{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T18:00:36Z","timestamp":1793124036348,"version":"4.5.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:00:00Z","timestamp":1595462400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:00:00Z","timestamp":1595462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100006198","name":"Goddard Space Flight Center","doi-asserted-by":"publisher","award":["NNX17AJ86A"],"award-info":[{"award-number":["NNX17AJ86A"]}],"id":[{"id":"10.13039\/100006198","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s00521-020-05186-x","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T19:03:23Z","timestamp":1595531003000},"page":"3235-3250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Data-driven system health monitoring technique using autoencoder for the safety management of commercial aircraft"],"prefix":"10.1007","volume":"33","author":[{"given":"Hyunseong","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9728-8191","authenticated-orcid":false,"given":"Hyung Jin","family":"Lim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aditi","family":"Chattopadhyay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,7,23]]},"reference":[{"key":"5186_CR1","doi-asserted-by":"publisher","first-page":"1832","DOI":"10.2514\/1.C032221","volume":"50","author":"SJ Gill","year":"2013","unstructured":"Gill SJ, Lowenberg MH, Neild SA, Krauskopf B, Puyou G, Coetzee E (2013) Upset dynamics of an airliner model: a nonlinear bifurcation analysis. J Aircr 50:1832\u20131842. https:\/\/doi.org\/10.2514\/1.C032221","journal-title":"J Aircr"},{"key":"5186_CR2","doi-asserted-by":"crossref","unstructured":"Lambregts A, Nesemeier G, Newman R, Wilborn J (2008) Airplane upsets: old problem, new issues. In: AIAA modeling and simulation technologies conference and exhibit. American Institute of Aeronautics and Astronautics, Reston, Virigina","DOI":"10.2514\/6.2008-6867"},{"key":"5186_CR3","doi-asserted-by":"crossref","unstructured":"Belcastro C, Foster J (2010) Aircraft loss-of-control accident analysis. In: AIAA guidance, navigation, and control conference. American Institute of Aeronautics and Astronautics, Reston, Virigina, pp 1\u201341","DOI":"10.2514\/6.2010-8004"},{"key":"5186_CR4","unstructured":"Boeing Commercial Airplanes (2019) Statistical summary of commercial jet airplane accidents: worldwide operations 1959\u20132018"},{"key":"5186_CR5","doi-asserted-by":"publisher","first-page":"733","DOI":"10.2514\/1.G002815","volume":"40","author":"CM Belcastro","year":"2017","unstructured":"Belcastro CM, Foster JV, Shah GH, Gregory IM, Cox DE, Crider DA, Groff L, Newman RL, Klyde DH (2017) Aircraft loss of control problem analysis and research toward a holistic solution. J Guid Control Dyn 40:733\u2013775. https:\/\/doi.org\/10.2514\/1.G002815","journal-title":"J Guid Control Dyn"},{"key":"5186_CR6","unstructured":"International Air Transport Airport (2018) Future of airline industry 2035"},{"key":"5186_CR7","unstructured":"Federal Aviation Administration (2016) NextGen implementation plan"},{"key":"5186_CR8","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.2514\/1.G001748","volume":"40","author":"H Pfifer","year":"2017","unstructured":"Pfifer H, Venkataraman R, Seiler P (2017) Quantifying loss-of-control envelopes via robust tracking analysis. J Guid Control Dyn 40:1042\u20131050. https:\/\/doi.org\/10.2514\/1.G001748","journal-title":"J Guid Control Dyn"},{"key":"5186_CR9","doi-asserted-by":"publisher","first-page":"831","DOI":"10.2514\/1.G001731","volume":"40","author":"V Stepanyan","year":"2017","unstructured":"Stepanyan V, Krishnakumar K, Dorais G, Reardon S, Barlow J, Lampton A, Hardy G (2017) Loss-of-control mitigation via predictive cuing. J Guid Control