{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T16:23:44Z","timestamp":1776788624436,"version":"3.51.2"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T00:00:00Z","timestamp":1699315200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T00:00:00Z","timestamp":1699315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72101065"],"award-info":[{"award-number":["72101065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72101106"],"award-info":[{"award-number":["72101106"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71971143"],"award-info":[{"award-number":["71971143"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2021A1515110336"],"award-info":[{"award-number":["2021A1515110336"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["RCBS20210609103119020"],"award-info":[{"award-number":["RCBS20210609103119020"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["RCBS20221008093124063"],"award-info":[{"award-number":["RCBS20221008093124063"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s10845-023-02242-w","type":"journal-article","created":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T11:02:07Z","timestamp":1699354927000},"page":"319-330","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Shape prior guided defect pattern classification and segmentation in wafer bin maps"],"prefix":"10.1007","volume":"36","author":[{"given":"Rui","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songhao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5822-8743","authenticated-orcid":false,"given":"Ben","family":"Niu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,7]]},"reference":[{"key":"2242_CR1","doi-asserted-by":"publisher","unstructured":"Abd Al Rahman, M., Danishvar, S., & Mousavi, A. (2021). An improved capsule network (wafercaps) for wafer bin map classification based on dcgan data upsampling. IEEE Transactions on Semiconductor Manufacturing, 35(1), 50\u201359. https:\/\/doi.org\/10.1109\/TSM.2021.3134625","DOI":"10.1109\/TSM.2021.3134625"},{"issue":"6","key":"2242_CR2","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1109\/TSM.2018.2841416","volume":"11","author":"F Adly","year":"2015","unstructured":"Adly, F., Alhussein, O., Yoo, P. D., Al-Hammadi, Y., Taha, K., Muhaidat, S., Jeong, Y.-S., Lee, U., & Ismail, M. (2015). Simplified subspaced regression network for identification of defect patterns in semiconductor wafer maps. IEEE Transactions on Industrial Informatics, 11(6), 1267\u20131276. https:\/\/doi.org\/10.1109\/TSM.2018.2841416","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"12","key":"2242_CR3","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481\u20132495. https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"3","key":"2242_CR4","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s10696-012-9161-4","volume":"25","author":"C-F Chien","year":"2013","unstructured":"Chien, C.-F., Hsu, C.-Y., & Chen, P.-N. (2013). Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence. Flexible Services and Manufacturing Journal, 25(3), 367\u2013388. https:\/\/doi.org\/10.1007\/s10696-012-9161-4","journal-title":"Flexible Services and Manufacturing Journal"},{"issue":"4","key":"2242_CR5","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1109\/TSM.2021.3118922","volume":"34","author":"M-C Chiu","year":"2021","unstructured":"Chiu, M.-C., & Chen, T.-M. (2021). Applying data augmentation and mask R-CNN-based instance segmentation method for mixed-type wafer maps defect patterns classification. IEEE Transactions on Semiconductor Manufacturing, 34(4), 455\u2013463. https:\/\/doi.org\/10.1109\/TSM.2021.3118922","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"2242_CR6","doi-asserted-by":"publisher","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., & Ronneberger, O. (2016). 3D U-Net: Learning dense volumetric segmentation from sparse annotation. In International conference on medical image computing and computer-assisted intervention (pp. 424-432). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49","DOI":"10.1007\/978-3-319-46723-8_49"},{"issue":"3","key":"2242_CR7","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s11263-006-7533-5","volume":"69","author":"D Cremers","year":"2006","unstructured":"Cremers, D., Osher, S. J., & Soatto, S. (2006). Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. International Journal of Computer Vision, 69(3), 335\u2013351. https:\/\/doi.org\/10.1007\/s11263-006-7533-5","journal-title":"International Journal of Computer Vision"},{"issue":"12","key":"2242_CR8","doi-asserted-by":"publisher","first-page":"5349","DOI":"10.1109\/TNNLS.2020.2966319","volume":"31","author":"F He","year":"2020","unstructured":"He, F., Liu, T., & Tao, D. (2020). Why resnet works? Residuals generalize. IEEE Transactions on neural Networks and Learning Systems, 31(12), 5349\u20135362. https:\/\/doi.org\/10.1109\/TNNLS.2020.2966319","journal-title":"IEEE Transactions on neural Networks and Learning Systems"},{"key":"2242_CR9","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). https:\/\/doi.org\/10.48550\/arXiv.1512.03385","DOI":"10.48550\/arXiv.1512.03385"},{"key":"2242_CR10","doi-asserted-by":"publisher","unstructured":"Hsu, C.-Y., Chen, W.-J., & Chien, J.-C. (2020). Similarity matching of wafer bin maps for manufacturing intelligence to empower industry 3.5 for semiconductor manufacturing. Computers & Industrial Engineering, 142, 106358. https:\/\/doi.org\/10.1016\/j.cie.2020.106358","DOI":"10.1016\/j.cie.2020.106358"},{"issue":"3","key":"2242_CR11","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1007\/s10845-020-01687-7","volume":"33","author":"C-Y Hsu","year":"2022","unstructured":"Hsu, C.-Y., & Chien, J.-C. (2022). Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification. Journal of Intelligent Manufacturing, 33(3), 831\u2013844. https:\/\/doi.org\/10.1007\/s10845-020-01687-7","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2242_CR12","doi-asserted-by":"publisher","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., & kavukcuoglu, K. (2015). Spatial transformer networks. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., & Garnett, R. (Eds.), Advances in neural information processing systems (Vol. 28). https:\/\/doi.org\/10.48550\/arXiv.1506.02025","DOI":"10.48550\/arXiv.1506.02025"},{"issue":"8","key":"2242_CR13","doi-asserted-by":"publisher","first-page":"1861","DOI":"10.1007\/s10845-020-01540-x","volume":"31","author":"CH Jin","year":"2020","unstructured":"Jin, C. H., Kim, H.-J., Piao, Y., Li, M., & Piao, M. (2020). Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes. Journal of Intelligent Manufacturing, 31(8), 1861\u20131875. https:\/\/doi.org\/10.1007\/s10845-020-01540-x","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"2","key":"2242_CR14","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1080\/24725854.2017.1386337","volume":"50","author":"J Kim","year":"2018","unstructured":"Kim, J., Lee, Y., & Kim, H. (2018). Detection and clustering of mixed-type defect patterns in wafer bin maps. IISE Transactions, 50(2), 99\u2013111. https:\/\/doi.org\/10.1080\/24725854.2017.1386337","journal-title":"IISE Transactions"},{"key":"2242_CR15","doi-asserted-by":"publisher","first-page":"3215","DOI":"10.1007\/s10845-022-01994-1","volume":"34","author":"T Kim","year":"2023","unstructured":"Kim, T., & Behdinan, K. (2023). Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: A review. Journal of Intelligent Manufacturing, 34, 3215\u20133247. https:\/\/doi.org\/10.1007\/s10845-022-01994-1","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"6","key":"2242_CR16","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.1007\/s10845-021-01755-6","volume":"33","author":"TS Kim","year":"2022","unstructured":"Kim, T. S., Lee, J. W., Lee, W. K., & Sohn, S. Y. (2022). Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm. Journal of Intelligent Manufacturing, 33(6), 1715\u20131724. https:\/\/doi.org\/10.1007\/s10845-021-01755-6","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2242_CR17","unstructured":"Kingma, D.P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"2242_CR18","doi-asserted-by":"publisher","unstructured":"Kong, Y., & Ni, D. (2019). Recognition and location of mixed-type patterns in wafer bin maps. In 2019 IEEE international conference on smart manufacturing, industrial & logistics engineering (SMILE) (pp. 4-8). https:\/\/doi.org\/10.1109\/SMILE45626.2019.8965309","DOI":"10.1109\/SMILE45626.2019.8965309"},{"issue":"4","key":"2242_CR19","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1109\/TSM.2020.3022431","volume":"33","author":"Y Kong","year":"2020","unstructured":"Kong, Y., & Ni, D. (2020). Qualitative and quantitative analysis of multi-pattern wafer bin maps. IEEE Transactions