{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T06:51:56Z","timestamp":1771051916591,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T00:00:00Z","timestamp":1599696000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T00:00:00Z","timestamp":1599696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673298"],"award-info":[{"award-number":["61673298"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61203149"],"award-info":[{"award-number":["61203149"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013105","name":"Shanghai Rising-Star Program","doi-asserted-by":"publisher","award":["17QA1404500"],"award-info":[{"award-number":["17QA1404500"]}],"id":[{"id":"10.13039\/501100013105","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["17ZR1445700"],"award-info":[{"award-number":["17ZR1445700"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","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":[[2020,12]]},"DOI":"10.1007\/s11063-020-10348-y","type":"journal-article","created":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T22:02:57Z","timestamp":1599775377000},"page":"2253-2274","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Finite Time Anti-synchronization of Quaternion-Valued Neural Networks with Asynchronous Time-Varying Delays"],"prefix":"10.1007","volume":"52","author":[{"given":"Zihan","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3165-443X","authenticated-orcid":false,"given":"Xiwei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,10]]},"reference":[{"issue":"10","key":"10348_CR1","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1109\/31.7601","volume":"35","author":"L Chua","year":"1988","unstructured":"Chua L, Yang L (1988) Cellular neural networks: applications. IEEE Trans Circuits Syst 35(10):1273\u20131290","journal-title":"IEEE Trans Circuits Syst"},{"issue":"3","key":"10348_CR2","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1145\/175247.175257","volume":"37","author":"B Widrow","year":"1994","unstructured":"Widrow B, Rumelhart D, Lehr M (1994) Neural networks: applications in industry, business and science. Commun ACM 37(3):93\u2013105","journal-title":"Commun ACM"},{"key":"10348_CR3","first-page":"318","volume":"2774","author":"T Isokawa","year":"2003","unstructured":"Isokawa T, Kusakabe T, Matsui N, Peper F (2003) Quaternion neural network and its application. Knowl-Based Intell Inf Eng Syst 2774:318\u2013324","journal-title":"Knowl-Based Intell Inf Eng Syst"},{"issue":"2","key":"10348_CR4","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1017\/S0305004100055638","volume":"85","author":"A Sudbery","year":"1979","unstructured":"Sudbery A (1979) Quaternionic analysis. Math Proc Camb Philos Soc 85(2):199\u2013225","journal-title":"Math Proc Camb Philos Soc"},{"key":"10348_CR5","first-page":"504","volume":"2017","author":"T Parcollet","year":"2017","unstructured":"Parcollet T, Morchid M, Linares G (2017) Deep quaternion neural networks for spoken language understanding. IEEE Autom Speech Recogn Underst Worksh (ASRU) 2017:504\u2013511","journal-title":"IEEE Autom Speech Recogn Underst Worksh (ASRU)"},{"key":"10348_CR6","doi-asserted-by":"crossref","unstructured":"Zhu X, Xu Y, Xu H, Chen C (2018) Quaternion convolutional neural networks. In: Process of the European conference on computer vision (ECCV), pp 645\u2013661","DOI":"10.1007\/978-3-030-01237-3_39"},{"key":"10348_CR7","doi-asserted-by":"crossref","unstructured":"Gaudet C, Maida A (2018) Deep quaternion networks. In: International joint conference on neural networks (IJCNN), pp 1\u20138","DOI":"10.1109\/IJCNN.2018.8489651"},{"issue":"10","key":"10348_CR8","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1109\/81.633887","volume":"44","author":"T Yang","year":"1997","unstructured":"Yang T, Chua L (1997) Impulsive stabilization for control and synchronization of chaotic systems: theory and application to secure communication. IEEE Trans Circuits Syst I Fundam Theor Appl 44(10):976\u2013988","journal-title":"IEEE Trans Circuits Syst I Fundam Theor Appl"},{"issue":"2","key":"10348_CR9","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.cnsns.2012.07.005","volume":"18","author":"A Wu","year":"2013","unstructured":"Wu A, Zeng Z (2013) Anti-synchronization control of a class of memristive recurrent neural networks. Commun Nonlinear Sci Numer Simul 18(2):373\u2013385","journal-title":"Commun Nonlinear Sci Numer Simul"},{"key":"10348_CR10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2018.04.008","volume":"105","author":"D Liu","year":"2018","unstructured":"Liu D, Zhu S, Sun K (2018) Anti-synchronization of complex-valued memristor-based delayed neural networks. Neural Netw 105:1\u201313","journal-title":"Neural Netw"},{"issue":"3","key":"10348_CR11","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1109\/TNNLS.2015.2415496","volume":"27","author":"X