{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T06:04:23Z","timestamp":1740117863558,"version":"3.37.3"},"reference-count":77,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011929","name":"Programa Operacional Tem\u00e1tico Factores de Competitividade","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011929","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["POCI-01-0247-FEDER-039719","UIDB\/00127\/2020"],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1016\/j.neucom.2024.128434","type":"journal-article","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T10:20:45Z","timestamp":1724754045000},"page":"128434","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting"],"prefix":"10.1016","volume":"608","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5796-6338","authenticated-orcid":false,"given":"Martim","family":"Sousa","sequence":"first","affiliation":[]},{"given":"Ana Maria","family":"Tom\u00e9","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9","family":"Moreira","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"year":"2023","series-title":"Long-term forecasting with TiDE: Time-series dense encoder","author":"Das","key":"10.1016\/j.neucom.2024.128434_b1"},{"issue":"1","key":"10.1016\/j.neucom.2024.128434_b2","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1111\/joes.12429","article-title":"Machine learning advances for time series forecasting","volume":"37","author":"Masini","year":"2023","journal-title":"J. Econ. Surv."},{"issue":"2","key":"10.1016\/j.neucom.2024.128434_b3","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1002\/for.3980010202","article-title":"The accuracy of extrapolation (time series) methods: Results of a forecasting competition","volume":"1","author":"Makridakis","year":"1982","journal-title":"J. Forecast."},{"issue":"1","key":"10.1016\/j.neucom.2024.128434_b4","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.ijforecast.2019.04.014","article-title":"The M4 competition: 100,000 time series and 61 forecasting methods","volume":"36","author":"Makridakis","year":"2020","journal-title":"Int. J. Forecast."},{"issue":"4","key":"10.1016\/j.neucom.2024.128434_b5","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1016\/j.ijforecast.2021.11.013","article-title":"M5 accuracy competition: Results, findings, and conclusions","volume":"38","author":"Makridakis","year":"2022","journal-title":"Int. J. Forecast."},{"issue":"4","key":"10.1016\/j.neucom.2024.128434_b6","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1016\/j.ijforecast.2021.10.009","article-title":"The M5 uncertainty competition: Results, findings and conclusions","volume":"38","author":"Makridakis","year":"2022","journal-title":"Int. J. Forecast."},{"issue":"1","key":"10.1016\/j.neucom.2024.128434_b7","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s42521-022-00050-0","article-title":"DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks","volume":"5","author":"Fatouros","year":"2023","journal-title":"Digit. Finance"},{"key":"10.1016\/j.neucom.2024.128434_b8","first-page":"1","article-title":"Probabilistic forecasting of hourly emergency department arrivals","author":"Rostami-Tabar","year":"2023","journal-title":"Health Syst."},{"issue":"1","key":"10.1016\/j.neucom.2024.128434_b9","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.ejor.2021.06.044","article-title":"Combining probabilistic forecasts of COVID-19 mortality in the united states","volume":"304","author":"Taylor","year":"2023","journal-title":"European J. Oper. Res."},{"key":"10.1016\/j.neucom.2024.128434_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.121370","article-title":"Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows","volume":"346","author":"Cramer","year":"2023","journal-title":"Appl. Energy"},{"key":"10.1016\/j.neucom.2024.128434_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2022.118796","article-title":"A novel ensemble probabilistic forecasting system for uncertainty in wind speed","volume":"313","author":"Wang","year":"2022","journal-title":"Appl. Energy"},{"key":"10.1016\/j.neucom.2024.128434_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.108875","article-title":"Interval forecasting for urban water demand using PSO optimized KDE distribution and LSTM neural networks","volume":"122","author":"Du","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.neucom.2024.128434_b13","series-title":"2023 IEEE 39th International Conference on Data Engineering","first-page":"992","article-title":"Uncertainty quantification for traffic forecasting: A unified approach","author":"Qian","year":"2023"},{"key":"10.1016\/j.neucom.2024.128434_b14","article-title":"Practical confidence and prediction intervals","volume":"9","author":"Heskes","year":"1996","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"2","key":"10.1016\/j.neucom.2024.128434_b15","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.strusafe.2008.06.020","article-title":"Aleatory or epistemic? Does it matter?","volume":"31","author":"Der Kiureghian","year":"2009","journal-title":"Struct. Saf."