{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T05:38:55Z","timestamp":1769146735721,"version":"3.49.0"},"reference-count":53,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"name":"Provincial Innovation and Entrepreneurship Training Program for College Students","award":["202210460073"],"award-info":[{"award-number":["202210460073"]}]},{"name":"Key Scientific and Technological Project of Henan Province","award":["232102320171"],"award-info":[{"award-number":["232102320171"]}]},{"name":"Soft Science Research Project of Henan Province","award":["242400410187"],"award-info":[{"award-number":["242400410187"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Foundation of China","doi-asserted-by":"crossref","award":["62077041"],"award-info":[{"award-number":["62077041"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Comput. Cult. Herit."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>At present, the dissemination of Tai Chi is predominantly facilitated through offline instructional methods combined with video practice, lacking effective feedback on movement poses and exhibiting relatively low efficiency. This article proposes a novel algorithm named TC-YOLO for pose estimation based on YOLOv8. TC-YOLO enhances the efficiency through utilizing real-time detection of key points in Tai Chi practitioners\u2019 movements to provide more accurate and intuitive feedback for instructional evaluation and pose correction. Focusing on the demonstration of the Essential Eighteen Movements of Chen-style Tai Chi as the research subject, Tai Chi movement dataset comprising 3,688 images is constructed. To enhance model efficiency, the backbone network is reparameterized through the Reparametrized C2f (RC2f) module, which optimizes feature extraction process by allowing more effective information flow and reducing computational complexity. Furthermore, a simplified neck network structure is designed to provide effective information transmission and multiscale feature fusion, thereby enhancing detection accuracy. Experimental results show that the TC-YOLO algorithm achieves a mAP of 97.2% and a recognition speed of 146.1 FPS with lower parameters and computational cost on the self-built dataset, which is better than YOLO-Pose and other models. TC-YOLO could make an instructive contribution to the field of sports science through the analysis of Tai Chi movement poses, promoting the inheritance and global dissemination of Tai Chi.<\/jats:p>","DOI":"10.1145\/3765960","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T14:02:03Z","timestamp":1761314523000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["TC-YOLO: An Improved Tai Chi Movement Pose Estimation Algorithm Based on YOLOv8"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7060-2846","authenticated-orcid":false,"given":"Shouming","family":"Hou","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0165-2209","authenticated-orcid":false,"given":"Zixuan","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7278-2421","authenticated-orcid":false,"given":"Huichao","family":"He","sequence":"additional","affiliation":[{"name":"School of Physical Education and Taijiquan, Henan Polytechnic University, Jiaozuo, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1552-5092","authenticated-orcid":false,"given":"Aoyu","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4307-2835","authenticated-orcid":false,"given":"Ziying","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2296-2983","authenticated-orcid":false,"given":"Mingmin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang University, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/7068349"},{"issue":"1","key":"e_1_3_1_3_2","article-title":"Stacked capsule graph autoencoders for geometry-aware 3D head pose estimation","volume":"208","author":"Hong C.","year":"2021","unstructured":"C. Hong, L. Chen, Y. Liang, and Z. Zeng. 2021. Stacked capsule graph autoencoders for geometry-aware 3D head pose estimation. Computer Vision and Image Understanding 208\u2013209, 1 (2021), 103224.","journal-title":"Computer Vision and Image Understanding"},{"key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/978-3-030-58452-8_23","volume-title":"Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference","author":"Cao Z.","year":"2020","unstructured":"Z. Cao, H. Gao, K. Mangalam, Q. Z. Cai, M. Vo, and J. Malik. 2020. Long-term human motion prediction with scene context. In Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference. Springer International Publishing, 387\u2013404."},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","unstructured":"H. Boudlal M. Serrhini and A. Tahiri. 