Liu","year":"2016","unstructured":"Liu X, Chen T (2016) Global exponential stability for complex-valued recurrent neural networks with asynchronous time delays. IEEE Trans Neural Netw Learn Syst 27(3):593\u2013606","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"6","key":"10348_CR12","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1109\/TNNLS.2012.2195028","volume":"23","author":"J Hu","year":"2012","unstructured":"Hu J, Wang J (2012) Global stability of complex-valued recurrent neural networks with time-delays. IEEE Trans Neural Netw Learn Syst 23(6):853\u2013865","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10348_CR13","doi-asserted-by":"crossref","first-page":"9128","DOI":"10.1109\/ACCESS.2019.2891721","volume":"7","author":"X Liu","year":"2019","unstructured":"Liu X, Li Z (2019) Global $$\\mu $$-stability of quaternion-valued neural networks with unbounded and asynchronous time-varying delays. IEEE Access 7:9128\u20139141","journal-title":"IEEE Access"},{"issue":"6","key":"10348_CR14","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/TSMC.2017.2720121","volume":"49","author":"Y Liu","year":"2019","unstructured":"Liu Y, Wang Z, Yuan Y, Liu W (2019) Event-triggered partial-nodes-based state estimation for delayed complex networks with bounded distributed delays. IEEE Trans Syst Man Cybern-Syst 49(6):1088\u20131098","journal-title":"IEEE Trans Syst Man Cybern-Syst"},{"key":"10348_CR15","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.ins.2016.04.033","volume":"360","author":"Y Liu","year":"2016","unstructured":"Liu Y, Zhang D, Lu J, Cao J (2016) Global $$\\mu $$-stability criteria for quaternion-valued neural networks with unbounded time-varying delays. Inf Sci 360:273\u2013288","journal-title":"Inf Sci"},{"issue":"5","key":"10348_CR16","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1016\/j.neunet.2005.03.015","volume":"19","author":"Y Liu","year":"2006","unstructured":"Liu Y, Wang Z, Liu X (2006) Global exponential stability of generalized recurrent neural networks with discrete and distributed delays. Neural Netw 19(5):667\u2013675","journal-title":"Neural Netw"},{"issue":"1","key":"10348_CR17","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s11071-016-3060-2","volume":"87","author":"Y Liu","year":"2017","unstructured":"Liu Y, Zhang D, Lu J (2017) Global exponential stability for quaternion-valued recurrent neural networks with time-varying delays. Nonlinear Dyn 87(1):553\u2013565","journal-title":"Nonlinear Dyn"},{"issue":"9","key":"10348_CR18","doi-asserted-by":"crossref","first-page":"4201","DOI":"10.1109\/TNNLS.2017.2755697","volume":"29","author":"Y Liu","year":"2018","unstructured":"Liu Y, Zhang D, Lou J, Lu J, Cao J (2018) Stability analysis of quaternion-valued neural networks: Decomposition and direct approaches. IEEE Trans Neural Netw Learn Syst 29(9):4201\u20134211","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10348_CR19","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.neucom.2017.03.052","volume":"247","author":"H Shu","year":"2017","unstructured":"Shu H, Song Q, Liu Y, Zhao Z, Alsaadi F (2017) Global $$\\mu $$-stability of quaternion-valued neural networks with non-differentiable time-varying delays. Neurocomputing 247:202\u2013212","journal-title":"Neurocomputing"},{"key":"10348_CR20","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.neucom.2018.04.044","volume":"303","author":"Y Li","year":"2018","unstructured":"Li Y, Li B, Yao S, Xiong L (2018) The global exponential pseudo almost periodic synchronization of quaternion-valued cellular neural networks with time-varying delays. Neurocomputing 303:75\u201387","journal-title":"Neurocomputing"},{"issue":"7","key":"10348_CR21","first-page":"2769","volume":"29","author":"X Chen","year":"2018","unstructured":"Chen X, Song Q, Li Z, Zhao Z, Liu Y (2018) Stability analysis of continuous-time and discrete-time quaternion-valued neural networks with linear threshold neurons. IEEE Trans Neural Netw Learn Syst 29(7):2769\u20132781","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"11","key":"10348_CR22","doi-asserted-by":"crossref","first-page":"5430","DOI":"10.1109\/TNNLS.2018.2801297","volume":"29","author":"Q Song","year":"2018","unstructured":"Song Q, Chen X (2018) Multistability analysis of quaternion-valued neural networks with time delays. IEEE Trans Neural Netw Learn Syst 29(11):5430\u20135440","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"10348_CR23","doi-asserted-by":"crossref","first-page":"361","DOI":"10.15388\/NA.2018.3.5","volume":"23","author":"Z Tu","year":"2018","unstructured":"Tu Z, Cao J, Alsaedi A, Ahmad B (2018) Stability analysis for delayed quaternion-valued neural networks via nonlinear measure approach. Nonlinear Anal-Model Control 23(3):361\u2013379","journal-title":"Nonlinear Anal-Model Control"},{"key":"10348_CR24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2016.04.012","volume":"81","author":"Q