{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T03:00:14Z","timestamp":1775098814880,"version":"3.50.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["61272338"],"award-info":[{"award-number":["61272338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["61703443"],"award-info":[{"award-number":["61703443"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["61673018"],"award-info":[{"award-number":["61673018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004000","name":"Guangzhou Municipal Science and Technology Program key projects","doi-asserted-by":"publisher","award":["201804010255"],"award-info":[{"award-number":["201804010255"]}],"id":[{"id":"10.13039\/501100004000","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Province Key Laboratory of Computational Science at Sun Yat-sen University","award":["2020B1212060032"],"award-info":[{"award-number":["2020B1212060032"]}]},{"DOI":"10.13039\/501100010226","name":"Department of Education of Guangdong Province","doi-asserted-by":"publisher","award":["2022KTSCX302"],"award-info":[{"award-number":["2022KTSCX302"]}],"id":[{"id":"10.13039\/501100010226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangzhou Vocational College of Technology&Business","award":["2022YB01"],"award-info":[{"award-number":["2022YB01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-20062-9","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T10:03:32Z","timestamp":1724407412000},"page":"24229-24253","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["HyperplaneGAN: a unified consistent translation framework for facial attribute editing"],"prefix":"10.1007","volume":"84","author":[{"given":"Defang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiqi","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9375-2214","authenticated-orcid":false,"given":"Weifu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guocan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"20062_CR1","doi-asserted-by":"publisher","first-page":"2002","DOI":"10.1007\/s11263-020-01308-z","volume":"128","author":"X Zheng","year":"2020","unstructured":"Zheng X, Guo Y, Huang H, Li Y, He R (2020) A survey of deep facial attribute analysis. Int J Comput Vision 128:2002\u20132034","journal-title":"Int J Comput Vision"},{"issue":"6","key":"20062_CR2","doi-asserted-by":"publisher","first-page":"2359","DOI":"10.1109\/TCSVT.2020.3024201","volume":"31","author":"S Xie","year":"2020","unstructured":"Xie S, Hu H, Chen Y (2020) Facial expression recognition with two-branch disentangled generative adversarial network. IEEE Trans Circuits Syst Video Technol 31(6):2359\u20132371","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"20062_CR3","doi-asserted-by":"crossref","unstructured":"Kim T, Chung C, Kim Y, Park S, Kim K, Choo J (2022) Style your hair: Latent optimization for pose-invariant hairstyle transfer via local-style-aware hair alignment. In: European conference on computer vision. Springer, pp 188\u2013203","DOI":"10.1007\/978-3-031-19790-1_12"},{"issue":"7","key":"20062_CR4","doi-asserted-by":"publisher","first-page":"4338","DOI":"10.1109\/TCSVT.2021.3133313","volume":"32","author":"Y Wu","year":"2021","unstructured":"Wu Y, Wang R, Gong M, Cheng J, Yu Z, Tao D (2021) Adversarial uv-transformation texture estimation for 3d face aging. IEEE Trans Circuits Syst Video Technol 32(7):4338\u20134350","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"20062_CR5","doi-asserted-by":"crossref","unstructured":"Choi Y, Uh Y, Yoo J, Ha J-W (2020) Stargan v2: Diverse image synthesis for multiple domains. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 8188\u20138197","DOI":"10.1109\/CVPR42600.2020.00821"},{"issue":"4","key":"20062_CR6","doi-asserted-by":"publisher","first-page":"2004","DOI":"10.1109\/TPAMI.2020.3034267","volume":"44","author":"Y Shen","year":"2020","unstructured":"Shen Y, Yang C, Tang X, Zhou B (2020) Interfacegan: Interpreting the disentangled face representation learned by gans. IEEE Trans Pattern Anal Mach Intell 44(4):2004\u20132018","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"20062_CR7","doi-asserted-by":"crossref","unstructured":"Shi Y, Yang X, Wan Y, Shen X (2022) Semanticstylegan: Learning compositional generative priors for controllable image synthesis and editing. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition. pp 11254\u201311264","DOI":"10.1109\/CVPR52688.2022.01097"},{"issue":"4","key":"20062_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2185520.2185613","volume":"31","author":"T Beeler","year":"2012","unstructured":"Beeler T, Bickel B, Noris G, Beardsley P, Marschner S, Sumner RW, Gross M (2012) Coupled 3d reconstruction of sparse facial hair and skin. ACM Trans Graph (ToG) 31(4):1\u201310","journal-title":"ACM Trans Graph (ToG)"},{"key":"20062_CR9","doi-asserted-by":"crossref","unstructured":"Yang F, Wang J, Shechtman E, Bourdev L, Metaxas D (2011) Expression flow for 3d-aware face component transfer. In: ACM SIGGRAPH. pp 1\u201310","DOI":"10.1145\/1964921.1964955"},{"issue":"6","key":"20062_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2816795.2818056","volume":"34","author":"J Thies","year":"2015","unstructured":"Thies J, Zollh\u00f6fer M, Nie\u00dfner M, Valgaerts L, Stamminger M, Theobalt C (2015) Real-time expression transfer for facial reenactment. ACM Trans Graph 34(6):1\u201314","journal-title":"ACM Trans Graph"},{"key":"20062_CR11","doi-asserted-by":"crossref","unstructured":"Leyvand T, Cohen-Or D, Dror G, Lischinski D (2006) Digital face beautification. In: ACM Siggraph 2006 Sketches","DOI":"10.1145\/1179849.1180060"},{"key":"20062_CR12","doi-asserted-by":"crossref","unstructured":"Chen Y-C, Shen X, Jia J (2017) Makeup-go: Blind reversion of portrait edit. In: Proceedings of the IEEE international conference on computer vision. pp 4501\u20134509","DOI":"10.1109\/ICCV.2017.482"},{"key":"20062_CR13","doi-asserted-by":"crossref","unstructured":"Kemelmacher-Shlizerman I, Suwajanakorn S, Seitz SM (2014) Illumination-aware age progression. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 3334\u20133341","DOI":"10.1109\/CVPR.2014.426"},{"key":"20062_CR14","doi-asserted-by":"crossref","unstructured":"Zhang J, Zhou K, Luximon Y, Lee T-Y, Li P (2023) Meshwgan: Mesh-to-mesh wasserstein gan with multi-task gradient penalty for 3d facial geometric age transformation. IEEE Trans Vis Comput Graph","DOI":"10.1109\/TVCG.2023.3284500"},{"key":"20062_CR15","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems. pp 2672\u20132680"},{"key":"20062_CR16","unstructured":"Lample G, Zeghidour N, Usunier N, Bordes A, Denoyer L, Ranzato M (2017) Fader networks: Manipulating images by sliding attributes. In: Advances in neural information processing systems. pp 5967\u20135976"},{"key":"20062_CR17","doi-asserted-by":"crossref","unstructured":"Choi Y, Choi M, Kim M, Ha J-W, Kim S, Choo J (2018) Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 8789\u20138797","DOI":"10.1109\/CVPR.2018.00916"},{"issue":"11","key":"20062_CR18","doi-asserted-by":"publisher","first-page":"5464","DOI":"10.1109\/TIP.2019.2916751","volume":"28","author":"Z He","year":"2019","unstructured":"He Z, Zuo W, Kan M, Shan S, Chen X (2019) Attgan: Facial attribute editing by only changing what you want. IEEE Trans Image Process 28(11):5464\u20135478","journal-title":"IEEE Trans Image Process"},{"key":"20062_CR19","doi-asserted-by":"crossref","unstructured":"Liu M, Ding Y, Xia M, Liu X, Ding E, Zuo W, Wen S (2019) Stgan: A unified selective transfer network for arbitrary image attribute editing. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 3673\u20133682","DOI":"10.1109\/CVPR.2019.00379"},{"key":"20062_CR20","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.neunet.2020.10.019","volume":"133","author":"Y Liu","year":"2021","unstructured":"Liu Y, Fan H, Ni F, Xiang J (2021) Clsgan: Selective attribute editing model based on classification adversarial network. Neural Netw 133:220\u2013228","journal-title":"Neural Netw"},{"key":"20062_CR21","doi-asserted-by":"crossref","unstructured":"Zhou S, Xiao T, Yang Y, Feng D, He Q, He W (2017) Genegan: Learning object transfiguration and attribute subspace from unpaired data. arXiv:1705.04932","DOI":"10.5244\/C.31.111"},{"key":"20062_CR22","doi-asserted-by":"crossref","unstructured":"Xiao T, Hong J, Ma J (2018) Elegant: Exchanging latent encodings with gan for transferring multiple face attributes. In: Proceedings of the European Conference on Computer Vision (ECCV). pp 168\u2013184","DOI":"10.1007\/978-3-030-01249-6_11"},{"key":"20062_CR23","doi-asserted-by":"crossref","unstructured":"Yin W, Liu Z, Loy CC (2019) Instance-level facial attributes transfer with geometry-aware flow. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33. pp 9111\u20139118","DOI":"10.1609\/aaai.v33i01.33019111"},{"key":"20062_CR24","unstructured":"Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training gans. In: Advances in neural information processing systems. pp 2234\u20132242"},{"key":"20062_CR25","unstructured":"Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: Proceedings of the 34th International conference on machine learning. pp 214\u2013223"},{"key":"20062_CR26","unstructured":"Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of wasserstein gans. In: Advances in neural information processing systems. pp 5767\u20135777"},{"key":"20062_CR27","unstructured":"Guo J, Qian Z, Zhou Z, Liu Y (2019) Mulgan: Facial attribute editing by exemplar. arXiv:1912.12396"},{"key":"20062_CR28","doi-asserted-by":"crossref","unstructured":"Zhang J, Huang Y, Li Y, Zhao W, Zhang L (2019) Multi-attribute transfer via disentangled representation. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33. pp 9195\u20139202","DOI":"10.1609\/aaai.v33i01.33019195"},{"key":"20062_CR29","unstructured":"Perarnau G, Van De\u00a0Weijer J, Raducanu B, \u00c1lvarez JM (2016) Invertible conditional gans for image editing. arXiv:1611.06355"},{"key":"20062_CR30","doi-asserted-by":"crossref","unstructured":"Yan X, Yang J, Sohn K, Lee H (2016) Attribute2image: Conditional image generation from visual attributes. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV 14. Springer, pp 776\u2013791","DOI":"10.1007\/978-3-319-46493-0_47"},{"key":"20062_CR31","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. Comput Sci 2672\u20132680"},{"key":"20062_CR32","doi-asserted-by":"crossref","unstructured":"Zhang G, Kan M, Shan S, Chen X (2018) Generative adversarial network with spatial attention for face attribute editing. In: Proceedings of the European Conference on Computer Vision (ECCV). pp 417\u2013432","DOI":"10.1007\/978-3-030-01231-1_26"},{"key":"20062_CR33","doi-asserted-by":"crossref","unstructured":"Lin J, Xia Y, Wang Y, Qin T, Chen Z (2019) Image-to-image translation with multi-path consistency regularization. In: Proceedings of the 28th International joint conference on artificial intelligence. pp 2980\u20132986","DOI":"10.24963\/ijcai.2019\/413"},{"key":"20062_CR34","unstructured":"Zhu D, Liu S, Jiang W, Gao C, Wu T, Guo G (2019) Ugan: Untraceable gan for multi-domain face translation. arXiv:1907.11418"},{"key":"20062_CR35","doi-asserted-by":"publisher","first-page":"4881","DOI":"10.1007\/s11042-020-09858-7","volume":"80","author":"D Li","year":"2021","unstructured":"Li D, Zhang M, Zhang L, Chen W, Feng G (2021) A novel attribute-based generation architecture for facial image editing. Multimedia Tools Appl 80:4881\u20134902","journal-title":"Multimedia Tools Appl"},{"key":"20062_CR36","unstructured":"Sohn K, Lee H, Yan X (2015) Learning structured output representation using deep conditional generative models. In: Advances in neural information processing systems. pp 3483\u20133491"},{"key":"20062_CR37","unstructured":"Xiao T, Hong J, Ma J (2017) Dna-gan: Learning disentangled representations from multi-attribute images. arXiv:1711.05415"},{"key":"20062_CR38","doi-asserted-by":"crossref","unstructured":"Lin C-H, Yumer E, Wang O, Shechtman E, Lucey S (2018) St-gan: Spatial transformer generative adversarial networks for image compositing. In: Proceedings of the ieee conference on computer vision and pattern recognition. pp 9455\u20139464","DOI":"10.1109\/CVPR.2018.00985"},{"key":"20062_CR39","doi-asserted-by":"crossref","unstructured":"Li X, Zhang S, Hu J, Cao L, Hong X, Mao X, Huang F, Wu Y, Ji R (2021) Image-to-image translation via hierarchical style disentanglement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 8639\u20138648","DOI":"10.1109\/CVPR46437.2021.00853"},{"key":"20062_CR40","doi-asserted-by":"crossref","unstructured":"Dalva Y, Alt\u0131ndi\u015f SF, Dundar A (2022) Vecgan: Image-to-image translation with interpretable latent directions. In: European Conference on Computer Vision. Springer, pp 