{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T23:47:32Z","timestamp":1782863252083,"version":"3.54.5"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T00:00:00Z","timestamp":1780704000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T00:00:00Z","timestamp":1780704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Key Research and Development Program of Shandong","award":["2023CXGC010410"],"award-info":[{"award-number":["2023CXGC010410"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["31972790"],"award-info":[{"award-number":["31972790"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Fujian-CAS (Chinese Academy of Sciences) STS Program","award":["2024T3049"],"award-info":[{"award-number":["2024T3049"]}]},{"name":"Liaoning Provincial Major Special Project","award":["2025JH1\/11700001"],"award-info":[{"award-number":["2025JH1\/11700001"]}]},{"name":"Central Guidance on Local Science and Technology Development Fund of Shandong Province","award":["YDZX2022093"],"award-info":[{"award-number":["YDZX2022093"]}]},{"name":"Technology Innovation Program of Yantai City","award":["2023XDRH001"],"award-info":[{"award-number":["2023XDRH001"]}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["ZR2023MF014"],"award-info":[{"award-number":["ZR2023MF014"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Taishan Scholars Program of Shandong Province","award":["NO. tsqn202103118"],"award-info":[{"award-number":["NO. tsqn202103118"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s00138-026-01853-6","type":"journal-article","created":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T15:48:29Z","timestamp":1780760909000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Amodal segmentation for occlusion-aware instance recovery of juvenile abalone"],"prefix":"10.1007","volume":"37","author":[{"given":"Ji","family":"Ruan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fucun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dapeng","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,6]]},"reference":[{"issue":"1","key":"1853_CR1","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1080\/00288330.2023.2261869","volume":"59","author":"PA Cook","year":"2025","unstructured":"Cook, P.A.: Worldwide abalone production: an update. NZ J. Mar. Freshwat. Res. 59(1), 4\u201310 (2025). https:\/\/doi.org\/10.1080\/00288330.2023.2261869","journal-title":"NZ J. Mar. Freshwat. Res."},{"key":"1853_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.marpol.2022.105405","volume":"148","author":"S Hern\u00e1ndez Casas","year":"2023","unstructured":"Hern\u00e1ndez Casas, S., Seijo, J.C., Beltr\u00e1n Morales, L.F., Hern\u00e1ndez-Flores, \u00c1., Arregu\u00edn-S\u00e1nchez, F., Ponce D\u00edaz, G.: Analysis of supply and demand in the international market of major abalone fisheries and aquaculture production. Mar. Policy 148, 105405 (2023). https:\/\/doi.org\/10.1016\/j.marpol.2022.105405","journal-title":"Mar. Policy"},{"issue":"4","key":"1853_CR3","doi-asserted-by":"publisher","first-page":"1274","DOI":"10.1111\/raq.12769","volume":"15","author":"X Gao","year":"2023","unstructured":"Gao, X., Zhang, M., Luo, X., You, W., Ke, C.: Transitions, challenges and trends in china\u2019s abalone culture industry. Rev. Aquac. 15(4), 1274\u20131293 (2023). https:\/\/doi.org\/10.1111\/raq.12769","journal-title":"Rev. Aquac."},{"issue":"1","key":"1853_CR4","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1111\/raq.12464","volume":"13","author":"X Yang","year":"2021","unstructured":"Yang, X., Zhang, S., Liu, J., Gao, Q., Dong, S., Zhou, C.: Deep learning for smart fish farming: applications, opportunities and challenges. Rev. Aquac. 13(1), 66\u201390 (2021). https:\/\/doi.org\/10.1111\/raq.12464","journal-title":"Rev. Aquac."},{"issue":"1","key":"1853_CR5","doi-asserted-by":"publisher","first-page":"13001","DOI":"10.1111\/raq.13001","volume":"17","author":"M Cui","year":"2025","unstructured":"Cui, M., Liu, X., Liu, H., Zhao, J., Li, D., Wang, W.: Fish tracking, counting, and behaviour analysis in digital aquaculture: a comprehensive survey. Rev. Aquac. 17(1), 13001 (2025). https:\/\/doi.org\/10.1111\/raq.13001","journal-title":"Rev. Aquac."