{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T13:46:57Z","timestamp":1777038417314,"version":"3.51.4"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"41","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-025-21103-7","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T08:37:14Z","timestamp":1756715834000},"page":"49355-49395","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A novel wavelet-transformer discriminator for semi-supervised GANs with controlled regularization and ensemble techniques"],"prefix":"10.1007","volume":"84","author":[{"given":"Mohammad Saber","family":"Iraji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"key":"21103_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108987","volume":"133","author":"R Wang","year":"2023","unstructured":"Wang R, Qi L, Shi Y, Gao Y (2023) Better pseudo-label: joint domain-aware label and dual-classifier for semi-supervised domain generalization. Pattern Recogn 133:108987","journal-title":"Pattern Recogn"},{"key":"21103_CR2","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.neunet.2020.10.018","volume":"133","author":"X Wei","year":"2021","unstructured":"Wei X, Wei X, Kong X, Lu S, Xing W, Lu W (2021) Fmixcutmatch for semi-supervised deep learning. Neural Netw 133:166\u2013176","journal-title":"Neural Netw"},{"key":"21103_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2023.104793","volume":"137","author":"A Krishnan","year":"2023","unstructured":"Krishnan A, Rattani A (2023) A novel approach for bias mitigation of gender classification algorithms using consistency regularization. Image Vis Comput 137:104793","journal-title":"Image Vis Comput"},{"key":"21103_CR4","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neunet.2021.10.008","volume":"145","author":"V Verma","year":"2022","unstructured":"Verma V, Kawaguchi K, Lamb A, Kannala J, Solin A, Bengio Y, Lopez-Paz D (2022) Interpolation consistency training for semi-supervised learning. Neural Netw 145:90\u2013106","journal-title":"Neural Netw"},{"key":"21103_CR5","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.neucom.2023.01.027","volume":"528","author":"A Gangwar","year":"2023","unstructured":"Gangwar A, Gonz\u00e1lez-Castro V, Alegre E, Fidalgo E (2023) Triple-BigGAN: semi-supervised generative adversarial networks for image synthesis and classification on sexual facial expression recognition. Neurocomputing 528:200\u2013216","journal-title":"Neurocomputing"},{"issue":"6","key":"21103_CR6","doi-asserted-by":"publisher","first-page":"2009","DOI":"10.1007\/s00371-021-02262-8","volume":"38","author":"L Wang","year":"2022","unstructured":"Wang L, Sun Y, Wang Z (2022) CCS-GAN: a semi-supervised generative adversarial network for image classification. Vis Comput 38(6):2009\u20132021","journal-title":"Vis Comput"},{"key":"21103_CR7","first-page":"10440","volume":"32","author":"J Dong","year":"2019","unstructured":"Dong J, Lin T (2019) Margingan: adversarial training in semi-supervised learning. Adv Neural Inf Process Syst 32:10440\u201310449","journal-title":"Adv Neural Inf Process Syst"},{"key":"21103_CR8","doi-asserted-by":"publisher","unstructured":"Iraji MS, Tanha J, Balafar M-A, Feizi-Derakhshi M-R (2024) A novel interpolation consistency for bad generative adversarial networks (IC-BGAN). Multimedia Tools Appl 1\u201345. https:\/\/doi.org\/10.1007\/s11042-024-20333-5","DOI":"10.1007\/s11042-024-20333-5"},{"key":"21103_CR9","first-page":"14608","volume":"35","author":"F Pinto","year":"2022","unstructured":"Pinto F, Yang H, Lim SN, Torr P, Dokania P (2022) Using mixup as a regularizer can surprisingly improve accuracy & out-of-distribution robustness. Adv Neural Inf Process Syst 35:14608\u201314622","journal-title":"Adv Neural Inf Process Syst"},{"issue":"4","key":"21103_CR10","doi-asserted-by":"publisher","first-page":"3081","DOI":"10.1109\/TCSVT.2022.3232112","volume":"35","author":"Z