{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T14:13:11Z","timestamp":1783951991074,"version":"3.55.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Henan Province Graduate Education Reform and Quality Enhancement Project","award":["YJS2026AL116"],"award-info":[{"award-number":["YJS2026AL116"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-026-08703-2","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T13:14:17Z","timestamp":1783948457000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FFEF-YOLO: a fine-grained feature extraction and fusion network for UAV small object detection"],"prefix":"10.1007","volume":"82","author":[{"given":"Bin","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengyu","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanming","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuwei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"issue":"5","key":"8703_CR1","doi-asserted-by":"publisher","first-page":"879","DOI":"10.3390\/rs16050879","volume":"16","author":"MH Rahman","year":"2024","unstructured":"Rahman MH, Sejan MAS, Aziz MA, Tabassum R, Baik J-I, Song H-K (2024) A comprehensive survey of unmanned aerial vehicles detection and classification using machine learning approach: challenges, solutions, and future directions. Remote Sens 16(5):879","journal-title":"Remote Sens"},{"issue":"1","key":"8703_CR2","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s44163-024-00209-1","volume":"4","author":"OK Pal","year":"2024","unstructured":"Pal OK, Shovon MSH, Mridha M, Shin J (2024) In-depth review of AI-enabled unmanned aerial vehicles: trends, vision, and challenges. Discover Artif Intell 4(1):97","journal-title":"Discover Artif Intell"},{"key":"8703_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.trip.2025.101591","volume":"33","author":"L Obaid","year":"2025","unstructured":"Obaid L, Hamad K, Al-Ruzouq R, Dabous SA, Ismail K, Alotaibi E (2025) State-of-the-art review of unmanned aerial vehicles (uavs) and artificial intelligence (AI) for traffic and safety analyses: Recent progress, applications, challenges, and opportunities. Transport Res Interdisciplinary Perspect 33:101591","journal-title":"Transport Res Interdisciplinary Perspect"},{"issue":"9","key":"8703_CR4","doi-asserted-by":"publisher","first-page":"2133","DOI":"10.3390\/ma18092133","volume":"18","author":"X Zhang","year":"2025","unstructured":"Zhang X, Cheng S, Wang P, Zheng H, Yang X, Guo Y (2025) 3d reconstruction and large-scale detection of roads based on uav imagery. Materials 18(9):2133","journal-title":"Materials"},{"issue":"14","key":"8703_CR5","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1007\/s11227-025-07836-0","volume":"81","author":"J Zhang","year":"2025","unstructured":"Zhang J, Gao M, Song L, Zhao H, Li W, Zhang Z, Rong C (2025) Rea-yolo for small object detection in uav aerial images. J Supercomput 81(14):1332","journal-title":"J Supercomput"},{"key":"8703_CR6","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1016\/j.isprsjprs.2024.04.015","volume":"211","author":"P Schuegraf","year":"2024","unstructured":"Schuegraf P, Shan J, Bittner K (2024) Planes4lod2: reconstruction of lod-2 building models using a depth attention-based fully convolutional neural network. ISPRS J Photogramm Remote Sens 211:425\u2013437","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"14","key":"8703_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11227-025-07790-x","volume":"81","author":"S Xu","year":"2025","unstructured":"Xu S, Cui K, Chen G, Zhang M (2025) Sga-yolov8: Sobel-gelan and aifi-dilated-attention fusion network for uav remote-sensing small target detection. J Supercomput 81(14):1\u201324","journal-title":"J Supercomput"},{"issue":"4","key":"8703_CR8","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1007\/s40745-024-00546-z","volume":"11","author":"J Li","year":"2024","unstructured":"Li J, Xie C, Wu S, Ren Y (2024) Uav-yolov5: a swin-transformer-enabled small object detection model for long-range uav images. Annals of Data Sci 11(4):1109\u20131138","journal-title":"Annals of Data Sci"},{"key":"8703_CR9","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), vol 1. Ieee, pp 