{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:05:47Z","timestamp":1783641947459,"version":"3.55.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s00521-026-11949-9","type":"journal-article","created":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T09:58:03Z","timestamp":1772618283000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Contrastive learning-based video quality assessment-jointed video vision transformer for video recognition"],"prefix":"10.1007","volume":"38","author":[{"given":"Jian","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Mahoor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,4]]},"reference":[{"key":"11949_CR1","doi-asserted-by":"publisher","DOI":"10.3390\/app14020948","author":"T Gao","year":"2024","unstructured":"Gao T, Zhang M, Zhu Y, Zhang Y, Pang X, Ying J, Liu W (2024) Sports video classification method based on improved deep learning. Appl Sci. https:\/\/doi.org\/10.3390\/app14020948","journal-title":"Appl Sci"},{"key":"11949_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121929","volume":"238","author":"J Sun","year":"2023","unstructured":"Sun J, Dodge HH, Mahoor MH (2023) Mc-ViViT: multi-branch classifier-ViViT to detect mild cognitive impairment in older adults using facial videos. Expert Syst Appl 238:121929","journal-title":"Expert Syst Appl"},{"key":"11949_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124185","volume":"252","author":"M Alsuhaibani","year":"2024","unstructured":"Alsuhaibani M, Dodge HH, Mahoor MH (2024) Mild cognitive impairment detection from facial video interviews by applying spatial-to-temporal attention module. Expert Syst Appl 252:124185","journal-title":"Expert Syst Appl"},{"issue":"1","key":"11949_CR4","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1109\/TBC.2022.3197904","volume":"69","author":"S Jiang","year":"2023","unstructured":"Jiang S, Sang Q, Hu Z, Liu L (2023) Self-supervised representation learning for video quality assessment. IEEE Trans Broadcast 69(1):118\u2013129. https:\/\/doi.org\/10.1109\/TBC.2022.3197904","journal-title":"IEEE Trans Broadcast"},{"key":"11949_CR5","doi-asserted-by":"publisher","unstructured":"Mitra S, Soundararajan R (2022) Multiview contrastive learning for completely blind video quality assessment of user generated content. In: Proceedings of the 30th ACM international conference on multimedia. MM \u201922. Association for Computing Machinery, New York, NY, USA, pp 1914\u20131924. https:\/\/doi.org\/10.1145\/3503161.3548064","DOI":"10.1145\/3503161.3548064"},{"key":"11949_CR6","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1109\/LSP.2023.3255011","volume":"30","author":"Y-C Chen","year":"2023","unstructured":"Chen Y-C, Saha A, Davis C, Qiu B, Wang X, Gowda R, Katsavounidis I, Bovik AC (2023) GAMIVAL: video quality prediction on mobile cloud gaming content. IEEE Signal Process Lett 30:324\u2013328","journal-title":"IEEE Signal Process Lett"},{"key":"11949_CR7","doi-asserted-by":"crossref","unstructured":"Mi Y, Li Y, Shu Y, Liu S (2024) ZE-FESG: a zero-shot feature extraction method based on semantic guidance for no-reference video quality assessment. In: ICASSP 2024\u20132024 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 3640\u20133644","DOI":"10.1109\/ICASSP48485.2024.10448422"},{"key":"11949_CR8","doi-asserted-by":"crossref","unstructured":"Campos W, Saavedra JM, Stears C (2025) A study on self-supervised sketch-based image retrieval on unpaired data (S3BIR). Neural Comput Appl 1\u201319","DOI":"10.1007\/s00521-025-11142-4"},{"key":"11949_CR9","doi-asserted-by":"crossref","unstructured":"Jayamohan M, Yuvaraj S (2025) A novel human actions recognition and classification using semantic segmentation with deep learning techniques. Neural Comput Appl 1\u201317","DOI":"10.1007\/s00521-024-10962-0"},{"key":"11949_CR10","doi-asserted-by":"crossref","unstructured":"Arnab A, Dehghani M, Heigold G, Sun C, Lu\u010di\u0107 M, Schmid C (2021) ViViT: a video vision transformer. