{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T01:40:57Z","timestamp":1768095657838,"version":"3.49.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T00:00:00Z","timestamp":1716595200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T00:00:00Z","timestamp":1716595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Scientific Research Project of Zhejiang Provincial Department of Education","award":["Y202352164"],"award-info":[{"award-number":["Y202352164"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51975171"],"award-info":[{"award-number":["51975171"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LZ23E050003"],"award-info":[{"award-number":["LZ23E050003"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s11227-024-06223-5","type":"journal-article","created":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T10:01:37Z","timestamp":1716631297000},"page":"19062-19090","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Improved YOLOv5s combining enhanced backbone network and optimized self-attention for PCB defect detection"],"prefix":"10.1007","volume":"80","author":[{"given":"Yongfa","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1312-6190","authenticated-orcid":false,"given":"Ming","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guojin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,25]]},"reference":[{"key":"6223_CR1","doi-asserted-by":"publisher","first-page":"23390","DOI":"10.1109\/JSEN.2021.3106057","volume":"21","author":"MK Li","year":"2021","unstructured":"Li MK, Yao NA, Liu S et al (2021) Multisensor image fusion for automated detection of defects in printed circuit boards. IEEE Sens J 21:23390\u201323399. https:\/\/doi.org\/10.1109\/JSEN.2021.3106057","journal-title":"IEEE Sens J"},{"key":"6223_CR2","doi-asserted-by":"publisher","first-page":"95092","DOI":"10.1109\/ACCESS.2023.3311260","volume":"11","author":"BY Chen","year":"2023","unstructured":"Chen BY, Dang ZC (2023) Fast PCB defect detection method based on fasternet backbone network and CBAM attention mechanism integrated with feature fusion module in improved YOLOv7. IEEE ACCESS 11:95092\u201395103. https:\/\/doi.org\/10.1109\/ACCESS.2023.3311260","journal-title":"IEEE ACCESS"},{"key":"6223_CR3","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.microrel.2018.07.090","volume":"88\u201390","author":"NE Alaoui","year":"2018","unstructured":"Alaoui NE, Boyer A, Tounsi P, Viard A (2018) New defect detection approach using near electromagnetic field probing of high density PCBAs. Microelectron Reliab 88\u201390:288\u2013293. https:\/\/doi.org\/10.1016\/j.microrel.2018.07.090","journal-title":"Microelectron Reliab"},{"key":"6223_CR4","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TSM.2019.2911062","volume":"32","author":"MH Annaby","year":"2019","unstructured":"Annaby MH, Fouda YM, Rushdi MA (2019) Improved normalized cross-correlation for defect detection in printed-circuit boards. IEEE Trans Semicond Manuf 32:199\u2013211. https:\/\/doi.org\/10.1109\/TSM.2019.2911062","journal-title":"IEEE Trans Semicond Manuf"},{"key":"6223_CR5","doi-asserted-by":"publisher","first-page":"2858","DOI":"10.1109\/TIM.2017.2717284","volume":"66","author":"DM Tsai","year":"2017","unstructured":"Tsai DM, Hsieh YC (2017) Machine vision-based positioning and inspection using expectation-maximization technique. IEEE Trans Instrum Meas 66:2858\u20132868. https:\/\/doi.org\/10.1109\/TIM.2017.2717284","journal-title":"IEEE Trans Instrum Meas"},{"key":"6223_CR6","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1109\/TCPMT.2018.2873744","volume":"9","author":"DM Tsai","year":"2019","unstructured":"Tsai DM, Huang CK (2019) Defect detection in electronic surfaces using template-based fourier image reconstruction. IEEE Trans Compon Packaging Manuf Technol 9:163\u2013172. https:\/\/doi.org\/10.1109\/TCPMT.2018.2873744","journal-title":"IEEE Trans Compon Packaging Manuf Technol"},{"key":"6223_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2020.103807","author":"ZC Liu","year":"2021","unstructured":"Liu ZC, Qu B (2021) Machine vision based online detection of PCB defect. Microprocess Microsyst. https:\/\/doi.org\/10.1016\/j.micpro.2020.103807","journal-title":"Microprocess Microsyst"},{"key":"6223_CR8","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1109\/TCPMT.2020.3047089","volume":"11","author":"YT Li","year":"2021","unstructured":"Li YT, Kuo P, Guo JI (2021) Automatic industry PCB board DIP process defect