{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:16:24Z","timestamp":1760710584605,"version":"3.37.3"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,5,9]],"date-time":"2021-05-09T00:00:00Z","timestamp":1620518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,9]],"date-time":"2021-05-09T00:00:00Z","timestamp":1620518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Genet Program Evolvable Mach"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s10710-021-09404-w","type":"journal-article","created":{"date-parts":[[2021,5,9]],"date-time":"2021-05-09T06:02:46Z","timestamp":1620540166000},"page":"297-324","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Genetic programming-based regression for temporal data"],"prefix":"10.1007","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2801-6974","authenticated-orcid":false,"given":"Cry","family":"Kuranga","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nelishia","family":"Pillay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,9]]},"reference":[{"issue":"2","key":"9404_CR1","first-page":"58","volume":"106","author":"A Tsymbal","year":"2004","unstructured":"A. Tsymbal, The problem of concept drift: definitions and related work. Comput. Sci. Dep, Trinity Coll Dublin 106(2), 58 (2004)","journal-title":"Comput. Sci. Dep, Trinity Coll Dublin"},{"key":"9404_CR2","unstructured":"T. Mitsa, Temporal Data Mining, Chapman & Hall\/CRC Data Mining and Knowledge Discovery Series (2010)"},{"key":"9404_CR3","unstructured":"J. Brownlee, A gentle introduction to concept drift in machine learning. Mach. Learn. Mastery (2018)"},{"key":"9404_CR4","doi-asserted-by":"crossref","unstructured":"L. Khan, W. Fan, In international conference on database systems for advanced applications, in Tutorial: Data Stream Mining and its Applications (Springer, Berlin, Heidelberg, 2012), pp. 328\u2013329","DOI":"10.1007\/978-3-642-29035-0_33"},{"key":"9404_CR5","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.ins.2017.06.038","volume":"415","author":"E Lughofer","year":"2017","unstructured":"E. Lughofer, On-line active learning: a new paradigm to improve practical useability of datastream modeling methods. Inf. Sci. 415, 356\u2013376 (2017)","journal-title":"Inf. Sci."},{"issue":"9","key":"9404_CR6","doi-asserted-by":"publisher","first-page":"3151","DOI":"10.1016\/j.patcog.2010.03.021","volume":"43","author":"Z Zhang","year":"2010","unstructured":"Z. Zhang, J. Zhou, Transfer estimation of evolving class priors in data stream classification. Pattern Recogn. 43(9), 3151\u20133161 (2010)","journal-title":"Pattern Recogn."},{"issue":"4","key":"9404_CR7","doi-asserted-by":"publisher","first-page":"44:1","DOI":"10.1145\/2523813","volume":"46","author":"J Gama","year":"2014","unstructured":"J. Gama, I. \u017dliobaite, A. Bifet, M. Pechenizkiy, A. Bouchachia, A survey on concept drift adaptation. ACM Comput. Surv. 46(4), 44:1-44:37 (2014)","journal-title":"ACM Comput. Surv."},{"issue":"10","key":"9404_CR8","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1109\/TNN.2011.2160459","volume":"22","author":"R Elwell","year":"2011","unstructured":"R. Elwell, R. Polikar, Incremental learning of concept drift in nonstationary environments. IEEE Trans. Neural Netw. 22(10), 1517\u20131531 (2011)","journal-title":"IEEE Trans. Neural Netw."},{"key":"9404_CR9","doi-asserted-by":"crossref","unstructured":"C. Alippi, G. Boracchi, M. Roveri, Just in time classifiers: managing the slow drift case, in Proc. Int. Joint Conf. Neural Networks (2009), pp. 114\u2013120","DOI":"10.1109\/IJCNN.2009.5178799"},{"key":"9404_CR10","doi-asserted-by":"crossref","unstructured":"L. Torrey, J. Shavlik, Transfer Learning, in Handbook of Research on Machine Learning Applications. ed. by J.M.R.M.M.M.A.A.S.E. Soria (IGI Global, 2009)","DOI":"10.4018\/978-1-60566-766-9.ch011"},{"issue":"3","key":"9404_CR11","first-page":"317","volume":"1","author":"JC Schlimmer","year":"1986","unstructured":"J.C. Schlimmer, R.H. Granger, Incremental learning from noisy data. Mach. Learn. 