{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T11:29:15Z","timestamp":1764588555166,"version":"3.37.3"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61862015"],"award-info":[{"award-number":["61862015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Science and Technology Project of Guangxi","award":["AD21220114"],"award-info":[{"award-number":["AD21220114"]}]},{"name":"the Guangxi Key Research and Development Program","award":["AB17195025"],"award-info":[{"award-number":["AB17195025"]}]},{"name":"Guangxi Natural Science Foundation","award":["2024GXNSFAA010493"],"award-info":[{"award-number":["2024GXNSFAA010493"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s00530-024-01463-0","type":"journal-article","created":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T17:02:52Z","timestamp":1725987772000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-scale motion contrastive learning for self-supervised skeleton-based action recognition"],"prefix":"10.1007","volume":"30","author":[{"given":"Yushan","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zengmin","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengwei","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianchi","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruxing","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongyuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"key":"1463_CR1","unstructured":"Qin, Z., Liu, Y., Perera, M., Gedeon, T., Ji, P., Kim, D., Anwar, S.: Anubis: Skeleton action recognition dataset, review, and benchmark. arXiv preprint arXiv:2211.09590. (2022)"},{"issue":"18","key":"1463_CR2","doi-asserted-by":"publisher","first-page":"27867","DOI":"10.1007\/s11042-021-10811-5","volume":"80","author":"MA Khan","year":"2021","unstructured":"Khan, M.A., Mittal, M., Goyal, L.M., Roy, S.: A deep survey on supervised learning based human detection and activity classification methods. Multimed. Tools and Appl. 80(18), 27867\u201327923 (2021)","journal-title":"Multimed. Tools and Appl."},{"issue":"16","key":"1463_CR3","doi-asserted-by":"publisher","first-page":"22307","DOI":"10.1007\/s11042-021-11131-4","volume":"81","author":"N Varshney","year":"2022","unstructured":"Varshney, N., Bakariya, B., Kushwaha, A.K.S.: Human activity recognition using deep transfer learning of cross position sensor based on vertical distribution of data. Multimed. Tools Appl. 81(16), 22307\u201322322 (2022)","journal-title":"Multimed. Tools Appl."},{"issue":"3","key":"1463_CR4","doi-asserted-by":"publisher","first-page":"4533","DOI":"10.1007\/s11042-022-13441-7","volume":"82","author":"Z Guo","year":"2023","unstructured":"Guo, Z., Hou, Y., Xiao, R., Li, C., Li, W.: Motion saliency based hierarchical attention network for action recognition. Multimed. Tools Appl. 82(3), 4533\u20134550 (2023)","journal-title":"Multimed. Tools Appl."},{"issue":"1","key":"1463_CR5","first-page":"8303","volume":"33","author":"J Gao","year":"2019","unstructured":"Gao, J., Zhang, T., Xu, C.: I know the relationships: zero-shot action recognition via two-stream graph convolutional networks and knowledge graphs. Proc. AAAI Conf. Artif. Intell. 33(1), 8303\u20138311 (2019)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"1463_CR6","doi-asserted-by":"crossref","unstructured":"Gao, J., Chen, M., Xu, C.: Vectorized evidential learning for weakly-supervised temporal action localization. IEEE transactions on pattern analysis and machine intelligence (2023)","DOI":"10.1109\/CVPR52729.2023.01416"},{"key":"1463_CR7","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489\u20134497 (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"1463_CR8","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.patcog.2016.08.003","volume":"61","author":"A Jalal","year":"2017","unstructured":"Jalal, A., Kim, Y.H., Kim, Y.J., Kamal, S., Kim, D.: Robust human activity recognition from depth video using spatiotemporal multi-fused features. Pattern Recognit. 61, 295\u2013308 (2017)","journal-title":"Pattern Recognit."},{"key":"1463_CR9","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.cogsys.2018.04.002","volume":"50","author":"A Akula","year":"2018","unstructured":"Akula, A., Shah, A.K., Ghosh, R.: Deep learning approach for human action recognition in infrared images. Cogn. Syst. Res. 50, 146\u2013154 (2018)","journal-title":"Cogn. Syst. Res."