Gao","year":"2022","unstructured":"Gao Z, Guo S, Xu C, Zhang J, Gong M, Ser JD, Li S (2022) Multi-domain adversarial variational bayesian inference for domain generalization. IEEE Trans Circuits Syst Video Technol 35(4):3081\u20133093","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"21103_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109552","volume":"253","author":"S Zuo","year":"2022","unstructured":"Zuo S, Xiao Y, Chang X, Wang X (2022) Vision transformers for dense prediction: a survey. Knowledge-Based Systems 253:109552","journal-title":"Knowledge-Based Systems"},{"key":"21103_CR12","first-page":"28522","volume":"34","author":"Y Xu","year":"2021","unstructured":"Xu Y, Zhang Q, Zhang J, Tao D (2021) Vitae: vision transformer advanced by exploring intrinsic inductive bias. Adv Neural Inf Process Syst 34:28522\u201328535","journal-title":"Adv Neural Inf Process Syst"},{"key":"21103_CR13","doi-asserted-by":"publisher","first-page":"8334358","DOI":"10.1155\/2024\/8334358","volume":"1","author":"B Mulugeta Abuhayi","year":"2024","unstructured":"Mulugeta Abuhayi B, Agegnehu Bezabh Y, Melese Ayalew A (2024) Alemayehu Lakew M (2024) Classification of gastrointestinal diseases using hybrid recurrent vision transformers with wavelet transform. Adv Multimed 1:8334358","journal-title":"Adv Multimed"},{"key":"21103_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3528228","author":"Z Lu","year":"2025","unstructured":"Lu Z, Liu C, Chang X, Zhang Y, Xie H (2025) DHVT: dynamic hybrid vision transformer for small dataset recognition. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2025.3528228","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"21103_CR15","doi-asserted-by":"crossref","unstructured":"Zhou A, Farimani AB (2024) Faultformer: pretraining Transformers for adaptable bearing fault classification. IEEE Access","DOI":"10.1109\/ACCESS.2024.3399670"},{"issue":"5","key":"21103_CR16","doi-asserted-by":"publisher","first-page":"6867","DOI":"10.1007\/s11042-022-13604-6","volume":"82","author":"AM Hafiz","year":"2023","unstructured":"Hafiz AM, Bhat R, Hassaballah M (2023) Image classification using convolutional neural network tree ensembles. Multimedia Tools Appl 82(5):6867\u20136884","journal-title":"Multimedia Tools Appl"},{"key":"21103_CR17","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.inffus.2021.09.012","volume":"79","author":"J Huertas-Tato","year":"2022","unstructured":"Huertas-Tato J, Mart\u00edn A, Fierrez J, Camacho D (2022) Fusing CNNs and statistical indicators to improve image classification. Inf Fusion 79:174\u2013187","journal-title":"Inf Fusion"},{"issue":"15","key":"21103_CR18","doi-asserted-by":"publisher","DOI":"10.3390\/app13158856","volume":"13","author":"T Jiang","year":"2023","unstructured":"Jiang T, Chen L, Chen W, Meng W, Qi P (2023) Reliamatch: Semi-supervised classification with reliable match. Appl Sci 13(15):8856","journal-title":"Appl Sci"},{"key":"21103_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110166","volume":"260","author":"H Xu","year":"2023","unstructured":"Xu H, Xiao H, Hao H, Dong L, Qiu X, Peng C (2023) Semi-supervised learning with pseudo-negative labels for image classification. Knowledge-Based Systems 260:110166","journal-title":"Knowledge-Based Systems"},{"key":"21103_CR20","unstructured":"Laine S, Aila T (2017) Temporal en sembling for semi-supervised learning. in 5th International Conference on Learning Representations, ICLR"},{"key":"21103_CR21","unstructured":"Tarvainen A, Valpola H (2017) Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems. 30"},{"key":"21103_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109521","volume":"140","author":"M Yang","year":"2023","unstructured":"Yang M, Ling J, Chen J, Feng M, Yang J (2023) Discriminative semi-supervised learning via deep and dictionary representation for image classification. Pattern