1(3), 317\u2013354 (1986)","journal-title":"Mach. Learn."},{"issue":"4","key":"9404_CR12","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/MCI.2015.2471196","volume":"10","author":"G Ditzler","year":"2015","unstructured":"G. Ditzler, M. Roveri, C. Alippi, Learning in nonstationary environments: a survey. IEEE Comput. Intell. Mag. 10(4), 12\u201325 (2015)","journal-title":"IEEE Comput. Intell. Mag."},{"issue":"4\u20135","key":"9404_CR13","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.knosys.2004.10.002","volume":"18","author":"S Delany","year":"2005","unstructured":"S. Delany, P. Cunningham, A. Tsymbal, L. Coyle, A case-based technique for tracking concept drift in spam filtering. Knowl. Based Syst 18(4\u20135), 187\u2013195 (2005)","journal-title":"Knowl. Based Syst"},{"key":"9404_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-05278-6","volume-title":"Intelligence for Embedded Systems","author":"C Alippi","year":"2014","unstructured":"C. Alippi, Intelligence for Embedded Systems (Springer, Berlin, 2014)."},{"key":"9404_CR15","doi-asserted-by":"crossref","unstructured":"J. Sarnelle, A. Sanchez, R. Capo, J. Haas, R. Polikar, Quantifying the limited and gradual concept drift assumption, in 2015 International Joint Conference on Neural Networks (IJCNN) (IEEE, 2015), pp. 1\u20138","DOI":"10.1109\/IJCNN.2015.7280850"},{"key":"9404_CR16","doi-asserted-by":"crossref","unstructured":"L.I. Kuncheva, Classifier ensembles for changing environments, in Proc. 5th Int Workshop of Multiple Classifier Systems (2004), pp. 1\u201315","DOI":"10.1007\/978-3-540-25966-4_1"},{"key":"9404_CR17","first-page":"1621","volume":"6","author":"G Brown","year":"2005","unstructured":"G. Brown, J.L. Wyatt, P. Tino, Managing diversity in regression ensembles. J. Mach. Learn. Res. 6, 1621\u20131650 (2005)","journal-title":"J. Mach. Learn. Res."},{"key":"9404_CR18","volume-title":"Detection of Abrupt Changes: Theory and Application","author":"M Basseville","year":"1993","unstructured":"M. Basseville, I.V. Nikiforov, Detection of Abrupt Changes: Theory and Application, vol. 104 (Prentice-Hall, Englewood Cliffs, 1993)."},{"issue":"1","key":"9404_CR19","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.inffus.2006.11.002","volume":"9","author":"A Tsymbal","year":"2008","unstructured":"A. Tsymbal, M. Pechenizkiy, P. Cunningham, S. Puuronen, Dynamic integration of classifiers for handling concept drift. Inform. Fusion 9(1), 56\u201368 (2008)","journal-title":"Inform. Fusion"},{"key":"9404_CR20","unstructured":"J.R. Koza, Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems. Stanford University Computer Science Department Technical Report STAN-CS-90-1314 (1990)"},{"key":"9404_CR21","doi-asserted-by":"crossref","unstructured":"S. Massimo, A. Tettamanzi, Genetic programming for financial time series prediction, in Genetic Programming (Springer, 2001), pp. 361\u2013370","DOI":"10.1007\/3-540-45355-5_29"},{"key":"9404_CR22","unstructured":"M. K\u013e\u00fa\u010dik, J. Juriova, M. K\u013e\u00fa\u010dik, Time series modeling with genetic programming relative to ARIMA models, in Conferences on New Techniques and Technologies for Statistics (2009), pp. 17\u201327"},{"issue":"2","key":"9404_CR23","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/TSMCC.2009.2033566","volume":"40","author":"PG Espejo","year":"2010","unstructured":"P.G. Espejo, S. Ventura, F. Herrera, A Survey on the Application of Genetic Programming to Classification. IEEE Trans. Syst., Man, Cybern., Part C, Appl. Rev. 40(2), 121\u2013144 (2010)","journal-title":"IEEE Trans. Syst., Man, Cybern., Part C, Appl. Rev."},{"issue":"2","key":"9404_CR24","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1109\/TCYB.2015.2404806","volume":"46","author":"K Nag","year":"2016","unstructured":"K. Nag, N. Pal, A Multiobjective genetic programming-based ensemble for simultaneous feature selection and classification. IEEE Trans. Cybern. 