{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T16:09:39Z","timestamp":1773936579887,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":20,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-026-01161-x","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T08:33:59Z","timestamp":1772181239000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-view Spatiotemporal Traffic Prediction Using Evolutionary-Optimized RNN-GCN Networks"],"prefix":"10.1007","volume":"19","author":[{"given":"Walaa N.","family":"Ismail","sequence":"first","affiliation":[]},{"given":"Nariman Adel","family":"Hussein","sequence":"additional","affiliation":[]},{"given":"Hoda M. O.","family":"Mokhtar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"key":"1161_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111946","volume":"297","author":"Z Li","year":"2024","unstructured":"Li, Z., Zhou, J., Lin, Z., Zhou, T.: Dynamic spatial aware graph transformer for spatiotemporal traffic flow forecasting. Knowl. Based Syst. 297, 111946 (2024)","journal-title":"Knowl. Based Syst."},{"issue":"2","key":"1161_CR2","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1049\/itr2.12448","volume":"18","author":"K Li","year":"2024","unstructured":"Li, K., Bai, W., Huang, S., Tan, G., Zhou, T., Li, K.: Lag-related noise shrinkage stacked lstm network for short-term traffic flow forecasting. IET Intel. Transport Syst. 18(2), 244\u2013257 (2024)","journal-title":"IET Intel. Transport Syst."},{"key":"1161_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106041","volume":"121","author":"M M\u00e9ndez","year":"2023","unstructured":"M\u00e9ndez, M., Merayo, M.G., N\u00fa\u00f1ez, M.: Long-term traffic flow forecasting using a hybrid cnn-bilstm model. Eng. Appl. Artif. Intell. 121, 106041 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"1161_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2024.116899","volume":"296","author":"J Man","year":"2024","unstructured":"Man, J., Chen, D., Wu, B., Wan, C., Yan, X.: An effective approach for Yangtze river vessel traffic flow forecasting: a case study of Wuhan area. Ocean Eng. 296, 116899 (2024)","journal-title":"Ocean Eng."},{"issue":"20","key":"1161_CR5","doi-asserted-by":"publisher","first-page":"31401","DOI":"10.1007\/s11042-020-10486-4","volume":"80","author":"A Ali","year":"2021","unstructured":"Ali, A., Zhu, Y., Zakarya, M.: A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing. Multimed. Tools Appl. 80(20), 31401\u201331433 (2021)","journal-title":"Multimed. Tools Appl."},{"key":"1161_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111637","volume":"293","author":"Q Luo","year":"2024","unstructured":"Luo, Q., He, S., Han, X., Wang, Y., Li, H.: Lsttn: a long-short term transformer-based spatiotemporal neural network for traffic flow forecasting. Knowl. Based Syst. 293, 111637 (2024)","journal-title":"Knowl. Based Syst."},{"issue":"6","key":"1161_CR7","doi-asserted-by":"publisher","first-page":"4775","DOI":"10.1007\/s00500-023-09173-x","volume":"28","author":"G Tan","year":"2024","unstructured":"Tan, G., Zhou, T., Huang, B., Dou, H., Song, Y., Lin, Z.: A noise-immune and attention-based multi-modal framework for short-term traffic flow forecasting. Soft. Comput. 28(6), 4775\u20134790 (2024)","journal-title":"Soft. Comput."},{"key":"1161_CR8","doi-asserted-by":"crossref","unstructured":"Alruban, A., Mengash, H.A., Eltahir, M.M., Almalki, N.S., Mahmud, A., Assiri, M.: Artificial hummingbird optimization algorithm with hierarchical deep learning for traffic management in intelligent transportation systems. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2023.3349032"},{"key":"1161_CR9","unstructured":"Li, H., Zhao, Y., Mao, Z., Qin, Y., Xiao, Z., Feng, J., Gu, Y., Ju, W., Luo, X., Zhang, M.: A survey on graph neural networks in intelligent transportation systems. arXiv preprint arXiv:2401.00713 (2024)"},{"issue":"4","key":"1161_CR10","doi-asserted-by":"publisher","first-page":"4477","DOI":"10.1007\/s11227-023-05597-2","volume":"80","author":"KHK Reddy","year":"2024","unstructured":"Reddy, K.H.K., Goswami, R.S., Roy, D.S.: A deep learning-based smart service model for context-aware intelligent transportation system. J. Supercomput. 80(4), 4477\u20134499 (2024)","journal-title":"J. Supercomput."},{"key":"1161_CR11","doi-asserted-by":"crossref","unstructured":"Cui, Z., Henrickson, K., Ke, R., Wang, Y.: Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting. IEEE Trans. Intell. Transport. Syst. (2019)","DOI":"10.1109\/TITS.2019.2950416"},{"key":"1161_CR12","doi-asserted-by":"crossref","unstructured":"Ali, A., Zhu, Y., Chen, Q., Yu, J., Cai, H.: Leveraging spatio-temporal patterns for predicting citywide traffic crowd flows using deep hybrid neural networks. In: 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), pp. 125\u2013132. IEEE (2019)","DOI":"10.1109\/ICPADS47876.2019.00025"},{"key":"1161_CR13","doi-asserted-by":"crossref","unstructured":"Rajalakshmi, V., Kala, A., et al.: Day\u2014ahead traffic flow forecast using lstm and cuckoo search optimization (2024)","DOI":"10.1007\/s42979-025-04276-8"},{"key":"1161_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102466","volume-title":"Enhancement of Traffic Forecasting Through Graph Neural Network-Based Information Fusion Techniques","author":"SF Ahmed","year":"2024","unstructured":"Ahmed, S.F., Kuldeep, S.A., Rafa, S.J., Fazal, J., Hoque, M., Liu, G., Gandomi, A.H.: Enhancement of Traffic Forecasting Through Graph Neural Network-Based Information Fusion Techniques. Elsevier, Amsterdam (2024)"},{"key":"1161_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111913","volume":"25","author":"A Khaled","year":"2024","unstructured":"Khaled, A., Elsir, A.M.T., Wang, P., Shen, Y., Zhang, Q.: A graph-based approach for traffic prediction using similarity and causal relations between nodes. Knowl. Based Syst. 25, 111913 (2024)","journal-title":"Knowl. Based Syst."},{"key":"1161_CR16","first-page":"1","volume":"7","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y., Xu, S., Zhang, L., Jiang, W., Alam, S., Xue, D.: Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional lstm network (agc-lstm). Neural Comput. Appl. 7, 1\u201320 (2024)","journal-title":"Neural Comput. Appl."},{"issue":"8","key":"1161_CR17","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.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"1161_CR18","unstructured":"Graves, A.: Generating sequences with recurrent neural networks. CoRR abs\/1308.0850 (2013)"},{"key":"1161_CR19","doi-asserted-by":"crossref","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN\u201995-international Conference on Neural Networks, vol. 4, pp. 1942\u20131948. IEEE (1995)","DOI":"10.1109\/ICNN.1995.488968"},{"key":"1161_CR20","doi-asserted-by":"crossref","unstructured":"Ali, A., Ullah, I., Shabaz, M., Sharafian, A., Khan, M.A., Bai, X., Qiu, L.: A resource-aware multi-graph neural network for urban traffic flow prediction in multi-access edge computing systems. IEEE Trans. Consum. Electron. (2024)","DOI":"10.1109\/TCE.2024.3439719"},{"key":"1161_CR21","doi-asserted-by":"crossref","unstructured":"Rajalakshmi, V., Ganesh Vaidyanathan, S.: Efficient traffic management on road network using edmonds\u2013karp algorithm. In: Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2017, vol. 2, pp. 577\u2013583. Springer (2019)","DOI":"10.1007\/978-981-13-0224-4_52"},{"key":"1161_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.trc.2017.02.024","volume":"79","author":"NG Polson","year":"2017","unstructured":"Polson, N.G., Sokolov, V.O.: Deep learning for short-term traffic flow prediction. Transport. Res. Part C Emerg. Technol. 79, 1\u201317 (2017)","journal-title":"Transport. Res. Part C Emerg. Technol."