{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T02:35:25Z","timestamp":1773887725531,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"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":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s41060-025-00932-7","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T07:35:43Z","timestamp":1764660943000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A novel sentiment correlation-based method with dual transformer model for stock price prediction"],"prefix":"10.1007","volume":"21","author":[{"given":"Qizhao","family":"Chen","sequence":"first","affiliation":[]},{"given":"Hiroaki","family":"Kawashima","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"key":"932_CR1","doi-asserted-by":"publisher","unstructured":"Chen, Q.: Stock price change prediction using prompt-based llms with rl-enhanced post-hoc adjustments. In: Proceedings of the 2025 4th International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2025), pp. 475\u2013483. Atlantis Press, (2025). https:\/\/doi.org\/10.2991\/978-94-6463-742-7_46","DOI":"10.2991\/978-94-6463-742-7_46"},{"key":"932_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.nlp.2025.100125","volume":"10","author":"X Chen","year":"2025","unstructured":"Chen, X., Xie, H., Li, Z., Zhang, H., Tao, X., Wang, F.L.: Sentiment analysis for stock market research: a bibliometric study. Nat. Language Process. J. 10, 100125 (2025). https:\/\/doi.org\/10.1016\/j.nlp.2025.100125","journal-title":"Nat. Language Process. J."},{"key":"932_CR3","doi-asserted-by":"publisher","unstructured":"Mohan, S., Mullapudi, S., Sammeta, S., Vijayvergia, P., Anastasiu, D.C.: Stock price prediction using news sentiment analysis. In: 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), pp. 205\u2013208 (2019). https:\/\/doi.org\/10.1109\/BigDataService.2019.00035","DOI":"10.1109\/BigDataService.2019.00035"},{"key":"932_CR4","doi-asserted-by":"publisher","DOI":"10.3390\/math10224255","author":"MP Cristescu","year":"2022","unstructured":"Cristescu, M.P., Nerisanu, R.A., Mara, D.A., Oprea, S.-V.: Using market news sentiment analysis for stock market prediction. Mathematics (2022). https:\/\/doi.org\/10.3390\/math10224255","journal-title":"Mathematics"},{"key":"932_CR5","doi-asserted-by":"crossref","unstructured":"Luan, Y., Zhang, H., Zhang, C., Mu, Y., Wang, W.: Stock price prediction with sentiment analysis for chinese market. In: Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing, pp. 167\u2013177. Association for Computational Linguistics, Torino, Italia (2024). https:\/\/aclanthology.org\/2024.finnlp-1.16","DOI":"10.63317\/4vxfwda6dv5i"},{"key":"932_CR6","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.7717\/peerj-cs.1293","volume":"9","author":"Q Xiao","year":"2023","unstructured":"Xiao, Q., Ihnaini, B.: Stock trend prediction using sentiment analysis. Peer J. Comput. Sci. 9, 1293 (2023). https:\/\/doi.org\/10.7717\/peerj-cs.1293","journal-title":"Peer J. Comput. Sci."},{"key":"932_CR7","unstructured":"Darapaneni, N., Paduri, A.R., Sharma, H., Manjrekar, M., Hindlekar, N., Bhagat, P., Aiyer, U., Agarwal, Y.: Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets (2022). 10.48550\/arXiv.2204.05783"},{"issue":"04","key":"932_CR8","doi-asserted-by":"publisher","first-page":"309","DOI":"10.4236\/jdaip.2020.84018","volume":"08","author":"SV Kolasani","year":"2020","unstructured":"Kolasani, S.V., Assaf, R.: Predicting stock movement using sentiment analysis of twitter feed with neural networks. J. Data Anal. Inf. Process. 08(04), 309\u2013319 (2020). https:\/\/doi.org\/10.4236\/jdaip.2020.84018","journal-title":"J. Data Anal. Inf. Process."},{"key":"932_CR9","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-82338-6","author":"X Wan","year":"2021","unstructured":"Wan, X., Yang, J., Marinov, S., Calliess, J.