{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T08:29:46Z","timestamp":1761294586635,"version":"3.37.3"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71971122 and 71501101"],"award-info":[{"award-number":["71971122 and 71501101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11042-023-14497-9","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T16:52:22Z","timestamp":1677171142000},"page":"22961-22979","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A deep learning-based nonlinear ensemble approach with biphasic feature selection for multivariate exchange rate forecasting"],"prefix":"10.1007","volume":"82","author":[{"given":"Jujie","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maolin","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjie","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Jing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,20]]},"reference":[{"issue":"2","key":"14497_CR1","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.jinteco.2004.09.003","volume":"66","author":"A Abhyankar","year":"2005","unstructured":"Abhyankar A, Sarno L, Valente G (Jul. 2005) Exchange rates and fundamentals: evidence on the economic value of predictability. J Int Econ 66(2):325\u2013348. https:\/\/doi.org\/10.1016\/j.jinteco.2004.09.003","journal-title":"J Int Econ"},{"key":"14497_CR2","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/j.neucom.2019.07.088","volume":"365","author":"A Amo Baffour","year":"2019","unstructured":"Amo Baffour A, Feng J, Taylor EK (Nov. 2019) A hybrid artificial neural network-GJR modeling approach to forecasting currency exchange rate volatility. Neurocomputing 365:285\u2013301. https:\/\/doi.org\/10.1016\/j.neucom.2019.07.088","journal-title":"Neurocomputing"},{"key":"14497_CR3","doi-asserted-by":"crossref","unstructured":"Bai Y, Bezak N, Sapa\u010d K, Klun M, Zhang J (2019) Short-term streamflow forecasting using the feature-enhanced regression model. Water Resour. Manag, p. 15","DOI":"10.1007\/s11269-019-02399-1"},{"issue":"1","key":"14497_CR4","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.ejor.2015.12.010","volume":"251","author":"J Barunik","year":"2016","unstructured":"Barunik J, Krehlik T, Vacha L (May 2016) Modeling and forecasting exchange rate volatility in time-frequency domain. Eur J Oper Res 251(1):329\u2013340. https:\/\/doi.org\/10.1016\/j.ejor.2015.12.010","journal-title":"Eur J Oper Res"},{"issue":"3","key":"14497_CR5","doi-asserted-by":"publisher","first-page":"1878","DOI":"10.1515\/1558-3708","volume":"16","author":"Z Cai","year":"2012","unstructured":"Cai Z, Chen L, Fang Y (2012) A new forecasting model for USD\/CNY exchange rate. Stud Nonlinear Dyn Econom 16(3):1878. https:\/\/doi.org\/10.1515\/1558-3708","journal-title":"Stud Nonlinear Dyn Econom"},{"issue":"4","key":"14497_CR6","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1016\/j.ijforecast.2010.07.003","volume":"27","author":"G Chortareas","year":"2011","unstructured":"Chortareas G, Jiang Y, Nankervis JC (2011) Forecasting exchange rate volatility using high-frequency data: is the euro different? Int J Forecast 27(4):1089\u20131107. https:\/\/doi.org\/10.1016\/j.ijforecast.2010.07.003","journal-title":"Int J Forecast"},{"key":"14497_CR7","doi-asserted-by":"publisher","unstructured":"de O Santos J\u00fanior DS, de Oliveira JFL, de Mattos Neto PSG (2019) An intelligent hybridization of ARIMA with machine learning models for time series forecasting. Knowl-Based Syst 175: 72\u201386. https:\/\/doi.org\/10.1016\/j.knosys.2019.03.011.","DOI":"10.1016\/j.knosys.2019.03.011"},{"issue":"5","key":"14497_CR8","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1002\/for.833","volume":"21","author":"CL