{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T04:19:24Z","timestamp":1743394764368,"version":"3.40.3"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T00:00:00Z","timestamp":1741564800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T00:00:00Z","timestamp":1741564800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306203","62241102"],"award-info":[{"award-number":["62306203","62241102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010881","name":"Suzhou Municipal Science and Technology Bureau","doi-asserted-by":"publisher","award":["SYG202351","SYG202129","62202205","U24A20263"],"award-info":[{"award-number":["SYG202351","SYG202129","62202205","U24A20263"]}],"id":[{"id":"10.13039\/501100010881","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s40747-025-01823-x","type":"journal-article","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T03:31:07Z","timestamp":1741577467000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modelling object mask interaction for compositional action recognition"],"prefix":"10.1007","volume":"11","author":[{"given":"Xinya","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6701-1965","authenticated-orcid":false,"given":"Zhongwei","family":"Shen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6709-6583","authenticated-orcid":false,"given":"Benlian","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5451-4870","authenticated-orcid":false,"given":"Rongchang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Mingli","family":"Lu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4249-7003","authenticated-orcid":false,"given":"Jinliang","family":"Cong","sequence":"additional","affiliation":[]},{"given":"Longxin","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,10]]},"reference":[{"key":"1823_CR1","doi-asserted-by":"crossref","unstructured":"Materzynska J, Xiao T, Herzig R Xu H, Wang X, Darrell T (2020) Something-else: compositional action recognition with spatial-temporal interaction networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. Springer, pp 1049\u20131059","DOI":"10.1109\/CVPR42600.2020.00113"},{"key":"1823_CR2","doi-asserted-by":"crossref","unstructured":"Zhou B, Andonian A, Oliva A,Torralba A (2018) Temporal relational reasoning in videos. In: Proceedings of the European conference on computer vision (ECCV). Springer, pp 803\u2013818","DOI":"10.1007\/978-3-030-01246-5_49"},{"key":"1823_CR3","doi-asserted-by":"crossref","unstructured":"Girdhar R, Carreira J, Doersch C, Zisserman A (2019) Video action transformer network. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. Springer, pp 244\u2013253","DOI":"10.1109\/CVPR.2019.00033"},{"key":"1823_CR4","doi-asserted-by":"crossref","unstructured":"Baradel F, Neverova N, Wolf C et\u00a0al (2018) Object level visual reasoning in videos. arXiv:1806.06157. https:\/\/api.semanticscholar.org\/CorpusID:49302513","DOI":"10.1007\/978-3-030-01261-8_7"},{"key":"1823_CR5","doi-asserted-by":"crossref","unstructured":"Sun C, Shrivastava A, Vondrick C et\u00a0al (2018) Actor-centric relation network. In: Proceedings of the European conference on computer vision (ECCV), pp 318\u2013334","DOI":"10.1007\/978-3-030-01252-6_20"},{"key":"1823_CR6","doi-asserted-by":"crossref","unstructured":"Herzig R, Ben-Avraham E, Mangalam K et\u00a0al (2021) Object-region video transformers. In: 2022 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 3138\u20133149. https:\/\/api.semanticscholar.org\/CorpusID:238744000","DOI":"10.1109\/CVPR52688.2022.00315"},{"key":"1823_CR7","doi-asserted-by":"crossref","unstructured":"Zhang Y, Tokmakov P, Hebert M et\u00a0al (2019) A structured model for action detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9975\u20139984","DOI":"10.1109\/CVPR.2019.01021"},{"key":"1823_CR8","doi-asserted-by":"crossref","unstructured":"Qi J, Ma L, Yu Cui Z, Y, (2024) Computer vision-based