{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T15:16:29Z","timestamp":1783178189891,"version":"3.54.6"},"reference-count":76,"publisher":"Elsevier BV","issue":"8","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2023YFC3303800"],"award-info":[{"award-number":["2023YFC3303800"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Processing & Management"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.ipm.2026.104969","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T02:44:24Z","timestamp":1781059464000},"page":"104969","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["A multi-expert adaptive framework for test-time personalization in federated learning"],"prefix":"10.1016","volume":"63","author":[{"given":"Sanchuan","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongyi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaozhuo","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ipm.2026.104969_b1","series-title":"Federated learning based on dynamic regularization","author":"Acar","year":"2021"},{"key":"10.1016\/j.ipm.2026.104969_b2","series-title":"Federated learning with personalization layers","author":"Arivazhagan","year":"2019"},{"issue":"6","key":"10.1016\/j.ipm.2026.104969_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2022.103061","article-title":"Federated learning review: Fundamentals, enabling technologies, and future applications","volume":"59","author":"Banabilah","year":"2022","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104969_b4","series-title":"2020 international joint conference on neural networks","first-page":"1","article-title":"Federated learning with hierarchical clustering of local updates to improve training on non-IID data","author":"Briggs","year":"2020"},{"issue":"2","key":"10.1016\/j.ipm.2026.104969_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.103991","article-title":"Trust driven on-demand scheme for client deployment in federated learning","volume":"62","author":"Chahoud","year":"2025","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104969_b6","series-title":"Fedbe: Making bayesian model ensemble applicable to federated learning","author":"Chen","year":"2020"},{"key":"10.1016\/j.ipm.2026.104969_b7","series-title":"On bridging generic and personalized federated learning for image classification","author":"Chen","year":"2021"},{"key":"10.1016\/j.ipm.2026.104969_b8","series-title":"International conference on machine learning","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"10.1016\/j.ipm.2026.104969_b9","series-title":"Federated meta-learning with fast convergence and efficient communication","author":"Chen","year":"2018"},{"key":"10.1016\/j.ipm.2026.104969_b10","series-title":"Advances in neural information processing systems","first-page":"65525","article-title":"Heterogeneity-guided client sampling: Towards fast and efficient non-IID federated learning","volume":"37","author":"Chen","year":"2024"},{"key":"10.1016\/j.ipm.2026.104969_b11","series-title":"Heterogeneous ensemble knowledge transfer for training large models in federated learning","author":"Cho","year":"2022"},{"key":"10.1016\/j.ipm.2026.104969_b12","series-title":"Multi-layer personalized federated learning for mitigating biases in student predictive analytics","author":"Chu","year":"2022"},{"key":"10.1016\/j.ipm.2026.104969_b13","series-title":"International conference on machine learning","first-page":"2089","article-title":"Exploiting shared representations for personalized federated learning","author":"Collins","year":"2021"},{"key":"10.1016\/j.ipm.2026.104969_b14","series-title":"Variational federated multi-task learning","author":"Corinzia","year":"2019"},{"key":"10.1016\/j.ipm.2026.104969_b15","series-title":"Cinic-10 is not imagenet or cifar-10","author":"Darlow","year":"2018"},{"key":"10.1016\/j.ipm.2026.104969_b16","series-title":"Heterofl: Computation and communication efficient federated learning for heterogeneous clients","author":"Diao","year":"2020"},{"key":"10.1016\/j.ipm.2026.104969_b17","series-title":"Fedgroup: Ternary cosine similarity-based clustered federated learning framework toward high accuracy in heterogeneous data","first-page":"129","author":"Duan","year":"2020"},{"key":"10.1016\/j.ipm.2026.104969_b18","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume":"33","author":"Fallah","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"10.1016\/j.ipm.2026.104969_b19","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/TIT.1975.1055330","article-title":"The estimation of the gradient of a density function, with applications in pattern