{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T06:49:56Z","timestamp":1776235796747,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"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":["62073295"],"award-info":[{"award-number":["62073295"]}],"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":["62072409"],"award-info":[{"award-number":["62072409"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"\u201cPioneer\u201d and \u201cLeading Goose\u201d R & D Program of Zhejiang","award":["2022C01050"],"award-info":[{"award-number":["2022C01050"]}]},{"DOI":"10.13039\/501100004731","name":"Zhejiang Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["LR21F020003"],"award-info":[{"award-number":["LR21F020003"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s10489-025-06506-1","type":"journal-article","created":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T02:41:10Z","timestamp":1743302470000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Convolution-aware networks for random missing traffic data imputation"],"prefix":"10.1007","volume":"55","author":[{"given":"Zhenzhen","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Guojiang","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Wenfeng","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Wenjie","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xiangjie","family":"Kong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,27]]},"reference":[{"issue":"3","key":"6506_CR1","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1109\/TBDATA.2020.2991152","volume":"8","author":"C Chen","year":"2020","unstructured":"Chen C, Yang S, Wang Y, Guo B, Zhang D (2020) Crowdexpress: a probabilistic framework for on-time crowdsourced package deliveries. IEEE Trans Big Data 8(3):827\u2013842","journal-title":"IEEE Trans Big Data"},{"issue":"6","key":"6506_CR2","first-page":"6","volume":"14","author":"C Zhao","year":"2022","unstructured":"Zhao C, Lv Y, Jin J, Tian Y, Wang J, Wang F (2022) Decast in transverse for parallel intelligent transportation systems and smart cities: Three decades and beyond. IEEE Intell Trans Syst Mag 14(6):6\u201317","journal-title":"IEEE Intell Trans Syst Mag"},{"issue":"11","key":"6506_CR3","doi-asserted-by":"publisher","first-page":"2140","DOI":"10.1080\/00949655.2015.1104683","volume":"86","author":"V Audigier","year":"2016","unstructured":"Audigier V, Husson F, Josse J (2016) Multiple imputation for continuous variables using a bayesian principal component analysis. J Stat Comput Simul 86(11):2140\u20132156","journal-title":"J Stat Comput Simul"},{"key":"6506_CR4","unstructured":"Cao W, Wang D, Li J, Zhou H, Li L, Li Y (2018) Brits: bidirectional recurrent imputation for time series. Adv Neural Inf Process Syst 31"},{"key":"6506_CR5","unstructured":"Yoon J, Jordon J, Schaar M (2018) Gain: missing data imputation using generative adversarial nets. In: International conference on machine learning, pp 5689\u20135698"},{"issue":"1","key":"6506_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-24271-9","volume":"8","author":"Z Che","year":"2018","unstructured":"Che Z, Purushotham S, Cho K, Sontag D, Liu Y (2018) Recurrent neural networks for multivariate time series with missing values. Sci Rep 8(1):1\u201312","journal-title":"Sci Rep"},{"key":"6506_CR7","doi-asserted-by":"publisher","first-page":"102674","DOI":"10.1016\/j.trc.2020.102674","volume":"118","author":"Z Cui","year":"2020","unstructured":"Cui Z, Ke R, Pu Z, Wang Y (2020) Stacked bidirectional and unidirectional lstm recurrent neural network for forecasting network-wide traffic state with missing values. Trans Res Part C: Emerging Technol 118:102674","journal-title":"Trans Res Part C: Emerging Technol"},{"key":"6506_CR8","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.neunet.2021.05.033","volume":"141","author":"Y Wang","year":"2021","unstructured":"Wang Y, Li D, Li X, Yang M (2021) Pc-gain: pseudo-label conditional generative adversarial imputation networks for incomplete data. Neural Netw 141:395\u2013403","journal-title":"Neural Netw"},{"issue":"1","key":"6506_CR9","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1109\/TBDATA.2022.3154097","volume":"9","author":"Y Yuan","year":"2023","unstructured":"Yuan Y, Zhang Y, Wang B, Peng Y, Hu Y, Yin B (2023) Stgan: spatio-temporal generative adversarial network for traffic data imputation. IEEE Trans Big Data 9(1):200\u2013211","journal-title":"IEEE Trans Big Data"},{"key":"6506_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2023.3280481","author":"M Hou","year":"2023","unstructured":"Hou M, Tang T, Xia F, Sultan I, Kaur R, Kong X (2023) Missii: missing information imputation for traffic data. IEEE Trans Emerging Top Comput. https:\/\/doi.org\/10.1109\/TETC.2023.3280481","journal-title":"IEEE Trans Emerging Top Comput"},{"key":"6506_CR11","doi-asserted-by":"publisher","first-page":"2528","DOI":"10.1007\/s10489-024-05314-3","volume":"54","author":"J Liu","year":"2024","unstructured":"Liu J, Yao J, Liu J, Wang Z, Huang L (2024) Missing data imputation and classification of small sample missing time series data based on gradient penalized adversarial multi-task learning. Appl Intell 54:2528\u20132550","journal-title":"Appl Intell"},{"issue":"1","key":"6506_CR12","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4\u201324","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"6506_CR13","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/MSP.2012.2235192","volume":"30","author":"DI Shuman","year":"2013","unstructured":"Shuman DI, Narang SK, Frossard P, Ortega A, Vandergheynst P (2013) The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process Mag 30(3):83\u201398","journal-title":"IEEE Signal Process Mag"},{"key":"6506_CR14","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of the 30th international conference on neural information processing systems, pp 3844\u20133852"},{"key":"6506_CR15","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of international conference on learning representations"},{"key":"6506_CR16","unstructured":"Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. In: Proceedings of the 31st international conference on neural information processing systems, pp 1025\u20131035"},{"key":"6506_CR17","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph attention networks. In: International conference on learning representations"},{"key":"6506_CR18","doi-asserted-by":"crossref","unstructured":"Han X, Shen G, Yang X, Kong X (2020) Congestion recognition for hybrid urban road systems via digraph convolutional network. Trans Res Part C: Emerging Technol 121:102877","DOI":"10.1016\/j.trc.2020.102877"},{"issue":"9","key":"6506_CR19","doi-asserted-by":"publisher","first-page":"14413","DOI":"10.1109\/TITS.2021.3128494","volume":"23","author":"G Shen","year":"2021","unstructured":"Shen G, Han X, Chin K, Kong X (2021) An attention-based digraph convolution network enabled framework for congestion recognition in three-dimensional road networks. IEEE Trans Intell Trans Syst 23(9):14413\u201314426","journal-title":"IEEE Trans Intell Trans Syst"},{"key":"6506_CR20","doi-asserted-by":"publisher","first-page":"119779","DOI":"10.1016\/j.eswa.2023.119779","volume":"222","author":"X Huang","year":"2023","unstructured":"Huang X, Ye Y, Yang X, Xiong L (2023) Multi-view dynamic graph convolution neural network for traffic flow prediction. Expert Syst Appl 222:119779","journal-title":"Expert Syst Appl"},{"key":"6506_CR21","doi-asserted-by":"publisher","first-page":"102291","DOI":"10.1016\/j.inffus.2024.102291","volume":"106","author":"L Sun","year":"2024","unstructured":"Sun L, Liu M, Liu G, Chen X, Yu X (2024) Fd-tgcn: fast and dynamic temporal graph convolution network for traffic flow prediction. Inf Fusion 106:102291","journal-title":"Inf Fusion"},{"key":"6506_CR22","doi-asserted-by":"crossref","unstructured":"Guo H, Peng Y, Fan Z, Zhu H, Song X (2024) Hhgnn: heterogeneous hypergraph neural network for traffic agents trajectory prediction in grouping scenarios. In: IEEE international conference on robotics and automation (ICRA), pp 14101\u201314108","DOI":"10.1109\/ICRA57147.2024.10611535"},{"issue":"10","key":"6506_CR23","doi-asserted-by":"publisher","first-page":"12472","DOI":"10.1007\/s10489-022-04122-x","volume":"53","author":"J Liu","year":"2023","unstructured":"Liu J, Kang Y, Li H, Wang H, Yang X (2023) Stghtn: spatial-temporal gated hybrid transformer network for traffic flow forecasting. Appl Intell 53(10):12472\u201312488","journal-title":"Appl Intell"},{"key":"6506_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3345872","author":"X Kong","year":"2024","unstructured":"Kong X, Shen Z, Wang K, Shen G, Fu Y (2024) Exploring bus stop mobility pattern: a multi-pattern deep learning prediction framework. IEEE Trans Intell Trans Syst. https:\/\/doi.org\/10.1109\/TITS.2023.3345872","journal-title":"IEEE Trans Intell Trans Syst"},{"key":"6506_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3478816","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Kong X, Zhou W, Liu J, Fu Y, Shen G (2024) A comprehensive survey on traffic missing data imputation. IEEE Trans Intell Trans Syst. https:\/\/doi.org\/10.1109\/TITS.2024.3478816","journal-title":"IEEE Trans Intell Trans