886\u2013893","DOI":"10.1109\/CVPR.2005.177"},{"key":"8703_CR10","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25"},{"key":"8703_CR11","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"issue":"6","key":"8703_CR12","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren S, He K, Girshick R, Sun J (2016) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8703_CR13","doi-asserted-by":"crossref","unstructured":"Cai Z, Vasconcelos N (2018) Cascade r-cnn: Delving into high quality object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp 6154\u20136162","DOI":"10.1109\/CVPR.2018.00644"},{"key":"8703_CR14","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. European Conference on Computer Vision. Springer, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"8703_CR15","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"8703_CR16","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. European Conference on Computer Vision. Springer, pp 213\u2013229","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"8703_CR17","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"16","key":"8703_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11227-025-08001-3","volume":"81","author":"Y Wang","year":"2025","unstructured":"Wang Y, Yang W (2025) Mrf-yolo: small object detection for uav and remote sensing images. J Supercomput 81(16):1\u201324","journal-title":"J Supercomput"},{"issue":"3","key":"8703_CR19","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","volume":"111","author":"Z Zou","year":"2023","unstructured":"Zou Z, Chen K, Shi Z, Guo Y, Ye J (2023) Object detection in 20 years: a survey. Proc IEEE 111(3):257\u2013276","journal-title":"Proc IEEE"},{"key":"8703_CR20","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"8703_CR21","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"8703_CR22","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement, arXiv preprint arXiv:1804.02767"},{"key":"8703_CR23","unstructured":"Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: optimal speed and accuracy of object detection, arXiv preprint arXiv:2004.10934"},{"key":"8703_CR24","unstructured":"Jocher G, Stoken A, Borovec J, Changyu L, Hogan A, Diaconu L, Poznanski J, Yu L, Rai P, Ferriday R (2020) ultralytics\/yolov5: v3. 0. Zenodo"},{"key":"8703_CR25","unstructured":"Jocher G, Chaurasia A, Qiu J (2023) Ultralytics yolo.(version 8.0. 0)[computer software]"},{"key":"8703_CR26","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Yeh I-H, Mark Liao H-Y (2024) Yolov9: Learning what you want to learn using programmable gradient information. European Conference on Computer Vision. Springer, pp 1\u201321","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"8703_CR27","doi-asserted-by":"publisher","first-page":"107984","DOI":"10.52202\/079017-3429","volume":"37","author":"A Wang","year":"2024","unstructured":"Wang A, Chen H, Liu L, Chen K, Lin Z, Han J et al (2024) Yolov10: real-time end-to-end object detection. Adv Neural Inf Process Syst 37:107984\u2013108011","journal-title":"Adv Neural Inf Process Syst"},{"key":"8703_CR28","unstructured":"Khanam R, Hussain M (2024) Yolov11: an overview of the key architectural enhancements, arXiv preprint arXiv:2410.17725"},{"key":"8703_CR29","unstructured":"Tian Y, Ye Q, Doermann D (2025) Yolov12: attention-centric real-time object detectors, arXiv preprint arXiv:2502.12524"},{"key":"8703_CR30","unstructured":"Lei M, Li S, Wu Y, Hu H, Zhou Y, Zheng X, Ding G, Du S, Wu Z, Gao Y (2025) Yolov13: real-time object detection with hypergraph-enhanced adaptive visual perception. arXiv preprint arXiv:2506.17733"},{"issue":"6","key":"8703_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-025-11150-9","volume":"58","author":"W Hua","year":"2025","unstructured":"Hua W, Chen Q (2025) A survey of small object detection based on deep learning in aerial images. Artif Intell Rev 58(6):1\u201367","journal-title":"Artif Intell Rev"},{"key":"8703_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.110488","volume":"149","author":"MS Ali","year":"2025","unstructured":"Ali MS, Latif A, Anwar MW, Ashraf MH (2025) Multiscale self-attention for unmanned ariel vehicle-based infrared thermal