{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T13:25:22Z","timestamp":1762953922883,"version":"3.37.3"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T00:00:00Z","timestamp":1689897600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T00:00:00Z","timestamp":1689897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021MF058"],"award-info":[{"award-number":["ZR2021MF058"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s00521-023-08844-y","type":"journal-article","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T16:03:04Z","timestamp":1689955384000},"page":"11711-11725","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["JointMETRO: a 3D reconstruction model for human figures in works of art based on transformer"],"prefix":"10.1007","volume":"36","author":[{"given":"Shanchen","family":"Pang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongrong","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yukun","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengtao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junqi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,21]]},"reference":[{"key":"8844_CR1","doi-asserted-by":"crossref","unstructured":"Bogo F, Kanazawa A, Lassner C, Gehler P, Romero J, Black MJ (2016) Keep it SMPL: automatic estimation of 3d human pose and shape from a single image. In: Computer vision\u2014ECCV 2016: 14th European conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part V 14. Springer, Berlin, pp 561\u2013578","DOI":"10.1007\/978-3-319-46454-1_34"},{"key":"8844_CR2","doi-asserted-by":"crossref","unstructured":"Dwivedi SK, Athanasiou N, Kocabas M, Black MJ (2021) Learning to regress bodies from images using differentiable semantic rendering. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 11250\u201311259","DOI":"10.1109\/ICCV48922.2021.01106"},{"key":"8844_CR3","doi-asserted-by":"crossref","unstructured":"Choi H, Moon G, Lee KM (2020) Pose2mesh: graph convolutional network for 3d human pose and mesh recovery from a 2d human pose. In: Computer vision\u2014ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328. Proceedings, Part VII 16. Springer, Berlin, pp 769\u2013787","DOI":"10.1007\/978-3-030-58571-6_45"},{"key":"8844_CR4","doi-asserted-by":"crossref","unstructured":"Zheng C, Mendieta M, Wang P, Lu A, Chen C (2022) A lightweight graph transformer network for human mesh reconstruction from 2d human pose. In: Proceedings of the 30th ACM international conference on multimedia, pp 5496\u20135507","DOI":"10.1145\/3503161.3547844"},{"key":"8844_CR5","doi-asserted-by":"crossref","unstructured":"Lin K, Wang L, Liu Z (2021) End-to-end human pose and mesh reconstruction with transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1954\u20131963","DOI":"10.1109\/CVPR46437.2021.00199"},{"key":"8844_CR6","doi-asserted-by":"crossref","unstructured":"Lin K, Wang L, Liu Z (2021) Mesh graphormer. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 12939\u201312948","DOI":"10.1109\/ICCV48922.2021.01270"},{"key":"8844_CR7","doi-asserted-by":"crossref","unstructured":"Li K, Wang S, Zhang X, Xu Y, Xu W, Tu Z (2021) Pose recognition with cascade transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1944\u20131953","DOI":"10.1109\/CVPR46437.2021.00198"},{"key":"8844_CR8","doi-asserted-by":"crossref","unstructured":"Luvizon DC, Picard D, Tabia H (2018) 2d\/3d pose estimation and action recognition using multitask deep learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5137\u20135146","DOI":"10.1109\/CVPR.2018.00539"},{"key":"8844_CR9","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.cag.2019.09.002","volume":"85","author":"DC Luvizon","year":"2019","unstructured":"Luvizon DC, Tabia H, Picard D (2019) Human pose regression by combining indirect part detection and contextual information. Comput Graph 85:15\u201322","journal-title":"Comput Graph"},{"key":"8844_CR10","doi-asserted-by":"crossref","unstructured":"Xia H, Wang Y, Wang X, Xiong S, Yu Z (2022) HKE-GCN: Heatmaps-guided keypoints encoder and graph convolutional network for human pose estimation. In: 2022 international joint conference on neural networks (IJCNN). IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN55064.2022.9892251"},{"key":"8844_CR11","doi-asserted-by":"crossref","unstructured":"Sun X, Shang J, Liang S, Wei Y (2017) Compositional human