{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T09:49:41Z","timestamp":1775209781461,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:00:00Z","timestamp":1769558400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:00:00Z","timestamp":1775174400000},"content-version":"vor","delay-in-days":65,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s44443-026-00494-z","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T05:36:02Z","timestamp":1769578562000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An improved SMOTE method based on triangular scoring mechanism with random perturbation for handling class-imbalanced classification problems"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6327-6272","authenticated-orcid":false,"given":"Shihao","family":"Song","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2301-3803","authenticated-orcid":false,"given":"Sibo","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"494_CR1","first-page":"285","volume-title":"Data classification","author":"CC Aggarwal","year":"2015","unstructured":"Aggarwal CC (2015) Data classification. The Textbook. Springer, Cham, Switzerland, Data Mining, pp 285\u2013344"},{"issue":"12","key":"494_CR2","doi-asserted-by":"publisher","first-page":"10347","DOI":"10.1007\/s11042-024-19361-y","volume":"84","author":"Z Ahmad","year":"2025","unstructured":"Ahmad Z, Jaffri ZUA, Chen M, Bao S (2025) Understanding gans: Fundamentals, variants, training challenges, applications, and open problems. Multimedia Tools and Applications 84(12):10347\u201310423","journal-title":"Multimedia Tools and Applications"},{"issue":"8","key":"494_CR3","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.3390\/electronics9081295","volume":"9","author":"M Ahmed","year":"2020","unstructured":"Ahmed M, Seraj R, Islam SMS (2020) The k-means algorithm: A comprehensive survey and performance evaluation. Electronics 9(8):1295","journal-title":"Electronics"},{"key":"494_CR4","unstructured":"Alahyari S, Domaratzki M (2025) Smogan: Synthetic minority oversampling with gan refinement for imbalanced regression. arXiv:2504.21152"},{"issue":"5","key":"494_CR5","doi-asserted-by":"publisher","first-page":"373","DOI":"10.5662\/wjm.v13.i5.373","volume":"13","author":"IM Alkhawaldeh","year":"2023","unstructured":"Alkhawaldeh IM, Albalkhi I, Naswhan AJ (2023) Challenges and limitations of synthetic minority oversampling techniques in machine learning. World journal of methodology 13(5):373","journal-title":"World journal of methodology"},{"key":"494_CR6","doi-asserted-by":"crossref","unstructured":"Alnuaimi AF, Albaldawi TH (2024) An overview of machine learning classification techniques. In: BIO Web of Conferences, vol. 97, p. 00133. EDP Sciences","DOI":"10.1051\/bioconf\/20249700133"},{"key":"494_CR7","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.procs.2019.09.167","volume":"159","author":"M Bach","year":"2019","unstructured":"Bach M, Werner A, Palt M (2019) The proposal of undersampling method for learning from imbalanced datasets. Procedia Computer Science 159:125\u2013134","journal-title":"Procedia Computer Science"},{"key":"494_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120351","volume":"665","author":"L Bai","year":"2024","unstructured":"Bai L, Ju T, Wang H, Lei M, Pan X (2024) Two-step ensemble under-sampling algorithm for massive imbalanced data classification. Inf Sci 665:120351","journal-title":"Inf Sci"},{"issue":"2","key":"494_CR9","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1109\/TKDE.2012.232","volume":"26","author":"S Barua","year":"2012","unstructured":"Barua S, Islam MM, Yao X, Murase K (2012) Mwmote-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 26(2):405\u2013425","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"494_CR10","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1016\/j.asoc.2007.03.007","volume":"8","author":"SD Bhavani","year":"2008","unstructured":"Bhavani SD, Rani TS, Bapi RS (2008) Feature selection using correlation fractal dimension: Issues and applications in binary classification problems. Appl Soft Comput 8(1):555\u2013563","journal-title":"Appl Soft Comput"},{"key":"494_CR11","doi-asserted-by":"crossref","unstructured":"Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) Smoteboost: Improving prediction of the minority class in boosting. In: European Conference on Principles of Data Mining and Knowledge Discovery, pp. 107\u2013119. Springer","DOI":"10.1007\/978-3-540-39804-2_12"},{"key":"494_CR12","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research 16:321\u2013357","journal-title":"Journal of artificial intelligence research"},{"key":"494_CR13","doi-asserted-by":"crossref","unstructured":"Dal\u00a0Pozzolo A, Caelen O, Johnson RA, Bontempi G (2015) Calibrating