{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T21:16:59Z","timestamp":1776374219157,"version":"3.51.2"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["No.2021YFC2801001"],"award-info":[{"award-number":["No.2021YFC2801001"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["No.2021YFC2801001"],"award-info":[{"award-number":["No.2021YFC2801001"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["No.2021YFC2801001"],"award-info":[{"award-number":["No.2021YFC2801001"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Research plan of the National Social Science Foundation of China","award":["No.20&ZD130"],"award-info":[{"award-number":["No.20&ZD130"]}]},{"name":"Major Research plan of the National Social Science Foundation of China","award":["No.20&ZD130"],"award-info":[{"award-number":["No.20&ZD130"]}]},{"name":"Major Research plan of the National Social Science Foundation of China","award":["No.20&ZD130"],"award-info":[{"award-number":["No.20&ZD130"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11227-024-06588-7","type":"journal-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T05:02:16Z","timestamp":1729918936000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["MST-ARGCN: modality-squeeze transformer with attentional recurrent graph capsule network for multimodal sentiment analysis"],"prefix":"10.1007","volume":"81","author":[{"given":"Chengyu","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingye","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meijing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huihua","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,26]]},"reference":[{"key":"6588_CR1","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.inffus.2017.02.003","volume":"37","author":"S Poria","year":"2017","unstructured":"Poria S, Cambria E, Bajpai R et al (2017) A review of affective computing: From unimodal analysis to multimodal fusion. Inf Fusion 37:98\u2013125. https:\/\/doi.org\/10.1016\/j.inffus.2017.02.003","journal-title":"Inf Fusion"},{"issue":"1","key":"6588_CR2","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1109\/TAFFC.2020.3038167","volume":"14","author":"S Poria","year":"2023","unstructured":"Poria S, Hazarika D, Majumder N et al (2023) Beneath the tip of the iceberg: current challenges and new directions in sentiment analysis research. IEEE Trans Aff Comput 14(1):108\u2013132. https:\/\/doi.org\/10.1109\/TAFFC.2020.3038167. arXiv:2005.00357","journal-title":"IEEE Trans Aff Comput"},{"key":"6588_CR3","doi-asserted-by":"publisher","unstructured":"Zadeh A, Chen M, Poria S, et\u00a0al (2017) Tensor fusion network for multimodal sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Copenhagen, Denmark, pp 1103\u20131114. https:\/\/doi.org\/10.18653\/v1\/D17-1115","DOI":"10.18653\/v1\/D17-1115"},{"key":"6588_CR4","doi-asserted-by":"publisher","unstructured":"Liu Z, Shen Y, Lakshminarasimhan VB, et\u00a0al (2018) Efficient low-rank multimodal fusion with modality-specific factors. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Melbourne, Australia, pp 2247\u20132256. https:\/\/doi.org\/10.18653\/v1\/P18-1209","DOI":"10.18653\/v1\/P18-1209"},{"key":"6588_CR5","doi-asserted-by":"publisher","unstructured":"Tsai YHH, Bai S, Liang PP, et\u00a0al (2019) Multimodal transformer for unaligned multimodal language sequences. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Volume 1: Long Papers. Association for Computational Linguistics, Italy, pp 6558\u20136569. https:\/\/doi.org\/10.18653\/v1\/p19-1656","DOI":"10.18653\/v1\/p19-1656"},{"key":"6588_CR6","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.patrec.2021.03.025","volume":"146","author":"H Wen","year":"2021","unstructured":"Wen H, You S, Fu Y (2021) Cross-modal context-gated convolution for multi-modal sentiment analysis. Pattern Recogn Lett 146:252\u2013259. https:\/\/doi.org\/10.1016\/j.patrec.2021.03.025 (https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167865521001124)","journal-title":"Pattern Recogn Lett"},{"key":"6588_CR7","doi-asserted-by":"publisher","unstructured":"Wu J, Mai S, Hu H (2021) Graph capsule aggregation for unaligned multimodal sequences. In: Proceedings of the 2021 International Conference on Multimodal Interaction. Association for Computing Machinery, New York, NY, USA, ICMI \u201921, pp 