Dunis","year":"2002","unstructured":"Dunis CL, Huang X (2002) Forecasting and trading currency volatility: an application of recurrent neural regression and model combination. J Forecast 21(5):317\u2013354. https:\/\/doi.org\/10.1002\/for.833","journal-title":"J Forecast"},{"key":"14497_CR9","doi-asserted-by":"publisher","first-page":"101294","DOI":"10.1016\/j.jup.2021.101294","volume":"73","author":"G-F Fan","year":"2021","unstructured":"Fan G-F, Yu M, Dong S-Q, Yeh Y-H, Hong W-C (2021) Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling. Util Policy 73:101294. https:\/\/doi.org\/10.1016\/j.jup.2021.101294","journal-title":"Util Policy"},{"key":"14497_CR10","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.neucom.2015.03.100","volume":"172","author":"S Galeshchuk","year":"2016","unstructured":"Galeshchuk S (Jan. 2016) Neural networks performance in exchange rate prediction. Neurocomputing 172:446\u2013452. https:\/\/doi.org\/10.1016\/j.neucom.2015.03.100","journal-title":"Neurocomputing"},{"key":"14497_CR11","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.physa.2018.05.135","volume":"510","author":"K He","year":"2018","unstructured":"He K, Chen Y, Tso GKF (2018) Forecasting exchange rate using Variational mode decomposition and entropy theory. Phys Stat Mech Its Appl 510:15\u201325. https:\/\/doi.org\/10.1016\/j.physa.2018.05.135","journal-title":"Phys Stat Mech Its Appl"},{"key":"14497_CR12","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1155\/2014\/795624","volume":"2014","author":"M-L Huang","year":"2014","unstructured":"Huang M-L, Hung Y-H, Lee WM, Li RK, Jiang B-R (2014) SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier. Sci World J 2014:10. https:\/\/doi.org\/10.1155\/2014\/795624","journal-title":"Sci World J"},{"key":"14497_CR13","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/j.jclepro.2018.10.128","volume":"209","author":"Y Huang","year":"2019","unstructured":"Huang Y, Shen L, Liu H (Feb. 2019) Grey relational analysis, principal component analysis and forecasting of carbon emissions based on long short-term memory in China. J Clean Prod 209:415\u2013423. https:\/\/doi.org\/10.1016\/j.jclepro.2018.10.128","journal-title":"J Clean Prod"},{"key":"14497_CR14","doi-asserted-by":"crossref","unstructured":"Li J, et al. (2021) A novel hybrid short-term load forecasting method of smart grid using MLR and LSTM neural network. IEEE Trans Ind Inform 14(4):2443","DOI":"10.1109\/TII.2020.3000184"},{"key":"14497_CR15","doi-asserted-by":"publisher","first-page":"105140","DOI":"10.1016\/j.eneco.2021.105140","volume":"95","author":"Y Li","year":"2021","unstructured":"Li Y, Jiang S, Li X, Wang S (Mar. 2021) The role of news sentiment in oil futures returns and volatility forecasting: data-decomposition based deep learning approach. Energy Econ 95:105140. https:\/\/doi.org\/10.1016\/j.eneco.2021.105140","journal-title":"Energy Econ"},{"key":"14497_CR16","doi-asserted-by":"crossref","unstructured":"Liu H (2019) Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression. Renew Energy, p. 13","DOI":"10.1016\/j.renene.2019.05.039"},{"issue":"1","key":"14497_CR17","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/S1044-0283(98)90012-6","volume":"9","author":"TH Lubecke","year":"1998","unstructured":"Lubecke TH, Nam KD, Markland RE, Kwok CCY (1998) Combining foreign exchange rate forecasts using neural networks. Glob Finance J 9(1):5\u201327. https:\/\/doi.org\/10.1016\/S1044-0283(98)90012-6","journal-title":"Glob Finance J"},{"issue":"4","key":"14497_CR18","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1109\/72.935089","volume":"12","author":"MC