@inproceedings{20652, author = {{Aßmuth, Verena and Teutenberg, Dominik and Meschut, Gerson and Philipp, Jens and Stammen, Elisabeth and Dilger, Klaus}}, booktitle = {{20. Kolloquium Gemeinsame Forschung in der Klebtechnik}}, location = {{Würzburg}}, pages = {{85--87}}, title = {{{Lokales Konzept zur Auslegung von elastischen Klebverbindungen (LoKAl)}}}, year = {{2020}}, } @inproceedings{42892, abstract = {{This paper presents the results of static short-term and long-term tensile tests for beta-nucleated joined polypropylene samples by the hot plate welding process. In the present study different dimensionless joining displacements are accounted for. The results show that high short-term tensile strength does not directly transfer to high long-term tensile strength. The morphology of the weld seam in the joined samples is examined by means of transmitted and reflected light microscopy. For the dimensionless joining displacements of 0.75 and 0.95, stretched spherulites are obtained. X-Ray diffraction can be used as a tool for qualitative and quantitative analysis and eventually for differentiation of samples of various joining displacements.}}, author = {{Wübbeke, Andrea and Schöppner, Volker and Paul, André and Tiemann, Michael and Austermeier, Laura and Fitze, Marcus and Chen, Mingie and Jakob, Fabian and Heim, Hans-Peter and Wu, Tao and Niendorf, Thomas and Röhricht, Marie-Luise and Schmidt, Michael}}, booktitle = {{SPE ANTEC 2020: The Virtual Edition 5 }}, title = {{{Long- and Short-Term Tensile Strength and Morphology of Joined Beta-Nucleated Polypropylene Parts}}}, year = {{2020}}, } @inproceedings{24472, author = {{Schöppner, Volker and Wübbeke, Andrea and Paul, A. and Tiemann, M. and Austermeier, Laura and Fitze, M. and Chen, M. and Jakob, F. and Heim, H.-P- and Wu, T. and Niendorf, T. and Röhricht, M.-L. and Schmidt, M.}}, booktitle = {{ANTEC20}}, location = {{Texas (USA)}}, title = {{{LONG- AND SHORT-TERM TENSILE STRENGTH AND MORPHOLOGY OF JOINED BETA-NUCLEATED POLYPROPYLENE PARTS}}}, year = {{2020}}, } @article{26526, author = {{Gappa, Monika and Filipiak‐Pittroff, Birgit and Libuda, Lars and Berg, Andrea and Koletzko, Sibylle and Bauer, Carl‐Peter and Heinrich, Joachim and Schikowski, Tamara and Berdel, Dietrich and Standl, Marie}}, issn = {{0105-4538}}, journal = {{Allergy}}, pages = {{1903--1907}}, title = {{{Long‐term effects of hydrolyzed formulae on atopic diseases in the GINI study}}}, doi = {{10.1111/all.14709}}, year = {{2020}}, } @misc{37507, author = {{Weber, Jutta and Pentenrieder, Annelie}}, booktitle = {{Technikanthropologie. Handbuch für Wissenschaft und Studium}}, editor = {{Heßler, Martina and Liggieri, Kevin}}, pages = {{215--225}}, publisher = {{Nomos}}, title = {{{Lucy Suchman}}}, year = {{2020}}, } @inbook{31732, author = {{Willeke, Stephanie}}, booktitle = {{Vormärz-Handbuch}}, editor = {{Eke, Norbert Otto }}, pages = {{209--227}}, publisher = {{Aisthesis }}, title = {{{Ludwig Tieck}}}, year = {{2020}}, } @inbook{49832, author = {{Diedrich, Alena}}, booktitle = {{Handbuch Vormärz}}, editor = {{Eke, Norbert Otto }}, publisher = {{Aisthesis}}, title = {{{Lyrik im Vormärz}}}, year = {{2020}}, } @inproceedings{16219, abstract = {{Network function virtualization (NFV) proposes to replace physical middleboxes with more flexible virtual network functions (VNFs). To dynamically adjust to everchanging traffic demands, VNFs have to be instantiated and their allocated resources have to be adjusted on demand. Deciding the amount of allocated resources is non-trivial. Existing optimization approaches often assume fixed resource requirements for each VNF instance. However, this can easily lead to either waste of resources or bad service quality if too many or too few resources are allocated. To solve this problem, we train machine learning models on real VNF data, containing measurements of performance and resource requirements. For each VNF, the trained models can then accurately predict the required resources to handle a certain traffic load. We integrate these machine learning models into an algorithm for joint VNF scaling and placement and evaluate their impact on resulting VNF placements. Our evaluation based on real-world data shows that using suitable machine learning models effectively avoids over- and underallocation of resources, leading to up to 12 times lower resource consumption and better service quality with up to 4.5 times lower total delay than using standard fixed resource allocation.}}, author = {{Schneider, Stefan Balthasar and Satheeschandran, Narayanan Puthenpurayil and Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization (NetSoft)}}, location = {{Ghent, Belgium}}, publisher = {{IEEE}}, title = {{{Machine Learning for Dynamic Resource Allocation in Network Function Virtualization}}}, year = {{2020}}, } @article{20834, author = {{Webb, Mary E and Fluck, Andrew and Magenheim, Johannes and Malyn-Smith, Joyce and Waters, Juliet and Deschênes , Michelle and Zagami, Jason}}, journal = {{Educational Technology Research and Development}}, pages = {{1--22}}, publisher = {{Springer}}, title = {{{Machine learning for human learners: opportunities, issues, tensions and threats}}}, doi = {{10.1007/s11423-020-09858-2}}, year = {{2020}}, } @inbook{21391, author = {{Reinhart, Felix and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation (Technologies for Intelligent Automation)}}, editor = {{Beyerer, Jürgen and Maier, Alexander and Niggemann, Oliver}}, pages = {{25--33}}, publisher = {{Springer Vieweg, Berlin, Heidelberg}}, title = {{{Machine Learning for Process-X: A Taxonomy}}}, volume = {{11}}, year = {{2020}}, }