},{"issue":"3","key":"1161_CR23","doi-asserted-by":"publisher","first-page":"45","DOI":"10.32604\/iasc.2022.024310","volume":"33","author":"V Rajalakshmi","year":"2022","unstructured":"Rajalakshmi, V., Vaidyanathan, S.G.: Mlp-pso framework with dynamic network tuning for traffic flow forecasting. Intell. Autom. Soft Comput. 33(3), 45 (2022)","journal-title":"Intell. Autom. Soft Comput."},{"key":"1161_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.03.007","volume":"534","author":"J Tang","year":"2019","unstructured":"Tang, J., Chen, X., Hu, Z., Zong, F., Han, C., Li, L.: Traffic flow prediction based on combination of support vector machine and data denoising schemes. Physica A 534, 120642 (2019)","journal-title":"Physica A"},{"key":"1161_CR25","doi-asserted-by":"crossref","unstructured":"Shin, Y., Yoon, Y.: Pgcn: progressive graph convolutional networks for spatial\u2013temporal traffic forecasting. IEEE Trans. Intell. Transport. Syst. (2024)","DOI":"10.1109\/TITS.2024.3349565"},{"key":"1161_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2023.102513","volume":"80","author":"H Xing","year":"2023","unstructured":"Xing, H., Chen, A., Zhang, X.: Rl-gcn: traffic flow prediction based on graph convolution and reinforcement learning for smart cities. Displays 80, 102513 (2023)","journal-title":"Displays"},{"key":"1161_CR27","doi-asserted-by":"crossref","unstructured":"Li, Z., Jiang, S., Li, L., Li, Y.: Building sparse models for traffic flow prediction: an empirical comparison between statistical heuristics and geometric heuristics for bayesian network approaches. Transport. Dynam. Transportmetrica B (2017)","DOI":"10.1080\/21680566.2017.1354737"},{"key":"1161_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2021.102076","volume":"69","author":"W Cai","year":"2021","unstructured":"Cai, W., Liu, D., Ning, X., Wang, C., Xie, G.: Voxel-based three-view hybrid parallel network for 3d object classification. Displays 69, 102076 (2021)","journal-title":"Displays"},{"key":"1161_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2023.104225","volume":"153","author":"Q Xu","year":"2023","unstructured":"Xu, Q., Pang, Y., Liu, Y.: Air traffic density prediction using bayesian ensemble graph attention network (began). Transport. Res. Part C Emerg. Technol. 153, 104225 (2023)","journal-title":"Transport. Res. Part C Emerg. Technol."},{"key":"1161_CR30","doi-asserted-by":"crossref","unstructured":"Xu, Q., Pang, Y., Zhou, X., Liu, Y.: Pigat: physics-informed graph attention transformer for air traffic state prediction. IEEE Trans. Intell. Transport. Syst. (2024)","DOI":"10.1109\/TITS.2024.3386128"},{"issue":"1","key":"1161_CR31","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/s12205-023-2457-y","volume":"28","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Ke, S., An, C., Lu, Z., Xia, J.: A hybrid framework combining lstm nn and bnn for short-term traffic flow prediction and uncertainty quantification. KSCE J. Civ. Eng. 28(1), 363\u2013374 (2024)","journal-title":"KSCE J. Civ. Eng."},{"key":"1161_CR32","doi-asserted-by":"crossref","unstructured":"Ali, A., Ullah, I., Ahmad, S., Wu, Z., Li, J., Bai, X.: An attention-driven spatio-temporal deep hybrid neural networks for traffic flow prediction in transportation systems. IEEE Trans. Intell. Transport. Syst. (2025)","DOI":"10.1109\/TITS.2025.3540852"},{"key":"1161_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2025.116898","volume":"199","author":"A Ali","year":"2025","unstructured":"Ali, A., Naeem, H.Y., Sharafian, A., Qiu, L., Wu, Z., Bai, X.: Dynamic multi-graph spatio-temporal learning for citywide traffic flow prediction in transportation systems. Chaos Solitons Fractals 199, 116898 (2025)","journal-title":"Chaos Solitons Fractals"},{"key":"1161_CR34","doi-asserted-by":"crossref","unstructured":"Ali, A., Ullah, I., Singh, S.K., Jiang, W., Alturise, F., Bai, X.: Attention-driven graph convolutional networks for deadline-constrained virtual machine task allocation in edge computing. IEEE Trans Consum. Electron. (2025)","DOI":"10.1109\/TCE.2025.3571035"},{"key":"1161_CR35","volume":"8","author":"W Li","year":"2025","unstructured":"Li, W., Fei, J., Jiang, Y., Guo, X., Geng, X., He, X.: Dynamic spatio-temporal graph interaction attention network for traffic flow prediction. Future Gener. Comput. Syst. 8, 108116 (2025)","journal-title":"Future Gener. Comput. Syst."