Dyn 40:831\u2013846. https:\/\/doi.org\/10.2514\/1.G001731","journal-title":"J Guid Control Dyn"},{"key":"5186_CR10","doi-asserted-by":"publisher","first-page":"847","DOI":"10.2514\/1.G001729","volume":"40","author":"S Schuet","year":"2017","unstructured":"Schuet S, Lombaerts T, Acosta D, Kaneshige J, Wheeler K, Shish K (2017) Autonomous flight envelope estimation for loss-of-control prevention. J Guid Control Dyn 40:847\u2013862. https:\/\/doi.org\/10.2514\/1.G001729","journal-title":"J Guid Control Dyn"},{"key":"5186_CR11","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1017\/aer.2016.42","volume":"120","author":"Z Dlamini","year":"2016","unstructured":"Dlamini Z, Jones T (2016) Fly-by-wire robustness to flight dynamics change under horizontal stabiliser damage. Aeronaut J 120:1005\u20131023. https:\/\/doi.org\/10.1017\/aer.2016.42","journal-title":"Aeronaut J"},{"key":"5186_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-17518-8_16","author":"T Lombaerts","year":"2015","unstructured":"Lombaerts T, Schuet S, Acosta D, Kaneshige J (2015) On-line safe flight envelope determination for impaired aircraft. Adv Aerosp Guid Navig Control. https:\/\/doi.org\/10.1007\/978-3-319-17518-8_16","journal-title":"Adv Aerosp Guid Navig Control"},{"key":"5186_CR13","doi-asserted-by":"publisher","first-page":"410","DOI":"10.2514\/1.C031847","volume":"50","author":"PK Menon","year":"2013","unstructured":"Menon PK, Sengupta P, Vaddi S, Yang BJ, Kwan J (2013) Impaired aircraft performance envelope estimation. J Aircr 50:410\u2013424. https:\/\/doi.org\/10.2514\/1.C031847","journal-title":"J Aircr"},{"key":"5186_CR14","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1016\/j.ins.2019.10.039","volume":"512","author":"T Li","year":"2020","unstructured":"Li T, Tang X, Ge J, Fei S (2020) Event-based fault-tolerant control for networked control systems applied to aircraft engine system. Inf Sci (NY) 512:1063\u20131077. https:\/\/doi.org\/10.1016\/j.ins.2019.10.039","journal-title":"Inf Sci (NY)"},{"key":"5186_CR15","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.ins.2015.08.033","volume":"328","author":"X Tao","year":"2016","unstructured":"Tao X, Li N, Li S (2016) Multiple model predictive control for large envelope flight of hypersonic vehicle systems. Inf Sci (NY) 328:115\u2013126. https:\/\/doi.org\/10.1016\/j.ins.2015.08.033","journal-title":"Inf Sci (NY)"},{"key":"5186_CR16","doi-asserted-by":"publisher","first-page":"1813","DOI":"10.1002\/aic.690460910","volume":"46","author":"S Yoon","year":"2000","unstructured":"Yoon S, MacGregor JF (2000) Statistical and causal model-based approaches to fault detection and isolation. AIChE J 46:1813\u20131824. https:\/\/doi.org\/10.1002\/aic.690460910","journal-title":"AIChE J"},{"key":"5186_CR17","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.inffus.2005.07.003","volume":"8","author":"O Basir","year":"2007","unstructured":"Basir O, Yuan X (2007) Engine fault diagnosis based on multi-sensor information fusion using Dempster\u2013Shafer evidence theory. Inf Fusion 8:379\u2013386. https:\/\/doi.org\/10.1016\/j.inffus.2005.07.003","journal-title":"Inf Fusion"},{"key":"5186_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04692-x","author":"S Schwartz","year":"2020","unstructured":"Schwartz S, Montero Jimenez JJ, Sala\u00fcn M, Vingerhoeds R (2020) A fault mode identification methodology based on self-organizing map. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-019-04692-x","journal-title":"Neural Comput Appl"},{"key":"5186_CR19","doi-asserted-by":"publisher","unstructured":"Melnyk I, Banerjee A, Matthews B, Oza N (2016) Semi-Markov switching vector autoregressive model-based anomaly detection in aviation systems. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining 13\u201317-August, pp 1065\u20131074. https:\/\/doi.org\/10.1145\/2939672.2939789","DOI":"10.1145\/2939672.2939789"},{"key":"5186_CR20","doi-asserted-by":"publisher","first-page":"753","DOI":"10.4103\/0377-2063.126967","volume":"59","author":"S Rouhani","year":"2013","unstructured":"Rouhani S, Sheikholeslami A, Hosseini H, Kazemi H (2013) Application of fuzzy subtractive clustering for optimal transient performance of automatic generation control. IETE J Res 59:753. https:\/\/doi.org\/10.4103\/0377-2063.126967","journal-title":"IETE J Res"},{"key":"5186_CR21","doi-asserted-by":"publisher","first-page":"101071","DOI":"10.1016\/j.aei.2020.101071","volume":"44","author":"H Lee","year":"2020","unstructured":"Lee H, Li G, Rai A, Chattopadhyay A (2020) Real-time anomaly detection framework using a support vector regression for the safety monitoring of commercial aircraft. Adv Eng Inform 44:101071. https:\/\/doi.org\/10.1016\/j.aei.2020.101071","journal-title":"Adv Eng Inform"},{"key":"5186_CR22","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/s00521-018-3669-9","volume":"31","author":"S Zhou","year":"2019","unstructured":"Zhou S, Zhou Y, Xu Z, Chang W, Cheng Y (2019) The landing safety prediction model by integrating pattern recognition and Markov chain with flight data. Neural Comput Appl 31:147\u2013159. https:\/\/doi.org\/10.1007\/s00521-018-3669-9","journal-title":"Neural Comput Appl"},{"key":"5186_CR23","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.trc.2016.01.007","volume":"64","author":"L Li","year":"2016","unstructured":"Li L, Hansman RJ, Palacios R, Welsch R (2016) Anomaly detection via a Gaussian mixture model for flight operation and safety monitoring. Transp Res Part C 64:45\u201357. https:\/\/doi.org\/10.1016\/j.trc.2016.01.007","journal-title":"Transp Res Part C"},{"key":"5186_CR24","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.trc.2019.11.011","volume":"110","author":"G Li","year":"2020","unstructured":"Li G, Lee H, Rai A, Chattopadhyay A (2020) Analysis of operational and mechanical anomalies in scheduled commercial flights using a logarithmic multivariate Gaussian model. Transp Res Part C Emerg Technol 110:20\u201339. https:\/\/doi.org\/10.1016\/j.trc.2019.11.011","journal-title":"Transp Res Part C Emerg Technol"},{"key":"5186_CR25","doi-asserted-by":"publisher","first-page":"745","DOI":"10.4103\/0377-2063.126971","volume":"59","author":"H Hosseini","year":"2013","unstructured":"Hosseini H, Tousi B, Razmjooy N, Khalilpour M (2013) Design robust controller for automatic generation control in restructured power system by imperialist competitive algorithm. IETE J Res 59:745. https:\/\/doi.org\/10.4103\/0377-2063.126971","journal-title":"IETE J Res"},{"key":"5186_CR26","doi-asserted-by":"publisher","unstructured":"Lee H, Li G, Rai A, Chattopadhyay A (2019) Health monitoring framework for aircraft engine system using deep neural network. In: PHM, vol 2019, pp 1\u20138. https:\/\/doi.org\/10.36001\/phmconf.2019.v11i1.869","DOI":"10.36001\/phmconf.2019.v11i1.869"},{"key":"5186_CR27","first-page":"53","volume":"10","author":"N Razmjooy","year":"2016","unstructured":"Razmjooy N, Ramezani M, Namadchian A (2016) A new LQR optimal control for a single-link flexible joint robot manipulator based on grey wolf optimizer. Majlesi J Electr Eng 10:53","journal-title":"Majlesi J Electr Eng"},{"key":"5186_CR28","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.ssci.2018.12.018","volume":"114","author":"J Oehling","year":"2019","unstructured":"Oehling J, Barry DJ (2019) Using machine learning methods in airline flight data monitoring to generate new operational safety knowledge from existing data. Saf Sci 