{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T01:22:44Z","timestamp":1780104164509,"version":"3.54.0"},"reference-count":72,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"vor","delay-in-days":70,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Video anomaly detection is vital for public safety but remains challenging due to complex motion patterns, limited robustness to motion\u2010related perturbations, and the heavy computation demands of modern transformers. To address these challenges, swin\u20103DART is introduced as a unified framework that improves both efficiency and resilience. First, the proposed  modality enhances RGB frames with temporal\u2010gradient motion cues, improving motion sensitivity. Second, it designs T\u2010GAP (temporal gradient adaptive perturbation), which generates worst\u2010case perturbations to expose vulnerabilities and strengthen the model through adversarial training. Third, an adversarial defence mechanism is embedded to ensure robustness, achieving consistently low attack success rates (4%\u20137%) across datasets. Finally, the framework incorporates the 3DART (3D adaptive receive transformer), which reduces memory footprint by ~12% and FLOPs by ~11.9%, making it suitable for deployment in real\u2010time surveillance or edge computing scenarios. Comprehensive evaluations show that swin\u20103DART achieves state\u2010of\u2010the\u2010art AUCs of 95% on UBI fights, 86% on UCF\u2010crime, and 99% on RLVS. These results highlight swin\u20103DART's potential as an efficient and robust solution for real\u2010time, safety\u2010critical video anomaly\u00a0detection.<\/jats:p>","DOI":"10.1049\/ipr2.70318","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T08:19:23Z","timestamp":1773303563000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Swin\u20103DART: An Efficient and Robust Lightweight Transformer for Video Anomaly Detection with TG\u2010RGB\n                    <sup>+<\/sup>"],"prefix":"10.1049","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3342-5497","authenticated-orcid":false,"given":"Intissar","family":"Ziani","sequence":"first","affiliation":[{"name":"MISC Laboratory, Department of Computer Science and its Applications, Faculty of NTIC University of Constantine 2 \u2010 Abdelhamid Mehri Constantine Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gueltoum","family":"Bendiab","sequence":"additional","affiliation":[{"name":"Department of Electronics, LIRE Laboratory University of Fr\u00e8res Mentouri Constantine Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mourad","family":"Bouzenada","sequence":"additional","affiliation":[{"name":"MISC Laboratory, Department of Computer Science and its Applications, Faculty of NTIC University of Constantine 2 \u2010 Abdelhamid Mehri Constantine Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meriem","family":"Guerar","sequence":"additional","affiliation":[{"name":"DIBRIS University of Genoa Genova Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"Mordor Intelligence \u201cEurope Video Surveillance Market Size & Share Analysis\u2014Growth Trends & Forecasts (2025\u20132030) \u201d accessed June 3 2025 https:\/\/www.mordorintelligence.com\/industry\u2010reports\/europe\u2010video\u2010surveillance\u2010systems\u2010market(2025)."},{"key":"e_1_2_11_3_1","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.neunet.2020.09.013","article-title":"Real\u2010Time Gun Detection in CCTV: An Open Problem","volume":"132","author":"Gonz\u00e1lez J. L. S.","year":"2020","journal-title":"Neural Networks"},{"issue":"24","key":"e_1_2_11_4_1","doi-asserted-by":"crossref","first-page":"35463","DOI":"10.1007\/s11042-021-11864-2","article-title":"Event Detection in Surveillance Videos: A Review","volume":"81","author":"Karbalaie A.","year":"2022","journal-title":"Multimedia Tools and Applications"},{"issue":"1","key":"e_1_2_11_5_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0212-5","article-title":"Intelligent Video Surveillance: A Review Through Deep Learning Techniques for Crowd Analysis","volume":"6","author":"Sreenu G.","year":"2019","journal-title":"Journal of Big Data"},{"key":"e_1_2_11_6_1","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1007\/978-3-642-23678-5_39","volume-title":"Computer Analysis of Images and Patterns","author":"Nievas E. B.","year":"2011"},{"key":"e_1_2_11_7_1","doi-asserted-by":"crossref","first-page":"107560","DOI":"10.1109\/ACCESS.2019.2932114","article-title":"A Review on State\u2010of\u2010the\u2010Art Violence Detection Techniques","volume":"7","author":"Ramzan M.","year":"2019","journal-title":"IEEE Access"},{"key":"e_1_2_11_8_1","doi-asserted-by":"crossref","first-page":"6479","DOI":"10.1109\/CVPR.2018.00678","volume-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Sultani