},{"issue":"10","key":"1161_CR36","doi-asserted-by":"publisher","first-page":"6782","DOI":"10.1002\/cpe.6782","volume":"34","author":"X Xu","year":"2022","unstructured":"Xu, X., Liu, C., Zhao, Y., Lv, X.: Short-term traffic flow prediction based on whale optimization algorithm optimized bilstm_attention. Concurr. Comput. Pract. Exp. 34(10), 6782 (2022)","journal-title":"Concurr. Comput. Pract. Exp."},{"issue":"10","key":"1161_CR37","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.3390\/math12101493","volume":"12","author":"C Zhang","year":"2024","unstructured":"Zhang, C., Wu, Y., Shen, Y., Wang, S., Zhu, X., Shen, W.: Adaptive graph convolutional recurrent network with transformer and whale optimization algorithm for traffic flow prediction. Mathematics 12(10), 1493 (2024)","journal-title":"Mathematics"},{"key":"1161_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2023.129448","volume":"634","author":"B Naheliya","year":"2024","unstructured":"Naheliya, B., Redhu, P., Kumar, K.: Mfoa-bi-lstm: an optimized bidirectional long short-term memory model for short-term traffic flow prediction. Physica A 634, 129448 (2024)","journal-title":"Physica A"},{"key":"1161_CR39","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1016\/j.aej.2024.08.083","volume":"107","author":"A Alzughaibi","year":"2024","unstructured":"Alzughaibi, A., Karim, F.K., Darwish, J.A.: Driven traffic flow prediction in smart cities using hunter-prey optimization with hybrid deep learning models. Alex. Eng. J. 107, 625\u2013633 (2024)","journal-title":"Alex. Eng. J."},{"issue":"1","key":"1161_CR40","first-page":"1702766","volume":"2022","author":"C Chai","year":"2022","unstructured":"Chai, C., Ren, C., Yin, C., Xu, H., Meng, Q., Teng, J., Gao, G.: A multifeature fusion short-term traffic flow prediction model based on deep learnings. J. Adv. Transp. 2022(1), 1702766 (2022)","journal-title":"J. Adv. Transp."},{"issue":"4\u20135","key":"1161_CR41","first-page":"3137","volume":"24","author":"S Zhang","year":"2024","unstructured":"Zhang, S.: Real-time application of grey system theory in intelligent traffic signal optimization. J. Comput. Methods Sci. Eng. 24(4\u20135), 3137\u20133153 (2024)","journal-title":"J. Comput. Methods Sci. Eng."},{"issue":"6","key":"1161_CR42","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1049\/iet-its.2017.0199","volume":"12","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Huang, G.: Traffic flow prediction model based on deep belief network and genetic algorithm. IET Intel. Transport Syst. 12(6), 533\u2013541 (2018)","journal-title":"IET Intel. Transport Syst."},{"issue":"4","key":"1161_CR43","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1080\/23249935.2020.1845250","volume":"17","author":"J Tang","year":"2021","unstructured":"Tang, J., Zeng, J., Wang, Y., Yuan, H., Liu, F., Huang, H.: Traffic flow prediction on urban road network based on license plate recognition data: combining attention-lstm with genetic algorithm. Transportmetrica Transport Sci. 17(4), 1217\u20131243 (2021)","journal-title":"Transportmetrica Transport Sci."},{"key":"1161_CR44","doi-asserted-by":"publisher","first-page":"2305","DOI":"10.1007\/s11063-019-09994-8","volume":"50","author":"C Luo","year":"2019","unstructured":"Luo, C., Huang, C., Cao, J., Lu, J., Huang, W., Guo, J., Wei, Y.: Short-term traffic flow prediction based on least square support vector machine with hybrid optimization algorithm. Neural Process. Lett. 50, 2305\u20132322 (2019)","journal-title":"Neural Process. Lett."