{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T22:48:05Z","timestamp":1775083685581,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T00:00:00Z","timestamp":1565049600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T00:00:00Z","timestamp":1565049600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Key Research and Development Plan","award":["No.2016YFC0401601"],"award-info":[{"award-number":["No.2016YFC0401601"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["2017B623X14"],"award-info":[{"award-number":["2017B623X14"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Postgraduate Research & Practice Innovation Program of Jiangsu Province","award":["KYCX17_0436"],"award-info":[{"award-number":["KYCX17_0436"]}]},{"DOI":"10.13039\/501100012246","name":"Priority Academic Program Development of Jiangsu Higher Education Institutions","doi-asserted-by":"crossref","award":["YS11001"],"award-info":[{"award-number":["YS11001"]}],"id":[{"id":"10.13039\/501100012246","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1007\/s00521-019-04375-7","type":"journal-article","created":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T16:03:42Z","timestamp":1565107422000},"page":"8503-8518","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":127,"title":["Gaussian process regression-based forecasting model of dam deformation"],"prefix":"10.1007","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5615-308X","authenticated-orcid":false,"given":"Chaoning","family":"Lin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6710-5745","authenticated-orcid":false,"given":"Tongchun","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1070-9761","authenticated-orcid":false,"given":"Siyu","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xiaoqing","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Chuan","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Siling","family":"Liang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,6]]},"reference":[{"issue":"6","key":"4375_CR1","first-page":"e2036","volume":"25","author":"G Prakash","year":"2017","unstructured":"Prakash G, Sadhu A, Narasimhan S et al (2017) Initial service life data towards structural health monitoring of a concrete arch dam. Struct Control Health Monit 25(6):e2036","journal-title":"Struct Control Health Monit"},{"key":"4375_CR2","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/j.ijdrr.2018.07.024","volume":"31","author":"AHA Mohammad","year":"2018","unstructured":"Mohammad AHA (2018) Risk, reliability, resilience (R3) and beyond in dam engineering: a state-of-the-art review. Int J Disaster Risk Reduct 31:806\u2013831","journal-title":"Int J Disaster Risk Reduct"},{"key":"4375_CR3","first-page":"1","volume":"2018","author":"S Chen","year":"2018","unstructured":"Chen S, Gu C, Lin C, et al (2018) Safety monitoring model of a super-high concrete dam by using RBF neural network coupled with kernel principal component analysis. Math Probl Eng 2018:1\u201313","journal-title":"Math Probl Eng"},{"key":"4375_CR4","unstructured":"Wang SJ, Gu YC, Pang Q (2017) Experience and prospect of dam surveillance system in China. In: Proceedings of the 85th annual meeting of international commission on large dams"},{"issue":"69","key":"4375_CR5","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.compstruc.2014.07.002","volume":"142","author":"J Mata","year":"2014","unstructured":"Mata J, Leit\u00e3o NS, Castro ATD et al (2014) Construction of decision rules for early detection of a developing concrete arch dam failure scenario. A discriminant approach. Comput Struct 142(69):45\u201353","journal-title":"Comput Struct"},{"issue":"1","key":"4375_CR6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11831-015-9157-9","volume":"24","author":"F Salazar","year":"2017","unstructured":"Salazar F, Mor\u00e1n R, Toledo M\u00c1 et al (2017) Data-based models for the prediction of dam behaviour: a review and some methodological considerations. Arch Comput Methods Eng 24(1):1\u201321","journal-title":"Arch Comput Methods Eng"},{"issue":"10","key":"4375_CR7","doi-asserted-by":"crossref","first-page":"e1997","DOI":"10.1002\/stc.1997","volume":"24","author":"F