booktitle = {{ML4CPS 2023}}, location = {{Hamburg}}, title = {{{Domain Knowledge Injection Guidance for Predictive Maintenance}}}, year = {{2024}}, } @inproceedings{49363, author = {{Scholtysik, Michel and Rohde, Malte and Koldewey, Christian and Dumitrescu, Roman}}, title = {{{Circular Product-Service-System Ideation Canvas – A Framework for the Design of circular Product-Service-System Ideas}}}, year = {{2024}}, } @inproceedings{49364, author = {{Scholtysik, Michel and Rohde, Malte and Koldewey, Christian and Dumitrescu, Roman}}, title = {{{Business strategy taxonomy and solution patterns for the circular economy}}}, year = {{2024}}, } @article{32097, author = {{Weich, Tobias and Guedes Bonthonneau, Yannick and Guillarmou, Colin}}, journal = {{Journal of Differential Geometry (to appear) -- arXiv:2103.12127}}, title = {{{SRB Measures of Anosov Actions}}}, year = {{2024}}, } @inproceedings{50287, author = {{Kruse, Stephan and Schwabe, Tobias and Kneuper, Pascal and Kurz, Heiko G. and Meinecke, March-Michael and Scheytt, Christoph}}, booktitle = {{German Microwave Conference (GeMiC) }}, title = {{{Analysis and Simulation of a Photonic Multiband FMCW Radar Sensor System using Nyquist Pulses}}}, year = {{2024}}, } @article{46469, abstract = {{We show how to learn discrete field theories from observational data of fields on a space-time lattice. For this, we train a neural network model of a discrete Lagrangian density such that the discrete Euler--Lagrange equations are consistent with the given training data. We, thus, obtain a structure-preserving machine learning architecture. Lagrangian densities are not uniquely defined by the solutions of a field theory. We introduce a technique to derive regularisers for the training process which optimise numerical regularity of the discrete field theory. Minimisation of the regularisers guarantees that close to the training data the discrete field theory behaves robust and efficient when used in numerical simulations. Further, we show how to identify structurally simple solutions of the underlying continuous field theory such as travelling waves. This is possible even when travelling waves are not present in the training data. This is compared to data-driven model order reduction based approaches, which struggle to identify suitable latent spaces containing structurally simple solutions when these are not present in the training data. Ideas are demonstrated on examples based on the wave equation and the Schrödinger equation. }}, author = {{Offen, Christian and Ober-Blöbaum, Sina}}, issn = {{1054-1500}}, journal = {{Chaos}}, number = {{1}}, publisher = {{AIP Publishing}}, title = {{{Learning of discrete models of variational PDEs from data}}}, doi = {{10.1063/5.0172287}}, volume = {{34}}, year = {{2024}}, } @inproceedings{50476, author = {{Krings, Sarah Claudia and Yigitbas, Enes}}, booktitle = {{Proceedings of the 16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2024) (to appear)}}, publisher = {{ACM}}, title = {{{TARPS: A Toolbox for Enhancing Privacy and Security for Collaborative AR}}}, year = {{2024}}, } @inbook{50554, author = {{Prediger, Susanne and Wessel, Lena}}, booktitle = {{Berufs-und Fachsprache Deutsch in Wissenschaft und Praxis}}, editor = {{Efing, Christian and Kalkavan-Aydin, Zeynep}}, isbn = {{978-3-11-074544-3}}, pages = {{363--372}}, publisher = {{DE GRUYTER}}, title = {{{31 Sprachbildung im berufsbezogenen Mathematikunterricht.}}}, volume = {{Band 3}}, year = {{2024}}, }