{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T21:29:47Z","timestamp":1776893387177,"version":"3.51.2"},"reference-count":63,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1016\/j.knosys.2025.114248","type":"journal-article","created":{"date-parts":[[2025,8,10]],"date-time":"2025-08-10T14:16:55Z","timestamp":1754835415000},"page":"114248","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"PA","title":["An Adaptive Active Learning Framework for Sparsely Labeled Multi-Label Drifting Data Streams"],"prefix":"10.1016","volume":"329","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3955-8479","authenticated-orcid":false,"given":"Reza","family":"Rahimian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0080-2743","authenticated-orcid":false,"given":"Hoda","family":"Mashayekhi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maryam","family":"Khodabakhsh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.knosys.2025.114248_bib0001","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","article-title":"A Review on Multi-Label Learning Algorithms","volume":"26","author":"Zhang","year":"2014","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10.1016\/j.knosys.2025.114248_bib0002","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.patcog.2019.01.007","article-title":"Multi-label classification via label correlation and first order feature dependance in a data stream","volume":"90","author":"Nguyen","year":"2019","journal-title":"Pattern Recognit"},{"issue":"2","key":"10.1016\/j.knosys.2025.114248_bib0003","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/s11390-020-9994-3","article-title":"Incremental Multi-Label Learning with Active Queries","volume":"35","author":"Huang","year":"2020","journal-title":"J Comput Sci Technol"},{"issue":"1","key":"10.1016\/j.knosys.2025.114248_bib0004","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/TNNLS.2021.3091681","article-title":"Online active learning for drifting data streams","volume":"34","author":"Liu","year":"2021","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10.1016\/j.knosys.2025.114248_bib0005","series-title":"Proceedings of the 38th ACM\/SIGAPP Symposium on Applied Computing","first-page":"390","article-title":"Aging and rejuvenating strategies for fading windows in multi-label classification on data streams","author":"Roseberry","year":"2023"},{"key":"10.1016\/j.knosys.2025.114248_bib0006","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.neucom.2022.01.075","article-title":"Adaptive ensemble of self-adjusting nearest neighbor subspaces for multi-label drifting data streams","volume":"481","author":"Alberghini","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2025.114248_bib0007","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1109\/ACCESS.2019.2962059","article-title":"A survey on multi-label data stream classification","volume":"8","author":"Zheng","year":"2019","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.knosys.2025.114248_bib0008","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.1007\/s10462-022-10232-2","article-title":"Unsupervised concept drift detection for multi-label data streams","volume":"56","author":"Gulcan","year":"2023","journal-title":"Artif Intell Rev"},{"key":"10.1016\/j.knosys.2025.114248_bib0009","series-title":"Proceedings of the 38th ACM\/SIGAPP symposium on applied computing","first-page":"382","article-title":"An active learning budget-based oversampling approach for partially labeled multi-class imbalanced data streams","author":"Aguiar","year":"2023"},{"key":"10.1016\/j.knosys.2025.114248_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109359","article-title":"Meta-learning for dynamic tuning of active learning on stream classification","volume":"138","author":"Martins","year":"2023","journal-title":"Pattern Recognit"},{"key":"10.1016\/j.knosys.2025.114248_bib0011","article-title":"Dynamic budget allocation for sparsely labeled drifting data streams","volume":"654","author":"Aguiar","year":"2024","journal-title":"Inf Sci (N Y)"},{"key":"10.1016\/j.knosys.2025.114248_bib0012","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1007\/s11390-020-9487-4","article-title":"Active learning query strategies for classification, regression, and clustering: A survey","volume":"35","author":"Kumar","year":"2020","journal-title":"J Comput Sci Technol"},{"issue":"2","key":"10.1016\/j.knosys.2025.114248_bib0013","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.ifacol.2022.04.206","article-title":"Streaming Machine Learning and Online Active Learning for Automated Visual Inspection","volume":"55","author":"Ro\u017eanec","year":"2022","journal-title":"IFAC-PapersOnLine"},{"key":"10.1016\/j.knosys.2025.114248_bib0014","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s10994-023-06454-2","article-title":"Active learning for data streams: a