{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T23:08:44Z","timestamp":1724886524008},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T00:00:00Z","timestamp":1643068800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T00:00:00Z","timestamp":1643068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Engineering with Computers"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s00366-021-01573-7","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T05:17:56Z","timestamp":1643087876000},"page":"1923-1933","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Bi-objective Bayesian optimization of engineering problems with cheap and expensive cost functions"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-4761-2368","authenticated-orcid":false,"given":"Nasrulloh","family":"Loka","sequence":"first","affiliation":[]},{"given":"Ivo","family":"Couckuyt","sequence":"additional","affiliation":[]},{"given":"Federico","family":"Garbuglia","sequence":"additional","affiliation":[]},{"given":"Domenico","family":"Spina","sequence":"additional","affiliation":[]},{"given":"Inneke","family":"Van Nieuwenhuyse","sequence":"additional","affiliation":[]},{"given":"Tom","family":"Dhaene","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,25]]},"reference":[{"issue":"1","key":"1573_CR1","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TEVC.2005.851274","volume":"10","author":"J Knowles","year":"2006","unstructured":"Knowles J (2006) ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans Evol Comput 10(1):50\u201366. https:\/\/doi.org\/10.1109\/TEVC.2005.851274 (ISSN 1089778X.)","journal-title":"IEEE Trans Evol Comput"},{"key":"1573_CR2","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/978-3-642-01020-0_8","volume-title":"Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics","author":"J Knowles","year":"2010","unstructured":"Knowles J, Corne D, Reynolds A (2010) Noisy multiobjective optimization on a budget of 250 evaluations. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics, vol 5467. Springer, Berlin, pp 36\u201350. https:\/\/doi.org\/10.1007\/978-3-642-01020-0_8 (ISSN 03029743ISSN 03029743)"},{"issue":"1\u20132","key":"1573_CR3","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s10898-016-0419-3","volume":"67","author":"J Davins-Valldaura","year":"2017","unstructured":"Davins-Valldaura J, Moussaoui S, Pita-Gil G, Plestan F (2017) ParEGO extensions for multi-objective optimization of expensive evaluation functions. J Global Optim 67(1\u20132):79\u201396. https:\/\/doi.org\/10.1007\/s10898-016-0419-3 (ISSN 15732916.)","journal-title":"J Global Optim"},{"key":"1573_CR4","unstructured":"Astudillo R, Frazier P (2017) Multi-attribute Bayesian optimization under utility uncertainty. In: 31st Conference on Neural Information Processing Systems (NIPS 2017), p 5"},{"issue":"2","key":"1573_CR5","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s40747-019-0113-4","volume":"6","author":"CCA Coello","year":"2020","unstructured":"Coello CCA, Brambila SG, Gamboa JF, Tapia MGC, G\u00f3mez RH (2020) Evolutionary multiobjective optimization: open research areas and some challenges lying ahead. Complex Intell Syst 6(2):221\u2013236. https:\/\/doi.org\/10.1007\/s40747-019-0113-4 (ISSN 2199-4536)","journal-title":"Complex Intell Syst"},{"issue":"4","key":"1573_CR6","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1080\/03052150108940926","volume":"33","author":"T Ray","year":"2001","unstructured":"Ray T, Tai K, Seow KC (2001) Multiobjective design optimization by an evolutionary algorithm. Eng Optim 33(4):399\u2013424. https:\/\/doi.org\/10.1080\/03052150108940926 (ISSN 0305215X)","journal-title":"Eng Optim"},{"issue":"1","key":"1573_CR7","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s00366-019-00844-8","volume":"37","author":"Q