{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T15:47:26Z","timestamp":1781884046778,"version":"3.54.5"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T00:00:00Z","timestamp":1678665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T00:00:00Z","timestamp":1678665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["TED2021-129488B-I00"],"award-info":[{"award-number":["TED2021-129488B-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015866","name":"Hezkuntza, Hizkuntza Politika Eta Kultura Saila, Eusko Jaurlaritza","doi-asserted-by":"publisher","award":["IT1427-22"],"award-info":[{"award-number":["IT1427-22"]}],"id":[{"id":"10.13039\/100015866","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003451","name":"Universidad del Pa\u00eds Vasco","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003451","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Detecting changes in data streams, with the data flowing continuously, is an important problem which Industry 4.0 has to deal with. In industrial monitoring, the data distribution may vary after a change in the machine\u2019s operating point; this situation is known as concept drift, and it is key to detecting this change. One drawback of conventional machine learning algorithms is that they are usually static, trained offline, and require monitoring at the input level. A change in the distribution of data, in the relationship between the input and the output data, would result in the deterioration of the predictive performance of the models due to the lack of an ability to generalize the model to new concepts. Drift detecting methods emerge as a solution to identify the concept drift in the data. This paper proposes a new approach for concept drift detection\u2014a novel approach to deal with sudden or abrupt drift, the most common drift found in industrial processes-, called CatSight. Briefly, this method is composed of two steps: (i) Use of Common Spatial Patterns (a statistical approach to deal with data streaming, closely related to Principal Component Analysis) to maximize the difference between two different distributions of a multivariate temporal data, and (ii) Machine Learning conventional algorithms to detect whether a change in the data flow has been occurred or not. The performance of the CatSight method, has been evaluated on a real use case, training six state of the art Machine Learning (ML) classifiers; obtained results indicate how adequate the new approach is.<\/jats:p>","DOI":"10.1007\/s13042-023-01810-z","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T08:03:42Z","timestamp":1678694622000},"page":"2925-2944","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["CatSight, a direct path to proper multi-variate time series change detection: perceiving a concept drift through common spatial pattern"],"prefix":"10.1007","volume":"14","author":[{"given":"Arantzazu","family":"Fl\u00f3rez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Itsaso","family":"Rodr\u00edguez-Moreno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arkaitz","family":"Artetxe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Igor Garc\u00eda","family":"Olaizola","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8062-9332","authenticated-orcid":false,"given":"Basilio","family":"Sierra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,13]]},"reference":[{"key":"1810_CR1","first-page":"1","volume":"2","author":"CA Escobar","year":"2021","unstructured":"Escobar CA, McGovern ME, Morales-Menendez R (2021) Quality 4.0: a review of big data challenges in manufacturing. J Intell Manuf 2:1\u201316","journal-title":"J Intell Manuf"},{"key":"1810_CR2","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.eswa.2017.04.008","volume":"82","author":"TS Sethi","year":"2017","unstructured":"Sethi TS, Kantardzic M (2017) On the reliable detection of concept drift from streaming