2023. Human activity monitoring system with commodity WiFi infrastructure using channel state information. Indonesian Journal of Electrical Engineering and Computer Science 31 2 (2023) 763.","DOI":"10.11591\/ijeecs.v31.i2.pp763-776"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2487860"},{"issue":"6","key":"e_1_3_1_7_2","first-page":"3742","article-title":"Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval","volume":"62","author":"Hong C.","year":"2014","unstructured":"C. Hong, J. Yu, D. Tao, and M. Wang. 2014. Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval. IEEE Transactions on Industrial Electronics 62, 6 (December 2014), 3742\u20133751.","journal-title":"IEEE Transactions on Industrial Electronics"},{"issue":"10","key":"e_1_3_1_8_2","doi-asserted-by":"crossref","first-page":"7107","DOI":"10.1109\/TII.2022.3143605","article-title":"ARHPE: Asymmetric relation-aware representation learning for head pose estimation in industrial human\u2013computer interaction","volume":"18","author":"Liu H.","year":"2022","unstructured":"H. Liu, T. Liu, Z. Zhang, A. K. Sangaiah, B. Yang, and Y. Li. 2022. ARHPE: Asymmetric relation-aware representation learning for head pose estimation in industrial human\u2013computer interaction. IEEE Transactions on Industrial Informatics 18, 10 (January 2022), 7107\u20137117.","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","unstructured":"W. Smith R. Philpot G. Gerdin K. Schenker S. Linn\u00e9r L. Larsson K. Mordal Moen and K. Westlie. 2021. School HPE: Its mandate responsibility and role in educating for social cohesion. Sport Education Society 26 5 (June 2021) 500\u2013513.","DOI":"10.1080\/13573322.2020.1742103"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3010248"},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","first-page":"102971","DOI":"10.1016\/j.ctim.2023.102971","article-title":"Effect of TAICHI CHUAN on health-related physical fitness in adults: A systematic review with meta-analysis","volume":"77","author":"Qi F.","year":"2023","unstructured":"F. Qi, K. G. Soh, N. J. Nasiruddin, O. S Leong, S. He, and H. Liu. 2023. Effect of TAICHI CHUAN on health-related physical fitness in adults: A systematic review with meta-analysis. Complementary Therapies in Medicine 77 (August 2023), 102971.","journal-title":"Complementary Therapies in Medicine"},{"key":"e_1_3_1_12_2","first-page":"1385","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR \u201911)","author":"Yang Y.","year":"2011","unstructured":"Y. Yang and D. Ramanan. 2011. Articulated pose estimation with flexible mixtures-of-parts. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR \u201911). IEEE, 1385\u20131392."},{"issue":"21","key":"e_1_3_1_13_2","doi-asserted-by":"crossref","first-page":"107712","DOI":"10.1016\/j.engappai.2023.107712","article-title":"Advancements in artificial intelligence for biometrics: A deep dive into model-based gait recognition techniques","volume":"130","author":"Parashar A.","year":"2024","unstructured":"A. Parashar, A. Parashar, M. Shabaz, D. Gupta, A. K. Sahu, and M. A. Khan. 2024. Advancements in artificial intelligence for biometrics: A deep dive into model-based gait recognition techniques. Engineering Applications of Artificial Intelligence 130, 21 (April 2024), 107712.","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"2","key":"e_1_3_1_14_2","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/TPAMI.2019.2932058","article-title":"Hierarchical deep click feature prediction for fine-grained image recognition","volume":"44","author":"Yu J.","year":"2019","unstructured":"J. Yu, M. Tan, H. Zhang, Y. Rui, and D. Tao. 2019. Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 2 (July 2019), 563\u2013578.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"e_1_3_1_15_2","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1007\/s13042-024-02262-9","article-title":"HOGFormer: High-order graph convolution transformer for 3D human pose estimation","volume":"16","author":"Xie Y.","year":"2024","unstructured":"Y. Xie, C. Hong, W. Zhuang, L. Liu, and J. Li. 2024. HOGFormer: High-order graph convolution transformer for 3D human pose estimation. International Journal of Machine Learning and Cybernetics 16 1 (2024), 599\u2013610.","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"e_1_3_1_16_2","first-page":"10769","article-title":"MfvPose: A multi-scale hybrid framework for human pose estimation","volume":"45","author":"Ran L.","year":"2023","unstructured":"L. Ran, C. Hong, X. Zhang, C. Tang, and Y. Xie. 2023. MfvPose: A multi-scale hybrid framework for human pose estimation. Journal of Intelligent & Fuzzy