{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:32:58Z","timestamp":1762342378609,"version":"3.36.0"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Young Doctoral Program","award":["2023QB019"],"award-info":[{"award-number":["2023QB019"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s13042-024-02316-y","type":"journal-article","created":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T09:02:28Z","timestamp":1724490148000},"page":"983-997","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Color attention tracking with score matching"],"prefix":"10.1007","volume":"16","author":[{"given":"Xuedong","family":"He","sequence":"first","affiliation":[]},{"given":"Jiehui","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"2316_CR1","first-page":"6552","volume":"45","author":"S Javed","year":"2023","unstructured":"Javed S, Danelljan M, Khan FS et al (2023) Visual object tracking with discriminative filters and siamese networks: a Survey and Outlook[J]. IEEE Trans Pattern Anal Mach Intell 45:6552\u20136574","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2316_CR2","doi-asserted-by":"crossref","unstructured":"Liu D, Cui Y, Tan W et al (2021) Sg-net: Spatial granularity network for one-stage video instance segmentation[C]. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition: 9816\u20139825","DOI":"10.1109\/CVPR46437.2021.00969"},{"key":"2316_CR3","doi-asserted-by":"crossref","unstructured":"Cui Y, Yan L, Cao Z et al (2021) Tf-blender: Temporal feature blender for video object detection[C]. Proceedings of the IEEE\/CVF international conference on computer vision: 8138\u20138147","DOI":"10.1109\/ICCV48922.2021.00803"},{"key":"2316_CR4","doi-asserted-by":"crossref","unstructured":"Chen F, Wang X, Zhao Y et al (2022) Visual object tracking: a survey[J]. Comput Vis Image Underst: 103508","DOI":"10.1016\/j.cviu.2022.103508"},{"key":"2316_CR5","doi-asserted-by":"crossref","unstructured":"Mayer C, Danelljan M, Bhat G et al (2022) Transforming Model Prediction for Tracking[C]. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition: 8731\u20138740","DOI":"10.1109\/CVPR52688.2022.00853"},{"key":"2316_CR6","doi-asserted-by":"crossref","unstructured":"Wang N, Zhou W, Wang J et al (2021) Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking[C]. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition: 1571\u20131580","DOI":"10.1109\/CVPR46437.2021.00162"},{"key":"2316_CR7","doi-asserted-by":"crossref","unstructured":"Danelljan M, Bhat G, Khan FS et al (2019) ATOM: Accurate Tracking by Overlap Maximization[C]. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition: 4660\u20134669","DOI":"10.1109\/CVPR.2019.00479"},{"key":"2316_CR8","doi-asserted-by":"crossref","unstructured":"Bhat G, Danelljan M, Van Gool L et al (2019) Learning Discriminative Model Prediction for Tracking[C]. Proceedings of the IEEE\/CVF International Conference on Computer Vision: 6182\u20136191","DOI":"10.1109\/ICCV.2019.00628"},{"key":"2316_CR9","doi-asserted-by":"crossref","unstructured":"Danelljan M, Bhat G, Khan FS et al (2017) ECO: Efficient Convolution Operators for Tracking[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 6638\u20136646","DOI":"10.1109\/CVPR.2017.733"},{"issue":"3","key":"2316_CR10","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","volume":"37","author":"JF Henriques","year":"2015","unstructured":"Henriques JF, Caseiro R, Martins P et al (2015) High-speed tracking with kernelized correlation Filters[J]. IEEE Trans Pattern Anal Mach Intell 37(3):583\u2013596","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2316_CR11","doi-asserted-by":"crossref","unstructured":"Bolme DS, Beveridge JR, Draper BA et al (2010) Visual object tracking using adaptive correlation filters[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 2544\u20132550","DOI":"10.1109\/CVPR.2010.5539960"},{"issue":"6","key":"2316_CR12","doi-asserted-by":"publisher","first-page":"6129","DOI":"10.1007\/s10489-021-02694-8","volume":"52","author":"J Zhang","year":"2022","unstructured":"Zhang J, Liu Y, Liu H et al (2022) Distractor-aware visual tracking using hierarchical correlation filters adaptive selection[J]. Appl Intell 52(6):6129\u20136147","journal-title":"Appl Intell"},{"key":"2316_CR13","doi-asserted-by":"crossref","unstructured":"Cui Y, Cheng J, Wang L et al (2022) MixFormer: End-to-End Tracking with Iterative Mixed Attention[C]. