{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T20:13:20Z","timestamp":1786047200665,"version":"3.56.0"},"reference-count":63,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"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\/501100003453","name":"Guangdong Provincial Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.neucom.2026.134634","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T15:41:16Z","timestamp":1785253276000},"page":"134634","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["CGVT-FSL: Concept-guided visual-textual few-shot learning for cross-domain hyperspectral image classification"],"prefix":"10.1016","volume":"702","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9365-1484","authenticated-orcid":false,"given":"Haojin","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqing","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7507-342X","authenticated-orcid":false,"given":"Hongyi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weixin","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.neucom.2026.134634_bib0005","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1109\/TCSVT.2022.3215513","article-title":"Dgssc: a deep generative spectral-spatial classifier for imbalanced hyperspectral imagery","volume":"33","author":"Xi","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.neucom.2026.134634_bib0010","article-title":"ConvMamba: combining Mamba with CNN for hyperspectral image classification","author":"Zhang","year":"2025","journal-title":"Neurocomputing"},{"issue":"4","key":"10.1016\/j.neucom.2026.134634_bib0015","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1109\/TCSVT.2021.3095250","article-title":"Global-local balanced low-rank approximation of hyperspectral images for classification","volume":"32","author":"Liu","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.neucom.2026.134634_bib0020","article-title":"A band grouping-based hybrid convolution for hyperspectral image super-resolution","author":"Liu","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134634_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111197","article-title":"Multi-branch feature transformation cross-domain few-shot learning for hyperspectral image classification","volume":"160","author":"Shi","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.neucom.2026.134634_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127742","article-title":"Cross-domain multimodal feature enhancement hypergraph neural network for few-shot hyperspectral images classification","volume":"283","author":"Zhang","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.134634_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113199","article-title":"Global\u2013local prototype-based few-shot learning for cross-domain hyperspectral image classification","volume":"314","author":"Tang","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.neucom.2026.134634_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125453","article-title":"Semantic guided prototype learning for cross-domain few-shot hyperspectral image classification","volume":"260","author":"Li","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.134634_bib0045","first-page":"1","article-title":"Language-aware domain generalization network for cross-scene hyperspectral image classification","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"12","key":"10.1016\/j.neucom.2026.134634_bib0050","doi-asserted-by":"crossref","first-page":"7789","DOI":"10.1109\/TCSVT.2023.3282777","article-title":"Few-shot learning meets transformer: unified query-support transformers for few-shot classification","volume":"33","author":"Wang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.neucom.2026.134634_bib0055","article-title":"Task-aware dynamic routing network for cross-domain few-shot learning","author":"Li","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134634_bib0060","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11719","article-title":"Task agnostic meta-learning for few-shot learning","author":"Jamal","year":"2019"},{"key":"10.1016\/j.neucom.2026.134634_bib0065","doi-asserted-by":"crossref","first-page":"1470","DOI":"10.1109\/JSTARS.2022.3140756","article-title":"Symmetric information\u2013theoretic metric learning for target detection in hyperspectral imagery","volume":"15","author":"Dong","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"issue":"4","key":"10.1016\/j.neucom.2026.134634_bib0070","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TGRS.2018.2872830","article-title":"Deep few-shot learning for hyperspectral image classification","volume":"57","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.neucom.2026.134634_bib0075","first-page":"1","article-title":"Deep cross-domain few-shot learning for hyperspectral image classification","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.neucom.2026.134634_bib0080","series-title":"Advances in Neural Information Processing Systems, 31","article-title":"Conditional adversarial domain adaptation","author":"Long","year":"2018"},{"key":"10.1016\/j.neucom.2026.134634_bib0085","author":"Li"},{"key":"10.1016\/j.neucom.2026.134634_bib0090","first-page":"1","article-title":"Parameter-efficient transfer learning for remote sensing image\u2013text retrieval","volume":"61","author":"Yuan","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.neucom.2026.134634_bib0095","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence, 