{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T04:15:51Z","timestamp":1773980151695,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"37","license":[{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A2058, 62203242, 62176136, 62176138"],"award-info":[{"award-number":["U22A2058, 62203242, 62176136, 62176138"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Outstanding Youth Funding","award":["ZR2023YQ054"],"award-info":[{"award-number":["ZR2023YQ054"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19260-2","type":"journal-article","created":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T05:01:38Z","timestamp":1714453298000},"page":"84743-84763","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Drone-captured vehicle re-identification via perspective mask segmentation and hard sample learning"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5516-2486","authenticated-orcid":false,"given":"Liu","family":"Chunsheng","sequence":"first","affiliation":[]},{"given":"Xue","family":"Baoqi","sequence":"additional","affiliation":[]},{"given":"Li","family":"Shuang","sequence":"additional","affiliation":[]},{"given":"Chang","family":"Faliang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,30]]},"reference":[{"key":"19260_CR1","doi-asserted-by":"crossref","unstructured":"Wang Z, Tang L, Liu X, Yao Z, Yi S, Shao J, Yan J, Wang S, Li H, Wang X (2017) Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 379\u2013387","DOI":"10.1109\/ICCV.2017.49"},{"key":"19260_CR2","doi-asserted-by":"crossref","unstructured":"Song Y, Liu C, Zhang W, Nie Z, Chen L (2020) View-decision based compound match learning for vehicle re-identification in uav surveillance. In: 2020 39th chinese control conference (CCC), pp 6594\u20136601. IEEE","DOI":"10.23919\/CCC50068.2020.9189528"},{"key":"19260_CR3","doi-asserted-by":"crossref","unstructured":"Meng D, Li L, Liu X, Li Y, Yang S, Zha Z, Gao X, Wang S, Huang Q (2020) Parsing-based view-aware embedding network for vehicle re-identification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7103\u20137112","DOI":"10.1109\/CVPR42600.2020.00713"},{"key":"19260_CR4","doi-asserted-by":"crossref","unstructured":"Lu Z, Lin R, He Q, Hu H (2023) Mask-aware pseudo label denoising for unsupervised vehicle re-identification. IEEE Trans Intell Transp Syst","DOI":"10.1109\/TITS.2022.3233565"},{"key":"19260_CR5","first-page":"1","volume":"19","author":"A Yao","year":"2022","unstructured":"Yao A, Huang M, Qi J, Zhong P (2022) Attention mask-based network with simple color annotation for uav vehicle re-identification. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"19260_CR6","doi-asserted-by":"publisher","unstructured":"Jiao B, Yang L, Gao L, Wang P, Zhang S, Zhang Y (2023) Vehicle re-identification in aerial images and videos: Dataset and approach. IEEE transactions on circuits and systems for video technology, pp 1\u201318. https:\/\/doi.org\/10.1109\/TCSVT.2023.3298788","DOI":"10.1109\/TCSVT.2023.3298788"},{"issue":"10","key":"19260_CR7","doi-asserted-by":"publisher","first-page":"19246","DOI":"10.1109\/TITS.2022.3165175","volume":"23","author":"C Liu","year":"2022","unstructured":"Liu C, Song Y, Chang F, Li S, Ke R, Wang Y (2022) Posture calibration based cross-view & hard-sensitive metric learning for uav-based vehicle re-identification. IEEE Trans Intell Transp Syst 23(10):19246\u201319257","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"19260_CR8","doi-asserted-by":"crossref","unstructured":"Meng D, Li L, Liu X, Li Y, Huang Q (2020) Parsing-based view-aware embedding network for vehicle re-identification. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 7103\u20137112","DOI":"10.1109\/CVPR42600.2020.00713"},{"issue":"10","key":"19260_CR9","doi-asserted-by":"publisher","first-page":"18695","DOI":"10.1109\/TITS.2022.3165619","volume":"23","author":"Q Li","year":"2022","unstructured":"Li Q, Liu C, Chang F, Li S, Liu H, Liu Z (2022) Adaptive short-temporal induced aware fusion network for predicting attention regions like a driver. IEEE Trans Intell Transp Syst 23(10):18695\u201318706","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"11","key":"19260_CR10","doi-asserted-by":"publisher","first-page":"11850","DOI":"10.1109\/TITS.2023.3285923","volume":"24","author":"Y Lu","year":"2023","unstructured":"Lu Y, Liu C, Chang F, Liu H, Huan H (2023) Jhpfa-net: Joint head pose and facial action network for driver yawning detection across arbitrary poses in videos. IEEE Trans Intell Transp Syst 