521\u2013529. https:\/\/doi.org\/10.1145\/3462244.3479931","DOI":"10.1145\/3462244.3479931"},{"key":"6588_CR8","volume-title":"Advances in neural information processing systems","author":"S Sabour","year":"2017","unstructured":"Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Guyon I, Luxburg UV, Bengio S et al (eds) Advances in neural information processing systems, vol 30. Curran Associates Inc, USA"},{"key":"6588_CR9","doi-asserted-by":"publisher","unstructured":"Gori M, Monfardini G, Scarselli F (2005) A new model for learning in graph domains. In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., pp 729\u2013734 vol. 2. https:\/\/doi.org\/10.1109\/IJCNN.2005.1555942","DOI":"10.1109\/IJCNN.2005.1555942"},{"issue":"8","key":"6588_CR10","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"6588_CR11","doi-asserted-by":"publisher","unstructured":"Poria S, Chaturvedi I, Cambria E, et\u00a0al (2016) Convolutional MKL based multimodal emotion recognition and sentiment analysis. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp 439\u2013448. https:\/\/doi.org\/10.1109\/ICDM.2016.0055","DOI":"10.1109\/ICDM.2016.0055"},{"key":"6588_CR12","doi-asserted-by":"crossref","unstructured":"Sarkar C, Bhatia S, Agarwal A, et\u00a0al (2014) Feature analysis for computational personality recognition using youtube personality data set. In: Proceedings of the 2014 ACM Multi Media on Workshop on Computational Personality Recognition. Association for Computing Machinery, New York, NY, USA, WCPR \u201914, pp 11\u201314. https:\/\/doi.org\/10.1145\/2659522.2659528","DOI":"10.1145\/2659522.2659528"},{"key":"6588_CR13","doi-asserted-by":"publisher","unstructured":"W\u00f6rtwein T, Scherer S (2017) What really matters\u2014An information gain analysis of questions and reactions in automated PTSD screenings. In: 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), pp 15\u201320. https:\/\/doi.org\/10.1109\/ACII.2017.8273573","DOI":"10.1109\/ACII.2017.8273573"},{"key":"6588_CR14","doi-asserted-by":"publisher","unstructured":"Yamasaki T, Fukushima Y, Furuta R, et\u00a0al (2015) Prediction of user ratings of oral presentations using label relations. In: Proceedings of the 1st International Workshop on Affect & Sentiment in Multimedia. Association for Computing Machinery, New York, NY, USA, ASM \u201915, pp 33\u201338. https:\/\/doi.org\/10.1145\/2813524.2813533","DOI":"10.1145\/2813524.2813533"},{"issue":"2","key":"6588_CR15","doi-asserted-by":"publisher","first-page":"4203","DOI":"10.32604\/cmc.2023.028291","volume":"74","author":"P Gong","year":"2023","unstructured":"Gong P, Liu J, Wu Z et al (2023) A multi-level circulant cross-modal transformer for multimodal speech emotion recognition. Comput Mater Continua 74(2):4203\u20134220. https:\/\/doi.org\/10.32604\/cmc.2023.028291","journal-title":"Comput Mater Continua"},{"key":"6588_CR16","doi-asserted-by":"publisher","unstructured":"Tsai YHH, Ma M, Yang M, et\u00a0al (2020) Multimodal routing: improving local and global interpretability of multimodal language analysis. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, pp 1823\u20131833. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.143","DOI":"10.18653\/v1\/2020.emnlp-main.143"},{"key":"6588_CR17","doi-asserted-by":"publisher","unstructured":"Sahay S, Okur E, H Kumar S, et\u00a0al (2020) Low rank fusion based transformers for multimodal sequences. In: Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML). Association for Computational Linguistics, Seattle, USA, pp 29\u201334. https:\/\/doi.org\/10.18653\/v1\/2020.challengehml-1.4","DOI":"10.18653\/v1\/2020.challengehml-1.4"},{"key":"6588_CR18","doi-asserted-by":"publisher","unstructured":"Liu Y, Li S, Wu Y, et\u00a0al (2022) UMT: unified multi-modal transformers for joint video moment retrieval and highlight detection. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 3032\u20133041. https:\/\/doi.org\/10.1109\/CVPR52688.2022.00305","DOI":"10.1109\/CVPR52688.2022.00305"},{"key":"6588_CR19","doi-asserted-by":"publisher","unstructured":"Gong P, Liu J, Zhang X, et\u00a0al (2023) A multi-stage hierarchical relational graph neural network for multimodal sentiment analysis. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 1\u20135. https:\/\/doi.org\/10.1109\/ICASSP49357.2023.10096644","DOI":"10.1109\/ICASSP49357.2023.10096644"},{"key":"6588_CR20","doi-asserted-by":"publisher","first-page":"79876","DOI":"10.1109\/ACCESS.2020.2990700","volume":"8","author":"S Chang","year":"2020","unstructured":"Chang S, Liu J (2020) Multi-lane capsule network for classifying images with complex background. IEEE Access 8:79876\u201379886. https:\/\/doi.org\/10.1109\/ACCESS.2020.2990700","journal-title":"IEEE Access"},{"key":"6588_CR21","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings. arXiv:1609.02907"},{"key":"6588_CR22","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.neucom.2019.09.012","volume":"371","author":"F Liu","year":"2020","unstructured":"Liu F, Zheng J, Zheng L et al (2020) Combining attention-based bidirectional gated recurrent neural network and two-dimensional convolutional neural network for document-level sentiment classification. Neurocomputing 371:39\u201350. https:\/\/doi.org\/10.1016\/j.neucom.2019.09.012","journal-title":"Neurocomputing"},{"key":"6588_CR23","doi-asserted-by":"publisher","unstructured":"Zhou P, Shi W, Tian J, et\u00a0al (2016) Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Berlin, Germany, pp 207\u2013212. https:\/\/doi.org\/10.18653\/v1\/P16-2034","DOI":"10.18653\/v1\/P16-2034"},{"key":"6588_CR24","volume-title":"Advances in neural information processing systems","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. In: Guyon I, Luxburg UV, Bengio S et al (eds) Advances in neural information processing systems, vol 30. Curran Associates Inc, USA"},{"key":"6588_CR25","doi-asserted-by":"publisher","unstructured":"Cho K, van Merri\u00ebnboer B, Bahdanau D, et\u00a0al (2014) On the properties of neural machine translation: encoder\u2013decoder approaches. In: Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation. Association for Computational Linguistics, Doha, Qatar, pp 103\u2013111. https:\/\/doi.org\/10.3115\/v1\/W14-4012","DOI":"10.3115\/v1\/W14-4012"},{"key":"6588_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-019-01344-9","author":"J Liu","year":"2019","unstructured":"Liu J, Yang Y, Lv S et al (2019) Attention-based BiGRU-CNN for Chinese question classification. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-019-01344-9","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"6","key":"6588_CR27","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MIS.2016.94","volume":"31","author":"A Zadeh","year":"2016","unstructured":"Zadeh A, Zellers R, Pincus E et al (2016) Multimodal sentiment intensity analysis in videos: facial gestures and verbal messages. IEEE Intell Syst 31(6):82\u201388. https:\/\/doi.org\/10.1109\/MIS.2016.94","journal-title":"IEEE Intell Syst"},{"key":"6588_CR28","doi-asserted-by":"publisher","unstructured":"Bagher Zadeh A, Liang PP, Poria S, et\u00a0al (2018) Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Melbourne, Australia, pp 2236\u20132246. https:\/\/doi.org\/10.18653\/v1\/P18-1208","DOI":"10.18653\/v1\/P18-1208"},{"key":"6588_CR29","doi-asserted-by":"publisher","unstructured":"Pennington J, Socher R, Manning C (2014) GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Doha, Qatar, pp 1532\u20131543. https:\/\/doi.org\/10.3115\/v1\/D14-1162","DOI":"10.3115\/v1\/D14-1162"},{"key":"6588_CR30","doi-asserted-by":"publisher","unstructured":"Degottex G, Kane J, Drugman T, et\u00a0al (2014) COVAREP\u2014A collaborative voice analysis repository for speech technologies. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 960\u2013964. https:\/\/doi.org\/10.1109\/ICASSP.2014.6853739","DOI":"10.1109\/ICASSP.2014.6853739"},{"key":"6588_CR31","unstructured":"iMotions (2017) Facial expression analysis"},{"issue":"5","key":"6588_CR32","doi-asserted-by":"publisher","first-page":"3878","DOI":"10.1121\/1.2935783","volume":"123","author":"J Yuan","year":"2008","unstructured":"Yuan J, Liberman M et al (2008) Speaker identification on the SCOTUS corpus. J Acoust Soc Am 123(5):3878. https:\/\/doi.org\/10.1121\/1.2935783","journal-title":"J Acoust Soc Am"},{"key":"6588_CR33","unstructured":"Loshchilov I, Hutter F (2019) Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019. arXiv:1711.05101"},{"key":"6588_CR34","unstructured":"Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. arXiv:1412.6980"},{"key":"6588_CR35","doi-asserted-by":"publisher","DOI":"10.3390\/e24071010","author":"F Liu","year":"2022","unstructured":"Liu F, Shen SY, Fu ZW et al (2022) Lgcct: a light gated and crossed complementation