Recognit 140:109521","journal-title":"Pattern Recognit"},{"issue":"8","key":"21103_CR23","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","volume":"41","author":"T Miyato","year":"2018","unstructured":"Miyato T, Maeda S-i, Koyama M, Ishii S (2018) Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans Pattern Anal Mach Intell 41(8):1979\u20131993","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"21103_CR24","doi-asserted-by":"crossref","unstructured":"Park S, Park J, Shin S-J, Moon I-C (2018) Adversarial dropout for supervised and semi-supervised learning. in Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11634"},{"key":"21103_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109032","volume":"133","author":"X Huo","year":"2023","unstructured":"Huo X, Zeng X, Wu S, Shen W, Wong H-S (2023) Collaborative learning with unreliability adaptation for semi-supervised image classification. Pattern Recogn 133:109032","journal-title":"Pattern Recogn"},{"key":"21103_CR26","doi-asserted-by":"crossref","unstructured":"Luo Y, Zhu J, Li M, Ren Y, Zhang B (2018) Smooth neighbors on teacher graphs for semi-supervised learning. in Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8896\u20138905","DOI":"10.1109\/CVPR.2018.00927"},{"key":"21103_CR27","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.neunet.2021.11.026","volume":"146","author":"E Tu","year":"2022","unstructured":"Tu E, Wang Z, Yang J, Kasabov N (2022) Deep semi-supervised learning via dynamic anchor graph embedding in latent space. Neural Netw 146:350\u2013360","journal-title":"Neural Netw"},{"key":"21103_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109061","volume":"133","author":"E Jung","year":"2023","unstructured":"Jung E, Luna M, Park SH (2023) Conditional GAN with 3D discriminator for MRI generation of Alzheimer\u2019s disease progression. Pattern Recogn 133:109061","journal-title":"Pattern Recogn"},{"key":"21103_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2022.109772","volume":"185","author":"K Zhou","year":"2023","unstructured":"Zhou K, Diehl E, Tang J (2023) Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations. Mech Syst Signal Process 185:109772","journal-title":"Mech Syst Signal Process"},{"key":"21103_CR30","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.neunet.2022.06.017","volume":"154","author":"J Zhao","year":"2022","unstructured":"Zhao J, Lan L, Huang D, Ren J, Yang W (2022) Heterogeneous pseudo-supervised learning for few-shot person re-identification. Neural Netw 154:521\u2013537","journal-title":"Neural Netw"},{"key":"21103_CR31","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.inffus.2022.10.017","volume":"91","author":"T Zhou","year":"2023","unstructured":"Zhou T, Li Q, Lu H, Cheng Q, Zhang X (2023) GAN review: models and medical image fusion applications. Inf Fusion 91:134\u2013148","journal-title":"Inf Fusion"},{"key":"21103_CR32","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.inffus.2022.08.010","volume":"89","author":"JE Arco","year":"2023","unstructured":"Arco JE, Ortiz A, Ramirez J, Martinez-Murcia FJ, Zhang Y-D, Gorriz JM (2023) Uncertainty-driven ensembles of multi-scale deep architectures for image classification. Inf Fusion 89:53\u201365","journal-title":"Inf Fusion"},{"issue":"7","key":"21103_CR33","doi-asserted-by":"publisher","first-page":"9335","DOI":"10.1007\/s11063-023-11204-5","volume":"55","author":"G Yang","year":"2023","unstructured":"Yang G, Luo S, Greer P (2023) A novel vision transformer model for skin cancer classification. Neural Process Lett 55(7):9335\u20139351","journal-title":"Neural Process Lett"},{"key":"21103_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106791","volume":"157","author":"ON Manzari","year":"2023","unstructured":"Manzari ON, Ahmadabadi H, Kashiani H, Shokouhi SB, Ayatollahi A (2023) MedViT: a robust vision transformer for