survey","volume":"113","author":"Cacciarelli","year":"2024","journal-title":"Mach Learn"},{"issue":"1","key":"10.1016\/j.knosys.2025.114248_bib0015","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/TNNLS.2012.2236570","article-title":"Active learning with drifting streaming data","volume":"25","author":"e \u017dliobait\\.e","year":"2013","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10.1016\/j.knosys.2025.114248_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107583","article-title":"Active k-labelsets ensemble for multi-label classification","volume":"109","author":"Wang","year":"2021","journal-title":"Pattern Recognit"},{"key":"10.1016\/j.knosys.2025.114248_bib0017","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s10115-017-1137-y","article-title":"Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM)","volume":"54","author":"Losing","year":"2018","journal-title":"Knowl Inf Syst"},{"key":"10.1016\/j.knosys.2025.114248_bib0018","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.neucom.2021.02.032","article-title":"Self-adjusting k nearest neighbors for continual learning from multi-label drifting data streams","volume":"442","author":"Roseberry","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2025.114248_bib0019","article-title":"Imbalance-Robust Multi-Label Self-Adjusting kNN","author":"de O. M. Nicola","year":"2024","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"8","key":"10.1016\/j.knosys.2025.114248_bib0020","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1007\/s10994-020-05879-3","article-title":"An empirical analysis of binary transformation strategies and base algorithms for multi-label learning","volume":"109","author":"Rivolli","year":"2020","journal-title":"Mach Learn"},{"key":"10.1016\/j.knosys.2025.114248_bib0021","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s10044-019-00779-2","article-title":"ML-SLSTSVM: a new structural least square twin support vector machine for multi-label learning","volume":"23","author":"Azad-Manjiri","year":"2020","journal-title":"Pattern Analysis and Applications"},{"key":"10.1016\/j.knosys.2025.114248_bib0022","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s10994-011-5256-5","article-title":"Classifier chains for multi-label classification","volume":"85","author":"Read","year":"2011","journal-title":"Mach Learn"},{"issue":"5","key":"10.1016\/j.knosys.2025.114248_bib0023","first-page":"1","article-title":"A weighted ensemble classification algorithm based on nearest neighbors for multi-label data stream","volume":"17","author":"Wu","year":"2023","journal-title":"ACM Trans Knowl Discov Data"},{"key":"10.1016\/j.knosys.2025.114248_bib0024","series-title":"Artificial Intelligence: Theories, Models and Applications: 5th Hellenic Conference on AI, SETN 2008","first-page":"401","article-title":"An empirical study of lazy multilabel classification algorithms","author":"Spyromitros","year":"2008"},{"issue":"7","key":"10.1016\/j.knosys.2025.114248_bib0025","doi-asserted-by":"crossref","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","article-title":"ML-KNN: A lazy learning approach to multi-label learning","volume":"40","author":"Zhang","year":"2007","journal-title":"Pattern Recognit"},{"key":"10.1016\/j.knosys.2025.114248_bib0026","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s10994-009-5127-5","article-title":"Combining instance-based learning and logistic regression for multilabel classification","volume":"76","author":"Cheng","year":"2009","journal-title":"Mach Learn"},{"key":"10.1016\/j.knosys.2025.114248_bib0027","series-title":"First International Workshop on Learning with Imbalanced Domains: Theory and Applications","first-page":"51","article-title":"Stacked-MLkNN: a stacking based improvement to multi-label k-nearest neighbours","author":"Pakrashi","year":"2017"},{"key":"10.1016\/j.knosys.2025.114248_bib0028","first-page":"23","article-title":"Multi-label kNN Classifier with Self Adjusting Memory for Drifting Data Streams","volume":"94","author":"Roseberry","year":"2018","journal-title":"Proc Mach Learn Res"},{"issue":"3","key":"10.1016\/j.knosys.2025.114248_bib0029","doi-asserted-by":"crossref","DOI":"10.1177\/1550147720911892","article-title":"A novel multi-label classification algorithm based on K-nearest neighbor and random walk","volume":"16","author":"Wang","year":"2020","journal-title":"Int J Distrib Sens Netw"},{"issue":"6","key":"10.1016\/j.knosys.2025.114248_bib0030","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3363573","article-title":"Multi-label punitive kNN with self-adjusting memory for drifting data streams","volume":"13","author":"Roseberry","year":"2019","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"key":"10.1016\/j.knosys.2025.114248_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109664","article-title":"Stream-based active learning with linear