unlabeled data. Expert Syst Appl 82:77\u201399","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1810_CR3","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1109\/TNNLS.2020.2978523","volume":"32","author":"A Liu","year":"2020","unstructured":"Liu A, Lu J, Zhang G (2020) Diverse instance-weighting ensemble based on region drift disagreement for concept drift adaptation. IEEE Trans Neural Netw Learn Syst 32(1):293\u2013307","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"12","key":"1810_CR4","first-page":"2346","volume":"31","author":"J Lu","year":"2018","unstructured":"Lu J, Liu A, Dong F, Gu F, Gama J, Zhang G (2018) Learning under concept drift: a review. IEEE Trans Knowl Data Eng 31(12):2346\u20132363","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"1810_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2523813","volume":"46","author":"J Gama","year":"2014","unstructured":"Gama J, \u017dliobait\u0117 I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv (CSUR) 46(4):1\u201337","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1810_CR6","doi-asserted-by":"publisher","unstructured":"Bahri M, Bifet A, Gama J, Gomes HM, Maniu S (2021) Data stream analysis: Foundations, major tasks and tools. WIREs Data Mining Knowl Discov 11(3):e1405. https:\/\/doi.org\/10.1002\/widm.1405. wires.onlinelibrary.wiley.com\/doi\/abs\/10.1002\/widm.1405","DOI":"10.1002\/widm.1405."},{"key":"1810_CR7","doi-asserted-by":"publisher","first-page":"1954","DOI":"10.1016\/j.neucom.2017.10.051","volume":"275","author":"RSM de Barros","year":"2018","unstructured":"de Barros RSM, Hidalgo JIG, de Lima Cabral D.R (2018) Wilcoxon rank sum test drift detector. Neurocomputing 275:1954\u20131963","journal-title":"Neurocomputing"},{"issue":"18","key":"1810_CR8","doi-asserted-by":"publisher","first-page":"8144","DOI":"10.1016\/j.eswa.2014.07.019","volume":"41","author":"PM Gon\u00e7alves Jr","year":"2014","unstructured":"Gon\u00e7alves PM Jr, de Carvalho Santos SG, Barros RS, Vieira DC (2014) A comparative study on concept drift detectors. Exp Syst Appl 41(18):8144\u20138156","journal-title":"Exp Syst Appl"},{"key":"1810_CR9","first-page":"286","volume-title":"Brazilian symposium on artificial intelligence","author":"J Gama","year":"2004","unstructured":"Gama J, Medas P, Castillo G, Rodrigues P (2004) Brazilian symposium on artificial intelligence. Springer, Berlin, pp 286\u2013295"},{"key":"1810_CR10","unstructured":"Baena-Garc\u0131a M, del Campo-\u00c1vila J, Fidalgo R, Bifet A, Gavalda R, Morales-Bueno R (2006) In: Fourth international workshop on knowledge discovery from data streams, vol.\u00a06 pp. 77\u201386"},{"key":"1810_CR11","unstructured":"Bifet A, Gavalda R (2007) In: Proceedings of the 2007 SIAM international conference on data mining (SIAM, 2007), pp. 443\u2013448"},{"key":"1810_CR12","unstructured":"Nishida K, Yamauchi K (2007) In: International conference on discovery science. Springer, Berlin, pp 264\u2013269"},{"key":"1810_CR13","unstructured":"Bach SH, Maloof MA (2008) in 2008 Eighth IEEE International Conference on Data Mining, pp 23\u201332"},{"issue":"2","key":"1810_CR14","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.patrec.2011.08.019","volume":"33","author":"GJ Ross","year":"2012","unstructured":"Ross GJ, Adams NM, Tasoulis DK, Hand DJ (2012) Exponentially weighted moving average charts for detecting concept drift. Pattern Recogn Lett 33(2):191\u2013198","journal-title":"Pattern Recogn Lett"},{"key":"1810_CR15","unstructured":"Sadreazami H, Amini M, Ahmad M.O, Swamy M (2021) in 2021 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1\u20135"},{"key":"1810_CR16","unstructured":"Sun Z, Tang J, Qiao J, Cui C (2020) in 2020 39th Chinese Control Conference (CCC), pp. 5754\u20135759"},{"key":"1810_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2019.106031","volume":"137","author":"J Zenisek","year":"2019","unstructured":"Zenisek