},{"key":"1161_CR45","doi-asserted-by":"publisher","first-page":"6505","DOI":"10.1109\/ACCESS.2019.2963784","volume":"8","author":"W Cai","year":"2020","unstructured":"Cai, W., Yang, J., Yu, Y., Song, Y., Zhou, T., Qin, J.: Pso-elm: a hybrid learning model for short-term traffic flow forecasting. IEEE Access 8, 6505\u20136514 (2020)","journal-title":"IEEE Access"},{"issue":"4","key":"1161_CR46","first-page":"1270","volume":"27","author":"Y-S Qian","year":"2020","unstructured":"Qian, Y.-S., Zeng, J.-W., Zhang, S.-F., Xu, D.-J., Wei, X.-T.: Short-term traffic prediction based on genetic algorithm improved neural network. Tehni\u010dki vjesnik 27(4), 1270\u20131276 (2020)","journal-title":"Tehni\u010dki vjesnik"},{"key":"1161_CR47","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)","DOI":"10.24963\/ijcai.2018\/505"},{"key":"1161_CR48","doi-asserted-by":"publisher","first-page":"8091","DOI":"10.1007\/s11042-020-10139-6","volume":"80","author":"S Katoch","year":"2021","unstructured":"Katoch, S., Chauhan, S.S., Kumar, V.: A review on genetic algorithm: past, present, and future. Multimed. Tools Appl. 80, 8091\u20138126 (2021)","journal-title":"Multimed. Tools Appl."},{"key":"1161_CR49","first-page":"1","volume":"6","author":"B Naheliya","year":"2024","unstructured":"Naheliya, B., Redhu, P., Kumar, K.: A review on developments in evolutionary computation approaches for road traffic flow prediction. Arch. Comput. Methods Eng. 6, 1\u201325 (2024)","journal-title":"Arch. Comput. Methods Eng."},{"issue":"1","key":"1161_CR50","doi-asserted-by":"publisher","first-page":"96","DOI":"10.3141\/1748-12","volume":"1748","author":"C Chen","year":"2001","unstructured":"Chen, C., Petty, K., Skabardonis, A., Varaiya, P., Jia, Z.: Freeway performance measurement system: mining loop detector data. Transp. Res. Rec. 1748(1), 96\u2013102 (2001)","journal-title":"Transp. Res. Rec."},{"key":"1161_CR51","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI) (2018)","DOI":"10.24963\/ijcai.2018\/505"},{"issue":"3","key":"1161_CR52","first-page":"82","volume":"21","author":"J Liu","year":"2004","unstructured":"Liu, J., Guan, W.: A summary of traffic flow forecasting methods. J. Highway Transport. Res. Dev. 21(3), 82\u201385 (2004)","journal-title":"J. Highway Transport. Res. Dev."},{"key":"1161_CR53","first-page":"97","volume":"722","author":"MS Ahmed","year":"1979","unstructured":"Ahmed, M.S., Cook, A.R.: Analysis of freeway traffic time-series data by using box-Jenkins. Techniques 722, 97 (1979)","journal-title":"Techniques"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-026-01161-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01161-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01161-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:17:18Z","timestamp":1773929838000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-026-01161-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,27]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1161"],"URL":"https:\/\/doi.org\/10.1007\/s44196-026-01161-x","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,27]]},"assertion":[{"value":"2 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2026","order":4,"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 Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable. This study does not involve human participants, animals, or sensitive personal data, and therefore did not require ethics approval or consent to participate.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable. No identifiable data or personal information is included in this manuscript that requires consent for publication.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"130"}}