pose regression. In: Proceedings of the IEEE international conference on computer vision, pp 2602\u20132611","DOI":"10.1109\/ICCV.2017.284"},{"key":"8844_CR12","doi-asserted-by":"crossref","unstructured":"Zhang F, Zhu X, Ye M (2019) Fast human pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3517\u20133526","DOI":"10.1109\/CVPR.2019.00363"},{"key":"8844_CR13","doi-asserted-by":"crossref","unstructured":"Liu Z, Feng R, Chen H, Wu S, Gao Y, Gao Y, Wang X (2022) Temporal feature alignment and mutual information maximization for video-based human pose estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11006\u201311016","DOI":"10.1109\/CVPR52688.2022.01073"},{"key":"8844_CR14","doi-asserted-by":"crossref","unstructured":"Lifshitz I, Fetaya E, Ullman S (2016) Human pose estimation using deep consensus voting. In: Computer vision\u2013ECCV 2016: 14th European conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part II 14. Springer, Berlin, pp 246\u2013260","DOI":"10.1007\/978-3-319-46475-6_16"},{"key":"8844_CR15","doi-asserted-by":"crossref","unstructured":"Jiao Y, Chen H, Feng R, Chen H, Wu S, Yin Y, Liu Z (2022) Glpose: global-local representation learning for human pose estimation. In: ACM transactions on multimedia computing, communications, and applications (TOMM), vol 18(2s), pp 1\u201316","DOI":"10.1145\/3519305"},{"issue":"13","key":"8844_CR16","doi-asserted-by":"publisher","first-page":"17615","DOI":"10.1007\/s11042-022-12245-z","volume":"81","author":"X Qin","year":"2022","unstructured":"Qin X, Guo H, He C, Zhang X (2022) Lightweight human pose estimation: CVC-net. Multimedia Tools Appl 81(13):17615\u201317637","journal-title":"Multimedia Tools Appl"},{"key":"8844_CR17","doi-asserted-by":"crossref","unstructured":"Toshev A, Szegedy C (2014) Deeppose: human pose estimation via deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1653\u20131660","DOI":"10.1109\/CVPR.2014.214"},{"issue":"6","key":"8844_CR18","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"key":"8844_CR19","doi-asserted-by":"crossref","unstructured":"Carreira J, Agrawal P, Fragkiadaki K, Malik J (2016) Human pose estimation with iterative error feedback. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4733\u20134742","DOI":"10.1109\/CVPR.2016.512"},{"key":"8844_CR20","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":"8844_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2021.103055","volume":"76","author":"L Song","year":"2021","unstructured":"Song L, Gang Yu, Yuan J, Liu Z (2021) Human pose estimation and its application to action recognition: a survey. J Vis Commun Image Represent 76:103055","journal-title":"J Vis Commun Image Represent"},{"key":"8844_CR22","doi-asserted-by":"crossref","unstructured":"Wei S-E, Ramakrishna V, Kanade T, Sheikh Y (2016) Convolutional pose machines. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4724\u20134732","DOI":"10.1109\/CVPR.2016.511"},{"key":"8844_CR23","doi-asserted-by":"crossref","unstructured":"Yu C, Xiao B, Gao C, Yuan L, Zhang L, Sang N, Wang J (2021) Lite-HRNET: a lightweight high-resolution network. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10440\u201310450","DOI":"10.1109\/CVPR46437.2021.01030"},{"key":"8844_CR24","doi-asserted-by":"crossref","unstructured":"Zhang H, Tian Y, Zhou X, Ouyang W, Liu Y, Wang L, Sun Z (2021) Pymaf: 3D human pose and shape regression with pyramidal mesh alignment feedback loop. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 11446\u201311456","DOI":"10.1109\/ICCV48922.2021.01125"},{"key":"8844_CR25","doi-asserted-by":"crossref","unstructured":"Kocabas M, Huang C-HP, Hilliges O, Black MJ (2021) Pare: part attention regressor for 3d human body estimation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 11127\u201311137","DOI":"10.1109\/ICCV48922.2021.01094"},{"key":"8844_CR26","doi-asserted-by":"crossref","unstructured":"Sun Y, Bao Q, Liu W, Fu Y, Black MJ, Mei T (2021) Monocular, one-stage, regression of multiple 3d people. In  Proceedings of the IEEE\/CVF international conference on computer vision, pp 11179\u201311188","DOI":"10.1109\/ICCV48922.2021.01099"},{"key":"8844_CR27","doi-asserted-by":"crossref","unstructured":"Anguelov D, Srinivasan P, Koller D, Thrun S, Rodgers J, Davis J (2005) Scape: shape completion and animation of people. In: ACM SIGGRAPH 2005 Papers, pp 