{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T09:47:11Z","timestamp":1775123231885,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"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":["62502040"],"award-info":[{"award-number":["62502040"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postdoctoral Fellowship Program of China Postdoctoral Science Foundation","award":["GZC20251056"],"award-info":[{"award-number":["GZC20251056"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-026-08462-0","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T08:49:52Z","timestamp":1775119792000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-preference graph learning for next point-of-interest recommendation"],"prefix":"10.1007","volume":"82","author":[{"given":"Sai","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caisen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"issue":"1","key":"8462_CR1","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s11227-024-06742-1","volume":"81","author":"J Zhang","year":"2025","unstructured":"Zhang J, Li B, Zhang Y, Xu Y, Li H (2025) Research on the recommendation method of urban location point of interest based on DTCN-EFFN-Transformer. J Supercomput 81(1):221. https:\/\/doi.org\/10.1007\/s11227-024-06742-1","journal-title":"J Supercomput"},{"issue":"11s","key":"8462_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3510409","volume":"54","author":"P S\u00e1nchez","year":"2022","unstructured":"S\u00e1nchez P, Bellog\u00edn A (2022) Point-of-interest recommender systems based on location-based social networks: a survey from an experimental perspective. ACM Comput Surv (CSUR) 54(11s):1\u201337. https:\/\/doi.org\/10.1145\/3510409","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"2","key":"8462_CR3","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1007\/s11227-025-06925-4","volume":"81","author":"N Huang","year":"2025","unstructured":"Huang N, Ding H, Hu R, Jiao P, Zhao Z, Yang B, Zheng Q (2025) Multi-time-scale with clockwork recurrent neural network modeling for sequential recommendation. J Supercomput 81(2):412. https:\/\/doi.org\/10.1007\/s11227-025-06925-4","journal-title":"J Supercomput"},{"issue":"1","key":"8462_CR4","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1007\/s11227-024-06695-5","volume":"81","author":"X Zang","year":"2025","unstructured":"Zang X, Xia H, Liu Y (2025) Diffusion social augmentation for social recommendation. J Supercomput 81(1):208. https:\/\/doi.org\/10.1007\/s11227-024-06695-5","journal-title":"J Supercomput"},{"issue":"8","key":"8462_CR5","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1007\/s11227-025-07385-6","volume":"81","author":"Z Fei","year":"2025","unstructured":"Fei Z, Qin X, Zhou H, Chen G, Xiang X (2025) Hybrid personalized sequence recommendation based on LSTM and filter enhancement. J Supercomput 81(8):919. https:\/\/doi.org\/10.1007\/s11227-025-07385-6","journal-title":"J Supercomput"},{"issue":"1","key":"8462_CR6","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1007\/s11227-024-06726-1","volume":"81","author":"AU Mat","year":"2025","unstructured":"Mat AU, Saran AN (2025) Enhancing session-based trip recommendations using matrix factorization: a study on algorithm efficiency and resource utilization. J Supercomput 81(1):292. https:\/\/doi.org\/10.1007\/s11227-024-06726-1","journal-title":"J Supercomput"},{"key":"8462_CR7","doi-asserted-by":"publisher","unstructured":"Duan C, Fan W, Zhou W, Liu H, Wen J (2023) CLSPRec: contrastive learning of long and short-term preferences for next POI recommendation. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp 473\u2013482. https:\/\/doi.org\/10.1145\/3583780.361481","DOI":"10.1145\/3583780.361481"},{"issue":"1","key":"8462_CR8","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s11227-024-06583-y","volume":"81","author":"H Zhang","year":"2025","unstructured":"Zhang H, Li H, Li Z, Chen P (2025) User preference and social relationship-aware recommendations base on a novel light graph convolutional network. J Supercomput 81(1):27. https:\/\/doi.org\/10.1007\/s11227-024-06583-y","journal-title":"J Supercomput"},{"key":"8462_CR9","doi-asserted-by":"publisher","unstructured":"Rao X, Chen L, Liu Y, Shang S, Yao B, Han P (2022) Graph-flashback network for next location recommendation. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp 1463\u20131471. https:\/\/doi.org\/10.1145\/3534678.353938","DOI":"10.1145\/3534678.353938"},{"key":"8462_CR10","doi-asserted-by":"publisher","unstructured":"Lim N, Hooi B, Ng S-K, Goh YL, Weng R, Tan R (2022) Hierarchical multi-task graph recurrent network for next POI recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1133\u20131143. https:\/\/doi.org\/10.1145\/3477495.3531989","DOI":"10.1145\/3477495.3531989"},{"key":"8462_CR11","doi-asserted-by":"publisher","unstructured":"Luo Y, Duan H, Liu Y, Chung F-L (2023) Timestamps as prompts for geography-aware location recommendation. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp 1697\u20131706. https:\/\/doi.org\/10.1145\/3583780.3615083","DOI":"10.1145\/3583780.3615083"},{"key":"8462_CR12","doi-asserted-by":"publisher","unstructured":"Yang S, Liu J, Zhao K (2022) GETNext: trajectory flow map enhanced transformer for next POI recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1144\u20131153. https:\/\/doi.org\/10.1145\/3477495.3531983","DOI":"10.1145\/3477495.3531983"},{"key":"8462_CR13","doi-asserted-by":"publisher","unstructured":"Zhang L, Sun Z, Wu Z, Zhang J, Ong YS, Qu X (2022) Next point-of-interest recommendation with inferring multi-step future preferences. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI, pp 3751\u20133757. https:\/\/doi.org\/10.24963\/ijcai.2022\/521","DOI":"10.24963\/ijcai.2022\/521"},{"issue":"5","key":"8462_CR14","doi-asserted-by":"publisher","first-page":"2512","DOI":"10.1109\/TKDE.2020.3007194","volume":"34","author":"P Zhao","year":"2020","unstructured":"Zhao P, Luo A, Liu Y, Xu J, Li Z, Zhuang F, Sheng VS, Zhou X (2020) Where to go next: a spatio-temporal gated network for next POI recommendation. IEEE Trans Knowl Data Eng 34(5):2512\u20132524. https:\/\/doi.org\/10.1109\/TKDE.2020.3007194","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8462_CR15","doi-asserted-by":"publisher","unstructured":"Huang Z, Ma J, Dong Y, Foutz NZ, Li J (2022) Empowering next POI recommendation with multi-relational modeling. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 2034\u20132038. https:\/\/doi.org\/10.1145\/3477495.3531801","DOI":"10.1145\/3477495.3531801"},{"key":"8462_CR16","doi-asserted-by":"publisher","unstructured":"Wang Z, Zhu Y, Liu H, Wang C (2022) Learning graph-based disentangled representations for next POI recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1154\u20131163. https:\/\/doi.org\/10.1145\/3477495.3532012","DOI":"10.1145\/3477495.3532012"},{"key":"8462_CR17","doi-asserted-by":"publisher","unstructured":"Wang Z, Zhu Y, Wang C, Ma W, Li B, Yu J (2023) Adaptive graph representation learning for next POI recommendation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 393\u2013402. https:\/\/doi.org\/10.1145\/3539618.3591634","DOI":"10.1145\/3539618.3591634"},{"key":"8462_CR18","doi-asserted-by":"publisher","unstructured":"Yan X, Song T, Jiao Y, He J, Wang J, Li R, Chu W (2023) Spatio-temporal hypergraph learning for next POI recommendation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 403\u2013412. https:\/\/doi.org\/10.1145\/3539618.3591770","DOI":"10.1145\/3539618.3591770"},{"key":"8462_CR19","doi-asserted-by":"publisher","unstructured":"Yin F, Liu Y, Shen Z, Chen L, Shang S, Han P (2023) Next POI recommendation with dynamic graph and explicit dependency. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 37, pp 4827\u20134834. https:\/\/doi.org\/10.1609\/aaai.v37i4.25608","DOI":"10.1609\/aaai.v37i4.25608"},{"issue":"4","key":"8462_CR20","doi-asserted-by":"publisher","first-page":"1944","DOI":"10.1109\/TKDE.2020.3002531","volume":"34","author":"Y Wu","year":"2020","unstructured":"Wu Y, Li K, Zhao G, Qian X (2020) Personalized long-and short-term preference learning for next POI recommendation. IEEE Trans Knowl Data Eng 34(4):1944\u20131957. https:\/\/doi.org\/10.1109\/TKDE.2020.3002531","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8462_CR21","doi-asserted-by":"publisher","unstructured":"Han H, Zhang M, Hou M, Zhang F, Wang Z, Chen E, Wang H, Ma J, Liu Q (2020) STGCN: a spatial-temporal aware graph learning method for POI recommendation. In: 2020 IEEE International Conference on Data Mining (ICDM), pp 1052\u20131057. https:\/\/doi.org\/10.1109\/ICDM50108.2020.00124","DOI":"10.1109\/ICDM50108.2020.00124"},{"key":"8462_CR22","doi-asserted-by":"publisher","unstructured":"Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp 811\u2013820. https:\/\/doi.org\/10.1145\/1772690.1772773","DOI":"10.1145\/1772690.1772773"},{"key":"8462_CR23","doi-asserted-by":"publisher","unstructured":"He R, McAuley J (2016) Fusing similarity models with Markov chains for sparse sequential recommendation. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp 191\u2013200. https:\/\/doi.org\/10.1109\/ICDM.2016.0030. IEEE","DOI":"10.1109\/ICDM.2016.0030"},{"key":"8462_CR24","doi-asserted-by":"publisher","unstructured":"Liu Q, Wu S, Wang L, Tan T (2016) Predicting the next location: A recurrent model with spatial and temporal contexts. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 30. https:\/\/doi.org\/10.1609\/aaai.v30i1.9971","DOI":"10.1609\/aaai.v30i1.9971"},{"key":"8462_CR25","doi-asserted-by":"publisher","unstructured":"Sun K, Qian T, Chen T, Liang Y, Nguyen QVH, Yin H (2020) Where to go next: Modeling long-and short-term user preferences for point-of-interest recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 214\u2013221. https:\/\/doi.org\/10.1609\/aaai.v34i01.5353","DOI":"10.1609\/aaai.v34i01.5353"},{"issue":"4","key":"8462_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3610584","volume":"1","author":"N Lim","year":"2023","unstructured":"Lim N, Hooi B, Ng S-K, Goh YL, Weng R, Tan R (2023) Learning hierarchical spatial tasks with visiting relations for next POI recommendation. ACM Trans Recomm Syst 1(4):1\u201326. https:\/\/doi.org\/10.1145\/3610584","journal-title":"ACM Trans Recomm Syst"},{"key":"8462_CR27","doi-asserted-by":"publisher","unstructured":"Lian D, Wu Y, Ge Y, Xie X, Chen E (2020) Geography-aware sequential location recommendation. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2009\u20132019. https:\/\/doi.org\/10.1145\/3394486.3403252","DOI":"10.1145\/3394486.3403252"},{"key":"8462_CR28","doi-asserted-by":"publisher","unstructured":"Luo Y, Liu Q, Liu Z (2021) STAN: spatio-temporal attention network for next location recommendation. In: Proceedings of the Web Conference 2021, pp 2177\u20132185. https:\/\/doi.org\/10.1145\/3442381.3449998","DOI":"10.1145\/3442381.3449998"},{"key":"8462_CR29","doi-asserted-by":"publisher","unstructured":"Lin Y, Wan H, Guo S, Lin Y (2021) Pre-training context and time aware location embeddings from spatial-temporal trajectories for user next location prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 35, pp 4241\u20134248. https:\/\/doi.org\/10.1609\/aaai.v35i5.16548","DOI":"10.1609\/aaai.v35i5.16548"},{"issue":"1","key":"8462_CR30","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1109\/TKDE.2023.3280859","volume":"36","author":"X Luo","year":"2023","unstructured":"Luo X, Zhao Y, Qin Y, Ju W, Zhang M (2023) Towards semi-supervised universal graph classification. IEEE Trans Knowl Data Eng 36(1):416\u2013428. https:\/\/doi.org\/10.1109\/TKDE.2023.3280859","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8462_CR31","doi-asserted-by":"publisher","unstructured":"Ju W, Gu Y, Chen B, Sun G, Qin Y, Liu X, Luo X, Zhang M (2023) GLCC: a general framework for graph-level clustering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 37, pp 4391\u20134399. https:\/\/doi.org\/10.1609\/aaai.v37i4.25559","DOI":"10.1609\/aaai.v37i4.25559"},{"issue":"4","key":"8462_CR32","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/s11227-024-06898-w","volume":"81","author":"B Zhang","year":"2025","unstructured":"Zhang B, Xu H, Shuang R, Wang K (2025) Heterogeneous information-based self-supervised graph learning for recommendation. J Supercomput 81(4):507. https:\/\/doi.org\/10.1007\/s11227-024-06898-w","journal-title":"J Supercomput"},{"key":"8462_CR33","doi-asserted-by":"publisher","unstructured":"Xie M, Yin H, Wang H, Xu F, Chen W, Wang S (2016) Learning graph-based POI embedding for location-based recommendation. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp 15\u201324. https:\/\/doi.org\/10.1145\/2983323.2983711","DOI":"10.1145\/2983323.2983711"},{"issue":"6","key":"8462_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3513092","volume":"16","author":"Z Wang","year":"2022","unstructured":"Wang Z, Zhu Y, Zhang Q, Liu H, Wang C, Liu T (2022) Graph-enhanced spatial-temporal network for next POI recommendation. ACM Trans Knowl Discov Data (TKDD) 16(6):1\u201321. https:\/\/doi.org\/10.1145\/3513092","journal-title":"ACM Trans Knowl Discov Data (TKDD)"},{"key":"8462_CR35","doi-asserted-by":"publisher","unstructured":"Wang X, He X, Wang M, Feng F, Chua T-S (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 165\u2013174. https:\/\/doi.org\/10.1145\/3331184.3331267","DOI":"10.1145\/3331184.3331267"},{"issue":"2","key":"8462_CR36","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/s10844-023-00816-x","volume":"62","author":"X