J, Holzinger F, Affenzeller M (2019) Machine learning based concept drift detection for predictive maintenance. Comput Ind Eng 137:106031","journal-title":"Comput Ind Eng"},{"key":"1810_CR18","unstructured":"Saurav S, Malhotra P, TV V, Gugulothu N, Vig L, Agarwal P, Shroff G (2018) in Proceedings of the acm india joint international conference on data science and management of data , pp. 78\u201387"},{"key":"1810_CR19","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.inffus.2021.04.011","volume":"76","author":"B Veloso","year":"2021","unstructured":"Veloso B, Gama J, Malheiro B, Vinagre J (2021) Hyperparameter self-tuning for data streams. Inform Fusion 76:75\u201386","journal-title":"Inform Fusion"},{"key":"1810_CR20","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.inffus.2019.03.006","volume":"52","author":"RSM de Barros","year":"2019","unstructured":"de Barros RSM, de Carvalho Santos S.G.T (2019) An overview and comprehensive comparison of ensembles for concept drift. Inform Fusion 52:213\u2013244","journal-title":"Inform Fusion"},{"key":"1810_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113786","volume":"163","author":"ES Bab\u00fcro\u011flu","year":"2021","unstructured":"Bab\u00fcro\u011flu ES, Durmu\u015fo\u011flu A, Dereli T (2021) Novel hybrid pair recommendations based on a large-scale comparative study of concept drift detection. Exp Syst Appl 163:113786","journal-title":"Exp Syst Appl"},{"key":"1810_CR22","first-page":"1","volume":"2","author":"B Wang","year":"2022","unstructured":"Wang B, Wang W, Wang N, Mao Z (2022) A robust novelty detection framework based on ensemble learning. Int J Mach Learn Cybern 2:1\u201318","journal-title":"Int J Mach Learn Cybern"},{"issue":"6","key":"1810_CR23","doi-asserted-by":"publisher","first-page":"3198","DOI":"10.1109\/TCYB.2020.2983962","volume":"51","author":"A Liu","year":"2020","unstructured":"Liu A, Lu J, Zhang G (2020) Concept drift detection via equal intensity k-means space partitioning. IEEE Trans Cybern 51(6):3198\u20133211","journal-title":"IEEE Trans Cybern"},{"key":"1810_CR24","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.ins.2019.02.031","volume":"485","author":"SG Santos","year":"2019","unstructured":"Santos SG, Barros RS, Gon\u00e7alves PM Jr (2019) A differential evolution based method for tuning concept drift detectors in data streams. Inf Sci 485:376\u2013393","journal-title":"Inf Sci"},{"key":"1810_CR25","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.ins.2018.02.054","volume":"442","author":"DR de Lima Cabral","year":"2018","unstructured":"de Lima Cabral DR, de Barros RSM (2018) Concept drift detection based on fisher\u2019s exact test. Inform Sci 442:220\u2013234","journal-title":"Inform Sci"},{"key":"1810_CR26","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.compeleceng.2016.09.006","volume":"58","author":"S Liu","year":"2017","unstructured":"Liu S, Feng L, Wu J, Hou G, Han G (2017) Concept drift detection for data stream learning based on angle optimized global embedding and principal component analysis in sensor networks. Comput Electr Eng 58:327\u2013336","journal-title":"Comput Electr Eng"},{"key":"1810_CR27","unstructured":"Li D, Chen D, Goh J, Ng SK (2018) Anomaly detection with generative adversarial networks for multivariate time series. arXiv preprint arXiv:1809.04758"},{"key":"1810_CR28","first-page":"2","volume":"2","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Chen Y, Wang J, Pan Z (2021) Unsupervised deep anomaly detection for multi-sensor time-series signals. IEEE Trans Knowl Data Eng 2:2","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1810_CR29","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1109\/T-C.1970.222918","volume":"4","author":"K Fukunaga","year":"1970","unstructured":"Fukunaga K, Koontz WL (1970) Application of the Karhunen-Loeve expansion to feature selection and ordering. IEEE Trans