detection system based on deep ensemble self-adaption method. IEEE Trans Compon Packaging Manuf Technol 11:312\u2013323. https:\/\/doi.org\/10.1109\/TCPMT.2020.3047089","journal-title":"IEEE Trans Compon Packaging Manuf Technol"},{"key":"6223_CR9","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"6223_CR10","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"KM He","year":"2015","unstructured":"He KM, Zhang XY, Ren SQ, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37:1904\u20131916. https:\/\/doi.org\/10.1109\/TPAMI.2015.2389824","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6223_CR11","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV). pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"6223_CR12","doi-asserted-by":"crossref","unstructured":"Liyun X, Boyu L, Hong M, Xingzhong L (2020) Improved faster R-CNN algorithm for defect detection in powertrain assembly line. In: 53rd CIRP Conference on Manufacturing Systems, CMS 2020, July 1, 2020 - July 3, 2020. Elsevier B.V., Chicago, IL, United states, pp 479\u2013484","DOI":"10.1016\/j.procir.2020.04.031"},{"key":"6223_CR13","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) 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":"6223_CR14","first-page":"21","volume-title":"14th European Conference on Computer Vision, ECCV 2016, October 8, 2016 - October 16, 2016","author":"W Liu","year":"2016","unstructured":"Liu W, Anguelov D, Erhan D et al (2016) SSD: Single shot multibox detector. 14th European Conference on Computer Vision, ECCV 2016, October 8, 2016 - October 16, 2016. Springer Verlag, Amsterdam, Netherlands, pp 21\u201337"},{"key":"6223_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100301","author":"VK Sharma","year":"2020","unstructured":"Sharma VK, Mir RN (2020) A comprehensive and systematic look up into deep learning based object detection techniques: a review. Comput Sci Rev. https:\/\/doi.org\/10.1016\/j.cosrev.2020.100301","journal-title":"Comput Sci Rev"},{"key":"6223_CR16","doi-asserted-by":"crossref","unstructured":"Tang J, Zhao Y, Bai D, Liu Q (2023) Rev-RetinaNet: PCB defect detection algorithm based on improved RetinaNet. In: 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). pp 653\u2013658","DOI":"10.1109\/EEBDA56825.2023.10090524"},{"key":"6223_CR17","doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le Q V (2020) EfficientDet: Scalable and Efficient Object Detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 10778\u201310787","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"6223_CR18","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1049\/trit.2019.0019","volume":"4","author":"RW Ding","year":"2019","unstructured":"Ding RW, Dai LH, Li GP, Liu H (2019) TDD-net: a tiny defect detection network for printed circuit boards. CAAI Trans Intell Technol 4:110\u2013116. https:\/\/doi.org\/10.1049\/trit.2019.0019","journal-title":"CAAI Trans Intell Technol"},{"key":"6223_CR19","doi-asserted-by":"publisher","first-page":"108335","DOI":"10.1109\/ACCESS.2020.3001349","volume":"8","author":"B Hu","year":"2020","unstructured":"Hu B, Wang JH (2020) Detection of PCB surface defects with improved faster-RCNN and feature pyramid network. IEEE ACCESS 8:108335\u2013108345. https:\/\/doi.org\/10.1109\/ACCESS.2020.3001349","journal-title":"IEEE ACCESS"},{"key":"6223_CR20","doi-asserted-by":"publisher","DOI":"10.3389\/fphy.2021.661091","author":"SY Xia","year":"2021","unstructured":"Xia SY, Wang F, Xie F et al (2021) An efficient and robust target detection algorithm for identifying minor defects of printed circuit board based on PHFE and FL-RFCN. Front Phys. https:\/\/doi.org\/10.3389\/fphy.2021.661091","journal-title":"Front Phys"},{"key":"6223_CR21","doi-asserted-by":"crossref","unstructured":"Ran G, Lei X, Li D, Guo Z (2020) Research on PCB Defect Detection Using Deep Convolutional Nerual Network. In: 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). pp 1310\u20131314","DOI":"10.1109\/ICMCCE51767.2020.00287"},{"key":"6223_CR22","doi-asserted-by":"crossref","unstructured":"Lan Z, Hong Y, Li Y (2021) An improved YOLOv3 method for PCB surface defect detection. In: 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA). pp 1009\u20131015","DOI":"10.1109\/ICPECA51329.2021.9362675"},{"key":"6223_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/app112411701","author":"XT Liao","year":"2021","unstructured":"Liao XT, Lv SP, Li DH et al (2021) YOLOv4-MN3 for PCB surface defect detection. Appl