Song","year":"2016","unstructured":"Song Q, Yan H, Zhao Z, Liu Y (2016) Global exponential stability of impulsive complex-valued neural networks with both asynchronous time-varying and continuously distributed delays. Neural Netw 81:1\u201310","journal-title":"Neural Netw"},{"issue":"2","key":"10348_CR25","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1007\/s11063-018-9849-x","volume":"49","author":"J Zhu","year":"2019","unstructured":"Zhu J, Sun J (2019) Stability of quaternion-valued neural networks with mixed delays. Neural Process Lett 49(2):819\u2013833","journal-title":"Neural Process Lett"},{"issue":"1","key":"10348_CR26","first-page":"36","volume":"25","author":"R Wei","year":"2020","unstructured":"Wei R, Cao J (2020) Global exponential synchronization of quaternion-valued memristive neural networks with time delays. Nonlinear Anal-Model Control 25(1):36\u201356","journal-title":"Nonlinear Anal-Model Control"},{"key":"10348_CR27","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neunet.2016.04.011","volume":"81","author":"W Lu","year":"2016","unstructured":"Lu W, Liu X, Chen T (2016) A note on finite-time and fixed-time stability. Neural Netw 81:11\u201315","journal-title":"Neural Netw"},{"issue":"1","key":"10348_CR28","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s11063-014-9401-6","volume":"43","author":"W Wang","year":"2016","unstructured":"Wang W, Li L, Peng H, Kurths J, Xiao J, Yang Y (2016) Finite-time anti-synchronization control of memristive neural networks with stochastic perturbations. Neural Process Lett 43(1):49\u201363","journal-title":"Neural Process Lett"},{"issue":"1","key":"10348_CR29","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/TCYB.2016.2630703","volume":"48","author":"X Liu","year":"2018","unstructured":"Liu X, Chen T (2018) Finite-time and fixed-time cluster synchronization with or without pinning control. IEEE Trans Cybern 48(1):240\u2013252","journal-title":"IEEE Trans Cybern"},{"key":"10348_CR30","doi-asserted-by":"crossref","first-page":"121351","DOI":"10.1016\/j.physa.2019.121351","volume":"527","author":"H Deng","year":"2019","unstructured":"Deng H, Bao H (2019) Fixed-time synchronization of quaternion-valued neural networks. Phys A 527:121351","journal-title":"Phys A"},{"issue":"3","key":"10348_CR31","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1007\/s11063-018-9791-y","volume":"48","author":"C Aouiti","year":"2018","unstructured":"Aouiti C, Miaadi F (2018) Finite-time stabilization of neutral Hopfield neural networks with mixed delays. Neural Process Lett 48(3):1645\u20131669","journal-title":"Neural Process Lett"},{"issue":"3","key":"10348_CR32","doi-asserted-by":"crossref","first-page":"2871","DOI":"10.1007\/s11063-019-10069-x","volume":"50","author":"J Hou","year":"2019","unstructured":"Hou J, Huang Y, Yang E (2019) Finite-time anti-synchronization of multi-weighted coupled neural networks with and without coupling delays. Neural Process Lett 50(3):2871\u20132898","journal-title":"Neural Process Lett"},{"issue":"19","key":"10348_CR33","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1016\/j.physleta.2019.04.032","volume":"383","author":"K Sun","year":"2019","unstructured":"Sun K, Zhu S, Wei Y, Zhang X, Gao F (2019) Finite-time synchronization of memristor-based complex-valued neural networks with time delays. Phys Lett A 383(19):2255\u20132263","journal-title":"Phys Lett A"},{"key":"10348_CR34","doi-asserted-by":"publisher","unstructured":"Feng L, Yu J, Hu C, Yang C, Jiang H (2020) Nonseparation method-based finite\/fixed-time synchronization of fully complex-valued discontinuous neural networks. IEEE Trans Cybern. https:\/\/doi.org\/10.1109\/TCYB.2020.2980684","DOI":"10.1109\/TCYB.2020.2980684"},{"issue":"2","key":"10348_CR35","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1007\/s11063-019-10180-z","volume":"51","author":"C Yang","year":"2020","unstructured":"Yang C, Xiong Z, Yang T (2020) Finite-time synchronization of coupled inertial memristive neural networks with mixed delays via nonlinear feedback control. Neural Process Lett 51(2):1921\u20131938","journal-title":"Neural Process Lett"},{"issue":"2","key":"10348_CR36","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1007\/s11063-018-9910-9","volume":"50","author":"X Xiong","year":"2019","unstructured":"Xiong X, Tang R, Yang X (2019) Finite-time synchronization of memristive neural networks with proportional delay. Neural Process Lett 50(2):1139\u20131152","journal-title":"Neural Process Lett"},{"issue":"1","key":"10348_CR37","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s11063-017-9590-x","volume":"46","author":"C Zhou","year":"2017","unstructured":"Zhou C, Zhang W, Yang X, Xu C, Feng J (2017) Finite-time synchronization of complex-valued neural networks with mixed delays and uncertain