{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T17:05:50Z","timestamp":1784307950261,"version":"3.55.0"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004335","name":"Southwest University of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004335","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers &amp; Graphics"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.cag.2026.104671","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T16:30:40Z","timestamp":1781800240000},"page":"104671","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Neural occlusion removal rendering based on occluded scene layered structure analysis"],"prefix":"10.1016","volume":"138","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0196-2901","authenticated-orcid":false,"given":"Xiaoqiang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cag.2026.104671_b1","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2025.3548133","article-title":"A comprehensive review on light field occlusion removal: Trends, challenges, and future directions","author":"Senussi","year":"2025","journal-title":"IEEE Access"},{"issue":"4","key":"10.1016\/j.cag.2026.104671_b2","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1016\/j.patcog.2011.10.003","article-title":"A novel multi-object detection method in complex scene using synthetic aperture imaging","volume":"45","author":"Pei","year":"2012","journal-title":"Pattern Recognit"},{"key":"10.1016\/j.cag.2026.104671_b3","series-title":"2007 IEEE 11th international conference on computer vision","first-page":"1","article-title":"Synthetic aperture tracking: Tracking through occlusions","author":"Joshi","year":"2007"},{"key":"10.1016\/j.cag.2026.104671_b4","series-title":"CVPR 2011","first-page":"3409","article-title":"Continuously tracking and see-through occlusion based on a new hybrid synthetic aperture imaging model","author":"Yang","year":"2011"},{"issue":"6","key":"10.1016\/j.cag.2026.104671_b5","doi-asserted-by":"crossref","first-page":"2590","DOI":"10.1109\/TCSVT.2022.3226227","article-title":"Effective light field de-occlusion network based on swin transformer","volume":"33","author":"Wang","year":"2022","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"10.1016\/j.cag.2026.104671_b6","doi-asserted-by":"crossref","unstructured":"Wang Y, Wu T, Yang J, Wang L, An W, Guo Y. DeOccNet: Learning to see through foreground occlusions in light fields. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision. 2020, p. 118\u201327.","DOI":"10.1109\/WACV45572.2020.9093448"},{"issue":"7","key":"10.1016\/j.cag.2026.104671_b7","doi-asserted-by":"crossref","first-page":"8660","DOI":"10.1109\/TPAMI.2022.3227448","article-title":"Learning to see through with events","volume":"45","author":"Yu","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"10.1016\/j.cag.2026.104671_b8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2766940","article-title":"A computational approach for obstruction-free photography","volume":"34","author":"Xue","year":"2015","journal-title":"ACM Trans Graph"},{"issue":"1","key":"10.1016\/j.cag.2026.104671_b9","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.patcog.2012.06.014","article-title":"Synthetic aperture imaging using pixel labeling via energy minimization","volume":"46","author":"Pei","year":"2013","journal-title":"Pattern Recognit"},{"key":"10.1016\/j.cag.2026.104671_b10","series-title":"European conference on computer vision","first-page":"1","article-title":"All-in-focus synthetic aperture imaging","author":"Yang","year":"2014"},{"key":"10.1016\/j.cag.2026.104671_b11","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.patcog.2016.07.019","article-title":"Synthetic aperture photography using a moving camera-IMU system","volume":"62","author":"Zhang","year":"2017","journal-title":"Pattern Recognit"},{"issue":"9","key":"10.1016\/j.cag.2026.104671_b12","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/TIP.2004.833105","article-title":"Region filling and object removal by exemplar-based image inpainting","volume":"13","author":"Criminisi","year":"2004","journal-title":"IEEE Trans Image Process"},{"key":"10.1016\/j.cag.2026.104671_b13","doi-asserted-by":"crossref","unstructured":"Bertalmio M, Sapiro G, Caselles V, Ballester C. Image inpainting. In: Proceedings of the 27th annual conference on computer graphics and interactive techniques. 