Kang","year":"2017","unstructured":"Kang F, Liu J, Li J et al (2017) Concrete dam deformation prediction model for health monitoring based on extreme learning machine. Struct Control Health Monit 24(10):e1997","journal-title":"Struct Control Health Monit"},{"issue":"1","key":"4375_CR8","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.engstruct.2006.04.022","volume":"29","author":"AD Sortis","year":"2007","unstructured":"Sortis AD, Paoliani P (2007) Statistical analysis and structural identification in concrete dam monitoring. Eng Struct 29(1):110\u2013120","journal-title":"Eng Struct"},{"issue":"5","key":"4375_CR9","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.3390\/s18051369","volume":"18","author":"LE Acosta","year":"2018","unstructured":"Acosta LE, Lacy MC, Ramos MI et al (2018) Displacements study of an earth fill dam based on high precision geodetic monitoring and numerical modeling. Sensors 18(5):1369","journal-title":"Sensors"},{"key":"4375_CR10","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1098\/rsta.2006.1938","volume":"365","author":"K Worden","year":"2007","unstructured":"Worden K, Manson G (2007) The application of machine learning to structural health monitoring. Philos Trans 365:515\u2013537","journal-title":"Philos Trans"},{"issue":"10","key":"4375_CR11","first-page":"877","volume":"8","author":"W Zhou","year":"2016","unstructured":"Zhou W, Li SL, Zhou ZW et al (2016) InSAR observation and numerical modeling of the earth-dam displacement of Shuibuya Dam (China). Sensors 8(10):877","journal-title":"Sensors"},{"issue":"13","key":"4375_CR12","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.compstruc.2009.03.001","volume":"87","author":"F Kang","year":"2009","unstructured":"Kang F, Li J, Xu Q (2009) Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Comput Struct 87(13):861\u2013870","journal-title":"Comput Struct"},{"issue":"5","key":"4375_CR13","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1007\/s00521-012-1334-2","volume":"24","author":"V Rankovi\u0107","year":"2014","unstructured":"Rankovi\u0107 V, Novakovi\u0107 A, Grujovi\u0107 N et al (2014) Predicting piezometric water level in dams via artificial neural networks. Neural Comput Appl 24(5):1115\u20131121","journal-title":"Neural Comput Appl"},{"key":"4375_CR14","volume-title":"Safety monitoring of dams and dam foundations\u2014theories & methods and their application","author":"CS Gu","year":"2006","unstructured":"Gu CS, Wu ZR (2006) Safety monitoring of dams and dam foundations\u2014theories & methods and their application. Hohai University Press, Nanjing"},{"key":"4375_CR15","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.engstruct.2018.05.084","volume":"171","author":"S Hadi","year":"2018","unstructured":"Hadi S, Rigoberto B (2018) Emerging artificial intelligence methods in structural engineering. Eng Struct 171:170\u2013189","journal-title":"Eng Struct"},{"key":"4375_CR16","volume-title":"Safety monitoring theory & it\u2019s application of hydraulic structures","author":"ZH Wu","year":"2003","unstructured":"Wu ZH (2003) Safety monitoring theory & it\u2019s application of hydraulic structures. Higher Education Press, Beijing"},{"issue":"21\u201322","key":"4375_CR17","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.1016\/j.compstruc.2011.05.013","volume":"89","author":"S Freitag","year":"2011","unstructured":"Freitag S, Graf W, Kaliske M et al (2011) Prediction of time-dependent structural behaviour with recurrent neural networks for fuzzy data. Comput Struct 89(21\u201322):1971\u20131981","journal-title":"Comput Struct"},{"issue":"10","key":"4375_CR18","first-page":"182","volume":"65","author":"M Milivojevic","year":"2013","unstructured":"Milivojevic M, Milivojevic M, Divac D et al (2013) Adaptive system for dam behavior modeling based on linear regression and genetic algorithms. Adv Eng Softw 65(10):182\u2013190","journal-title":"Adv Eng Softw"},{"key":"4375_CR19","first-page":"125","volume":"52","author":"M Fanelli","year":"1975","unstructured":"Fanelli M (1975) Control