Sci-Basel. https:\/\/doi.org\/10.3390\/app112411701","journal-title":"Appl Sci-Basel"},{"key":"6223_CR24","doi-asserted-by":"publisher","DOI":"10.3389\/fphy.2021.708097","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Xie F, Huang L et al (2021) A lightweight one-stage defect detection network for small object based on dual attention mechanism and PAFPN. Front Phys. https:\/\/doi.org\/10.3389\/fphy.2021.708097","journal-title":"Front Phys"},{"key":"6223_CR25","doi-asserted-by":"crossref","unstructured":"Jin J, Feng W, Lei Q, et al (2021) Defect Detection of Printed Circuit Boards Using EfficientDet. In: 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP). pp 287\u2013293","DOI":"10.1109\/ICSIP52628.2021.9688801"},{"key":"6223_CR26","doi-asserted-by":"publisher","first-page":"12059","DOI":"10.1109\/ACCESS.2023.3241808","volume":"11","author":"R Shen","year":"2023","unstructured":"Shen R, Zhen T, Li ZH (2023) YOLOv5-based model integrating separable convolutions for detection of wheat head images. IEEE ACCESS 11:12059\u201312074. https:\/\/doi.org\/10.1109\/ACCESS.2023.3241808","journal-title":"IEEE ACCESS"},{"key":"6223_CR27","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You Only Look Once: Unified, Real-Time Object Detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"6223_CR28","doi-asserted-by":"crossref","unstructured":"Jin Y, Cai L, Cheng K, et al (2023) PCB bare board defect detection based on improved YOLOv5s. In: 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS). pp 1\u20136","DOI":"10.1109\/SAFEPROCESS58597.2023.10295682"},{"key":"6223_CR29","doi-asserted-by":"crossref","unstructured":"Chen X, Zhou Y (2023) PCB defect target detection based on improved YOLOv5s. In: 7th International Conference on Innovation in Artificial Intelligence, ICIAI 2023, March 3, 2023 - March 5, 2023. Association for Computing Machinery, Harbin, China, pp 26\u201331","DOI":"10.1145\/3594409.3594414"},{"key":"6223_CR30","doi-asserted-by":"crossref","unstructured":"Liu F, Shen Y (2022) A modified-yolov5s model for defect detection of printed circuit board. In: 2022 China Automation Congress (CAC). pp 351\u2013356","DOI":"10.1109\/CAC57257.2022.10055693"},{"key":"6223_CR31","doi-asserted-by":"crossref","unstructured":"He B, Zhuo J, Zhuo X, et al (2022) Defect detection of printed circuit board based on improved YOLOv5. In: 2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT). pp 1\u20134","DOI":"10.1109\/AICIT55386.2022.9930318"},{"key":"6223_CR32","doi-asserted-by":"publisher","DOI":"10.3390\/su15075963","author":"JL Tang","year":"2023","unstructured":"Tang JL, Liu SB, Zhao DX et al (2023) PCB-YOLO: an improved detection algorithm of PCB surface defects based on YOLOv5. Sustainability. https:\/\/doi.org\/10.3390\/su15075963","journal-title":"Sustainability"},{"key":"6223_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-36854-2","author":"KW Xia","year":"2023","unstructured":"Xia KW, Lv ZL, Liu K et al (2023) Global contextual attention augmented YOLO with ConvMixer prediction heads for PCB surface defect detection. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-023-36854-2","journal-title":"Sci Rep"},{"key":"6223_CR34","doi-asserted-by":"crossref","unstructured":"Zhao Y, Yang H, Feng H (2022) An improved YOLOv5 PCB defect detection. In: 2022 International Conference on Advanced Sensing and Smart Manufacturing, ASSM 2022, July 1, 2022 - July 3, 2022. SPIE, Nanjing, China, p Academic Exchange Information Center (AEIC)","DOI":"10.1117\/12.2652341"},{"key":"6223_CR35","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, et al (2017) Feature Pyramid Networks for Object Detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp 936\u2013944","DOI":"10.1109\/CVPR.2017.106"},{"key":"6223_CR36","doi-asserted-by":"crossref","unstructured":"Liu S, Qi L, Qin H, et al (2018) Path Aggregation Network for Instance Segmentation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 8759\u20138768","DOI":"10.1109\/CVPR.2018.00913"},{"key":"6223_CR37","unstructured":"Wang Y, Song S (2023) Improve the defect detection of printed circuit board by Yolov5s. In: 7th International Conference on Computer Science and Artificial Intelligence, CSAI 2023, December 8, 2023 - December 10, 2023. Association for Computing Machinery, Beijing, China, pp 246\u2013250"},{"key":"6223_CR38","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s10694-023-01492-7","volume":"60","author":"Z Dou","year":"2024","unstructured":"Dou Z, Zhou