W.","year":"2018"},{"issue":"12","key":"e_1_2_11_9_1","doi-asserted-by":"crossref","first-page":"4016","DOI":"10.3390\/s24124016","article-title":"Literature Review of Deep\u2010Learning\u2010Based Detection of Violence in Video","volume":"24","author":"Negre P.","year":"2024","journal-title":"Sensors"},{"key":"e_1_2_11_10_1","first-page":"3202","volume-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Liu Z.","year":"2022"},{"issue":"1","key":"e_1_2_11_11_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","article-title":"Determining Optical Flow","volume":"17","author":"Horn B. K.","year":"1981","journal-title":"Artificial Intelligence"},{"key":"e_1_2_11_12_1","first-page":"20","volume-title":"Proceedings of the European Conference on Computer Vision","year":"2016"},{"key":"e_1_2_11_13_1","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.1109\/CVPR.2018.00151","volume-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Sun S.","year":"2018"},{"key":"e_1_2_11_14_1","first-page":"3252","volume-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Xiao J.","year":"2022"},{"key":"e_1_2_11_15_1","first-page":"6299","volume-title":"2017 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Carreira J.","year":"2017"},{"issue":"2","key":"e_1_2_11_16_1","doi-asserted-by":"crossref","first-page":"317","DOI":"10.3390\/s24020317","article-title":"Conv3D\u2010Based Video Violence Detection Network Using Optical Flow and RGB Data","volume":"24","author":"Park J. H.","year":"2024","journal-title":"Sensors"},{"key":"e_1_2_11_17_1","first-page":"6202","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Feichtenhofer C.","year":"2019"},{"key":"e_1_2_11_18_1","first-page":"16020","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Kondratyuk D.","year":"2021"},{"key":"e_1_2_11_19_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.118523","article-title":"Multimodal fusion methods with deep neural networks and meta\u2010information for aggression detection in surveillance","volume":"211","author":"Jaafar N.","year":"2023","journal-title":"Expert Systems With Applications"},{"key":"e_1_2_11_20_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.chb.2024.108482","article-title":"Extended Multi\u2010Stream Temporal\u2010Attention Module for Skeleton\u2010Based Human Action Recognition (HAR)","volume":"163","author":"Mehmood F.","year":"2025","journal-title":"Computers in Human Behavior"},{"key":"e_1_2_11_21_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130015","article-title":"PRG\u2010Net: Point Relationship\u2010Guided Network for 3D Human Action Recognition","volume":"635","author":"Du Y.","year":"2025","journal-title":"Neurocomputing"},{"key":"e_1_2_11_22_1","first-page":"38","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations","author":"Wolf T.","year":"2020"},{"key":"e_1_2_11_23_1","first-page":"6836","volume-title":"2021 IEEE\/CVF International Conference on Computer Vision (ICCV)","author":"Arnab A.","year":"2021"},{"key":"e_1_2_11_24_1","first-page":"139","volume-title":"Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning","author":"Iftee M. A. R.","year":"2023"},{"key":"e_1_2_11_25_1","volume-title":"arXiv:2310.03108","author":"Mohammadi H.","year":"2023"},{"key":"e_1_2_11_26_1","first-page":"88","volume-title":"BIM: International Conference on Big Data, IoT and Machine Learning","author":"Zhou L.","year":"2023"},{"issue":"17","key":"e_1_2_11_27_1","doi-asserted-by":"crossref","first-page":"10835","DOI":"10.1007\/s00521-025-10978-0","article-title":"A Novel Human Action Recognition Using Grad\u2010CAM Visualization With Gated Recurrent Units","volume":"37","author":"Jayamohan M.","year":"2025","journal-title":"Neural Computing and Applications"},{"issue":"1","key":"e_1_2_11_28_1","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1007\/s44196-025-00848-x","article-title":"A Novel Human Action Recognition Model by Grad\u2010CAM Visualization With Multi\u2010Level Feature Extraction Using Global Average Pooling With Sequence Modeling by Bidirectional Gated Recurrent Units","volume":"18","author":"Manoharan J.","year":"2025","journal-title":"International Journal of Computational Intelligence Systems"},{"key":"e_1_2_11_29_1","first-page":"10012","volume-title":"2021 IEEE\/CVF International Conference on Computer Vision (ICCV)","author":"Liu Z.","year":"2021"},{"issue":"7","key":"e_1_2_11_30_1","doi-asserted-by":"crossref","first-page":"4048","DOI":"10.1109\/JBHI.2024.3397047","article-title":"HST\u2010MRF: Heterogeneous Swin Transformer With Multi\u2010Receptive Field for Medical Image Segmentation","volume":"28","author":"Huang X.","year":"2024","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_2_11_31_1","volume-title":"arXiv:2403.19882","author":"Heidari M.","year":"2024"},{"key":"e_1_2_11_32_1","volume-title":"arXiv:1807.00458","author":"Li