46(2), 499\u2013510 (2016)","journal-title":"IEEE Trans. Cybern."},{"key":"9404_CR25","unstructured":"L. Vanneschi, G. Cuccu, A study of genetic programming variable population size for dynamic optimization problems, in IJCCI (2009), pp. 119\u2013126"},{"key":"9404_CR26","unstructured":"Z. Yin, A. Brabazon, C. O\u2019Sullivan, M. O\u2019Neill, Genetic programming for dynamic environments, in 2nd international symposium advances in artificial intelligence and applications, vol. 2, pp. 437\u2013446"},{"key":"9404_CR27","doi-asserted-by":"crossref","unstructured":"M. Rieket, K. M. Malan, and A. P. Engelbrecht, Adaptive genetic programming for dynamic classification problems, in 2009 IEEE congress on evolutionary computation (2009), pp. 674\u2013681","DOI":"10.1109\/CEC.2009.4983010"},{"issue":"4","key":"9404_CR28","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1109\/TEVC.2006.882430","volume":"11","author":"N Wagner","year":"2007","unstructured":"N. Wagner, Z. Michalewicz, M. Khouja, R. McGregor, Time series forecasting for dynamic environments: the DyFor genetic program model. IEEE Trans. Evol. Comput. 11(4), 433\u2013452 (2007)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"9404_CR29","doi-asserted-by":"crossref","unstructured":"S. Kelly, J. Newsted, W. Banzhaf, C. Gondro, A modular memory framework for time series prediction, in Proceedings of the 2020 Genetic and Evolutionary Computation Conference (2020), pp. 949\u2013957","DOI":"10.1145\/3377930.3390216"},{"issue":"2","key":"9404_CR30","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s10710-016-9276-6","volume":"18","author":"AJ Turner","year":"2017","unstructured":"A.J. Turner, J.F. Miller, Recurrent cartesian genetic programming of artificial neural networks. Genet. Progr. Evol. Mach. 18(2), 185\u2013212 (2017)","journal-title":"Genet. Progr. Evol. Mach."},{"key":"9404_CR31","doi-asserted-by":"publisher","DOI":"10.1002\/9781118625590","volume-title":"Applied Regression Analysis","author":"NR Draper","year":"1998","unstructured":"N.R. Draper, H. Smith, Applied Regression Analysis, vol. 326 (Wiley, New York, 1998)."},{"key":"9404_CR32","doi-asserted-by":"crossref","unstructured":"T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics, 2nd edn. (Springer, 2009).","DOI":"10.1007\/978-0-387-84858-7"},{"key":"9404_CR33","volume-title":"Linear Algebra","author":"JB Fraleigh","year":"1995","unstructured":"J.B. Fraleigh, R.A. Beauregard, Linear Algebra, 3rd edn. (Addison-Wesley Publishing Company, Upper Saddle River, 1995).","edition":"3"},{"issue":"3","key":"9404_CR34","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1214\/aos\/1176345451","volume":"9","author":"SM Stigler","year":"1981","unstructured":"S.M. Stigler, Gauss and the invention of least squares. Ann. Stat. 9(3), 465\u2013474 (1981)","journal-title":"Ann. Stat."},{"key":"9404_CR35","doi-asserted-by":"crossref","unstructured":"A. Kordon, Future trends in soft computing industrial applications, in Proceedings of the 2006 IEEE Congress on Evolutionary Computation (2006), pp. 7854\u20137861","DOI":"10.1109\/FUZZY.2006.1681930"},{"key":"9404_CR36","doi-asserted-by":"crossref","unstructured":"E. Alfaro-Cid, A.I. Esparcia-Alc\u00e1zar, P. Moya, B. Femenia-Ferrer, K. Sharman, J.J. Merelo, Modeling pheromone dispensers using genetic programming, in Lecture Notes in Computer Science, vol 5484 (Springer, Berlin\/Heidelberg, 2008), pp. 635\u2013644","DOI":"10.1007\/978-3-642-01129-0_73"},{"key":"9404_CR37","unstructured":"D.P. Searson, D.E. Leahy, M.J. Willis, GPTIPS: an open-source genetic programming toolbox for multigene symbolic regression, in Proceedings of the International Multiconference of Engineers and Computer Scientists, vol 1 (Citeseer, 2010), pp. 77\u201380"},{"issue":"2","key":"9404_CR38","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s10710-010-9121-2","volume":"12","author":"NQ Uy","year":"2011","unstructured":"N.Q. Uy, N.X. Hoai, M. O\u2019Neill, R.I. McKay, E. Galv\u00e1n-L\u00f3pez, Semantically-based crossover in genetic programming: application to real-valued symbolic regression. Genet. Progr. Evol. Mach. 12(2), 91\u2013119 (2011)","journal-title":"Genet. Progr. Evol. Mach."