{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T09:42:01Z","timestamp":1773999721153,"version":"3.50.1"},"reference-count":62,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100007839","name":"Yunnan University","doi-asserted-by":"publisher","award":["ZC-24248298"],"award-info":[{"award-number":["ZC-24248298"]}],"id":[{"id":"10.13039\/501100007839","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62266051"],"award-info":[{"award-number":["62266051"]}],"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":["61802337"],"award-info":[{"award-number":["61802337"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.knosys.2026.115414","type":"journal-article","created":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T06:52:37Z","timestamp":1769323957000},"page":"115414","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["RAATrack: Reliable appearance aggregation for video-level multimodal tracking"],"prefix":"10.1016","volume":"337","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2407-1194","authenticated-orcid":false,"given":"Yingran","family":"Jin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1711-3066","authenticated-orcid":false,"given":"Yun","family":"Gao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5453-8345","authenticated-orcid":false,"given":"Qianyun","family":"Feng","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.115414_bib0001","first-page":"1","article-title":"A novel target-aware dual matching and compensatory segmentation tracker for aerial videos","volume":"70","author":"Sun","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"11","key":"10.1016\/j.knosys.2026.115414_bib0002","doi-asserted-by":"crossref","first-page":"12320","DOI":"10.1109\/JSEN.2023.3266653","article-title":"Adaptive image dehazing and object tracking in UAV videos based on the template updating siamese network","volume":"23","author":"Sun","year":"2023","journal-title":"IEEE Sens. J."},{"issue":"18","key":"10.1016\/j.knosys.2026.115414_bib0003","doi-asserted-by":"crossref","first-page":"10355","DOI":"10.1109\/JSEN.2020.2995271","article-title":"Progress and prospects of multimodal fusion methods in physical human-robot interaction: a review","volume":"20","author":"Xue","year":"2020","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.knosys.2026.115414_bib0004","unstructured":"J. Xie, B. Zhong, Q. Liang, N. Li, Z. Mo, S. Song, Robust Tracking via Mamba-based Context-aware Token Learning, 2024, (http:\/\/arxiv.org\/abs\/2412.13611). Online; accessed on [insert date]."},{"key":"10.1016\/j.knosys.2026.115414_bib0005","series-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","first-page":"341","article-title":"Joint feature learning and relation modeling for tracking: a one-stream framework","volume":"13682 LNCS","author":"Ye","year":"2022"},{"issue":"2","key":"10.1016\/j.knosys.2026.115414_bib0006","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.1109\/TCSVT.2023.3289624","article-title":"Transformer tracking via frequency fusion","volume":"34","author":"Hu","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.knosys.2026.115414_bib0007","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3336665","article-title":"Multiple source domain adaptation for multiple object tracking in satellite video","volume":"61","author":"Zheng","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.knosys.2026.115414_bib0008","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"4838","article-title":"Explicit visual prompts for visual object tracking","volume":"38","author":"Shi","year":"2024"},{"key":"10.1016\/j.knosys.2026.115414_bib0009","series-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","first-page":"19300","article-title":"Autoregressive queries for adaptive tracking with spatio-temporal transformers","author":"Xie","year":"2024"},{"key":"10.1016\/j.knosys.2026.115414_bib0010","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"927","article-title":"Bi-directional adapter for multimodal tracking","volume":"38","author":"Cao","year":"2024"},{"key":"10.1016\/j.knosys.2026.115414_bib0011","series-title":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","first-page":"9516","article-title":"Visual prompt multi-modal tracking","author":"Zhu","year":"2023"},{"key":"10.1016\/j.knosys.2026.115414_bib0012","series-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","first-page":"13630","article-title":"Bridging search region