S.","year":"2018"},{"key":"e_1_2_11_33_1","doi-asserted-by":"crossref","first-page":"88170","DOI":"10.1109\/ACCESS.2020.2993373","article-title":"Dual Discriminator Generative Adversarial Network for Video Anomaly Detection","volume":"8","author":"Dong F.","year":"2020","journal-title":"IEEE Access"},{"key":"e_1_2_11_34_1","volume-title":"arXiv:2312.10911","author":"Izza Y.","year":"2023"},{"key":"e_1_2_11_35_1","first-page":"3199","volume-title":"2021 IEEE Winter Conference on Applications of Computer Vision (WACV)","author":"Chen Z.","year":"2021"},{"key":"e_1_2_11_36_1","first-page":"4325","volume-title":"2023 IEEE\/CVF International Conference on Computer Vision (ICCV)","author":"Kim H. S.","year":"2023"},{"issue":"3","key":"e_1_2_11_37_1","doi-asserted-by":"crossref","first-page":"1914","DOI":"10.3390\/app13031914","article-title":"A Robust Adversarial Example Attack Based on Video Augmentation","volume":"13","author":"Yin M.","year":"2023","journal-title":"Applied Sciences"},{"key":"e_1_2_11_38_1","first-page":"1","volume-title":"2024 IEEE International Conference on Multimedia and Expo (ICME)","author":"Pan Y.","year":"2024"},{"key":"e_1_2_11_39_1","first-page":"15064","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Wei Z.","year":"2022"},{"key":"e_1_2_11_40_1","unstructured":"J.Li K.Gao Y.Bai J.Zhang S. T.Xia andY.Wang \u201cFMM\u2010Attack: A Flow\u2010Based Multi\u2010Modal Adversarial Attack on Video\u2010Based LLMs \u201darXiv:2403.13507(2024)."},{"key":"e_1_2_11_41_1","volume-title":"arXiv:2308.14597","author":"Inkawhich N.","year":"2023"},{"key":"e_1_2_11_42_1","doi-asserted-by":"crossref","first-page":"5549","DOI":"10.1109\/CVPRW59228.2023.00587","volume-title":"2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","author":"Chen W.","year":"2023"},{"issue":"1","key":"e_1_2_11_43_1","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-73462-0","article-title":"Multimodal and Multiscale Feature Fusion for Weakly Supervised Video Aanomaly Detection","volume":"14","author":"Sun W.","year":"2024","journal-title":"Scientific Reports"},{"key":"e_1_2_11_44_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.cviu.2023.103739","article-title":"Human Skeletons and Change Detection for Efficient Violence Detection in Surveillance Videos","volume":"233","author":"Garcia\u2010Cobo G.","year":"2023","journal-title":"Computer Vision and Image Understanding"},{"key":"e_1_2_11_45_1","first-page":"1965","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Ghadiya A.","year":"2024"},{"key":"e_1_2_11_46_1","doi-asserted-by":"crossref","first-page":"37096","DOI":"10.1109\/ACCESS.2023.3267409","article-title":"Violence Detection Enhancement by Involving Convolutional Block Attention Modules Into Various Deep Learning Architectures: Comprehensive Case Study for UBI\u2010Fights Dataset","volume":"11","author":"Abbass M. A. B.","year":"2023","journal-title":"IEEE Access"},{"key":"e_1_2_11_47_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.patrec.2021.01.031","article-title":"Iterative Weak\/Self\u2010Supervised Classification Framework for Abnormal Events Detection","volume":"145","author":"Degardin B.","year":"2021","journal-title":"Pattern Recognition Letters"},{"key":"e_1_2_11_48_1","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1109\/CSR61664.2024.10679423","volume-title":"2024 IEEE International Conference on Cyber Security and Resilience (CSR)","author":"Ziani I.","year":"2024"},{"key":"e_1_2_11_49_1","unstructured":"B. M.Degardin \u201cWeakly and Partially Supervised Learning Frameworks for Anomaly Detection \u201d (master's thesis Universidade da Beira Interior 2020)."},{"key":"e_1_2_11_50_1","first-page":"12009","volume-title":"2024 IEEE International Conference on Cyber Security and Resilience (CSR)","author":"Liu Z.","year":"2022"},{"key":"e_1_2_11_51_1","unstructured":"H. S.Heo J. W.Jung H. J.Shim I. H.Yang andH. J.Yu \u201cCosine Similarity\u2010Based Adversarial Process \u201darXiv:1907.00542(2019)."},{"key":"e_1_2_11_52_1","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/ICICIS46948.2019.9014714","volume-title":"2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)","author":"Soliman M. M.","year":"2019"},{"issue":"3","key":"e_1_2_11_53_1","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1093\/biomet\/52.3-4.591","article-title":"An Analysis of Variance Test for Normality (Complete Samples)","volume":"52","author":"Shapiro S. S.","year":"1965","journal-title":"Biometrika"},{"key":"e_1_2_11_54_1","unstructured":"H.Hsu P.Lachenbruch P.Armitage andT.Colton \u201cPairedtTest. in Encyclopedia of Biostatistics \u201d (Wiley 2005)."