on Semiconductor Manufacturing, 33(4), 578\u2013586. https:\/\/doi.org\/10.1109\/TSM.2020.3022431","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"issue":"3","key":"2242_CR20","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1109\/TSM.2018.2841416","volume":"31","author":"K Kyeong","year":"2018","unstructured":"Kyeong, K., & Kim, H. (2018). Classification of mixed-type defect patterns in wafer bin maps using convolutional neural networks. IEEE Transactions on Semiconductor Manufacturing, 31(3), 395\u2013402. https:\/\/doi.org\/10.1109\/TSM.2018.2841416","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"issue":"11","key":"2242_CR21","doi-asserted-by":"publisher","first-page":"2596","DOI":"10.1109\/TMI.2019.2905990","volume":"38","author":"MCH Lee","year":"2019","unstructured":"Lee, M. C. H., Petersen, K., Pawlowski, N., Glocker, B., & Schaap, M. (2019). TETRIS: Template transformer networks for image segmentation with shape priors. IEEE Transactions on Medical Imaging, 38(11), 2596\u20132606. https:\/\/doi.org\/10.1109\/TMI.2019.2905990","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2242_CR22","doi-asserted-by":"publisher","unstructured":"Milletari, F., Rothberg, A., Jia, J., & Sofka, M. (2017). Integrating statistical prior knowledge into convolutional neural networks. In International conference on medical image computing and computer-assisted intervention (pp. 161-168). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_19","DOI":"10.1007\/978-3-319-66182-7_19"},{"key":"2242_CR23","doi-asserted-by":"publisher","unstructured":"Nag, S., Makwana, D., & R, S. C. T., Mittal, S., & Mohan, C. K. (2022). Wafersegclassnet\u2013A light-weight network for classification and segmentation of semiconductor wafer defects. Computers in Industry, 142, 103720. https:\/\/doi.org\/10.1016\/j.compind.2022.103720","DOI":"10.1016\/j.compind.2022.103720"},{"issue":"2","key":"2242_CR24","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1109\/TSM.2018.2795466","volume":"31","author":"T Nakazawa","year":"2018","unstructured":"Nakazawa, T., & Kulkarni, D. V. (2018). Wafer map defect pattern classification and image retrieval using convolutional neural network. IEEE Transactions on Semiconductor Manufacturing, 31(2), 309\u2013314. https:\/\/doi.org\/10.1109\/TSM.2018.2795466","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"issue":"1","key":"2242_CR25","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/TSM.2019.2963656","volume":"33","author":"J O\u2019Leary","year":"2020","unstructured":"O\u2019Leary, J., Sawlani, K., & Mesbah, A. (2020). Deep learning for classification of the chemical composition of particle defects on semiconductor wafers. IEEE Transactions on Semiconductor Manufacturing, 33(1), 72\u201385. https:\/\/doi.org\/10.1109\/TSM.2019.2963656","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"2242_CR26","doi-asserted-by":"publisher","unstructured":"Pak, D.H., Caballero, A., Sun, W., & Duncan, J.S. (2020). Efficient aortic valve multilabel segmentation using a spatial transformer network. In 2020 IEEE 17th international symposium on biomedical imaging (ISBI) (pp. 1738-1742). https:\/\/doi.org\/10.1109\/TMI.2019.2905990","DOI":"10.1109\/TMI.2019.2905990"},{"key":"2242_CR27","doi-asserted-by":"publisher","unstructured":"Piao, M., & Jin, C. H. (2023). CNN and ensemble learning based wafer map failure pattern recognition based on local property based features. Journal of Intelligent Manufacturing, 34, 3599\u20133621. https:\/\/doi.org\/10.1007\/s10845-022-02023-x","DOI":"10.1007\/s10845-022-02023-x"},{"key":"2242_CR28","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18 (pp. 234-241). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"4","key":"2242_CR29","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer, E., Long, J., & Darrell, T. (2017). Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640\u2013651. https:\/\/doi.org\/10.1109\/TPAMI.2016.2572683","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2242_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2022.107996","volume":"167","author":"W Shin","year":"2022","unstructured":"Shin, W., Kahng, H., & Kim, S. B. (2022). Mixup-based classification of mixed-type defect patterns in wafer bin maps. Computers & Industrial Engineering, 167, 107996. https:\/\/doi.org\/10.1016\/j.cie.2022.107996","journal-title":"Computers & Industrial Engineering"},{"issue":"3","key":"2242_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/jdwm.2007070101","volume":"3","author":"G Tsoumakas","year":"2007","unstructured":"Tsoumakas, G., & Katakis, I. (2007). Multi-label classification: An overview. International Journal of Data Warehousing and Mining (IJDWM), 3(3), 1\u201313. https:\/\/doi.org\/10.4018\/jdwm.2007070101","journal-title":"International Journal of Data Warehousing and Mining (IJDWM)"},{"issue":"4","key":"2242_CR32","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1109\/TSM.2020.3020985","volume":"33","author":"J Wang","year":"2020","unstructured":"Wang, J., Xu, C., Yang, Z., Zhang, J., & Li, X. (2020). Deformable convolutional networks for efficient mixed-type wafer defect pattern recognition. IEEE Transactions on Semiconductor Manufacturing, 33(4), 587\u2013596. https:\/\/doi.org\/10.1109\/TSM.2020.3020985","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"issue":"3","key":"2242_CR33","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1109\/TSM.2022.3183008","volume":"35","author":"R Wang","year":"2022","unstructured":"Wang, R., & Chen, N. (2022). Detection and recognition of mixed-type defect patterns in wafer bin maps via tensor voting. IEEE Transactions on Semiconductor Manufacturing, 35(3), 485\u2013494. https:\/\/doi.org\/10.1109\/TSM.2022.3183008","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"2242_CR34","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2023.3274958","author":"J Yan","year":"2023","unstructured":"Yan, J., Sheng, Y., & Piao, M. (2023). Semantic segmentation based wafer map mixed-type defect pattern recognition. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (Early Access). https:\/\/doi.org\/10.1109\/TCAD.2023.3274958","journal-title":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (Early Access)"},{"issue":"1","key":"2242_CR35","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.ejor.2007.06.007","volume":"190","author":"T Yuan","year":"2008","unstructured":"Yuan, T., & Kuo, W. (2008). Spatial defect pattern recognition on semiconductor wafers using model-based clustering and bayesian inference. European Journal of Operational Research, 190(1), 228\u2013240. https:\/\/doi.org\/10.1016\/j.ejor.2007.06.007","journal-title":"European Journal of Operational Research"},{"issue":"9","key":"2242_CR36","doi-asserted-by":"publisher","first-page":"8789","DOI":"10.1109\/TIE.2020.3013492","volume":"68","author":"J Yu","year":"2020","unstructured":"Yu, J., & Liu, J. (2020). Two-dimensional principal component analysis-based convolutional autoencoder for wafer map defect detection. IEEE Transactions on Industrial Electronics, 68(9), 8789\u20138797. https:\/\/doi.org\/10.1109\/TIE.2020.3013492","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"2242_CR37","doi-asserted-by":"publisher","unstructured":"Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P.H. (2015). Conditional random fields as recurrent neural networks. In Proceedings of the IEEE international conference on computer vision (pp. 1529-1537). https:\/\/doi.org\/10.1109\/ICCV.2015.179","DOI":"10.1109\/ICCV.2015.179"},{"issue":"3","key":"2242_CR38","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1109\/TMI.2019.2905990","volume":"23","author":"C Zotti","year":"2018","unstructured":"Zotti, C., Luo, Z., Lalande, A., & Jodoin, P.-M. (2018). Convolutional neural network with shape prior applied to cardiac MRI segmentation. IEEE Journal of Biomedical and Health Informatics, 23(3), 1119\u20131128. https:\/\/doi.org\/10.1109\/TMI.2019.2905990","journal-title":"IEEE Journal of Biomedical and Health Informatics"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02242-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-023-02242-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02242-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T23:12:28Z","timestamp":1736032348000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-023-02242-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,7]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["2242"],"URL":"https:\/\/doi.org\/10.1007\/s10845-023-02242-w","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,7]]},"assertion":[{"value":"16 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2023","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 interests"}}]}}