{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:15:33Z","timestamp":1757312133447,"version":"3.37.3"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100005145","name":"Basic Research Program of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20191457"],"award-info":[{"award-number":["BK20191457"]}],"id":[{"id":"10.13039\/501100005145","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176107","62076111","62076215"],"award-info":[{"award-number":["62176107","62076111","62076215"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012154","name":"Graduate Research and Innovation Projects of Jiangsu Province","doi-asserted-by":"publisher","award":["SJCX22_1901"],"award-info":[{"award-number":["SJCX22_1901"]}],"id":[{"id":"10.13039\/501100012154","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s11063-022-11089-w","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T12:48:06Z","timestamp":1669898886000},"page":"5245-5267","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Active Learning by Extreme Learning Machine with Considering Exploration and Exploitation Simultaneously"],"prefix":"10.1007","volume":"55","author":[{"given":"Yan","family":"Gu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9621-4158","authenticated-orcid":false,"given":"Hualong","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Xibei","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Shang","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,1]]},"reference":[{"issue":"8","key":"11089_CR1","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1109\/TNNLS.2014.2356470","volume":"26","author":"S Chakraborty","year":"2015","unstructured":"Chakraborty S, Balasubramanian V, Panchanathan S (2015) Adaptive batch mode active learning. IEEE Trans Neural Netw Learn Syst 26(8):1747\u20131760","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"11089_CR2","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1007\/s11063-021-10671-y","volume":"54","author":"BB Hazarika","year":"2021","unstructured":"Hazarika BB, Gupta D (2021) Density weighted twin support vector machines for binary class imbalance learning. Neural Process Lett 54(2):1091\u20131130","journal-title":"Neural Process Lett"},{"issue":"9","key":"11089_CR3","doi-asserted-by":"publisher","first-page":"4243","DOI":"10.1007\/s00521-020-05240-8","volume":"33","author":"BB Hazarika","year":"2020","unstructured":"Hazarika BB, Gupta D (2020) Density-weighted support vector machines for binary class imbalance learning. Neural Comput Appl 33(9):4243\u20134261","journal-title":"Neural Comput Appl"},{"issue":"1","key":"11089_CR4","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/TCYB.2015.2496974","volume":"47","author":"B Du","year":"2017","unstructured":"Du B, Wang Z, Zhang L et al (2017) Exploring representativeness and informativeness for active learning. IEEE Trans Cybern 47(1):14\u201326","journal-title":"IEEE Trans Cybern"},{"key":"11089_CR5","unstructured":"Settles B (2011) From theories to queries: active learning in practice. In: JMLR workshop and conference proceedings, vol 16, pp 1\u201318"},{"key":"11089_CR6","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1016\/j.patcog.2018.01.017","volume":"78","author":"Y Yang","year":"2018","unstructured":"Yang Y, Loog M (2018) A variance maximization criterion for active learning. Pattern Recognit 78:358\u2013370","journal-title":"Pattern Recognit"},{"key":"11089_CR7","doi-asserted-by":"crossref","unstructured":"Konyushkova K, Sznitman R, Fua P (2015) Introducing geometry in active learning for image segmentation. In: 2015 IEEE international conference on computer vision (ICCV), Santiago, Chile, pp\u00a02974\u20132982","DOI":"10.1109\/ICCV.2015.340"},{"key":"11089_CR8","doi-asserted-by":"crossref","unstructured":"Liu B, Ferrari V (2017) Active learning for human pose estimation. In: 2017 IEEE international conference on computer vision (ICCV), Venice, Italy, pp\u00a04363\u20134372","DOI":"10.1109\/ICCV.2017.468"},{"key":"11089_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/3287589","volume":"2020","author":"Q She","year":"2020","unstructured":"She Q, Chen K, Luo Z et al (2020) Double-criteria active learning for multiclass brain\u2013computer interfaces. Comput Intell Neurosci 2020:1\u201313","journal-title":"Comput Intell Neurosc"},{"key":"11089_CR10","doi-asserted-by":"crossref","unstructured":"Malhotra K, Bansal S, Ganapathy S (2019) Active learning methods for low resource end-to-end speech recognition. In: Interspeech, Graz, Austria, pp 2215\u20132219","DOI":"10.21437\/Interspeech.2019-2316"},{"key":"11089_CR11","doi-asserted-by":"crossref","unstructured":"Han X, Kwoh CK, Kim J (2016) Clustering based active learning for biomedical named entity recognition. In: 2016 International joint conference on neural networks (IJCNN), Vancouver, BC, Canada, pp\u00a01253\u20131260","DOI":"10.1109\/IJCNN.2016.7727341"},{"key":"11089_CR12","doi-asserted-by":"publisher","first-page":"38767","DOI":"10.1109\/ACCESS.2021.3064000","volume":"9","author":"CA Flores","year":"2021","unstructured":"Flores CA, Figueroa RL, Pezoa JE (2021) Active learning for biomedical text classification based on automatically generated regular expressions. IEEE Access 9:38767\u201338777","journal-title":"IEEE Access"},{"key":"11089_CR13","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1007\/s10618-016-0460-3","volume":"31","author":"M Sharma","year":"2016","unstructured":"Sharma M, Bilgic M (2016) Evidence-based uncertainty sampling for active learning. Data Min Knowl Disc 31:164\u2013202","journal-title":"Data Min Knowl Disc"},{"issue":"1","key":"11089_CR14","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1109\/TFUZZ.2017.2654504","volume":"26","author":"E Lughofer","year":"2018","unstructured":"Lughofer E, Pratama M (2018) Online active learning in data stream regression using uncertainty sampling based on evolving generalized fuzzy models. IEEE Trans Fuzzy Syst 26(1):292\u2013309","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"1","key":"11089_CR15","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1109\/TIP.2018.2867913","volume":"28","author":"G Wang","year":"2019","unstructured":"Wang G, Hwang JN, Rose C, Wallace F (2019) Uncertainty-based active learning via sparse modeling for image classification. IEEE Trans Image Process 28(1):316\u2013329","journal-title":"IEEE Trans Image Process"},{"issue":"10","key":"11089_CR16","doi-asserted-by":"publisher","first-page":"3751","DOI":"10.1016\/j.patcog.2012.03.022","volume":"45","author":"R Wang","year":"2012","unstructured":"Wang R, Kwong S, Chen D (2012) Inconsistency-based active learning for support vector machines. Pattern Recognit 45(10):3751\u20133767","journal-title":"Pattern Recognit"},{"key":"11089_CR17","doi-asserted-by":"publisher","first-page":"103481","DOI":"10.1016\/j.jbi.2020.103481","volume":"108","author":"G Yu","year":"2020","unstructured":"Yu G, Yang Y, Wang X et al (2020) Adversarial active learning for the identification of medical concepts and annotation inconsistency. J Biomed Inform 108:103481","journal-title":"J Biomed Inform"},{"issue":"24","key":"11089_CR18","doi-asserted-by":"publisher","first-page":"241733","DOI":"10.1063\/1.5023802","volume":"148","author":"JS Smith","year":"2018","unstructured":"Smith JS, Nebgen B, Lubbers N et al (2018) Less is more: sampling chemical space with active learning. J Chem Phys 148(24):241733","journal-title":"J Chem Phys"},{"key":"11089_CR19","doi-asserted-by":"crossref","unstructured":"Settles B, Craven M (2008) An analysis of active learning strategies for sequence labeling tasks. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), Honolulu, Hawaii, USA, pp\u00a01070\u20131079","DOI":"10.3115\/1613715.1613855"},{"issue":"8","key":"11089_CR20","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1109\/TPAMI.2006.156","volume":"28","author":"IK Mingkun Li, Sethi","year":"2006","unstructured":"Mingkun Li, Sethi IK (2006) Confidence-based active learning. IEEE Trans Pattern Anal Mach Intell 28(8):1251\u20131261","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11089_CR21","unstructured":"Roy N, McCallum A (2001) Toward optimal active learning through monte carlo estimation of error reduction. In: Proceedings of the international conference on machine learning (ICML), Williamstown, MA, USA, vol\u00a02, pp\u00a0441\u2013448"},{"key":"11089_CR22","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.ress.2019.03.004","volume":"188","author":"C Ling","year":"2019","unstructured":"Ling C, Lu Z, Zhu X (2019) Efficient methods by active learning kriging coupled with variance reduction based sampling methods for time-dependent failure probability. Reliab Eng Syst Saf 188:23\u201335","journal-title":"Reliab Eng Syst Safe"},{"key":"11089_CR23","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1016\/j.patcog.2018.01.017","volume":"78","author":"Y Yang","year":"2018","unstructured":"Yang Y, Loog M (2018) A variance maximization criterion for active learning. Pattern Recognit 78:358\u2013370","journal-title":"Pattern Recognit"},{"issue":"10","key":"11089_CR24","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1109\/TPAMI.2014.2307881","volume":"36","author":"SJ Huang","year":"2014","unstructured":"Huang SJ, Jin R, Zhou ZH (2014) Active learning by querying informative and representative examples. IEEE Trans Pattern Anal Mach Intell 36(10):1936\u20131949","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"11089_CR25","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s11263-014-0781-x","volume":"113","author":"Y Yang","year":"2014","unstructured":"Yang Y, Ma Z, Nie F et al (2014) Multi-class active learning by uncertainty sampling with diversity maximization. Int J Comput Vis 113(2):113\u2013127","journal-title":"Int J Comput