},{"issue":"1","key":"e_1_2_11_55_1","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1093\/biomet\/52.1-2.203","article-title":"A Generalized Wilcoxon Test for Comparing Arbitrarily Singly\u2010Censored Samples","volume":"52","author":"Gehan E. A.","year":"1965","journal-title":"Biometrika"},{"key":"e_1_2_11_56_1","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1109\/COMNETSAT53002.2021.9530829","volume-title":"2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","author":"Abdali A. R.","year":"2021"},{"key":"e_1_2_11_57_1","volume-title":"arXiv:2101.10030","author":"Tian Y.","year":"2021"},{"key":"e_1_2_11_58_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120599","article-title":"Sequential Attention Mechanism for Weakly Supervised Video Anomaly Detection","volume":"230","author":"Ullah W.","year":"2023","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"e_1_2_11_59_1","doi-asserted-by":"crossref","DOI":"10.1142\/S0218001425500417","article-title":"Weakly Supervised Video Anomaly Detection Based on Adaptive Fusion of Multimodal Features","volume":"40","author":"Dang W.","year":"2025","journal-title":"International Journal of Pattern Recognition and Artificial Intelligence"},{"key":"e_1_2_11_60_1","doi-asserted-by":"crossref","first-page":"4067","DOI":"10.1109\/TMM.2021.3112814","article-title":"Contrastive Attention for Video Anomaly Detection","volume":"24","author":"Chang S.","year":"2021","journal-title":"IEEE Transactions on Multimedia"},{"key":"e_1_2_11_61_1","doi-asserted-by":"crossref","first-page":"125052","DOI":"10.1109\/ACCESS.2022.3224952","article-title":"Real\u2010World Video Anomaly Detection by Extracting Salient Features in Videos","volume":"10","author":"Watanabe Y.","year":"2022","journal-title":"IEEE Access"},{"issue":"5","key":"e_1_2_11_62_1","doi-asserted-by":"crossref","first-page":"4135","DOI":"10.1109\/TCSVT.2023.3321235","article-title":"Towards Video Anomaly Detection in the Real World: A Binarization Embedded Weakly\u2010Supervised Network","volume":"34","author":"Yang Z.","year":"2023","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"e_1_2_11_63_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128698","article-title":"A Lightweight Video Anomaly Detection Model With Weak Supervision and Adaptive Instance Selection","volume":"613","author":"Wang Y.","year":"2025","journal-title":"Neurocomputing"},{"key":"e_1_2_11_64_1","first-page":"14009","volume-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Feng J. C.","year":"2021"},{"key":"e_1_2_11_65_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2023.104629","article-title":"Spatial\u2013Temporal Graph Attention Network for Video Aanomaly Detection","volume":"131","author":"Chen H.","year":"2023","journal-title":"Image and Vision Computing"},{"key":"e_1_2_11_66_1","volume-title":"arXiv:1706.06083","author":"Madry A.","year":"2017"},{"key":"e_1_2_11_67_1","volume-title":"arXiv:1412.6572","author":"Goodfellow I. J.","year":"2014"},{"key":"e_1_2_11_68_1","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s11633-019-1211-x","article-title":"Adversarial Attacks and Defenses in Images, Graphs and Text: A Review","volume":"17","author":"Xu H.","year":"2020","journal-title":"International Journal of Automation and Computing"},{"key":"e_1_2_11_69_1","first-page":"206","volume-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","author":"Mumcu F.","year":"2022"},{"issue":"16","key":"e_1_2_11_70_1","doi-asserted-by":"crossref","first-page":"5429","DOI":"10.3390\/s24165429","article-title":"Transformer and Adaptive Threshold Sliding Window for Improving Violence Detection in Videos","volume":"24","author":"Rend\u00f3n\u2010Segador F. J.","year":"2024","journal-title":"Sensors"},{"key":"e_1_2_11_71_1","first-page":"1","volume-title":"Proceedings of the 38th International Conference on Machine Learning","author":"Bertasius G.","year":"2021"},{"key":"e_1_2_11_72_1","first-page":"6824","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Fan H.","year":"2021"},{"key":"e_1_2_11_73_1","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/ISPDS56360.2022.9874052","volume-title":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","author":"Chen L.","year":"2022"}],"container-title":["IET Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ipr2.70318","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/ipr2.70318","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ipr2.70318","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T08:19:40Z","timestamp":1773303580000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/ipr2.70318"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":72,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1049\/ipr2.70318"],"URL":"https:\/\/doi.org\/10.1049\/ipr2.70318","archive":["Portico"],"relation":{},"ISSN":["1751-9659","1751-9667"],"issn-type":[{"value":"1751-9659","type":"print"},{"value":"1751-9667","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2025-07-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70318"}}