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition: 13608\u201313618","DOI":"10.1109\/CVPR52688.2022.01324"},{"key":"2316_CR14","doi-asserted-by":"crossref","unstructured":"Yan B, Peng H, Fu J et al (2021) Learning Spatio-Temporal Transformer for Visual Tracking[C]. Proceedings of the IEEE\/CVF International Conference on Computer Vision: 10448\u201310457","DOI":"10.1109\/ICCV48922.2021.01028"},{"key":"2316_CR15","doi-asserted-by":"crossref","unstructured":"Chen X, Yan B, Zhu J et al (2021) Transformer Tracking[C]. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition: 8126\u20138135","DOI":"10.1109\/CVPR46437.2021.00803"},{"key":"2316_CR16","doi-asserted-by":"crossref","unstructured":"Voigtlaender P, Luiten J, Torr PHS et al (2020) Siam R-CNN: Visual Tracking by Re-Detection[C]. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition: 6578\u20136588","DOI":"10.1109\/CVPR42600.2020.00661"},{"key":"2316_CR17","doi-asserted-by":"crossref","unstructured":"Li B, Yan J, Wu W et al (2018) High Performance Visual Tracking with Siamese Region Proposal Network[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 8971\u20138980","DOI":"10.1109\/CVPR.2018.00935"},{"key":"2316_CR18","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Henriques JF et al (2016) Fully-Convolutional Siamese Networks for Object Tracking[C]. Proceedings of the European Conference on Computer Vision Workshops: 850\u2013865","DOI":"10.1007\/978-3-319-48881-3_56"},{"issue":"10","key":"2316_CR19","doi-asserted-by":"publisher","first-page":"3607","DOI":"10.1007\/s13042-023-01853-2","volume":"14","author":"J Li","year":"2023","unstructured":"Li J, Li B, Ding G et al (2023) Siamese global location-aware network for visual object tracking[J]. Int J Mach Learn Cybernet 14(10):3607\u20133620","journal-title":"Int J Mach Learn Cybernet"},{"key":"2316_CR20","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et al (2016) Deep Residual Learning for Image Recognition[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2316_CR21","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition[C]. International Conference on Learning Representations: 1\u201314"},{"key":"2316_CR22","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A et al (2021) An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale[C]. International Conference on Learning Representations 1\u201321"},{"key":"2316_CR23","unstructured":"Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need[C]. Adv Neural Inf Process Syst: 5998\u20136008"},{"issue":"3","key":"2316_CR24","doi-asserted-by":"publisher","first-page":"2401","DOI":"10.1109\/TETCI.2024.3360303","volume":"8","author":"F Gu","year":"2024","unstructured":"Gu F, Lu J, Cai C et al (2024) VTST: efficient visual Tracking with a stereoscopic Transformer[J]. IEEE Trans Emerg Top Comput Intell 8(3):2401\u20132416","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"key":"2316_CR25","doi-asserted-by":"crossref","unstructured":"Yan B, Zhang X, Wang D et al (2021) Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation[C]. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition: 5289\u20135298","DOI":"10.1109\/CVPR46437.2021.00525"},{"key":"2316_CR26","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.neucom.2023.01.041","volume":"527","author":"B Wei","year":"2023","unstructured":"Wei B, Chen H, Cao S et al (2023) An IoU-aware siamese network for real-time visual tracking[J]. Neurocomputing 527:13\u201326","journal-title":"Neurocomputing"},{"issue":"5","key":"2316_CR27","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1109\/TPAMI.2019.2957464","volume":"43","author":"L Huang","year":"2021","unstructured":"Huang L, Zhao X, Huang K (2021) GOT-10k: a large high-diversity benchmark for generic object tracking in the Wild[J]. IEEE Trans Pattern Anal Mach Intell 43(5):1562\u20131577","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2316_CR28","doi-asserted-by":"crossref","unstructured":"Fan H, Lin L, Yang F et al (2019) LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking[C]. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition: 5369\u20135378","DOI":"10.1109\/CVPR.2019.00552"},{"key":"2316_CR29","doi-asserted-by":"crossref","unstructured":"M\u00fcller M, Bibi A, Giancola S et al (2018) TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild[C]. Proceedings of the European Conference on Computer Vision: 310\u2013327","DOI":"10.1007\/978-3-030-01246-5_19"},{"key":"2316_CR30","doi-asserted-by":"crossref","unstructured":"Kiani Galoogahi H, Fagg A, Huang C et al (2017) Need for speed: A benchmark for higher frame rate object tracking[C]. Proceedings of the IEEE International Conference on Computer Vision: 1125\u20131134","DOI":"10.1109\/ICCV.2017.128"},{"key":"2316_CR31","doi-asserted-by":"crossref","unstructured":"Mueller M, Smith