{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T15:05:38Z","timestamp":1784905538696,"version":"3.55.0"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/fundrefid","name":"Funder Name","doi-asserted-by":"publisher","award":["Grant number"],"award-info":[{"award-number":["Grant number"]}],"id":[{"id":"10.13039\/fundrefid","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012547","name":"Natural Science Foundation of Guangxi Zhuang Autonomous Region","doi-asserted-by":"publisher","award":["AA24206025"],"award-info":[{"award-number":["AA24206025"]}],"id":[{"id":"10.13039\/100012547","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation","doi-asserted-by":"crossref","award":["AD22080061"],"award-info":[{"award-number":["AD22080061"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Innovation Project of Guangxi Graduate Education","award":["YCSW2024346"],"award-info":[{"award-number":["YCSW2024346"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s40747-025-02126-x","type":"journal-article","created":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T06:30:16Z","timestamp":1762842616000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A UAV aerial image small object detection algorithm based on fine-grained feature preservation and multi-scale feature pyramid balancing"],"prefix":"10.1007","volume":"12","author":[{"given":"Jian","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaixuan","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0217-9115","authenticated-orcid":false,"given":"Jianhua","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiyan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanfa","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,11]]},"reference":[{"issue":"6","key":"2126_CR1","doi-asserted-by":"publisher","first-page":"147","DOI":"10.3390\/drones6060147","volume":"6","author":"SAH Mohsan","year":"2022","unstructured":"Mohsan SAH, Khan MA, Noor F et al (2022) Towards the unmanned aerial vehicles (UAVs): a comprehensive review. Drones 6(6):147","journal-title":"Drones"},{"issue":"1","key":"2126_CR2","doi-asserted-by":"publisher","first-page":"149","DOI":"10.3390\/rs16010149","volume":"16","author":"G Tang","year":"2023","unstructured":"Tang G, Ni J, Zhao Y et al (2023) A survey of object detection for UAVs based on deep learning. Remote Sens 16(1):149","journal-title":"Remote Sens"},{"issue":"5","key":"2126_CR3","doi-asserted-by":"publisher","first-page":"108","DOI":"10.3390\/drones6050108","volume":"6","author":"J Feng","year":"2022","unstructured":"Feng J, Yi C (2022) Lightweight detection network for arbitrary-oriented vehicles in UAV imagery via global attentive relation and multi-path fusion. Drones 6(5):108","journal-title":"Drones"},{"issue":"7","key":"2126_CR4","doi-asserted-by":"publisher","first-page":"154","DOI":"10.3390\/drones6070154","volume":"6","author":"SH Alsamhi","year":"2022","unstructured":"Alsamhi SH, Shvetsov AV, Kumar S et al (2022) UAV computing-assisted search and rescue mission framework for disaster and harsh environment mitigation. Drones 6(7):154","journal-title":"Drones"},{"key":"2126_CR5","unstructured":"Muchiri G, Kimathi S (2022) A review of applications and potential applications of UAV. In: Proceedings of the sustainable research and innovation conference, pp 280\u2013283"},{"issue":"6","key":"2126_CR6","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(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2126_CR7","doi-asserted-by":"publisher","unstructured":"He K, Gkioxari G, Doll\u00e1r P et\u00a0al (2017) Mask R-CNN. In: 2017 IEEE international conference on computer vision (ICCV), pp 2980\u20132988. https:\/\/doi.org\/10.1109\/ICCV.2017.322","DOI":"10.1109\/ICCV.2017.322"},{"key":"2126_CR8","doi-asserted-by":"crossref","unstructured":"Woo S, Debnath S, Hu R et\u00a0al (2023) ConvNeXt V2: co-designing and scaling convnets with masked autoencoders. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16133\u201316142","DOI":"10.1109\/CVPR52729.2023.01548"},{"key":"2126_CR9","unstructured":"Jocher G, Chaurasia A, Qiu J (2023) YOLO by ultralytics"},{"key":"2126_CR10","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"2126_CR11","first-page":"1","volume-title":"Computer vision and pattern recognition","author":"A Farhadi","year":"2018","unstructured":"Farhadi A, Redmon J (2018) YOLOv3: an incremental improvement. Computer vision and pattern recognition. Springer, Berlin\/Heidelberg, pp 1\u20136"},{"key":"2126_CR12","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934"},{"key":"2126_CR13","unstructured":"Li C, Li L, Jiang H et\u00a0al (2022) YOLOv6: a single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976"},{"key":"2126_CR14","doi-asserted-by":"crossref","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: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"issue":"3","key":"2126_CR15","doi-asserted-by":"publisher","first-page":"190","DOI":"10.3390\/drones7030190","volume":"7","author":"C Chen","year":"2023","unstructured":"Chen C, Zheng Z, Xu T et al (2023) YOLO-based UAV technology: a review of the research and its applications. Drones 7(3):190","journal-title":"Drones"},{"issue":"17","key":"2126_CR16","doi-asserted-by":"publisher","first-page":"5496","DOI":"10.3390\/s24175496","volume":"24","author":"Y Kong","year":"2024","unstructured":"Kong Y, Shang X, Jia S (2024) Drone-DETR: efficient small object detection for remote sensing image using enhanced RT-DETR model. Sensors 24(17):5496","journal-title":"Sensors"},{"key":"2126_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.121366","volume":"686","author":"Q Fan","year":"2025","unstructured":"Fan Q, Li Y, Deveci M et al (2025) LUD-YOLO: a novel lightweight object detection network for unmanned aerial vehicle. Inf Sci 686:121366","journal-title":"Inf Sci"},{"issue":"1","key":"2126_CR18","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1007\/s40747-024-01652-4","volume":"11","author":"D Liao","year":"2025","unstructured":"Liao D, Zhang J, Tao Y et al (2025) ATBHC-YOLO: aggregate transformer and bidirectional hybrid convolution for small object detection. Complex Intell Syst 11(1):38","journal-title":"Complex Intell Syst"},{"key":"2126_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2025.105469","volume":"156","author":"C Bai","year":"2025","unstructured":"Bai C, Zhang K, Jin H et al (2025) SFFEF-YOLO: small object detection network based on fine-grained feature extraction and fusion for unmanned aerial images. Image Vis Comput 156:105469","journal-title":"Image Vis Comput"},{"key":"2126_CR20","doi-asserted-by":"crossref","unstructured":"Feng C, Wang C, Zhang D et\u00a0al (2024) Enhancing dense small object detection in UAV images based on hybrid transformer. Comput Mater Continua 78(3):3993\u20134013","DOI":"10.32604\/cmc.2024.048351"},{"key":"2126_CR21","first-page":"1","volume":"61","author":"G Cheng","year":"2023","unstructured":"Cheng G, Li Q, Wang G et al (2023) SFRNet: fine-grained oriented object recognition via separate feature refinement. IEEE Trans Geosci Remote Sens 61:1\u201310","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2126_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3494868","volume":"62","author":"W Lu","year":"2024","unstructured":"Lu W, Zhang Z, Nguyen M (2024) A lightweight CNN-transformer network with Laplacian loss for low-altitude UAV imagery semantic segmentation. IEEE Trans Geosci Remote Sens 62:1\u201320","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2126_CR23","doi-asserted-by":"crossref","unstructured":"Chen Q, Chen X, Wang J et\u00a0al (2023) Group DETR: fast DETR training with group-wise one-to-many assignment. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6633\u20136642","DOI":"10.1109\/ICCV51070.2023.00610"},{"key":"2126_CR24","doi-asserted-by":"crossref","unstructured":"Ma S, Zhang Y, Peng L et\u00a0al (2025) OWRT-DETR: a novel real-time transformer network for small object detection in open water search and rescue from UAV aerial imagery. IEEE Trans Geosci Remote Sens 63:1\u201313. https:\/\/doi.org\/10.1109\/TGRS.2025.3560928","DOI":"10.1109\/TGRS.2025.3560928"},{"key":"2126_CR25","doi-asserted-by":"crossref","unstructured":"Zhang H, Liu K, Gan Z et\u00a0al (2025) UAV-DETR: efficient end-to-end object detection for unmanned aerial vehicle imagery. arXiv preprint arXiv:2501.01855","DOI":"10.1109\/IROS60139.2025.11246176"},{"key":"2126_CR26","doi-asserted-by":"crossref","unstructured":"Chen J, Kao Sh, He H et\u00a0al (2023) Run, don\u2019t walk: chasing higher flops for faster neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12021\u201312031","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"2126_CR27","doi-asserted-by":"crossref","unstructured":"Li Y, Hu J, Wen Y et\u00a0al (2023) Rethinking vision transformers for MobileNet size and speed. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 16889\u201316900","DOI":"10.1109\/ICCV51070.2023.01549"},{"key":"2126_CR28","doi-asserted-by":"crossref","unstructured":"Zhang J, Zhang Y, Shi Z et\u00a0al (2024) Unmanned aerial vehicle object detection based on information-preserving and fine-grained feature aggregation. Remote Sens 16(14)","DOI":"10.3390\/rs16142590"},{"key":"2126_CR29","first-page":"1","volume":"61","author":"W Lu","year":"2023","unstructured":"Lu W, Chen SB, Tang J et al (2023) A robust feature downsampling module for remote-sensing visual tasks. IEEE Trans Geosci Remote Sens 61:1\u201312","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"6","key":"2126_CR30","doi-asserted-by":"publisher","first-page":"8773","DOI":"10.1109\/TII.2024.3367043","volume":"20","author":"D Chen","year":"2024","unstructured":"Chen D, Miao D, Zhao X (2024) Hyneter: hybrid network transformer for multiple computer vision tasks. IEEE Trans Ind Inf 20(6):8773\u20138785","journal-title":"IEEE Trans Ind Inf"},{"key":"2126_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3328078","volume":"72","author":"S Chan","year":"2023","unstructured":"Chan S, Yu M, Chen Z et al (2023) Regional contextual information modeling for small object detection on highways. IEEE Trans Instrum Meas 72:1\u201313","journal-title":"IEEE Trans Instrum Meas"},{"key":"2126_CR32","doi-asserted-by":"crossref","unstructured":"Cai X, Lai Q, Wang Y et\u00a0al (2024) Poly kernel inception network for remote sensing detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 27706\u201327716","DOI":"10.1109\/CVPR52733.2024.02617"},{"key":"2126_CR33","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S et\u00a0al (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"2126_CR34","doi-asserted-by":"crossref","unstructured":"Lin TY, Doll\u00e1r P, Girshick R et\u00a0al (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":"2126_CR35","doi-asserted-by":"crossref","unstructured":"Qiao S, Chen LC, Yuille A (2021) Detectors: Detecting objects with recursive feature pyramid and switchable Atrous convolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10213\u201310224","DOI":"10.1109\/CVPR46437.2021.01008"},{"issue":"1","key":"2126_CR36","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/s00371-024-03355-w","volume":"41","author":"Z Li","year":"2025","unstructured":"Li Z, He Q, Yang W (2025) E-FPN: an enhanced feature pyramid network for UAV scenarios detection. Vis Comput 41(1):675\u2013693","journal-title":"Vis Comput"},{"issue":"1","key":"2126_CR37","doi-asserted-by":"publisher","first-page":"16233","DOI":"10.1038\/s41598-025-00008-3","volume":"15","author":"Z Chen","year":"2025","unstructured":"Chen Z, Ma Y, Gong Z et al (2025) R-AFPN: a residual asymptotic feature pyramid network for UAV aerial photography of small targets. Sci Rep 15(1):16233","journal-title":"Sci Rep"},{"key":"2126_CR38","doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le QV (2020) EfficientDet: scalable and efficient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10781\u201310790","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"2126_CR39","doi-asserted-by":"publisher","first-page":"4183","DOI":"10.1109\/TMM.2023.3321394","volume":"26","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Zhang T, Wu C et al (2023) Multi-scale spatiotemporal feature fusion network for video saliency prediction. IEEE Trans Multimed 26:4183\u20134193","journal-title":"IEEE Trans Multimed"},{"key":"2126_CR40","unstructured":"Wei H, Liu X, Xu S et\u00a0al (2022) DWRSeg: rethinking efficient acquisition of multi-scale contextual information for real-time semantic segmentation. arXiv preprint arXiv:2212.01173"},{"key":"2126_CR41","doi-asserted-by":"crossref","unstructured":"Liu S, Qi L, Qin H et\u00a0al (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8759\u20138768","DOI":"10.1109\/CVPR.2018.00913"},{"key":"2126_CR42","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY et\u00a0al (2018) CBAM: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"2126_CR43","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S et\u00a0al (2014) Microsoft COCO: common objects in context. In: Computer vision\u2013ECCV 2014: 13th European conference, Zurich, Switzerland, September 6\u201312, 2014, proceedings, Part V 13. Springer, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"2126_CR44","doi-asserted-by":"crossref","unstructured":"Cao Y, He Z, Wang L et\u00a0al (2021) Visdrone-DET2021: the vision meets drone object detection challenge results. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 2847\u20132854","DOI":"10.1109\/ICCVW54120.2021.00319"},{"key":"2126_CR45","doi-asserted-by":"crossref","unstructured":"Wang J, Yang W, Guo H et\u00a0al (2021) Tiny object detection in aerial images. In: 2020 25th international conference on pattern recognition (ICPR). IEEE, pp 3791\u20133798","DOI":"10.1109\/ICPR48806.2021.9413340"},{"key":"2126_CR46","doi-asserted-by":"crossref","unstructured":"Sun P, Zhang R, Jiang Y et\u00a0al (2021) Sparse R-CNN: end-to-end object detection with learnable proposals. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14454\u201314463","DOI":"10.1109\/CVPR46437.2021.01422"},{"key":"2126_CR47","doi-asserted-by":"crossref","unstructured":"Feng C, Zhong Y, Gao Y et\u00a0al (2021) TOOD: task-aligned one-stage object detection. In: 2021 IEEE\/CVF international conference on computer vision (ICCV). IEEE Computer Society, pp 3490\u20133499","DOI":"10.1109\/ICCV48922.2021.00349"},{"key":"2126_CR48","unstructured":"Lyu C, Zhang W, Huang H et\u00a0al (2022) RTMDet: an empirical study of designing real-time object detectors. arXiv preprint arXiv:2212.07784"},{"key":"2126_CR49","doi-asserted-by":"crossref","unstructured":"Wang CY, Yeh IH, Mark\u00a0Liao HY (2024) YOLOv9: learning what you want to learn using programmable gradient information. In: European conference on computer vision. Springer, pp 1\u201321","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"2126_CR50","first-page":"107984","volume":"37","author":"A Wang","year":"2024","unstructured":"Wang A, Chen H, Liu L 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":"2126_CR51","unstructured":"Khanam R, Hussain M (2024) YOLOv11: an overview of the key architectural enhancements. arXiv preprint arXiv:2410.17725"},{"key":"2126_CR52","doi-asserted-by":"crossref","unstructured":"Qin D, Leichner C, Delakis M et\u00a0al (2024) MobileNetV4: universal models for the mobile ecosystem. In: European conference on computer vision. Springer, pp 78\u201396","DOI":"10.1007\/978-3-031-73661-2_5"},{"key":"2126_CR53","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y et\u00a0al (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"},{"key":"2126_CR54","doi-asserted-by":"crossref","unstructured":"Zhao Y, Lv W, Xu S et\u00a0al (2024) DETRs beat YOLOs on real-time object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16965\u201316974","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"2126_CR55","unstructured":"Tian Y, Ye Q, Doermann D (2025) YOLOv12: attention-centric real-time object detectors. arXiv preprint arXiv:2502.12524"},{"key":"2126_CR56","doi-asserted-by":"publisher","unstructured":"Jocher G (2020) YOLOv5 by ultralytics. https:\/\/doi.org\/10.5281\/zenodo.3908559","DOI":"10.5281\/zenodo.3908559"},{"key":"2126_CR57","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2126_CR58","unstructured":"Yang L, Zhang RY, Li L et\u00a0al (2021) SimAM: a simple, parameter-free attention module for convolutional neural networks. In: International conference on machine learning. PMLR, pp 11863\u201311874"},{"key":"2126_CR59","doi-asserted-by":"crossref","unstructured":"Zhang S, Chi C, Yao Y et\u00a0al (2020) Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9759\u20139768","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"2126_CR60","doi-asserted-by":"crossref","unstructured":"Zhu X, Lyu S, Wang X et\u00a0al (2021) TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 2778\u20132788","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"2126_CR61","doi-asserted-by":"crossref","unstructured":"Zhang Z (2023) Drone-YOLO: an efficient neural network method for target detection in drone images. Drones 7(8):526","DOI":"10.3390\/drones7080526"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02126-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-02126-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02126-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T11:48:10Z","timestamp":1769773690000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-02126-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,11]]},"references-count":61,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["2126"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-02126-x","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,11]]},"assertion":[{"value":"8 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 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 competingfinancial interests or personal relationships that could have appeared to influence thework reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"12"}}