Comput 4:311\u2013318","journal-title":"IEEE Trans Comput"},{"issue":"4","key":"1810_CR30","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1109\/86.895946","volume":"8","author":"H Ramoser","year":"2000","unstructured":"Ramoser H, Muller-Gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial eeg during imagined hand movement. IEEE Trans Rehabil Eng 8(4):441\u2013446","journal-title":"IEEE Trans Rehabil Eng"},{"issue":"1","key":"1810_CR31","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/MSP.2008.4408441","volume":"25","author":"B Blankertz","year":"2007","unstructured":"Blankertz B, Tomioka R, Lemm S, Kawanabe M, Muller KR (2007) Optimizing spatial filters for robust eeg single-trial analysis. IEEE Signal Process Mag 25(1):41\u201356","journal-title":"IEEE Signal Process Mag"},{"issue":"7","key":"1810_CR32","doi-asserted-by":"publisher","first-page":"1378","DOI":"10.1109\/TNSRE.2019.2922713","volume":"27","author":"Y Park","year":"2019","unstructured":"Park Y, Chung W (2019) Frequency-optimized local region common spatial pattern approach for motor imagery classification. IEEE Trans Neural Syst Rehabil Eng 27(7):1378\u20131388","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"1810_CR33","doi-asserted-by":"publisher","first-page":"27873","DOI":"10.1109\/ACCESS.2018.2841051","volume":"6","author":"T Nguyen","year":"2018","unstructured":"Nguyen T, Hettiarachchi I, Khatami A, Gordon-Brown L, Lim CP, Nahavandi S (2018) Classification of multi-class BCI data by common spatial pattern and fuzzy system. IEEE Access 6:27873\u201327884","journal-title":"IEEE Access"},{"key":"1810_CR34","doi-asserted-by":"crossref","unstructured":"Xygonakis I, Athanasiou A, Pandria N, Kugiumtzis D, Bamidis P.D (2018) Decoding motor imagery through common spatial pattern filters at the eeg source space. Comput Intell Neurosci 2018","DOI":"10.1155\/2018\/7957408"},{"issue":"8","key":"1810_CR35","doi-asserted-by":"publisher","first-page":"2436","DOI":"10.3390\/s20082436","volume":"20","author":"I Rodr\u00edguez-Moreno","year":"2020","unstructured":"Rodr\u00edguez-Moreno I, Mart\u00ednez-Otzeta JM, Goienetxea I, Rodriguez-Rodriguez I, Sierra B (2020) Shedding light on people action recognition in social robotics by means of common spatial patterns. Sensors 20(8):2436","journal-title":"Sensors"},{"key":"1810_CR36","doi-asserted-by":"publisher","unstructured":"Rodr\u00edguez-Moreno I, Mart\u00ednez-Otzeta J.M, Sierra B, Irigoien I, Rodriguez-Rodriguez I, Goienetxea I (2020) Using common spatial patterns to select relevant pixels for video activity recognition. Appl Sci 10(22). https:\/\/doi.org\/10.3390\/app10228075. https:\/\/www.mdpi.com\/2076-3417\/10\/22\/8075","DOI":"10.3390\/app10228075"},{"key":"1810_CR37","unstructured":"R\u00f6sler O, Suendermann D (2013)"},{"key":"1810_CR38","unstructured":"Roesler O (2013) UCI machine learning repository . http:\/\/archive.ics.uci.edu\/ml"},{"key":"1810_CR39","unstructured":"Ho TK (1995) in Proceedings of 3rd international conference on document analysis and recognition, vol.\u00a01, IEEE, pp 278\u2013282"},{"key":"1810_CR40","volume-title":"The nature of statistical learning theory","author":"V Vapnik","year":"1999","unstructured":"Vapnik V (1999) The nature of statistical learning theory. Springer Science & Business Media, Berlin"},{"issue":"2","key":"1810_CR41","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","volume":"7","author":"RA Fisher","year":"1936","unstructured":"Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7(2):179\u2013188","journal-title":"Ann Eugen"},{"key":"1810_CR42","first-page":"2","volume":"17","author":"J Goldberger","year":"2004","unstructured":"Goldberger J, Hinton GE, Roweis S, Salakhutdinov RR (2004) Neighbourhood components analysis. Adv Neural Inf Process Syst 17:2","journal-title":"Adv Neural Inf Process Syst"},{"issue":"2","key":"1810_CR43","first-page":"3","volume":"1","author":"H