perturbations. Neural Process Lett 46(1):271\u2013291","journal-title":"Neural Process Lett"},{"issue":"2","key":"10348_CR38","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1007\/s11063-018-9958-6","volume":"50","author":"Y Liu","year":"2019","unstructured":"Liu Y, Qin Y, Huang J, Huang T, Yang X (2019) Finite-time synchronization of complex-valued neural networks with multiple time-varying delays and infinite distributed delays. Neural Process Lett 50(2):1773\u20131787","journal-title":"Neural Process Lett"},{"key":"10348_CR39","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.neucom.2019.05.012","volume":"356","author":"Z Zhang","year":"2019","unstructured":"Zhang Z, Zheng T, Yu S (2019) Finite-time anti-synchronization of neural networks with time-varying delays via inequality skills. Neurocomputing 356:60\u201368","journal-title":"Neurocomputing"},{"issue":"5","key":"10348_CR40","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1109\/TNNLS.2018.2868800","volume":"30","author":"Z Zhang","year":"2019","unstructured":"Zhang Z, Cao J (2019) Novel finite-time synchronization criteria for inertial neural networks with time delays via integral inequality method. IEEE Trans Neural Netw Learn Syst 30(5):1476\u20131485","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10348_CR41","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.neucom.2018.08.063","volume":"318","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Li A, Yu S (2018) Finite-time synchronization for delayed complex-valued neural networks via integrating inequality method. Neurocomputing 318:248\u2013260","journal-title":"Neurocomputing"},{"key":"10348_CR42","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.neucom.2019.09.034","volume":"373","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Chen M, Li A (2020) Further study on finite-time synchronization for delayed inertial neural networks via inequality skills. Neurocomputing 373:15\u201323","journal-title":"Neurocomputing"},{"key":"10348_CR43","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.neucom.2020.01.035","volume":"387","author":"X Liu","year":"2020","unstructured":"Liu X, Li Z (2020) Finite time anti-synchronization of complex-valued neural networks with bounded asynchronous time-varying delays. Neurocomputing 387:129\u2013138","journal-title":"Neurocomputing"},{"key":"10348_CR44","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.1016\/j.neucom.2017.09.097","volume":"275","author":"L Wang","year":"2018","unstructured":"Wang L, Chen T (2018) Finite-time anti-synchronization of neural networks with time-varying delays. Neurocomputing 275:1595\u20131600","journal-title":"Neurocomputing"},{"key":"10348_CR45","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.neucom.2018.10.057","volume":"329","author":"L Wang","year":"2019","unstructured":"Wang L, Chen T (2019) Finite-time and fixed-time anti-synchronization of neural networks with time-varying delays. Neurocomputing 329:165\u2013171","journal-title":"Neurocomputing"},{"key":"10348_CR46","unstructured":"Liu X (2020) Adaptive finite time stability of delayed systems with applications to network synchronization. IEEE Trans Cybern. arXiv:2002.00145"},{"key":"10348_CR47","unstructured":"Liu X, Ma H (2020) Adaptive finite time stability of delayed systems via aperiodically intermittent control and quantized control. arXiv:2002.08851"},{"key":"10348_CR48","unstructured":"Wang J, Liu X (2020) Global $$\\mu $$-stability and finite-time control of octonion-valued neural networks with unbounded delays. IEEE Trans Syst Man Cybern-Syst. arXiv:2003.11330"},{"key":"10348_CR49","unstructured":"Liu X, Lin W (2020) Fixed-time stability of delayed systems: adaptive rule and network synchronization. Submitted"},{"issue":"6","key":"10348_CR50","doi-asserted-by":"crossref","first-page":"1836","DOI":"10.1109\/TNN.2007.902716","volume":"18","author":"T Chen","year":"2007","unstructured":"Chen T, Wang L (2007) Global $$\\mu $$-stability of delayed neural networks with unbounded time-varying delays. IEEE Trans Neural Netw 18(6):1836\u20131840","journal-title":"IEEE Trans Neural Netw"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10348-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-020-10348-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10348-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T01:37:41Z","timestamp":1631237861000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-020-10348-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,10]]},"references-count":50,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["10348"],"URL":"https:\/\/doi.org\/10.1007\/s11063-020-10348-y","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,10]]},"assertion":[{"value":"2 September 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2020","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}