{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:05:52Z","timestamp":1773248752279,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This study unveils the Named Entity Recognition (NER) system specifically designed for Urdu news headlines, aimed at bridging crucial linguistic resource gaps. We meticulously developed a comprehensive corpus from diverse news sources, specifically tailored to reflect Urdu\u2019s unique orthographic and morphological characteristics. Our approach incorporates state-of-the-art (SOTA) neural technologies including transformers for deep contextual embeddings, Graph Convolutional Networks (GCN) for detailed syntactic analysis, and Biaffine Attention mechanisms to enhance inter-token relationships. A Conditional Random Field (CRF) layer further ensures accurate and consistent entity labeling, improving the system\u2019s precision. Initially, our model was rigorously benchmarked using established transformer models such as XLM-R, mBERT, and XLNet to set initial performance benchmarks. Subsequent enhancements involved integrating encoder functionalities from generative models like mBART and mT5, allowing a thorough comparative evaluation of these advanced encoders against our benchmarks. This phase aimed to assess their potential in effectively detecting implicit entities, thus enhancing our model\u2019s functionality for complex searches and automated content categorization on Urdu digital platforms. Our improvements notably contribute to computational linguistics by extending SOTA language technologies to under-resourced languages and promoting greater inclusivity in Natural Language Processing (NLP).<\/jats:p>","DOI":"10.1007\/s40747-025-02066-6","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T06:24:19Z","timestamp":1761719059000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Advancing Urdu named entity recognition: deep learning for aspect targeting"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8355-6471","authenticated-orcid":false,"given":"Kamran","family":"Aziz","sequence":"first","affiliation":[]},{"given":"Naveed","family":"Ahmed","sequence":"additional","affiliation":[]},{"given":"Yaoxiang","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Hassan Jalil","family":"Hadi","sequence":"additional","affiliation":[]},{"given":"Mohammaed Ali","family":"Alshara","sequence":"additional","affiliation":[]},{"given":"Umair","family":"Tariq","sequence":"additional","affiliation":[]},{"given":"Donghong","family":"Ji","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,29]]},"reference":[{"key":"2066_CR1","doi-asserted-by":"crossref","unstructured":"Yu J, Bohnet B, Poesio M (2020) Named entity recognition as dependency parsing. CoRR. arXiv:2005.07150","DOI":"10.18653\/v1\/2020.acl-main.577"},{"key":"2066_CR2","doi-asserted-by":"publisher","unstructured":"Song B, Li F, Liu Y, Zeng X (2021) Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison. Brief Bioinform 22:bbab282, https:\/\/doi.org\/10.1093\/bib\/bbab282. arXiv:https:\/\/academic.oup.com\/bib\/article-pdf\/22\/6\/bbab282\/41089553\/bbab282.pdf","DOI":"10.1093\/bib\/bbab282"},{"key":"2066_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103422","volume":"107","author":"X Li","year":"2020","unstructured":"Li X, Zhang H, Zhou X-H (2020) Chinese clinical named entity recognition with variant neural structures based on bert methods. J Biomed Inform 107:103422. https:\/\/doi.org\/10.1016\/j.jbi.2020.103422","journal-title":"J Biomed Inform"},{"key":"2066_CR4","doi-asserted-by":"publisher","unstructured":"Liu Z et al (2021) Crossner: evaluating cross-domain named entity recognition. In: Proceedings of the AAAI Conference on artificial intelligence 35:13452\u201313460. https:\/\/doi.org\/10.1609\/aaai.v35i15.17587","DOI":"10.1609\/aaai.v35i15.17587"},{"key":"2066_CR5","doi-asserted-by":"publisher","unstructured":"Wang J, Shou L, Chen K, Chen G Pyramid: a layered model for nested named entity recognition. In: Jurafsky D, Chai J, Schluter N, Tetreault J (eds) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 