recognition","volume":"21","author":"Fukunaga","year":"1975","journal-title":"Institute of Electrical and Electronics Engineers. Transactions on Information Theory"},{"issue":"1","key":"10.1016\/j.ipm.2026.104969_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.103929","article-title":"PRoT-FL: A privacy-preserving and robust training manager for federated learning","volume":"62","author":"Gamiz","year":"2025","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104969_b21","first-page":"19586","article-title":"An efficient framework for clustered federated learning","volume":"33","author":"Ghosh","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104969_b22","doi-asserted-by":"crossref","unstructured":"Gong, X., Sharma, A., Karanam, S., Wu, Z., Chen, T., Doermann, D., & Innanje, A. (2021). Ensemble attention distillation for privacy-preserving federated learning. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 15076\u201315086).","DOI":"10.1109\/ICCV48922.2021.01480"},{"issue":"2","key":"10.1016\/j.ipm.2026.104969_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2022.103162","article-title":"Seeing is believing: Towards interactive visual exploration of data privacy in federated learning","volume":"60","author":"Guo","year":"2023","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104969_b24","series-title":"FedRC: Tackling diverse distribution shifts challenge in federated learning by robust clustering","author":"Guo","year":"2024"},{"key":"10.1016\/j.ipm.2026.104969_b25","series-title":"FedML: A research library and benchmark for federated machine learning","author":"He","year":"2020"},{"key":"10.1016\/j.ipm.2026.104969_b26","doi-asserted-by":"crossref","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\u2013778).","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5","key":"10.1016\/j.ipm.2026.104969_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104157","article-title":"Continual and wisdom learning for federated learning: A comprehensive framework for robustness and debiasing","volume":"62","author":"Iqbal","year":"2025","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104969_b28","first-page":"2427","article-title":"Test-time classifier adjustment module for model-agnostic domain generalization","volume":"34","author":"Iwasawa","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"10.1016\/j.ipm.2026.104969_b29","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1162\/neco.1991.3.1.79","article-title":"Adaptive mixtures of local experts","volume":"3","author":"Jacobs","year":"1991","journal-title":"Neural Computation"},{"key":"10.1016\/j.ipm.2026.104969_b30","series-title":"Test-time robust personalization for federated learning","author":"Jiang","year":"2022"},{"key":"10.1016\/j.ipm.2026.104969_b31","series-title":"International conference on machine learning","first-page":"5132","article-title":"Scaffold: Stochastic controlled averaging for federated learning","author":"Karimireddy","year":"2020"},{"key":"10.1016\/j.ipm.2026.104969_b32","article-title":"Adaptive gradient-based meta-learning methods","volume":"32","author":"Khodak","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104969_b33","series-title":"Proceedings of the 41st international conference on machine learning","article-title":"Clustered federated learning via gradient-based partitioning","author":"Kim","year":"2024"},{"key":"10.1016\/j.ipm.2026.104969_b34","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104969_b35","doi-asserted-by":"crossref","unstructured":"Li, Q., He, B., & Song, D. (2021). Model-contrastive federated learning. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 10713\u201310722).","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"10.1016\/j.ipm.2026.104969_b36","series-title":"International conference on machine learning","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","author":"Li","year":"2021"},{"key":"10.1016\/j.ipm.2026.104969_b37","series-title":"2019 IEEE international conference on big data (big data)","first-page":"215","article-title":"Online federated multitask learning","author":"Li","year":"2019"},{"key":"10.1016\/j.ipm.2026.104969_b38","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"vol. 2","author":"Li","year":"2020","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"10.1016\/j.ipm.2026.104969_b39","series-title":"International conference on machine learning","first-page":"6028","article-title":"Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation","author":"Liang","year":"2020"},{"key":"10.1016\/j.ipm.2026.104969_b40","series-title":"Think locally, act globally: Federated learning