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6836\u20136846","DOI":"10.1109\/ICCV48922.2021.00676"},{"issue":"1","key":"11949_CR11","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/JSTSP.2020.2966864","volume":"14","author":"M Xu","year":"2020","unstructured":"Xu M, Li C, Zhang S, Callet PL (2020) State-of-the-art in $$360^{\\circ }$$ video\/image processing: perception, assessment and compression. IEEE J Sel Top Signal Process 14(1):5\u201326. https:\/\/doi.org\/10.1109\/JSTSP.2020.2966864","journal-title":"IEEE J Sel Top Signal Process"},{"issue":"2","key":"11949_CR12","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1007\/s00521-023-09068-w","volume":"36","author":"B Zhang","year":"2024","unstructured":"Zhang B, Chen J, Xu Y, Zhang H, Yang X, Geng X (2024) Auto-encoding score distribution regression for action quality assessment. Neural Comput Appl 36(2):929\u2013942","journal-title":"Neural Comput Appl"},{"issue":"10s","key":"11949_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3507901","volume":"54","author":"T Schlett","year":"2022","unstructured":"Schlett T, Rathgeb C, Henniger O, Galbally J, Fierrez J, Busch C (2022) Face image quality assessment: a literature survey. ACM Comput Surv (CSUR) 54(10s):1\u201349","journal-title":"ACM Comput Surv (CSUR)"},{"key":"11949_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.iswa.2021.200059","volume":"13","author":"H Imani","year":"2022","unstructured":"Imani H, Islam MB, Arica N (2022) Three-stream 3d deep cnn for no-reference stereoscopic video quality assessment. Intell Syst Appl 13:200059. https:\/\/doi.org\/10.1016\/j.iswa.2021.200059","journal-title":"Intell Syst Appl"},{"issue":"4","key":"11949_CR15","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1109\/TCSVT.2019.2898732","volume":"30","author":"HG Kim","year":"2020","unstructured":"Kim HG, Lim H-T, Ro YM (2020) Deep virtual reality image quality assessment with human perception guider for omnidirectional image. IEEE Trans Circuits Syst Video Technol 30(4):917\u2013928. https:\/\/doi.org\/10.1109\/TCSVT.2019.2898732","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"11949_CR16","doi-asserted-by":"publisher","unstructured":"Sun W, Wang T, Min X, Yi F, Zhai G (2021) Deep learning based full-reference and no-reference quality assessment models for compressed UGC videos. In: 2021 IEEE international conference on multimedia and expo workshops (ICMEW). IEEE Computer Society, Los Alamitos, CA, USA, pp 1\u20136. https:\/\/doi.org\/10.1109\/ICMEW53276.2021.9455999","DOI":"10.1109\/ICMEW53276.2021.9455999"},{"key":"11949_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-022-10939-x","author":"Y Tan","year":"2022","unstructured":"Tan Y, Kong G, Duan X, Long H, Wu Y (2022) No-reference video quality assessment based on spatio-temporal perception feature fusion. Neural Process Lett. https:\/\/doi.org\/10.1007\/s11063-022-10939-x","journal-title":"Neural Process Lett"},{"key":"11949_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110955","volume":"158","author":"L Zhang","year":"2025","unstructured":"Zhang L, Zhou K, Lu F, Li Z, Shao X, Zhou X-D, Shi Y (2025) ESMformer: error-aware self-supervised transformer for multi-view 3d human pose estimation. Pattern Recogn 158:110955","journal-title":"Pattern Recogn"},{"key":"11949_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3265109","volume":"72","author":"W Mao","year":"2023","unstructured":"Mao W, Liu K, Zhang Y, Liang X, Wang Z (2023) Self-supervised deep tensor domain-adversarial regression adaptation for online remaining useful life prediction across machines. IEEE Trans Instrum Meas 72:1\u201316. https:\/\/doi.org\/10.1109\/TIM.2023.3265109","journal-title":"IEEE Trans Instrum Meas"},{"key":"11949_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.111021","volume":"158","author":"Q