hand gesture recognition for human-robot interaction: a review. Complex Intell Syst 10(1):1581\u20131606","DOI":"10.1007\/s40747-023-01173-6"},{"key":"1823_CR9","doi-asserted-by":"crossref","unstructured":"Zhang C, Gupta A, Zisserman A (2022) Is an object-centric video representation beneficial for transfer? arXiv e-prints","DOI":"10.1007\/978-3-031-26316-3_23"},{"key":"1823_CR10","doi-asserted-by":"crossref","unstructured":"Carreira J, Zisserman A (2017) Quo vadis, action recognition? A new model and the kinetics dataset. In: proceedings of the IEEE conference on computer vision and pattern recognition IEEE, pp 6299\u20136308","DOI":"10.1109\/CVPR.2017.502"},{"key":"1823_CR11","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Fan H, Malik J He K (2019) Slowfast networks for video recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision. IEEE, pp 6202\u20136211","DOI":"10.1109\/ICCV.2019.00630"},{"key":"1823_CR12","unstructured":"Bertasius G, Wang H, Torresani L (2021) Is space-time attention all you need for video understanding? In: ICML, vol 2(3), p\u00a04"},{"key":"1823_CR13","unstructured":"Patrick M, Campbell D, Misra Asano Y, I, Metze F, Feichtenhofer C, Vedaldi A, Henriques JF, (2021) Keeping your eye on the ball: trajectory attention in video transformers. Adv Neural Inf Process Syst 34:12493\u201312506"},{"key":"1823_CR14","doi-asserted-by":"crossref","unstructured":"Ge D, Cheng Y, Ma Cao S, Y, Wu Y, (2024) An enhanced abnormal information expression spatiotemporal model for anomaly detection in multivariate time-series. Complex Intell Syst 10(2):2937\u20132950","DOI":"10.1007\/s40747-023-01306-x"},{"issue":"6","key":"1823_CR15","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R et al (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1823_CR16","doi-asserted-by":"crossref","unstructured":"Kuehne H, Jhuang H, Garrote E et\u00a0al (2011) HMDB: a large video database for human motion recognition. In: 2011 International conference on computer vision. IEEE, pp 2556\u20132563","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"1823_CR17","unstructured":"Soomro K, Zamir AR, Shah M (2012) UCF101: a dataset of 101 human actions classes from videos in the wild. Comput Sci"},{"key":"1823_CR18","doi-asserted-by":"crossref","unstructured":"Sigurdsson GA, Varol G, Wang X Farhadi A, Laptev I, Gupta A (2016) Hollywood in homes: crowdsourcing data collection for activity understanding. Springer, Cham","DOI":"10.1007\/978-3-319-46448-0_31"},{"key":"1823_CR19","doi-asserted-by":"crossref","unstructured":"Jiang Y-G, Wu Z et\u00a0al (2017) Exploiting feature and class relationships in video categorization with regularized deep neural networks. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2017.2670560"},{"key":"1823_CR20","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Pinz A, Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1933\u20131941","DOI":"10.1109\/CVPR.2016.213"},{"key":"1823_CR21","doi-asserted-by":"crossref","unstructured":"Lin J, Gan C, Han S (2019) TSM: temporal shift module for efficient video understanding. In: Proceedings of the IEEE\/CVF international conference on computer vision. IEEE, pp 7083\u20137093","DOI":"10.1109\/ICCV.2019.00718"},{"key":"1823_CR22","unstructured":"Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. Adv Neural Inf Process Syst 1"},{"key":"1823_CR23","doi-asserted-by":"crossref","unstructured":"Wang L, Xiong Y, Wang Z Qiao, Y, Lin, D, Tang X, Van Gool L (2016) Temporal segment networks: towards good practices for deep action recognition. arXiv e-prints","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"1823_CR24","doi-asserted-by":"crossref","unstructured":"Wang L, Xiong Y, Wang Z et\u00a0al (2016) Temporal segment networks: towards good practices for deep action recognition. In: European conference on computer vision. Springer, Berlin, pp 