},{"key":"1463_CR10","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"1463_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lan, C., Zeng, W., Chen, Z.: Densely semantically aligned person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 667\u2013676 (2019)","DOI":"10.1109\/CVPR.2019.00076"},{"key":"1463_CR12","doi-asserted-by":"crossref","unstructured":"Karianakis, N., Liu, Z., Chen, Y., Soatto, S.: Reinforced temporal attention and split-rate transfer for depth-based person re-identification. In: Proceedings of the European Conference on Computer Vision, pp. 715\u2013733 (2018)","DOI":"10.1007\/978-3-030-01228-1_44"},{"key":"1463_CR13","unstructured":"Ge, Y., Zhu, F., Chen, D., Zhao, R., Li, H.: Self-paced contrastive learning with hybrid memory for domain adaptive object re-id. In: Proceedings of the Annual Conference on Neural Information Processing Systems (2020)"},{"key":"1463_CR14","doi-asserted-by":"crossref","unstructured":"Wang, Y., Li, M., Cai, H., Chen, W., Han, S.: Lite pose: Efficient architecture design for 2d human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 13126\u201313136 (2022)","DOI":"10.1109\/CVPR52688.2022.01278"},{"key":"1463_CR15","unstructured":"Zhou, Y., Li, C., Cheng, Z.Q., Geng, Y., Xie, X., Keuper, M.: Hypergraph transformer for skeleton-based action recognition. arXiv preprint arXiv:2211.09590 (2022)"},{"issue":"3","key":"1463_CR16","first-page":"3427","volume":"37","author":"J Zhang","year":"2023","unstructured":"Zhang, J., Lin, L., Liu, J.: Hierarchical consistent contrastive learning for skeleton-based action recognition with growing augmentations. Proc. AAAI Conf. Artif. Intell. 37(3), 3427\u20133435 (2023)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"5","key":"1463_CR17","first-page":"4487","volume":"38","author":"K Peng","year":"2024","unstructured":"Peng, K., Yin, C., Zheng, J., Liu, R., Schneider, D., Zhang, J., Yang, K., Saquib Sarfraz, M., Stiefelhagen, R., Roitberg, A.: Navigating open set scenarios for skeleton-based action recognition. Proc. AAAI Conf. Artif. Intell. 38(5), 4487\u20134496 (2024)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"1463_CR18","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, Z., Yuan, C., Li, B., Deng, Y., Hu, W.: Channel-wise topology refinement graph convolution for skeleton-based action recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 13359\u201313368 (2021)","DOI":"10.1109\/ICCV48922.2021.01311"},{"key":"1463_CR19","doi-asserted-by":"crossref","unstructured":"Chi, H., Ha, M.H., Chi, S., Lee, S.W., Huang, Q., Ramani, K.: Infogcn: Representation learning for human skeleton-based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 20186\u201320196 (2022)","DOI":"10.1109\/CVPR52688.2022.01955"},{"key":"1463_CR20","doi-asserted-by":"crossref","unstructured":"Ye, F., Pu, S., Zhong, Q., Li, C., Xie, D., Tang, H.: Dynamic gcn: Context-enriched topology learning for skeleton-based action recognition. In: Proceedings of the 28th ACM international conference on multimedia, pp. 55\u201363 (2020)","DOI":"10.1145\/3394171.3413941"},{"key":"1463_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, P., Lan, C., Zeng, W., Xing, J., Xue, J., Zheng, N.: Semantics-guided neural networks for efficient skeleton-based human action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1112\u20131121 (2020)","DOI":"10.1109\/CVPR42600.2020.00119"},{"key":"1463_CR22","doi-asserted-by":"crossref","unstructured":"Chen, Z., Li, S., Yang, B., Li, Q., Liu, H.: Multi-scale spatial temporal graph convolutional network for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1113\u20131122 (2021)","DOI":"10.1609\/aaai.v35i2.16197"},{"key":"1463_CR23","doi-asserted-by":"crossref","unstructured":"Kim, B., Chang, H.J., Kim, J., Choi, J.Y.: Global-local motion transformer for unsupervised skeleton-based action learning. In: Proceedings of the European Conference on Computer Vision, pp. 209\u2013225 (2022)","DOI":"10.1007\/978-3-031-19772-7_13"},{"key":"1463_CR24","doi-asserted-by":"crossref","unstructured":"Su, K., Liu, X., Shlizerman, E.: Predict cluster: Unsupervised skeleton based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9631\u20139640 (2020)","DOI":"10.1109\/CVPR42600.2020.00965"},{"key":"1463_CR25","doi-asserted-by":"crossref","unstructured":"Yang, S., Liu, J., Lu, S., Er, M.H., Kot, A.C.: Skeleton cloud colorization for unsupervised 3d action representation learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 13423\u201313433 (2021)","DOI":"10.1109\/ICCV48922.2021.01317"},{"key":"1463_CR26","doi-asserted-by":"crossref","unstructured":"Zheng, N., Wen, J., Liu, R., Long, L., Dai, J., Gong, Z.: Unsupervised representation learning with long-term dynamics for skeleton based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)","DOI":"10.1609\/aaai.v32i1.11853"},{"key":"1463_CR27","doi-asserted-by":"crossref","unstructured":"Wu, W., Hua, Y., Zheng, C., Wu, S., Chen, C., Lu, A.: Skeletonmae: Spatial-temporal masked autoencoders for self-supervised skeleton action recognition. In: Proceedings of the IEEE International Conference on Multimedia and Expo Workshops, pp. 224\u2013229 (2023)","DOI":"10.1109\/ICMEW59549.2023.00045"},{"key":"1463_CR28","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.ins.2021.04.023","volume":"569","author":"H Rao","year":"2021","unstructured":"Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented skeleton based contrastive action learning with momentum lstm for unsupervised action recognition. Inf. Sci. 569, 90\u2013109 (2021)","journal-title":"Inf. Sci."},{"issue":"1","key":"1463_CR29","first-page":"762","volume":"36","author":"T Guo","year":"2022","unstructured":"Guo, T., Liu, H., Chen, Z., Liu, M., Wang, T., Ding, R.: Contrastive learning from extremely augmented skeleton sequences for self-supervised action recognition. Inf. Sci. 36(1), 762\u2013770 (2022)","journal-title":"Inf. Sci."},{"key":"1463_CR30","doi-asserted-by":"crossref","unstructured":"Dang, L., Nie, Y., Long, C., Zhang, Q., Li, G.: Msr-gcn: Multi-scale residual graph convolution networks for human motion prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 11467\u201311476 (2021)","DOI":"10.1109\/ICCV48922.2021.01127"},{"key":"1463_CR31","unstructured":"Rao, H., Miao, C.: Skeleton prototype contrastive learning with multi-level graph relation modeling for unsupervised person re-identification. arXiv preprint arXiv:2208.11814. (2022)"},{"key":"1463_CR32","unstructured":"Xu, B., Shu, X.: Pyramid self-attention polymerization learning for semi-supervised skeleton-based action recognition. arXiv preprint arXiv:2302.02327 (2023)"},{"key":"1463_CR33","doi-asserted-by":"crossref","unstructured":"Jiang, S., Sun, B., Wang, L., Bai, Y., Li, K., Fu, Y.: Skeleton aware multi-modal sign language recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3413\u20133423 (2021)","DOI":"10.1109\/CVPRW53098.2021.00380"},{"key":"1463_CR34","doi-asserted-by":"crossref","unstructured":"Li, L., Wang, M., Ni, B., Wang, H., Yang, J., Zhang, W.: 3d human action representation learning via cross-view consistency pursuit. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4741\u20134750 (2021)","DOI":"10.1109\/CVPR46437.2021.00471"},{"key":"1463_CR35","unstructured":"Chen, Z., Liu, H., Guo, T., Chen, Z., Song, P., Tang, H.: Contrastive learning from spatio-temporal mixed skeleton sequences for self-supervised skeleton-based action recognition. arXiv preprint arXiv:2207.03065 (2022)"},{"key":"1463_CR36","doi-asserted-by":"crossref","unstructured":"R., V., Chellapa, R.: Rolling rotations for recognizing human actions from 3d skeletal data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4471\u20134479 (2016)","DOI":"10.1109\/CVPR.2016.484"},{"key":"1463_CR37","unstructured":"Chen, X., Fan, H., Girshick, R., He, K.: Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 (2020)"},{"key":"1463_CR38","unstructured":"Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"1463_CR39","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser., Polosukhin, I.: Attention is all you need. In: Proceedings of the Advances in Neural Information Processing Systems, 30 (2017)"},{"issue":"10s","key":"1463_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3505244","volume":"54","author":"S Khan","year":"2021","unstructured":"Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S., Shah, M.: Transformers in vision: a survey. ACM Comput. Surv. 54(10s), 1\u201341 (2021)","journal-title":"ACM Comput. Surv."},{"key":"1463_CR41","doi-asserted-by":"crossref","unstructured":"Li, M., Chen, S., Zhao, Y., Zhang, Y., Wang, Y., Tian, Q.: Dynamic multi-scale graph neural networks for 3d skeleton based human motion prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 214\u2013223 (2020)","DOI":"10.1109\/CVPR42600.2020.00029"},{"key":"1463_CR42","doi-asserted-by":"crossref","unstructured":"Gebotys, B., Wong, A., Clausi, D.A.: M2a: Motion aware attention for accurate video action recognition. In: Proceedings of the 19th IEEE Conference on Robots and Vision, pp. 83\u201389 (2022)","DOI":"10.1109\/CRV55824.2022.00019"},{"issue":"5","key":"1463_CR43","first-page":"5549","volume":"45","author":"X Wang","year":"2022","unstructured":"Wang, X., Qi, G.J.: Contrastive learning with stronger augmentations. IEEE Trans. Pattern Anal. Mach. Intell. 