Yang","year":"2025","unstructured":"Yang Q, Wang C, Liu P, Jiang Z, Li J (2025) Video anomaly detection via self-supervised and spatio-temporal proxy tasks learning. Pattern Recogn 158:111021","journal-title":"Pattern Recogn"},{"key":"11949_CR21","doi-asserted-by":"publisher","unstructured":"Kwong N-W, Chan Y-L, Tsang S-H, Lun DP-K (2023) Optimized quality feature learning for video quality assessment. In: ICASSP 2023\u20132023 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1\u20135. https:\/\/doi.org\/10.1109\/ICASSP49357.2023.10095975","DOI":"10.1109\/ICASSP49357.2023.10095975"},{"key":"11949_CR22","unstructured":"Tarvainen A, Valpola H (2017) Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Adv Neural Inf Process Syst 30"},{"key":"11949_CR23","doi-asserted-by":"crossref","unstructured":"Pozzi A, Incremona A, Tessera D, Toti D (2025) Mitigating exposure bias in large language model distillation: an imitation learning approach. Neural Comput Appl 1\u201317","DOI":"10.1007\/s00521-025-11162-0"},{"key":"11949_CR24","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International conference on machine learning. PMLR, pp 1597\u20131607"},{"key":"11949_CR25","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y, Xie S, Girshick R (2020) Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9729\u20139738","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"11949_CR26","unstructured":"Chen X, Fan H, Girshick R, He K (2020) Improved baselines with momentum contrastive learning. arXiv:2003.04297"},{"key":"11949_CR27","doi-asserted-by":"crossref","unstructured":"Chen X, Xie S, He K (2021) An empirical study of training self-supervised vision transformers. In: 2021 IEEE\/CVF international conference on computer vision (ICCV), pp 9620\u20139629","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"11949_CR28","doi-asserted-by":"crossref","unstructured":"Meng T, Ai W, Li J, Wang Z, Li K (2025) SE-GCL: an event-based simple and effective graph contrastive learning for text representation. Neural Comput Appl 1\u201314","DOI":"10.1007\/s00521-024-10686-1"},{"key":"11949_CR29","doi-asserted-by":"crossref","unstructured":"Wang J, Yoshie O, Ieiri Y (2025) Conducting patch contrastive learning with mixture of experts on mixed datasets for medical image segmentation. Neural Comput Appl 1\u201328","DOI":"10.1007\/s00521-025-11234-1"},{"key":"11949_CR30","unstructured":"Zhao J, Mathieu M, LeCun Y (2017) Energy-based generative adversarial networks. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=ryh9pmcee"},{"key":"11949_CR31","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Fan H, Xiong B, Girshick R, He K (2021) A large-scale study on unsupervised spatiotemporal representation learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3299\u20133309","DOI":"10.1109\/CVPR46437.2021.00331"},{"key":"11949_CR32","doi-asserted-by":"publisher","unstructured":"Feng Y, Li S, Chang Y (2021) Multi-scale feature-guided stereoscopic video quality assessment based on 3d convolutional neural network. In: ICASSP 2021\u20132021 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2095\u20132099. https:\/\/doi.org\/10.1109\/ICASSP39728.2021.9414231","DOI":"10.1109\/ICASSP39728.2021.9414231"},{"issue":"1","key":"11949_CR33","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s10846-023-01952-w","volume":"109","author":"J Sun","year":"2023","unstructured":"Sun J, Fard AP, Mahoor MH (2023) Xnodr and xnidr: Two accurate and fast fully connected layers for convolutional neural networks. J Intell Robot Syst 109(1):17","journal-title":"J Intell Robot Syst"},{"key":"11949_CR34","doi-asserted-by":"crossref","unstructured":"Lin E, Sun J, Chen H, Mahoor MH (2024) Data quality matters: suicide intention detection on social media posts using RoBERTa-CNN. In: 2024 46th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 