Zhou","year":"2021","unstructured":"Zhou Q, Wu J, Xue T, Jin P (2021) A two-stage adaptive multi-fidelity surrogate model-assisted multi-objective genetic algorithm for computationally expensive problems. Eng Comput 37(1):623\u2013639. https:\/\/doi.org\/10.1007\/s00366-019-00844-8 (ISSN 14355663)","journal-title":"Eng Comput"},{"issue":"2","key":"1573_CR8","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s00366-011-0229-7","volume":"28","author":"W Shao","year":"2012","unstructured":"Shao W, Deng H, Ma Y, Wei Z (2012) Extended Gaussian Kriging for computer experiments in engineering design. Engi Comput 28(2):161\u2013178. https:\/\/doi.org\/10.1007\/s00366-011-0229-7 (ISSN 01770667)","journal-title":"Engi Comput"},{"key":"1573_CR9","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3206.001.0001","volume-title":"Gaussian processes for machine learning","author":"CE Rasmussen","year":"2018","unstructured":"Rasmussen CE, Williams CKI (2018) Gaussian processes for machine learning. The MIT Press, London. https:\/\/doi.org\/10.7551\/mitpress\/3206.001.0001"},{"issue":"1","key":"1573_CR10","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","volume":"104","author":"B Shahriari","year":"2015","unstructured":"Shahriari B, Swersky K, Wang Z, Adams RP, De Freitas N (2015) Taking the human out of the loop: a review of Bayesian optimization. Proc IEEE 104(1):148\u2013756 (ISSN 15582256)","journal-title":"Proc IEEE"},{"key":"1573_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-021-01404-9","author":"Y He","year":"2021","unstructured":"He Y, Sun J, Song P, Wang X (2021) Variable-fidelity hypervolume-based expected improvement criteria for multi-objective efficient global optimization of expensive functions. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-021-01404-9 (ISSN 14355663)","journal-title":"Eng Comput"},{"issue":"16","key":"1573_CR12","doi-asserted-by":"publisher","first-page":"5863","DOI":"10.1177\/0954410019864485","volume":"233","author":"G Sun","year":"2019","unstructured":"Sun G, Wang S (2019) A review of the artificial neural network surrogate modeling in aerodynamic design. Proc Inst Mech Eng Part G 233(16):5863\u20135872. https:\/\/doi.org\/10.1177\/0954410019864485 (ISSN 20413025)","journal-title":"Proc Inst Mech Eng Part G"},{"key":"1573_CR13","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.promfg.2020.02.075","volume":"42","author":"M Shahriari","year":"2020","unstructured":"Shahriari M, Pardo D, Moser B, Sobieczky F (2020) A deep neural network as surrogate model for forward simulation of borehole resistivity measurement. Proced Manuf 42:235\u2013238. https:\/\/doi.org\/10.1016\/j.promfg.2020.02.075 (ISSN 23519789)","journal-title":"Proced Manuf"},{"key":"1573_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-021-01291-0","author":"B Bhattacharyya","year":"2021","unstructured":"Bhattacharyya B (2021) Uncertainty quantification and reliability analysis by an adaptive sparse Bayesian inference based PCE model. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-021-01291-0 (ISSN 14355663)","journal-title":"Eng Comput"},{"key":"1573_CR15","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.ymssp.2019.03.032","volume":"128","author":"Y Zhou","year":"2019","unstructured":"Zhou Y, Lu Z, Cheng K, Shi Y (2019) An expanded sparse Bayesian learning method for polynomial chaos expansion. Mech Syst Signal Process 128:153\u2013171. https:\/\/doi.org\/10.1016\/j.ymssp.2019.03.032 (ISSN 10961216)","journal-title":"Mech Syst Signal Process"},{"key":"1573_CR16","unstructured":"Bergstra J, Bardenet R, Bengio Y , K\u00e9gl B (2011) Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011, pp 1\u20139"},{"key":"1573_CR17","doi-asserted-by":"publisher","first-page":"5090","DOI":"10.1109\/CEFC.2016.7816315","volume":"15","author":"M