models","volume":"254","author":"Cacciarelli","year":"2022","journal-title":"Knowl Based Syst"},{"key":"10.1016\/j.knosys.2025.114248_bib0032","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.neucom.2021.08.063","article-title":"Cost-effective batch-mode multi-label active learning","volume":"463","author":"Gui","year":"2021","journal-title":"Neurocomputing"},{"issue":"12","key":"10.1016\/j.knosys.2025.114248_bib0033","doi-asserted-by":"crossref","first-page":"7177","DOI":"10.1002\/int.22585","article-title":"Cost-effective multi-instance multilabel active learning","volume":"36","author":"Su","year":"2021","journal-title":"International Journal of Intelligent Systems"},{"issue":"4","key":"10.1016\/j.knosys.2025.114248_bib0034","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3161606","article-title":"Evolutionary strategy to perform batch-mode active learning on multi-label data","volume":"9","author":"Reyes","year":"2018","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"issue":"2","key":"10.1016\/j.knosys.2025.114248_bib0035","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3379504","article-title":"Multi-label active learning algorithms for image classification: Overview and future promise","volume":"53","author":"Wu","year":"2020","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"10.1016\/j.knosys.2025.114248_bib0036","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.neucom.2017.08.001","article-title":"Effective active learning strategy for multi-label learning","volume":"273","author":"Reyes","year":"2018","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2025.114248_bib0037","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s12530-017-9202-z","article-title":"Multi-label active learning: key issues and a novel query strategy","volume":"10","author":"Cherman","year":"2019","journal-title":"Evolving Systems"},{"key":"10.1016\/j.knosys.2025.114248_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2017.06.038","article-title":"On-line active learning: A new paradigm to improve practical useability of data stream modeling methods","author":"Lughofer","year":"2017","journal-title":"Inf Sci (N Y)"},{"issue":"6","key":"10.1016\/j.knosys.2025.114248_bib0039","doi-asserted-by":"crossref","first-page":"2680","DOI":"10.1109\/TKDE.2019.2955078","article-title":"Online adaptive asymmetric active learning with limited budgets","volume":"33","author":"Zhang","year":"2019","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"10","key":"10.1016\/j.knosys.2025.114248_bib0040","doi-asserted-by":"crossref","first-page":"6714","DOI":"10.1109\/TNNLS.2022.3222265","article-title":"An online active broad learning approach for real-time safety assessment of dynamic systems in nonstationary environments","volume":"34","author":"Liu","year":"2022","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10.1016\/j.knosys.2025.114248_bib0041","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.patcog.2019.06.001","article-title":"Multi-label classification via incremental clustering on an evolving data stream","volume":"95","author":"Nguyen","year":"2019","journal-title":"Pattern Recognit"},{"key":"10.1016\/j.knosys.2025.114248_bib0042","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s10994-012-5279-6","article-title":"Scalable and efficient multi-label classification for evolving data streams","volume":"88","author":"Read","year":"2012","journal-title":"Mach Learn"},{"key":"10.1016\/j.knosys.2025.114248_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111489","article-title":"Balancing efficiency vs. effectiveness and providing missing label robustness in multi-label stream classification","volume":"289","author":"Bakhshi","year":"2024","journal-title":"Knowl Based Syst"},{"key":"10.1016\/j.knosys.2025.114248_bib0044","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.neucom.2022.09.065","article-title":"Nonstationary data stream classification with online active learning and siamese neural networks\u2729","volume":"512","author":"Malialis","year":"2022","journal-title":"Neurocomputing"},{"issue":"7","key":"10.1016\/j.knosys.2025.114248_bib0045","article-title":"Worst-Case Analysis of Selective Sampling for Linear Classification","volume":"7","author":"Cesa-Bianchi","year":"2006","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.knosys.2025.114248_bib0046","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.106778","article-title":"A comprehensive active learning method for multiclass imbalanced data streams with concept drift","volume":"215","author":"Liu","year":"2021","journal-title":"Knowl Based Syst"},{"key":"10.1016\/j.knosys.2025.114248_bib0047","series-title":"2016 IEEE 16th international conference on data mining (ICDM)","first-page":"291","article-title":"KNN classifier with self adjusting memory for heterogeneous concept