with local and global representations","author":"Liang","year":"2020"},{"key":"10.1016\/j.ipm.2026.104969_b41","series-title":"Proceedings of the 38th international conference on neural information processing systems","article-title":"FOOGD: federated collaboration for both out-of-distribution generalization and detection","author":"Liao","year":"2024"},{"key":"10.1016\/j.ipm.2026.104969_b42","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume":"33","author":"Lin","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"4","key":"10.1016\/j.ipm.2026.104969_b43","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1109\/TPDS.2023.3240767","article-title":"Auction-based cluster federated learning in mobile edge computing systems","volume":"34","author":"Lu","year":"2023","journal-title":"IEEE Transactions on Parallel and Distributed Systems"},{"key":"10.1016\/j.ipm.2026.104969_b44","first-page":"5972","article-title":"No fear of heterogeneity: Classifier calibration for federated learning with non-iid data","volume":"34","author":"Luo","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104969_b45","doi-asserted-by":"crossref","unstructured":"Ma, X., Zhang, J., Guo, S., & Xu, W. (2022). Layer-wised model aggregation for personalized federated learning. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 10092\u201310101).","DOI":"10.1109\/CVPR52688.2022.00985"},{"key":"10.1016\/j.ipm.2026.104969_b46","series-title":"Three approaches for personalization with applications to federated learning","author":"Mansour","year":"2020"},{"key":"10.1016\/j.ipm.2026.104969_b47","series-title":"International conference on machine learning","first-page":"15070","article-title":"Personalized federated learning through local memorization","author":"Marfoq","year":"2022"},{"key":"10.1016\/j.ipm.2026.104969_b48","series-title":"Artificial intelligence and statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"10.1016\/j.ipm.2026.104969_b49","series-title":"NIPS workshop on deep learning and unsupervised feature learning","first-page":"7","article-title":"Reading digits in natural images with unsupervised feature learning","volume":"2011","author":"Netzer","year":"2011"},{"key":"10.1016\/j.ipm.2026.104969_b50","series-title":"Towards stable test-time adaptation in dynamic wild world","author":"Niu","year":"2023"},{"key":"10.1016\/j.ipm.2026.104969_b51","series-title":"Fedbabu: Towards enhanced representation for federated image classification","author":"Oh","year":"2021"},{"issue":"2, Part B","key":"10.1016\/j.ipm.2026.104969_b52","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104485","article-title":"Federated cross-domain CTR prediction with triple-view contrastive learning and LLM augmentation","volume":"63","author":"Qin","year":"2026","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104969_b53","series-title":"2025 IEEE\/CVF conference on computer vision and pattern recognition workshops","first-page":"1802","article-title":"FedDG-MoE: Test-time mixture-of-experts fusion for federated domain generalization","author":"Radwan","year":"2025"},{"key":"10.1016\/j.ipm.2026.104969_b54","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"8124","article-title":"Fedsoft: Soft clustered federated learning with proximal local updating","volume":"vol. 36","author":"Ruan","year":"2022"},{"issue":"9","key":"10.1016\/j.ipm.2026.104969_b55","doi-asserted-by":"crossref","first-page":"5531","DOI":"10.1109\/TNNLS.2021.3129371","article-title":"Fedaux: Leveraging unlabeled auxiliary data in federated learning","volume":"34","author":"Sattler","year":"2021","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"8","key":"10.1016\/j.ipm.2026.104969_b56","doi-asserted-by":"crossref","first-page":"3710","DOI":"10.1109\/TNNLS.2020.3015958","article-title":"Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints","volume":"32","author":"Sattler","year":"2020","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.ipm.2026.104969_b57","series-title":"Federated learning on heterogeneous and long-tailed data via classifier re-training with federated features","author":"Shang","year":"2022"},{"issue":"4","key":"10.1016\/j.ipm.2026.104969_b58","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104599","article-title":"Grouping-enhanced personalization for federated recommendation","volume":"63","author":"Shen","year":"2026","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104969_b59","doi-asserted-by":"crossref","unstructured":"Shen, Y., Zhou, Y., & Yu, L. (2022). Cd2-pfed: Cyclic distillation-guided channel decoupling for model personalization in federated learning. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 10041\u201310050).","DOI":"10.1109\/CVPR52688.2022.00980"},{"key":"10.1016\/j.ipm.2026.104969_b60","article-title":"Federated multi-task learning","volume":"30","author":"Smith","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104969_b61","series-title":"International conference on machine learning","first-page":"9229","article-title":"Test-time training with self-supervision for generalization under distribution shifts","author":"Sun","year":"2020"},{"key":"10.1016\/j.ipm.2026.104969_b62","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","volume":"33","author":"T Dinh","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104969_b63","series-title":"Tent: Fully test-time adaptation by entropy minimization","author":"Wang","year":"2020"},{"key":"10.1016\/j.ipm.2026.104969_b64","series-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"10.1016\/j.ipm.2026.104969_b65","series-title":"Multi-center federated learning","first-page":"arXiv","author":"Xie","year":"2021"},{"key":"10.1016\/j.ipm.2026.104969_b66","doi-asserted-by":"crossref","unstructured":"Xu, J., & Huang, S.-L. (2023). A Joint Training-Calibration Framework for Test-Time Personalization with Label Shift in Federated Learning. In Proceedings of the 32nd ACM international conference on information and knowledge management (pp. 4370\u20134374).","DOI":"10.1145\/3583780.3615173"},{"key":"10.1016\/j.ipm.2026.104969_b67","series-title":"International conference on machine learning","first-page":"39879","article-title":"Feddisco: Federated learning with discrepancy-aware collaboration","author":"Ye","year":"2023"},{"key":"10.1016\/j.ipm.2026.104969_b68","doi-asserted-by":"crossref","first-page":"34077","DOI":"10.52202\/068431-2470","article-title":"Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition","volume":"35","author":"Zhang","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104969_b69","doi-asserted-by":"crossref","first-page":"38629","DOI":"10.52202\/068431-2799","article-title":"Memo: Test time robustness via adaptation and augmentation","volume":"35","author":"Zhang","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104969_b70","doi-asserted-by":"crossref","unstructured":"Zhang, L., Shen, L., Ding, L., Tao, D., & Duan, L.-Y. (2022). Fine-tuning global model via data-free knowledge distillation for non-iid federated learning. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 10174\u201310183).","DOI":"10.1109\/CVPR52688.2022.00993"},{"issue":"12","key":"10.1016\/j.ipm.2026.104969_b71","doi-asserted-by":"crossref","first-page":"5586","DOI":"10.1109\/TKDE.2021.3070203","article-title":"A survey on multi-task learning","volume":"34","author":"Zhang","year":"2021","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.ipm.2026.104969_b72","series-title":"Federated out-of-distribution generalization: A causal augmentation view","author":"Zhang","year":"2025"},{"key":"10.1016\/j.ipm.2026.104969_b73","series-title":"Federated learning with non-iid data","author":"Zhao","year":"2018"},{"key":"10.1016\/j.ipm.2026.104969_b74","series-title":"International conference on machine learning","first-page":"12878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","author":"Zhu","year":"2021"},{"key":"10.1016\/j.ipm.2026.104969_b75","article-title":"Deep leakage from gradients","volume":"32","author":"Zhu","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104969_b76","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.neucom.2021.07.098","article-title":"Federated learning on non-IID data: A survey","volume":"465","author":"Zhu","year":"2021","journal-title":"Neurocomputing"}],"container-title":["Information Processing & Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326003602?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326003602?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T14:35:00Z","timestamp":1783175700000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306457326003602"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":76,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["S0306457326003602"],"URL":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104969","relation":{},"ISSN":["0306-4573"],"issn-type":[{"value":"0306-4573","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A multi-expert adaptive framework for test-time personalization in federated learning","name":"articletitle","label":"Article Title"},{"value":"Information Processing & Management","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104969","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":"104969"}}