408\u2013416","DOI":"10.1145\/1186822.1073207"},{"issue":"6","key":"8844_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2816795.2818013","volume":"34","author":"M Loper","year":"2015","unstructured":"Loper M, Mahmood N, Romero J, Pons-Moll G, Black MJ (2015) SMPL: a skinned multi-person linear model. ACM Trans Graph (TOG) 34(6):1\u201316","journal-title":"ACM Trans Graph (TOG)"},{"key":"8844_CR29","doi-asserted-by":"crossref","unstructured":"Osman AAA, Bolkart T, Black MJ (2020) STAR: sparse trained articulated human body regressor. In: Computer vision\u2013ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part VI 16. Springer, Berlin, pp 598\u2013613","DOI":"10.1007\/978-3-030-58539-6_36"},{"key":"8844_CR30","doi-asserted-by":"crossref","unstructured":"Kolotouros N, Pavlakos G, Daniilidis K (2019) Convolutional mesh regression for single-image human shape reconstruction. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4501\u2013510","DOI":"10.1109\/CVPR.2019.00463"},{"key":"8844_CR31","doi-asserted-by":"crossref","unstructured":"Moon G, Lee KM (2020) I2l-meshnet: image-to-lixel prediction network for accurate 3d human pose and mesh estimation from a single RGB image. In: Computer vision\u2014ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part VII 16. Springer, Berlin, pp 752\u2013768","DOI":"10.1007\/978-3-030-58571-6_44"},{"key":"8844_CR32","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: Computer Vision\u2013ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part I 16. Springer, Berlin, pp. 213\u2013229","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"8844_CR33","unstructured":"Zhu X, Su W, Lu L, Li B, Wang X, Dai J (2020) Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159"},{"key":"8844_CR34","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P (2021) SegFormer: simple and efficient design for semantic segmentation with transformers. Adv Neural Inf Process Syst 34:12077\u201312090","journal-title":"Adv Neural Inf Process Syst"},{"key":"8844_CR35","doi-asserted-by":"crossref","unstructured":"Strudel R, Garcia R, Laptev I, Schmid C (2021) Segmenter: transformer for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 7262\u20137272","DOI":"10.1109\/ICCV48922.2021.00717"},{"key":"8844_CR36","doi-asserted-by":"crossref","unstructured":"Chen C-F, Fan Q, Panda R (2021) CrossViT: cross-attention multi-scale vision transformer for image classification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 357\u2013366","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"8844_CR37","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3854635","author":"D Datta","year":"2022","unstructured":"Datta D, Mallick PK, Bhoi AKD, Ijaz MF, Shafi J, Choi J (2022) Hyperspectral image classification: potentials, challenges, and future directions. Comput Intell Neurosci. https:\/\/doi.org\/10.1155\/2022\/3854635","journal-title":"Comput Intell Neurosci"},{"key":"8844_CR38","doi-asserted-by":"crossref","unstructured":"Zhao Weixi, Tian Yunjie, Ye Qixiang, Jiao Jianbin, Wang Weiqiang (2021) Graformer: Graph convolution transformer for 3d pose estimation. arXiv preprint arXiv:2109.08364","DOI":"10.1109\/CVPR52688.2022.01979"},{"key":"8844_CR39","doi-asserted-by":"crossref","unstructured":"Zheng C, Zhu S, Mendieta M, Yang T, Chen C, Ding Z (2021) 3d human pose estimation with spatial and temporal transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 11656\u201311665","DOI":"10.1109\/ICCV48922.2021.01145"},{"key":"8844_CR40","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"8844_CR41","doi-asserted-by":"crossref","unstructured":"Wang N, Zhang Ya, Li Z, Fu Y, Liu W, Jiang Y-G (2018) Pixel2mesh: generating 3D mesh models from single RGB images. In: Proceedings of the European conference on computer vision (ECCV), pp 52\u201367","DOI":"10.1007\/978-3-030-01252-6_4"},{"key":"8844_CR42","unstructured":"Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in pytorch. In: Proceedings of the 31st Conference on Neural Information Processing Systems, pp 1\u20134"},{"key":"8844_CR43","doi-asserted-by":"crossref","unstructured":"Kolotouros N, Pavlakos G, Black MJ, Daniilidis K (2019) Learning to reconstruct 3d human pose and shape via model-fitting in the loop. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 2252\u20132261","DOI":"10.1109\/ICCV.2019.00234"},{"key":"8844_CR44","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"issue":"7","key":"8844_CR45","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1109\/TPAMI.2013.248","volume":"36","author":"C Ionescu","year":"2013","unstructured":"Ionescu C, Papava D, Olaru V, Sminchisescu C (2013) Human3.6m: large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE Trans Pattern Anal Mach Intell 36(7):1325\u20131339","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8844_CR46","doi-asserted-by":"crossref","unstructured":"Von Marcard T, Henschel R, Black MJ, Rosenhahn B, Pons-Moll G (2018) Recovering accurate 3d human pose in the wild using IMUS and a moving camera. In: Proceedings of the European conference on computer vision (ECCV), pp 601\u2013617","DOI":"10.1007\/978-3-030-01249-6_37"},{"key":"8844_CR47","doi-asserted-by":"crossref","unstructured":"Mehta D, Sotnychenko O, Mueller F, Xu W, Sridhar S, Pons-Moll G, Theobalt C (2018) Single-shot multi-person 3D pose estimation from monocular RGB. In: 2018 international conference on 3D vision (3DV), pp 120\u2013130. IEEE","DOI":"10.1109\/3DV.2018.00024"},{"key":"8844_CR48","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft COCO: common objects in context. In: Computer vision\u2014ECCV 2014: 13th European conference, Zurich, Switzerland, September 6\u201312, 2014, Proceedings, Part V 13, Springer, Berlin. pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"8844_CR49","doi-asserted-by":"crossref","unstructured":"Kanazawa A, Black MJ, Jacobs DW, Malik J (2018) End-to-end recovery of human shape and pose. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7122\u20137131","DOI":"10.1109\/CVPR.2018.00744"},{"key":"8844_CR50","doi-asserted-by":"crossref","unstructured":"Kocabas M, Athanasiou N, Black MJ (2020) Vibe: video inference for human body pose and shape estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5253\u20135263","DOI":"10.1109\/CVPR42600.2020.00530"},{"issue":"4","key":"8844_CR51","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1109\/TPAMI.2018.2816031","volume":"41","author":"X Zhou","year":"2018","unstructured":"Zhou X, Zhu M, Pavlakos G, Leonardos S, Derpanis KG, Daniilidis K (2018) Monocap: monocular human motion capture using a CNN coupled with a geometric prior. IEEE Tans Pattern Anal Mach Intell 41(4):901\u2013914","journal-title":"IEEE Tans Pattern Anal Mach Intell"},{"key":"8844_CR52","doi-asserted-by":"crossref","unstructured":"Pavlakos G, Zhu L, Zhou X, Daniilidis K (2018) Learning to estimate 3D human pose and shape from a single color image. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 459\u2013468","DOI":"10.1109\/CVPR.2018.00055"},{"key":"8844_CR53","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/BF02291478","volume":"40","author":"JC Gower","year":"1975","unstructured":"Gower JC (1975) Generalized procrustes analysis. Psychometrika 40:33\u201351","journal-title":"Psychometrika"},{"issue":"10","key":"8844_CR54","doi-asserted-by":"publisher","first-page":"1667","DOI":"10.3390\/rs12101667","volume":"12","author":"T Hoeser","year":"2020","unstructured":"Hoeser T, Kuenzer C (2020) Object detection and image segmentation with deep learning on earth observation data: a review-part I: evolution and recent trends. Remote Sens 12(10):1667","journal-title":"Remote Sens"},{"issue":"6","key":"8844_CR55","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.3390\/rs14061498","volume":"14","author":"Z Huang","year":"2022","unstructured":"Huang Z, Zhang Q, Zhang G (2022) MLCRNET: multi-level context refinement for semantic segmentation in aerial images. Remote Sens 14(6):1498","journal-title":"Remote Sens"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08844-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08844-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08844-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T14:05:24Z","timestamp":1720447524000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08844-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,21]]},"references-count":55,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["8844"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08844-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,7,21]]},"assertion":[{"value":"28 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"None.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}