-P., Zohren, S., Dong, X.: Sentiment correlation in financial news networks and associated market movements. Sci. Rep. (2021). https:\/\/doi.org\/10.1038\/s41598-021-82338-6","journal-title":"Sci. Rep."},{"key":"932_CR10","doi-asserted-by":"publisher","first-page":"2312","DOI":"10.7717\/peerj-cs.2312","volume":"10","author":"Z Aksehir","year":"2024","unstructured":"Aksehir, Z., Kilic, E.: Analyzing the critical steps in deep learning-based stock forecasting: a literature review. Peer J. Comput. Sci. 10, 2312 (2024). https:\/\/doi.org\/10.7717\/peerj-cs.2312","journal-title":"Peer J. Comput. Sci."},{"key":"932_CR11","doi-asserted-by":"publisher","first-page":"51353","DOI":"10.1109\/ACCESS.2023.3278790","volume":"11","author":"G Mu","year":"2023","unstructured":"Mu, G., Gao, N., Wang, Y., Dai, L.: A stock price prediction model based on investor sentiment and optimized deep learning. IEEE Access 11, 51353\u201351367 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3278790","journal-title":"IEEE Access"},{"key":"932_CR12","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1016\/j.procs.2023.01.086","volume":"218","author":"J Maqbool","year":"2023","unstructured":"Maqbool, J., Aggarwal, P., Kaur, R., Mittal, A., Ganaie, I.A.: Stock prediction by integrating sentiment scores of financial news and mlp-regressor: A machine learning approach. Procedia Comput. Sci. 218, 1067\u20131078 (2023). https:\/\/doi.org\/10.1016\/j.procs.2023.01.086","journal-title":"Procedia Comput. Sci."},{"issue":"4","key":"932_CR13","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1080\/00051144.2023.2217602","volume":"64","author":"A John","year":"2023","unstructured":"John, A., Latha, T.: Stock market prediction based on deep hybrid rnn model and sentiment analysis. Automatika 64(4), 981\u2013995 (2023). https:\/\/doi.org\/10.1080\/00051144.2023.2217602","journal-title":"Automatika"},{"key":"932_CR14","doi-asserted-by":"publisher","first-page":"1168","DOI":"10.1016\/j.procs.2020.03.049","volume":"170","author":"A Moghar","year":"2020","unstructured":"Moghar, A., Hamiche, M.: Stock market prediction using lstm recurrent neural network. Procedia Comput. Sci. 170, 1168\u20131173 (2020). https:\/\/doi.org\/10.1016\/j.procs.2020.03.049","journal-title":"Procedia Comput. Sci."},{"key":"932_CR15","doi-asserted-by":"publisher","unstructured":"Reddy, K.R., Kumar, B.T., Ganesh, V.R., Swetha, P., Sarangi, P.K.: Stock market prediction using recurrent neural network. In: 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET), pp. 1\u20134 (2022). https:\/\/doi.org\/10.1109\/CCET56606.2022.10080154","DOI":"10.1109\/CCET56606.2022.10080154"},{"key":"932_CR16","doi-asserted-by":"publisher","unstructured":"Islam, S.B., Hasan, M.M., Khan, M.M.: Prediction of stock market using recurrent neural network. In: 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 0479\u20130483 (2021). https:\/\/doi.org\/10.1109\/IEMCON53756.2021.9623206","DOI":"10.1109\/IEMCON53756.2021.9623206"},{"key":"932_CR17","doi-asserted-by":"publisher","unstructured":"Mehtab, S., Sen, J.: Stock price prediction using cnn and lstm-based deep learning models. In: 2020 International Conference on Decision Aid Sciences and Application (DASA), pp. 447\u2013453 (2020). https:\/\/doi.org\/10.1109\/DASA51403.2020.9317207","DOI":"10.1109\/DASA51403.2020.9317207"},{"issue":"1","key":"932_CR18","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/435\/1\/012026","volume":"435","author":"S Chen","year":"2018","unstructured":"Chen, S., He, H.: Stock prediction using convolutional neural network. IOP Conf. Ser. Mater. Sci. Eng. 435(1), 012026 (2018). https:\/\/doi.org\/10.1088\/1757-899X\/435\/1\/012026","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"932_CR19","doi-asserted-by":"publisher","first-page":"476","DOI":"10.7717\/peerj-cs.476","volume":"7","author":"P Mehta","year":"2021","unstructured":"Mehta, P., Pandya, S., Kotecha, K.: Harvesting social media sentiment analysis to enhance stock market prediction using deep learning. Peer J. Comput. Sci. 7, 476 (2021). https:\/\/doi.org\/10.7717\/peerj-cs.476","journal-title":"Peer J. Comput. Sci."