N, Ghanem BA (2016) Benchmark and Simulator for UAV Tracking[C]. Proceedings of the European Conference on Computer Vision: 445\u2013461","DOI":"10.1007\/978-3-319-46448-0_27"},{"issue":"9","key":"2316_CR32","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","volume":"37","author":"Y Wu","year":"2015","unstructured":"Wu Y, Lim J, Yang M-H (2015) Object tracking Benchmark[J]. IEEE Trans Pattern Anal Mach Intell 37(9):1834\u20131848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"2316_CR33","doi-asserted-by":"publisher","first-page":"8896","DOI":"10.1109\/TPAMI.2021.3127492","volume":"44","author":"J Shen","year":"2022","unstructured":"Shen J, Liu Y, Dong X et al (2022) Distilled siamese networks for Visual Tracking[J]. IEEE Trans Pattern Anal Mach Intell 44(12):8896\u20138909","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"2316_CR34","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/TCSVT.2022.3177215","volume":"34","author":"S Zhao","year":"2024","unstructured":"Zhao S, Xu T, Wu XJ et al (2024) Distillation, ensemble and selection for building a better and faster siamese Based Tracker[J]. IEEE Trans Circuits Syst Video Technol 34(1):182\u2013194","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"2316_CR35","doi-asserted-by":"crossref","unstructured":"Danelljan M, Khan FS, Felsberg M et al (2014) Adaptive Color Attributes for Real-Time Visual Tracking[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 1090\u20131097","DOI":"10.1109\/CVPR.2014.143"},{"key":"2316_CR36","doi-asserted-by":"crossref","unstructured":"Bertinetto L, Valmadre J, Golodetz S et al (2016) Staple: Complementary Learners for Real-Time Tracking[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 1401\u20131409","DOI":"10.1109\/CVPR.2016.156"},{"key":"2316_CR37","doi-asserted-by":"crossref","unstructured":"Danelljan M, Van Gool L, Timofte R (2020) Probabilistic Regression for Visual Tracking[C]. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition: 7183\u20137192","DOI":"10.1109\/CVPR42600.2020.00721"},{"key":"2316_CR38","doi-asserted-by":"crossref","unstructured":"Jiang B, Luo R, Mao J et al (2018) Acquisition of Localization Confidence for Accurate Object Detection[C]. Proceedings of the European Conference on Computer Vision (ECCV): 784\u2013799","DOI":"10.1007\/978-3-030-01264-9_48"},{"key":"2316_CR39","doi-asserted-by":"crossref","unstructured":"Ma C, Huang J, Yang X et al (2015) Hierarchical Convolutional Features for Visual Tracking[C]. Proceedings of the IEEE International Conference on Computer Vision: 3074\u20133082","DOI":"10.1109\/ICCV.2015.352"},{"key":"2316_CR40","doi-asserted-by":"crossref","unstructured":"Bhat G, Johnander J, Danelljan M et al (2018) Unveiling the Power of Deep Tracking[C]. Proceedings of the European Conference on Computer Vision: 493\u2013509","DOI":"10.1007\/978-3-030-01216-8_30"},{"key":"2316_CR41","doi-asserted-by":"publisher","first-page":"110860","DOI":"10.1016\/j.knosys.2023.110860","volume":"278","author":"X He","year":"2023","unstructured":"He X, Chen CY-C (2023) Attention fusion and target-uncertain detection for discriminative tracking[J]. Knowl Based Syst 278:110860","journal-title":"Knowl Based Syst"},{"key":"2316_CR42","doi-asserted-by":"crossref","unstructured":"Danelljan M, H\u00e4ger G, Khan FS et al (2015) Learning Spatially Regularized Correlation Filters for Visual Tracking[C]. Proceedings of the IEEE International Conference on Computer Vision: 4310\u20134318","DOI":"10.1109\/ICCV.2015.490"},{"issue":"8","key":"2316_CR43","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1109\/TPAMI.2016.2609928","volume":"39","author":"M Danelljan","year":"2017","unstructured":"Danelljan M, H\u00e4ger G, Khan FS et al (2017) Discriminative Scale Space Tracking[J]. IEEE Trans Pattern Anal Mach Intell 39(8):1561\u20131575","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2316_CR44","doi-asserted-by":"crossref","unstructured":"Li Y, Zhu J (2014) A scale adaptive kernel correlation filter tracker with feature integration[C]. Proceedings of the European Conference on Computer Vision Workshops: 254\u2013265","DOI":"10.1007\/978-3-319-16181-5_18"},{"issue":"7","key":"2316_CR45","doi-asserted-by":"publisher","first-page":"1512","DOI":"10.1109\/TIP.2009.2019809","volume":"18","author":"JVD Weijer","year":"2009","unstructured":"Weijer JVD, Schmid C, Verbeek J et al (2009) Learning Color names for Real-World Applications[J]. IEEE Trans Image Process 18(7):1512\u20131523","journal-title":"IEEE Trans Image Process"},{"key":"2316_CR46","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 