2000, p. 417\u201324.","DOI":"10.1145\/344779.344972"},{"key":"10.1016\/j.cag.2026.104671_b14","series-title":"Asian conference on computer vision","first-page":"422","article-title":"Image de-fencing revisited","author":"Park","year":"2010"},{"issue":"7","key":"10.1016\/j.cag.2026.104671_b15","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1109\/TCSVT.2013.2241351","article-title":"Video de-fencing","volume":"24","author":"Mu","year":"2013","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"4","key":"10.1016\/j.cag.2026.104671_b16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1778765.1778820","article-title":"Repfinder: Finding approximately repeated scene elements for image editing","volume":"29","author":"Cheng","year":"2010","journal-title":"ACM Trans Graph"},{"key":"10.1016\/j.cag.2026.104671_b17","series-title":"Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004","article-title":"Using plane+ parallax for calibrating dense camera arrays","volume":"vol. 1","author":"Vaish","year":"2004"},{"key":"10.1016\/j.cag.2026.104671_b18","series-title":"2005 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905)-workshops","article-title":"Synthetic aperture focusing using a shear-warp factorization of the viewing transform","author":"Vaish","year":"2005"},{"key":"10.1016\/j.cag.2026.104671_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107669","article-title":"All-in-focus synthetic aperture imaging using generative adversarial network-based semantic inpainting","volume":"111","author":"Pei","year":"2021","journal-title":"Pattern Recognit"},{"key":"10.1016\/j.cag.2026.104671_b20","doi-asserted-by":"crossref","unstructured":"Zhang X, Liao W, Yu L, Yang W, Xia G-S. Event-based synthetic aperture imaging with a hybrid network. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2021, p. 14235\u201344.","DOI":"10.1109\/CVPR46437.2021.01401"},{"issue":"10","key":"10.1016\/j.cag.2026.104671_b21","article-title":"SwinSccNet: Swin-unet encoder-decoder structured-light field occlusion removal network","volume":"63","author":"Zhang","year":"2024","journal-title":"Opt Eng, Bellingham"},{"key":"10.1016\/j.cag.2026.104671_b22","series-title":"European conference on computer vision","first-page":"405","article-title":"NeRF: Representing scenes as neural radiance fields for view synthesis","author":"Mildenhall","year":"2020"},{"key":"10.1016\/j.cag.2026.104671_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.cag.2025.104447","article-title":"CeRF: Convolutional neural radiance derivative fields for new view synthesis","author":"Liu","year":"2025","journal-title":"Comput Graph"},{"key":"10.1016\/j.cag.2026.104671_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.cag.2024.104157","article-title":"MT-NeRF: Neural implicit representation based on multi-resolution geometric feature planes","volume":"126","author":"Jiang","year":"2025","journal-title":"Comput Graph"},{"key":"10.1016\/j.cag.2026.104671_b25","doi-asserted-by":"crossref","unstructured":"Martin-Brualla R, Radwan N, Sajjadi MS, Barron JT, Dosovitskiy A, Duckworth D. Nerf in the wild: Neural radiance fields for unconstrained photo collections. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2021, p. 7210\u20139.","DOI":"10.1109\/CVPR46437.2021.00713"},{"key":"10.1016\/j.cag.2026.104671_b26","doi-asserted-by":"crossref","unstructured":"Chen X, Zhang Q, Li X, Chen Y, Feng Y, Wang X, Wang J. Hallucinated neural radiance fields in the wild. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2022, p. 12943\u201352.","DOI":"10.1109\/CVPR52688.2022.01260"},{"key":"10.1016\/j.cag.2026.104671_b27","doi-asserted-by":"crossref","unstructured":"Zhu C, Wan R, Tang Y, Shi B. Occlusion-free scene recovery via neural radiance fields. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2023, p. 20722\u201331.","DOI":"10.1109\/CVPR52729.2023.01985"},{"issue":"4","key":"10.1016\/j.cag.2026.104671_b28","doi-asserted-by":"crossref","first-page":"1742","DOI":"10.1109\/TIP.2011.2179057","article-title":"Automatic single-image-based rain streaks removal via image decomposition","volume":"21","author":"Kang","year":"2011","journal-title":"IEEE Trans Image Process"},{"issue":"12","key":"10.1016\/j.cag.2026.104671_b29","doi-asserted-by":"crossref","first-page":"6925","DOI":"10.1007\/s11042-015-2618-1","article-title":"Kinect based real-time synthetic aperture imaging through occlusion","volume":"75","author":"Yang","year":"2016","journal-title":"Multimedia Tools