of dam displacements. Energia Elettrica 52:125\u2013139","journal-title":"Energia Elettrica"},{"issue":"12","key":"4375_CR20","first-page":"135","volume":"82","author":"D Tonini","year":"1956","unstructured":"Tonini D (1956) Observed behavior of several leakier arch dams. J Power Div 82(12):135\u2013139","journal-title":"J Power Div"},{"issue":"9","key":"4375_CR21","first-page":"42","volume":"29","author":"P Bonaldi","year":"1977","unstructured":"Bonaldi P, Fanelli M, Giuseppetti G (1977) Displacement forecasting for concrete dams. Int Water Power Dam Constr 29(9):42\u201350","journal-title":"Int Water Power Dam Constr"},{"key":"4375_CR22","first-page":"3984","volume":"4","author":"L Piroddi","year":"2004","unstructured":"Piroddi L, Spinelli W (2004) Long-range nonlinear prediction: a case study. IEEE Conf Decision Control 4:3984\u20133989","journal-title":"IEEE Conf Decision Control"},{"issue":"3","key":"4375_CR23","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1002\/stc.1575","volume":"21","author":"J Mata","year":"2014","unstructured":"Mata J, Castro ATD, Costa JSD (2014) Constructing statistical models for arch dam deformation. Struct Control Health Monit 21(3):423\u2013437","journal-title":"Struct Control Health Monit"},{"issue":"12","key":"4375_CR24","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1007\/s00521-016-2666-0","volume":"29","author":"KTT Bui","year":"2018","unstructured":"Bui KTT, Bui DT, Zou J et al (2018) A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam. Neural Comput Appl 29(12):1495\u20131506","journal-title":"Neural Comput Appl"},{"issue":"8","key":"4375_CR25","doi-asserted-by":"crossref","first-page":"e2188","DOI":"10.1002\/stc.2188","volume":"25","author":"B Wei","year":"2018","unstructured":"Wei B, Yuan D, Xu Z et al (2018) Modified hybrid forecast model considering chaotic residual errors for dam deformation. Structural Control and Health Monitoring 25(8):e2188","journal-title":"Structural Control and Health Monitoring"},{"issue":"11","key":"4375_CR26","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1007\/s00521-016-2588-x","volume":"29","author":"H Karami","year":"2018","unstructured":"Karami H, Karimi S, Bonakdari H et al (2018) Predicting discharge coefficient of triangular labyrinth weir using extreme learning machine, artificial neural network and genetic programming. Neural Comput Appl 29(11):983\u2013989","journal-title":"Neural Comput Appl"},{"issue":"12","key":"4375_CR27","doi-asserted-by":"crossref","first-page":"3709","DOI":"10.1007\/s00521-016-2255-2","volume":"28","author":"FF Ahmadi","year":"2016","unstructured":"Ahmadi FF (2016) Integration of industrial videogrammetry and artificial neural networks for monitoring and modeling the deformation or displacement of structures. Neural Comput Appl 28(12):3709\u20133716","journal-title":"Neural Comput Appl"},{"issue":"7\u20138","key":"4375_CR28","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1007\/s00521-014-1675-0","volume":"25","author":"SA Akrami","year":"2014","unstructured":"Akrami SA, El-Shafie A, Naseri M et al (2014) Rainfall data analyzing using moving average (MA) model and wavelet multi-resolution intelligent model for noise evaluation to improve the forecasting accuracy. Neural Comput Appl 25(7\u20138):1853\u20131861","journal-title":"Neural Comput Appl"},{"key":"4375_CR29","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.ymssp.2018.03.022","volume":"110","author":"HZ Su","year":"2018","unstructured":"Su HZ, Li X, Yang BB et al (2018) Wavelet support vector machine-based prediction model of dam deformation. Mech Syst Signal Process 110:412\u2013427","journal-title":"Mech Syst Signal Process"},{"issue":"3","key":"4375_CR30","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1177\/1475921710365269","volume":"9","author":"JP Ou","year":"2010","unstructured":"Ou JP, Li H (2010) Structural Health Monitoring in mainland China: review and Future Trends. Struct Health Monit 9(3):219\u2013231","journal-title":"Struct Health Monit"},{"issue":"1","key":"4375_CR31","first-page":"71","volume":"200","author":"VS Devi","year":"2015","unstructured":"Devi