Medeiros","year":"2001","unstructured":"Medeiros MC, Veiga A, Pedreira CE (Jul. 2001) Modeling exchange rates: smooth transitions, neural networks, and linear models. IEEE Trans Neural Netw 12(4):755\u2013764. https:\/\/doi.org\/10.1109\/72.935089","journal-title":"IEEE Trans Neural Netw"},{"issue":"1\u20132","key":"14497_CR19","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/0022-1996(83)90017-X","volume":"14","author":"RA Meese","year":"1983","unstructured":"Meese RA, Rogoff K (Feb. 1983) Empirical exchange rate models of the seventies. J Int Econ 14(1\u20132):3\u201324. https:\/\/doi.org\/10.1016\/0022-1996(83)90017-X","journal-title":"J Int Econ"},{"key":"14497_CR20","doi-asserted-by":"publisher","first-page":"119887","DOI":"10.1016\/j.energy.2021.119887","volume":"221","author":"T Peng","year":"2021","unstructured":"Peng T, Zhang C, Zhou J, Nazir MS (Apr. 2021) An integrated framework of bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting. Energy 221:119887. https:\/\/doi.org\/10.1016\/j.energy.2021.119887","journal-title":"Energy"},{"key":"14497_CR21","doi-asserted-by":"publisher","first-page":"116704","DOI":"10.1016\/j.energy.2019.116704","volume":"193","author":"W Qiao","year":"2020","unstructured":"Qiao W, Yang Z (2020) Forecast the electricity price of U.S. using a wavelet transform-based hybrid model. Energy 193:116704. https:\/\/doi.org\/10.1016\/j.energy.2019.116704","journal-title":"Energy"},{"issue":"8","key":"14497_CR22","doi-asserted-by":"publisher","first-page":"2318","DOI":"10.1109\/TSMC.2017.2661996","volume":"47","author":"CG Raji","year":"2017","unstructured":"Raji CG, Chandra SSV (2017) Long-term forecasting the survival in liver transplantation using multilayer perceptron networks. IEEE Trans Syst Man Cybern Syst 47(8):2318\u20132329. https:\/\/doi.org\/10.1109\/TSMC.2017.2661996","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"2","key":"14497_CR23","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.ijforecast.2005.09.006","volume":"22","author":"DE Rapach","year":"2006","unstructured":"Rapach DE, Wohar ME (Apr. 2006) The out-of-sample forecasting performance of nonlinear models of real exchange rate behavior. Int J Forecast 22(2):341\u2013361. https:\/\/doi.org\/10.1016\/j.ijforecast.2005.09.006","journal-title":"Int J Forecast"},{"issue":"3","key":"14497_CR24","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1080\/03610918.2019.1664578","volume":"49","author":"PC Rodrigues","year":"2020","unstructured":"Rodrigues PC, Mahmoudvand R (2020) A new approach for the vector forecast algorithm in singular spectrum analysis. Commun Stat - Simul Comput 49(3):591\u2013605. https:\/\/doi.org\/10.1080\/03610918.2019.1664578","journal-title":"Commun Stat - Simul Comput"},{"key":"14497_CR25","doi-asserted-by":"publisher","unstructured":"Sermpinis G, Dunis C, Laws J, Stasinakis C (Dec. 2012) Forecasting and trading the EUR\/USD exchange rate with stochastic neural network combination and time-varying leverage. Decis Support Syst 54(1):316\u2013329. https:\/\/doi.org\/10.1016\/j.dss.2012.05.039","DOI":"10.1016\/j.dss.2012.05.039"},{"issue":"3","key":"14497_CR26","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1016\/j.ejor.2015.06.052","volume":"247","author":"G Sermpinis","year":"2015","unstructured":"Sermpinis G, Stasinakis C, Theofilatos K, Karathanasopoulos A (Dec. 2015) Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms\u2014support vector regression forecast combinations. Eur J Oper Res 247(3):831\u2013846. https:\/\/doi.org\/10.1016\/j.ejor.2015.06.052","journal-title":"Eur J Oper