{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T10:05:06Z","timestamp":1766138706892,"version":"3.37.3"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176225"],"award-info":[{"award-number":["62176225"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00530-024-01338-4","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T03:16:05Z","timestamp":1716779765000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Improving collaborative filtering with SNE\u2013GCN: a second-order neighbor enhanced graph convolutional network"],"prefix":"10.1007","volume":"30","author":[{"given":"Tianyang","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Langcai","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peihua","family":"Chai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenbao","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,27]]},"reference":[{"key":"1338_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s00530-023-01107-9","author":"X Pan","year":"2023","unstructured":"Pan, X., Gan, M.: Multi-behavior recommendation based on intent learning. Multimed. Syst. (2023). https:\/\/doi.org\/10.1007\/s00530-023-01107-9","journal-title":"Multimed. Syst."},{"key":"1338_CR2","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s00530-010-0189-6","volume":"16","author":"F Hopfgartner","year":"2010","unstructured":"Hopfgartner, F., Jose, J.M.: Semantic user profiling techniques for personalised multimedia recommendation. Multimedia Syst. 16, 255\u2013274 (2010). https:\/\/doi.org\/10.1007\/s00530-010-0189-6","journal-title":"Multimedia Syst."},{"key":"1338_CR3","doi-asserted-by":"publisher","unstructured":"Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook, pp. 1\u201335 (2011). https:\/\/doi.org\/10.1007\/978-0-387-85820-3_1","DOI":"10.1007\/978-0-387-85820-3_1"},{"key":"1338_CR4","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.neucom.2021.05.114","volume":"472","author":"MA Islam","year":"2022","unstructured":"Islam, M.A., Mohammad, M.M., Sarathi Das, S.S., et al.: A survey on deep learning based point-of-interest (poi) recommendations. Neurocomputing 472, 306\u2013325 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2021.05.114","journal-title":"Neurocomputing"},{"issue":"6","key":"1338_CR5","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1109\/TKDE.2005.99","volume":"17","author":"G Adomavicius","year":"2005","unstructured":"Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734\u2013749 (2005). https:\/\/doi.org\/10.1109\/TKDE.2005.99","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1338_CR6","doi-asserted-by":"publisher","unstructured":"Kang, W., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM). IEEE Computer Society, Los Alamitos, pp. 197\u2013206 (2018). https:\/\/doi.org\/10.1109\/ICDM.2018.00035","DOI":"10.1109\/ICDM.2018.00035"},{"issue":"8","key":"1338_CR7","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30\u201337 (2009). https:\/\/doi.org\/10.1109\/MC.2009.263","journal-title":"Computer"},{"issue":"5","key":"1338_CR8","doi-asserted-by":"publisher","first-page":"1621","DOI":"10.1007\/s00530-022-00923-9","volume":"28","author":"H Zhang","year":"2022","unstructured":"Zhang, H., Kong, X., Member, I., et al.: Cross-domain collaborative recommendation without overlapping entities based on domain adaptation. Multimed. Syst. 28(5), 1621\u20131637 (2022). https:\/\/doi.org\/10.1007\/s00530-022-00923-9","journal-title":"Multimed. Syst."},{"key":"1338_CR9","doi-asserted-by":"publisher","unstructured":"He, X., Deng, K., Wang, X., et\u00a0al.: Lightgcn: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, SIGIR \u201920, pp. 639\u2013648 (2020). https:\/\/doi.org\/10.1145\/3397271.3401063","DOI":"10.1145\/3397271.3401063"},{"key":"1338_CR10","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., et\u00a0al.: Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. AUAI Press, Arlington, Virginia, USA, UAI \u201909, pp. 452\u2013461 (2009)"},{"key":"1338_CR11","doi-asserted-by":"publisher","unstructured":"Lin, Z., Tian, C., Hou, Y., et al.: Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In: Proceedings of the ACM Web Conference 2022, pp. 2320\u20132329 (2022). https:\/\/doi.org\/10.1145\/3485447.3512104","DOI":"10.1145\/3485447.3512104"},{"key":"1338_CR12","unstructured":"Berg, R.vd., Kipf, T.N., Welling, M.: Graph convolutional matrix completion (2017). arXiv preprint arXiv:1706.02263"},{"key":"1338_CR13","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"1338_CR14","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou, J., Cui, G., Hu, S., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57\u201381 (2020). https:\/\/doi.org\/10.1016\/j.aiopen.2021.01.001","journal-title":"AI Open"},{"key":"1338_CR15","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks (2016). arXiv preprint arXiv:1609.02907"},{"key":"1338_CR16","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., et\u00a0al.: Graph attention networks (2017). arXiv preprint arXiv:1710.10903"},{"key":"1338_CR17","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2022.3164474","author":"Y Shi","year":"2022","unstructured":"Shi, Y., Quan, P., Xiao, Y., et al.: Graph influence network. IEEE Trans. Cybern. (2022). https:\/\/doi.org\/10.1109\/TCYB.2022.3164474","journal-title":"IEEE Trans. Cybern."},{"key":"1338_CR18","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., et\u00a0al.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263\u20131272. PMLR (2017)"},{"key":"1338_CR19","unstructured":"Gao, H., Ji, S.: Graph u-nets. In: International Conference on Machine Learning, pp. 2083\u20132092. , PMLR (2019)"},{"key":"1338_CR20","doi-asserted-by":"publisher","unstructured":"Wang, Z., Xu, Q., Yang, Z., et\u00a0al.: Implicit feedbacks are not always favorable: iterative relabeled one-class collaborative filtering against noisy interactions. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3070\u20133078 (2021). https:\/\/doi.org\/10.1145\/3474085.3475446","DOI":"10.1145\/3474085.3475446"},{"issue":"1","key":"1338_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3568022","volume":"1","author":"C Gao","year":"2023","unstructured":"Gao, C., Zheng, Y., Li, N., et al.: A survey of graph neural networks for recommender systems: challenges, methods, and directions. ACM Trans. Recomm. Syst. 1(1), 1\u201351 (2023). https:\/\/doi.org\/10.1145\/3568022","journal-title":"ACM Trans. Recomm. Syst."},{"key":"1338_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126272","volume":"543","author":"F Mo","year":"2023","unstructured":"Mo, F., Yamana, H.: Ept-gcn: edge propagation-based time-aware graph convolution network for poi recommendation. Neurocomputing 543, 126272 (2023). https:\/\/doi.org\/10.1016\/j.neucom.2023.126272","journal-title":"Neurocomputing"},{"key":"1338_CR23","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.aiopen.2022.03.002","volume":"3","author":"J Liu","year":"2022","unstructured":"Liu, J., Shi, C., Yang, C., et al.: A survey on heterogeneous information network based recommender systems: concepts, methods, applications and resources. AI Open 3, 40\u201357 (2022). https:\/\/doi.org\/10.1016\/j.aiopen.2022.03.002","journal-title":"AI Open"},{"key":"1338_CR24","unstructured":"Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering (2013). arXiv preprint arXiv:1301.7363"},{"key":"1338_CR25","doi-asserted-by":"publisher","unstructured":"Sarwar, B., Karypis, G., Konstan, J., et\u00a0al.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web. Association for Computing Machinery, New York, NY, USA, WWW \u201901, pp. 285\u2013295 (2001). https:\/\/doi.org\/10.1145\/371920.372071","DOI":"10.1145\/371920.372071"},{"key":"1338_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s00530-020-00713-1","author":"X Hua","year":"2020","unstructured":"Hua, X., Han, J., Zhao, C., et al.: A novel method for ecg signal classification via one-dimensional convolutional neural network. Multimed. Syst. (2020). https:\/\/doi.org\/10.1007\/s00530-020-00713-1","journal-title":"Multimed. Syst."},{"issue":"6","key":"1338_CR27","doi-asserted-by":"publisher","first-page":"2193","DOI":"10.1007\/s00530-022-00926-6","volume":"28","author":"L Nixon","year":"2022","unstructured":"Nixon, L., Foss, J., Apostolidis, K., et al.: Data-driven personalisation of television content: a survey. Multimed. Syst. 28(6), 2193\u20132225 (2022). https:\/\/doi.org\/10.1007\/s00530-022-00926-6","journal-title":"Multimed. Syst."