[{"publication":"Chemosensors","file":[{"relation":"main_file","success":1,"content_type":"application/pdf","file_name":"chemosensors-12-00178.pdf","access_level":"closed","file_id":"56000","file_size":3275869,"date_created":"2024-09-03T13:58:18Z","creator":"cweinber","date_updated":"2024-09-03T13:58:18Z"}],"abstract":[{"lang":"eng","text":"Clean hydrogen is a key aspect of carbon neutrality, necessitating robust methods for monitoring hydrogen concentration in critical infrastructures like pipelines or power plants. While semiconducting metal oxides such as In2O3 can monitor gas concentrations down to the ppm range, they often exhibit cross-sensitivity to other gases like H2O. In this study, we investigated whether cyclic optical illumination of a gas-sensitive In2O3 layer creates identifiable changes in a gas sensor´s electronic resistance that can be linked to H2 and H2O concentrations via machine learning. We exposed nanostructured In2O3 with a large surface area of 95 m2 g-1 to H2 concentrations (0-800 ppm) and relative humidity (0-70%) under cyclic activation utilizing blue light. The sensors were tested for 20 classes of gas combinations. A support vector machine achieved classification rates up to 92.0%, with reliable reproducibility (88.2 ± 2.7%) across five individual sensors using 10-fold cross-validation. Our findings suggest that cyclic optical activation can be used as a tool to classify H2 and H2O concentrations."}],"language":[{"iso":"eng"}],"keyword":["resistive gas sensor","chemiresistor","semiconductor","metal oxide","In2O3","mesoporous","hydrogen","humidtiy","machine learning","sustainable"],"ddc":["540"],"issue":"9","quality_controlled":"1","year":"2024","date_created":"2024-09-03T13:49:42Z","publisher":"MDPI","title":"Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O","type":"journal_article","status":"public","department":[{"_id":"2"},{"_id":"307"}],"user_id":"11848","_id":"55999","file_date_updated":"2024-09-03T13:58:18Z","article_type":"original","publication_identifier":{"issn":["2227-9040"]},"has_accepted_license":"1","publication_status":"published","intvolume":"        12","page":"178","citation":{"bibtex":"@article{Baier_Krüger_Wagner_Tiemann_Weinberger_2024, title={Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O}, volume={12}, DOI={<a href=\"https://doi.org/10.3390/chemosensors12090178\">10.3390/chemosensors12090178</a>}, number={9}, journal={Chemosensors}, publisher={MDPI}, author={Baier, Dominik  and Krüger, Alexander  and Wagner, Thorsten  and Tiemann, Michael and Weinberger, Christian}, year={2024}, pages={178} }","short":"D. Baier, A. Krüger, T. Wagner, M. Tiemann, C. Weinberger, Chemosensors 12 (2024) 178.","mla":"Baier, Dominik, et al. “Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O.” <i>Chemosensors</i>, vol. 12, no. 9, MDPI, 2024, p. 178, doi:<a href=\"https://doi.org/10.3390/chemosensors12090178\">10.3390/chemosensors12090178</a>.","apa":"Baier, D., Krüger, A., Wagner, T., Tiemann, M., &#38; Weinberger, C. (2024). Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O. <i>Chemosensors</i>, <i>12</i>(9), 178. <a href=\"https://doi.org/10.3390/chemosensors12090178\">https://doi.org/10.3390/chemosensors12090178</a>","chicago":"Baier, Dominik , Alexander  Krüger, Thorsten  Wagner, Michael Tiemann, and Christian Weinberger. “Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O.” <i>Chemosensors</i> 12, no. 9 (2024): 178. <a href=\"https://doi.org/10.3390/chemosensors12090178\">https://doi.org/10.3390/chemosensors12090178</a>.","ieee":"D. Baier, A. Krüger, T. Wagner, M. Tiemann, and C. Weinberger, “Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O,” <i>Chemosensors</i>, vol. 12, no. 9, p. 178, 2024, doi: <a href=\"https://doi.org/10.3390/chemosensors12090178\">10.3390/chemosensors12090178</a>.","ama":"Baier D, Krüger A, Wagner T, Tiemann M, Weinberger C. Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O. <i>Chemosensors</i>. 2024;12(9):178. doi:<a href=\"https://doi.org/10.3390/chemosensors12090178\">10.3390/chemosensors12090178</a>"},"volume":12,"author":[{"first_name":"Dominik ","full_name":"Baier, Dominik ","last_name":"Baier"},{"first_name":"Alexander ","last_name":"Krüger","full_name":"Krüger, Alexander "},{"first_name":"Thorsten ","last_name":"Wagner","full_name":"Wagner, Thorsten "},{"first_name":"Michael","id":"23547","full_name":"Tiemann, Michael","orcid":"0000-0003-1711-2722","last_name":"Tiemann"},{"id":"11848","full_name":"Weinberger, Christian","last_name":"Weinberger","first_name":"Christian"}],"date_updated":"2025-11-26T12:14:21Z","oa":"1","doi":"10.3390/chemosensors12090178","main_file_link":[{"open_access":"1","url":"https://www.mdpi.com/2227-9040/12/9/178"}]},{"abstract":[{"text":"Hass im Netz ist nicht nur ein Problem, sondern ermöglicht es einer bürgerlichen Position, hegemoniale Ansprüche zu bekräftigen und zu legitimieren. Der Text geht somit dem nach, was im Gegensatz zum abzulehnenden Diskurs als ‚gut‘ gezeichnet wird. In diesem Gegensatz steht die bürgerliche Welt des Etablierten/Status quo als Ort, in dem der ‚gute‘ Diskurs bereits erfüllt ist. Sie dient sich somit als Standard und Vorbild für die sozialen Medien an und kann dabei gesellschaftliche Kräfte mobilisieren, indem sie sich als bedroht und schützenswert darstellt.","lang":"ger"}],"status":"public","type":"journal_article","publication":"merzWissenschaft","article_type":"original","language":[{"iso":"ger"}],"_id":"56020","user_id":"89772","year":"2024","citation":{"ieee":"S. Althoff, “Hass im Netz und die Konstruktion des ‘guten’ Diskurses: Eine machtkritische Analyse,” <i>merzWissenschaft</i>, vol. 68, no. 6, pp. 113–125, 2024, doi: <a href=\"https://doi.org/10.21240/merz/2024.6.10\">https://doi.org/10.21240/merz/2024.6.10</a>.","chicago":"Althoff, Sebastian. “Hass im Netz und die Konstruktion des ‘guten’ Diskurses: Eine machtkritische Analyse.” <i>merzWissenschaft</i> 68, no. 6 (2024): 113–25. <a href=\"https://doi.org/10.21240/merz/2024.6.10\">https://doi.org/10.21240/merz/2024.6.10</a>.","ama":"Althoff S. Hass im Netz und die Konstruktion des “guten” Diskurses: Eine machtkritische Analyse. <i>merzWissenschaft</i>. 