{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T17:39:50Z","timestamp":1781631590096,"version":"3.54.5"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61962036"],"award-info":[{"award-number":["61962036"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangxi Provincial Department of Water Resources Science & Technology Program Foundation","award":["202325ZDKT17"],"award-info":[{"award-number":["202325ZDKT17"]}]},{"name":"Jiangxi Provincial Department of Water Resources Science & Technology Program Foundation","award":["202426ZDKT13"],"award-info":[{"award-number":["202426ZDKT13"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s44443-025-00278-x","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T09:22:04Z","timestamp":1764235324000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A lightweight object detection algorithm for resource-constrained UAVs via multi-module optimization and channel pruning"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3433-2925","authenticated-orcid":false,"given":"Wenfeng","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaomin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhong","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuming","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"key":"278_CR1","doi-asserted-by":"publisher","unstructured":"Chen J, Kao S, He H et al (2023) Run, Don\u2019t Walk: Chasing Higher FLOPS for Faster Neural Networks. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 12021\u201312031. https:\/\/doi.org\/10.48550\/arXiv.2303.03667","DOI":"10.48550\/arXiv.2303.03667"},{"key":"278_CR2","doi-asserted-by":"publisher","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","volume":"54","author":"G Cheng","year":"2016","unstructured":"Cheng G, Zhou P, Han J (2016) Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans Geosci Remote Sens 54:7405\u20137415. https:\/\/doi.org\/10.1109\/TGRS.2016.2601622","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"278_CR3","doi-asserted-by":"publisher","unstructured":"Chollet F (2017) Xception: Deep Learning with Depthwise Separable Convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp 1800\u20131807. https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"278_CR4","doi-asserted-by":"publisher","first-page":"7807","DOI":"10.3390\/s24237807","volume":"24","author":"C Ers\u00fc","year":"2024","unstructured":"Ers\u00fc C, Petlenkov E, Janson K (2024) A systematic review of cutting-edge radar technologies: applications for unmanned ground vehicles. Sensors 24:7807. https:\/\/doi.org\/10.3390\/s24237807","journal-title":"Sensors"},{"key":"278_CR5","doi-asserted-by":"publisher","DOI":"10.3390\/rs16183532","volume":"16","author":"X Fei","year":"2024","unstructured":"Fei X, Guo M, Li Y et al (2024) Acdf-yolo: attentive and cross-differential fusion network for multimodal remote sensing object detection. Remote Sens 16:3532. https:\/\/doi.org\/10.3390\/rs16183532","journal-title":"Remote Sens"},{"key":"278_CR6","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1049\/cvi2.12042","volume":"15","author":"Y Guo","year":"2021","unstructured":"Guo Y, Zou Q, Jin L (2021) A coarse to fine network for fast and accurate object detection in high resolution images. IET Comput Vision 15:274\u2013282. https:\/\/doi.org\/10.1049\/cvi2.12042","journal-title":"IET Comput Vision"},{"key":"278_CR7","doi-asserted-by":"publisher","unstructured":"Han K, Wang Y, Tian Q et al (2019) GhostNet: More Features from Cheap Operations. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 1577\u20131586. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00165","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"278_CR8","doi-asserted-by":"publisher","unstructured":"Howard AG, Zhu M, Chen B et al (2017) MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. https:\/\/doi.org\/10.48550\/arXiv.1704.04861","DOI":"10.48550\/arXiv.1704.04861"},{"key":"278_CR9","unstructured":"Hu J F, LI B C, Zhu H et al (2024) Improved YOLOv8 Lightweight UAV Target Detection Algorithm. Computer Engineering and Applications 60: 182\u2013191. http:\/\/cea.ceaj.org\/EN\/Y2024\/V60\/I8\/182"},{"key":"278_CR10","doi-asserted-by":"publisher","first-page":"21103","DOI":"10.1109\/JIOT.2024.3361857","volume":"11","author":"B Jia","year":"2024","unstructured":"Jia B, Gao Z, Jing J, Huang B, Liu S et al (2024) Coverage path planning for IoUAVs with tiny machine learning in complex