45(5), 5549\u20135560 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1463_CR44","doi-asserted-by":"publisher","first-page":"2317","DOI":"10.1007\/s10489-021-02487-z","volume":"52","author":"Y Yoon","year":"2022","unstructured":"Yoon, Y., Yu, J., Jeon, M.: Predictively encoded graph convolutional network for noise-robust skeleton-based action recognition. Appl. Intell. 52, 2317\u20132331 (2022)","journal-title":"Appl. Intell."},{"issue":"1","key":"1463_CR45","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 60 (2019)","journal-title":"J. Big Data"},{"key":"1463_CR46","doi-asserted-by":"crossref","unstructured":"Shahroudy, A., Liu, J., Ng, T.T., Wang, G.: Ntu rgb+d: A large scale dataset for 3D human activity analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1010\u20131019 (2016)","DOI":"10.1109\/CVPR.2016.115"},{"key":"1463_CR47","doi-asserted-by":"crossref","unstructured":"Liu, J., Shahroudy, A., Perez, M.L., Wang, G., Duan, L.Y., Chichung, A.K.: Ntu rgb+d 120: A large-scale benchmark for 3D human activity understanding. In: IEEE transactions on pattern analysis and machine intelligence (2019)","DOI":"10.1109\/TPAMI.2019.2916873"},{"key":"1463_CR48","doi-asserted-by":"crossref","unstructured":"Liu, C., Hu, Y., Li, Y., Song, S., Liu, J.: Pku-mmd: A large scale benchmark for skeleton-based human action understanding. In: Proceedings of the Workshop on Visual Analysis in Smart and Connected Communities, pp. 1\u20138 (2017)","DOI":"10.1145\/3132734.3132739"},{"key":"1463_CR49","doi-asserted-by":"crossref","unstructured":"Lin, L., Song, S., Yang, W., Liu, J.: Ms2l: Multi-task self-supervised learning for skeleton based action recognition. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2490\u20132498 (2020)","DOI":"10.1145\/3394171.3413548"},{"key":"1463_CR50","doi-asserted-by":"crossref","unstructured":"Nie, Q., Liu, Z.W., Liu, Y.H.: Unsupervised 3d human pose representation with viewpoint and pose disentanglement. In: Proceedings of the European Conference on Computer Vision, pp. 102\u2013118 (2020)","DOI":"10.1007\/978-3-030-58529-7_7"},{"issue":"3","key":"1463_CR51","first-page":"3825","volume":"37","author":"Y Zhou","year":"2023","unstructured":"Zhou, Y., Duan, H., Rao, A., Su, B., Wang, J.: Self-supervised action representation learning from partial spatio-temporal skeleton sequences. Proc. AAAI Conf. Artif. Intell. 37(3), 3825\u20133833 (2023)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"1463_CR52","doi-asserted-by":"crossref","unstructured":"Thoker, F.M., Doughty, H., Snoek, C.G.M.: Skeleton-contrastive 3D action representation learning. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 1655\u20131663 (2021)","DOI":"10.1145\/3474085.3475307"},{"issue":"8","key":"1463_CR53","doi-asserted-by":"publisher","first-page":"1963","DOI":"10.1109\/TPAMI.2019.2896631","volume":"41","author":"P Zhang","year":"2019","unstructured":"Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View adaptive neural networks for high performance skeleton-based human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1963\u20131978 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"1463_CR54","first-page":"2579","volume":"9","author":"L Van Der Maaten","year":"2008","unstructured":"Van Der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01463-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-024-01463-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01463-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T18:12:55Z","timestamp":1730139175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-024-01463-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,10]]},"references-count":54,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["1463"],"URL":"https:\/\/doi.org\/10.1007\/s00530-024-01463-0","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"type":"print","value":"0942-4962"},{"type":"electronic","value":"1432-1882"}],"subject":[],"published":{"date-parts":[[2024,9,10]]},"assertion":[{"value":"12 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"267"}}