2024;68(6):113-125. doi:<a href=\"https://doi.org/10.21240/merz/2024.6.10\">https://doi.org/10.21240/merz/2024.6.10</a>","mla":"Althoff, Sebastian. “Hass im Netz und die Konstruktion des ‘guten’ Diskurses: Eine machtkritische Analyse.” <i>merzWissenschaft</i>, vol. 68, no. 6, 2024, pp. 113–25, doi:<a href=\"https://doi.org/10.21240/merz/2024.6.10\">https://doi.org/10.21240/merz/2024.6.10</a>.","bibtex":"@article{Althoff_2024, title={Hass im Netz und die Konstruktion des “guten” Diskurses: Eine machtkritische Analyse}, volume={68}, DOI={<a href=\"https://doi.org/10.21240/merz/2024.6.10\">https://doi.org/10.21240/merz/2024.6.10</a>}, number={6}, journal={merzWissenschaft}, author={Althoff, Sebastian}, year={2024}, pages={113–125} }","short":"S. Althoff, merzWissenschaft 68 (2024) 113–125.","apa":"Althoff, S. (2024). Hass im Netz und die Konstruktion des “guten” Diskurses: Eine machtkritische Analyse. <i>merzWissenschaft</i>, <i>68</i>(6), 113–125. <a href=\"https://doi.org/10.21240/merz/2024.6.10\">https://doi.org/10.21240/merz/2024.6.10</a>"},"page":"113-125","intvolume":"        68","publication_status":"published","issue":"6","title":"Hass im Netz und die Konstruktion des 'guten' Diskurses: Eine machtkritische Analyse","main_file_link":[{"url":"https://www.merz-zeitschrift.de/article/view/2820/2872","open_access":"1"}],"doi":"https://doi.org/10.21240/merz/2024.6.10","date_updated":"2025-11-27T09:52:55Z","oa":"1","author":[{"first_name":"Sebastian","last_name":"Althoff","full_name":"Althoff, Sebastian","id":"89772"}],"date_created":"2024-09-04T12:56:23Z","volume":68},{"language":[{"iso":"eng"}],"user_id":"39217","department":[{"_id":"58"}],"_id":"59223","status":"public","type":"misc","doi":"10.5281/zenodo.15114897","title":"Precise optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter presentation 2024 SPP 2111 ","author":[{"first_name":"Tobias","full_name":"Schwabe, Tobias","id":"39217","last_name":"Schwabe"},{"first_name":"Khaleda","full_name":"Mallick, Khaleda","last_name":"Mallick"},{"first_name":"Karanveer","full_name":"Singh, Karanveer","last_name":"Singh"},{"full_name":"Schneider, Thomas","last_name":"Schneider","first_name":"Thomas"},{"orcid":"0000-0002-5950-6618 ","last_name":"Scheytt","id":"37144","full_name":"Scheytt, J. Christoph","first_name":"J. Christoph"}],"date_created":"2025-04-01T07:36:59Z","publisher":"Zenodo","date_updated":"2025-11-27T08:51:56Z","citation":{"chicago":"Schwabe, Tobias, Khaleda Mallick, Karanveer Singh, Thomas Schneider, and J. Christoph Scheytt. <i>Precise Optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter Presentation 2024 SPP 2111 </i>. Zenodo, 2024. <a href=\"https://doi.org/10.5281/zenodo.15114897\">https://doi.org/10.5281/zenodo.15114897</a>.","ieee":"T. Schwabe, K. Mallick, K. Singh, T. Schneider, and J. C. Scheytt, <i>Precise optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter presentation 2024 SPP 2111 </i>. Zenodo, 2024.","ama":"Schwabe T, Mallick K, Singh K, Schneider T, Scheytt JC. <i>Precise Optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter Presentation 2024 SPP 2111 </i>. Zenodo; 2024. doi:<a href=\"https://doi.org/10.5281/zenodo.15114897\">10.5281/zenodo.15114897</a>","mla":"Schwabe, Tobias, et al. <i>Precise Optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter Presentation 2024 SPP 2111 </i>. Zenodo, 2024, doi:<a href=\"https://doi.org/10.5281/zenodo.15114897\">10.5281/zenodo.15114897</a>.","bibtex":"@book{Schwabe_Mallick_Singh_Schneider_Scheytt_2024, title={Precise optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter presentation 2024 SPP 2111 }, DOI={<a href=\"https://doi.org/10.5281/zenodo.15114897\">10.5281/zenodo.15114897</a>}, publisher={Zenodo}, author={Schwabe, Tobias and Mallick, Khaleda and Singh, Karanveer and Schneider, Thomas and Scheytt, J. Christoph}, year={2024} }","short":"T. Schwabe, K. Mallick, K. Singh, T. Schneider, J.C. Scheytt, Precise Optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter Presentation 2024 SPP 2111 , Zenodo, 2024.","apa":"Schwabe, T., Mallick, K., Singh, K., Schneider, T., &#38; Scheytt, J. C. (2024). <i>Precise optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter presentation 2024 SPP 2111 </i>. Zenodo. <a href=\"https://doi.org/10.5281/zenodo.15114897\">https://doi.org/10.5281/zenodo.15114897</a>"},"year":"2024"},{"language":[{"iso":"eng"}],"department":[{"_id":"58"}],"user_id":"39217","_id":"59224","status":"public","type":"misc","doi":"10.5281/zenodo.15114631","title":"Precise optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter (PONyDAC II) 2024 SPP 2111 ","author":[{"first_name":"Tobias","full_name":"Schwabe, Tobias","id":"39217","last_name":"Schwabe"},{"full_name":"Singh, Karanveer","last_name":"Singh","first_name":"Karanveer"},{"last_name":"Schneider","full_name":"Schneider, Thomas","first_name":"Thomas"},{"orcid":"0000-0002-5950-6618 ","last_name":"Scheytt","id":"37144","full_name":"Scheytt, J. Christoph","first_name":"J. Christoph"}],"date_created":"2025-04-01T07:38:40Z","date_updated":"2025-11-27T08:52:52Z","publisher":"Zenodo","citation":{"apa":"Schwabe, T., Singh, K., Schneider, T., &#38; Scheytt, J. C. (2024). <i>Precise optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter (PONyDAC II) 2024 SPP 2111 </i>. Zenodo. <a href=\"https://doi.org/10.5281/zenodo.15114631\">https://doi.org/10.5281/zenodo.15114631</a>","bibtex":"@book{Schwabe_Singh_Schneider_Scheytt_2024, title={Precise optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter (PONyDAC II) 2024 SPP 2111 }, DOI={<a href=\"https://doi.org/10.5281/zenodo.15114631\">10.5281/zenodo.15114631</a>}, publisher={Zenodo}, author={Schwabe, Tobias and Singh, Karanveer and Schneider, Thomas and Scheytt, J. Christoph}, year={2024} }","short":"T. Schwabe, K. Singh, T. Schneider, J.C. Scheytt, Precise Optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter (PONyDAC II) 2024 SPP 2111 , Zenodo, 2024.","mla":"Schwabe, Tobias, et al. <i>Precise Optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter (PONyDAC II) 2024 SPP 2111 </i>. Zenodo, 2024, doi:<a href=\"https://doi.org/10.5281/zenodo.15114631\">10.5281/zenodo.15114631</a>.","ama":"Schwabe T, Singh K, Schneider T, Scheytt JC. <i>Precise Optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter (PONyDAC II) 2024 SPP 2111 </i>. Zenodo; 2024. doi:<a href=\"https://doi.org/10.5281/zenodo.15114631\">10.5281/zenodo.15114631</a>","chicago":"Schwabe, Tobias, Karanveer Singh, Thomas Schneider, and J. Christoph Scheytt. <i>Precise Optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter (PONyDAC II) 2024 SPP 2111 </i>. Zenodo, 2024. <a href=\"https://doi.org/10.5281/zenodo.15114631\">https://doi.org/10.5281/zenodo.15114631</a>.","ieee":"T. Schwabe, K. Singh, T. Schneider, and J. C. Scheytt, <i>Precise optical Nyquist Pulse Synthesizer Digital- to-Analog-Converter (PONyDAC II) 2024 SPP 2111 </i>. Zenodo, 2024."