Appl"},{"issue":"3","key":"10.1016\/j.cag.2026.104671_b30","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MCG.2019.2896024","article-title":"Synthetic aperture imaging with drones","volume":"39","author":"Bimber","year":"2019","journal-title":"IEEE Comput Graph Appl"},{"issue":"7","key":"10.1016\/j.cag.2026.104671_b31","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1109\/JSTSP.2017.2715012","article-title":"Seeing beyond foreground occlusion: A joint framework for SAP-based scene depth and appearance reconstruction","volume":"11","author":"Xiao","year":"2017","journal-title":"IEEE J Sel Top Signal Process"},{"issue":"3","key":"10.1016\/j.cag.2026.104671_b32","doi-asserted-by":"crossref","first-page":"607","DOI":"10.3390\/s19030607","article-title":"Occluded-object 3D reconstruction using camera array synthetic aperture imaging","volume":"19","author":"Pei","year":"2019","journal-title":"Sensors"},{"key":"10.1016\/j.cag.2026.104671_b33","doi-asserted-by":"crossref","unstructured":"Liu G, Reda FA, Shih KJ, Wang T-C, Tao A, Catanzaro B. Image inpainting for irregular holes using partial convolutions. In: Proceedings of the European conference on computer vision. ECCV, 2018, p. 85\u2013100.","DOI":"10.1007\/978-3-030-01252-6_6"},{"key":"10.1016\/j.cag.2026.104671_b34","doi-asserted-by":"crossref","unstructured":"Li J, Wang N, Zhang L, Du B, Tao D. Recurrent feature reasoning for image inpainting. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2020, p. 7760\u20138.","DOI":"10.1109\/CVPR42600.2020.00778"},{"key":"10.1016\/j.cag.2026.104671_b35","doi-asserted-by":"crossref","unstructured":"Xie C, Liu S, Li C, Cheng M-M, Zuo W, Liu X, Wen S, Ding E. Image inpainting with learnable bidirectional attention maps. In: Proceedings of the IEEE\/CVF international conference on computer vision. 2019, p. 8858\u201367.","DOI":"10.1109\/ICCV.2019.00895"},{"key":"10.1016\/j.cag.2026.104671_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.image.2022.116853","article-title":"Light field occlusion removal network via foreground location and background recovery","volume":"109","author":"Zhang","year":"2022","journal-title":"Signal Process, Image Commun"},{"key":"10.1016\/j.cag.2026.104671_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.cag.2024.104140","article-title":"Dyn-e: Local appearance editing of dynamic neural radiance fields","author":"ShenTu","year":"2025","journal-title":"Comput Graph"},{"issue":"4","key":"10.1016\/j.cag.2026.104671_b38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3528223.3530127","article-title":"Instant neural graphics primitives with a multiresolution hash encoding","volume":"41","author":"M\u00fcller","year":"2022","journal-title":"ACM Trans Graph"},{"key":"10.1016\/j.cag.2026.104671_b39","doi-asserted-by":"crossref","unstructured":"Barron JT, Mildenhall B, Tancik M, Hedman P, Martin-Brualla R, Srinivasan PP. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In: Proceedings of the IEEE\/CVF international conference on computer vision. 2021, p. 5855\u201364.","DOI":"10.1109\/ICCV48922.2021.00580"},{"key":"10.1016\/j.cag.2026.104671_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.cag.2025.104163","article-title":"CtrlNeRF: The generative neural radiation fields for the controllable synthesis of high-fidelity 3D-aware images","volume":"126","author":"Liu","year":"2025","journal-title":"Comput Graph"},{"key":"10.1016\/j.cag.2026.104671_b41","series-title":"European conference on computer vision","first-page":"333","article-title":"Tensorf: Tensorial radiance fields","author":"Chen","year":"2022"},{"key":"10.1016\/j.cag.2026.104671_b42","doi-asserted-by":"crossref","unstructured":"Deng K, Liu A, Zhu J-Y, Ramanan D. Depth-supervised nerf: Fewer views and faster training for free. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2022, p. 12882\u201391.","DOI":"10.1109\/CVPR52688.2022.01254"},{"key":"10.1016\/j.cag.2026.104671_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.cag.2024.104025","article-title":"SP-SeaNeRF: Underwater neural radiance fields with strong scattering perception","volume":"123","author":"Chen","year":"2024","journal-title":"Comput Graph"},{"key":"10.1016\/j.cag.2026.104671_b44","doi-asserted-by":"crossref","unstructured":"Sabour S, Vora S, Duckworth D, Krasin I, Fleet DJ, Tagliasacchi A. Robustnerf: Ignoring distractors with robust losses. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2023, p. 20626\u201336.","DOI":"10.1109\/CVPR52729.2023.01976"},{"key":"10.1016\/j.cag.2026.104671_b45","doi-asserted-by":"crossref","unstructured":"Liu W, Xiong Z, Li X, Jacobs N. DeclutterNeRF: Generative-Free 3D Scene Recovery for Occlusion Removal. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2025, p. 380\u201390.","DOI":"10.1109\/CVPRW67362.2025.00042"},{"key":"10.1016\/j.cag.2026.104671_b46","doi-asserted-by":"crossref","unstructured":"Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo W-Y, et al. Segment anything. In: Proceedings of the IEEE\/CVF international conference on computer vision. 2023, p. 4015\u201326.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"10.1016\/j.cag.2026.104671_b47","doi-asserted-by":"crossref","unstructured":"Chen H, Loy CC, Pan X. Mvip-nerf: Multi-view 3d inpainting on nerf scenes via diffusion prior. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2024, p. 5344\u201353.","DOI":"10.1109\/CVPR52733.2024.00511"},{"key":"10.1016\/j.cag.2026.104671_b48","doi-asserted-by":"crossref","unstructured":"Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B. High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2022, p. 10684\u201395.","DOI":"10.1109\/CVPR52688.2022.01042"},{"issue":"11","key":"10.1016\/j.cag.2026.104671_b49","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun ACM"},{"issue":"11","key":"10.1016\/j.cag.2026.104671_b50","first-page":"120","article-title":"The opencv library","volume":"25","author":"Bradski","year":"2000","journal-title":"Dr Dobb\u2019s J: Softw Tools Program"},{"key":"10.1016\/j.cag.2026.104671_b51","series-title":"UCSD light field archive","year":"2007"},{"issue":"3","key":"10.1016\/j.cag.2026.104671_b52","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1145\/1141911.1141955","article-title":"Natural video matting using camera arrays","volume":"25","author":"Joshi","year":"2006","journal-title":"ACM Trans Graph"},{"key":"10.1016\/j.cag.2026.104671_b53","series-title":"Ultralytics YOLOv8","author":"Jocher","year":"2023"},{"key":"10.1016\/j.cag.2026.104671_b54","series-title":"Stanford light field dataset","year":"2011"},{"key":"10.1016\/j.cag.2026.104671_b55","series-title":"International conference on learning representations","article-title":"NeRF-SOS: Any-view self-supervised object segmentation on complex scenes","author":"Fan","year":"2023"},{"issue":"4","key":"10.1016\/j.cag.2026.104671_b56","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans Image Process"},{"key":"10.1016\/j.cag.2026.104671_b57","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros AA, Shechtman E, Wang O. The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, p. 586\u201395.","DOI":"10.1109\/CVPR.2018.00068"},{"issue":"4","key":"10.1016\/j.cag.2026.104671_b58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3306346.3322980","article-title":"Local light field fusion: Practical view synthesis with prescriptive sampling guidelines","volume":"38","author":"Mildenhall","year":"2019","journal-title":"ACM Trans Graph (ToG)"},{"issue":"4","key":"10.1016\/j.cag.2026.104671_b59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3592433","article-title":"3D gaussian splatting for real-time radiance field rendering","volume":"42","author":"Kerbl","year":"2023","journal-title":"ACM Trans Graph"},{"key":"10.1016\/j.cag.2026.104671_b60","series-title":"European conference on computer vision","first-page":"145","article-title":"Fsgs: Real-time few-shot view synthesis using gaussian splatting","author":"Zhu","year":"2024"}],"container-title":["Computers &amp; Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0097849326001421?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0097849326001421?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T16:32:37Z","timestamp":1784305957000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0097849326001421"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":60,"alternative-id":["S0097849326001421"],"URL":"https:\/\/doi.org\/10.1016\/j.cag.2026.104671","relation":{},"ISSN":["0097-8493"],"issn-type":[{"value":"0097-8493","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Neural occlusion removal rendering based on occluded scene layered structure analysis","name":"articletitle","label":"Article Title"},{"value":"Computers & Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cag.2026.104671","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104671"}}