20\u201336","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"1823_CR25","doi-asserted-by":"crossref","unstructured":"Wu Z, Jiang YG, Wang X et\u00a0al (2016) Multi-stream multi-class fusion of deep networks for video classification. In: Proceedings of the 24th ACM international conference on multimedia, pp 791\u2013800","DOI":"10.1145\/2964284.2964328"},{"key":"1823_CR26","doi-asserted-by":"crossref","unstructured":"Wu Z, Wang X, Jiang YG et\u00a0al (2015) Modeling spatial-temporal clues in a hybrid deep learning framework for video classification. IEEE Trans Multimed","DOI":"10.1145\/2733373.2806222"},{"key":"1823_CR27","doi-asserted-by":"crossref","unstructured":"Karpathy A, Toderici G, Shetty S et\u00a0al (2014) Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1725\u20131732","DOI":"10.1109\/CVPR.2014.223"},{"key":"1823_CR28","doi-asserted-by":"crossref","unstructured":"Zhang D, Zhang H, Tang J et\u00a0al (2020) Feature pyramid transformer. arXiv:2007.09451. https:\/\/api.semanticscholar.org\/CorpusID:220647080","DOI":"10.1007\/978-3-030-58604-1_20"},{"key":"1823_CR29","doi-asserted-by":"crossref","unstructured":"Hara K, Kataoka H, Satoh Y (2018) Can spatiotemporal 3D CNNs retrace the history of 2D CNNs and ImageNet? In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6546\u20136555","DOI":"10.1109\/CVPR.2018.00685"},{"key":"1823_CR30","doi-asserted-by":"crossref","unstructured":"Li Y, Lin W, Wang T et\u00a0al (2020) Finding action tubes with a sparse-to-dense framework. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v34i07.6811"},{"key":"1823_CR31","unstructured":"Liu J, Shahroudy A, Xu D et\u00a0al (2017) Skeleton-based action recognition using spatio-temporal LSTM network with trust gates. IEEE Trans Pattern Anal Mach Intell PP(99):1"},{"key":"1823_CR32","doi-asserted-by":"crossref","unstructured":"Tran D, Bourdev L, Fergus R Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE international conference on computer vision IEEE 4489\u20134497","DOI":"10.1109\/ICCV.2015.510"},{"key":"1823_CR33","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick RB, Gupta AK et\u00a0al (2017) Non-local neural networks. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition, pp 7794\u20137803. https:\/\/api.semanticscholar.org\/CorpusID:4852647","DOI":"10.1109\/CVPR.2018.00813"},{"key":"1823_CR34","doi-asserted-by":"crossref","unstructured":"Arnab A, Dehghani M, Heigold G et\u00a0al (2021) ViViT: a video vision transformer. In: 2021 IEEE\/CVF international conference on computer vision (ICCV), pp 6816\u20136826. https:\/\/api.semanticscholar.org\/CorpusID:232417054","DOI":"10.1109\/ICCV48922.2021.00676"},{"key":"1823_CR35","doi-asserted-by":"crossref","unstructured":"Neimark D, Bar O, Zohar M et\u00a0al (2021) Video transformer network. In: 2021 IEEE\/CVF international conference on computer vision workshops (ICCVW), pp 3156\u20133165. https:\/\/api.semanticscholar.org\/CorpusID:231741093","DOI":"10.1109\/ICCVW54120.2021.00355"},{"key":"1823_CR36","doi-asserted-by":"crossref","unstructured":"Liu Z, Ning J, Cao Y et\u00a0al (2022) Video swin transformer. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 3202\u20133211","DOI":"10.1109\/CVPR52688.2022.00320"},{"key":"1823_CR37","doi-asserted-by":"crossref","unstructured":"Kato K, Li Y, Gupta A (2018) Compositional learning for human object interaction. In: Proceedings of the European conference on computer vision (ECCV), pp 234\u2013251","DOI":"10.1007\/978-3-030-01264-9_15"},{"key":"1823_CR38","doi-asserted-by":"crossref","unstructured":"Shao D, Zhao Y, Dai B et\u00a0al (2020) Finegym: a hierarchical video dataset for fine-grained action understanding. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2616\u20132625","DOI":"10.1109\/CVPR42600.2020.00269"},{"key":"1823_CR39","doi-asserted-by":"crossref","unstructured":"Ji J, Krishna R, Fei-Fei L et\u00a0al (2019) Action genome: actions as compositions of spatio-temporal scene graphs. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 10233\u201310244. https:\/\/api.semanticscholar.org\/CorpusID:209376177","DOI":"10.1109\/CVPR42600.2020.01025"},{"key":"1823_CR40","unstructured":"Yan R, Xie L, Shu X et\u00a0al (2020) Interactive fusion of multi-level features for compositional activity recognition. arXiv preprint. arXiv:2012.05689"},{"key":"1823_CR41","doi-asserted-by":"crossref","unstructured":"Sun P, Wu B, Li X Li W, Duan L, Gan C (2021) Counterfactual debiasing inference for compositional action recognition. In: Proceedings of the 29th ACM international conference on multimedia, ACM, pp 3220\u20133228","DOI":"10.1145\/3474085.3475472"},{"issue":"1\u20133","key":"1823_CR42","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/0010-0277(84)90022-2","volume":"18","author":"DD Hoffman","year":"1984","unstructured":"Hoffman DD, Richards WA (1984) Parts of recognition. Cognition 18(1\u20133):65\u201396","journal-title":"Cognition"},{"key":"1823_CR43","doi-asserted-by":"crossref","unstructured":"Tulsiani S, Su H, Guibas LJ et\u00a0al (2017) Learning shape abstractions by assembling volumetric primitives. IEEE","DOI":"10.1109\/CVPR.2017.160"},{"key":"1823_CR44","doi-asserted-by":"crossref","unstructured":"Li R, Feng Z, Xu T Li L, Wu XJ, Awais, M, Atito S, Kittler Josef (2024) C2C: component-to-composition learning for zero-shot compositional action recognition. In: European conference on computer vision. Springer, Berlin, pp 369\u2013388","DOI":"10.1007\/978-3-031-72920-1_21"},{"key":"1823_CR45","doi-asserted-by":"crossref","unstructured":"Shao D, Zhao Y, Dai B et\u00a0al (2020) Intra-and inter-action understanding via temporal action parsing. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 730\u2013739","DOI":"10.1109\/CVPR42600.2020.00081"},{"key":"1823_CR46","doi-asserted-by":"crossref","unstructured":"Ji S, Xu W, Yang M et\u00a0al (2010) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35:221\u2013231. https:\/\/api.semanticscholar.org\/CorpusID:1923924","DOI":"10.1109\/TPAMI.2012.59"},{"key":"1823_CR47","doi-asserted-by":"crossref","unstructured":"Ma CY, Kadav A, Melvin I et\u00a0al (2018) Attend and interact: higher-order object interactions for video understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6790\u20136800","DOI":"10.1109\/CVPR.2018.00710"},{"key":"1823_CR48","doi-asserted-by":"crossref","unstructured":"Krishna R, Zhu Y, Groth O et\u00a0al (2017) Visual genome: connecting language and vision using crowdsourced dense image annotations. Int J Comput Vis 123(1)","DOI":"10.1007\/s11263-016-0981-7"},{"key":"1823_CR49","doi-asserted-by":"crossref","unstructured":"Jiao Y, Yang W, Zeng Xing W, S, Geng L, (2024) TAN: a temporal-aware attention network with context-rich representation for boosting proposal generation. Complex Intell Syst 10(3):3691\u20133708","DOI":"10.1007\/s40747-024-01343-0"},{"key":"1823_CR50","doi-asserted-by":"crossref","unstructured":"Kumie GA, Habtie MA, Ayall TA Zhou C, Liu H, Seid AM, Erbad A (2024) Dual-attention network for view-invariant action recognition. Complex Intell Syst 10(1):305\u2013321","DOI":"10.1007\/s40747-023-01171-8"},{"key":"1823_CR51","unstructured":"Yan R, Xie L, Tang J et\u00a0al (2020) HiGCIN: hierarchical graph-based cross inference network for group activity recognition. IEEE Trans Pattern Anal Mach Intell PP(99):1"},{"key":"1823_CR52","doi-asserted-by":"crossref","unstructured":"Wang X, Gupta AK (2018) Videos as space-time region graphs. arXiv:1806.01810. https:\/\/api.semanticscholar.org\/CorpusID:46940850","DOI":"10.1007\/978-3-030-01228-1_25"},{"key":"1823_CR53","doi-asserted-by":"crossref","unstructured":"Kirillov A, Mintun E, Ravi N Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo W-Y, Dollar P, Girshick R (2023) Segment anything. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV). IEEE, pp 4015\u20134026","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"1823_CR54","unstructured":"Radford A, Kim JW, Hallacy C Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J et al (2021) Learning transferable visual models from natural language supervision. In: International conference on machine learning, PMLR, pp 8748\u20138763"},{"key":"1823_CR55","doi-asserted-by":"crossref","unstructured":"Goyal R, Kahou SE, Michalski V Materzynska J, Westphal S, Kim H, Haenel V, Fruend I, Yianilos P, Mueller-Freitag M et al (2017) The \u201csomething something\u201d video database for learning and evaluating visual common sense. In:Proceedings of the IEEE international conference on computer vision. IEEE, pp 5842\u20135850","DOI":"10.1109\/ICCV.2017.622"},{"key":"1823_CR56","doi-asserted-by":"crossref","unstructured":"Ben-Shabat Y, Yu X, Saleh F Campbell D, Rodriguez-Opazo C, Li H, Gould S (2021) The IKEA ASM dataset: understanding people assembling furniture through actions, objects and pose. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision. IEEE, pp 847\u2013859","DOI":"10.1109\/WACV48630.2021.00089"},{"key":"1823_CR57","doi-asserted-by":"crossref","unstructured":"Kim TS, Jones J, Hager GD (2021) Motion guided attention fusion to recognize interactions from videos. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 13076\u201313086","DOI":"10.1109\/ICCV48922.2021.01283"},{"key":"1823_CR58","doi-asserted-by":"crossref","unstructured":"Yan R, Xie L, Zhang ShuX, L, Tang J, (2023) Progressive instance-aware feature learning for compositional action recognition. IEEE Trans Pattern Anal Mach Intell 45(8):10317\u201310330","DOI":"10.1109\/TPAMI.2023.3261659"},{"key":"1823_CR59","unstructured":"Loshchilov I, Hutter F (2016) SGDR: stochastic gradient descent with warm restarts. arXiv preprint. arXiv:1608.03983"},{"key":"1823_CR60","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv Learning"},{"key":"1823_CR61","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition IEEE pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1823_CR62","doi-asserted-by":"crossref","unstructured":"Li X, Sun P, Liu Y Duan L, Li W (2025) Simultaneous detection and interaction reasoning for object-centric action recognition. IEEE Trans Multimedia, IEEE","DOI":"10.1109\/TMM.2025.3543033"},{"key":"1823_CR63","doi-asserted-by":"crossref","unstructured":"Ma L, Zheng Y, Yao Zhang Z, Y, Fan X, Ye Q, (2022) Motion stimulation for compositional action recognition. IEEE Trans Circuits Syst Video Technol 33(5):2061\u20132074","DOI":"10.1109\/TCSVT.2022.3222305"},{"key":"1823_CR64","doi-asserted-by":"crossref","unstructured":"Yan R, Huang P, Shu X Zhang J, Pan Y, Tang J (2022) Look less think more: rethinking compositional action recognition. In: Proceedings of the 30th ACM international conference on multimedia. ACM, pp 3666\u20133675","DOI":"10.1145\/3503161.3547862"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01823-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-01823-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01823-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T21:25:23Z","timestamp":1743369923000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-01823-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,10]]},"references-count":64,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["1823"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-01823-x","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2025,3,10]]},"assertion":[{"value":"14 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2025","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 that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All authors have read and agreed to the published version of the manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}],"article-number":"206"}}