},"year":"2024"},{"citation":{"ieee":"V. Surendranath Shroff, M. Bahmanian, S. Kruse, and J. C. Scheytt, “Design of an Ultra-Low Phase Noise Broadband Amplifier in 130 nm SiGe BiCMOS Technology,” presented at the 2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS) , Fort Lauderdale, Florida, 2024, doi: <a href=\"https://doi.org/10.1109/BCICTS59662.2024.10745663\">10.1109/BCICTS59662.2024.10745663</a>.","chicago":"Surendranath Shroff, Vijayalakshmi, Meysam Bahmanian, Stephan Kruse, and J. Christoph Scheytt. “Design of an Ultra-Low Phase Noise Broadband Amplifier in 130 Nm SiGe BiCMOS Technology.” In <i>2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS) </i>. IEEE, 2024. <a href=\"https://doi.org/10.1109/BCICTS59662.2024.10745663\">https://doi.org/10.1109/BCICTS59662.2024.10745663</a>.","ama":"Surendranath Shroff V, Bahmanian M, Kruse S, Scheytt JC. Design of an Ultra-Low Phase Noise Broadband Amplifier in 130 nm SiGe BiCMOS Technology. In: <i>2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS) </i>. IEEE; 2024. doi:<a href=\"https://doi.org/10.1109/BCICTS59662.2024.10745663\">10.1109/BCICTS59662.2024.10745663</a>","bibtex":"@inproceedings{Surendranath Shroff_Bahmanian_Kruse_Scheytt_2024, title={Design of an Ultra-Low Phase Noise Broadband Amplifier in 130 nm SiGe BiCMOS Technology}, DOI={<a href=\"https://doi.org/10.1109/BCICTS59662.2024.10745663\">10.1109/BCICTS59662.2024.10745663</a>}, booktitle={2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS) }, publisher={IEEE}, author={Surendranath Shroff, Vijayalakshmi and Bahmanian, Meysam and Kruse, Stephan and Scheytt, J. Christoph}, year={2024} }","mla":"Surendranath Shroff, Vijayalakshmi, et al. “Design of an Ultra-Low Phase Noise Broadband Amplifier in 130 Nm SiGe BiCMOS Technology.” <i>2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS) </i>, IEEE, 2024, doi:<a href=\"https://doi.org/10.1109/BCICTS59662.2024.10745663\">10.1109/BCICTS59662.2024.10745663</a>.","short":"V. Surendranath Shroff, M. Bahmanian, S. Kruse, J.C. Scheytt, in: 2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS) , IEEE, 2024.","apa":"Surendranath Shroff, V., Bahmanian, M., Kruse, S., &#38; Scheytt, J. C. (2024). Design of an Ultra-Low Phase Noise Broadband Amplifier in 130 nm SiGe BiCMOS Technology. <i>2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS) </i>. 2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS) , Fort Lauderdale, Florida. <a href=\"https://doi.org/10.1109/BCICTS59662.2024.10745663\">https://doi.org/10.1109/BCICTS59662.2024.10745663</a>"},"year":"2024","conference":{"name":"2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS) ","location":"Fort Lauderdale, Florida"},"doi":"10.1109/BCICTS59662.2024.10745663","title":"Design of an Ultra-Low Phase Noise Broadband Amplifier in 130 nm SiGe BiCMOS Technology","date_created":"2024-11-15T09:57:42Z","author":[{"last_name":"Surendranath Shroff","id":"76626","full_name":"Surendranath Shroff, Vijayalakshmi","first_name":"Vijayalakshmi"},{"first_name":"Meysam","id":"69233","full_name":"Bahmanian, Meysam","last_name":"Bahmanian"},{"last_name":"Kruse","full_name":"Kruse, Stephan","id":"38254","first_name":"Stephan"},{"first_name":"J. Christoph","last_name":"Scheytt","orcid":"0000-0002-5950-6618 ","id":"37144","full_name":"Scheytt, J. Christoph"}],"date_updated":"2025-11-28T05:39:07Z","publisher":"IEEE","status":"public","type":"conference","publication":"2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS) ","language":[{"iso":"eng"}],"ddc":["620"],"user_id":"76626","department":[{"_id":"58"}],"_id":"57103"},{"citation":{"short":"A. Werning, R. Haeb-Umbach, in: 32nd European Signal Processing Conference (EUSIPCO 2024), 2024.","bibtex":"@inproceedings{Werning_Haeb-Umbach_2024, title={Target-Specific Dataset Pruning for Compression of Audio Tagging Models}, booktitle={32nd European Signal Processing Conference (EUSIPCO 2024)}, author={Werning, Alexander and Haeb-Umbach, Reinhold}, year={2024} }","mla":"Werning, Alexander, and Reinhold Haeb-Umbach. “Target-Specific Dataset Pruning for Compression of Audio Tagging Models.” <i>32nd European Signal Processing Conference (EUSIPCO 2024)</i>, 2024.","apa":"Werning, A., &#38; Haeb-Umbach, R. (2024). Target-Specific Dataset Pruning for Compression of Audio Tagging Models. <i>32nd European Signal Processing Conference (EUSIPCO 2024)</i>. 32nd European Signal Processing Conference, Lyon.","ama":"Werning A, Haeb-Umbach R. Target-Specific Dataset Pruning for Compression of Audio Tagging Models. In: <i>32nd European Signal Processing Conference (EUSIPCO 2024)</i>. ; 2024.","ieee":"A. Werning and R. Haeb-Umbach, “Target-Specific Dataset Pruning for Compression of Audio Tagging Models,” presented at the 32nd European Signal Processing Conference, Lyon, 2024.","chicago":"Werning, Alexander, and Reinhold Haeb-Umbach. “Target-Specific Dataset Pruning for Compression of Audio Tagging Models.” In <i>32nd European Signal Processing Conference (EUSIPCO 2024)</i>, 2024."