5918\u20135928, https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.525 (Association for Computational Linguistics, Online, 2020)","DOI":"10.18653\/v1\/2020.acl-main.525"},{"key":"2066_CR6","doi-asserted-by":"publisher","unstructured":"Huang J et\u00a0al (2021) Few-shot named entity recognition: an empirical baseline study. In: Moens M-F, Huang X, Specia L, Yih SW-t (eds) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp 10408\u201310423, https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.813 (Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 2021)","DOI":"10.18653\/v1\/2021.emnlp-main.813"},{"key":"2066_CR7","doi-asserted-by":"crossref","unstructured":"Ma J et\u00a0al (2022) Label semantics for few shot named entity recognition. arXiv:2203.08985","DOI":"10.18653\/v1\/2022.findings-acl.155"},{"key":"2066_CR8","doi-asserted-by":"publisher","first-page":"7897","DOI":"10.1038\/s41598-024-56567-4","volume":"14","author":"S Maham","year":"2024","unstructured":"Maham S et al (2024) Ann: adversarial news net for robust fake news classification. Sci Rep 14:7897","journal-title":"Sci Rep"},{"key":"2066_CR9","unstructured":"Riaz K (2010) Rule-based named entity recognition in Urdu. In: Proceedings of the 2010 Named Entities Workshop, pp 126\u2013135"},{"key":"2066_CR10","doi-asserted-by":"crossref","unstructured":"Lin BY, et\u00a0al (2020) Triggerner: learning with entity triggers as explanations for named entity recognition. CoRR abs\/2004.07493 . arXiv:2004.07493","DOI":"10.18653\/v1\/2020.acl-main.752"},{"key":"2066_CR11","doi-asserted-by":"publisher","unstructured":"Riaz F, Anwar MW, Muqades H (2020) Maximum entropy based Urdu named entity recognition. In: 2020 International Conference on Engineering and Emerging Technologies (ICEET), pp 1\u20135, https:\/\/doi.org\/10.1109\/ICEET48479.2020.9048203","DOI":"10.1109\/ICEET48479.2020.9048203"},{"key":"2066_CR12","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1093\/comjnl\/bxac047","volume":"66","author":"R Haq","year":"2023","unstructured":"Haq R, Zhang X, Khan W, Feng Z (2023) Urdu named entity recognition system using deep learning approaches. Comput J 66:1856\u20131869","journal-title":"Comput J"},{"key":"2066_CR13","doi-asserted-by":"publisher","unstructured":"Aziz K et al (2024) Urduaspectnet: fusing transformers and dual gcn for urdu aspect-based sentiment detection. ACM Trans Asian Low-Resour Lang Inf Process. https:\/\/doi.org\/10.1145\/3663367. Just Accepted","DOI":"10.1145\/3663367"},{"key":"2066_CR14","doi-asserted-by":"publisher","first-page":"90","DOI":"10.4218\/etrij.2018-0553","volume":"42","author":"W Khan","year":"2020","unstructured":"Khan W, Daud A, Alotaibi F, Aljohani N, Arafat S (2020) Deep recurrent neural networks with word embeddings for Urdu named entity recognition. ETRI J 42:90\u2013100","journal-title":"ETRI J"},{"key":"2066_CR15","unstructured":"Khana W, Daudb A, Nasira JA, Amjada T (2016) Named entity dataset for urdu named entity recognition task. Lang Technol 51"},{"key":"2066_CR16","unstructured":"Jahangir F, Anwar W, Bajwa UI, Wang X (2012) N-gram and gazetteer list based named entity recognition for Urdu: a scarce resourced language. In: Proceedings of the 10th Workshop on Asian Language Resources, pp 95\u2013104"},{"key":"2066_CR17","first-page":"2724","volume":"9","author":"MS Tahir","year":"2024","unstructured":"Tahir MS, Amjad M, Ahmad M, Ikram M, Fazal N (2024) Named entity recognition for Urdu language. Remit Rev 9:2724\u20132732","journal-title":"Remit Rev"},{"key":"2066_CR18","doi-asserted-by":"publisher","first-page":"37736","DOI":"10.1109\/ACCESS.2020.2973319","volume":"8","author":"M Al-Smadi","year":"2020","unstructured":"Al-Smadi M, Al-Zboon S, Jararweh Y, Juola P (2020) Transfer learning for Arabic named entity recognition with deep neural networks. IEEE Access 8:37736\u201337745. https:\/\/doi.org\/10.1109\/ACCESS.2020.2973319","journal-title":"IEEE Access"},{"key":"2066_CR19","doi-asserted-by":"publisher","unstructured":"He B, Chen J (2021) Named entity recognition method in network security domain based on bert-bilstm-crf. In: 2021 IEEE 21st International