Systems 45, 6 (January 2023), 10769\u201310778.","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"e_1_3_1_17_2","first-page":"1","article-title":"A comprehensive survey on real time human pose estimation","author":"Datir A. P.","unstructured":"A. P. Datir, S. S. Funde, N. T. Bhore, S. B. Gawande, P. Dhade and P. Nehete. A comprehensive survey on real time human pose estimation. In 2023 IEEE International Students\u2019 Conference on Electrical, Electronics and Computer Science, Bhopal, India, 1\u20139.","journal-title":"2023 IEEE International Students\u2019 Conference on Electrical, Electronics and Computer Science,"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-025-20650-3"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.214"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.511"},{"key":"e_1_3_1_21_2","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/978-3-319-46484-8_29","volume-title":"Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference","author":"Newell A.","year":"2016","unstructured":"A. Newell, K. Yang, and J. Deng. 2016. Stacked hourglass networks for human pose estimation. In Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference. Springer International Publishing, 483\u2013499."},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00584"},{"issue":"4","key":"e_1_3_1_23_2","doi-asserted-by":"crossref","first-page":"3673","DOI":"10.1007\/s11760-024-03031-5","article-title":"A novel approach for simultaneous human activity recognition and pose estimation via skeleton-based leveraging WiFi CSI with YOLOv8 and media pipe frameworks","volume":"18","author":"Boudlal H.","year":"2024","unstructured":"H. Boudlal, M. Serrhini, and A. Tahiri. 2024. A novel approach for simultaneous human activity recognition and pose estimation via skeleton-based leveraging WiFi CSI with YOLOv8 and media pipe frameworks. Signal, Image and Video Processing 18, 4 (2024), 3673\u20133689.","journal-title":"Signal, Image and Video Processing"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00543"},{"issue":"6","key":"e_1_3_1_25_2","doi-asserted-by":"crossref","first-page":"7157","DOI":"10.1109\/TPAMI.2022.3222784","article-title":"AlphaPose: Whole-body regional multi-person pose estimation and tracking in real-time","volume":"45","author":"Fang H. S.","year":"2022","unstructured":"H. S. Fang, J. Li, H. Tang, C. Xu, H. Zhu, Y. Xiu, Y. L. Li, and C. Lu. 2022. AlphaPose: Whole-body regional multi-person pose estimation and tracking in real-time. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 6 (November 2022), 7157\u20137173.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00297"},{"key":"e_1_3_1_27_2","first-page":"38571","article-title":"Simple vision transformer baselines for human pose estimation","volume":"35","author":"Xu Y.","year":"2022","unstructured":"Y. Xu, J. Zhang, Q. Zhang, and T. D. Vitpose. 2022. Simple vision transformer baselines for human pose estimation. Advances in Neural Information Processing Systems 35 (December 2022), 38571\u201338584.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_28_2","doi-asserted-by":"crossref","first-page":"107866","DOI":"10.1016\/j.engappai.2024.107866","article-title":"Improved YOLOv8-GD deep learning model for defect detection in electroluminescence images of solar photovoltaic modules","volume":"131","author":"Cao Y.","year":"2024","unstructured":"Y. Cao, D. Pang, Q. Zhao, Y. Yan, Y. Jiang, C. Tian, F. Wang, and J. Li. 2024. Improved YOLOv8-GD deep learning model for defect detection in electroluminescence images of solar photovoltaic modules. Engineering Applications of Artificial Intelligence 131 (May 2024), 107866.","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.3390\/make5040083"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"e_1_3_1_31_2","unstructured":"A. Dosovitskiy L. Beyer A. Kolesnikov D. Weissenborn X. Zhai T. Unterthiner M. Dehghani M. Minderer G. Heigold S. Gelly et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929. Retrieved from https:\/\/arxiv.org\/abs\/2010.11929"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127280"},{"key":"e_1_3_1_33_2","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1109\/ICME55011.2023.00054","volume-title":"Proceedings of the 2023 IEEE International Conference on Multimedia and Expo (ICME)","author":"Zhang M.","year":"2023","unstructured":"M. Zhang, X. Yu, J. Rong, and L. Ou. 2023. RepNAS: Searching for efficient re-parameterizing blocks. In Proceedings of the 2023 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 270\u2013275."