Zhang","year":"2004","unstructured":"Zhang H (2004) The optimality of naive bayes. AA 1(2):3","journal-title":"AA"},{"issue":"3","key":"1810_CR44","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1007\/s11760-019-01580-8","volume":"14","author":"MD Basar","year":"2020","unstructured":"Basar MD, Duru AD, Akan A (2020) Emotional state detection based on common spatial patterns of eeg. SIViP 14(3):473\u2013481","journal-title":"SIViP"},{"issue":"395","key":"1810_CR45","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1080\/01621459.1986.10478341","volume":"81","author":"JP Shaffer","year":"1986","unstructured":"Shaffer JP (1986) Modified sequentially rejective multiple test procedures. J Am Stat Assoc 81(395):826\u2013831","journal-title":"J Am Stat Assoc"},{"key":"1810_CR46","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1007\/978-3-642-52307-6_8","volume-title":"Multiple hypothesenpr\u00fcfung\/multiple hypotheses testing","author":"B Bergmann","year":"1988","unstructured":"Bergmann B, Hommel G (1988) Multiple hypothesenpr\u00fcfung\/multiple hypotheses testing. Springer, Berlin, pp 100\u2013115"},{"key":"1810_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.32614\/RJ-2016-017","volume":"8","author":"B Calvo","year":"2016","unstructured":"Calvo B, Santaf\u00e9 Rodrigo G (2016) scmamp: statistical comparison of multiple algorithms in multiple problems. R J 8:1","journal-title":"R J"},{"key":"1810_CR48","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.ins.2017.11.046","volume":"430","author":"S Ren","year":"2018","unstructured":"Ren S, Liao B, Zhu W, Li K (2018) Knowledge-maximized ensemble algorithm for different types of concept drift. Inf Sci 430:261\u2013281","journal-title":"Inf Sci"},{"issue":"6","key":"1810_CR49","doi-asserted-by":"publisher","first-page":"1721","DOI":"10.1007\/s13042-020-01270-9","volume":"12","author":"I Goienetxea","year":"2021","unstructured":"Goienetxea I, Mendialdua I, Rodr\u00edguez I, Sierra B (2021) Problems selection under dynamic selection of the best base classifier in one versus one: Pseudovo. Int J Mach Learn Cybern 12(6):1721\u20131735","journal-title":"Int J Mach Learn Cybern"},{"issue":"10","key":"1810_CR50","doi-asserted-by":"publisher","first-page":"3071","DOI":"10.1007\/s13042-022-01581-z","volume":"13","author":"C Li","year":"2022","unstructured":"Li C, He C, Zhang H, Yao J, Zhang J, Zhuo L (2022) Streamer temporal action detection in live video by co-attention boundary matching. Int J Mach Learn Cybern 13(10):3071\u20133088","journal-title":"Int J Mach Learn Cybern"},{"key":"1810_CR51","first-page":"1","volume":"2","author":"JM Barrera","year":"2022","unstructured":"Barrera JM, Reina A, Mate A, Trujillo JC (2022) Fault detection and diagnosis for industrial processes based on clustering and autoencoders: a case of gas turbines. Int J Mach Learn Cybern 2:1\u201317","journal-title":"Int J Mach Learn Cybern"},{"issue":"6","key":"1810_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11222-022-10176-1","volume":"32","author":"KL Hallgren","year":"2022","unstructured":"Hallgren KL, Heard NA, Adams NM (2022) Changepoint detection in non-exchangeable data. Stat Comput 32(6):1\u201319","journal-title":"Stat Comput"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01810-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01810-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01810-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T19:07:20Z","timestamp":1690052840000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01810-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,13]]},"references-count":52,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["1810"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01810-z","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,13]]},"assertion":[{"value":"20 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}