H, Liu Z et al (2024) An improved YOLOv5s fire detection model. Fire Technol 60:135\u2013166. https:\/\/doi.org\/10.1007\/s10694-023-01492-7","journal-title":"Fire Technol"},{"key":"6223_CR39","doi-asserted-by":"crossref","unstructured":"Chen H, Qi J, Wang M, Wu C (2023) Helmet-Wearing Detection Algorithm Based on Improved YOLOv5s. In: 2023 42nd Chinese Control Conference (CCC). pp 8564\u20138569","DOI":"10.23919\/CCC58697.2023.10240843"},{"key":"6223_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2022.103733","author":"Y Lan","year":"2022","unstructured":"Lan Y, Peng B, Wu X, Teng F (2022) Infrared dim and small targets detection via self-attention mechanism and pipeline correlator. Digit Signal Process. https:\/\/doi.org\/10.1016\/j.dsp.2022.103733","journal-title":"Digit Signal Process"},{"key":"6223_CR41","first-page":"252","volume-title":"4th International Conference on Computer Engineering and Application, ICCEA 2023, April 7, 2023 - April 9, 2023","author":"J Yang","year":"2023","unstructured":"Yang J, Tong Q, Zhong Y, Li Q (2023) Improved YOLOv5 for stroller and luggage detection. 4th International Conference on Computer Engineering and Application, ICCEA 2023, April 7, 2023 - April 9, 2023. Institute of Electrical and Electronics Engineers Inc., Hangzhou, China, pp 252\u2013257"},{"key":"6223_CR42","doi-asserted-by":"crossref","unstructured":"Feng Y, Wei Y, Li K, et al (2022) Improved Pedestrian Fall Detection Model Based on YOLOv5. In: 2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). pp 410\u2013413","DOI":"10.1109\/IAEAC54830.2022.9930104"},{"key":"6223_CR43","doi-asserted-by":"crossref","unstructured":"Kisantal M, Wojna Z, Murawski J, et al (2019) Augmentation for small object detection. arXiv","DOI":"10.5121\/csit.2019.91713"},{"key":"6223_CR44","doi-asserted-by":"publisher","first-page":"167267","DOI":"10.1109\/ACCESS.2021.3135973","volume":"9","author":"N Ban\u00fas","year":"2021","unstructured":"Ban\u00fas N, Boada I, Bardera A, Toldr\u00e0 P (2021) A deep-learning based solution to automatically control closure and seal of pizza packages. IEEE Access 9:167267\u2013167281. https:\/\/doi.org\/10.1109\/ACCESS.2021.3135973","journal-title":"IEEE Access"},{"key":"6223_CR45","doi-asserted-by":"crossref","unstructured":"Zhang W, Huang J (2022) Research on Camera Calibration of Binocular Vision System Based on Halcon. In: 2022 14th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). pp 123\u2013127","DOI":"10.1109\/IHMSC55436.2022.00037"},{"key":"6223_CR46","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.egyr.2021.10.039","volume":"7","author":"G Han","year":"2021","unstructured":"Han G, He M, Zhao F et al (2021) Insulator detection and damage identification based on improved lightweight YOLOv4 network. Energy Rep 7:187\u2013197. https:\/\/doi.org\/10.1016\/j.egyr.2021.10.039","journal-title":"Energy Rep"},{"key":"6223_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/s10921-021-00835-0","author":"ZW Zhang","year":"2022","unstructured":"Zhang ZW, Zhang YY, Wen YT et al (2022) Intelligent defect detection method for additive manufactured lattice structures based on a modified YOLOv3 model. J Nondestr Eval. https:\/\/doi.org\/10.1007\/s10921-021-00835-0","journal-title":"J Nondestr Eval"},{"key":"6223_CR48","doi-asserted-by":"publisher","first-page":"20910","DOI":"10.1109\/JSEN.2022.3208580","volume":"22","author":"X Wang","year":"2022","unstructured":"Wang X, Gao JS, Hou BJ et al (2022) A lightweight modified YOLOX network using coordinate attention mechanism for pcb surface defect detection. IEEE Sens J 22:20910\u201320920. https:\/\/doi.org\/10.1109\/JSEN.2022.3208580","journal-title":"IEEE Sens J"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06223-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06223-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06223-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T10:17:46Z","timestamp":1721902666000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06223-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,25]]},"references-count":48,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["6223"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06223-5","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,25]]},"assertion":[{"value":"12 May 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2024","order":2,"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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}