},"year":"2024","quality_controlled":"1","has_accepted_license":"1","conference":{"location":"Lyon","name":"32nd European Signal Processing Conference"},"title":"Target-Specific Dataset Pruning for Compression of Audio Tagging Models","author":[{"last_name":"Werning","id":"62152","full_name":"Werning, Alexander","first_name":"Alexander"},{"first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold","id":"242","last_name":"Haeb-Umbach"}],"date_created":"2024-11-18T09:29:16Z","date_updated":"2025-11-28T13:22:00Z","file":[{"file_id":"57200","access_level":"closed","file_name":"Eusipco__Target_specific_Dataset_Pruning_for_Compression_of_Audio_Tagging_Models.pdf","file_size":183539,"creator":"awerning","date_created":"2024-11-18T12:10:09Z","date_updated":"2024-11-18T12:10:09Z","relation":"main_file","success":1,"content_type":"application/pdf"}],"status":"public","abstract":[{"text":"Large audio tagging models are usually trained or pre-trained on AudioSet, a dataset that encompasses a large amount of different sound classes and acoustic environments. Knowledge distillation has emerged as a method to compress such models without compromising their effectiveness. There are many different applications for audio tagging, some of which require a specialization to a narrow domain of sounds to be classified. For these scenarios, it is beneficial to distill the large audio tagger with respect to a specific subset of sounds of interest. A method to prune a general dataset with respect to a target dataset is presented. By distilling with such a specialized pruned dataset, we obtain a compressed model with better classification accuracy in the specific target domain than with target-agnostic distillation.","lang":"eng"}],"type":"conference","publication":"32nd European Signal Processing Conference (EUSIPCO 2024)","language":[{"iso":"eng"}],"file_date_updated":"2024-11-18T12:10:09Z","ddc":["000"],"keyword":["data pruning","knowledge distillation","audio tagging"],"user_id":"62152","department":[{"_id":"54"}],"project":[{"name":"WestAI - AI Service Center West","_id":"512"}],"_id":"57160"},{"status":"public","abstract":[{"text":"<jats:p>Clifford algebras are a natural extension of division algebras, including real numbers, complex numbers, quaternions, and octonions. Previous research in knowledge graph embeddings has focused exclusively on Clifford algebras of a specific type, which do not include nilpotent base vectors—elements that square to zero. In this work, we introduce a novel approach by incorporating nilpotent base vectors with a nilpotency index of two, leading to a more general form of Clifford algebras named degenerate Clifford algebras. This generalization to degenerate Clifford algebras does allow for covering dual numbers and as such include translations and rotations models under the same generalization paradigm for the first time. We develop two models to determine the parameters that define the algebra: one using a greedy search and another predicting the parameters based on neural network embeddings of the input knowledge graph. Our evaluation on seven benchmark datasets demonstrates that this incorporation of nilpotent vectors enhances the quality of embeddings. Additionally, our method outperforms state-of-the-art approaches in terms of generalization, particularly regarding the mean reciprocal rank achieved on validation data. Finally, we show that even a simple greedy search can effectively discover optimal or near-optimal parameters for the algebra.</jats:p>","lang":"eng"}],"publication":"Frontiers in Artificial Intelligence and Applications","type":"book_chapter","popular_science":"1","language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"574"}],"user_id":"101165","_id":"62702","citation":{"bibtex":"@inbook{Kamdem Teyou_Demir_Ngonga Ngomo_2024, title={Embedding Knowledge Graphs in Degenerate Clifford Algebras}, DOI={<a href=\"https://doi.org/10.3233/faia240627\">10.3233/faia240627</a>}, booktitle={Frontiers in Artificial Intelligence and Applications}, publisher={IOS Press}, author={Kamdem Teyou, Louis Mozart and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, year={2024} }","short":"L.M. Kamdem Teyou, C. Demir, A.-C. Ngonga Ngomo, in: Frontiers in Artificial Intelligence and Applications, IOS Press, 2024.","mla":"Kamdem Teyou, Louis Mozart, et al. “Embedding Knowledge Graphs in Degenerate Clifford Algebras.” <i>Frontiers in Artificial Intelligence and Applications</i>, IOS Press, 2024, doi:<a href=\"https://doi.org/10.3233/faia240627\">10.3233/faia240627</a>.","apa":"Kamdem Teyou, L. M., Demir, C., &#38; Ngonga Ngomo, A.-C. (2024). Embedding Knowledge Graphs in Degenerate Clifford Algebras. In <i>Frontiers in Artificial Intelligence and Applications</i>. 27TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, Santiago de Compostela. IOS Press. <a href=\"https://doi.org/10.3233/faia240627\">https://doi.org/10.3233/faia240627</a>","ama":"Kamdem Teyou LM, Demir C, Ngonga Ngomo A-C. Embedding Knowledge Graphs in Degenerate Clifford Algebras. In: <i>Frontiers in Artificial Intelligence and Applications</i>. 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In H. van Vorst (Ed.), <i>Frühe naturwissenschaftliche Bildung</i> (Vol. 44).","chicago":"Pollmeier, Pascal, Christoph Vogelsang, and Tim Rogge. “Eigenvideografien Als Instrument Zur Professionalisierung Angehender Lehrkräfte.” In <i>Frühe Naturwissenschaftliche Bildung</i>, edited by Helena van Vorst, Vol. 44. Tagungsband Zur GDCP Jahrestagung. Essen, 2024.","ieee":"P. Pollmeier, C. Vogelsang, and T. Rogge, “Eigenvideografien als Instrument zur Professionalisierung angehender Lehrkräfte,” in <i>Frühe naturwissenschaftliche Bildung</i>, Hamburg, 2024, vol. 44.","ama":"Pollmeier P, Vogelsang C, Rogge T. Eigenvideografien als Instrument zur Professionalisierung angehender Lehrkräfte. In: van Vorst H, ed. <i>Frühe Naturwissenschaftliche Bildung</i>. Vol 44. Tagungsband zur GDCP Jahrestagung. ; 2024."},"intvolume":"        44","language":[{"iso":"eng"}],"_id":"54811","series_title":"Tagungsband zur GDCP Jahrestagung","user_id":"4245","department":[{"_id":"386"},{"_id":"33"},{"_id":"450"}],"editor":[{"full_name":"van Vorst, Helena","last_name":"van Vorst","first_name":"Helena"}],"status":"public","type":"conference","publication":"Frühe naturwissenschaftliche Bildung"},{"date_updated":"2025-12-03T12:51:26Z","publisher":"Springer Science and Business Media LLC","author":[{"first_name":"Rasoul","full_name":"Najafi Koopas, Rasoul","last_name":"Najafi Koopas"},{"last_name":"Rezaei","full_name":"Rezaei, Shahed","first_name":"Shahed"},{"first_name":"Natalie","last_name":"Rauter","full_name":"Rauter, Natalie"},{"first_name":"Richard","last_name":"Ostwald","orcid":"0000-0003-2147-8444","id":"106876","full_name":"Ostwald, Richard"},{"last_name":"Lammering","full_name":"Lammering, Rolf","first_name":"Rolf"}],"date_created":"2025-12-03T12:37:08Z","volume":75,"title":"Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space","doi":"10.1007/s00466-024-02568-z","publication_status":"published","publication_identifier":{"issn":["0178-7675","1432-0924"]},"quality_controlled":"1","issue":"4","year":"2024","citation":{"apa":"Najafi Koopas, R., Rezaei, S., Rauter, N., Ostwald, R., &#38; Lammering, R. (2024). Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space. <i>Computational Mechanics</i>, <i>75</i>(4), 1377–1406. <a href=\"https://doi.org/10.1007/s00466-024-02568-z\">https://doi.org/10.1007/s00466-024-02568-z</a>","short":"R. Najafi Koopas, S. Rezaei, N. Rauter, R. Ostwald, R. Lammering, Computational Mechanics 75 (2024) 1377–1406.","bibtex":"@article{Najafi Koopas_Rezaei_Rauter_Ostwald_Lammering_2024, title={Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space}, volume={75}, DOI={<a href=\"https://doi.org/10.1007/s00466-024-02568-z\">10.1007/s00466-024-02568-z</a>}, number={4}, journal={Computational Mechanics}, publisher={Springer Science and Business Media LLC}, author={Najafi Koopas, Rasoul and Rezaei, Shahed and Rauter, Natalie and Ostwald, Richard and Lammering, Rolf}, year={2024}, pages={1377–1406} }","mla":"Najafi Koopas, Rasoul, et al. “Introducing a Microstructure-Embedded Autoencoder Approach for Reconstructing High-Resolution Solution Field Data from a Reduced Parametric Space.” <i>Computational Mechanics</i>, vol. 75, no. 4, Springer Science and Business Media LLC, 2024, pp. 1377–406, doi:<a href=\"https://doi.org/10.1007/s00466-024-02568-z\">10.1007/s00466-024-02568-z</a>.","ama":"Najafi Koopas R, Rezaei S, Rauter N, Ostwald R, Lammering R. Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space. <i>Computational Mechanics</i>. 2024;75(4):1377-1406. doi:<a href=\"https://doi.org/10.1007/s00466-024-02568-z\">10.1007/s00466-024-02568-z</a>","chicago":"Najafi Koopas, Rasoul, Shahed Rezaei, Natalie Rauter, Richard Ostwald, and Rolf Lammering. “Introducing a Microstructure-Embedded Autoencoder Approach for Reconstructing High-Resolution Solution Field Data from a Reduced Parametric Space.” <i>Computational Mechanics</i> 75, no. 4 (2024): 1377–1406. <a href=\"https://doi.org/10.1007/s00466-024-02568-z\">https://doi.org/10.1007/s00466-024-02568-z</a>.","ieee":"R. Najafi Koopas, S. Rezaei, N. Rauter, R. Ostwald, and R. Lammering, “Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space,” <i>Computational Mechanics</i>, vol. 75, no. 4, pp. 1377–1406, 2024, doi: <a href=\"https://doi.org/10.1007/s00466-024-02568-z\">10.1007/s00466-024-02568-z</a>."},"intvolume":"        75","page":"1377-1406","_id":"62767","user_id":"85414","department":[{"_id":"952"},{"_id":"321"}],"language":[{"iso":"eng"}],"type":"journal_article","publication":"Computational Mechanics","abstract":[{"lang":"eng","text":"<jats:title>Abstract</jats:title>\r\n          <jats:p>In this study, we develop a novel multi-fidelity deep learning approach that transforms low-fidelity solution maps into high-fidelity ones by incorporating parametric space information into an autoencoder architecture. This method’s integration of parametric space information significantly reduces the amount of training data needed to effectively predict high-fidelity solutions from low-fidelity ones. In this study, we examine a two-dimensional steady-state heat transfer analysis within a heterogeneous materials microstructure. The heat conductivity coefficients for two different materials are condensed from a 101 <jats:inline-formula>\r\n              <jats:alternatives>\r\n                <jats:tex-math>$$\\times $$</jats:tex-math>\r\n                <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\">\r\n                  <mml:mo>×</mml:mo>\r\n                </mml:math>\r\n              </jats:alternatives>\r\n            </jats:inline-formula> 101 grid to smaller grids. We then solve the boundary value problem on the coarsest grid using a pre-trained physics-informed neural operator network known as Finite Operator Learning (FOL). The resulting low-fidelity solution is subsequently upscaled back to a 101 <jats:inline-formula>\r\n              <jats:alternatives>\r\n                <jats:tex-math>$$\\times $$</jats:tex-math>\r\n                <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\">\r\n                  <mml:mo>×</mml:mo>\r\n                </mml:math>\r\n              </jats:alternatives>\r\n            </jats:inline-formula> 101 grid using a newly designed enhanced autoencoder. The novelty of the developed enhanced autoencoder lies in the concatenation of heat conductivity maps of different resolutions to the decoder segment in distinct steps. Hence the developed algorithm is named microstructure-embedded autoencoder (MEA). We compare the MEA outcomes with those from finite element methods, the standard U-Net, and an interpolation approach as an upscaling technique. Our analysis shows that MEA outperforms these methods in terms of computational efficiency and error on representative test cases. As a result, the MEA serves as a potential supplement to neural operator networks, effectively upscaling low-fidelity solutions to high-fidelity while preserving critical details often lost in traditional upscaling methods, such as sharp interfaces features lost in the context of interpolation approaches.</jats:p>"}],"status":"public"},{"language":[{"iso":"eng"}],"user_id":"85414","department":[{"_id":"952"},{"_id":"321"}],"_id":"62770","status":"public","abstract":[{"lang":"eng","text":"<jats:title>Abstract</jats:title><jats:p>The open-source parameter identification tool ADAPT (A diversely applicable parameter identification Tool) is integrated with a machine learning-based approach for start value prediction in order to calibrate a Gurson–Tvergaard–Needleman (GTN) and a Lemaitre damage model. As representative example case-hardened steel 16MnCrS5 is elaborated. An artificial neural network (ANN) is initially trained by using load–displacement curves derived from simulations of a boundary value problem—instead of using data generated for homogeneous states of deformation at material point or one-element level—with varying material parameter combinations. The ANN is then employed so as to predict sets of material parameters that already provide close solutions to the experiment. These predicted parameter sets serve as starting values for a subsequent multi-objective parameter identification by using ADAPT. ADAPT allows for the consideration of input data from multiple scales, including integral data such as load–displacement curves, full-field data such as displacement and strain fields, and high-resolution experimental void data at the micro-scale. The influence of each data set on prediction quality is analyzed. Using various types of input data introduces additional information, enhancing prediction accuracy. The validation is carried out with respect to experimental void measurements of forward rod extruded parts. The results demonstrate, by incorporating void measurements in the optimization process, that it is possible to improve the quantitative prediction of ductile damage in the sense of void area fractions by factor 28 in forward rod extrusion.