Conference on Communication Technology (ICCT), 508\u2013512, https:\/\/doi.org\/10.1109\/ICCT52962.2021.9657857","DOI":"10.1109\/ICCT52962.2021.9657857"},{"key":"2066_CR20","unstructured":"Jarrar M, Khalilia M, Ghanem S (2022) Wojood: nested Arabic named entity corpus and recognition using bert. arXiv preprint arXiv:2205.09651"},{"key":"2066_CR21","doi-asserted-by":"crossref","unstructured":"Zhou J et\u00a0al (2021) Generative sentiment analysis via latent category distribution and constrained decoding. In: International Conference on artificial neural networks, pp 209\u2013223 (Springer)","DOI":"10.1007\/978-3-031-72350-6_14"},{"key":"2066_CR22","doi-asserted-by":"publisher","DOI":"10.1145\/3129290","author":"MK Malik","year":"2017","unstructured":"Malik MK (2017) Urdu named entity recognition and classification system using artificial neural network. ACM Trans Asian Low-Resour Lang Inf Process. https:\/\/doi.org\/10.1145\/3129290","journal-title":"ACM Trans Asian Low-Resour Lang Inf Process"},{"key":"2066_CR23","doi-asserted-by":"publisher","DOI":"10.1145\/3329710","author":"S Kanwal","year":"2019","unstructured":"Kanwal S, Malik K, Shahzad K, Aslam F, Nawaz Z (2019) Urdu named entity recognition: Corpus generation and deep learning applications. ACM Trans Asian Low-Resour Lang Inf Process. https:\/\/doi.org\/10.1145\/3329710","journal-title":"ACM Trans Asian Low-Resour Lang Inf Process"},{"key":"2066_CR24","unstructured":"Malmasi S, Fang A, Fetahu B, Kar S, Rokhlenko O (2022) Multiconer: a large-scale multilingual dataset for complex named entity recognition. arXiv:2208.14536"},{"key":"2066_CR25","first-page":"481","volume":"5","author":"AR Mahlous","year":"2024","unstructured":"Mahlous AR (2024) The impact of fake news on social media users during the covid-19 pandemic, health, political and religious conflicts: A deep look. Int J Psychol Relig 5:481\u2013492","journal-title":"Int J Psychol Relig"},{"key":"2066_CR26","doi-asserted-by":"crossref","unstructured":"Dutta AK, et\u00a0al (2023) Optimal weighted extreme learning machine for cybersecurity fake news classification. Comput Syst Sci Eng 44","DOI":"10.32604\/csse.2023.027502"},{"key":"2066_CR27","doi-asserted-by":"publisher","first-page":"14646","DOI":"10.1038\/s41598-024-61886-7","volume":"14","author":"K Aziz","year":"2024","unstructured":"Aziz K et al (2024) Unifying aspect-based sentiment analysis bert and multi-layered graph convolutional networks for comprehensive sentiment dissection. Sci Rep 14:14646","journal-title":"Sci Rep"},{"key":"2066_CR28","doi-asserted-by":"crossref","unstructured":"Yusufu A et\u00a0al (2024) Uzbek news corpus for named entity recognition. In: Language resources and evaluation, pp 1\u201314","DOI":"10.1007\/s10579-024-09786-0"},{"key":"2066_CR29","unstructured":"Riaz K (2010) Rule-based named entity recognition in Urdu. In: Kumaran A, Li H (eds) Proceedings of the 2010 Named Entities Workshop, 126\u2013135 (Association for Computational Linguistics, Uppsala, Sweden, 2010)"},{"key":"2066_CR30","doi-asserted-by":"crossref","unstructured":"Becker D, Riaz K (2002) A study in Urdu corpus construction. In: COLING-02: The 3rd Workshop on Asian Language Resources and International Standardization","DOI":"10.3115\/1118759.1118760"},{"key":"2066_CR31","unstructured":"Singh U, Goyal V, Lehal GS (2012) Named entity recognition system for Urdu. In: Kay M, Boitet C (eds) Proceedings of COLING, 2507\u20132518 (The COLING 2012 Organizing Committee, Mumbai, India, 2012)"},{"key":"2066_CR32","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.artint.2005.03.001","volume":"165","author":"O Etzioni","year":"2005","unstructured":"Etzioni O et al (2005) Unsupervised named entity extraction from the web: an experimental study. Artif Intell 165:91\u2013134. https:\/\/doi.org\/10.1016\/j.artint.2005.03.001","journal-title":"Artif Intell"},{"key":"2066_CR33","unstructured":"Sekine S, Nobata C (2004) Definition, dictionaries and tagger for extended named entity hierarchy. In: Proceedings of the Language Resources and Evaluation Conference (LREC), 1977\u20131980 (Lisbon, Portugal, 