},{"issue":"5","key":"1338_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3535101","volume":"55","author":"S Wu","year":"2022","unstructured":"Wu, S., Sun, F., Zhang, W., et al.: Graph neural networks in recommender systems: a survey. ACM Comput. Surv. 55(5), 1\u201337 (2022). https:\/\/doi.org\/10.1145\/3535101","journal-title":"ACM Comput. Surv."},{"key":"1338_CR29","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.neucom.2023.03.030","volume":"536","author":"H Xu","year":"2023","unstructured":"Xu, H., Zhang, S., Jiang, B., et al.: Graph context-attention network via low and high order aggregation. Neurocomputing 536, 152\u2013163 (2023). https:\/\/doi.org\/10.1016\/j.neucom.2023.03.030","journal-title":"Neurocomputing"},{"key":"1338_CR30","unstructured":"Xu, K., Hu, W., Leskovec, J., et\u00a0al.: How powerful are graph neural networks? (2018). arXiv:1810.00826"},{"key":"1338_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/tsc.2023.3288872","author":"W Tang","year":"2023","unstructured":"Tang, W., Sun, H., Wang, J., et al.: Identifying users across social media networks for interpretable fine-grained neighborhood matching by adaptive gat. IEEE Trans. Serv. Comput. (2023). https:\/\/doi.org\/10.1109\/tsc.2023.3288872","journal-title":"IEEE Trans. Serv. Comput."},{"key":"1338_CR32","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.neucom.2020.11.046","volume":"436","author":"D Zhang","year":"2021","unstructured":"Zhang, D., Zheng, Z., Li, M., et al.: Csart: channel and spatial attention-guided residual learning for real-time object tracking. Neurocomputing 436, 260\u2013272 (2021). https:\/\/doi.org\/10.1016\/j.neucom.2020.11.046","journal-title":"Neurocomputing"},{"key":"1338_CR33","doi-asserted-by":"publisher","unstructured":"Chen, X., Zhang, D., Zheng, Z., et\u00a0al.: Deep regression tracking with graph attention. In: 2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) (2022). https:\/\/doi.org\/10.1109\/icicml57342.2022.10009671","DOI":"10.1109\/icicml57342.2022.10009671"},{"key":"1338_CR34","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2021.3102964","author":"X Fan","year":"2021","unstructured":"Fan, X., Gong, M., Wu, Y., et al.: Propagation enhanced neural message passing for graph representation learning. IEEE Trans. Knowl. Data Eng. (2021). https:\/\/doi.org\/10.1109\/tkde.2021.3102964","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"12","key":"1338_CR35","doi-asserted-by":"publisher","first-page":"7172","DOI":"10.1109\/tnnls.2021.3084319","volume":"33","author":"X Fan","year":"2022","unstructured":"Fan, X., Gong, M., Tang, Z., et al.: Deep neural message passing with hierarchical layer aggregation and neighbor normalization. IEEE Trans. Neural Netw. Learn. Syst. 33(12), 7172\u20137184 (2022). https:\/\/doi.org\/10.1109\/tnnls.2021.3084319","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"10","key":"1338_CR36","doi-asserted-by":"publisher","first-page":"10735","DOI":"10.1109\/TKDE.2023.3264512","volume":"35","author":"X Fan","year":"2023","unstructured":"Fan, X., Gong, M., Wu, Y., et al.: Maximizing mutual information across feature and topology views for representing graphs. IEEE Trans. Knowl. Data Eng. 35(10), 10735\u201310747 (2023). https:\/\/doi.org\/10.1109\/TKDE.2023.3264512","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1338_CR37","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.neucom.2021.03.053","volume":"454","author":"L Sang","year":"2021","unstructured":"Sang, L., Xu, M., Qian, S., et al.: Knowledge graph enhanced neural collaborative filtering with residual recurrent network. Neurocomputing 454, 417\u2013429 (2021). https:\/\/doi.org\/10.1016\/j.neucom.2021.03.053","journal-title":"Neurocomputing"},{"key":"1338_CR38","unstructured":"Li, Y., Tarlow, D., Brockschmidt, M., et\u00a0al.: Gated graph sequence neural networks (2015). arXiv preprint arXiv:1511.05493"},{"key":"1338_CR39","doi-asserted-by":"publisher","unstructured":"Wu, J., Wang, X., Feng, F., et\u00a0al.: Self-supervised graph learning for recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 726\u2013735 (2021). https:\/\/doi.org\/10.1145\/3404835.3462862","DOI":"10.1145\/3404835.3462862"},{"key":"1338_CR40","doi-asserted-by":"publisher","unstructured":"Sun, J., Zhang, Y., Ma, C., et\u00a0al.: Multi-graph convolution collaborative filtering. In: 2019 IEEE International Conference on Data Mining (ICDM), pp 1306\u20131311. IEEE (2019). https:\/\/doi.org\/10.1109\/ICDM.2019.00165","DOI":"10.1109\/ICDM.2019.00165"},{"key":"1338_CR41","doi-asserted-by":"publisher","unstructured":"Wu, L., Sun, P., Fu, Y., et\u00a0al.: A neural influence diffusion model for social recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 235\u2013244 (2019). https:\/\/doi.org\/10.1145\/3331184.3331214","DOI":"10.1145\/3331184.3331214"},{"issue":"3","key":"1338_CR42","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1007\/s10844-022-00727-3","volume":"59","author":"Y He","year":"2022","unstructured":"He, Y., Mao, Y., Xie, X., et al.: An improved recommendation based on graph convolutional network. J. Intell. Inf. Syst. 59(3), 801\u2013823 (2022). https:\/\/doi.org\/10.1007\/s10844-022-00727-3","journal-title":"J. Intell. Inf. Syst."},{"key":"1338_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2021.08.018","volume":"580","author":"Z Lin","year":"2021","unstructured":"Lin, Z., Feng, L., Yin, R., et al.: Glimg: global and local item graphs for top-n recommender systems. Inf. Sci. 580, 1\u201314 (2021). https:\/\/doi.org\/10.1016\/j.ins.2021.08.018","journal-title":"Inf. Sci."},{"issue":"2","key":"1338_CR44","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s11063-024-11545-9","volume":"56","author":"J Chen","year":"2024","unstructured":"Chen, J., Zhou, J., Ma, L.: Gnncl: a graph neural network recommendation model based on contrastive learning. Neural Process. Lett. 56(2), 45 (2024). https:\/\/doi.org\/10.1007\/s11063-024-11545-9","journal-title":"Neural Process. Lett."},{"key":"1338_CR45","doi-asserted-by":"publisher","unstructured":"Wang, X., He, X., Wang, M., et\u00a0al.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, SIGIR\u201919, pp. 165\u2013174 (2019). https:\/\/doi.org\/10.1145\/3331184.3331267","DOI":"10.1145\/3331184.3331267"},{"key":"1338_CR46","doi-asserted-by":"publisher","unstructured":"Jin, X., Li, J., Xie, Y., et\u00a0al.: Enhancing graph collaborative filtering via neighborhood structure embedding. In: 2023 IEEE International Conference on Data Mining (ICDM), pp. 190\u2013199. IEEE (2023). https:\/\/doi.org\/10.1109\/ICDM58522.2023.00028","DOI":"10.1109\/ICDM58522.2023.00028"},{"issue":"4","key":"1338_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2827872","volume":"5","author":"FM Harper","year":"2015","unstructured":"Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TIIS) 5(4), 1\u201319 (2015). https:\/\/doi.org\/10.1145\/2827872","journal-title":"ACM Trans. Interact. Intell. Syst. (TIIS)"},{"key":"1338_CR48","doi-asserted-by":"publisher","unstructured":"Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1082\u20131090 (2011). https:\/\/doi.org\/10.1145\/2020408.2020579","DOI":"10.1145\/2020408.2020579"},{"key":"1338_CR49","doi-asserted-by":"publisher","unstructured":"McAuley, J., Targett, C., Shi, Q., et\u00a0al.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, SIGIR \u201915, pp. 43\u201352 (2015). https:\/\/doi.org\/10.1145\/2766462.2767755","DOI":"10.1145\/2766462.2767755"},{"key":"1338_CR50","unstructured":"He, S., Zha, H., Ye, X.: Network diffusions via neural mean-field dynamics. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01338-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-024-01338-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01338-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T17:24:09Z","timestamp":1720200249000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-024-01338-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,27]]},"references-count":50,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["1338"],"URL":"https:\/\/doi.org\/10.1007\/s00530-024-01338-4","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"type":"print","value":"0942-4962"},{"type":"electronic","value":"1432-1882"}],"subject":[],"published":{"date-parts":[[2024,5,27]]},"assertion":[{"value":"13 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2024","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 declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"160"}}