</jats:p>"}],"type":"journal_article","publication":"Archive of Applied Mechanics","doi":"10.1007/s00419-024-02634-1","title":"Enhancing damage prediction in bulk metal forming through machine learning-assisted parameter identification","author":[{"full_name":"Gerlach, Jan","last_name":"Gerlach","first_name":"Jan"},{"first_name":"Robin","last_name":"Schulte","full_name":"Schulte, Robin"},{"full_name":"Schowtjak, Alexander","last_name":"Schowtjak","first_name":"Alexander"},{"last_name":"Clausmeyer","full_name":"Clausmeyer, Till","first_name":"Till"},{"last_name":"Ostwald","orcid":"0000-0003-2147-8444","id":"106876","full_name":"Ostwald, Richard","first_name":"Richard"},{"first_name":"A. Erman","full_name":"Tekkaya, A. Erman","last_name":"Tekkaya"},{"first_name":"Andreas","full_name":"Menzel, Andreas","last_name":"Menzel"}],"date_created":"2025-12-03T12:46:31Z","volume":94,"publisher":"Springer Science and Business Media LLC","date_updated":"2025-12-03T12:50:41Z","citation":{"bibtex":"@article{Gerlach_Schulte_Schowtjak_Clausmeyer_Ostwald_Tekkaya_Menzel_2024, title={Enhancing damage prediction in bulk metal forming through machine learning-assisted parameter identification}, volume={94}, DOI={<a href=\"https://doi.org/10.1007/s00419-024-02634-1\">10.1007/s00419-024-02634-1</a>}, number={8}, journal={Archive of Applied Mechanics}, publisher={Springer Science and Business Media LLC}, author={Gerlach, Jan and Schulte, Robin and Schowtjak, Alexander and Clausmeyer, Till and Ostwald, Richard and Tekkaya, A. Erman and Menzel, Andreas}, year={2024}, pages={2217–2242} }","short":"J. Gerlach, R. Schulte, A. Schowtjak, T. Clausmeyer, R. Ostwald, A.E. Tekkaya, A. Menzel, Archive of Applied Mechanics 94 (2024) 2217–2242.","mla":"Gerlach, Jan, et al. “Enhancing Damage Prediction in Bulk Metal Forming through Machine Learning-Assisted Parameter Identification.” <i>Archive of Applied Mechanics</i>, vol. 94, no. 8, Springer Science and Business Media LLC, 2024, pp. 2217–42, doi:<a href=\"https://doi.org/10.1007/s00419-024-02634-1\">10.1007/s00419-024-02634-1</a>.","apa":"Gerlach, J., Schulte, R., Schowtjak, A., Clausmeyer, T., Ostwald, R., Tekkaya, A. E., &#38; Menzel, A. (2024). Enhancing damage prediction in bulk metal forming through machine learning-assisted parameter identification. <i>Archive of Applied Mechanics</i>, <i>94</i>(8), 2217–2242. <a href=\"https://doi.org/10.1007/s00419-024-02634-1\">https://doi.org/10.1007/s00419-024-02634-1</a>","chicago":"Gerlach, Jan, Robin Schulte, Alexander Schowtjak, Till Clausmeyer, Richard Ostwald, A. Erman Tekkaya, and Andreas Menzel. “Enhancing Damage Prediction in Bulk Metal Forming through Machine Learning-Assisted Parameter Identification.” <i>Archive of Applied Mechanics</i> 94, no. 8 (2024): 2217–42. <a href=\"https://doi.org/10.1007/s00419-024-02634-1\">https://doi.org/10.1007/s00419-024-02634-1</a>.","ieee":"J. Gerlach <i>et al.</i>, “Enhancing damage prediction in bulk metal forming through machine learning-assisted parameter identification,” <i>Archive of Applied Mechanics</i>, vol. 94, no. 8, pp. 2217–2242, 2024, doi: <a href=\"https://doi.org/10.1007/s00419-024-02634-1\">10.1007/s00419-024-02634-1</a>.","ama":"Gerlach J, Schulte R, Schowtjak A, et al. Enhancing damage prediction in bulk metal forming through machine learning-assisted parameter identification. <i>Archive of Applied Mechanics</i>. 2024;94(8):2217-2242. doi:<a href=\"https://doi.org/10.1007/s00419-024-02634-1\">10.1007/s00419-024-02634-1</a>"},"intvolume":"        94","page":"2217-2242","year":"2024","issue":"8","publication_status":"published","quality_controlled":"1","publication_identifier":{"issn":["0939-1533","1432-0681"]}},{"title":"A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: Efficient mapping of concrete microstructures to its fracture properties","doi":"10.1016/j.engfracmech.2024.110675","publisher":"Elsevier BV","date_updated":"2025-12-03T12:51:12Z","volume":314,"author":[{"last_name":"Najafi Koopas","full_name":"Najafi Koopas, Rasoul","first_name":"Rasoul"},{"last_name":"Rezaei","full_name":"Rezaei, Shahed","first_name":"Shahed"},{"full_name":"Rauter, Natalie","last_name":"Rauter","first_name":"Natalie"},{"first_name":"Richard","orcid":"0000-0003-2147-8444","last_name":"Ostwald","full_name":"Ostwald, Richard","id":"106876"},{"first_name":"Rolf","last_name":"Lammering","full_name":"Lammering, Rolf"}],"date_created":"2025-12-03T12:40:42Z","year":"2024","intvolume":"       314","citation":{"ieee":"R. Najafi Koopas, S. Rezaei, N. Rauter, R. Ostwald, and R. Lammering, “A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: Efficient mapping of concrete microstructures to its fracture properties,” <i>Engineering Fracture Mechanics</i>, vol. 314, Art. no. 110675, 2024, doi: <a href=\"https://doi.org/10.1016/j.engfracmech.2024.110675\">10.1016/j.engfracmech.2024.110675</a>.","chicago":"Najafi Koopas, Rasoul, Shahed Rezaei, Natalie Rauter, Richard Ostwald, and Rolf Lammering. “A Spatiotemporal Deep Learning Framework for Prediction of Crack Dynamics in Heterogeneous Solids: Efficient Mapping of Concrete Microstructures to Its Fracture Properties.” <i>Engineering Fracture Mechanics</i> 314 (2024). <a href=\"https://doi.org/10.1016/j.engfracmech.2024.110675\">https://doi.org/10.1016/j.engfracmech.2024.110675</a>.","ama":"Najafi Koopas R, Rezaei S, Rauter N, Ostwald R, Lammering R. A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: Efficient mapping of concrete microstructures to its fracture properties. <i>Engineering Fracture Mechanics</i>. 