areas based on convex decomposition. IEEE Internet Things J 11:21103\u201321111. https:\/\/doi.org\/10.1109\/JIOT.2024.3361857","journal-title":"IEEE Internet Things J"},{"key":"278_CR11","unstructured":"Jocher G, Chaurasia A, Qiu J (2023) Ultralytics YOLO. https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"278_CR12","doi-asserted-by":"publisher","unstructured":"Jocher G, Stoken A, et al (2020) Ultralytics\/yolov5: v3.1 - Bug Fixes and Performance Improvements. B-ug Fixes and Performance Improvements. https:\/\/doi.org\/10.5281\/ZENODO.4154370","DOI":"10.5281\/ZENODO.4154370"},{"key":"278_CR13","doi-asserted-by":"publisher","unstructured":"Khanam R, Hussain M (2024) YOLOv11: An Overview of the Key Architectural Enhancements. https:\/\/doi.org\/10.48550\/arXiv.2410.17725","DOI":"10.48550\/arXiv.2410.17725"},{"key":"278_CR14","doi-asserted-by":"publisher","unstructured":"Lee J, Park S, Mo S et al (2021) Layer-adaptive sparsity for the Magnitude-based Pruning. ICLR 2021. https:\/\/doi.org\/10.48550\/arXiv.2010.07611","DOI":"10.48550\/arXiv.2010.07611"},{"key":"278_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-024-01436-6","author":"H Li","year":"2024","unstructured":"Li H, Li J, Wei H et al (2024) Slim-neck by GSConv: a lightweight-design for real-time detector architectures. J Real-Time Image Process. https:\/\/doi.org\/10.1007\/s11554-024-01436-6","journal-title":"J Real-Time Image Process"},{"key":"278_CR16","doi-asserted-by":"publisher","unstructured":"Li J, Wen Y, He L (2023) SCConv: Spatial and Channel Reconstruction Convolution for Feature Redundancy. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 6153\u20136162. https:\/\/doi.org\/10.1109\/CVPR52729.2023.00596","DOI":"10.1109\/CVPR52729.2023.00596"},{"key":"278_CR17","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume":"2016","author":"W Liu","year":"2016","unstructured":"Liu W, Anguelov D, Erhan D et al (2016) SSD: Single Shot MultiBox Detector. Computer Vision - ECCV 2016:21\u201337. https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","journal-title":"Computer Vision - ECCV"},{"issue":"13","key":"278_CR18","doi-asserted-by":"publisher","DOI":"10.3390\/rs14133232","volume":"14","author":"S Liu","year":"2022","unstructured":"Liu S, Chen P, Wo\u017aniak M (2022) Image enhancement-based detection with small infrared targets. Remote Sens 14(13):3232. https:\/\/doi.org\/10.3390\/rs14133232","journal-title":"Remote Sens"},{"key":"278_CR19","doi-asserted-by":"publisher","first-page":"253","DOI":"10.3778\/j.issn.1002-8331.2212-0045","volume":"59","author":"T Liu","year":"2023","unstructured":"Liu T, Ding XY, Zhang BB et al (2023a) Improved yolov5 for remote sensing image detection. Comput Eng Appl 59:253\u2013261. https:\/\/doi.org\/10.3778\/j.issn.1002-8331.2212-0045","journal-title":"Comput Eng Appl"},{"key":"278_CR20","doi-asserted-by":"publisher","first-page":"1742","DOI":"10.1109\/ACCESS.2023.3233964","volume":"11","author":"Z Liu","year":"2023","unstructured":"Liu Z, Gao Y, Du Q et al (2023b) YOLO-extract: improved YOLOv5 for aircraft object detection in remote sensing images. IEEE Access 11:1742\u20131751. https:\/\/doi.org\/10.1109\/ACCESS.2023.3233964","journal-title":"IEEE Access"},{"key":"278_CR21","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 et al (2023c) An improved YOLOv5 method for small object detection in UAV capture scenes. IEEE Access 11:14365\u201314374. https:\/\/doi.org\/10.1109\/ACCESS.2023.3241005","journal-title":"IEEE Access"},{"key":"278_CR22","doi-asserted-by":"publisher","first-page":"9904","DOI":"10.1109\/JSTARS.2023.3325376","volume":"16","author":"S Liu","year":"2023","unstructured":"Liu S, Chen P, Zhang Y (2023d) A multiscale feature pyramid SAR ship detection network with robust background interference. IEEE J Sel Top Appl Earth Observ Remote Sens 16:9904\u20139915. https:\/\/doi.org\/10.1109\/JSTARS.2023.3325376","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"key":"278_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/s24227166","volume":"24","author":"B Liu","year":"2024","unstructured":"Liu B, Mo P, Wang S et al (2024) A refined and efficient CNN algorithm for remote sensing object detection. Sensors 24:7166. https:\/\/doi.org\/10.3390\/s24227166","journal-title":"Sensors"},{"key":"278_CR24","doi-asserted-by":"publisher","unstructured":"Liu R, Yu Z, Mo D et al (2020) An Improved Faster-RCNN Algorithm for Object Detection