},{"year":"2023","series-title":"Conformal prediction for time series","author":"Xu","key":"10.1016\/j.neucom.2024.128434_b16"},{"key":"10.1016\/j.neucom.2024.128434_b17","article-title":"LightGBM: A highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"year":"2023","series-title":"Heteroskedastic conformal regression","author":"Dewolf","key":"10.1016\/j.neucom.2024.128434_b18"},{"year":"2022","series-title":"A review of probabilistic forecasting and prediction with machine learning","author":"Tyralis","key":"10.1016\/j.neucom.2024.128434_b19"},{"key":"10.1016\/j.neucom.2024.128434_b20","series-title":"Principles of Forecasting: A Handbook for Researchers and Practitioners","first-page":"475","article-title":"Prediction intervals for time-series forecasting","author":"Chatfield","year":"2001"},{"year":"2002","series-title":"Introduction to Time Series and Forecasting","author":"Brockwell","key":"10.1016\/j.neucom.2024.128434_b21"},{"key":"10.1016\/j.neucom.2024.128434_b22","series-title":"Time Series for Macroeconomics and Finance","first-page":"16","author":"Cochrane","year":"2005"},{"year":"2018","series-title":"Forecasting: Principles and Practice","author":"Hyndman","key":"10.1016\/j.neucom.2024.128434_b23"},{"issue":"2","key":"10.1016\/j.neucom.2024.128434_b24","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1080\/07474939608800344","article-title":"Bootstrapping time series models","volume":"15","author":"Hongyi Li","year":"1996","journal-title":"Econometric Rev."},{"key":"10.1016\/j.neucom.2024.128434_b25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.csda.2018.08.006","article-title":"Bootstrap estimation of uncertainty in prediction for generalized linear mixed models","volume":"130","author":"Flores-Agreda","year":"2019","journal-title":"Comput. Statist. Data Anal."},{"key":"10.1016\/j.neucom.2024.128434_b26","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume":"30","author":"Lakshminarayanan","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2024.128434_b27","series-title":"International Conference on Machine Learning","first-page":"1050","article-title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","author":"Gal","year":"2016"},{"issue":"2","key":"10.1016\/j.neucom.2024.128434_b28","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.ijforecast.2013.07.018","article-title":"Empirical prediction intervals revisited","volume":"30","author":"Lee","year":"2014","journal-title":"Int. J. Forecast."},{"key":"10.1016\/j.neucom.2024.128434_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.csda.2019.106816","article-title":"Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation","volume":"142","author":"Kwon","year":"2020","journal-title":"Comput. Statist. Data Anal."},{"issue":"3","key":"10.1016\/j.neucom.2024.128434_b30","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1111\/j.2517-6161.1976.tb01586.x","article-title":"Bayesian forecasting","volume":"38","author":"Harrison","year":"1976","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"year":"1995","series-title":"Bayesian Data Analysis","author":"Gelman","key":"10.1016\/j.neucom.2024.128434_b31"},{"key":"10.1016\/j.neucom.2024.128434_b32","article-title":"Gaussian processes for regression","volume":"8","author":"Williams","year":"1995","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"4","key":"10.1016\/j.neucom.2024.128434_b33","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1257\/jep.15.4.143","article-title":"Quantile regression","volume":"15","author":"Koenker","year":"2001","journal-title":"J. Econ. Perspect."},{"key":"10.1016\/j.neucom.2024.128434_b34","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1146\/annurev-economics-063016-103651","article-title":"Quantile regression: 40 years on","volume":"9","author":"Koenker","year":"2017","journal-title":"Annu. Rev. Econ."},{"key":"10.1016\/j.neucom.2024.128434_b35","series-title":"International Conference on Machine Learning","first-page":"4075","article-title":"High-quality prediction intervals for deep learning: A distribution-free, ensembled approach","author":"Pearce","year":"2018"},{"year":"2024","series-title":"A comprehensive survey on uncertainty quantification for deep learning","author":"He","key":"10.1016\/j.neucom.2024.128434_b36"},{"issue":"1","key":"10.1016\/j.neucom.2024.128434_b37","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1007\/s10462-022-10178-5","article-title":"Valid prediction intervals for regression problems","volume":"56","author":"Dewolf","year":"2023","journal-title":"Artif. Intell. Rev."