},{"issue":"17","key":"1853_CR6","doi-asserted-by":"publisher","first-page":"13388","DOI":"10.3390\/ijms241713388","volume":"24","author":"KH Kho","year":"2023","unstructured":"Kho, K.H., Sukhan, Z.P., Hossen, S., Cho, Y., Lee, W.K., Nou, I.S.: Age-dependent growth-related qtl variations in pacific abalone, haliotis discus hannai. Int. J. Mol. Sci. 24(17), 13388 (2023). https:\/\/doi.org\/10.3390\/ijms241713388","journal-title":"Int. J. Mol. Sci."},{"key":"1853_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.107744","volume":"207","author":"T Napier","year":"2023","unstructured":"Napier, T., Lee, I.: Using mobile-based augmented reality and object detection for real-time abalone growth monitoring. Comput. Electron. Agric. 207, 107744 (2023). https:\/\/doi.org\/10.1016\/j.compag.2023.107744","journal-title":"Comput. Electron. Agric."},{"issue":"2\u20133","key":"1853_CR8","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.fishres.2006.04.009","volume":"80","author":"DJ White","year":"2006","unstructured":"White, D.J., Svellingen, C., Strachan, N.J.: Automated measurement of species and length of fish by computer vision. Fish. Res. 80(2\u20133), 203\u2013210 (2006). https:\/\/doi.org\/10.1016\/j.fishres.2006.04.009","journal-title":"Fish. Res."},{"key":"1853_CR9","doi-asserted-by":"publisher","first-page":"1171625","DOI":"10.3389\/fmars.2023.1171625","volume":"10","author":"D Marrable","year":"2023","unstructured":"Marrable, D., Tippaya, S., Barker, K., Harvey, E., Bierwagen, S.L., Wyatt, M., Bainbridge, S., Stowar, M.: Generalised deep learning model for semi-automated length measurement of fish in stereo-bruvs. Front. Mar. Sci. 10, 1171625 (2023). https:\/\/doi.org\/10.3389\/fmars.2023.1171625","journal-title":"Front. Mar. Sci."},{"issue":"1","key":"1853_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.fsi.2013.09.034","volume":"36","author":"M Cardinaud","year":"2014","unstructured":"Cardinaud, M., Offret, C., Huchette, S., Moraga, D., Paillard, C.: The impacts of handling and air exposure on immune parameters, gene expression, and susceptibility to vibriosis of european abalone haliotis tuberculata. Fish Shellfish Immunol. 36(1), 1\u20138 (2014). https:\/\/doi.org\/10.1016\/j.fsi.2013.09.034","journal-title":"Fish & Shellfish Immunology"},{"issue":"2","key":"1853_CR11","doi-asserted-by":"publisher","first-page":"222","DOI":"10.3390\/w14020222","volume":"14","author":"T Hong Khai","year":"2022","unstructured":"Hong Khai, T., Abdullah, S.N.H.S., Hasan, M.K., Tarmizi, A.: Underwater fish detection and counting using mask regional convolutional neural network. Water 14(2), 222 (2022). https:\/\/doi.org\/10.3390\/w14020222","journal-title":"Water"},{"issue":"12","key":"1853_CR12","doi-asserted-by":"publisher","first-page":"1426","DOI":"10.3390\/electronics10121426","volume":"10","author":"C Yu","year":"2021","unstructured":"Yu, C., Hu, Z., Han, B., Wang, P., Zhao, Y., Wu, H.: Intelligent measurement of morphological characteristics of fish using improved u-net. Electronics 10(12), 1426 (2021). https:\/\/doi.org\/10.3390\/electronics10121426","journal-title":"Electronics"},{"issue":"14","key":"1853_CR13","doi-asserted-by":"publisher","first-page":"6325","DOI":"10.3390\/s23146325","volume":"23","author":"M Zhou","year":"2023","unstructured":"Zhou, M., Shen, P., Zhu, H., Shen, Y.: In-water fish body-length measurement system based on stereo vision. Sensors 23(14), 6325 (2023). https:\/\/doi.org\/10.3390\/s23146325","journal-title":"Sensors"},{"key":"1853_CR14","doi-asserted-by":"publisher","first-page":"1010565","DOI":"10.3389\/fmars.2022.1010565","volume":"9","author":"A Haider","year":"2022","unstructured":"Haider, A., Arsalan, M., Choi, J., Sultan, H., Park, K.R.: Robust segmentation of underwater fish based on multi-level feature accumulation. Front. Mar. Sci. 9, 1010565 (2022). https:\/\/doi.org\/10.3389\/fmars.2022.1010565","journal-title":"Front. Mar. Sci."},{"issue":"6","key":"1853_CR15","doi-asserted-by":"publisher","first-page":"326","DOI":"10.3390\/fishes7060326","volume":"7","author":"Y Deng","year":"2022","unstructured":"Deng, Y., Tan, H., Tong, M., Zhou, D., Li, Y., Zhu, M.: An automatic recognition method for fish species and length using an underwater stereo vision system. Fishes 7(6), 326 (2022). https:\/\/doi.org\/10.3390\/fishes7060326","journal-title":"Fishes"},{"issue":"14","key":"1853_CR16","doi-asserted-by":"publisher","first-page":"4142","DOI":"10.1049\/ipr2.12924","volume":"17","author":"Y Yu","year":"2023","unstructured":"Yu, Y., Zhang, H., Yuan, F.: Key point detection method for fish size measurement based on deep learning. IET Image Proc. 17(14), 4142\u20134158 (2023). https:\/\/doi.org\/10.1049\/ipr2.12924","journal-title":"IET Image Proc."