Res"},{"key":"14497_CR27","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.neucom.2015.04.071","volume":"167","author":"F Shen","year":"2015","unstructured":"Shen F, Chao J, Zhao J (Nov. 2015) Forecasting exchange rate using deep belief networks and conjugate gradient method. Neurocomputing 167:243\u2013253. https:\/\/doi.org\/10.1016\/j.neucom.2015.04.071","journal-title":"Neurocomputing"},{"key":"14497_CR28","doi-asserted-by":"publisher","unstructured":"Sun S, Wang S, Wei Y, Zhang G (2018) A clustering-based nonlinear ensemble approach for exchange rates forecasting. IEEE Trans Syst Man Cybern Syst:1\u20139. https:\/\/doi.org\/10.1109\/TSMC.2018.2799869","DOI":"10.1109\/TSMC.2018.2799869"},{"key":"14497_CR29","doi-asserted-by":"publisher","first-page":"101160","DOI":"10.1016\/j.aei.2020.101160","volume":"46","author":"S Sun","year":"2020","unstructured":"Sun S, Wang S, Wei Y (Oct. 2020) A new ensemble deep learning approach for exchange rates forecasting and trading. Adv Eng Inform 46:101160. https:\/\/doi.org\/10.1016\/j.aei.2020.101160","journal-title":"Adv Eng Inform"},{"key":"14497_CR30","unstructured":"Tyree EW, Long JA (1995) Forecasting currency exchange rates: Neural networks and the random walk model, in Forecasting Currency Exchange Rates: Neural Networks and the Random Walk Model, Wall Street New York, p. 11"},{"key":"14497_CR31","doi-asserted-by":"publisher","unstructured":"Wang J, Cheng Q, Sun X (2021) Carbon price forecasting using multiscale nonlinear integration model coupled optimal feature reconstruction with biphasic deep learning. Environmental Science and Pollution Research, https:\/\/doi.org\/10.1007\/s11356-021-16089-2","DOI":"10.1007\/s11356-021-16089-2"},{"key":"14497_CR32","doi-asserted-by":"publisher","first-page":"108498","DOI":"10.1016\/j.patcog.2021.108498","volume":"124","author":"C Wang","year":"2022","unstructured":"Wang C, Wang X, Zhang J, Zhang L, Bai X, Ning X, Zhou J, Hancock E (Apr. 2022) Uncertainty estimation for stereo matching based on evidential deep learning. Pattern Recogn 124:108498. https:\/\/doi.org\/10.1016\/j.patcog.2021.108498","journal-title":"Pattern Recogn"},{"issue":"1","key":"14497_CR33","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.jmse.2019.02.001","volume":"4","author":"Y Wei","year":"2019","unstructured":"Wei Y, Sun S, Ma J, Wang S, Lai KK (2019) A decomposition clustering ensemble learning approach for forecasting foreign exchange rates. J Manag Sci Eng 4(1):45\u201354. https:\/\/doi.org\/10.1016\/j.jmse.2019.02.001","journal-title":"J Manag Sci Eng"},{"issue":"2","key":"14497_CR34","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/0304-4076(94)01654-I","volume":"69","author":"KD West","year":"1995","unstructured":"West KD, Cho D (Oct. 1995) The predictive ability of several models of exchange rate volatility. J Econom 69(2):367\u2013391. https:\/\/doi.org\/10.1016\/0304-4076(94)01654-I","journal-title":"J Econom"},{"key":"14497_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-018-1140-3","volume-title":"Nonlinear feature selection using Gaussian kernel SVM-RFE for fault diagnosis","author":"Y Xue","year":"2018","unstructured":"Xue Y, Zhang L, Wang B, Zhang Z, Li F (2018) Nonlinear feature selection using Gaussian kernel SVM-RFE for fault diagnosis. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-018-1140-3"},{"key":"14497_CR36","doi-asserted-by":"publisher","first-page":"103176","DOI":"10.1016\/j.artint.2019.103176","volume":"277","author":"H-F Yang","year":"2019","unstructured":"Yang H-F, Chen Y-PP (2019) Representation learning with extreme learning machines and empirical mode decomposition for wind speed forecasting methods. Artif Intell 277:103176. https:\/\/doi.org\/10.1016\/j.artint.2019.103176","journal-title":"Artif Intell"},{"issue":"1","key":"14497_CR37","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s10614-015-9549-9","volume":"49","author":"H-L Yang","year":"2017","unstructured":"Yang H-L, Lin H-C (2017) Applying the hybrid model of EMD, PSR, and ELM to exchange rates forecasting. Comput Econ 49(1):99\u2013116. https:\/\/doi.org\/10.1007\/s10614-015-9549-9","journal-title":"Comput Econ"},{"key":"14497_CR38","doi-asserted-by":"publisher","first-page":"137117","DOI":"10.1016\/j.scitotenv.2020.137117","volume":"716","author":"S Yang","year":"2020","unstructured":"Yang S, Chen D, Li S, Wang W (2020) Carbon price forecasting based on modified ensemble empirical mode decomposition and long short-term memory optimized by improved whale optimization algorithm. Sci Total Environ 716:137117. https:\/\/doi.org\/10.1016\/j.scitotenv.2020.137117","journal-title":"Sci Total Environ"},{"issue":"16","key":"14497_CR39","doi-asserted-by":"publisher","first-page":"3295","DOI":"10.1016\/j.neucom.2008.04.029","volume":"71","author":"L Yu","year":"2008","unstructured":"Yu L, Lai KK, Wang S (2008) Multistage RBF neural network ensemble learning for exchange rates forecasting. Neurocomputing 71(16):3295\u20133302. https:\/\/doi.org\/10.1016\/j.neucom.2008.04.029","journal-title":"Neurocomputing"},{"issue":"2","key":"14497_CR40","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1007\/s42835-020-00358-0","volume":"15","author":"Q Zhang","year":"2020","unstructured":"Zhang Q, Zhang J (2020) Short-term load forecasting method based on EWT and IDBSCAN. J Electr Eng Technol 15(2):635\u2013644. https:\/\/doi.org\/10.1007\/s42835-020-00358-0","journal-title":"J Electr Eng Technol"},{"issue":"1","key":"14497_CR41","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/S0169-2070(97)00044-7","volume":"14","author":"G Zhang","year":"1998","unstructured":"Zhang G, Eddy Patuwo B, Hu MY (1998) Forecasting with artificial neural networks: The state of the art. Int J Forecast 14(1):35\u201362. https:\/\/doi.org\/10.1016\/S0169-2070(97)00044-7","journal-title":"Int J Forecast"},{"key":"14497_CR42","doi-asserted-by":"publisher","first-page":"118601","DOI":"10.1016\/j.apenergy.2022.118601","volume":"311","author":"F Zhou","year":"2022","unstructured":"Zhou F, Huang Z, Zhang C (Apr. 2022) Carbon price forecasting based on CEEMDAN and LSTM. Appl Energy 311:118601. https:\/\/doi.org\/10.1016\/j.apenergy.2022.118601","journal-title":"Appl Energy"},{"key":"14497_CR43","doi-asserted-by":"publisher","unstructured":"Zhu J, Liu J, Wu P, Chen H, Zhou L (2019) A novel decomposition-ensemble approach to crude oil price. Int J Mach Learn Cybern. no. 10, pp. 3349\u20133362. https:\/\/doi.org\/10.1007\/s13042-019-00922-9.","DOI":"10.1007\/s13042-019-00922-9"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14497-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-14497-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14497-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T09:35:56Z","timestamp":1685439356000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-14497-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,20]]},"references-count":43,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["14497"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-14497-9","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2023,2,20]]},"assertion":[{"value":"15 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2023","order":4,"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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}]}}