2004)"},{"key":"2066_CR34","unstructured":"Jahangir F, Anwar W, Bajwa UI, Wang X (2012) N-gram and gazetteer list based named entity recognition for Urdu: a scarce resourced language. In: Weerasinghe R, Hussain S, Sornlertlamvanich V, Roxas REO (eds) Proceedings of the 10th Workshop on Asian Language Resources, 95\u2013104 (The COLING 2012 Organizing Committee, Mumbai, India, 2012)"},{"key":"2066_CR35","unstructured":"Malik MK, Sarwar SM (2017) Urdu named entity recognition system using hidden Markov model. Pak J Eng Appl Sci"},{"key":"2066_CR36","doi-asserted-by":"publisher","unstructured":"Riaz F, Anwar MW, Muqades H (2020) Maximum entropy based Urdu named entity recognition. In: 2020 International Conference on Engineering and Emerging Technologies (ICEET), 1\u20135, https:\/\/doi.org\/10.1109\/ICEET48479.2020.9048203","DOI":"10.1109\/ICEET48479.2020.9048203"},{"key":"2066_CR37","doi-asserted-by":"publisher","DOI":"10.3390\/app12136391","author":"W Khan","year":"2022","unstructured":"Khan W et al (2022) Named entity recognition using conditional random fields. Appl Sci. https:\/\/doi.org\/10.3390\/app12136391","journal-title":"Appl Sci"},{"key":"2066_CR38","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0300725","volume":"19","author":"R Anam","year":"2024","unstructured":"Anam R et al (2024) A deep learning approach for named entity recognition in Urdu language. PLoS ONE 19:e0300725","journal-title":"PLoS ONE"},{"key":"2066_CR39","doi-asserted-by":"publisher","first-page":"258","DOI":"10.3390\/computers13100258","volume":"13","author":"F Ullah","year":"2024","unstructured":"Ullah F, Gelbukh A, Zamir MT, Rivern EMF, Sidorov G (2024) Enhancement of named entity recognition in low-resource languages with data augmentation and bert models: a case study on urdu. Computers 13:258","journal-title":"Computers"},{"key":"2066_CR40","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-031-19496-2_1","volume-title":"Advances in computational intelligence","author":"F Ullah","year":"2022","unstructured":"Ullah F, Ullah I, Kolesnikova O (2022) Urdu named entity recognition with attention bi-lstm-crf model. In: Pichardo Lagunas O, Mart\u00ednez-Miranda J, Mart\u00ednez Seis B (eds) Advances in computational intelligence. Springer Nature Switzerland, Cham, pp 3\u201317"},{"key":"2066_CR41","doi-asserted-by":"crossref","unstructured":"Biswas S, Mohanty S, Mishra SP (2009) A hybrid oriya named entity recognition system: Integrating hmm with maxent. In: 2009 Second International Conference on Emerging Trends in Engineering & Technology, 639\u2013643 (IEEE, 2009)","DOI":"10.1109\/ICETET.2009.10"},{"key":"2066_CR42","doi-asserted-by":"publisher","first-page":"33","DOI":"10.17562\/PB-38-4","volume":"38","author":"SK Saha","year":"2008","unstructured":"Saha SK, Ghosh PS, Sarkar S, Mitra P (2008) Named entity recognition in Hindi using maximum entropy and transliteration. Polibits 38:33\u201341","journal-title":"Polibits"},{"key":"2066_CR43","unstructured":"Gali K, Surana H, Vaidya A, Shishtla PM, Sharma DM (2008) Aggregating machine learning and rule based heuristics for named entity recognition. In: Proceedings of the IJCNLP-08 Workshop on Named Entity Recognition for South and South East Asian Languages"},{"key":"2066_CR44","doi-asserted-by":"crossref","unstructured":"Bikel DM, Miller S, Schwartz R, Weischedel R (1998) Nymble: a high-performance learning name-finder. arXiv preprint cmp-lg\/9803003","DOI":"10.3115\/974557.974586"},{"key":"2066_CR45","unstructured":"Kumar P, Kiran VR (2008) A hybrid named entity recognition system for south Asian languages. In: Proceedings of The IJCNLP-08 Workshop on NER For South And South East Asian Languages, 83\u201388"},{"key":"2066_CR46","unstructured":"Chaudhuri BB, Bhattacharya S (2008) An experiment on automatic detection of named entities in Bangla. In: Proceedings of the IJCNLP-08 Workshop on Named Entity Recognition for South and South East Asian Languages"},{"key":"2066_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2024.102221","volume":"36","author":"K Aziz","year":"2024","unstructured":"Aziz K et al (2024) Enhanced