Vis"},{"key":"11089_CR26","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.eswa.2017.05.046","volume":"85","author":"M Wang","year":"2017","unstructured":"Wang M, Min F, Zhang ZH, Wu YX (2017) Active learning through density clustering. Expert Syst Appl 85:305\u2013317","journal-title":"Expert Syst Appl"},{"issue":"5","key":"11089_CR27","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.3233\/IDA-205393","volume":"25","author":"D He","year":"2021","unstructured":"He D, Yu H, Wang G, Li J (2021) A two-stage clustering-based cold-start method for active learning. Intell Data Anal 25(5):1169\u20131185","journal-title":"Intell Data Anal"},{"key":"11089_CR28","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.ins.2014.09.009","volume":"293","author":"T Reitmaier","year":"2015","unstructured":"Reitmaier T, Calma A, Sick B (2015) Transductive active learning: a new semi-supervised learning approach based on iteratively refined generative models to capture structure in data. Inf Sci 293:275\u2013298","journal-title":"Inf Sci"},{"key":"11089_CR29","doi-asserted-by":"crossref","unstructured":"Yu K, Bi J, Tresp V (2006) Active learning via transductive experimental design. In: Proceedings of the 23rd international conference on machine learning (ICML), Pittsburgh, Pennsylvania, pp\u00a01081\u20131088","DOI":"10.1145\/1143844.1143980"},{"key":"11089_CR30","doi-asserted-by":"publisher","first-page":"51452","DOI":"10.1109\/ACCESS.2021.3053003","volume":"9","author":"Y Yang","year":"2021","unstructured":"Yang Y, Yin X, Zhao Y et al (2021) Batch mode active learning based on multi-set clustering. IEEE Access 9:51452\u201351463","journal-title":"IEEE Access"},{"key":"11089_CR31","doi-asserted-by":"publisher","first-page":"171447","DOI":"10.1109\/ACCESS.2020.3025036","volume":"8","author":"DW Chen","year":"2020","unstructured":"Chen DW, Jin YH (2020) An active learning algorithm based on Shannon entropy for constraint-based clustering. IEEE Access 8:171447\u2013171456","journal-title":"IEEE Access"},{"issue":"1\u20133","key":"11089_CR32","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133):489\u2013501","journal-title":"Neurocomputing"},{"issue":"2","key":"11089_CR33","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","volume":"2","author":"GB Huang","year":"2011","unstructured":"Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107\u2013122","journal-title":"Int J Mach Learn Cybern"},{"issue":"2","key":"11089_CR34","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"GB Huang","year":"2012","unstructured":"Huang GB, Zhou HM, Ding XJ, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B 42(2):513\u2013529","journal-title":"IEEE Trans Syst Man Cybern B"},{"issue":"4","key":"11089_CR35","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1007\/s10489-019-01596-0","volume":"50","author":"P Borah","year":"2020","unstructured":"Borah P, Gupta D (2020) Unconstrained convex minimization based implicit lagrangian twin extreme learning machine for classification (ULTELMC). Appl Intell 50(4):1327\u20131344","journal-title":"Appl Intell"},{"issue":"9","key":"11089_CR36","doi-asserted-by":"publisher","first-page":"2675","DOI":"10.1007\/s13762-020-02967-8","volume":"18","author":"BB Hazarika","year":"2020","unstructured":"Hazarika BB, Gupta D, Berlin M (2020) A Coiflet LDMR and coiflet OB-elm for river suspended sediment load prediction. Int J Environ Sci Technol 18(9):2675\u20132692","journal-title":"Int J Environ Sci Technol"},{"key":"11089_CR37","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.neucom.2015.04.019","volume":"166","author":"H Yu","year":"2015","unstructured":"Yu H, Sun C, Yang W et al (2015) AL-ELM: one uncertainty-based active learning algorithm using extreme learning machine. Neurocomputing 166:140\u2013150","journal-title":"Neurocomputing"},{"issue":"4","key":"11089_CR38","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1109\/TNNLS.2018.2855446","volume":"30","author":"H Yu","year":"2019","unstructured":"Yu H, Yang X, Zheng S, Sun C (2019) Active learning from imbalanced data: a solution of online weighted extreme learning machine. IEEE Trans Neural Netw Learn Syst 30(4):1088\u20131103","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"11089_CR39","doi-asserted-by":"publisher","first-page":"107385","DOI":"10.1016\/j.knosys.2021.107385","volume":"231","author":"J Qin","year":"2021","unstructured":"Qin J, Wang C, Zou Q et al (2021) Active learning with extreme learning machine for online imbalanced multiclass classification. Knowl Based Syst 231:107385","journal-title":"Knowl Based Syst"},{"key":"11089_CR40","unstructured":"Yoon J, Hwang SJ (2017) Combined group and exclusive sparsity for deep neural networks. In: Proceedings of international conference on machine learning (ICML), Sydney, NSW, Australia, vol\u00a070, pp\u00a03958\u20133966"},{"key":"11089_CR41","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.patcog.2019.01.009","volume":"90","author":"V