probability with undersampling for unbalanced classification. In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 159\u2013166. IEEE","DOI":"10.1109\/SSCI.2015.33"},{"key":"494_CR14","first-page":"255","volume":"17","author":"J Derrac","year":"2015","unstructured":"Derrac J, Garcia S, Sanchez L, Herrera F (2015) Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework. J Mult Valued Logic Soft Comput 17:255\u2013287","journal-title":"J Mult Valued Logic Soft Comput"},{"key":"494_CR15","doi-asserted-by":"crossref","unstructured":"Dietterich TG (2000) Ensemble methods in machine learning. In: International Workshop on Multiple Classifier Systems, pp. 1\u201315. Springer","DOI":"10.1007\/3-540-45014-9_1"},{"key":"494_CR16","doi-asserted-by":"crossref","unstructured":"El Naqa I, Murphy MJ (2015) What is machine learning? Machine Learning in Radiation Oncology: Theory and Applications. Springer, Cham, Switzerland, pp 3\u201311","DOI":"10.1007\/978-3-319-18305-3_1"},{"key":"494_CR17","doi-asserted-by":"publisher","first-page":"25","DOI":"10.2495\/DATA050031","volume":"35","author":"RP Esp\u00edndola","year":"2005","unstructured":"Esp\u00edndola RP, Ebecken NF (2005) On extending f-measure and g-mean metrics to multi-class problems. WIT Transactions on Information and Communication Technologies 35:25\u201334","journal-title":"WIT Transactions on Information and Communication Technologies"},{"key":"494_CR18","first-page":"97","volume":"99","author":"W Fan","year":"1999","unstructured":"Fan W, Stolfo SJ, Zhang J, Chan PK (1999) Adacost: misclassification cost-sensitive boosting. Icml 99:97\u2013105","journal-title":"Icml"},{"key":"494_CR19","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1613\/jair.1.11192","volume":"61","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez A, Garcia S, Herrera F, Chawla NV (2018) Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. Journal of artificial intelligence research 61:863\u2013905","journal-title":"Journal of artificial intelligence research"},{"issue":"8","key":"494_CR20","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.1016\/j.patcog.2011.02.019","volume":"44","author":"F Fern\u00e1ndez-Navarro","year":"2011","unstructured":"Fern\u00e1ndez-Navarro F, Herv\u00e1s-Mart\u00ednez C, Guti\u00e9rrez PA (2011) A dynamic over-sampling procedure based on sensitivity for multi-class problems. Pattern Recogn 44(8):1821\u20131833","journal-title":"Pattern Recogn"},{"issue":"2","key":"494_CR21","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s11222-009-9153-8","volume":"21","author":"T Fushiki","year":"2011","unstructured":"Fushiki T (2011) Estimation of prediction error by using k-fold cross-validation. Stat Comput 21(2):137\u2013146","journal-title":"Stat Comput"},{"issue":"4","key":"494_CR22","first-page":"42","volume":"2","author":"V Ganganwar","year":"2012","unstructured":"Ganganwar V (2012) An overview of classification algorithms for imbalanced datasets. International Journal of Emerging Technology and Advanced Engineering 2(4):42\u201347","journal-title":"International Journal of Emerging Technology and Advanced Engineering"},{"key":"494_CR23","doi-asserted-by":"crossref","unstructured":"Han H, Wang W-Y, Mao B-H (2005) Borderline-smote: a new over-sampling method in imbalanced data sets learning. In: International Conference on Intelligent Computing, pp. 878\u2013887. Springer","DOI":"10.1007\/11538059_91"},{"issue":"9","key":"494_CR24","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263\u20131284","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"494_CR25","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/5254.708428","volume":"13","author":"MA Hearst","year":"1998","unstructured":"Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intelligent Systems and their applications 13(4):18\u201328","journal-title":"IEEE Intelligent Systems and their applications"},{"key":"494_CR26","doi-asserted-by":"crossref","unstructured":"He H, Bai Y, Garcia EA, Li S (2008) Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322\u20131328. Ieee","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"494_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.108288","volume":"115","author":"A Islam","year":"2022","unstructured":"Islam A, Belhaouari SB, Rehman AU, Bensmail H (2022) Knnor: An oversampling technique for imbalanced datasets. Appl Soft Comput 115:108288","journal-title":"Appl Soft Comput"},{"issue":"6245","key":"494_CR28","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"MI Jordan","year":"2015","unstructured":"Jordan MI, Mitchell TM (2015) Machine learning: Trends, perspectives, and prospects. Science 349(6245):255\u2013260","journal-title":"Science"},{"key":"494_CR29","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y (2017) Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30"},{"issue":"12","key":"494_CR30","doi-asserted-by":"publisher","first-page":"17760","DOI":"10.1007\/s11227-024-06132-7","volume":"80","author":"X Li","year":"2024","unstructured":"Li X, Liu Q (2024) Ddsc-smote: an imbalanced data oversampling algorithm based on data distribution and spectral clustering. J Supercomput 80(12):17760\u201317789","journal-title":"J Supercomput"},{"key":"494_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.121193","volume":"686","author":"LCM Liaw","year":"2025","unstructured":"Liaw LCM, Tan SC, Goh PY, Lim CP (2025) A histogram smote-based sampling algorithm with incremental learning for imbalanced data classification. Inf Sci 686:121193","journal-title":"Inf Sci"},{"key":"494_CR32","doi-asserted-by":"crossref","unstructured":"Liberti L, Lavor C (2017) Euclidean distance geometry. An Introduction","DOI":"10.1007\/978-3-319-60792-4"},{"key":"494_CR33","doi-asserted-by":"publisher","first-page":"8889","DOI":"10.1109\/ACCESS.2024.3351569","volume":"12","author":"S Limanto","year":"2024","unstructured":"Limanto S, Buliali JL, Saikhu A (2024) Glow smote-d: Oversampling technique to improve prediction model performance of students failure in courses. IEEE Access 12:8889\u20138901","journal-title":"IEEE Access"},{"key":"494_CR34","doi-asserted-by":"crossref","unstructured":"Liu X-Y, Wu J, Zhou Z-H (2008) Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39(2):539\u2013550","DOI":"10.1109\/TSMCB.2008.2007853"},{"key":"494_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109008","volume":"133","author":"Y Liu","year":"2023","unstructured":"Liu Y, Liu Y, Bruce X, Zhong S, Hu Z (2023) Noise-robust oversampling for imbalanced data classification. Pattern Recogn 133:109008","journal-title":"Pattern Recogn"},{"issue":"2","key":"494_CR36","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1111\/j.1466-8238.2007.00358.x","volume":"17","author":"JM Lobo","year":"2008","unstructured":"Lobo JM, Jim\u00e9nez-Valverde A, Real R (2008) Auc: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17(2):145\u2013151","journal-title":"Glob Ecol Biogeogr"},{"issue":"3","key":"494_CR37","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/0098-3004(93)90090-R","volume":"19","author":"A Ma\u0107kiewicz","year":"1993","unstructured":"Ma\u0107kiewicz A, Ratajczak W (1993) Principal components analysis (pca). Computers & Geosciences 19(3):303\u2013342","journal-title":"Computers & Geosciences"},{"key":"494_CR38","doi-asserted-by":"crossref","unstructured":"Majeed A, Hwang SO (2023) Ctgan-mos: Conditional generative adversarial network based minority-class-augmented oversampling scheme for imbalanced problems. IEEE Access 11:85878\u201385899","DOI":"10.1109\/ACCESS.2023.3303509"},{"issue":"1","key":"494_CR39","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1186\/s40537-024-00943-4","volume":"11","author":"M Mujahid","year":"2024","unstructured":"Mujahid M, K\u0131na E, Rustam F, Villar MG, Alvarado ES, De La Torre DI, Ashraf I (2024) Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engineering. Journal of Big Data 11(1):87","journal-title":"Journal of Big Data"},{"issue":"5","key":"494_CR40","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1007\/s11222-024-10479-5","volume":"34","author":"H Park","year":"2024","unstructured":"Park H, Kim H (2024) Ar-adasyn: angle radius-adaptive synthetic data generation approach for imbalanced learning. Stat Comput 34(5):166","journal-title":"Stat Comput"},{"issue":"10","key":"494_CR41","doi-asserted-by":"publisher","first-page":"2636","DOI":"10.1080\/03610918.2014.931971","volume":"44","author":"DG Pereira","year":"2015","unstructured":"Pereira DG, Afonso A, Medeiros FM (2015) Overview of friedman\u2019s test and post-hoc analysis. Communications in Statistics-Simulation and Computation 44(10):2636\u20132653","journal-title":"Communications in Statistics-Simulation and Computation"},{"issue":"2","key":"494_CR42","doi-asserted-by":"publisher","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","volume":"4","author":"LE Peterson","year":"2009","unstructured":"Peterson LE (2009) K-nearest neighbor Scholarpedia 4(2):1883","journal-title":"K-nearest neighbor Scholarpedia"},{"key":"494_CR43","doi-asserted-by":"crossref","unstructured":"Pradipta GA, Wardoyo R, Musdholifah A, Sanjaya INH, Ismail M (2021) Smote for handling imbalanced data problem: A review. In: 2021 Sixth International Conference on Informatics and Computing (ICIC), pp. 1\u20138 . IEEE","DOI":"10.1109\/ICIC54025.2021.9632912"},{"issue":"1","key":"494_CR44","doi-asserted-by":"publisher","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","volume":"47","author":"SJ Rigatti","year":"2017","unstructured":"Rigatti SJ (2017) Random