{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T21:12:29Z","timestamp":1778101949866,"version":"3.51.4"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100015308","name":"Xinjiang Uygur Autonomous Region Department of Science and Technology","doi-asserted-by":"publisher","award":["2023TSYCLJ0052"],"award-info":[{"award-number":["2023TSYCLJ0052"]}],"id":[{"id":"10.13039\/501100015308","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.engappai.2026.114826","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T10:36:17Z","timestamp":1776076577000},"page":"114826","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P2","title":["Dynamic feature enhancement adversarial network for domain adaptive fault diagnosis of rotating machinery"],"prefix":"10.1016","volume":"176","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0828-5896","authenticated-orcid":false,"given":"Jiasong","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9212-9898","authenticated-orcid":false,"given":"Xiangfeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaige","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minglie","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114826_bib1","doi-asserted-by":"crossref","first-page":"5031","DOI":"10.1109\/TII.2022.3146552","article-title":"From artificial intelligence to explainable artificial intelligence in industry 4.0: a survey on what, how, and where","volume":"18","author":"Ahmed","year":"2022","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.114826_bib2","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111063","article-title":"Unsupervised multiple-target domain adaptation for bearing fault diagnosis","volume":"154","author":"Bai","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114826_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112766","article-title":"Intelligent fault diagnosis via unsupervised domain adaptation: the role of intermediate domain construction","volume":"310","author":"Cao","year":"2025","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.engappai.2026.114826_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110427","article-title":"Collaborative fault diagnosis of rotating machinery via dual adversarial guided unsupervised multi-domain adaptation network","volume":"198","author":"Chen","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2026.114826_bib5","series-title":"Transferrable Contrastive Learning for Visual Domain Adaptation","author":"Chen","year":"2021"},{"key":"10.1016\/j.engappai.2026.114826_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.107968","article-title":"Global wavelet-integrated residual frequency attention regularized network for hypersonic flight vehicle fault diagnosis with imbalanced data","volume":"132","author":"Dong","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114826_bib7","doi-asserted-by":"crossref","first-page":"5171","DOI":"10.1109\/TII.2025.3552655","article-title":"Digital twin assisted degradation assessment of bearing cage performance","volume":"21","author":"Fan","year":"2025","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.114826_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108590","article-title":"Trend attention fully convolutional network for remaining useful life estimation","volume":"225","author":"Fan","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.engappai.2026.114826_bib9","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.engappai.2026.114826_bib10","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109516","article-title":"Unsupervised domain adaptation for drive-by condition monitoring of multiple railway tracks","volume":"139","author":"Ghiasi","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114826_bib11","first-page":"1","article-title":"An analysis method for interpretability of convolutional neural network in bearing fault diagnosis","volume":"73","author":"Guo","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.114826_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.109197","article-title":"Hybrid distance-guided adversarial network for intelligent fault diagnosis under different working conditions","volume":"176","author":"Han","year":"2021","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.114826_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110958","article-title":"Novel joint transfer fine-grained metric network for cross-domain few-shot fault diagnosis","volume":"279","author":"Hu","year":"2023","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.engappai.2026.114826_bib14","first-page":"1","article-title":"Adaptive sparse attention wavelet network for the robust open-circuit fault diagnosis in PMSM drives","volume":"73","author":"Jiang","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.114826_bib15","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111490","article-title":"Double-classifier adversarial learning for fault diagnosis of rotating machinery considering cross domains","volume":"216","author":"Jin","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2026.114826_bib16","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1109\/TCYB.2023.3256080","article-title":"Filter-informed spectral graph wavelet networks for multiscale feature extraction and intelligent fault diagnosis","volume":"54","author":"Li","year":"2024","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.engappai.2026.114826_bib17","doi-asserted-by":"crossref","first-page":"5397","DOI":"10.1007\/s10845-024-02511-2","article-title":"A novel unsupervised graph wavelet autoencoder for mechanical system fault detection","volume":"36","author":"Li","year":"2025","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.engappai.2026.114826_bib18","doi-asserted-by":"crossref","first-page":"2833","DOI":"10.1109\/TII.2020.3008010","article-title":"Intelligent fault diagnosis by fusing domain adversarial training and maximum mean discrepancy via ensemble learning","volume":"17","author":"Li","year":"2021","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.114826_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111151","article-title":"Cross-machine