2024;314. doi:<a href=\"https://doi.org/10.1016/j.engfracmech.2024.110675\">10.1016/j.engfracmech.2024.110675</a>","apa":"Najafi Koopas, R., Rezaei, S., Rauter, N., Ostwald, R., &#38; Lammering, R. (2024). A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: Efficient mapping of concrete microstructures to its fracture properties. <i>Engineering Fracture Mechanics</i>, <i>314</i>, Article 110675. <a href=\"https://doi.org/10.1016/j.engfracmech.2024.110675\">https://doi.org/10.1016/j.engfracmech.2024.110675</a>","bibtex":"@article{Najafi Koopas_Rezaei_Rauter_Ostwald_Lammering_2024, title={A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: Efficient mapping of concrete microstructures to its fracture properties}, volume={314}, DOI={<a href=\"https://doi.org/10.1016/j.engfracmech.2024.110675\">10.1016/j.engfracmech.2024.110675</a>}, number={110675}, journal={Engineering Fracture Mechanics}, publisher={Elsevier BV}, author={Najafi Koopas, Rasoul and Rezaei, Shahed and Rauter, Natalie and Ostwald, Richard and Lammering, Rolf}, year={2024} }","mla":"Najafi Koopas, Rasoul, et al. “A Spatiotemporal Deep Learning Framework for Prediction of Crack Dynamics in Heterogeneous Solids: Efficient Mapping of Concrete Microstructures to Its Fracture Properties.” <i>Engineering Fracture Mechanics</i>, vol. 314, 110675, Elsevier BV, 2024, doi:<a href=\"https://doi.org/10.1016/j.engfracmech.2024.110675\">10.1016/j.engfracmech.2024.110675</a>.","short":"R. Najafi Koopas, S. Rezaei, N. Rauter, R. Ostwald, R. Lammering, Engineering Fracture Mechanics 314 (2024)."},"quality_controlled":"1","publication_identifier":{"issn":["0013-7944"]},"publication_status":"published","article_number":"110675","language":[{"iso":"eng"}],"_id":"62768","department":[{"_id":"952"},{"_id":"321"}],"user_id":"85414","status":"public","publication":"Engineering Fracture Mechanics","type":"journal_article"},{"status":"public","type":"conference","publication":"Americas Conference on Information Systems (AMCIS)","language":[{"iso":"eng"}],"_id":"54589","user_id":"56760","department":[{"_id":"195"}],"year":"2024","citation":{"apa":"Brennig, K., Löhr, B., Brock, J., Reineke, M. F., &#38; Bartelheimer, C. (2024). Maximizing the Impact of Process Mining Research: Four Strategic Guidelines. <i>Americas Conference on Information Systems (AMCIS)</i>. 30th Americas Conference on Information Systems (AMCIS).","short":"K. Brennig, B. Löhr, J. Brock, M.F. Reineke, C. Bartelheimer, in: Americas Conference on Information Systems (AMCIS), 2024.","bibtex":"@inproceedings{Brennig_Löhr_Brock_Reineke_Bartelheimer_2024, title={Maximizing the Impact of Process Mining Research: Four Strategic Guidelines}, booktitle={Americas Conference on Information Systems (AMCIS)}, author={Brennig, Katharina and Löhr, Bernd and Brock, Jonathan and Reineke, Malte Fabian and Bartelheimer, Christian}, year={2024} }","mla":"Brennig, Katharina, et al. “Maximizing the Impact of Process Mining Research: Four Strategic Guidelines.” <i>Americas Conference on Information Systems (AMCIS)</i>, 2024.","ama":"Brennig K, Löhr B, Brock J, Reineke MF, Bartelheimer C. Maximizing the Impact of Process Mining Research: Four Strategic Guidelines. In: <i>Americas Conference on Information Systems (AMCIS)</i>. ; 2024.","chicago":"Brennig, Katharina, Bernd Löhr, Jonathan Brock, Malte Fabian Reineke, and Christian Bartelheimer. “Maximizing the Impact of Process Mining Research: Four Strategic Guidelines.” In <i>Americas Conference on Information Systems (AMCIS)</i>, 2024.","ieee":"K. Brennig, B. Löhr, J. Brock, M. F. Reineke, and C. Bartelheimer, “Maximizing the Impact of Process Mining Research: Four Strategic Guidelines,” presented at the 30th Americas Conference on Information Systems (AMCIS), 2024."},"title":"Maximizing the Impact of Process Mining Research: Four Strategic Guidelines","main_file_link":[{"url":"https://aisel.aisnet.org/amcis2024/dsa/dsa/14/"}],"conference":{"name":"30th Americas Conference on Information Systems (AMCIS)"},"date_updated":"2025-12-03T14:12:14Z","date_created":"2024-06-04T12:36:52Z","author":[{"id":"51905","full_name":"Brennig, Katharina","last_name":"Brennig","first_name":"Katharina"},{"first_name":"Bernd","full_name":"Löhr, Bernd","id":"56760","last_name":"Löhr","orcid":"0000-0001-9581-4602"},{"last_name":"Brock","full_name":"Brock, Jonathan","first_name":"Jonathan"},{"last_name":"Reineke","full_name":"Reineke, Malte Fabian","id":"60641","first_name":"Malte Fabian"},{"last_name":"Bartelheimer","full_name":"Bartelheimer, Christian","id":"49160","first_name":"Christian"}]},{"language":[{"iso":"eng"}],"article_number":"11","department":[{"_id":"195"}],"user_id":"60641","_id":"62796","status":"public","publication":"AMCIS 2024 TREOs","type":"conference","conference":{"location":"Salt Lake City, Utah, USA"},"main_file_link":[{"url":"https://aisel.aisnet.org/treos_amcis2024/11/"}],"title":"Assessing the impact of organizational culture on workarounds: A maturity model","date_created":"2025-12-03T14:07:18Z","author":[{"full_name":"Reineke, Malte Fabian","id":"60641","last_name":"Reineke","first_name":"Malte Fabian"},{"first_name":"Christian","full_name":"Bartelheimer, Christian","id":"49160","last_name":"Bartelheimer"}],"date_updated":"2025-12-03T14:08:49Z","citation":{"ieee":"M. F. Reineke and C. Bartelheimer, “Assessing the impact of organizational culture on workarounds: A maturity model,” Salt Lake City, Utah, USA, 2024.","chicago":"Reineke, Malte Fabian, and Christian Bartelheimer. “Assessing the Impact of Organizational Culture on Workarounds: A Maturity Model.” In <i>AMCIS 2024 TREOs</i>, 2024.","ama":"Reineke MF, Bartelheimer C. Assessing the impact of organizational culture on workarounds: A maturity model. In: <i>AMCIS 2024 TREOs</i>. ; 2024.","apa":"Reineke, M. F., &#38; Bartelheimer, C. (2024). Assessing the impact of organizational culture on workarounds: A maturity model. <i>AMCIS 2024 TREOs</i>, Article 11.","mla":"Reineke, Malte Fabian, and Christian Bartelheimer. “Assessing the Impact of Organizational Culture on Workarounds: A Maturity Model.” <i>AMCIS 2024 TREOs</i>, 11, 2024.","short":"M.F. Reineke, C. Bartelheimer, in: AMCIS 2024 TREOs, 2024.","bibtex":"@inproceedings{Reineke_Bartelheimer_2024, title={Assessing