},{"key":"9404_CR39","first-page":"240","volume":"8353","author":"K Georgieva","year":"2014","unstructured":"K. Georgieva, A.P. Engelbrecht, dynamic differential evolution algorithm for clustering temporal data. Large Scale Sci. Comput., Lect. Notes Comput. Sci. 8353, 240\u2013247 (2014)","journal-title":"Large Scale Sci. Comput., Lect. Notes Comput. Sci."},{"key":"9404_CR40","unstructured":"C. Kuranga, Genetic programming approach for nonstationary data analytics. Ph.D Thesis, University of Pretoria, Pretoria, South Africa (2020)"},{"key":"9404_CR41","unstructured":"R. Poli, W.B. Langdon, N.F. McPhee, A field guide to genetic programming. Lulu Enterprise, UK Ltd, http:\/\/lulu.com (2008)"},{"key":"9404_CR42","volume-title":"Handbook of Natural Computing: Theory, Experiments and Applications","author":"L Vanneschi","year":"2010","unstructured":"L. Vanneschi, R. Poli, Genetic programming: introduction, application, theory and open issues, in Handbook of Natural Computing: Theory, Experiments and Applications. ed. by T.B.A.J.K. Grzegorz Rosenberg (Springer, Berlin, 2010)"},{"key":"9404_CR43","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0055923","volume-title":"Genetic Programming: An Introduction","author":"W Banzhaf","year":"1998","unstructured":"W. Banzhaf, P. Nordin, R.E. Keller, F.D. Francone, Genetic Programming: An Introduction, vol. 1 (Morgan Kaufmann, San Francisco, 1998)."},{"key":"9404_CR44","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.patcog.2018.10.024","volume":"87","author":"A Canoa","year":"2019","unstructured":"A. Canoa, B. Krawczyk, Evolving rule-based classifiers with genetic programming on GPUs for drifting data streams. Pattern Recogn. 87, 248\u2013268 (2019)","journal-title":"Pattern Recogn."},{"issue":"2","key":"9404_CR45","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1177\/1077546311404731","volume":"18","author":"A Soundarrajan","year":"2012","unstructured":"A. Soundarrajan, S. Sumathi, G. Sivamurugan, Voltage and frequency control in power generating system using hybrid evolutionary algorithms. J. Vib. Control 18(2), 214\u2013227 (2012)","journal-title":"J. Vib. Control"},{"key":"9404_CR46","unstructured":"MATLAB, version 8.5.0 (R2015a) (The MathWorks Inc., Natick, MA, 2015)"},{"key":"9404_CR47","unstructured":"R.W. Morrison, Performance measure in dynamic environments, in GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, No. 5\u20138 (2003)"},{"key":"9404_CR48","unstructured":"R.W. Morrison, K.A. De Jong, A test problem generator for non-stationary environments, in Proc. of the 1999 Congr. on Evol. Comput. (1999), pp. 2047\u20132053"},{"key":"9404_CR49","unstructured":"J. Branke, Memory enhanced evolutionary algorithms for changing optimization problems, in Proc. of the 1999 Congr. on Evol. Comput. (1999), pp. 1875\u20131882"},{"key":"9404_CR50","first-page":"526","volume":"3005","author":"Y Jin","year":"2004","unstructured":"Y. Jin, B. Sendhoff, Constructing dynamic optimization test problems using the multiobjective optimization concept. EvoWorkshop 2004 LNCS 3005, 526\u2013536 (2004)","journal-title":"EvoWorkshop 2004 LNCS"},{"key":"9404_CR51","doi-asserted-by":"crossref","unstructured":"C. Li, M. Yang, L. Kang, A new approach to solving dynamic TSP, in Proc of the 6th Int. Conf. on Simulated Evolution and Learning (2006), pp. 236\u2013243","DOI":"10.1007\/11903697_31"},{"key":"9404_CR52","doi-asserted-by":"crossref","unstructured":"C. Li, S. Yang, A generalized approach to construct benchmark problems for dynamic optimization, in Proc. of the 7th Int. Conf. on Simulated Evolution and Learning (Springer, Berlin, Heidelberg, 2008), pp. 391\u2013400.","DOI":"10.1007\/978-3-540-89694-4_40"},{"issue":"2","key":"9404_CR53","first-page":"289","volume":"47","author":"L Zhang","year":"2017","unstructured":"L. Zhang, J. Lin, R. Karim, Sliding window-based fault detection from high-dimensional data streams. IEEE Trans. Syst., Man, Cybern.: Syst. 47(2), 289\u2013303 (2017)","journal-title":"IEEE Trans. Syst., Man, Cybern.: Syst."