drift","author":"Losing","year":"2016"},{"key":"10.1016\/j.knosys.2025.114248_bib0048","series-title":"Proceedings of the 2007 SIAM international conference on data mining","first-page":"443","article-title":"Learning from time-changing data with adaptive windowing","author":"Bifet","year":"2007"},{"key":"10.1016\/j.knosys.2025.114248_bib0049","first-page":"2411","article-title":"Mulan: A java library for multi-label learning","volume":"12","author":"Tsoumakas","year":"2011","journal-title":"The Journal of Machine Learning Research"},{"issue":"21","key":"10.1016\/j.knosys.2025.114248_bib0050","first-page":"1","article-title":"Meka: a multi-label\/multi-target extension to weka","volume":"17","author":"Read","year":"2016","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.knosys.2025.114248_bib0051","series-title":"Proceedings of the first workshop on applications of pattern analysis","first-page":"44","article-title":"Moa: Massive online analysis, a framework for stream classification and clustering","author":"Bifet","year":"2010"},{"key":"10.1016\/j.knosys.2025.114248_bib0052","series-title":"Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining","first-page":"71","article-title":"Mining high-speed data streams","author":"Domingos","year":"2000"},{"key":"10.1016\/j.knosys.2025.114248_bib0053","series-title":"Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining","first-page":"139","article-title":"New ensemble methods for evolving data streams","author":"Bifet","year":"2009"},{"key":"10.1016\/j.knosys.2025.114248_bib0054","series-title":"AI 2007: Advances in Artificial Intelligence: 20th Australian Joint Conference on Artificial Intelligence, Gold Coast","first-page":"90","article-title":"New options for hoeffding trees","author":"Pfahringer","year":"2007"},{"key":"10.1016\/j.knosys.2025.114248_bib0055","series-title":"Fourth international workshop on knowledge discovery from data streams","first-page":"77","article-title":"Early drift detection method","author":"Baena-Garc\\ia","year":"2006"},{"key":"10.1016\/j.knosys.2025.114248_bib0056","series-title":"Database Systems for Advanced Applications: 17th International Conference","first-page":"309","article-title":"Stream data mining using the MOA framework","author":"Kranen","year":"2012"},{"key":"10.1016\/j.knosys.2025.114248_bib0057","series-title":"Proceedings of the 28th annual ACM symposium on applied computing","first-page":"801","article-title":"Efficient data stream classification via probabilistic adaptive windows","author":"Bifet","year":"2013"},{"key":"10.1016\/j.knosys.2025.114248_bib0058","series-title":"2016 IEEE 16th international conference on data mining (ICDM)","first-page":"291","article-title":"KNN classifier with self adjusting memory for heterogeneous concept drift","author":"Losing","year":"2016"},{"key":"10.1016\/j.knosys.2025.114248_bib0059","series-title":"Advances in Artificial Intelligence: 17th Conference of the Spanish Association for Artificial Intelligence","first-page":"58","article-title":"Online multi-label classification with adaptive model rules","author":"Sousa","year":"2016"},{"key":"10.1016\/j.knosys.2025.114248_bib0060","series-title":"Proceedings of the Second Workshop on Applications of Pattern Analysis","first-page":"19","article-title":"Streaming multi-label classification","author":"Read","year":"2011"},{"issue":"7","key":"10.1016\/j.knosys.2025.114248_bib0061","doi-asserted-by":"crossref","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","article-title":"ML-KNN: A lazy learning approach to multi-label learning","volume":"40","author":"Zhang","year":"2007","journal-title":"Pattern Recognit"},{"key":"10.1016\/j.knosys.2025.114248_bib0062","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105487","article-title":"Local-based k values for multi-label k-nearest neighbors rule","volume":"116","author":"Romero-del-Castillo","year":"2022","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.knosys.2025.114248_bib0063","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s10994-012-5320-9","article-title":"On evaluating stream learning algorithms","volume":"90","author":"Gama","year":"2013","journal-title":"Mach Learn"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705125012894?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705125012894?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T20:06:38Z","timestamp":1763669198000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705125012894"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":63,"alternative-id":["S0950705125012894"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2025.114248","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An Adaptive Active Learning Framework for Sparsely Labeled Multi-Label Drifting Data Streams","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2025.114248","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114248"}}