the impact of organizational culture on workarounds: A maturity model}, number={11}, booktitle={AMCIS 2024 TREOs}, author={Reineke, Malte Fabian and Bartelheimer, Christian}, year={2024} }"},"year":"2024"},{"type":"journal_article","publication":"Carbon","status":"public","user_id":"98120","_id":"62670","language":[{"iso":"eng"}],"article_number":"118946","publication_status":"published","publication_identifier":{"issn":["0008-6223"]},"citation":{"chicago":"André, Rémi F., Jessica Brandt, Johannes Schmidt, Nieves López-Salas, Mateusz Odziomek, and Markus Antonietti. “Inductively Coupled Plasma Spectroscopy for Heteroatom-Doped Carbonaceous Materials: Limitations and Acid Choice for Digestion.” <i>Carbon</i> 223 (2024). <a href=\"https://doi.org/10.1016/j.carbon.2024.118946\">https://doi.org/10.1016/j.carbon.2024.118946</a>.","ieee":"R. F. André, J. Brandt, J. Schmidt, N. López-Salas, M. Odziomek, and M. Antonietti, “Inductively coupled plasma spectroscopy for heteroatom-doped carbonaceous materials: Limitations and acid choice for digestion,” <i>Carbon</i>, vol. 223, Art. no. 118946, 2024, doi: <a href=\"https://doi.org/10.1016/j.carbon.2024.118946\">10.1016/j.carbon.2024.118946</a>.","ama":"André RF, Brandt J, Schmidt J, López-Salas N, Odziomek M, Antonietti M. Inductively coupled plasma spectroscopy for heteroatom-doped carbonaceous materials: Limitations and acid choice for digestion. <i>Carbon</i>. 2024;223. doi:<a href=\"https://doi.org/10.1016/j.carbon.2024.118946\">10.1016/j.carbon.2024.118946</a>","apa":"André, R. F., Brandt, J., Schmidt, J., López-Salas, N., Odziomek, M., &#38; Antonietti, M. (2024). Inductively coupled plasma spectroscopy for heteroatom-doped carbonaceous materials: Limitations and acid choice for digestion. <i>Carbon</i>, <i>223</i>, Article 118946. <a href=\"https://doi.org/10.1016/j.carbon.2024.118946\">https://doi.org/10.1016/j.carbon.2024.118946</a>","mla":"André, Rémi F., et al. “Inductively Coupled Plasma Spectroscopy for Heteroatom-Doped Carbonaceous Materials: Limitations and Acid Choice for Digestion.” <i>Carbon</i>, vol. 223, 118946, Elsevier BV, 2024, doi:<a href=\"https://doi.org/10.1016/j.carbon.2024.118946\">10.1016/j.carbon.2024.118946</a>.","bibtex":"@article{André_Brandt_Schmidt_López-Salas_Odziomek_Antonietti_2024, title={Inductively coupled plasma spectroscopy for heteroatom-doped carbonaceous materials: Limitations and acid choice for digestion}, volume={223}, DOI={<a href=\"https://doi.org/10.1016/j.carbon.2024.118946\">10.1016/j.carbon.2024.118946</a>}, number={118946}, journal={Carbon}, publisher={Elsevier BV}, author={André, Rémi F. and Brandt, Jessica and Schmidt, Johannes and López-Salas, Nieves and Odziomek, Mateusz and Antonietti, Markus}, year={2024} }","short":"R.F. André, J. Brandt, J. Schmidt, N. López-Salas, M. Odziomek, M. Antonietti, Carbon 223 (2024)."},"intvolume":"       223","year":"2024","author":[{"full_name":"André, Rémi F.","last_name":"André","first_name":"Rémi F."},{"first_name":"Jessica","full_name":"Brandt, Jessica","last_name":"Brandt"},{"first_name":"Johannes","full_name":"Schmidt, Johannes","last_name":"Schmidt"},{"last_name":"López-Salas","full_name":"López-Salas, Nieves","first_name":"Nieves"},{"full_name":"Odziomek, Mateusz","last_name":"Odziomek","first_name":"Mateusz"},{"first_name":"Markus","full_name":"Antonietti, Markus","last_name":"Antonietti"}],"date_created":"2025-11-27T13:15:58Z","volume":223,"publisher":"Elsevier BV","date_updated":"2025-12-03T14:57:29Z","doi":"10.1016/j.carbon.2024.118946","title":"Inductively coupled plasma spectroscopy for heteroatom-doped carbonaceous materials: Limitations and acid choice for digestion"},{"year":"2024","page":"53-64","citation":{"ieee":"P. Pollmeier, D. Stroop, and S. Fechner, “Digitale Messwerterfassung im Chemieunterricht,” in <i>Lehrkräftebildung in der digitalen Welt - Zukunftsorientierte Forschungs- und Praxisperspektiven</i>, 1st ed., B. Herzig, B. Eickelmann, F. Schwabl, J. Schulze, and J. Niemann, Eds. Waxmann, 2024, pp. 53–64.","chicago":"Pollmeier, Pascal, Dietlinde Stroop, and Sabine Fechner. “Digitale Messwerterfassung im Chemieunterricht.” In <i>Lehrkräftebildung in der digitalen Welt - Zukunftsorientierte Forschungs- und Praxisperspektiven</i>, edited by Bardo Herzig, Birgit Eickelmann, Franszika Schwabl, Johanna Schulze, and Jan Niemann, 1st ed., 53–64. Paderborner Beiträge zur Bildungsforschung und Lehrkräftebildung. Waxmann, 2024.","ama":"Pollmeier P, Stroop D, Fechner S. Digitale Messwerterfassung im Chemieunterricht. In: Herzig B, Eickelmann B, Schwabl F, Schulze J, Niemann J, eds. <i>Lehrkräftebildung in der digitalen Welt - Zukunftsorientierte Forschungs- und Praxisperspektiven</i>. 1st ed. Paderborner Beiträge zur Bildungsforschung und Lehrkräftebildung. Waxmann; 2024:53-64.","bibtex":"@inbook{Pollmeier_Stroop_Fechner_2024, edition={1}, series={Paderborner Beiträge zur Bildungsforschung und Lehrkräftebildung}, title={Digitale Messwerterfassung im Chemieunterricht}, booktitle={Lehrkräftebildung in der digitalen Welt - Zukunftsorientierte Forschungs- und Praxisperspektiven}, publisher={Waxmann}, author={Pollmeier, Pascal and Stroop, Dietlinde and Fechner, Sabine}, editor={Herzig, Bardo and Eickelmann, Birgit and Schwabl, Franszika and Schulze, Johanna and Niemann, Jan}, year={2024}, pages={53–64}, collection={Paderborner Beiträge zur Bildungsforschung und Lehrkräftebildung} }","short":"P. Pollmeier, D. Stroop, S. Fechner, in: B. Herzig, B. Eickelmann, F. Schwabl, J. Schulze, J. Niemann (Eds.), Lehrkräftebildung in der digitalen Welt - Zukunftsorientierte Forschungs- und Praxisperspektiven, 1st ed., Waxmann, 2024, pp. 53–64.","mla":"Pollmeier, Pascal, et al. “Digitale Messwerterfassung im Chemieunterricht.” <i>Lehrkräftebildung in der digitalen Welt - Zukunftsorientierte Forschungs- und Praxisperspektiven</i>, edited by Bardo Herzig et al., 1st ed., Waxmann, 2024, pp. 53–64.","apa":"Pollmeier, P., Stroop, D., &#38; Fechner, S. (2024). Digitale Messwerterfassung im Chemieunterricht. In B. Herzig, B. Eickelmann, F. Schwabl, J. Schulze, &#38; J. Niemann (Eds.), <i>Lehrkräftebildung in der digitalen Welt - Zukunftsorientierte Forschungs- und Praxisperspektiven</i> (1st ed., pp. 53–64). 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