VS (2015) Introduction to pattern recognition and machine learning. J Cell Physiol 200(1):71\u201381","journal-title":"J Cell Physiol"},{"issue":"3","key":"4375_CR32","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1016\/j.engstruct.2010.12.011","volume":"33","author":"J Mata","year":"2011","unstructured":"Mata J (2011) Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Eng Struct 33(3):903\u2013910","journal-title":"Eng Struct"},{"issue":"3","key":"4375_CR33","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1002\/stc.492","volume":"20","author":"CY Kao","year":"2013","unstructured":"Kao CY, Loh CH (2013) Monitoring of long-term static deformation data of Fei-Tsui arch dam using artificial neural network-based approaches. Struct Control Health Monit 20(3):282\u2013303","journal-title":"Struct Control Health Monit"},{"key":"4375_CR34","first-page":"2137","volume":"170\u2013173","author":"WS Hu","year":"2012","unstructured":"Hu WS, Zhang F, Song L et al (2012) Study of dam deformation model based on neural network. Appl Mech Mater 170\u2013173:2137\u20132142","journal-title":"Appl Mech Mater"},{"key":"4375_CR35","first-page":"261","volume":"675\u2013677","author":"GH Xu","year":"2014","unstructured":"Xu GH (2014) Application of rbf neural network in dam deformation prediction. Appl Mech Mater 675\u2013677:261\u2013264","journal-title":"Appl Mech Mater"},{"key":"4375_CR36","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1016\/j.engstruct.2018.11.065","volume":"180","author":"F Kang","year":"2019","unstructured":"Kang F, Li J, Zhao S et al (2019) Structural health monitoring of concrete dams using long-term air temperature for thermal effect simulation. Eng Struct 180:642\u2013653","journal-title":"Eng Struct"},{"key":"4375_CR37","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.procs.2017.03.120","volume":"107","author":"J Cheng","year":"2017","unstructured":"Cheng J, Xiong Y (2017) Application of extreme learning machine combination model for dam displacement prediction. Proc Comput Sci 107:373\u2013378","journal-title":"Proc Comput Sci"},{"key":"4375_CR38","unstructured":"International Commission on Large Dams (2012) Dam surveillance guide. Tech. rep. B-158, ICOLD"},{"issue":"48","key":"4375_CR39","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.strusafe.2014.02.004","volume":"48","author":"V Rankovi\u0107","year":"2014","unstructured":"Rankovi\u0107 V, Grujovi\u0107 N, Divac D et al (2014) Development of support vector regression identification model for prediction of dam structural behaviour. Struct Saf 48(48):33\u201339","journal-title":"Struct Saf"},{"issue":"2","key":"4375_CR40","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1002\/stc.1767","volume":"23","author":"H Su","year":"2016","unstructured":"Su H, Chen Z, Wen Z (2016) Performance improvement method of support vector machine-based model monitoring dam safety. Struct Control Health Monit 23(2):252\u2013266","journal-title":"Struct Control Health Monit"},{"key":"4375_CR41","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.strusafe.2015.05.001","volume":"56","author":"F Salazar","year":"2015","unstructured":"Salazar F, Toledo MA, O\u00f1ate E et al (2015) An empirical comparison of machine learning techniques for dam behaviour modelling. Struct Saf 56:9\u201317","journal-title":"Struct Saf"},{"issue":"11","key":"4375_CR42","doi-asserted-by":"crossref","first-page":"e2012","DOI":"10.1002\/stc.2012","volume":"24","author":"F Salazar","year":"2017","unstructured":"Salazar F, Toledo M\u00c1, Gonz\u00e1lez JM et al (2017) Early detection of anomalies in dam performance: a methodology based on boosted regression trees. Struct Control Health Monit 24(11):e2012","journal-title":"Struct Control Health Monit"},{"issue":"4","key":"4375_CR43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/en11040935","volume":"11","author":"J Maritz","year":"2018","unstructured":"Maritz J, Maritz J, Lubbe F et al (2018) A practical guide to gaussian process regression for energy measurement and verification within the Bayesian Framework. Energies 