in Remote Sensing Images. In: 2020 39th Chinese Control Conference (CCC). pp 7188\u20137192. https:\/\/doi.org\/10.23919\/CCC50068.2020.9189024","DOI":"10.23919\/CCC50068.2020.9189024"},{"key":"278_CR25","doi-asserted-by":"publisher","unstructured":"Ma S, Xu Y (2023) MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression. https:\/\/doi.org\/10.48550\/arXiv.2307.07662","DOI":"10.48550\/arXiv.2307.07662"},{"key":"278_CR26","doi-asserted-by":"publisher","first-page":"246","DOI":"10.3778\/j.issn.1002-8331.2310-0279","volume":"60","author":"R Miao","year":"2024","unstructured":"Miao R, Yue M, Zhou K et al (2024) Small target detection method in remote sensing images based on improved YOLOv7. Comput Eng Appl 60:246\u2013255. https:\/\/doi.org\/10.3778\/j.issn.1002-8331.2310-0279","journal-title":"Comput Eng Appl"},{"key":"278_CR27","doi-asserted-by":"publisher","unstructured":"Min F, Kuang YG, Mao YX et al (2024) Improved YOLOv4 algorithm for remote sensing image object detection. Computer Engineering and Design 45: 396\u2013404. https:\/\/doi.org\/10.16208\/j.issn1000-7024.2024.02.010","DOI":"10.16208\/j.issn1000-7024.2024.02.010"},{"key":"278_CR28","doi-asserted-by":"publisher","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: An Incremental Improvement. https:\/\/doi.org\/10.48550\/arXiv.1804.02767","DOI":"10.48550\/arXiv.1804.02767"},{"key":"278_CR29","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 et al (2016) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39:1137\u20131149. https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"278_CR30","doi-asserted-by":"publisher","unstructured":"Sheng PF, Yang J (2023) Object detection of remote sensing images by using lightweight backbone network. Science of Surveying and Mapping 48: 58\u201367. https:\/\/doi.org\/10.16251\/j.cnki.1009-2307.2023.05.008","DOI":"10.16251\/j.cnki.1009-2307.2023.05.008"},{"key":"278_CR31","doi-asserted-by":"publisher","unstructured":"Shi F, Liu Y, Wang H (2023) Target Detection in Remote Sensing Images Based on Multi-scale Fusion Faster RCNN. In: 2023 35th Chinese Control and Decision Conference (CCDC). pp 4043\u20134046. https:\/\/doi.org\/10.1109\/CCDC58219.2023.10327230","DOI":"10.1109\/CCDC58219.2023.10327230"},{"key":"278_CR32","doi-asserted-by":"publisher","unstructured":"Singh KK (2018) An artificial intelligence and cloud based collaborative platform for plant disease identification, tracking and forecasting for farmers. In: 2018 IEEE International Conference on Cloud Computing in Emerging Markets. pp 49\u201356. https:\/\/doi.org\/10.1109\/CCEM.2018.00016","DOI":"10.1109\/CCEM.2018.00016"},{"key":"278_CR33","doi-asserted-by":"publisher","first-page":"2706","DOI":"10.11834\/jrs.20231638","volume":"27","author":"ZZ Tian","year":"2023","unstructured":"Tian ZZ, Zhang HW, Wang K et al (2023) Application of an improved centernet in remote sensing images object detection. National Remote Sensing Bulletin 27:2706\u20132715. https:\/\/doi.org\/10.11834\/jrs.20231638","journal-title":"National Remote Sensing Bulletin"},{"key":"278_CR34","doi-asserted-by":"publisher","first-page":"613","DOI":"10.3390\/rs15030614","volume":"15","author":"D Wan","year":"2023","unstructured":"Wan D, Lu R, Wang S et al (2023) YOLO-HR: improved yolov5 for object detection in high-resolution optical remote sensing images. Remote Sens 15:613\u2013614. https:\/\/doi.org\/10.3390\/rs15030614","journal-title":"Remote Sens"},{"key":"278_CR35","doi-asserted-by":"publisher","DOI":"10.3390\/rs17101768","volume":"17","author":"Z Wan","year":"2025","unstructured":"Wan Z, Lan Y, Xu Z, Shang K, Zhang F (2025) Dau-yolo: a lightweight and effective method for small object detection in UAV images. Remote Sens 17:1768. https:\/\/doi.org\/10.3390\/rs17101768","journal-title":"Remote Sens"},{"key":"278_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-72751-1_1","volume":"2024","author":"CY Wang","year":"2024","unstructured":"Wang CY, Yeh IH, Liao H (2024a) YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. Computer Vision - ECCV 2024:1\u201321. https:\/\/doi.org\/10.1007\/978-3-031-72751-1_1","journal-title":"Computer Vision - ECCV"},{"key":"278_CR37","doi-asserted-by":"publisher","unstructured":"Wang CY, Bochkovskiy A, Liao HYM (2023) YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 17\u201324. https:\/\/doi.org\/10.1109\/CVPR52729.2023.00721","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"278_CR38","doi-asserted-by":"publisher","unstructured":"Wang A, Chen H, Liu L et al (2024) YOLOv10: Real-Time End-to-End Object Detection. The 38th Con-ference on Neural Information Processing Systems (NeurIPS 2024). https:\/\/doi.org\/10.48550\/arXiv.2405.14458","DOI":"10.48550\/arXiv.2405.14458"},{"key":"278_CR39","doi-asserted-by":"publisher","unstructured":"Xia GS, Bai X, Ding J et al (2019) DOTA: A Large-scale Dataset for Object Detection in Aerial Images. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 3974\u20133983. https:\/\/doi.org\/10.1109\/CVPR.2018.00418","DOI":"10.1109\/CVPR.2018.00418"},{"key":"278_CR40","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1109\/JSTARS.2023.3329235","volume":"17","author":"S Xie","year":"2024","unstructured":"Xie S, Zhou M, Wang C et al (2024) Csppartial-YOLO: a lightweight YOLO-based method for typical objects detection in remote sensing images. IEEE J Sel Top Appl Earth Obs Remote Sens 17:388\u2013399. https:\/\/doi.org\/10.1109\/JSTARS.2023.3329235","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"278_CR41","doi-asserted-by":"publisher","first-page":"19743","DOI":"10.1109\/JSTARS.2024.3474689","volume":"17","author":"S Xu","year":"2024","unstructured":"Xu S, Song L, Yin J et al (2024) MFFCI\u2013YOLOv8: a lightweight remote sensing object detection network based on multiscale features fusion and context information. IEEE J Sel Top Appl Earth Obs Remote Sens 17:19743\u201319755. https:\/\/doi.org\/10.1109\/JSTARS.2024.3474689","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"278_CR42","doi-asserted-by":"publisher","first-page":"319","DOI":"10.3979\/j.issn.1673-825X.202302120032","volume":"36","author":"X Yu","year":"2024","unstructured":"Yu X, Pang ZH (2024) YOLOX remote sensing image object detection algorithm based on FEB. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition) 36:319\u2013327. https:\/\/doi.org\/10.3979\/j.issn.1673-825X.202302120032","journal-title":"Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition)"},{"key":"278_CR43","doi-asserted-by":"publisher","unstructured":"Zhang H, Zhang S (2024) Focaler-IoU: More Focused Intersection over Union Loss. https:\/\/doi.org\/10.48550\/arXiv.2401.10525","DOI":"10.48550\/arXiv.2401.10525"},{"key":"278_CR44","doi-asserted-by":"publisher","unstructured":"Zhang X, Zhou X, Lin M et al (2017) ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. https:\/\/doi.org\/10.48550\/arXiv.1707.01083","DOI":"10.48550\/arXiv.1707.01083"},{"key":"278_CR45","doi-asserted-by":"publisher","first-page":"46024","DOI":"10.1109\/ACCESS.2024.3382245","volume":"12","author":"C Zhao","year":"2024","unstructured":"Zhao C, Guo D, Shao C et al (2024) Satdetx-yolo: a more accurate method for vehicle target detection in satellite remote sensing imagery. IEEE Access 12:46024\u201346041. https:\/\/doi.org\/10.1109\/ACCESS.2024.3382245","journal-title":"IEEE Access"},{"key":"278_CR46","doi-asserted-by":"publisher","unstructured":"Zhao Q, Yang YC (2023) Small object detection algorithm for lightweight remote sensing vehicles with multiple pyramids. Electronic Measurement Technology 46: 88\u201394. https:\/\/doi.org\/10.19651\/j.cnki.emt.2211674","DOI":"10.19651\/j.cnki.emt.2211674"},{"key":"278_CR47","doi-asserted-by":"publisher","first-page":"4374","DOI":"10.3390\/rs16234374","volume":"16","author":"X Zheng","year":"2024","unstructured":"Zheng X, Qiu Y, Zhang G et al (2024) Esl-yolo: small object detection with effective feature enhancement and spatial-context-guided fusion network for remote sensing. Remote Sens 16:4374. https:\/\/doi.org\/10.3390\/rs16234374","journal-title":"Remote Sens"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00278-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00278-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00278-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:47:32Z","timestamp":1767638852000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00278-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,27]]},"references-count":47,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["278"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00278-x","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,27]]},"assertion":[{"value":"17 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2025","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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"339"}}