},{"key":"932_CR20","doi-asserted-by":"publisher","unstructured":"Nguyen, T.H., Shirai, K.: Topic modeling based sentiment analysis on social media for stock market prediction. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 1354\u20131364 (2015). https:\/\/doi.org\/10.3115\/v1\/P15-1131","DOI":"10.3115\/v1\/P15-1131"},{"key":"932_CR21","doi-asserted-by":"publisher","unstructured":"Batra, R., Daudpota, S.M.: Integrating stocktwits with sentiment analysis for better prediction of stock price movement. In: 2018 International Conference on Computing, Mathematics and Engineering Technologies (ICoMET), pp. 1\u20135 (2018). https:\/\/doi.org\/10.1109\/ICOMET.2018.8346382 . IEEE","DOI":"10.1109\/ICOMET.2018.8346382"},{"key":"932_CR22","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.knosys.2014.04.022","volume":"69","author":"X Li","year":"2014","unstructured":"Li, X., Xie, H., Chen, L., Wang, J., Deng, X.: News impact on stock price return via sentiment analysis. Knowl. Based Syst. 69, 14\u201323 (2014). https:\/\/doi.org\/10.1016\/j.knosys.2014.04.022","journal-title":"Knowl. Based Syst."},{"key":"932_CR23","doi-asserted-by":"publisher","unstructured":"Pagolu, V.S., Reddy, K.N., Panda, G., Majhi, B.: Sentiment analysis of twitter data for predicting stock market movements. In: 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), pp. 1345\u20131350 (2016). https:\/\/doi.org\/10.1109\/SCOPES.2016.7955659 . IEEE","DOI":"10.1109\/SCOPES.2016.7955659"},{"key":"932_CR24","unstructured":"Chen, Q.: Comparing different transformer model structures for stock prediction (2025). https:\/\/arxiv.org\/abs\/2504.16361"},{"key":"932_CR25","doi-asserted-by":"publisher","unstructured":"Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: a survey. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. IJCAI \u201923 (2023). https:\/\/doi.org\/10.24963\/ijcai.2023\/759","DOI":"10.24963\/ijcai.2023\/759"},{"key":"932_CR26","doi-asserted-by":"publisher","DOI":"10.3390\/math13050814","author":"R Caetano","year":"2025","unstructured":"Caetano, R., Oliveira, J.M., Ramos, P.: Transformer-based models for probabilistic time series forecasting with explanatory variables. Mathematics (2025). https:\/\/doi.org\/10.3390\/math13050814","journal-title":"Mathematics"},{"key":"932_CR27","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need (2023). https:\/\/arxiv.org\/abs\/1706.03762"},{"key":"932_CR28","doi-asserted-by":"publisher","unstructured":"Chen, Q., Kawashima, H.: Stock price prediction using llm-based sentiment analysis. In: Proceedings of the IEEE BigData 2024, pp. 4828\u20134835. IEEE, Washington DC, USA (2024). https:\/\/doi.org\/10.1109\/BigData62323.2024.10825946","DOI":"10.1109\/BigData62323.2024.10825946"},{"issue":"1","key":"932_CR29","doi-asserted-by":"publisher","first-page":"23762","DOI":"10.1038\/s41598-024-72045-3","volume":"14","author":"Y Ji","year":"2024","unstructured":"Ji, Y., Luo, Y., Lu, A., Xia, D., Yang, L., Wee-Chung Liew, A.: Galformer: a transformer with generative decoding and a hybrid loss function for multi-step stock market index prediction. Sci. Rep. 14(1), 23762 (2024). https:\/\/doi.org\/10.1038\/s41598-024-72045-3","journal-title":"Sci. Rep."},{"key":"932_CR30","doi-asserted-by":"crossref","unstructured":"Li, T., Liu, Z., Shen, Y., Wang, X., Chen, H., Huang, S.: MASTER: market-guided stock transformer for stock price forecasting (2023). 