Syst"},{"issue":"3","key":"6506_CR26","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1080\/15472450.2012.694788","volume":"16","author":"W Yin","year":"2012","unstructured":"Yin W, Murray-Tuite P, Rakha H (2012) Imputing erroneous data of single-station loop detectors for nonincident conditions: comparison between temporal and spatial methods. J Intell Trans Syst 16(3):159\u2013176","journal-title":"J Intell Trans Syst"},{"key":"6506_CR27","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.trc.2016.09.015","volume":"72","author":"Y Duan","year":"2016","unstructured":"Duan Y, Lv Y, Liu Y-L, Wang F-Y (2016) An efficient realization of deep learning for traffic data imputation. Trans Res Part C: Emerging Technol 72:168\u2013181","journal-title":"Trans Res Part C: Emerging Technol"},{"issue":"3","key":"6506_CR28","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1109\/TITS.2009.2026312","volume":"10","author":"L Qu","year":"2009","unstructured":"Qu L, Li L, Zhang Y, Hu J (2009) Ppca-based missing data imputation for traffic flow volume: a systematical approach. IEEE Trans Intell Trans Syst 10(3):512\u2013522","journal-title":"IEEE Trans Intell Trans Syst"},{"key":"6506_CR29","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.trc.2018.11.003","volume":"98","author":"X Chen","year":"2019","unstructured":"Chen X, He Z, Sun L (2019) A bayesian tensor decomposition approach for spatiotemporal traffic data imputation. Trans Res Part C: Emerging Technol 98:73\u201384","journal-title":"Trans Res Part C: Emerging Technol"},{"issue":"9","key":"6506_CR30","first-page":"4659","volume":"44","author":"X Chen","year":"2022","unstructured":"Chen X, Sun L (2022) Bayesian temporal factorization for multidimensional time series prediction. IEEE Trans Pattern Ana Mach Intell 44(9):4659\u20134673","journal-title":"IEEE Trans Pattern Ana Mach Intell"},{"issue":"10","key":"6506_CR31","doi-asserted-by":"publisher","first-page":"11363","DOI":"10.1007\/s10489-021-03060-4","volume":"52","author":"J Li","year":"2022","unstructured":"Li J, Xu L, Li R, Wu P, Huang Z (2022) Deep spatial-temporal bi-directional residual optimisation based on tensor decomposition for traffic data imputation on urban road network. Appl Intell 52(10):11363\u201311381","journal-title":"Appl Intell"},{"issue":"10","key":"6506_CR32","doi-asserted-by":"publisher","first-page":"11001","DOI":"10.1109\/TITS.2023.3279321","volume":"24","author":"X Xu","year":"2023","unstructured":"Xu X, Lin M, Luo X, Xu Z (2023) Hrst-lr: a hessian regularization spatio-temporal low rank algorithm for traffic data imputation. IEEE Trans Intell Trans Syst 24(10):11001\u201311017","journal-title":"IEEE Trans Intell Trans Syst"},{"issue":"10","key":"6506_CR33","doi-asserted-by":"publisher","first-page":"2188","DOI":"10.1109\/JAS.2024.124278","volume":"11","author":"H Chen","year":"2024","unstructured":"Chen H, Lin M, Liu J, Xu Z (2024) Scalable temporal dimension preserved tensor completion for missing traffic data imputation with orthogonal initialization. IEEE\/CAA J Autom Sinica 11(10):2188\u20132190","journal-title":"IEEE\/CAA J Autom Sinica"},{"key":"6506_CR34","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3486529","author":"H Yang","year":"2024","unstructured":"Yang H, Lin M, Chen H, Luo X, Xu Z (2024) Latent factor analysis model with temporal regularized constraint for road traffic data imputation. IEEE Trans Intell Trans Syst. https:\/\/doi.org\/10.1109\/TITS.2024.3486529","journal-title":"IEEE Trans Intell Trans Syst"},{"key":"6506_CR35","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3486963","author":"M Lin","year":"2024","unstructured":"Lin M, Liu J, Chen H, Xu X, Luo X, Xu Z (2024) A 3d convolution-incorporated dimension preserved decomposition model for traffic data prediction. IEEE Trans Intell Trans Syst. https:\/\/doi.org\/10.1109\/TITS.2024.3486963","journal-title":"IEEE Trans Intell Trans Syst"},{"issue":"7553","key":"6506_CR36","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"6506_CR37","doi-asserted-by":"crossref","unstructured":"Tian Y, Zhang K, Li J, Lin X, Yang B (2018) Lstm-based traffic flow prediction with missing data. Neurocomputing 318:297\u2013305","DOI":"10.1016\/j.neucom.2018.08.067"},{"key":"6506_CR38","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.neucom.2023.02.017","volume":"531","author":"G Shen","year":"2023","unstructured":"Shen G, Zhou W, Zhang W, Liu N, Liu Z, Kong X (2023) Bidirectional spatial-temporal traffic data imputation via graph attention recurrent neural network. Neurocomputing 531:151\u2013162","journal-title":"Neurocomputing"},{"key":"6506_CR39","doi-asserted-by":"crossref","unstructured":"Liu