886\u2013893","DOI":"10.1109\/CVPR.2005.177"},{"key":"2316_CR47","doi-asserted-by":"publisher","first-page":"108502","DOI":"10.1016\/j.patcog.2021.108502","volume":"124","author":"X Sun","year":"2022","unstructured":"Sun X, Han G, Guo L et al (2022) Two-stage aware attentional siamese network for visual tracking[J]. Pattern Recogn 124:108502","journal-title":"Pattern Recogn"},{"key":"2316_CR48","doi-asserted-by":"publisher","first-page":"119670","DOI":"10.1016\/j.eswa.2023.119670","volume":"219","author":"X He","year":"2023","unstructured":"He X, Yu-Chian Chen C (2023) Enhancing discriminative appearance model for visual tracking[J]. Expert Syst Appl 219:119670","journal-title":"Expert Syst Appl"},{"key":"2316_CR49","doi-asserted-by":"crossref","unstructured":"Mayer C, Danelljan M, Paudel DP et al (2021) Learning Target Candidate Association to Keep Track of What Not to Track[C]. Proceedings of the IEEE\/CVF International Conference on Computer Vision: 13444\u201313454","DOI":"10.1109\/ICCV48922.2021.01319"},{"key":"2316_CR50","doi-asserted-by":"crossref","unstructured":"Wang M, Liu Y (2017) Zeyihuang. Large Margin Object Tracking With Circulant Feature Maps[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 4021\u20134029","DOI":"10.1109\/CVPR.2017.510"},{"key":"2316_CR51","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.ins.2021.09.002","volume":"582","author":"X He","year":"2022","unstructured":"He X, Chen CY-C (2022) Learning object-uncertainty policy for visual tracking[J]. Inf Sci 582:60\u201372","journal-title":"Inf Sci"},{"key":"2316_CR52","doi-asserted-by":"crossref","unstructured":"Bhat G, Danelljan M, Van Gool L et al (2020) Know Your Surroundings: Exploiting Scene Information for Object Tracking[C]. Proceedings of the European Conference on Computer Vision: 205\u2013221","DOI":"10.1007\/978-3-030-58592-1_13"},{"issue":"5","key":"2316_CR53","doi-asserted-by":"publisher","first-page":"2976","DOI":"10.1109\/TCSVT.2021.3094645","volume":"32","author":"Z Zhou","year":"2021","unstructured":"Zhou Z, Li X, Zhang T et al (2021) Object tracking via spatial-temporal memory network[J]. IEEE Trans Circuits Syst Video Technol 32(5):2976\u20132989","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"2316_CR54","doi-asserted-by":"publisher","first-page":"109965","DOI":"10.1016\/j.patcog.2023.109965","volume":"146","author":"X Sun","year":"2024","unstructured":"Sun X, Sun H, Li J (2024) Updatable siamese tracker with two-stage one-shot learning[J]. Pattern Recogn 146:109965","journal-title":"Pattern Recogn"},{"key":"2316_CR55","doi-asserted-by":"crossref","unstructured":"Zhou L, Zhou Z, Mao K et al (2023) Joint visual grounding and tracking with natural language specification[C]. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition: 23151\u201323160","DOI":"10.1109\/CVPR52729.2023.02217"},{"issue":"9","key":"2316_CR56","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","volume":"37","author":"Y Wu","year":"2015","unstructured":"Wu Y, Lim J, Yang M (2015) Object tracking Benchmark[J]. IEEE Trans Pattern Anal Mach Intell 37(9):1834\u20131848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2316_CR57","doi-asserted-by":"crossref","unstructured":"Kristan M, Leonardis A, Matas J et al (2020) The Eighth Visual Object Tracking VOT2020 Challenge Results[C]. Proceedings of the European Conference on Computer Vision Workshops: 547\u2013601","DOI":"10.1007\/978-3-030-68238-5_39"},{"key":"2316_CR58","doi-asserted-by":"crossref","unstructured":"Yu B, Tang M, Zheng L et al (2021) High-Performance Discriminative Tracking With Transformers[C]. Proceedings of the IEEE\/CVF International Conference on Computer Vision: 9856\u20139865","DOI":"10.1109\/ICCV48922.2021.00971"},{"key":"2316_CR59","doi-asserted-by":"crossref","unstructured":"Zhou Z, Pei W, Li X et al (2021) Saliency-associated object tracking[C]. Proceedings of the IEEE\/CVF International Conference on Computer Vision: 9866\u20139875","DOI":"10.1109\/ICCV48922.2021.00972"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02316-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02316-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02316-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T10:41:06Z","timestamp":1738665666000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02316-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":59,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["2316"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02316-y","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"20 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}