Zhang","year":"2024","unstructured":"Zhang X, Gan M (2024) C-GDN: core features activated graph dual-attention network for personalized recommendation. J Intell Inf Syst 62(2):317\u2013338. https:\/\/doi.org\/10.1007\/s10844-023-00816-x","journal-title":"J Intell Inf Syst"},{"key":"8462_CR37","doi-asserted-by":"publisher","unstructured":"Lim N, Hooi B, Ng S-K, Wang X, Goh YL, Weng R, Varadarajan J (2020) STP-UDGAT: spatial-temporal-preference user dimensional graph attention network for next POI recommendation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp 845\u2013854. https:\/\/doi.org\/10.1145\/3340531.3411876","DOI":"10.1145\/3340531.3411876"},{"issue":"1","key":"8462_CR38","doi-asserted-by":"publisher","first-page":"1287","DOI":"10.1109\/TPAMI.2022.3148707","volume":"45","author":"K Zhang","year":"2022","unstructured":"Zhang K, Li D, Luo W, Ren W, Liu W (2022) Enhanced spatio-temporal interaction learning for video deraining: faster and better. IEEE Trans Pattern Anal Mach Intell 45(1):1287\u20131293. https:\/\/doi.org\/10.1109\/TPAMI.2022.3148707","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"8462_CR39","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/TIP.2018.2867733","volume":"28","author":"K Zhang","year":"2018","unstructured":"Zhang K, Luo W, Zhong Y, Ma L, Liu W, Li H (2018) Adversarial spatio-temporal learning for video deblurring. IEEE Trans Image Process 28(1):291\u2013301. https:\/\/doi.org\/10.1109\/TIP.2018.2867733","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"8462_CR40","doi-asserted-by":"publisher","first-page":"3755","DOI":"10.1109\/TCSVT.2023.3319330","volume":"34","author":"K Zhang","year":"2023","unstructured":"Zhang K, Wang T, Luo W, Ren W, Stenger B, Liu W, Li H, Yang M-H (2023) MC-Blur: a comprehensive benchmark for image deblurring. IEEE Trans Circuits Syst Video Technol 34(5):3755\u20133767. https:\/\/doi.org\/10.1109\/TCSVT.2023.3319330","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"8462_CR41","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907"},{"issue":"2","key":"8462_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3624475","volume":"42","author":"Y Qin","year":"2023","unstructured":"Qin Y, Wu H, Ju W, Luo X, Zhang M (2023) A diffusion model for POI recommendation. ACM Trans Inf Syst 42(2):1\u201327. https:\/\/doi.org\/10.1145\/3624475","journal-title":"ACM Trans Inf Syst"},{"key":"8462_CR43","doi-asserted-by":"publisher","unstructured":"Ma C, Zhang Y, Wang Q, Liu X (2018) Point-of-interest recommendation: exploiting self-attentive autoencoders with neighbor-aware influence. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp 697\u2013706. https:\/\/doi.org\/10.1145\/3269206.3271733","DOI":"10.1145\/3269206.3271733"},{"key":"8462_CR44","doi-asserted-by":"publisher","unstructured":"Zhou X, Mascolo C, Zhao Z (2019) Topic-enhanced memory networks for personalised point-of-interest recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 3018\u20133028. https:\/\/doi.org\/10.1145\/3292500.3330781","DOI":"10.1145\/3292500.3330781"},{"issue":"8","key":"8462_CR45","doi-asserted-by":"publisher","first-page":"8655","DOI":"10.1109\/TITS.2024.3355292","volume":"25","author":"B Wang","year":"2024","unstructured":"Wang B, Li H, Wang W, Wang M, Jin Y, Xu Y (2024) $$\\text{ PG}^2$$ Net: personalized and group preferences guided network for next place prediction. IEEE Trans Intell Transp Syst 25(8):8655\u20138670. https:\/\/doi.org\/10.1109\/TITS.2024.3355292","journal-title":"IEEE Trans Intell Transp Syst"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08462-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-026-08462-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08462-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T08:49:54Z","timestamp":1775119794000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-026-08462-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,2]]},"references-count":45,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["8462"],"URL":"https:\/\/doi.org\/10.1007\/s11227-026-08462-0","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,2]]},"assertion":[{"value":"5 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 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 declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This research did not require ethical approval.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"315"}}