generalized medical image classification. Comput Biol Med 157:106791","journal-title":"Comput Biol Med"},{"key":"21103_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111190","volume":"283","author":"Y Feng","year":"2024","unstructured":"Feng Y, Zhu J, Song R, Wang X (2024) S2EFT: spectral-spatial-elevation fusion transformer for hyperspectral image and lidar classification. Knowledge-Based Systems 283:111190","journal-title":"Knowledge-Based Systems"},{"key":"21103_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107486","volume":"107","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Gong Y, Zhu H, Bai X, Tang W (2020) Multi-head enhanced self-attention network for novelty detection. Pattern Recogn 107:107486","journal-title":"Pattern Recogn"},{"key":"21103_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121789","volume":"238","author":"HT Mustafa","year":"2024","unstructured":"Mustafa HT, Shamsolmoali P, Lee IH (2024) TGF: multiscale transformer graph attention network for multi-sensor image fusion. Expert Syst Appl 238:121789","journal-title":"Expert Syst Appl"},{"key":"21103_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106137","volume":"150","author":"Y Huang","year":"2022","unstructured":"Huang Y, Si Y, Hu B, Zhang Y, Wu S, Wu D, Wang Q (2022) Transformer-based factorized encoder for classification of pneumoconiosis on 3D CT images. Comput Biol Med 150:106137","journal-title":"Comput Biol Med"},{"key":"21103_CR39","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.aiopen.2022.10.001","volume":"3","author":"T Lin","year":"2022","unstructured":"Lin T, Wang Y, Liu X, Qiu X (2022) A survey of transformers. AI Open 3:111\u2013132","journal-title":"AI Open"},{"key":"21103_CR40","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.patrec.2024.04.018","volume":"182","author":"S Raj","year":"2024","unstructured":"Raj S, Mathew J, Mondal A (2024) Generalized and robust model for GAN-generated image detection. Pattern Recognit Lett 182:104\u2013110","journal-title":"Pattern Recognit Lett"},{"key":"21103_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109050","volume":"134","author":"C Tian","year":"2023","unstructured":"Tian C, Zheng M, Zuo W, Zhang B, Zhang Y, Zhang D (2023) Multi-stage image denoising with the wavelet transform. Pattern Recogn 134:109050","journal-title":"Pattern Recogn"},{"key":"21103_CR42","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.cogsys.2019.11.002","volume":"60","author":"IJ Kadhim","year":"2020","unstructured":"Kadhim IJ, Premaratne P, Vial PJ (2020) High capacity adaptive image steganography with cover region selection using dual-tree complex wavelet transform. Cogn Syst Res 60:20\u201332","journal-title":"Cogn Syst Res"},{"issue":"5","key":"21103_CR43","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1007\/s00371-020-01838-0","volume":"37","author":"Z Rahman","year":"2021","unstructured":"Rahman Z, Pu Y-F, Aamir M, Wali S (2021) Structure revealing of low-light images using wavelet transform based on fractional-order denoising and multiscale decomposition. Vis Comput 37(5):865\u2013880","journal-title":"Vis Comput"},{"key":"21103_CR44","doi-asserted-by":"publisher","first-page":"166883","DOI":"10.1016\/j.ijleo.2021.166883","volume":"241","author":"HH Maria","year":"2021","unstructured":"Maria HH, Jossy AM, Malarvizhi G, Jenitta A (2021) Analysis of lifting scheme based double density dual-tree complex wavelet transform for de-noising medical images. Optik 241:166883","journal-title":"Optik"},{"key":"21103_CR45","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1016\/j.inffus.2022.09.023","volume":"90","author":"M Abdar","year":"2023","unstructured":"Abdar M, Salari S, Qahremani S, Lam H-K, Karray F, Hussain S, Khosravi A, Acharya UR, Makarenkov V, Nahavandi S (2023) Uncertaintyfusenet: robust uncertainty-aware hierarchical feature fusion model with ensemble