Franti\u0161ek","year":"2017","unstructured":"Franti\u0161ek M (2017) Bayesian approach to design optimization of electromagnetic systems under uncertainty. IEEE CEFC Bienn Conf Electromagn Field Comput 15:5090. https:\/\/doi.org\/10.1109\/CEFC.2016.7816315","journal-title":"IEEE CEFC Bienn Conf Electromagn Field Comput"},{"key":"1573_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/NEMO.2018.8503107","author":"N Knudde","year":"2018","unstructured":"Knudde N, Couckuyt I, Spina D, Lukasik K, Barmuta P, Schreurs D, Dhaene T (2018) Data-efficient Bayesian optimization with constraints for power amplifier design. IEEE MTT-S Int Conf Numer Electromagn Multiphys Model Optim. https:\/\/doi.org\/10.1109\/NEMO.2018.8503107","journal-title":"IEEE MTT-S Int Conf Numer Electromagn Multiphys Model Optim"},{"issue":"5","key":"1573_CR19","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1002\/mop.30498","volume":"59","author":"F Passos","year":"2017","unstructured":"Passos F, Ye Y, Spina D, Roca E, Castro-Lopez R, Dhaene T, Fern\u00e1ndez FV (2017) Parametric macromodeling of integrated inductors for RF circuit design. Microw Opt Technol Lett 59(5):1207\u20131212. https:\/\/doi.org\/10.1002\/mop.30498","journal-title":"Microw Opt Technol Lett"},{"issue":"9","key":"1573_CR20","doi-asserted-by":"publisher","first-page":"3765","DOI":"10.2514\/1.J058979","volume":"58","author":"C Conlan-Smith","year":"2020","unstructured":"Conlan-Smith C, Ramos-Garc\u00eda N, Sigmund O, Andreasen CS (2020) Aerodynamic shape optimization of aircraft wings using panel methods. AIAA J 58(9):3765\u20133776. https:\/\/doi.org\/10.2514\/1.J058979 (ISSN 00011452)","journal-title":"AIAA J"},{"issue":"May","key":"1573_CR21","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1016\/j.ast.2019.05.044","volume":"91","author":"LR Zuhal","year":"2019","unstructured":"Zuhal LR, Palar PS, Shimoyama K (2019) A comparative study of multi-objective expected improvement for aerodynamic design. Aerosp Sci Technol 91(May):548\u2013560. https:\/\/doi.org\/10.1016\/j.ast.2019.05.044 (ISSN 12709638)","journal-title":"Aerosp Sci Technol"},{"key":"1573_CR22","doi-asserted-by":"publisher","DOI":"10.1145\/3377929.3398122","author":"TM Jim","year":"2020","unstructured":"Jim TM, Faza GA, Palar PS, Shimoyama K (2020) Bayesian methods for multi-objective optimization of a supersonic wing planform. Proc Genet Evolut Comput Conf Companion. https:\/\/doi.org\/10.1145\/3377929.3398122 (ISBN 9781450371278)","journal-title":"Proc Genet Evolut Comput Conf Companion"},{"issue":"5","key":"1573_CR23","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1016\/j.ijadhadh.2008.09.009","volume":"29","author":"LFM da\u00a0Silva","year":"2009","unstructured":"da\u00a0Silva LFM, Lopes MJCQ (2009) Joint strength optimization by the mixed-adhesive technique. Int J Adhes Adhes 29(5):509\u2013514. https:\/\/doi.org\/10.1016\/j.ijadhadh.2008.09.009 (ISSN 01437496)","journal-title":"Int J Adhes Adhes"},{"issue":"3","key":"1573_CR24","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1007\/s10898-013-0118-2","volume":"60","author":"I Couckuyt","year":"2014","unstructured":"Couckuyt I, Deschrijver D, Dhaene T (2014) Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization. J Global Optim 60(3):575\u2013594. https:\/\/doi.org\/10.1007\/s10898-013-0118-2 (ISSN 1573-2916)","journal-title":"J Global Optim"},{"key":"1573_CR25","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1109\/VLSID.2012.91","volume":"25","author":"AB Sachid","year":"2016","unstructured":"Sachid AB, Paliwal P, Joshi S, Shojaei M, Sharma D, Rao V (2016) Circuit optimization at 22nm technology node. Proc IEEE Int Conf VLSI Des 25:322\u2013327. https:\/\/doi.org\/10.1109\/VLSID.2012.91 (ISSN 