transformer for multimodal speech emotion recognition. Entropy. https:\/\/doi.org\/10.3390\/e24071010","journal-title":"Entropy"},{"key":"6588_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2021.103178","volume":"78","author":"H Wen","year":"2021","unstructured":"Wen H, You S, Fu Y (2021) Cross-modal dynamic convolution for multi-modal emotion recognition. J Vis Commun Image Represent 78:103178. https:\/\/doi.org\/10.1016\/j.jvcir.2021.103178 (https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1047320321001085)","journal-title":"J Vis Commun Image Represent"},{"key":"6588_CR37","doi-asserted-by":"publisher","DOI":"10.1145\/3542927","author":"S Mai","year":"2023","unstructured":"Mai S, Xing S, He J et al (2023) Multimodal graph for unaligned multimodal sequence analysis via graph convolution and graph pooling. ACM Trans Multimedia Comput Commun Appl. https:\/\/doi.org\/10.1145\/3542927","journal-title":"ACM Trans Multimedia Comput Commun Appl"},{"key":"6588_CR38","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1007\/978-3-030-98358-1_48","volume-title":"MultiMedia modeling","author":"B Wang","year":"2022","unstructured":"Wang B, Dong G, Zhao Y et al (2022) Non-uniform attention network for multi-modal sentiment analysis. In: \u00de\u00f3r J\u00f3nsson B, Gurrin C, Tran MT et al (eds) MultiMedia modeling. Springer, Cham, pp 612\u2013623"},{"key":"6588_CR39","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.inffus.2020.08.006","volume":"65","author":"Q Li","year":"2021","unstructured":"Li Q, Gkoumas D, Lioma C et al (2021) Quantum-inspired multimodal fusion for video sentiment analysis. Inf Fusion 65:58\u201371. https:\/\/doi.org\/10.1016\/j.inffus.2020.08.006","journal-title":"Inf Fusion"},{"key":"6588_CR40","doi-asserted-by":"publisher","unstructured":"Lv F, Chen X, Huang Y, et\u00a0al (2021) Progressive modality reinforcement for human multimodal emotion recognition from unaligned multimodal sequences. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 2554\u20132562. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00258","DOI":"10.1109\/CVPR46437.2021.00258"},{"key":"6588_CR41","doi-asserted-by":"publisher","unstructured":"Pham H, Liang PP, Manzini T, et\u00a0al (2019) Found in translation: learning robust joint representations by cyclic translations between modalities. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 6892\u20136899. https:\/\/doi.org\/10.1609\/aaai.v33i01.33016892","DOI":"10.1609\/aaai.v33i01.33016892"},{"key":"6588_CR42","doi-asserted-by":"publisher","unstructured":"Wang Y, Shen Y, Liu Z, et\u00a0al (2019) Words can shift: dynamically adjusting word representations using nonverbal behaviors. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 7216\u20137223. https:\/\/doi.org\/10.1609\/aaai.v33i01.33017216","DOI":"10.1609\/aaai.v33i01.33017216"},{"key":"6588_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2022.101588","volume":"52","author":"Z Wang","year":"2022","unstructured":"Wang Z, Gao P, Chu X (2022) Sentiment analysis from Customer-generated online videos on product review using topic modeling and Multi-attention BLSTM. Adv Eng Inform 52:101588. https:\/\/doi.org\/10.1016\/j.aei.2022.101588","journal-title":"Adv Eng Inform"},{"key":"6588_CR44","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2022.3197994","author":"Z Wang","year":"2022","unstructured":"Wang Z, Xu G, Zhou X et al (2022) Deep tensor evidence fusion network for sentiment classification. IEEE Trans Comput Soc Syst. https:\/\/doi.org\/10.1109\/TCSS.2022.3197994","journal-title":"IEEE Trans Comput Soc Syst"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06588-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06588-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06588-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T05:23:01Z","timestamp":1729920181000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06588-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,26]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["6588"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06588-7","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,26]]},"assertion":[{"value":"7 October 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"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":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"86"}}