interaction with template for RGB-T tracking","volume":"2023-June","author":"Hui","year":"2023"},{"key":"10.1016\/j.knosys.2026.115414_bib0013","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"5436","article-title":"Temporal adaptive RGBT tracking with modality prompt","volume":"38","author":"Wang","year":"2024"},{"key":"10.1016\/j.knosys.2026.115414_bib0014","first-page":"1","article-title":"Dual-modality space-time memory network for RGBT tracking","volume":"72","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.knosys.2026.115414_bib0015","series-title":"Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI 2022)","first-page":"2831","article-title":"Attribute-based progressive fusion network for RGBT tracking","volume":"36","author":"Xiao","year":"2022"},{"key":"10.1016\/j.knosys.2026.115414_bib0016","series-title":"SDSTrack: self-distillation symmetric adapter learning for multi-modal visual object tracking","author":"Hou","year":"2024"},{"key":"10.1016\/j.knosys.2026.115414_bib0017","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"10428","article-title":"Learning spatio-temporal transformer for visual tracking","author":"Yan","year":"2021"},{"key":"10.1016\/j.knosys.2026.115414_bib0018","series-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","first-page":"14572","article-title":"SeqTrack: sequence to sequence learning for visual object tracking","author":"Chen","year":"2023"},{"key":"10.1016\/j.knosys.2026.115414_bib0019","series-title":"Advances in Neural Information Processing Systems","first-page":"1","article-title":"SwinTrack: a simple and strong baseline for transformer tracking","volume":"35","author":"Lin","year":"2022"},{"key":"10.1016\/j.knosys.2026.115414_bib0020","series-title":"Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI 2023)","first-page":"2321","article-title":"Compact transformer tracker with correlative masked modeling","volume":"37","author":"Song","year":"2023"},{"key":"10.1016\/j.knosys.2026.115414_bib0021","series-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","first-page":"13598","article-title":"MixFormer: end-to-end tracking with iterative mixed attention","volume":"2022-June","author":"Cui","year":"2022"},{"key":"10.1016\/j.knosys.2026.115414_bib0022","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"7588","article-title":"ODTrack: online dense temporal token learning for visual tracking","volume":"38","author":"Zheng","year":"2024"},{"key":"10.1016\/j.knosys.2026.115414_bib0023","doi-asserted-by":"crossref","unstructured":"X. Wei, Autoregressive Visual Tracking, 2023, pp. 9697\u20139706.","DOI":"10.1109\/CVPR52729.2023.00935"},{"key":"10.1016\/j.knosys.2026.115414_bib0024","series-title":"First Conference on Language Modeling","article-title":"Mamba: Linear-time Sequence Modeling with Selective State Spaces","author":"Gu","year":"2024"},{"key":"10.1016\/j.knosys.2026.115414_bib0025","series-title":"Proceedings of Machine Learning Research","first-page":"62429","article-title":"Vision mamba: efficient visual representation learning with bidirectional state space model","volume":"235","author":"Zhu","year":"2024"},{"key":"10.1016\/j.knosys.2026.115414_bib0026","first-page":"103031","article-title":"Vmamba: Visual State Space Model","volume":"37","author":"Liu","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.knosys.2026.115414_bib0027","series-title":"ICLR 2021 - 9Th International Conference on Learning Representations","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2021"},{"key":"10.1016\/j.knosys.2026.115414_bib0028","doi-asserted-by":"crossref","unstructured":"Z. Wang, J.-Q. Zheng, Y. Zhang, G. Cui, L. Li, Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation, 2024, (http:\/\/arxiv.org\/abs\/2402.05079).","DOI":"10.1007\/978-3-031-72111-3_34"},{"key":"10.1016\/j.knosys.2026.115414_bib0029","doi-asserted-by":"crossref","first-page":"16591","DOI":"10.1109\/ACCESS.2021.3053408","article-title":"UNet: convolutional networks for biomedical image segmentation","volume":"9","author":"Weng","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.knosys.2026.115414_bib0030","series-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","first-page":"237","article-title":"VideoMamba: state space model for efficient video understanding","volume":"15084 LNCS","author":"Li","year":"2025"},{"key":"10.1016\/j.knosys.2026.115414_bib0031","doi-asserted-by":"crossref","unstructured":"Q. Xu, C. Zhang, L. Zhang, Denoising convolutional neural network(2015) 1184\u20131187. 