24(11):11850\u201311863","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"19260_CR11","first-page":"17489","volume":"32","author":"A Zheng","year":"2020","unstructured":"Zheng A, Lin X, Dong J, Wang W, Tang J, Luo B (2020) Multi-scale attention vehicle re-identification review. Multimed Tools Appl 32:17489\u201317503","journal-title":"Multimed Tools Appl"},{"key":"19260_CR12","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Rabinovich A (2014) Going deeper with convolutions. IEEE Computer Society, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"19260_CR13","doi-asserted-by":"crossref","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556v6","DOI":"10.1109\/ICCV.2015.314"},{"issue":"6","key":"19260_CR14","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton G (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"key":"19260_CR15","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, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"19260_CR16","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1109\/TMM.2017.2751966","volume":"20","author":"X Liu","year":"2017","unstructured":"Liu X, Liu W, Mei T, Ma H (2017) Provid: Progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans Multimed 20(3):645\u2013658","journal-title":"IEEE Trans Multimed"},{"key":"19260_CR17","doi-asserted-by":"publisher","unstructured":"Liu H, Tian Y, Wang Y, Pang L, Huang T (2016) Deep relative distance learning: Tell the difference between similar vehicles. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 2167\u20132175. https:\/\/doi.org\/10.1109\/CVPR.2016.238","DOI":"10.1109\/CVPR.2016.238"},{"key":"19260_CR18","doi-asserted-by":"publisher","unstructured":"Liu X, Liu W, Ma H, Fu H (2016) Large-scale vehicle re-identification in urban surveillance videos. In: 2016 IEEE international conference on multimedia and expo (ICME), pp 1\u20136. https:\/\/doi.org\/10.1109\/ICME.2016.7553002","DOI":"10.1109\/ICME.2016.7553002"},{"key":"19260_CR19","unstructured":"Wang W, Han C, Zhou T, Liu D (2023) Visual recognition with deep nearest centroids. In: International conference on learning representations (ICLR), pp 1\u201314"},{"key":"19260_CR20","unstructured":"Qin Z, Han C, Wang Q, Liu X, Yin Y, Lu X (2023) Unified 3d segmenter as prototypical classifiers. In: Conference on neural information processing systems (NeurIPS), pp 1\u201314"},{"key":"19260_CR21","unstructured":"Liang J, Zhou T, Liu D, Wang W (2023) Clustseg: clustering for universal segmentation. In: Proceedings of the 40th international conference on machine learning, pp 20787\u201320809"},{"key":"19260_CR22","unstructured":"Liang JC, Cui Y, Wang Q, Geng T, Wang W, Liu D (2023) Clusterformer: Clustering as a universal visual learner. In: Neural information processing systems (NeurIPS), pp 1\u201314"},{"issue":"10","key":"19260_CR23","doi-asserted-by":"publisher","first-page":"6642","DOI":"10.1109\/TCSVT.2022.3177320","volume":"32","author":"L Yan","year":"2022","unstructured":"Yan L, Ma S, Wang Q, Chen Y, Zhang X, Savakis A, Liu D (2022) Video captioning using global-local representation. IEEE Trans Circuits Syst Video Technol 32(10):6642\u20136656","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"19260_CR24","doi-asserted-by":"crossref","unstructured":"Liu D, Cui Y, Yan L, Mousas C, Yang B, Chen Y (2021) Densernet: Weakly supervised visual localization using multi-scale feature aggregation. In: Proceedings of the AAAI conference on artificial intelligence, pp 6101\u20136109","DOI":"10.1609\/aaai.v35i7.16760"},{"key":"19260_CR25","unstructured":"Wang W, Liang J, Liu D (2022) Learning equivariant segmentation with instance-unique querying, pp 1\u201320"},{"issue":"1","key":"19260_CR26","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1109\/TCSVT.2022.3202574","volume":"33","author":"L Yan","year":"2023","unstructured":"Yan L, Wang Q, Ma S, Wang J, Yu C (2023) Solve the puzzle of instance segmentation in videos: A weakly supervised framework with spatio-temporal collaboration. IEEE Trans Circuits Syst Video Technol 33(1):393\u2013406","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"19260_CR27","doi-asserted-by":"publisher","unstructured":"Li Y, Li Y, Yan H, Liu J (2017) Deep joint discriminative learning for vehicle re-identification and retrieval. In: 2017 IEEE international conference on image processing (ICIP), pp 395\u2013399. https:\/\/doi.org\/10.1109\/ICIP.2017.8296310","DOI":"10.1109\/ICIP.2017.8296310"},{"key":"19260_CR28","doi-asserted-by":"publisher","unstructured":"Zhang Y, Liu D, Zha Z-J (2017) Improving