},{"issue":"12","key":"1853_CR17","doi-asserted-by":"publisher","first-page":"10138","DOI":"10.1109\/TPAMI.2024.3434373","volume":"46","author":"X Li","year":"2024","unstructured":"Li, X., Ding, H., Yuan, H., Zhang, W., Pang, J., Cheng, G., Chen, K., Liu, Z., Loy, C.C.: Transformer-based visual segmentation: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 46(12), 10138\u201310163 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"1853_CR18","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s11263-026-02729-y","volume":"134","author":"H Ding","year":"2026","unstructured":"Ding, H., Liu, C., He, S., Jiang, X., Jiang, Y.G.: Grex: Generalized referring expression segmentation, comprehension, and generation. Int. J. Comput. Vision 134(2), 79 (2026)","journal-title":"Int. J. Comput. Vision"},{"key":"1853_CR19","unstructured":"Ding, H., Tang, S., He, S., Liu, C., Wu, Z., Jiang, Y.G.: Multimodal referring segmentation: a survey, (2025). arXiv:2508.00265 arXiv preprint"},{"key":"1853_CR20","doi-asserted-by":"crossref","unstructured":"Qi, L., Jiang, L., Liu, S., Shen, X., Jia, J.: Amodal instance segmentation with kins dataset. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3014\u20133023. (2019)","DOI":"10.1109\/CVPR.2019.00313"},{"key":"1853_CR21","doi-asserted-by":"publisher","unstructured":"Follmann, P., K\u00f6nig, R., H\u00e4rtinger, P., Klostermann, M., B\u00f6ttger, T.: Learning to see the invisible: End-to-end trainable amodal instance segmentation. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1328\u20131336 (2019). https:\/\/doi.org\/10.1109\/WACV.2019.00146","DOI":"10.1109\/WACV.2019.00146"},{"key":"1853_CR22","doi-asserted-by":"crossref","unstructured":"Ke, L., Tai, Y.W., Tang, C.K.: Deep occlusion-aware instance segmentation with overlapping bilayers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4019\u20134028. (2021)","DOI":"10.1109\/CVPR46437.2021.00401"},{"key":"1853_CR23","doi-asserted-by":"publisher","unstructured":"Tran, M., Vo, K., Yamazaki, K., Fernandes, A., Kidd, M., Le, N.: Aisformer: Amodal instance segmentation with transformer. arXiv preprint arXiv:2210.06323 (2022) https:\/\/doi.org\/10.48550\/arXiv.2210.06323","DOI":"10.48550\/arXiv.2210.06323"},{"key":"1853_CR24","doi-asserted-by":"publisher","first-page":"2995","DOI":"10.1609\/aaai.v35i4.16407","volume":"35","author":"Y Xiao","year":"2021","unstructured":"Xiao, Y., Xu, Y., Zhong, Z., Luo, W., Li, J., Gao, S.: Amodal segmentation based on visible region segmentation and shape prior. Proceedings of the AAAI Conference on Artificial Intelligence 35, 2995\u20133003 (2021). https:\/\/doi.org\/10.1609\/aaai.v35i4.16407","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"1853_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfoodeng.2023.111435","volume":"348","author":"X Wu","year":"2023","unstructured":"Wu, X., Meng, Y., Zhang, J., Wei, J., Zhai, X.: Amodal segmentation of cane sugar crystal via deep neural networks. J. Food Eng. 348, 111435 (2023). https:\/\/doi.org\/10.1016\/j.jfoodeng.2023.111435","journal-title":"J. Food Eng."},{"key":"1853_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2025.105539","volume":"158","author":"Y Yang","year":"2025","unstructured":"Yang, Y., Deng, H., Wu, Y.: Mfc-net: Amodal instance segmentation with multi-path fusion and context-awareness. Image Vis. Comput. 158, 105539 (2025). https:\/\/doi.org\/10.1016\/j.imavis.2025.105539","journal-title":"Image Vis. Comput."},{"key":"1853_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.107854","volume":"209","author":"J Gen\u00e9 Mola","year":"2023","unstructured":"Gen\u00e9 Mola, J., Ferrer Ferrer, M., Gregorio, E., Blok, P.M., Hemming, J., Morros, J.R., Rosell Polo, J.R., Vilaplana, V., Ruiz Hidalgo, J.: Looking behind occlusions: a study on amodal segmentation for robust on-tree apple fruit size estimation. Comput. Electron. Agric. 209, 107854 (2023). https:\/\/doi.org\/10.1016\/j.compag.2023.107854","journal-title":"Comput. Electron. Agric."},{"key":"1853_CR28","doi-asserted-by":"crossref","unstructured":"Ding, H., Liu, C., He, S., Jiang, X., Torr, P.H., Bai, S.: Mose: A new dataset for video object segmentation in complex scenes. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 20224\u201320234. (2023)","DOI":"10.1109\/ICCV51070.2023.01850"},{"key":"1853_CR29","unstructured":"Ding, H., Ying, K., Liu, C., He, S., Jiang, X., Jiang, Y.G., Torr, P.H., Bai, S.: Mosev2: A more challenging dataset for video object segmentation in complex scenes. arXiv preprint arXiv:2508.05630 (2025)"},{"issue":"12","key":"1853_CR30","doi-asserted-by":"publisher","first-page":"11400","DOI":"10.1109\/TPAMI.2025.3600507","volume":"47","author":"H Ding","year":"2025","unstructured":"Ding, H., Liu, C., He, S., Ying, K., Jiang, X., Loy, C.C., Jiang, Y.G.: Mevis: A multi-modal dataset for referring motion expression video segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 47(12), 11400\u201311416 (2025)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1853_CR31","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1853_CR32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778. (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1853_CR33","doi-asserted-by":"publisher","unstructured":"Taud, H., Mas, J.F.: Multilayer perceptron (mlp). In: Geomatic Approaches for Modeling Land Change Scenarios, pp. 451\u2013455. Springer, (2017). https:\/\/doi.org\/10.1007\/978-3-319-60801-3_27","DOI":"10.1007\/978-3-319-60801-3_27"},{"key":"1853_CR34","unstructured":"Xu, J., Sun, X., Zhang, Z., Zhao, G., Lin, J.: Understanding and improving layer normalization. Adv. Neural. Inf. Process. Syst. 32, (2019)"},{"key":"1853_CR35","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456. (2015) . (pmlr)"},{"key":"1853_CR36","doi-asserted-by":"publisher","unstructured":"Agarap, A.F.: Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375 (2018) https:\/\/doi.org\/10.48550\/arXiv.1803.08375","DOI":"10.48550\/arXiv.1803.08375"},{"issue":"1","key":"1853_CR37","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.neuroimage.2009.03.068","volume":"47","author":"HH Chang","year":"2009","unstructured":"Chang, H.H., Zhuang, A.H., Valentino, D.J., Chu, W.C.: Performance measure characterization for evaluating neuroimage segmentation algorithms. Neuroimage 47(1), 122\u2013135 (2009). https:\/\/doi.org\/10.1016\/j.neuroimage.2009.03.068","journal-title":"Neuroimage"},{"issue":"2","key":"1853_CR38","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.media.2013.12.002","volume":"18","author":"G Litjens","year":"2014","unstructured":"Litjens, G., Toth, R., Van De Ven, W., Hoeks, C., Kerkstra, S., Van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., et al.: Evaluation of prostate segmentation algorithms for mri: the promise12 challenge. Med. Image Anal. 18(2), 359\u2013373 (2014). https:\/\/doi.org\/10.1016\/j.media.2013.12.002","journal-title":"Med. Image Anal."},{"key":"1853_CR39","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969. (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"1853_CR40","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154\u20136162. (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"1853_CR41","doi-asserted-by":"crossref","unstructured":"Nguyen, K., Todorovic, S.: A weakly supervised amodal segmenter with boundary uncertainty estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7396\u20137405. (2021)","DOI":"10.1109\/ICCV48922.2021.00730"},{"key":"1853_CR42","doi-asserted-by":"crossref","unstructured":"Gao, J., Qian, X., Wang, Y., Xiao, T., He, T., Zhang, Z., Fu, Y.: Coarse-to-fine amodal segmentation with shape prior. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1262\u20131271. (2023)","DOI":"10.1109\/ICCV51070.2023.00122"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-026-01853-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-026-01853-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-026-01853-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T22:52:22Z","timestamp":1782859942000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-026-01853-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,6]]},"references-count":42,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["1853"],"URL":"https:\/\/doi.org\/10.1007\/s00138-026-01853-6","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,6]]},"assertion":[{"value":"3 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 2026","order":4,"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":"85"}}