urduaspectnet: leveraging biaffine attention for superior aspect-based sentiment analysis. J King Saud Univ-Comput Inform Sci 36:102221","journal-title":"J King Saud Univ-Comput Inform Sci"},{"key":"2066_CR48","doi-asserted-by":"crossref","unstructured":"Aziz K, Ji D, Li B, Li F, Zhou J (2024) Advancing Urdu nlp: aspect-based sentiment analysis with graph attention networks. In: 2024 International Joint Conference on Neural Networks (IJCNN), 1\u20138 (IEEE, 2024)","DOI":"10.1109\/IJCNN60899.2024.10650054"},{"key":"2066_CR49","doi-asserted-by":"crossref","unstructured":"Ullah F, Ullah I, Kolesnikova O (2022) Urdu named entity recognition with attention bi-lstm-crf model. In: Mexican International Conference on Artificial Intelligence, 3\u201317 (Springer, 2022)","DOI":"10.1007\/978-3-031-19496-2_1"},{"key":"2066_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102383","volume":"57","author":"A Nawaz","year":"2020","unstructured":"Nawaz A et al (2020) Extractive text summarization models for Urdu language. Inform Process Manag 57:102383. https:\/\/doi.org\/10.1016\/j.ipm.2020.102383","journal-title":"Inform Process Manag"},{"key":"2066_CR51","unstructured":"Sang EFTK, Meulder FD (2003) Introduction to the conll-2003 shared task: Language-independent named entity recognition. CoRR cs.CL\/0306050"},{"key":"2066_CR52","unstructured":"Ji H, Grishman R (2008) Refining event extraction through cross-document inference. In: Proceedings of ACL-08: Hlt, 254\u2013262"},{"key":"2066_CR53","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1080\/13488678.2010.10801269","volume":"13","author":"A Kirkpatrick","year":"2010","unstructured":"Kirkpatrick A (2010) Researching English as a lingua franca in Asia: The Asian corpus of English (ace) project. Asian Eng 13:4\u201318","journal-title":"Asian Eng"},{"key":"2066_CR54","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1017\/S1351324904003559","volume":"11","author":"G Zhou","year":"2005","unstructured":"Zhou G, Su J (2005) Machine learning-based named entity recognition via effective integration of various evidences. Nat Lang Eng 11:189\u2013206","journal-title":"Nat Lang Eng"},{"key":"2066_CR55","unstructured":"Chinchor NA (1998) Overview of MUC-7. In: Seventh Message Understanding Conference (MUC-7): Proceedings of a Conference Held in Fairfax, Virginia, April 29 - May 1, 1998"},{"key":"2066_CR56","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0222916","volume":"14","author":"R Delgado","year":"2019","unstructured":"Delgado R, Tibau X-A (2019) Why Cohen\u2019s kappa should be avoided as performance measure in classification. PLoS ONE 14:e0222916","journal-title":"PLoS ONE"},{"key":"2066_CR57","doi-asserted-by":"publisher","first-page":"223","DOI":"10.2466\/pr0.1989.65.1.223","volume":"65","author":"TO Kv\u00e5lseth","year":"1989","unstructured":"Kv\u00e5lseth TO (1989) Note on Cohen\u2019s kappa. Psychol Rep 65:223\u2013226","journal-title":"Psychol Rep"},{"key":"2066_CR58","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1002\/(SICI)1097-0258(20000315)19:5<723::AID-SIM379>3.0.CO;2-A","volume":"19","author":"NJ-M Blackman","year":"2000","unstructured":"Blackman NJ-M, Koval JJ (2000) Interval estimation for Cohen\u2019s kappa as a measure of agreement. Stat Med 19:723\u2013741","journal-title":"Stat Med"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02066-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-02066-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02066-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T17:01:52Z","timestamp":1763139712000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-02066-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,29]]},"references-count":58,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["2066"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-02066-6","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,29]]},"assertion":[{"value":"10 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 October 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":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"489"}}