triplet-wise training of convolutional neural network for vehicle re-identification. In: 2017 IEEE international conference on multimedia and expo (ICME), pp 1386\u20131391. https:\/\/doi.org\/10.1109\/ICME.2017.8019491","DOI":"10.1109\/ICME.2017.8019491"},{"key":"19260_CR29","unstructured":"Hermans A, Beyer L, Leibe B (2017) In defense of the triplet loss for person re-identification. arXiv:1703.07737"},{"key":"19260_CR30","doi-asserted-by":"crossref","unstructured":"Wang D, Yu H, Wang D, Li G (2020) Face recognition system based on cnn. In: 2020 International conference on computer information and big data applications (CIBDA), pp 470\u2013473","DOI":"10.1109\/CIBDA50819.2020.00111"},{"key":"19260_CR31","doi-asserted-by":"crossref","unstructured":"Zhang L, Xiang T, Gong S (2016) Learning a discriminative null space for person re-identification. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 1239\u20131248","DOI":"10.1109\/CVPR.2016.139"},{"key":"19260_CR32","doi-asserted-by":"publisher","first-page":"32731","DOI":"10.1007\/s11042-020-09356-w","volume":"79","author":"J Peng","year":"2020","unstructured":"Peng J, Hao Y, Xu F, Fu X (2020) Vehicle re-identification using multi-task deep learning network and spatio-temporal model. Multimed Tools Appl 79:32731\u201332747","journal-title":"Multimed Tools Appl"},{"key":"19260_CR33","unstructured":"Liu X, Xia T, Wang J, Yang Y, Zhou F, Lin Y (2016) Fully convolutional attention networks for fine-grained recognition. arXiv:1603.06765"},{"key":"19260_CR34","doi-asserted-by":"crossref","unstructured":"Lin TY, Roychowdhury A, Maji S (2015) Bilinear cnn models for fine-grained visual recognition. In: Proceedings of the IEEE international conference on computer vision, pp 1449\u20131457","DOI":"10.1109\/ICCV.2015.170"},{"key":"19260_CR35","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.1007\/s10044-016-0559-6","volume":"20","author":"Noppakun Boonsim","year":"2017","unstructured":"Boonsim Noppakun (2017) Prakoonwit, Simant: Car make and model recognition under limited lighting conditions at night. Pattern Anal Appl 20:1195\u20131207","journal-title":"Pattern Anal Appl"},{"key":"19260_CR36","doi-asserted-by":"crossref","unstructured":"Wang Z, Tang L, Liu X, Yao Z, Yi S, Shao J, Yan J, Wang S, Li H, Wang X (2017) Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In: International conference on computer vision, pp 379\u2013387","DOI":"10.1109\/ICCV.2017.49"},{"issue":"7","key":"19260_CR37","doi-asserted-by":"publisher","first-page":"3275","DOI":"10.1109\/TIP.2018.2819820","volume":"27","author":"Y Zhou","year":"2018","unstructured":"Zhou Y, Liu L, Shao L (2018) Vehicle re-identification by deep hidden multi-view inference. IEEE Trans Image Process 27(7):3275\u20133287. https:\/\/doi.org\/10.1109\/TIP.2018.2819820","journal-title":"IEEE Trans Image Process"},{"key":"19260_CR38","doi-asserted-by":"publisher","unstructured":"Song Y, Liu C, Zhang W, Nie Z, Chen L (2020) View-decision based compound match learning for vehicle re-identification in uav surveillance. In: 2020 39th Chinese control conference (CCC), pp 6594\u20136601. https:\/\/doi.org\/10.23919\/CCC50068.2020.9189528","DOI":"10.23919\/CCC50068.2020.9189528"},{"key":"19260_CR39","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-17070-6","author":"HA Asghar","year":"2023","unstructured":"Asghar HA, Khan B, Zafar Z, Sabri AQM, Fraz MM (2023) Pakvehicle-reid: a multi-perspective benchmark for vehicle re-identification in unconstrained urban road environment. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-023-17070-6","journal-title":"Multimed Tools Appl"},{"key":"19260_CR40","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"19260_CR41","doi-asserted-by":"publisher","unstructured":"Yang L, Han Y, Chen X, Song S, Dai J, Huang G (2020) Resolution adaptive networks for efficient inference. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 2366\u20132375. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00244","DOI":"10.1109\/CVPR42600.2020.00244"},{"key":"19260_CR42","doi-asserted-by":"publisher","unstructured":"Vaswani A, Ramachandran P, Srinivas A, Parmar N, Hechtman B, Shlens J (2021) Scaling local self-attention for parameter efficient visual backbones. In: 2021 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 12889\u201312899. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01270","DOI":"10.1109\/CVPR46437.2021.01270"},{"key":"19260_CR43","doi-asserted-by":"publisher","unstructured":"Zhang H, Wu C, Zhang Z, Zhu Y, Lin H, Zhang Z, Sun Y, He T, Mueller J, Manmatha R, Li M, Smola A (2022) Resnest: Split-attention networks. In: 2022 IEEE\/CVF conference on computer vision and pattern recognition workshops (CVPRW), pp 2735\u20132745. https:\/\/doi.org\/10.1109\/CVPRW56347.2022.00309","DOI":"10.1109\/CVPRW56347.2022.00309"},{"key":"19260_CR44","doi-asserted-by":"publisher","unstructured":"Ji R, Wen L, Zhang L, Du D, Wu Y, Zhao C, Liu X, Huang F (2020) Attention convolutional binary neural tree for fine-grained visual categorization. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 10465\u201310474. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01048","DOI":"10.1109\/CVPR42600.2020.01048"},{"key":"19260_CR45","doi-asserted-by":"publisher","unstructured":"Chen Y, Bai Y, Zhang W, Mei T (2019) Destruction and construction learning for fine-grained image recognition. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 5152\u20135161. https:\/\/doi.org\/10.1109\/CVPR.2019.00530","DOI":"10.1109\/CVPR.2019.00530"},{"key":"19260_CR46","doi-asserted-by":"publisher","first-page":"117944","DOI":"10.1109\/ACCESS.2019.2936118","volume":"7","author":"M Tan","year":"2019","unstructured":"Tan M, Wang G, Zhou J, Peng Z, Zheng M (2019) Fine-grained classification via hierarchical bilinear pooling with aggregated slack mask. IEEE Access 7:117944\u2013117953. https:\/\/doi.org\/10.1109\/ACCESS.2019.2936118","journal-title":"IEEE Access"},{"key":"19260_CR47","doi-asserted-by":"publisher","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 815\u2013823. https:\/\/doi.org\/10.1109\/CVPR.2015.7298682","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"19260_CR48","doi-asserted-by":"publisher","unstructured":"Wang P, Jiao B, Yang L, Yang Y, Zhang S, Wei W, Zhang Y (2019) Vehicle re-identification in aerial imagery: Dataset and approach. In: 2019 IEEE\/CVF international conference on computer vision (ICCV), pp 460\u2013469 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00055","DOI":"10.1109\/ICCV.2019.00055"},{"key":"19260_CR49","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1007\/s11263-020-01402-2","volume":"129","author":"S Teng","year":"2021","unstructured":"Teng S, Zhang S, Huang Q, Sebe N (2021) Viewpoint and scale consistency reinforcement for uav vehicle re-identification. Int J Comput Vis 129:719\u2013735","journal-title":"Int J Comput Vis"},{"key":"19260_CR50","doi-asserted-by":"crossref","unstructured":"Shen Y, Xiao T, Li H (2017) Learning deep neural networks for vehicle re-id with visual-spatio-temporal path proposals. In: Proceedings of the IEEE international conference on computer vision, pp 1900\u20131909","DOI":"10.1109\/ICCV.2017.210"},{"key":"19260_CR51","doi-asserted-by":"crossref","unstructured":"Teng S, Liu X, Zhang S (2018) Scan: Spatial and channel attention network for vehicle re-identification. In: Pacific-rim conference on multimedia, pp 350\u2013361","DOI":"10.1007\/978-3-030-00764-5_32"},{"key":"19260_CR52","doi-asserted-by":"crossref","unstructured":"Liu X, Zhang S, Huang Q (2018) Ram: a region-aware deep model for vehicle re-identification. In: IEEE international conference on multimedia and expo, pp 1\u20136","DOI":"10.1109\/ICME.2018.8486589"},{"key":"19260_CR53","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"19260_CR54","doi-asserted-by":"crossref","unstructured":"Yao H, Zhang S, Zhang Y (2017) One-shot fine-grained instance retrieval. In: IEEE international conference on multimedia, pp 342\u2013350","DOI":"10.1145\/3123266.3123278"},{"key":"19260_CR55","first-page":"1","volume":"19","author":"A Yao","year":"2021","unstructured":"Yao A, Huang M, Qi J (2021) Attention mask-based network with simple color annotation for uav vehicle re-identification. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19260-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19260-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19260-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T09:11:14Z","timestamp":1731834674000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19260-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,30]]},"references-count":55,"journal-issue":{"issue":"37","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["19260"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19260-2","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,30]]},"assertion":[{"value":"2 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2024","order":4,"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 they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}