deep subdomain adaptation network for wind turbines fault diagnosis","volume":"210","author":"Liu","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2026.114826_bib20","doi-asserted-by":"crossref","first-page":"6038","DOI":"10.1109\/TII.2022.3141783","article-title":"Deep adversarial subdomain adaptation network for intelligent fault diagnosis","volume":"18","author":"Liu","year":"2022","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.114826_bib21","series-title":"Conditional Adversarial Domain Adaptation","author":"Long","year":"2017"},{"key":"10.1016\/j.engappai.2026.114826_bib22","article-title":"Conditional adversarial domain adaptation","volume":"31","author":"Long","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.114826_bib23","doi-asserted-by":"crossref","first-page":"21552","DOI":"10.1109\/JIOT.2025.3547406","article-title":"Semi-supervised contrastive domain adaptation network for fault diagnosis of rotating machinery under cross-working conditions","volume":"12","author":"Lu","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.engappai.2026.114826_bib24","doi-asserted-by":"crossref","first-page":"4296","DOI":"10.1109\/TNNLS.2024.3376449","article-title":"Adaptive intermediate class-wise distribution alignment: a universal domain adaptation and generalization method for machine fault diagnosis","volume":"36","author":"Qian","year":"2025","journal-title":"IEEE Transact. Neural Networks Learn. Syst."},{"key":"10.1016\/j.engappai.2026.114826_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111544","article-title":"Variance discrepancy representation: a vibration characteristic-guided distribution alignment metric for fault transfer diagnosis","volume":"217","author":"Qian","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2026.114826_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110748","article-title":"Maximum mean square discrepancy: a new discrepancy representation metric for mechanical fault transfer diagnosis","volume":"276","author":"Qian","year":"2023","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.engappai.2026.114826_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111057","article-title":"DG-Softmax: a new domain generalization intelligent fault diagnosis method for planetary gearboxes","volume":"260","author":"Qian","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.engappai.2026.114826_bib28","doi-asserted-by":"crossref","first-page":"9898","DOI":"10.1109\/TII.2022.3232842","article-title":"Relationship transfer domain generalization network for rotating machinery fault diagnosis under different working conditions","volume":"19","author":"Qian","year":"2023","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.114826_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2020.108900","article-title":"Cross-domain fault diagnosis of rolling bearing using similar features-based transfer approach","volume":"172","author":"Qin","year":"2021","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.114826_bib30","doi-asserted-by":"crossref","first-page":"3128","DOI":"10.1109\/TCYB.2022.3162957","article-title":"Deep joint distribution alignment: a novel enhanced-domain adaptation mechanism for fault transfer diagnosis","volume":"53","author":"Qin","year":"2023","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.engappai.2026.114826_bib31","first-page":"1","article-title":"Large model for rotating machine fault diagnosis based on a dense connection network with depthwise separable convolution","volume":"73","author":"Qin","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.114826_bib32","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.112965","article-title":"Out-of-domain generalization for remaining useful life prediction of rotating machinery from a single source: an adversarial contrastive learning approach","volume":"236","author":"Shang","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2026.114826_bib33","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ad67f6","article-title":"Application of wavelet dynamic joint adaptive network guided by pseudo-label alignment mechanism in gearbox fault diagnosis","volume":"35","author":"Shao","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.engappai.2026.114826_bib34","author":"Shen"},{"key":"10.1016\/j.engappai.2026.114826_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.109834","article-title":"MiDAN: a framework for cross-domain intelligent fault diagnosis with imbalanced datasets","volume":"183","author":"Tan","year":"2021","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.114826_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109891","article-title":"A novel generalized source-free domain adaptation approach for cross-domain industrial fault diagnosis","volume":"243","author":"Tian","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.engappai.2026.114826_bib37","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102739","article-title":"A multi-source domain feature-decision dual fusion adversarial transfer network for cross-domain anti-noise mechanical fault diagnosis in sustainable city","volume":"115","author":"Wang","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.engappai.2026.114826_bib38","doi-asserted-by":"crossref","first-page":"7950","DOI":"10.1109\/TSMC.2025.3598790","article-title":"A virtual domain-driven semi-supervised hyperbolic metric network with domain-class adversarial decoupling for aircraft engine intershaft bearings fault diagnosis","volume":"55","author":"Wang","year":"2025","journal-title":"IEEE Trans. Syst. Man Cybern.: Systems"},{"key":"10.1016\/j.engappai.2026.114826_bib39","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103287","article-title":"Addressing unknown faults diagnosis of transport ship propellers system based on adaptive evolutionary reconstruction metric network","volume":"65","author":"Wang","year":"2025","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.engappai.2026.114826_bib40","doi-asserted-by":"crossref","first-page":"8270","DOI":"10.1109\/TII.2025.3588608","article-title":"Continuous