images detection. Eng Appl Artif Intell 149:110488","journal-title":"Eng Appl Artif Intell"},{"issue":"16","key":"8703_CR33","doi-asserted-by":"publisher","first-page":"2930","DOI":"10.3390\/rs16162930","volume":"16","author":"T Hanyu","year":"2024","unstructured":"Hanyu T, Yamazaki K, Tran M, McCann RA, Liao H, Rainwater C, Adkins M, Cothren J, Le N (2024) Aerialformer: multi-resolution transformer for aerial image segmentation. Remote Sensing 16(16):2930","journal-title":"Remote Sensing"},{"key":"8703_CR34","unstructured":"Wu X, Li W, Hong D, Tao R, Du Q (2021) Deep learning for uav-based object detection and tracking: A survey, arXiv preprint arXiv:2110.12638"},{"key":"8703_CR35","doi-asserted-by":"crossref","unstructured":"Zhu X, Lyu S, Wang X, Zhao Q (2021) Tph-yolov5: Improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios. Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 2778\u20132788","DOI":"10.1109\/ICCVW54120.2021.00312"},{"issue":"16","key":"8703_CR36","doi-asserted-by":"publisher","first-page":"7190","DOI":"10.3390\/s23167190","volume":"23","author":"G Wang","year":"2023","unstructured":"Wang G, Chen Y, An P, Hong H, Hu J, Huang T (2023) Uav-yolov8: a small-object-detection model based on improved yolov8 for uav aerial photography scenarios. Sensors 23(16):7190","journal-title":"Sensors"},{"key":"8703_CR37","doi-asserted-by":"crossref","unstructured":"Xiao Y, Xu T, Xin Y, Li J (2025) Fbrt-yolo: Faster and better for real-time aerial image detection. Proceedings of the AAAI Conference on Artificial Intelligence, vol 39. pp 8673\u20138681","DOI":"10.1609\/aaai.v39i8.32937"},{"issue":"8","key":"8703_CR38","doi-asserted-by":"publisher","first-page":"526","DOI":"10.3390\/drones7080526","volume":"7","author":"Z Zhang","year":"2023","unstructured":"Zhang Z (2023) Drone-yolo: an efficient neural network method for target detection in drone images. Drones 7(8):526","journal-title":"Drones"},{"issue":"16","key":"8703_CR39","doi-asserted-by":"publisher","first-page":"3057","DOI":"10.3390\/rs16163057","volume":"16","author":"Y Li","year":"2024","unstructured":"Li Y, Li Q, Pan J, Zhou Y, Zhu H, Wei H, Liu C (2024) Sod-yolo: small-object-detection algorithm based on improved yolov8 for uav images. Remote Sensing 16(16):3057","journal-title":"Remote Sensing"},{"key":"8703_CR40","doi-asserted-by":"crossref","unstructured":"Do M-T, Ha M-H, Nguyen D-C, Thai K, Do\u00a0Ba Q-H (2023) Human detection based yolo backbones-transformer in uavs. In: 2023 International Conference on System Science and Engineering (ICSSE), pp 576\u2013580. IEEE","DOI":"10.1109\/ICSSE58758.2023.10227141"},{"key":"8703_CR41","doi-asserted-by":"crossref","unstructured":"Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S (2019) Generalized intersection over union: a metric and a loss for bounding box regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 658\u2013666","DOI":"10.1109\/CVPR.2019.00075"},{"key":"8703_CR42","doi-asserted-by":"crossref","unstructured":"Zheng Z, Wang P, Liu W, Li J, Ye R, Ren D (2020) Distance-iou loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence, vol 34. pp 12993\u201313000","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"8703_CR43","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","volume":"506","author":"Y-F Zhang","year":"2022","unstructured":"Zhang Y-F, Ren W, Zhang Z, Jia Z, Wang L, Tan T (2022) Focal and efficient iou loss for accurate bounding box regression. Neurocomputing 506:146\u2013157","journal-title":"Neurocomputing"},{"key":"8703_CR44","unstructured":"Gevorgyan Z (2022) Siou loss: More powerful learning for bounding box regression. arXiv preprint arXiv:2205.12740"},{"key":"8703_CR45","doi-asserted-by":"crossref","unstructured":"Sohan M, Sai Ram T, Rami Reddy CV (2024) A review on yolov8 and its advancements. International Conference on Data Intelligence and Cognitive Informatics. Springer, pp 529\u2013545","DOI":"10.1007\/978-981-99-7962-2_39"},{"key":"8703_CR46","doi-asserted-by":"publisher","first-page":"14365","DOI":"10.1109\/ACCESS.2023.3241005","volume":"11","author":"Z Liu","year":"2023","unstructured":"Liu Z, Gao X, Wan Y, Wang J, Lyu H (2023) An improved yolov5 method for small object detection in uav capture scenes. IEEe Access 11:14365\u201314374","journal-title":"IEEe Access"},{"key":"8703_CR47","unstructured":"Zhang X, Liu C, Yang D, Song T, Ye Y, Li K, Song Y (2023) Rfaconv: Innovating spatial attention and standard convolutional operation. arXiv preprint arXiv:2304.03198"},{"key":"8703_CR48","doi-asserted-by":"crossref","unstructured":"Sunkara R, Luo T (2022) No more strided convolutions or pooling: A new cnn building block for low-resolution images and small objects. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, pp 443\u2013459","DOI":"10.1007\/978-3-031-26409-2_27"},{"key":"8703_CR49","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 13713\u201313722","DOI":"10.1109\/CVPR46437.2021.01350"},{"issue":"1","key":"8703_CR50","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1109\/TNNLS.2023.3336563","volume":"36","author":"Y Liu","year":"2024","unstructured":"Liu Y, Li H, Hu C, Luo S, Luo Y, Chen CW (2024) Learning to aggregate multi-scale context for instance segmentation in remote sensing images. IEEE Trans Neural Networks Learn Syst 36(1):595\u2013609","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"key":"8703_CR51","doi-asserted-by":"crossref","unstructured":"Cao Y, Xu J, Lin S, Wei F, Hu H (2019) Gcnet: Non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, pp 0\u20130","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"8703_CR52","unstructured":"Ma S, Xu Y (2023) Mpdiou: a loss for efficient and accurate bounding box regression. arXiv preprint arXiv:2307.07662"},{"key":"8703_CR53","doi-asserted-by":"crossref","unstructured":"Du D, Zhu P, Wen L, Bian X, Lin H, Hu Q, Peng T, Zheng J, Wang X, Zhang Y, et al (2019) Visdrone-det2019: The vision meets drone object detection in image challenge results. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, pp 0\u20130","DOI":"10.1109\/ICCVW.2019.00031"},{"key":"8703_CR54","doi-asserted-by":"crossref","unstructured":"Du D, Qi Y, Yu H, Yang Y, Duan K, Li G, Zhang W, Huang Q, Tian Q (2018) The unmanned aerial vehicle benchmark: object detection and tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 370\u2013386","DOI":"10.1007\/978-3-030-01249-6_23"},{"issue":"5","key":"8703_CR55","doi-asserted-by":"publisher","first-page":"2486","DOI":"10.1109\/TGRS.2016.2645610","volume":"55","author":"Y Long","year":"2017","unstructured":"Long Y, Gong Y, Xiao Z, Liu Q (2017) Accurate object localization in remote sensing images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 55(5):2486\u20132498","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"8","key":"8703_CR56","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s40747-025-01971-0","volume":"11","author":"Z Ma","year":"2025","unstructured":"Ma Z, Luo P, Shen X (2025) Lmsoe-net: lightweight multi-scale small object enhancement network for uav aerial images. Complex Intell Syst 11(8):333","journal-title":"Complex Intell Syst"},{"key":"8703_CR57","doi-asserted-by":"crossref","unstructured":"Zhao Y, Lv W, Xu S, Wei J, Wang G, Dang Q, Liu Y, Chen J (2024) Detrs beat yolos on real-time object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 16965\u201316974","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"8703_CR58","doi-asserted-by":"crossref","unstructured":"Hou T, Leng C, Wang J, Pei Z, Peng J, Cheng I, Basu A (2025) Mfel-yolo for small object detection in uav aerial images. Expert Syst Appl 128459","DOI":"10.1016\/j.eswa.2025.128459"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08703-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-026-08703-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08703-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T13:14:26Z","timestamp":1783948466000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-026-08703-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,13]]},"references-count":58,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["8703"],"URL":"https:\/\/doi.org\/10.1007\/s11227-026-08703-2","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,13]]},"assertion":[{"value":"6 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2026","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"563"}}