J, Zheng F, Liu X, Guo G (2021) Dynamic traffic flow prediction based on long-short term memory framework with feature organization. IEEE Intell Trans Syst Mag 14(6):221\u2013236","DOI":"10.1109\/MITS.2021.3116156"},{"issue":"2","key":"6506_CR40","first-page":"190","volume":"14","author":"T Zhang","year":"2020","unstructured":"Zhang T, Guo G (2020) Graph attention lstm: a spatiotemporal approach for traffic flow forecasting. IEEE Intell Trans Syst Mag 14(2):190\u2013196","journal-title":"IEEE Intell Trans Syst Mag"},{"key":"6506_CR41","doi-asserted-by":"crossref","unstructured":"Kong X, Zhou W, Shen G, Zhang W, Liu N, Yang Y (2023) Dynamic graph convolutional recurrent imputation network for spatiotemporal traffic missing data. Knowl-Based Syst 110188","DOI":"10.1016\/j.knosys.2022.110188"},{"issue":"3","key":"6506_CR42","first-page":"1","volume":"17","author":"L Jiang","year":"2023","unstructured":"Jiang L, Chen C-X, Chen C (2023) L2mm: learning to map matching with deep models for low-quality gps trajectory data. ACM Trans Knowl Disc Data 17(3):1\u201325","journal-title":"ACM Trans Knowl Disc Data"},{"key":"6506_CR43","doi-asserted-by":"crossref","unstructured":"Wu Z, Pan S, Long G, Jiang J, Zhang C (2019) Graph wavenet for deep spatial-temporal graph modeling. In: Proceedings of the 28th international joint conference on artificial intelligence, pp 1907\u20131913","DOI":"10.24963\/ijcai.2019\/264"},{"key":"6506_CR44","doi-asserted-by":"crossref","unstructured":"Lea C, Flynn MD, Vidal R, Reiter A, Hager GD (2017) Temporal convolutional networks for action segmentation and detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 156\u2013165","DOI":"10.1109\/CVPR.2017.113"},{"key":"6506_CR45","unstructured":"Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:1803.01271"},{"key":"6506_CR46","unstructured":"Luo Y, Cai X, Zhang Y, Xu J, et al (2018) Multivariate time series imputation with generative adversarial networks. Adv Neural Inf Process Syst 31"},{"issue":"4","key":"6506_CR47","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.1109\/TITS.2019.2910295","volume":"21","author":"Y Chen","year":"2019","unstructured":"Chen Y, Lv Y, Wang F (2019) Traffic flow imputation using parallel data and generative adversarial networks. IEEE Trans Intell Trans Syst 21(4):1624\u20131630","journal-title":"IEEE Trans Intell Trans Syst"},{"issue":"1","key":"6506_CR48","first-page":"198","volume":"14","author":"W Zou","year":"2020","unstructured":"Zou W, Sun Y, Zhou Y, Lu Q, Nie Y, Sun T, Peng L (2020) Limited sensing and deep data mining: a new exploration of developing city-wide parking guidance systems. IEEE Intell Trans Syst Mag 14(1):198\u2013215","journal-title":"IEEE Intell Trans Syst Mag"},{"key":"6506_CR49","unstructured":"Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In: International conference on learning representations"},{"key":"6506_CR50","doi-asserted-by":"crossref","unstructured":"Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the 27th international joint conference on artificial intelligence, pp 3634\u20133640","DOI":"10.24963\/ijcai.2018\/505"},{"key":"6506_CR51","doi-asserted-by":"crossref","unstructured":"Xie Y, Li S, Yang C, Wong RC-W, Han J (2020) When do gnns work: understanding and improving neighborhood aggregation. In: Proceedings of the 29th international joint conference on artificial intelligence, pp 1303\u20131309","DOI":"10.24963\/ijcai.2020\/181"},{"key":"6506_CR52","doi-asserted-by":"crossref","unstructured":"Ye Y, Zhang S, Yu JJ (2021) Spatial-temporal traffic data imputation via graph attention convolutional network. In: International conference on artificial neural networks. Springer, pp 241\u2013252","DOI":"10.1007\/978-3-030-86362-3_20"},{"key":"6506_CR53","unstructured":"Andrea C, Ivan M, Alippi C, et al (2021) Filling the g_ap_s: multivariate time series imputation by graph neural networks. In: ICLR 2022, pp 1\u201320"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06506-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06506-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06506-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:38:06Z","timestamp":1758310686000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06506-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,27]]},"references-count":53,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["6506"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06506-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,27]]},"assertion":[{"value":"22 March 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"587"}}