evolution learning: a lightweight expansion-based continuous learning method for train transmission systems fault diagnosis","volume":"21","author":"Wang","year":"2025","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.114826_bib41","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111121","article-title":"Joint wasserstein distance matching under conditional probability distribution for cross-domain fault diagnosis of rotating machinery","volume":"210","author":"Wang","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2026.114826_bib42","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103616","article-title":"An adaptive fused domain-cycling variational generative adversarial network for machine fault diagnosis under data scarcity","volume":"126","author":"Wang","year":"2026","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.engappai.2026.114826_bib43","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.105814","article-title":"A deep transfer maximum classifier discrepancy method for rolling bearing fault diagnosis under few labeled data","volume":"196","author":"Wu","year":"2020","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.engappai.2026.114826_bib44","series-title":"2019 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"1426","article-title":"Larger norm more transferable: an adaptive feature norm approach for unsupervised domain adaptation","author":"Xu","year":"2019"},{"key":"10.1016\/j.engappai.2026.114826_bib45","doi-asserted-by":"crossref","first-page":"9463","DOI":"10.1109\/TIE.2022.3212415","article-title":"Deep targeted transfer learning along designable adaptation trajectory for fault diagnosis across different machines","volume":"70","author":"Yang","year":"2023","journal-title":"IEEE Trans. Ind. Electron."},{"key":"10.1016\/j.engappai.2026.114826_bib46","doi-asserted-by":"crossref","first-page":"3871","DOI":"10.3390\/s25133871","article-title":"Bearing fault diagnosis based on time\u2013frequency dual domains and feature fusion of ResNet-CACNN-BiGRU-SDPA","volume":"25","author":"Yasenjiang","year":"2025","journal-title":"Sensors"},{"key":"10.1016\/j.engappai.2026.114826_bib47","first-page":"1","article-title":"Conditional adversarial domain adaptation with discrimination embedding for locomotive fault diagnosis","volume":"70","author":"Yu","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.114826_bib48","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103434","article-title":"A novel digital twin-enabled three-stage feature imputation framework for non-contact intelligent fault diagnosis","volume":"66","author":"Yu","year":"2025","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.engappai.2026.114826_bib49","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111812","article-title":"A novel multi-source sensor correlation adaptive fusion framework with uncertainty quantification for intelligent fault diagnosis","volume":"267","author":"Yu","year":"2026","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.engappai.2026.114826_bib50","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111194","article-title":"A new multi-source information domain adaption network based on domain attributes and features transfer for cross-domain fault diagnosis","volume":"211","author":"Yu","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2026.114826_bib51","series-title":"Mixup: Beyond Empirical Risk Minimization","author":"Zhang","year":"2017"},{"key":"10.1016\/j.engappai.2026.114826_bib52","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108986","article-title":"A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition","volume":"231","author":"Zhang","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.engappai.2026.114826_bib53","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.110242","article-title":"Fault diagnosis for small samples based on attention mechanism","volume":"187","author":"Zhang","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.114826_bib54","series-title":"Bridging Theory and Algorithm for Domain Adaptation","author":"Zhang","year":"2019"},{"key":"10.1016\/j.engappai.2026.114826_bib55","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.neucom.2020.02.049","article-title":"Sparse filtering based domain adaptation for mechanical fault diagnosis","volume":"393","author":"Zhang","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.engappai.2026.114826_bib56","doi-asserted-by":"crossref","first-page":"9091","DOI":"10.1109\/TII.2022.3224979","article-title":"A fault diagnosis method for rotating machinery based on CNN with mixed information","volume":"19","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.114826_bib57","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.111047","article-title":"An unsupervised transfer learning method based on SOCNN and FBNN and its application on bearing fault diagnosis","volume":"208","author":"Zheng","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2026.114826_bib58","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1109\/TNNLS.2020.2988928","article-title":"Deep subdomain adaptation network for image classification","volume":"32","author":"Zhu","year":"2021","journal-title":"IEEE Transact. Neural Networks Learn. Syst."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626011085?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626011085?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T20:25:13Z","timestamp":1778099113000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626011085"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":58,"alternative-id":["S0952197626011085"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114826","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Dynamic feature enhancement adversarial network for domain adaptive fault diagnosis of rotating machinery","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114826","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":"114826"}}