114:89\u2013104. https:\/\/doi.org\/10.1016\/j.ssci.2018.12.018","journal-title":"Saf Sci"},{"key":"5186_CR29","doi-asserted-by":"publisher","first-page":"2157","DOI":"10.1007\/s00521-015-1990-0","volume":"27","author":"S Kiakojoori","year":"2016","unstructured":"Kiakojoori S, Khorasani K (2016) Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis. Neural Comput Appl 27:2157\u20132192. https:\/\/doi.org\/10.1007\/s00521-015-1990-0","journal-title":"Neural Comput Appl"},{"key":"5186_CR30","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.19026\/rjaset.5.4902","volume":"5","author":"M Khalilpour","year":"2013","unstructured":"Khalilpour M, Valipour K, Shayeghi H, Razmjooy N (2013) Designing a robust and adaptive PID controller for gas turbine connected to the generator. Res J Appl Sci Eng Technol 5:1544\u20131551. https:\/\/doi.org\/10.19026\/rjaset.5.4902","journal-title":"Res J Appl Sci Eng Technol"},{"key":"5186_CR31","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1111\/coin.12257","volume":"36","author":"M Mir","year":"2020","unstructured":"Mir M, Dayyani M, Sutikno T, Mohammadi Zanjireh M, Razmjooy N (2020) Employing a Gaussian particle swarm optimization method for tuning multi input multi output-fuzzy system as an integrated controller of a micro-grid with stability analysis. Comput Intell 36:225\u2013258. https:\/\/doi.org\/10.1111\/coin.12257","journal-title":"Comput Intell"},{"key":"5186_CR32","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE (2006) Reducing the dimensionality of data with neural networks. Science (80-) 313:504\u2013507. https:\/\/doi.org\/10.1126\/science.1127647","journal-title":"Science (80-)"},{"key":"5186_CR33","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371\u20133408","journal-title":"J Mach Learn Res"},{"key":"5186_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/BF00758335","author":"J An","year":"2015","unstructured":"An J, Cho S (2015) Variational autoencoder based anomaly detection using reconstruction probability. Spec Lect IE. https:\/\/doi.org\/10.1007\/BF00758335","journal-title":"Spec Lect IE"},{"key":"5186_CR35","doi-asserted-by":"publisher","unstructured":"Zhou C, Paffenroth RC (2017) Anomaly detection with robust deep autoencoders. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining part F1, vol 296, pp 665\u2013674. https:\/\/doi.org\/10.1145\/3097983.3098052","DOI":"10.1145\/3097983.3098052"},{"key":"5186_CR36","doi-asserted-by":"crossref","unstructured":"Chalapathy R, Chawla S (2019) Deep learning for anomaly detection: a survey, pp 1\u201350","DOI":"10.1007\/978-1-4842-5177-5_1"},{"key":"5186_CR37","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.ins.2019.08.077","volume":"510","author":"Z Peng","year":"2020","unstructured":"Peng Z, Xiao X, Hu G, Kumar Sangaiah A, Atiquzzaman M, Xia S (2020) ABFL: an autoencoder based practical approach for software fault localization. Inf Sci (NY) 510:108\u2013121. https:\/\/doi.org\/10.1016\/j.ins.2019.08.077","journal-title":"Inf Sci (NY)"},{"key":"5186_CR38","unstructured":"National Aeronautics and Space Administration (2020) DASHlink sample flight data. https:\/\/c3.nasa.gov\/dashlink\/projects\/85\/"},{"key":"5186_CR39","unstructured":"United States Department of Transportation (2020) Bureau of transportation statistics. https:\/\/www.transtats.bts.gov\/"},{"key":"5186_CR40","unstructured":"Arneson HM, Hegde P, La Scola ME, Evans AD, Keller RM, Schade JE (2019) Sherlock data warehouse"},{"key":"5186_CR41","unstructured":"National Transportation Safety Board (2020) Aviation accident database. https:\/\/www.ntsb.gov\/_layouts\/ntsb.aviation\/index.aspx"},{"key":"5186_CR42","unstructured":"National Transportation Safety Board (2010) NTSB ID: DCA09MA027"},{"key":"5186_CR43","unstructured":"The Dutch Safety