1\u20135","DOI":"10.1109\/EMBC53108.2024.10782647"},{"key":"11949_CR35","doi-asserted-by":"crossref","unstructured":"Alsuhaibani M, Fard AP, Sun J, Poor FF, Pressman PS, Mahoor MH (2025) A review of machine learning approaches for non-invasive cognitive impairment detection. IEEE Access","DOI":"10.1109\/ACCESS.2025.3555176"},{"key":"11949_CR36","doi-asserted-by":"publisher","unstructured":"Tang J, Dong Y, Xie R, Gu X, Song L, Li L, Zhou B (2020) Deep blind video quality assessment for user generated videos. In: 2020 IEEE international conference on visual communications and image processing (VCIP), pp 156\u2013159. https:\/\/doi.org\/10.1109\/VCIP49819.2020.9301757","DOI":"10.1109\/VCIP49819.2020.9301757"},{"issue":"3","key":"11949_CR37","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1111\/jgs.17607","volume":"70","author":"C-Y Wu","year":"2022","unstructured":"Wu C-Y, Mattek N, Wild K, Miller LM, Kaye JA, Silbert LC, Dodge HH (2022) Can changes in social contact (frequency and mode) mitigate low mood before and during the COVID-19 pandemic? The i-CONECT project. J Am Geriatr Soc 70(3):669\u2013676","journal-title":"J Am Geriatr Soc"},{"key":"11949_CR38","doi-asserted-by":"crossref","unstructured":"Deng J, Guo J, Ververas E, Kotsia I, Zafeiriou S (2020) Retinaface: Single-shot multi-level face localisation in the wild. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5203\u20135212","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"11949_CR39","doi-asserted-by":"crossref","unstructured":"Liu D, Zhang H, Zhou P (2021) Video-based facial expression recognition using graph convolutional networks. In: 2020 25th International conference on pattern recognition (ICPR), pp 607\u2013614","DOI":"10.1109\/ICPR48806.2021.9413094"},{"key":"11949_CR40","doi-asserted-by":"publisher","unstructured":"Mohan B, Popa M (2021) Temporal based emotion recognition inspired by activity recognition models. In: 2021 9th International conference on affective computing and intelligent interaction workshops and demos (ACIIW), pp 01\u201308. https:\/\/doi.org\/10.1109\/ACIIW52867.2021.9666356","DOI":"10.1109\/ACIIW52867.2021.9666356"},{"key":"11949_CR41","doi-asserted-by":"crossref","unstructured":"Bermejo Nievas E, Deniz Suarez O, Bueno Garc\u00eda G, Sukthankar R (2011) Violence detection in video using computer vision techniques. In: Computer analysis of images and patterns: 14th international conference, CAIP 2011, Seville, Spain, August 29\u201331, 2011, Proceedings, Part II 14. Springer, pp 332\u2013339","DOI":"10.1007\/978-3-642-23678-5_39"},{"issue":"8","key":"11949_CR42","first-page":"173","volume":"51","author":"W Bai","year":"2022","unstructured":"Bai W, Chen P, Cai H, Zhang Q, Su Z, Cheung T, Jackson T, Sha S, Xiang Y-T (2022) Worldwide prevalence of mild cognitive impairment among community dwellers aged 50 years and older: a meta-analysis and systematic review of epidemiology studies. Age Ageing 51(8):173","journal-title":"Age Ageing"},{"issue":"1","key":"11949_CR43","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1038\/s41398-023-02361-1","volume":"13","author":"P Chen","year":"2023","unstructured":"Chen P, Cai H, Bai W, Su Z, Tang Y-L, Ungvari GS, Ng CH, Zhang Q, Xiang Y-T (2023) Global prevalence of mild cognitive impairment among older adults living in nursing homes: a meta-analysis and systematic review of epidemiological surveys. Transl Psychiatry 13(1):88","journal-title":"Transl Psychiatry"},{"key":"11949_CR44","doi-asserted-by":"crossref","unstructured":"Chen L, Dodge HH, Asgari M (2020) Topic-based measures of conversation for detecting mild cognitive impairment. In: Proceedings of the conference. Association for Computational Linguistics. Meeting, vol. 2020. NIH Public Access, p 63","DOI":"10.18653\/v1\/2020.nlpmc-1.9"},{"key":"11949_CR45","doi-asserted-by":"crossref","unstructured":"Liu