10639667)","journal-title":"Proc IEEE Int Conf VLSI Des"},{"key":"1573_CR26","unstructured":"S\u00e1nchez CA, Basler R, Zogg M, Ermanni P (2012) Multistep heating to optimizethe curing process of a paste adhesive. In: ECCM 2012-Composites at Venice, Proceedings of the 15th European Conference on Composite Materials. (ISBN 9788888785332)"},{"key":"1573_CR27","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2012.6256586","author":"I Couckuyt","year":"2012","unstructured":"Couckuyt I, Deschrijver D, Dhaene T (2012) Towards efficient multiobjective optimization: multiobjective statistical criterions. IEEE Congr Evolut Comput. https:\/\/doi.org\/10.1109\/CEC.2012.6256586","journal-title":"IEEE Congr Evolut Comput"},{"key":"1573_CR28","first-page":"32","volume-title":"Advances in neural information processing systems","author":"S Belakaria","year":"2019","unstructured":"Belakaria S, Deshwal A, Doppa JR (2019) Max-value entropy search for multi-objective Bayesian optimization. Advances in neural information processing systems. Springer, Berlin, p 32 (ISSN 10495258)"},{"key":"1573_CR29","unstructured":"Daulton S, Balandat M, Bakshy E (2020) Differentiable expected hypervolume improvement for parallel multi-objective bayesian optimization. Advances in Neural Information Processing Systems. 33, pp 1\u201330 (ISSN 23318422)"},{"key":"1573_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/1-84628-137-7_6","volume-title":"Scalable test problems for evolutionary multiobjective optimization","author":"K Deb","year":"2005","unstructured":"Deb K, Thiele L, Laumanns M, Zitzler E (2005) Scalable test problems for evolutionary multiobjective optimization. Wiley, New Jersey. https:\/\/doi.org\/10.1007\/1-84628-137-7_6"},{"key":"1573_CR31","doi-asserted-by":"publisher","DOI":"10.1002\/nme.2750","author":"FAC Viana","year":"2010","unstructured":"Viana FAC, Venter G, Balabanov V (2010) An algorithm for fast optimal Latin hypercube design of experiments. Int J Numer Methods Eng. https:\/\/doi.org\/10.1002\/nme.2750 (ISSN 00295981)","journal-title":"Int J Numer Methods Eng"},{"issue":"3\u20134 SPEC. ISS","key":"1573_CR32","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.geoderma.2005.04.003","volume":"128","author":"B Minasny","year":"2005","unstructured":"Minasny B, McBratney AB (2005) The Mat\u00e9rn function as a general model for soil variograms. Geoderma 128(3\u20134 SPEC. ISS):192\u2013207. https:\/\/doi.org\/10.1016\/j.geoderma.2005.04.003 (ISSN 00167061)","journal-title":"Geoderma"},{"key":"1573_CR33","first-page":"2951","volume":"4","author":"J Snoek","year":"2012","unstructured":"Snoek J, Larochelle Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. Adv Neural Inf Process Syst 4:2951\u20132959 (ISSN 10495258)","journal-title":"Adv Neural Inf Process Syst"},{"key":"1573_CR34","doi-asserted-by":"publisher","unstructured":"Zitzler E, Thiele L, Laumanns M, Fonseca CM, Da Fonseca VG (2003) Performance assessment of multiobjective optimizers: an analysis and review in IEEE Transactions on Evolutionary Computation, vol 7, no 2. pp 117\u2013132, April 2003. doi: https:\/\/doi.org\/10.1109\/TEVC.2003.810758. (ISSN 1089778X)","DOI":"10.1109\/TEVC.2003.810758"},{"issue":"3","key":"1573_CR35","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1109\/TEVC.2009.2033671","volume":"14","author":"Q Zhang","year":"2010","unstructured":"Zhang Q, Liu W, Tsang E, Virginas B (2010) Expensive multiobjective optimization by MOEA\/D with gaussian process model. IEEE Trans Evol Comput 14(3):456\u2013474. https:\/\/doi.org\/10.1109\/TEVC.2009.2033671 (ISSN 1089778X)","journal-title":"IEEE Trans Evol Comput"},{"key":"1573_CR36","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2011.5949880","author":"MTM Emmerich","year":"2011","unstructured":"Emmerich MTM, Deutz