},{"key":"9404_CR54","doi-asserted-by":"crossref","unstructured":"A.S. Rakitianskaia, A.P. Engelbrecht, Training Feedforward Neural Network with Dynamic Particle Swarm Optimisation (Computer Science Department, University of Pretoria, 2011).","DOI":"10.1007\/s11721-012-0071-6"},{"key":"9404_CR55","volume-title":"Superconductivity Magnetization Modeling","author":"L Bennett","year":"1994","unstructured":"L. Bennett, L. Swartzendruber, H. Brown, Superconductivity Magnetization Modeling (National Institute of Standards and Technology (NIST), US Department of Commerce, USA, 1994)."},{"issue":"4","key":"9404_CR56","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1109\/72.508939","volume":"7","author":"V Cherkassky","year":"1996","unstructured":"V. Cherkassky, D. Gehring, F. Mulier, Comparison of adaptive methods for function estimation from samples. IEEE Trans. Neural Netw. 7(4), 969\u2013984 (1996)","journal-title":"IEEE Trans. Neural Netw."},{"key":"9404_CR57","unstructured":"M. Harries, Splice-2 comparative evaluation: electricity pricing. Technical Report UNSW-CSE-TR-9905, Artificial Intelligence Group, School of Computer Science and Engineering, The University of New South Wales, Sydney 2052, Australia (1999)"},{"key":"9404_CR58","unstructured":"R.J. Shiller, Stock Market Data Used in Irrational Exuberance (Princeton University Press, 2005)."},{"key":"9404_CR59","first-page":"10","volume":"38","author":"J Kitchen","year":"2003","unstructured":"J. Kitchen, R. Monaco, Real-time forecasting in practice. Bus. Econ.: J. Natl. Assoc. Bus. Econ. 38, 10\u201319 (2003)","journal-title":"Bus. Econ.: J. Natl. Assoc. Bus. Econ."},{"key":"9404_CR60","first-page":"43","volume":"3","author":"M L\u00f3pez-Ib\u00e1\u00f1ez","year":"2016","unstructured":"M. L\u00f3pez-Ib\u00e1\u00f1ez, J. Dubois-Lacoste, L. P\u00e9rez-C\u00e1ceres, T. St\u00fctzle, M. Birattari, The irace package: Iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43\u201358 (2016)","journal-title":"Oper. Res. Perspect."},{"key":"9404_CR61","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.knosys.2018.01.015","volume":"145","author":"X Qiu","year":"2018","unstructured":"X. Qiu, P.N. Suganthan, G.A. Amaratunga, Ensemble incremental learning random vector functional link network for short-term electric load forecasting. Knowl.-Based Syst. 145, 182\u2013196 (2018)","journal-title":"Knowl.-Based Syst."},{"issue":"10","key":"9404_CR62","doi-asserted-by":"publisher","first-page":"1911","DOI":"10.1016\/j.enconman.2010.02.023","volume":"51","author":"J Che","year":"2010","unstructured":"J. Che, J. Wang, Short-term electricity prices forecasting based on support vector regression and auto-regressive integrated moving average modeling. Energy Convers. Manage. 51(10), 1911\u20131917 (2010)","journal-title":"Energy Convers. Manage."}],"container-title":["Genetic Programming and Evolvable Machines"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10710-021-09404-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10710-021-09404-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10710-021-09404-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T20:06:50Z","timestamp":1672085210000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10710-021-09404-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,9]]},"references-count":62,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["9404"],"URL":"https:\/\/doi.org\/10.1007\/s10710-021-09404-w","relation":{},"ISSN":["1389-2576","1573-7632"],"issn-type":[{"type":"print","value":"1389-2576"},{"type":"electronic","value":"1573-7632"}],"subject":[],"published":{"date-parts":[[2021,5,9]]},"assertion":[{"value":"6 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}