Monte Carlo dropout for COVID-19 detection. Inf Fusion 90:364\u2013381","journal-title":"Inf Fusion"},{"key":"21103_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108975","volume":"250","author":"Y Xie","year":"2022","unstructured":"Xie Y, Lin T, Chen Z, Xiong W, Ran Q, Shang C (2022) A lightweight ensemble discriminator for generative adversarial networks. Knowledge-Based Systems 250:108975","journal-title":"Knowledge-Based Systems"},{"key":"21103_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107646","volume":"110","author":"W Li","year":"2021","unstructured":"Li W, Fan L, Wang Z, Ma C, Cui X (2021) Tackling mode collapse in multi-generator GANs with orthogonal vectors. Pattern Recogn 110:107646","journal-title":"Pattern Recogn"},{"key":"21103_CR48","unstructured":"Netzer Y, Wang T, Coates A, Bissacco A, Wu B, Ng AY (2011) Reading digits In natural images with unsupervised feature learning. In NIPS workshop on deep learning and unsupervised feature learning. Granada, Spain, p 7"},{"key":"21103_CR49","unstructured":"Coates A, Ng A, Lee H (2011) An analysis of single-layer networks in unsupervised feature learning. in Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings. pp. 215\u2013223"},{"key":"21103_CR50","unstructured":"Darlow LN, Crowley EJ, Antoniou A, Storkey AJ (2018) Cinic-10 is not imagenet or cifar-10. arXiv preprint arXiv:1810.03505"},{"key":"21103_CR51","unstructured":"Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images."},{"key":"21103_CR52","first-page":"14745","volume":"34","author":"Y Jiang","year":"2021","unstructured":"Jiang Y, Chang S, Wang Z (2021) Transgan: two pure transformers can make one strong gan, and that can scale up. Adv Neural Inf Process Syst 34:14745\u201314758","journal-title":"Adv Neural Inf Process Syst"},{"key":"21103_CR53","unstructured":"Verma V, Lamb A, Beckham C, Najafi A, Mitliagkas I, Lopez-Paz D, Bengio Y (2019) Manifold mixup: Better representations by interpolating hidden states. in International conference on machine learning. PMLR. pp. 6438\u20136447"},{"key":"21103_CR54","unstructured":"Oliver A, Odena A, Raffel CA, Cubuk ED, Goodfellow I (2018) Realistic evaluation of deep semi-supervised learning algorithms. Advances in neural information processing systems. 31"},{"key":"21103_CR55","unstructured":"Zhang H, Cisse M, Dauphin YN, Lopez-Paz D (2017) mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412"},{"key":"21103_CR56","unstructured":"Athiwaratkun B, Finzi M, Izmailov P, Wilson AG (2019) There are many consistent explanations of unlabeled data: Why you should average. International conference on learning representations. URL: https:\/\/openreview.net\/forum?id=rkgKBhA5Y7"},{"key":"21103_CR57","doi-asserted-by":"publisher","first-page":"150579","DOI":"10.1109\/ACCESS.2021.3125920","volume":"9","author":"T Zoppi","year":"2021","unstructured":"Zoppi T, Ceccarelli A (2021) Detect adversarial attacks against deep neural networks with GPU monitoring. IEEE Access 9:150579\u2013150591","journal-title":"IEEE Access"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-21103-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-025-21103-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-21103-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T17:02:19Z","timestamp":1766682139000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-025-21103-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,1]]},"references-count":57,"journal-issue":{"issue":"41","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["21103"],"URL":"https:\/\/doi.org\/10.1007\/s11042-025-21103-7","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,1]]},"assertion":[{"value":"28 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The author has reviewed the final version of the manuscript and consents to its publication.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}