38","first-page":"2714","article-title":"Vlcounter: text-aware visual representation for zero-shot object counting","author":"Kang","year":"2024"},{"key":"10.1016\/j.neucom.2026.134634_bib0100","first-page":"1","article-title":"Improving few-shot remote sensing scene classification with class name semantics","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.neucom.2026.134634_bib0105","series-title":"International Conference on Machine Learning","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"10.1016\/j.neucom.2026.134634_bib0110","first-page":"1","article-title":"Cross-domain few-shot hyperspectral image classification with cross-modal alignment and supervised contrastive learning","volume":"62","author":"Li","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.neucom.2026.134634_bib0115","first-page":"1","article-title":"RemoteCLIP: a vision language foundation model for remote sensing","volume":"62","author":"Liu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.neucom.2026.134634_bib0120","series-title":"2023 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"15700","article-title":"Waffling around for performance: visual classification with random words and broad concepts","author":"Roth","year":"2023"},{"key":"10.1016\/j.neucom.2026.134634_bib0125","series-title":"ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1","article-title":"Teaw: text-aware few-shot remote sensing image scene classification","author":"Cheng","year":"2023"},{"key":"10.1016\/j.neucom.2026.134634_bib0130","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"19113","article-title":"Maple: multi-modal prompt learning","author":"Khattak","year":"2023"},{"key":"10.1016\/j.neucom.2026.134634_bib0135","author":"Zhou"},{"key":"10.1016\/j.neucom.2026.134634_bib0140","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"19996","article-title":"Test-time visual in-context tuning","author":"Xie","year":"2025"},{"issue":"1","key":"10.1016\/j.neucom.2026.134634_bib0145","doi-asserted-by":"crossref","DOI":"10.1080\/10106049.2023.2231428","article-title":"Attention-based multiscale deep learning with unsampled pixel utilization for hyperspectral image classification","volume":"38","author":"AL-Kubaisi","year":"2023","journal-title":"Geocarto Int."},{"key":"10.1016\/j.neucom.2026.134634_bib0150","series-title":"2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)","first-page":"1","article-title":"Hyperspectral image classification based on multi-level spectral-spatial transformer network","author":"Yang","year":"2022"},{"key":"10.1016\/j.neucom.2026.134634_bib0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111470","article-title":"Tensor transformer for hyperspectral image classification","volume":"163","author":"Zhang","year":"2025","journal-title":"Pattern Recognit."},{"issue":"12","key":"10.1016\/j.neucom.2026.134634_bib0160","doi-asserted-by":"crossref","first-page":"12680","DOI":"10.1109\/TCSVT.2025.3570466","article-title":"Cross-domain few-shot learning method based on fractional domain information for hyperspectral image multi-class change detection","volume":"35","author":"Feng","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"10","key":"10.1016\/j.neucom.2026.134634_bib0165","doi-asserted-by":"crossref","first-page":"17856","DOI":"10.1109\/TNNLS.2025.3586714","article-title":"An adaptive weighted metric learning network based on fractional domain decoupling for hyperspectral change detection","volume":"36","author":"Feng","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"4","key":"10.1016\/j.neucom.2026.134634_bib0170","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1049\/cit2.12181","article-title":"Deep transformer and few-shot learning for hyperspectral image classification","volume":"8","author":"Ran","year":"2023","journal-title":"CAAI Trans. Intell. Technol."},{"key":"10.1016\/j.neucom.2026.134634_bib0175","first-page":"1","article-title":"Hyperspectral image transformer classification networks","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.neucom.2026.134634_bib0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108705","article-title":"Multi-level graph learning network for hyperspectral image classification","volume":"129","author":"Wan","year":"2022","journal-title":"Pattern Recognit."},{"issue":"1","key":"10.1016\/j.neucom.2026.134634_bib0185","doi-asserted-by":"crossref","first-page":"67","DOI":"10.3390\/rs9010067","article-title":"Spectral\u2013spatial classification of hyperspectral imagery with 3D convolutional neural network","volume":"9","author":"Li","year":"2017","journal-title":"Remote Sens."},{"issue":"2","key":"10.1016\/j.neucom.2026.134634_bib0190","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral\u2013spatial residual network for hyperspectral image classification: a 3-D deep learning framework","volume":"56","author":"Zhong","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.neucom.2026.134634_bib0195","first-page":"1","article-title":"MASSFormer: memory-augmented spectral-spatial transformer for hyperspectral image classification","volume":"62","author":"Sun","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.neucom.2026.134634_bib0200","first-page":"1","article-title":"Cross-domain contrastive learning for hyperspectral image classification","volume":"60","author":"Guan","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"2","key":"10.1016\/j.neucom.2026.134634_bib0205","doi-asserted-by":"crossref","DOI":"10.1117\/1.JRS.17.026509","article-title":"HFC-SST: improved spatial-spectral transformer for hyperspectral few-shot classification","volume":"17","author":"Huang","year":"2023","journal-title":"J. Appl. Remote Sens."