Board (2010) Turkish Airlines, crashed during approach, Boeing 737\u2013800, near Amsterdam Schiphol Airport"},{"key":"5186_CR44","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-60566-178-0","volume-title":"Multirate filtering for digital signal processing","author":"L Milic","year":"2009","unstructured":"Milic L (2009) Multirate filtering for digital signal processing. IGI Global, Hershey"},{"key":"5186_CR45","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1515\/acgeo-2015-0062","volume":"64","author":"Y Liu","year":"2016","unstructured":"Liu Y, Dang B, Li Y, Lin H, Ma H (2016) Applications of Savitzky\u2013Golay filter for seismic random noise reduction. Acta Geophys 64:101\u2013124. https:\/\/doi.org\/10.1515\/acgeo-2015-0062","journal-title":"Acta Geophys"},{"key":"5186_CR46","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 807\u2013814"},{"key":"5186_CR47","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: 32nd international conference on machine learning ICML 2015, vol 1, pp 448\u2013456"},{"key":"5186_CR48","doi-asserted-by":"publisher","first-page":"14767","DOI":"10.1007\/s11042-017-5061-7","volume":"77","author":"S Amraee","year":"2018","unstructured":"Amraee S, Vafaei A, Jamshidi K, Adibi P (2018) Anomaly detection and localization in crowded scenes using connected component analysis. Multimed Tools Appl 77:14767\u201314782. https:\/\/doi.org\/10.1007\/s11042-017-5061-7","journal-title":"Multimed Tools Appl"},{"key":"5186_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2013.03.002","author":"R Kromanis","year":"2013","unstructured":"Kromanis R, Kripakaran P (2013) Support vector regression for anomaly detection from measurement histories. Adv Eng Inform. https:\/\/doi.org\/10.1016\/j.aei.2013.03.002","journal-title":"Adv Eng Inform"},{"key":"5186_CR50","doi-asserted-by":"crossref","unstructured":"Gang N, Lau D, Pecht M (2010) Improving computer manufacturing management through Lean Six Sigma and PHM. In: 2010 prognostics and system health management conference. IEEE, pp 1\u20137","DOI":"10.1109\/PHM.2010.5413497"},{"key":"5186_CR51","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"5186_CR52","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization, pp 1\u201315"},{"key":"5186_CR53","doi-asserted-by":"publisher","DOI":"10.1201\/9780203497111","volume-title":"Statistical and econometric methods for transportation data analysis","author":"SP Washington","year":"2003","unstructured":"Washington SP, Karlaftis MG, Mannering F (2003) Statistical and econometric methods for transportation data analysis. Chapman and Hall\/CRC, Boca Raton"},{"key":"5186_CR54","volume-title":"Flight dynamics principles: a linear systems approach to aircraft stability and control","author":"MV Cook","year":"2012","unstructured":"Cook MV (2012) Flight dynamics principles: a linear systems approach to aircraft stability and control. Butterworth-Heinemann, Oxford"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05186-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05186-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05186-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T23:35:17Z","timestamp":1626996917000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05186-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,23]]},"references-count":54,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["5186"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05186-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,23]]},"assertion":[{"value":"15 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2020","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 declared that they have no conflicts of interest to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}