153\u2013169","DOI":"10.1007\/978-3-031-19787-1_9"},{"key":"20062_CR41","doi-asserted-by":"publisher","unstructured":"Dalva Y, Pehlivan H, Hatipoglu OI, Moran C, Dundar A (2023) Image-to-image translation with disentangled latent vectors for face editing. IEEE Trans Pattern Anal Mach Intell 1\u201312. https:\/\/doi.org\/10.1109\/TPAMI.2023.3308102","DOI":"10.1109\/TPAMI.2023.3308102"},{"key":"20062_CR42","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 4401\u20134410","DOI":"10.1109\/CVPR.2019.00453"},{"key":"20062_CR43","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T (2020) Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 8110\u20138119","DOI":"10.1109\/CVPR42600.2020.00813"},{"issue":"1","key":"20062_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3544777","volume":"42","author":"D Roich","year":"2022","unstructured":"Roich D, Mokady R, Bermano AH, Cohen-Or D (2022) Pivotal tuning for latent-based editing of real images. ACM Trans Graph (TOG) 42(1):1\u201313","journal-title":"ACM Trans Graph (TOG)"},{"key":"20062_CR45","doi-asserted-by":"crossref","unstructured":"Hu X, Huang Q, Shi Z, Li S, Gao C, Sun L, Li Q (2022) Style transformer for image inversion and editing. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 11337\u201311346","DOI":"10.1109\/CVPR52688.2022.01105"},{"key":"20062_CR46","doi-asserted-by":"crossref","unstructured":"Alaluf Y, Tov O, Mokady R, Gal R, Bermano A (2022) Hyperstyle: Stylegan inversion with hypernetworks for real image editing. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 18511\u201318521","DOI":"10.1109\/CVPR52688.2022.01796"},{"key":"20062_CR47","doi-asserted-by":"crossref","unstructured":"Pehlivan H, Dalva Y, Dundar A (2023) Styleres: Transforming the residuals for real image editing with stylegan. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 1828\u20131837","DOI":"10.1109\/CVPR52729.2023.00182"},{"key":"20062_CR48","unstructured":"Wu P-W, Lin Y-J, Chang C-H, Chang EY, Liao S-W (2019) Relgan: Multi-domain image-to-image translation via relative attributes. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 5914\u20135922"},{"key":"20062_CR49","doi-asserted-by":"crossref","unstructured":"Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision. pp 2223\u20132232","DOI":"10.1109\/ICCV.2017.244"},{"key":"20062_CR50","unstructured":"Kim T, Cha M, Kim H, Lee JK, Kim J (2017) Learning to discover cross-domain relations with generative adversarial networks. In: Proceedings of the 34th International conference on machine learning. pp 1857\u20131865, JMLR. org"},{"key":"20062_CR51","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning. PMLR, pp 448\u2013456"},{"key":"20062_CR52","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv:1412.6980"},{"key":"20062_CR53","doi-asserted-by":"crossref","unstructured":"Liu Z, Luo P, Wang X, Tang X (2016) Deep learning face attributes in the wild. In: IEEE international conference on computer vision. pp 3730\u20133738","DOI":"10.1109\/ICCV.2015.425"},{"issue":"4","key":"20062_CR54","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"20062_CR55","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros AA, Shechtman E, Wang O (2018) The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 586\u2013595","DOI":"10.1109\/CVPR.2018.00068"},{"key":"20062_CR56","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"20062_CR57","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp 1097\u20131105"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20062-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-20062-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20062-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T14:02:32Z","timestamp":1751464952000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-20062-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"references-count":57,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["20062"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-20062-9","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,22]]},"assertion":[{"value":"6 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2024","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 certify that there is no confict of interest in the subject matter discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Confict of interest"}}]}}