10.48550\/arXiv.2312.15235","DOI":"10.1609\/aaai.v38i1.27767"},{"key":"932_CR31","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s00181-024-02644-6","volume":"68","author":"S Li","year":"2025","unstructured":"Li, S., Xu, S.: Enhancing stock price prediction using gans and transformer-based attention mechanisms. Empir. Econ. 68, 373\u2013403 (2025). https:\/\/doi.org\/10.1007\/s00181-024-02644-6","journal-title":"Empir. Econ."},{"key":"932_CR32","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting (2021). 10.48550\/arXiv.2012.07436","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"932_CR33","unstructured":"Lim, B., Arik, S.O., Loeff, N., Pfister, T.: Temporal fusion transformers for interpretable multi-horizon time series forecasting (2020). https:\/\/arxiv.org\/abs\/1912.09363"},{"key":"932_CR34","unstructured":"Oreshkin, B.N., Carpov, D., Chapados, N., Bengio, Y.: N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2020). https:\/\/arxiv.org\/abs\/1905.10437"},{"key":"932_CR35","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-031-70011-8_2","volume-title":"Complex, Intelligent and Software Intensive Systems","author":"A Upadhyay","year":"2024","unstructured":"Upadhyay, A., Jain, H., Dhingra, P., Kandhoul, N., Dhurandher, S.K., Woungang, I.: Stock market prediction using social media sentiments. In: Barolli, L. (ed.) Complex, Intelligent and Software Intensive Systems, pp. 14\u201326. Springer, Cham (2024)"},{"key":"932_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-023-01190-w","author":"N Das","year":"2024","unstructured":"Das, N., Sadhukhan, B., Bhakta, S.S., et al.: Integrating eemd and ensemble cnn with x (twitter) sentiment for enhanced stock price predictions. Soc. Netw. Anal. Min (2024). https:\/\/doi.org\/10.1007\/s13278-023-01190-w","journal-title":"Soc. Netw. Anal. Min"},{"key":"932_CR37","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.14984969","author":"J Liu","year":"2025","unstructured":"Liu, J.: Multimodal data-driven factor models for stock market forecasting. J. Comput. Technol. Softw. (2025). https:\/\/doi.org\/10.5281\/zenodo.14984969","journal-title":"J. Comput. Technol. Softw."},{"issue":"7","key":"932_CR38","doi-asserted-by":"publisher","first-page":"22","DOI":"10.5815\/ijisa.2017.07.03","volume":"9","author":"AE Khedr","year":"2017","unstructured":"Khedr, A.E., Yaseen, N., et al.: Predicting stock market behavior using data mining technique and news sentiment analysis. Int. J. Intelli. Syst. Appl 9(7), 22 (2017). https:\/\/doi.org\/10.5815\/ijisa.2017.07.03","journal-title":"Int. J. Intelli. Syst. Appl"},{"key":"932_CR39","doi-asserted-by":"publisher","first-page":"10262","DOI":"10.1038\/s41598-024-61106-2","volume":"14","author":"H Lee","year":"2024","unstructured":"Lee, H., Kim, J.H., Jung, H.S.: Deep-learning-based stock market prediction incorporating esg sentiment and technical indicators. Sci. Rep. 14, 10262 (2024). https:\/\/doi.org\/10.1038\/s41598-024-61106-2","journal-title":"Sci. Rep."},{"key":"932_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123966","volume":"250","author":"W-J Liu","year":"2024","unstructured":"Liu, W.-J., Ge, Y.-B., Gu, Y.-C.: News-driven stock market index prediction based on trellis network and sentiment attention mechanism. Exp. Syst. Appl. 250, 123966 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2024.123966","journal-title":"Exp. Syst. Appl."},{"key":"932_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2024.105227","volume":"62","author":"K Kirtac","year":"2024","unstructured":"Kirtac, K., Germano, G.: Sentiment trading with large language models. Finance Res. Lett. 62, 105227 (2024). https:\/\/doi.org\/10.1016\/j.frl.2024.105227","journal-title":"Finance Res. Lett."