Kumar","year":"2019","unstructured":"Kumar V, Pujari AK, Padmanabhan V, Kagita VR (2019) Group preserving label embedding for multi-label classification. Pattern Recognit 90:23\u201334","journal-title":"Pattern Recognit"},{"key":"11089_CR42","doi-asserted-by":"crossref","unstructured":"Ert\u00f6z L, Steinbach M, Kumar V (2003) Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data. In: Proceedings of the 2003 SIAM international conference on data mining (SDM), San Francisco, CA, USA, pp\u00a047\u201358","DOI":"10.1137\/1.9781611972733.5"},{"issue":"6","key":"11089_CR43","doi-asserted-by":"publisher","first-page":"1411","DOI":"10.1109\/TNN.2006.880583","volume":"17","author":"NY Liang","year":"2006","unstructured":"Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw Learn Syst 17(6):1411\u20131423","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"11089_CR44","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1109\/TKDE.2019.2923211","volume":"33","author":"Z Wang","year":"2021","unstructured":"Wang Z, Du B, Tu W et al (2021) Incorporating distribution matching into uncertainty for multiple kernel active learning. IEEE Trans Knowl Data Eng 33(1):128\u2013142","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"11","key":"11089_CR45","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.1109\/T-C.1973.223640","volume":"\u201322","author":"RA Jarvis","year":"1973","unstructured":"Jarvis RA, Patrick EA (1973) Clustering using a similarity measure based on shared near neighbors. IEEE Trans Comput C \u201322(11):1025\u20131034","journal-title":"IEEE Trans Comput C"},{"key":"11089_CR46","doi-asserted-by":"crossref","unstructured":"Wang WT, Wu YL, Tang CY, Hor MK (2015) Adaptive density-based spatial clustering of applications with noise (DBSCAN) according to Data. In: 2015 International conference on machine learning and cybernetics (ICMLC), GuangDong, China, vol\u00a01, pp\u00a0445\u2013451","DOI":"10.1109\/ICMLC.2015.7340962"},{"issue":"4","key":"11089_CR47","first-page":"329","volume":"1","author":"K Sawant","year":"2014","unstructured":"Sawant K (2014) Adaptive methods for determining dbscan parameters. Int J Innov Sci Eng Technol 1(4):329\u2013334","journal-title":"Int J Innovative Sci Eng Technol"},{"key":"11089_CR48","unstructured":"Blake C, Keogh E, Merz CJ (1998) UCI repository of machine learning databases, Department of Information and Computer Science, University of California, Technical Report 213, Irvine, CA"},{"key":"11089_CR49","unstructured":"https:\/\/www.kaggle.com\/datasets\/brjapon\/gearbox-fault-diagnosis-stdev-of-accelerations"},{"key":"11089_CR50","unstructured":"https:\/\/www.kaggle.com\/datasets\/subhajournal\/credit-card-fraud-dataset"},{"key":"11089_CR51","doi-asserted-by":"crossref","unstructured":"Xu Z, Yu K, Tresp V et al (2003) Representative sampling for text classification using support vector machines. In: European conference on information retrieval (ECIR), Berlin, Heidelberg, pp 393\u2013407","DOI":"10.1007\/3-540-36618-0_28"},{"key":"11089_CR52","doi-asserted-by":"publisher","first-page":"107410","DOI":"10.1016\/j.sigpro.2019.107410","volume":"169","author":"X Zhang","year":"2020","unstructured":"Zhang X, Delpha C, Diallo D (2020) Incipient fault detection and estimation based on Jensen\u2013Shannon divergence in a data-driven approach. Signal Process 169:107410","journal-title":"Signal Process"},{"key":"11089_CR53","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"key":"11089_CR54","first-page":"12","volume":"9","author":"S Garcia","year":"2008","unstructured":"Garcia S, Herrera F (2008) An extension on \"statistical comparisons of classifiers over multiple data sets\" for all pairwise comparisons. J Mach Learn Res 9:12","journal-title":"J Mach Learn Res"},{"issue":"10","key":"11089_CR55","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1016\/j.ins.2009.12.010","volume":"180","author":"S Garc\u00eda","year":"2010","unstructured":"Garc\u00eda S, Fern\u00e1ndez A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 180(10):2044\u20132064","journal-title":"Inf Sci"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11089-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-11089-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11089-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T16:56:54Z","timestamp":1690822614000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-11089-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,1]]},"references-count":55,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["11089"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-11089-w","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2022,12,1]]},"assertion":[{"value":"10 November 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2022","order":2,"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"}}]}}