},{"issue":"2","key":"10.1016\/j.neucom.2026.134634_bib0210","doi-asserted-by":"crossref","first-page":"1912","DOI":"10.1109\/TNNLS.2022.3185795","article-title":"Graph information aggregation cross-domain few-shot learning for hyperspectral image classification","volume":"35","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"5","key":"10.1016\/j.neucom.2026.134634_bib0215","doi-asserted-by":"crossref","first-page":"4392","DOI":"10.1007\/s10489-024-05384-3","article-title":"Multi-level relation learning for cross-domain few-shot hyperspectral image classification","volume":"54","author":"Liu","year":"2024","journal-title":"Appl. Intell."},{"key":"10.1016\/j.neucom.2026.134634_bib0220","first-page":"1","article-title":"A hybrid multi-task learning network for hyperspectral image classification with few labels","volume":"62","author":"Liu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"10.1016\/j.neucom.2026.134634_bib0225","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1038\/s41746-024-01258-7","article-title":"A framework for human evaluation of large language models in healthcare derived from literature review","volume":"7","author":"Tam","year":"2024","journal-title":"NPJ Digit. Med."},{"key":"10.1016\/j.neucom.2026.134634_bib0230","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neucom.2016.09.010","article-title":"Convolutional neural networks for hyperspectral image classification","volume":"219","author":"Yu","year":"2017","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.134634_bib0235","doi-asserted-by":"crossref","first-page":"24835","DOI":"10.1109\/ACCESS.2023.3255164","article-title":"Hyperspectral image classification: an analysis employing CNN, LSTM, transformer, and attention mechanism","volume":"11","author":"Viel","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2026.134634_bib0240","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.134634_bib0245","series-title":"International Conference on Learning Representations","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"issue":"7","key":"10.1016\/j.neucom.2026.134634_bib0250","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"10.1016\/j.neucom.2026.134634_bib0255","first-page":"5","article-title":"Airborne hyperspectral data over Chikusei","volume":"5","author":"Yokoya","year":"2016","journal-title":"Space Appl. Lab."},{"issue":"6","key":"10.1016\/j.neucom.2026.134634_bib0260","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1109\/JSTARS.2014.2305441","article-title":"Hyperspectral and LiDAR data fusion: outcome of the 2013 GRSS data fusion contest","volume":"7","author":"Debes","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.neucom.2026.134634_bib0265","series-title":"Hyperspectral Remote Sensing Datasets: Indian Pines, Pavia University, Botswana and Salinas","author":"Amin","year":"2025"},{"key":"10.1016\/j.neucom.2026.134634_bib0270","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2020.112012","article-title":"WHU-hi: UAV-Borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF","volume":"250","author":"Zhong","year":"2020","journal-title":"Remote Sens. Environ."},{"issue":"8","key":"10.1016\/j.neucom.2026.134634_bib0275","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.neucom.2026.134634_bib0280","doi-asserted-by":"crossref","first-page":"5079","DOI":"10.1109\/TIP.2022.3192712","article-title":"Few-shot learning with class-covariance metric for hyperspectral image classification","volume":"31","author":"Xi","year":"2022","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"10.1016\/j.neucom.2026.134634_bib0285","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.neucom.2026.134634_bib0290","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.neucom.2026.134634_bib0295","series-title":"European Conference on Computer Vision (ECCV) Workshops","first-page":"443","article-title":"Deep CORAL: correlation alignment for deep domain adaptation","author":"Sun","year":"2016"},{"issue":"1","key":"10.1016\/j.neucom.2026.134634_bib0300","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1186\/s12885-023-11325-z","article-title":"Kappa statistic considerations in evaluating inter-rater reliability between two raters: which, when and context matters","volume":"23","author":"Li","year":"2023","journal-title":"BMC Cancer"},{"key":"10.1016\/j.neucom.2026.134634_bib0305","author":"Liu"},{"key":"10.1016\/j.neucom.2026.134634_bib0310","author":"Xiao"},{"key":"10.1016\/j.neucom.2026.134634_bib0315","author":"Sun"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226020321?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226020321?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:36:04Z","timestamp":1786037764000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226020321"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":63,"alternative-id":["S0925231226020321"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134634","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"CGVT-FSL: Concept-guided visual-textual few-shot learning for cross-domain hyperspectral image classification","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134634","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"134634"}}