},{"key":"e_1_3_1_34_2","first-page":"1298","article-title":"ExpandNets: Linear over-parameterization to train compact convolutional networks","volume":"33","author":"Guo S.","year":"2020","unstructured":"S. Guo, J. M. Alvarez, and M. Salzmann. 2020. ExpandNets: Linear over-parameterization to train compact convolutional networks. Advances in Neural Information Processing Systems 33 (2020), 1298\u20131310.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_35_2","first-page":"568","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Hu M.","year":"2022","unstructured":"M. Hu, J. Feng, J. Hua, B. Lai, J. Huang, X. Gong, and X. S. Hua. 2022. Online convolutional re-parameterization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 568\u2013577."},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00913"},{"key":"e_1_3_1_38_2","first-page":"528","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Zhou P.","year":"2018","unstructured":"P. Zhou, B. Ni, C. Geng, J. Hu, and Y. Xu. 2018. Scale-transferrable object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 528\u2013537."},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00720"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"e_1_3_1_41_2","unstructured":"Y. Jiang Z. Tan J. Wang X. Sun M. Lin and H. Li. 2022. GiraffeDet: A heavy-neck paradigm for object detection. arXiv:2202.04256. Retrieved from https:\/\/arxiv.org\/abs\/2202.04256"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00667"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"e_1_3_1_44_2","doi-asserted-by":"crossref","first-page":"109579","DOI":"10.1016\/j.patcog.2023.109579","article-title":"RA-YOLOX: Re-parameterization align decoupled head and novel label assignment scheme based on YOLOX","volume":"140","author":"Zhao Z.","year":"2023","unstructured":"Z. Zhao, C. He, G. Zhao, J. Zhou, and K. Hao. 2023. RA-YOLOX: Re-parameterization align decoupled head and novel label assignment scheme based on YOLOX. Pattern Recognition 140 (August 2023), 109579.","journal-title":"Pattern Recognition"},{"key":"e_1_3_1_45_2","first-page":"5349","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Aboah A.","year":"2023","unstructured":"A. Aboah, B. Wang, U. Bagci, and Y. Adu-Gyamfi. 2023. Real-time multi-class helmet violation detection using few-shot data sampling technique and YOLOv8. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 5349\u20135357."},{"key":"e_1_3_1_46_2","unstructured":"A. Bochkovskiy C. Y. Wang and H. Y. Liao. 2020. YOLOv4: Optimal speed and accuracy of object detection. arXiv:2004.10934. Retrieved from https:\/\/arxiv.org\/abs\/2004.10934"},{"key":"e_1_3_1_47_2","unstructured":"A. Shen. 2019. AI Toolbox. Retrieved from https:\/\/github.com\/monkeyDemo\/AI-ToolBox"},{"key":"e_1_3_1_48_2","first-page":"529","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV)","author":"Sun X.","year":"2018","unstructured":"X. Sun, B. Xiao, F. Wei, S. Liang, and Y. Wei. 2018. Integral human pose regression. In Proceedings of the European Conference on Computer Vision (ECCV), 529\u2013545."},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01084"},{"key":"e_1_3_1_51_2","first-page":"89","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Li Y.","year":"2022","unstructured":"Y. Li, S. Yang, P. Liu, S. Zhang, Y. Wang, Z. Wang, W. Yang, and S. T. Xia. 2022. SimCC: A simple coordinate classification perspective for human pose estimation. In Proceedings of the European Conference on Computer Vision. Springer Nature Switzerland, 89\u2013106."},{"key":"e_1_3_1_52_2","unstructured":"T. Jiang P. Lu L. Zhang N. Ma R. Han C. Lyu Y. Li and K. Chen. 2023. RTMPose: Real-time multi-person pose estimation based on MMPose. arXiv:2303.07399. Retrieved from https:\/\/arxiv.org\/abs\/2303.07399"},{"key":"e_1_3_1_53_2","unstructured":"G. Jocher. 2023. Ultralytics. Retrieved from https:\/\/docs.ultralytics.com\/zh\/models\/yolov8\/"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.daach.2024.e00343"}],"container-title":["Journal on Computing and Cultural Heritage"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3765960","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T14:21:22Z","timestamp":1769091682000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3765960"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,22]]},"references-count":53,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,3,31]]}},"alternative-id":["10.1145\/3765960"],"URL":"https:\/\/doi.org\/10.1145\/3765960","relation":{},"ISSN":["1556-4673","1556-4711"],"issn-type":[{"value":"1556-4673","type":"print"},{"value":"1556-4711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,22]]},"assertion":[{"value":"2024-05-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}