10.1109\/ICInfA.2015.7279466.","DOI":"10.1109\/ICInfA.2015.7279466"},{"issue":"9","key":"10.1016\/j.knosys.2026.115414_bib0032","doi-asserted-by":"crossref","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","article-title":"FFDNet: toward a fast and flexible solution for CNN-Based image denoising","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.knosys.2026.115414_bib0033","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.isatra.2024.12.023","article-title":"End-to-end multi-scale residual network with parallel attention mechanism for fault diagnosis under noise and small samples","volume":"157","author":"Sun","year":"2025","journal-title":"ISA Trans."},{"key":"10.1016\/j.knosys.2026.115414_bib0034","doi-asserted-by":"crossref","unstructured":"P. Li, C. Liu, T. Li, X. Wang, S. Zhang, D. Yu, EMDFNet: efficient multi-scale and diverse feature network for traffic sign detection (2024) 120\u2013136.","DOI":"10.1007\/978-3-031-72335-3_9"},{"key":"10.1016\/j.knosys.2026.115414_bib0035","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1109\/TIP.2021.3130533","article-title":"LasHeR: a large-scale high-diversity benchmark for RGBT tracking","volume":"31","author":"Li","year":"2022","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"10.1016\/j.knosys.2026.115414_bib0036","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.1109\/TCYB.2023.3318601","article-title":"VisEvent: reliable object tracking via collaboration of frame and event flows","volume":"54","author":"Wang","year":"2024","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.knosys.2026.115414_bib0037","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"10705","article-title":"DepthTrack: unveiling the power of RGBD tracking","author":"Yan","year":"2021"},{"key":"10.1016\/j.knosys.2026.115414_bib0038","doi-asserted-by":"crossref","unstructured":"X. Wang, J. Li, L. Zhu, Z. Zhang, Z. Chen, X. Li, Y. Wang, Y. Tian, F. Wu, VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows, 2024. 10.1109\/TCYB.2023.3318601.","DOI":"10.1109\/TCYB.2023.3318601"},{"key":"10.1016\/j.knosys.2026.115414_bib0039","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2019.106977","article-title":"RGB-T object tracking: benchmark and baseline","volume":"96","author":"Li","year":"2019","journal-title":"Pattern Recognit."},{"issue":"3","key":"10.1016\/j.knosys.2026.115414_bib0040","doi-asserted-by":"crossref","first-page":"2202","DOI":"10.1109\/TCSVT.2024.3494725","article-title":"EMTrack: efficient multimodal object tracking","volume":"35","author":"Liu","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"December 2024","key":"10.1016\/j.knosys.2026.115414_bib0041","article-title":"Two-stage unidirectional fusion network for RGBT tracking","volume":"310","author":"Liu","year":"2025","journal-title":"Knowl. Based Syst."},{"issue":"January","key":"10.1016\/j.knosys.2026.115414_bib0042","article-title":"MKFTracker: an RGBT tracker via multimodal knowledge embedding and feature interaction","volume":"310","author":"Li","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115414_bib0043","doi-asserted-by":"crossref","unstructured":"X. Wang, et al., MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving, 2024, (http:\/\/arxiv.org\/abs\/2409.16149). Online; accessed on [insert date].","DOI":"10.1109\/IROS60139.2025.11245874"},{"key":"10.1016\/j.knosys.2026.115414_bib0044","series-title":"Single-model and any-modality for video object tracking","volume":"1","author":"Wu","year":"2023"},{"key":"10.1016\/j.knosys.2026.115414_bib0045","series-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","first-page":"26541","article-title":"SDSTrack: self-distillation symmetric adapter learning for multi-modal visual object tracking","author":"Hou","year":"2024"},{"key":"10.1016\/j.knosys.2026.115414_bib0046","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"5189","article-title":"Generative-based fusion mechanism for multi-modal tracking","volume":"38","author":"Tang","year":"2024"},{"issue":"8","key":"10.1016\/j.knosys.2026.115414_bib0047","first-page":"1","article-title":"Prior knowledge-driven hybrid prompter learning for RGB-event tracking","volume":"PP","author":"Wang","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.knosys.2026.115414_bib0048","series-title":"ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings","first-page":"1","article-title":"SNNPTrack: spiking neural network based prompt for high-accuracy RGBE tracking","author":"Ji","year":"2025"},{"issue":"7","key":"10.1016\/j.knosys.2026.115414_bib0049","doi-asserted-by":"crossref","first-page":"5519","DOI":"10.1109\/TCSVT.2024.3352573","article-title":"Knowledge synergy learning for multi-Modal tracking","volume":"34","author":"He","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.knosys.2026.115414_bib0050","series-title":"OneTracker: unifying visual object tracking with foundation models and efficient tuning","author":"Hong","year":"2024"},{"key":"10.1016\/j.knosys.2026.115414_bib0051","series-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","first-page":"10138","article-title":"Adaptive fusion of single-view and multi-view depth for autonomous driving","author":"Cheng","year":"2024"},{"key":"10.1016\/j.knosys.2026.115414_bib0052","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"564","article-title":"The visual object tracking VOT2015 challenge results","volume":"2015-Febru","author":"Kristan","year":"2016"},{"key":"10.1016\/j.knosys.2026.115414_bib0053","series-title":"Proceedings - IEEE International Conference on Multimedia and Expo","first-page":"1","article-title":"DepthRefiner: adapting RGB trackers to RGBD scenes via depth-fused refinement","author":"Lai","year":"2024"},{"issue":"August 2024","key":"10.1016\/j.knosys.2026.115414_bib0054","article-title":"Temporal adaptive bidirectional bridging for RGB-D tracking","volume":"158","author":"Ying","year":"2025","journal-title":"Pattern Recognit."},{"issue":"3","key":"10.1016\/j.knosys.2026.115414_bib0055","doi-asserted-by":"crossref","DOI":"10.3390\/math10030512","article-title":"Multiscale balanced-attention interactive network for salient object detection","volume":"10","author":"Yang","year":"2022","journal-title":"Mathematics"},{"key":"10.1016\/j.knosys.2026.115414_bib0056","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110543","article-title":"Self-supervised learning for RGB-D object tracking","volume":"155","author":"Zhu","year":"2024","journal-title":"Pattern Recognit."},{"issue":"4","key":"10.1016\/j.knosys.2026.115414_bib0057","doi-asserted-by":"crossref","first-page":"2779","DOI":"10.1007\/s13042-024-02420-z","article-title":"FADSiamNet: feature affinity drift siamese network for RGB-T target tracking","volume":"16","author":"Li","year":"2025","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.knosys.2026.115414_bib0058","doi-asserted-by":"crossref","DOI":"10.1016\/j.optcom.2024.131394","article-title":"MCSSAFNet: a multi-scale state-space attention fusion network for RGBT tracking","volume":"577","author":"Zhao","year":"2025","journal-title":"Opt. Commun."},{"key":"10.1016\/j.knosys.2026.115414_bib0059","doi-asserted-by":"crossref","unstructured":"Y. Du, B. Zeng, Q. Wei, B. Zhang, H. Hu, Transformer-mamba-based trident- branch RGB-T tracker (2024) 27\u201340. 10.1007\/978-981-96-0122-6_4.","DOI":"10.1007\/978-981-96-0122-6_4"},{"key":"10.1016\/j.knosys.2026.115414_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128908","article-title":"RGBT tracking via frequency-aware feature enhancement and unidirectional mixed attention","volume":"616","author":"Zhang","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2026.115414_bib0061","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110917","article-title":"Multi-scale feature extraction and fusion with attention interaction for RGB-T tracking","volume":"157","author":"Xing","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.115414_bib0062","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","article-title":"Temporal adaptive RGBT tracking with modality prompt","author":"Wang","year":"2024"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126001577?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126001577?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T06:19:26Z","timestamp":1773987566000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126001577"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":62,"alternative-id":["S0950705126001577"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115414","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"RAATrack: Reliable appearance aggregation for video-level multimodal tracking","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115414","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":"115414"}}