11(4):1\u201312","journal-title":"Energies"},{"key":"4375_CR44","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.asoc.2017.07.011","volume":"60","author":"F Kang","year":"2017","unstructured":"Kang F, Xu B, Li J et al (2017) Slope stability evaluation using Gaussian processes with various covariance functions. Appl Soft Comput 60:387\u2013396","journal-title":"Appl Soft Comput"},{"key":"4375_CR45","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.ymssp.2016.07.039","volume":"84","author":"SA Aye","year":"2017","unstructured":"Aye SA, Heyns PS (2017) An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission. Mech Syst Signal Process 84:485\u2013498","journal-title":"Mech Syst Signal Process"},{"issue":"5","key":"4375_CR46","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1007\/s10706-016-0044-4","volume":"34","author":"K Roushangar","year":"2016","unstructured":"Roushangar K, Garekhani S, Alizadeh F (2016) Forecasting daily seepage discharge of an earth dam using wavelet-mutual information-gaussian process regression approaches. Geotech Geol Eng 34(5):1313\u20131326","journal-title":"Geotech Geol Eng"},{"key":"4375_CR47","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.ymssp.2017.11.021","volume":"104","author":"D Kong","year":"2018","unstructured":"Kong D, Chen Y, Li N (2018) Gaussian process regression for tool wear prediction. Mech Syst Signal Process 104:556\u2013574","journal-title":"Mech Syst Signal Process"},{"issue":"5","key":"4375_CR48","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1007\/s12206-019-0426-7","volume":"33","author":"S Lee","year":"2019","unstructured":"Lee S, Chai J (2019) An enhanced prediction model for the on-line monitoring of the sensors using the Gaussian process regression. J Mech Sci Technol 33(5):2249\u20132257","journal-title":"J Mech Sci Technol"},{"issue":"1","key":"4375_CR49","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ijmachtools.2007.07.011","volume":"48","author":"J Yuan","year":"2008","unstructured":"Yuan J, Wang K, Yu T et al (2008) Reliable multi-objective optimization of high-speed WEDM process based on Gaussian process regression. Int J Mach Tools Manuf 48(1):47\u201360","journal-title":"Int J Mach Tools Manuf"},{"issue":"157","key":"4375_CR50","first-page":"332","volume":"38","author":"MR Hestenes","year":"1980","unstructured":"Hestenes MR (1980) Conjugate direction methods in optimization. Math Comput 38(157):332","journal-title":"Math Comput"},{"issue":"3","key":"4375_CR51","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1007\/s11431-010-0053-0","volume":"53","author":"CS Gu","year":"2010","unstructured":"Gu CS, Li B, Xu GL et al (2010) Back analysis of mechanical parameters of roller compacted concrete dam. Sci China Technol Sci 53(3):848\u2013853","journal-title":"Sci China Technol Sci"},{"issue":"4","key":"4375_CR52","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.isatra.2007.04.001","volume":"46","author":"K Azman","year":"2007","unstructured":"Azman K, Kocijan J (2007) Application of Gaussian processes for black-box modelling of biosystems. ISA Trans 46(4):443\u2013457","journal-title":"ISA Trans"},{"key":"4375_CR53","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.patcog.2017.03.025","volume":"69","author":"G Jiang","year":"2017","unstructured":"Jiang G, Wang W (2017) Error estimation based on variance analysis of k-fold cross-validation. Pattern Recogn 69:94\u2013106","journal-title":"Pattern Recogn"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04375-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04375-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04375-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T07:04:19Z","timestamp":1664089459000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04375-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,6]]},"references-count":53,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["4375"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04375-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,6]]},"assertion":[{"value":"16 February 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}