forest Journal of insurance medicine 47(1):31\u201339","journal-title":"Random forest Journal of insurance medicine"},{"key":"494_CR45","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez NOR, Alatriste CR, Paredes AA, Rey YV, M\u00e1rquez CY, Cruz AJA (2024) Exploring kans: Theory and applications for binary classification. In: 2024 13th International Conference On Software Process Improvement (CIMPS), pp. 01\u201309. IEEE","DOI":"10.1109\/CIMPS65195.2024.11095956"},{"key":"494_CR46","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.jbi.2015.09.012","volume":"58","author":"MS Santos","year":"2015","unstructured":"Santos MS, Abreu PH, Garc\u00eda-Laencina PJ, Sim\u00e3o A, Carvalho A (2015) A new cluster-based oversampling method for improving survival prediction of hepatocellular carcinoma patients. J Biomed Inform 58:49\u201359","journal-title":"J Biomed Inform"},{"issue":"3","key":"494_CR47","doi-asserted-by":"publisher","first-page":"193","DOI":"10.3233\/ICA-2009-0314","volume":"16","author":"C Seiffert","year":"2009","unstructured":"Seiffert C, Khoshgoftaar TM, Van Hulse J (2009) Hybrid sampling for imbalanced data. Integrated Computer-Aided Engineering 16(3):193\u2013210","journal-title":"Integrated Computer-Aided Engineering"},{"key":"494_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121848","volume":"238","author":"P Sun","year":"2024","unstructured":"Sun P, Wang Z, Jia L, Xu Z (2024) Smote-ktlnn: A hybrid re-sampling method based on smote and a two-layer nearest neighbor classifier. Expert Syst Appl 238:121848","journal-title":"Expert Syst Appl"},{"key":"494_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.125855","volume":"265","author":"S Wang","year":"2025","unstructured":"Wang S, Bao Y, Yang S (2025) Hs-smote: Oversampling method for multiple dynamic interpolations based on regular hexagon scoring mechanism. Expert Syst Appl 265:125855","journal-title":"Expert Syst Appl"},{"key":"494_CR50","doi-asserted-by":"crossref","unstructured":"Woolson RF (2007) Wilcoxon signed-rank test. Wiley encyclopedia of clinical trials 1\u20133","DOI":"10.1002\/9780471462422.eoct979"},{"key":"494_CR51","doi-asserted-by":"crossref","unstructured":"Yacouby R, Axman D (2020) Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models. In: Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, pp. 79\u201391","DOI":"10.18653\/v1\/2020.eval4nlp-1.9"},{"issue":"5","key":"494_CR52","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1093\/comjnl\/bxad098","volume":"67","author":"Q Zhang","year":"2024","unstructured":"Zhang Q, He J, Li T, Lan X, Fang W, Li Y (2024) Spaw-smote: space partitioning adaptive weighted synthetic minority oversampling technique for imbalanced data set learning. Comput J 67(5):1747\u20131762","journal-title":"Comput J"},{"issue":"5","key":"494_CR53","first-page":"1017","volume":"34","author":"Z Zheng","year":"2015","unstructured":"Zheng Z, Cai Y, Li Y (2015) Oversampling method for imbalanced classification. Computing and Informatics 34(5):1017\u20131037","journal-title":"Computing and Informatics"},{"key":"494_CR54","doi-asserted-by":"publisher","DOI":"10.1201\/9781003587774","volume-title":"Ensemble Methods: Foundations and Algorithms","author":"Z-H Zhou","year":"2025","unstructured":"Zhou Z-H (2025) Ensemble Methods: Foundations and Algorithms. CRC Press, Boca Raton, USA"},{"issue":"12","key":"494_CR55","doi-asserted-by":"publisher","first-page":"4135","DOI":"10.1007\/s13042-023-01886-7","volume":"14","author":"H Zhou","year":"2023","unstructured":"Zhou H, Wu Z, Xu N, Xiao H (2023) Pdr-smote: an imbalanced data processing method based on data region partition and k nearest neighbors. Int J Mach Learn Cybern 14(12):4135\u20134150","journal-title":"Int J Mach Learn Cybern"},{"key":"494_CR56","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.patrec.2020.03.004","volume":"133","author":"R Zhu","year":"2020","unstructured":"Zhu R, Guo Y, Xue J-H (2020) Adjusting the imbalance ratio by the dimensionality of imbalanced data. Pattern Recogn Lett 133:217\u2013223","journal-title":"Pattern Recogn Lett"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-026-00494-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-026-00494-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-026-00494-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T08:53:42Z","timestamp":1775206422000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-026-00494-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,28]]},"references-count":56,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["494"],"URL":"https:\/\/doi.org\/10.1007\/s44443-026-00494-z","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,28]]},"assertion":[{"value":"24 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"100"}}