},{"key":"932_CR42","doi-asserted-by":"publisher","unstructured":"Mo, K., Liu, W., Xu, X., Yu, C., Zou, Y., Xia, F.: Fine-tuning gemma-7b for enhanced sentiment analysis of financial news headlines. In: 2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI), 130\u2013135 (2024). https:\/\/doi.org\/10.1109\/ICETCI61221.2024.10594605","DOI":"10.1109\/ICETCI61221.2024.10594605"},{"issue":"21","key":"932_CR43","doi-asserted-by":"publisher","first-page":"10823","DOI":"10.3390\/app122110823","volume":"12","author":"K Srijiranon","year":"2022","unstructured":"Srijiranon, K., Lertratanakham, Y., Tanantong, T.: A hybrid framework using pca, emd and lstm methods for stock market price prediction with sentiment analysis. Appl. Sci. 12(21), 10823 (2022). https:\/\/doi.org\/10.3390\/app122110823","journal-title":"Appl. Sci."},{"key":"932_CR44","unstructured":"Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting (2022). https:\/\/arxiv.org\/abs\/2106.13008"},{"key":"932_CR45","doi-asserted-by":"crossref","unstructured":"Kaeley, H., Qiao, Y., Bagherzadeh, N.: Support for stock trend prediction using transformers and sentiment analysis (2023). https:\/\/arxiv.org\/abs\/2305.14368","DOI":"10.20472\/EFC.2023.018.004"},{"key":"932_CR46","unstructured":"Lu, Y., Zhang, H., Guo, Q.: Stock and market index prediction using Informer network (2023). 10.48550\/arXiv.2305.14382"},{"key":"932_CR47","doi-asserted-by":"publisher","unstructured":"Chen, Q.: Explore the use of prompt-based llm for credit risk classification. J. Comput. Commun. 13, 33\u201346 (2025). https:\/\/doi.org\/10.4236\/jcc.2025.136003","DOI":"10.4236\/jcc.2025.136003"},{"key":"932_CR48","unstructured":"Lundberg, S., Lee, S.-I.: A Unified approach to interpreting model predictions (2017). https:\/\/arxiv.org\/abs\/1705.07874"},{"key":"932_CR49","doi-asserted-by":"crossref","unstructured":"Han, M., Zhang, D.J., Wang, Y., Yan, R., Yao, L., Chang, X., Qiao, Y.: Dual-AI: Dual-path actor interaction learning for group activity recognition (2022). https:\/\/arxiv.org\/abs\/2204.02148","DOI":"10.1109\/CVPR52688.2022.00300"},{"key":"932_CR50","unstructured":"Michelucci, U.: An introduction to autoencoders (2022). https:\/\/arxiv.org\/abs\/2201.03898"},{"key":"932_CR51","doi-asserted-by":"publisher","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019). https:\/\/doi.org\/10.1145\/3292500.3330701","DOI":"10.1145\/3292500.3330701"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-025-00932-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-025-00932-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-025-00932-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:36:43Z","timestamp":1773481003000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-025-00932-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,2]]},"references-count":51,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["932"],"URL":"https:\/\/doi.org\/10.1007\/s41060-025-00932-7","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-6479946\/v1","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.3.rs-6479946\/v2","asserted-by":"object"}]},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,2]]},"assertion":[{"value":"18 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No funding was received for conducting this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Funding Declaration"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors consent to the publication of this work in its current form.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Materials availability"}},{"order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"The authors declare no Conflict of interest.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"39"}}