AH, Klinkenberg JW (2011) Hypervolume-based expected improvement: monotonicity properties and exact computation. IEEE Congr Evolut Comput. https:\/\/doi.org\/10.1109\/CEC.2011.5949880","journal-title":"IEEE Congr Evolut Comput"},{"key":"1573_CR37","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1023\/A:1008306431147","volume":"13","author":"DR Jones","year":"1998","unstructured":"Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13:455\u2013492","journal-title":"J Glob Optim"},{"key":"1573_CR38","unstructured":"Knudde N, Van Der Herten J, Dhaene T, Couckuyt I (2017) GPflowOpt: a Bayesian optimization library using tensorflow. arXiv preprint arXiv:1711.03845. ISSN 23318422"},{"issue":"3","key":"1573_CR39","doi-asserted-by":"publisher","first-page":"1653","DOI":"10.1016\/j.ejor.2006.08.008","volume":"181","author":"N Beume","year":"2007","unstructured":"Beume N, Naujoks B, Emmerich M (2007) SMS-EMOA: multiobjective selection based on dominated hypervolume. Eur J Oper Res 181(3):1653\u20131669. https:\/\/doi.org\/10.1016\/j.ejor.2006.08.008 (ISSN 03772217)","journal-title":"Eur J Oper Res"},{"key":"1573_CR40","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/978-3-642-01020-0_18","volume-title":"Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics","author":"JJ Durillo","year":"2010","unstructured":"Durillo JJ, Nebro AJ, Luna F, Alba E (2010) On the effect of the steady-state selection scheme in multi-objective genetic algorithms. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics, vol 5467. Springer, Berlin, pp 183\u2013197. https:\/\/doi.org\/10.1007\/978-3-642-01020-0_18 (ISSN 03029743)"},{"key":"1573_CR41","doi-asserted-by":"publisher","DOI":"10.23919\/EuCAP48036.2020.9136051","author":"J Qing","year":"2020","unstructured":"Qing J, Knudde N, Couckuyt I, Spina D, Dhaene T (2020) Bayesian active learning for electromagnetic structure design. Eur Conf Anten Propag. https:\/\/doi.org\/10.23919\/EuCAP48036.2020.9136051","journal-title":"Eur Conf Anten Propag"},{"issue":"6","key":"1573_CR42","doi-asserted-by":"publisher","first-page":"2346","DOI":"10.1109\/TMTT.2006.875271","volume":"54","author":"A Manchec","year":"2006","unstructured":"Manchec A, Quendo C, Favennec JF, Rius E, Person C (2006) Synthesis of capacitive-coupled dual-behavior resonator (ccdbr) filters. IEEE Trans Microw Theory Tech 54(6):2346\u20132355. https:\/\/doi.org\/10.1109\/TMTT.2006.875271 (ISSN 15579670)","journal-title":"IEEE Trans Microw Theory Tech"},{"issue":"6","key":"1573_CR43","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1109\/MMM.2008.929554","volume":"9","author":"S Koziel","year":"2008","unstructured":"Koziel S, Cheng QS, Bandler JW (2008) Space mapping. IEEE Microw Mag 9(6):105\u2013122. https:\/\/doi.org\/10.1109\/MMM.2008.929554 (ISSN 15273342.)","journal-title":"IEEE Microw Mag"}],"container-title":["Engineering with Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-021-01573-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00366-021-01573-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-021-01573-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T19:12:07Z","timestamp":1685560327000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00366-021-01573-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,25]]},"references-count":43,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["1573"],"URL":"https:\/\/doi.org\/10.1007\/s00366-021-01573-7","relation":{},"ISSN":["0177-0667","1435-5663"],"issn-type":[{"value":"0177-0667","type":"print"},{"value":"1435-5663","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,25]]},"assertion":[{"value":"7 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2022","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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}