G, Xue Z, Zhan L, Dodge HH, Zhou J (2022) Detection of mild cognitive impairment from language markers with crossmodal augmentation. In: Pacific symposium on biocomputing 2023: Kohala Coast, Hawaii, USA, 3\u20137 January 2023. World Scientific, pp 7\u201318","DOI":"10.1142\/9789811270611_0002"},{"key":"11949_CR46","doi-asserted-by":"crossref","unstructured":"Islam Z, Rukonuzzaman M, Ahmed R, Kabir MH, Farazi M (2021) Efficient two-stream network for violence detection using separable convolutional LSTM. In: 2021 International joint conference on neural networks (IJCNN). IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN52387.2021.9534280"},{"key":"11949_CR47","doi-asserted-by":"crossref","unstructured":"Eitta AA, Barghash T, Nafea Y, Gomaa W (2021) Automatic detection of violence in video scenes. In: 2021 International joint conference on neural networks (IJCNN). IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN52387.2021.9533669"},{"issue":"10","key":"11949_CR48","doi-asserted-by":"publisher","first-page":"4787","DOI":"10.1109\/TIP.2018.2845742","volume":"27","author":"I Serrano","year":"2018","unstructured":"Serrano I, Deniz O, Espinosa-Aranda JL, Bueno G (2018) Fight recognition in video using hough forests and 2d convolutional neural network. IEEE Trans Image Process 27(10):4787\u20134797","journal-title":"IEEE Trans Image Process"},{"issue":"26","key":"11949_CR49","doi-asserted-by":"publisher","first-page":"38151","DOI":"10.1007\/s11042-022-13169-4","volume":"81","author":"HT Irfanullah","year":"2022","unstructured":"Irfanullah HT, Iqbal A, Yang B, Hussain A (2022) Real time violence detection in surveillance videos using convolutional neural networks. Multimed Tools Appl 81(26):38151\u201338173","journal-title":"Multimed Tools Appl"},{"key":"11949_CR50","doi-asserted-by":"publisher","first-page":"160580","DOI":"10.1109\/ACCESS.2021.3131315","volume":"9","author":"P Sernani","year":"2021","unstructured":"Sernani P, Falcionelli N, Tomassini S, Contardo P, Dragoni AF (2021) Deep learning for automatic violence detection: tests on the AIRTLab dataset. IEEE Access 9:160580\u2013160595","journal-title":"IEEE Access"},{"issue":"12","key":"11949_CR51","doi-asserted-by":"publisher","first-page":"10400","DOI":"10.1002\/int.22537","volume":"37","author":"FUM Ullah","year":"2022","unstructured":"Ullah FUM, Obaidat MS, Muhammad K, Ullah A, Baik SW, Cuzzolin F, Rodrigues JJ, Albuquerque VHC (2022) An intelligent system for complex violence pattern analysis and detection. Int J Intell Syst 37(12):10400\u201310422","journal-title":"Int J Intell Syst"},{"key":"11949_CR52","doi-asserted-by":"crossref","unstructured":"Hanson A, Pnvr K, Krishnagopal S, Davis L (2018) Bidirectional convolutional LSTM for the detection of violence in videos. In: Proceedings of the European conference on computer vision (ECCV) workshops","DOI":"10.1007\/978-3-030-11012-3_24"},{"key":"11949_CR53","doi-asserted-by":"crossref","unstructured":"Li J, Jiang X, Sun T, Xu K (2019) Efficient violence detection using 3d convolutional neural networks. In: 2019 16th IEEE international conference on advanced video and signal based surveillance (AVSS). IEEE, pp 1\u20138","DOI":"10.1109\/AVSS.2019.8909883"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11949-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-026-11949-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11949-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T04:49:55Z","timestamp":1774414195000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-026-11949-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":53,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["11949"],"URL":"https:\/\/doi.org\/10.1007\/s00521-026-11949-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"22 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Materials availability"}}],"article-number":"107"}}