{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:46:30Z","timestamp":1777567590558,"version":"3.51.4"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T00:00:00Z","timestamp":1681948800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T00:00:00Z","timestamp":1681948800000},"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":["62162045"],"award-info":[{"award-number":["62162045"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61866028"],"award-info":[{"award-number":["61866028"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Technology Innovation Guidance Program Project of Jiangxi Province","award":["20212BDH81003"],"award-info":[{"award-number":["20212BDH81003"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s00521-023-08550-9","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T19:43:34Z","timestamp":1682019814000},"page":"16671-16688","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["RGRN: Relation-aware graph reasoning network for object detection"],"prefix":"10.1007","volume":"35","author":[{"given":"Jianjun","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2408-3982","authenticated-orcid":false,"given":"Jun","family":"Chu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Leng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaolin","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,20]]},"reference":[{"key":"8550_CR1","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn, In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"8550_CR2","doi-asserted-by":"crossref","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster r-cnn: towards realtime object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell (6):1137\u20131149","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"8550_CR3","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"8550_CR4","doi-asserted-by":"crossref","unstructured":"Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks, In Proceedings of the IEEE international conference on computer vision, pp 764\u2013773","DOI":"10.1109\/ICCV.2017.89"},{"issue":"4","key":"8550_CR5","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.3390\/s20041010","volume":"20","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Chu J, Leng L, Miao J (2020) Mask-refined r-cnn: a network for refining object details in instance segmentation. Sensors 20(4):1010","journal-title":"Sensors"},{"key":"8550_CR6","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1109\/ACCESS.2018.2815149","volume":"6","author":"J Chu","year":"2018","unstructured":"Chu J, Guo Z, Leng L (2018) Object detection based on multi-layer convolution feature fusion and online hard example mining. IEEE Access 6:19\u2013959","journal-title":"IEEE Access"},{"key":"8550_CR7","doi-asserted-by":"crossref","unstructured":"Park H-J, Choi Y-J, Lee Y-W, Kim B-G (2022) SSFPN: scale sequence ($$s^{}$$2) feature based feature pyramid network for object detection, arXiv preprint arXiv:2208.11533","DOI":"10.3390\/s23094432"},{"key":"8550_CR8","doi-asserted-by":"crossref","unstructured":"Bell S, Zitnick CL, Bala K,\u00a0Girshick R (2016) Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks, In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2874\u20132883","DOI":"10.1109\/CVPR.2016.314"},{"key":"8550_CR9","doi-asserted-by":"crossref","unstructured":"Ouyang W, Luo P, Zeng X, Qiu S, Tian Y, Li H, Yang S, Wang Z, Xiong Y, Qian C et\u00a0al (2014) Deepid-net: multi-stage and deformable deep convolutional neural networks for object detection, arXiv preprint arXiv:1409.3505","DOI":"10.1109\/CVPR.2015.7298854"},{"key":"8550_CR10","doi-asserted-by":"crossref","unstructured":"Chen X, Li L-J, Fei-Fei L, Gupta A (2018) Iterative visual reasoning beyond convolutions, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 7239\u20137248","DOI":"10.1109\/CVPR.2018.00756"},{"issue":"1","key":"8550_CR11","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1007\/s11263-020-01370-7","volume":"129","author":"Z Chen","year":"2021","unstructured":"Chen Z, Zhang J, Tao D (2021) Recursive context routing for object detection. Int J Comput Vision 129(1):142\u2013160","journal-title":"Int J Comput Vision"},{"key":"8550_CR12","doi-asserted-by":"crossref","unstructured":"Chen S, Li Z, Huang F, Zhang C, Ma H (2020) Improving object detection with relation mining network. In: 2020 IEEE International Conference on Data Mining (ICDM). IEEE, pp 52\u201361","DOI":"10.1109\/ICDM50108.2020.00014"},{"key":"8550_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-022-07368-1","volume":"34","author":"P Cao","year":"2022","unstructured":"Cao P, Zhu Z, Wang Z, Zhu Y, Niu Q (2022) Applications of graph convolutional networks in computer vision. Neural Comput Appl 34:1\u201319","journal-title":"Neural Comput Appl"},{"key":"8550_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06667-3","author":"H Wang","year":"2022","unstructured":"Wang H, Qin K, Zakari RY, Lu G, Yin J (2022) Deep neural network-based relation extraction: an overview. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-021-06667-3","journal-title":"Neural Comput Appl"},{"issue":"Suppl 2","key":"8550_CR15","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1142\/S0218488521400134","volume":"29","author":"AA Pise","year":"2021","unstructured":"Pise AA, Vadapalli H, Sanders I (2021) Relational reasoning using neural networks: a survey. Intern J Uncertain Fuzziness Knowl-Based Syst 29(Suppl 2):237\u2013258","journal-title":"Intern J Uncertain Fuzziness Knowl-Based Syst"},{"issue":"5","key":"8550_CR16","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1109\/TMM.2016.2642789","volume":"19","author":"J Li","year":"2016","unstructured":"Li J, Wei Y, Liang X, Dong J, Xu T, Feng J, Yan S (2016) Attentive contexts for object detection. IEEE Trans Multimed 19(5):944\u2013954","journal-title":"IEEE Trans Multimed"},{"key":"8550_CR17","doi-asserted-by":"crossref","unstructured":"Liu Y, Wang R, Shan S, Chen X (2018) Structure inference net: object detection using scene-level context and instance-level relationships, In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 6985\u20136994","DOI":"10.1109\/CVPR.2018.00730"},{"key":"8550_CR18","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: European conference on computer vision (ECCV). Springer, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"2","key":"8550_CR19","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88(2):303\u2013338","journal-title":"Int J Comput Vis"},{"key":"8550_CR20","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: single shot multibox detector, In: European conference on computer vision (ECCV). Springer, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"8550_CR21","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"8550_CR22","doi-asserted-by":"crossref","unstructured":"Mi L,\u00a0Chen Z (2020) Hierarchical graph attention network for visual relationship detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 13\u00a0886\u201313\u00a0895","DOI":"10.1109\/CVPR42600.2020.01390"},{"key":"8550_CR23","doi-asserted-by":"crossref","unstructured":"Lin X, Zou Q, Xu X (2021) Action-guided attention mining and relation reasoning network for human-object interaction detection. In: Proceedings of the Twenty-Ninth international conference on international joint conferences on artificial intelligence (IJCAI), pp 1104\u20131110","DOI":"10.24963\/ijcai.2020\/154"},{"key":"8550_CR24","doi-asserted-by":"crossref","unstructured":"Zhai Q, Li X, Yang F, Chen C, Cheng H, Fan D-P (2021) Mutual graph learning for camouflaged object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 12\u00a0997\u201313\u00a0007","DOI":"10.1109\/CVPR46437.2021.01280"},{"key":"8550_CR25","doi-asserted-by":"crossref","unstructured":"Chen X,\u00a0Gupta A (2017) Spatial memory for context reasoning in object detection. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 4086\u20134096","DOI":"10.1109\/ICCV.2017.440"},{"key":"8550_CR26","doi-asserted-by":"crossref","unstructured":"Li Z, Du X, Cao Y (2020) Gar: graph assisted reasoning for object detection. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision (WACV), pp 1295\u20131304","DOI":"10.1109\/WACV45572.2020.9093559"},{"issue":"3","key":"8550_CR27","doi-asserted-by":"publisher","first-page":"205","DOI":"10.33851\/JMIS.2020.7.3.205","volume":"7","author":"S Jang","year":"2020","unstructured":"Jang S, Battulga L, Nasridinov A (2020) Detection of dangerous situations using deep learning model with relational inference. J Multimed Inf Syst 7(3):205\u2013214","journal-title":"J Multimed Inf Syst"},{"key":"8550_CR28","doi-asserted-by":"crossref","unstructured":"Liu F, Liu J, Wang W, Lu H (2021) Hair: hierarchical visual-semantic relational reasoning for video question answering. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 1698\u20131707","DOI":"10.1109\/ICCV48922.2021.00172"},{"key":"8550_CR29","unstructured":"Santoro A, Raposo D, Barrett DG et al (2017) A simple neural network module for relational reasoning[J]. Adv Neural Inf Process Syst (30):1\u20130"},{"issue":"1","key":"8550_CR30","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2008) The graph neural network model. IEEE Trans Neural Netw 20(1):61\u201380","journal-title":"IEEE Trans Neural Netw"},{"key":"8550_CR31","unstructured":"Kipf TN,\u00a0Welling M (2016) Semi-supervised classification with graph convolutional networks, arXiv preprint arXiv:1609.02907"},{"issue":"4","key":"8550_CR32","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1089\/big.2020.0274","volume":"9","author":"Y-J Choi","year":"2021","unstructured":"Choi Y-J, Lee Y-W, Kim B-G (2021) Residual-based graph convolutional network for emotion recognition in conversation for smart internet of things. Big Data 9(4):279\u2013288","journal-title":"Big Data"},{"key":"8550_CR33","first-page":"1","volume":"1050","author":"P Velickovic","year":"2017","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. ICLR 1050:1\u201312","journal-title":"ICLR"},{"key":"8550_CR34","doi-asserted-by":"crossref","unstructured":"Arnab A, Sun C, Schmid C (2021) Unified graph structured models for video understanding. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 8117\u20138126","DOI":"10.1109\/ICCV48922.2021.00801"},{"key":"8550_CR35","first-page":"1","volume":"60","author":"S Tian","year":"2021","unstructured":"Tian S, Kang L, Xing X, Tian J, Fan C, Zhang Y (2021) A relation-augmented embedded graph attention network for remote sensing object detection. IEEE Trans Geosci Remote Sens 60:1\u201318","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"1","key":"8550_CR36","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1007\/s00521-021-06450-4","volume":"34","author":"V Jain","year":"2022","unstructured":"Jain V, Kaliyar RK, Goswami A, Narang P, Sharma Y (2022) Aenet: an attention-enabled neural architecture for fake news detection using contextual features. Neural Comput Appl 34(1):771\u2013782","journal-title":"Neural Comput Appl"},{"key":"8550_CR37","doi-asserted-by":"crossref","unstructured":"Li L, Gan Z, Cheng Y, Liu J (2019) Relation-aware graph attention network for visual question answering. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 10\u00a0313\u201310\u00a0322","DOI":"10.1109\/ICCV.2019.01041"},{"key":"8550_CR38","doi-asserted-by":"crossref","unstructured":"Ladicky L, Russell C, Kohli P, Torr PH (2010) Graph cut based inference with co-occurrence statistics. In: Computer Vision\u2013ECCV 2010: 11th European conference on computer vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part V 11. Springer, pp 239\u2013253","DOI":"10.1007\/978-3-642-15555-0_18"},{"key":"8550_CR39","doi-asserted-by":"crossref","unstructured":"Chen Z-M, Wei X-S, Wang P, Guo Y (2019) Multi-label image recognition with graph convolutional networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 5177\u20135186","DOI":"10.1109\/CVPR.2019.00532"},{"issue":"9","key":"8550_CR40","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627\u20131645","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8550_CR41","doi-asserted-by":"crossref","unstructured":"Hu H, Gu J, Zhang Z, Dai J, .\u00a0Wei Y (2018) Relation networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3588\u20133597","DOI":"10.1109\/CVPR.2018.00378"},{"key":"8550_CR42","doi-asserted-by":"crossref","unstructured":"Xu H, Jiang C, Liang X, Li Z (2019) Spatial-aware graph relation network for large-scale object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 9298\u20139307","DOI":"10.1109\/CVPR.2019.00952"},{"key":"8550_CR43","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998\u20136008"},{"key":"8550_CR44","doi-asserted-by":"crossref","unstructured":"Zhao G, Ge W, Yu Y (2021) Graphfpn: Graph feature pyramid network for object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 2763\u20132772","DOI":"10.1109\/ICCV48922.2021.00276"},{"key":"8550_CR45","doi-asserted-by":"crossref","unstructured":"Liang T, Wang Y, Tang Z, Hu G, Ling H (2021) Opanas: One-shot path aggregation network architecture search for object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10\u00a0195\u201310\u00a0203","DOI":"10.1109\/CVPR46437.2021.01006"},{"key":"8550_CR46","doi-asserted-by":"publisher","first-page":"104615","DOI":"10.1016\/j.imavis.2022.104615","volume":"130","author":"H Liu","year":"2022","unstructured":"Liu H, You X, Wang T, Li Y (2022) Object detection via inner-inter relational reasoning network. Image Vis Comput 130:104615","journal-title":"Image Vis Comput"},{"key":"8550_CR47","unstructured":"PASZKE A, GROSS S, MASSA F et al (2019) Pytorch: an imperative style, high performance deep learning library[J]. arXiv preprint arXiv:1912.01703"},{"key":"8550_CR48","unstructured":"Simonyan K,\u00a0Zisserman A (2014) Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556"},{"key":"8550_CR49","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 (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"8550_CR50","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L, Imagenet: A large-scale hierarchical image database. In: (2009) IEEE conference on computer vision and pattern recognition (CVPR). IEEE 2009:248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"8550_CR51","doi-asserted-by":"crossref","unstructured":"S.\u00a0Zhang, L.\u00a0Wen, X.\u00a0Bian, Z.\u00a0Lei, and S.\u00a0Z. Li, Single-shot refinement neural network for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp 4203\u20134212","DOI":"10.1109\/CVPR.2018.00442"},{"key":"8550_CR52","doi-asserted-by":"crossref","unstructured":"Liu S, Huang D et\u00a0al (2018) Receptive field block net for accurate and fast object detection. In: Proceedings of the European conference on computer vision (ECCV), pp 385\u2013400","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"8550_CR53","doi-asserted-by":"publisher","first-page":"107149","DOI":"10.1016\/j.patcog.2019.107149","volume":"100","author":"W Ma","year":"2020","unstructured":"Ma W, Wu Y, Cen F, Wang G (2020) Mdfn: multi-scale deep feature learning network for object detection. Pattern Recogn 100:107149","journal-title":"Pattern Recogn"},{"key":"8550_CR54","unstructured":"Fu C-Y, Liu W, Ranga A, Tyagi A, Berg AC (2017) Dssd: deconvolutional single shot detector, arXiv preprint arXiv:1701.06659"},{"key":"8550_CR55","doi-asserted-by":"crossref","unstructured":"Duan K, Bai S, Xie L, Qi H, Huang Q, Tian Q (2019) Centernet: keypoint triplets for object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6569\u20136578","DOI":"10.1109\/ICCV.2019.00667"},{"key":"8550_CR56","doi-asserted-by":"crossref","unstructured":"Hoiem D, Chodpathumwan Y, Dai Q (2012) Diagnosing error in object detectors. In European conference on computer vision (ECCV). Springer, pp 340\u2013353","DOI":"10.1007\/978-3-642-33712-3_25"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08550-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08550-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08550-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T20:50:23Z","timestamp":1689195023000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08550-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,20]]},"references-count":56,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["8550"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08550-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,20]]},"assertion":[{"value":"16 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 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":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}