},{"issue":"3","key":"10.1016\/j.neucom.2024.128434_b38","article-title":"A tutorial on conformal prediction","volume":"9","author":"Shafer","year":"2008","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"10.1016\/j.neucom.2024.128434_b39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3150\/21-BEJ1447","article-title":"Conformal prediction: A unified review of theory and new challenges","volume":"29","author":"Fontana","year":"2023","journal-title":"Bernoulli"},{"issue":"4","key":"10.1016\/j.neucom.2024.128434_b40","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1561\/2200000101","article-title":"Conformal prediction: A gentle introduction","volume":"16","author":"Angelopoulos","year":"2023","journal-title":"Found. Trends\u00ae Mach. Learn."},{"issue":"2","key":"10.1016\/j.neucom.2024.128434_b41","first-page":"455","article-title":"The limits of distribution-free conditional predictive inference","volume":"10","author":"Foygel Barber","year":"2021","journal-title":"Inf. Inference J. IMA"},{"key":"10.1016\/j.neucom.2024.128434_b42","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s10994-014-5453-0","article-title":"Regression conformal prediction with random forests","volume":"97","author":"Johansson","year":"2014","journal-title":"Mach. Learn."},{"issue":"8","key":"10.1016\/j.neucom.2024.128434_b43","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.neunet.2011.05.008","article-title":"Reliable prediction intervals with regression neural networks","volume":"24","author":"Papadopoulos","year":"2011","journal-title":"Neural Netw."},{"key":"10.1016\/j.neucom.2024.128434_b44","article-title":"Conformalized quantile regression","volume":"32","author":"Romano","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2024.128434_b45","series-title":"International Conference on Machine Learning","first-page":"11559","article-title":"Conformal prediction interval for dynamic time-series","author":"Xu","year":"2021"},{"key":"10.1016\/j.neucom.2024.128434_b46","article-title":"Ensemble conformalized quantile regression for probabilistic time series forecasting","author":"Jensen","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.neucom.2024.128434_b47","first-page":"1660","article-title":"Adaptive conformal inference under distribution shift","volume":"34","author":"Gibbs","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"8","key":"10.1016\/j.neucom.2024.128434_b48","doi-asserted-by":"crossref","first-page":"7067","DOI":"10.1016\/j.eswa.2012.01.039","article-title":"A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition","volume":"39","author":"Taieb","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2024.128434_b49","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.eneco.2013.07.028","article-title":"Beyond one-step-ahead forecasting: evaluation of alternative multi-step-ahead forecasting models for crude oil prices","volume":"40","author":"Xiong","year":"2013","journal-title":"Energy Econ."},{"key":"10.1016\/j.neucom.2024.128434_b50","series-title":"2015 International Conference on Advanced Computing and Applications","first-page":"142","article-title":"Comparison of strategies for multi-step-ahead prediction of time series using neural network","author":"An","year":"2015"},{"issue":"1","key":"10.1016\/j.neucom.2024.128434_b51","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.ejor.2014.02.036","article-title":"\u2018Horses for Courses\u2019 in demand forecasting","volume":"237","author":"Petropoulos","year":"2014","journal-title":"European J. Oper. Res."},{"key":"10.1016\/j.neucom.2024.128434_b52","series-title":"Artificial Intelligence Applications and Innovations: 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, September 30\u2013October 2, 2013, Proceedings 9","first-page":"348","article-title":"Transductive conformal predictors","author":"Vovk","year":"2013"},{"year":"2021","series-title":"Predictive inference with the jackknife+","author":"Barber","key":"10.1016\/j.neucom.2024.128434_b53"},{"key":"10.1016\/j.neucom.2024.128434_b54","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s10472-013-9368-4","article-title":"Cross-conformal predictors","volume":"74","author":"Vovk","year":"2015","journal-title":"Ann. Math. Artif. Intell."},{"key":"10.1016\/j.neucom.2024.128434_b55","series-title":"Tools in Artificial Intelligence","article-title":"Inductive conformal prediction: Theory and application to neural networks","author":"Papadopoulos","year":"2008"},{"issue":"6","key":"10.1016\/j.neucom.2024.128434_b56","article-title":"Quantile regression forests","volume":"7","author":"Meinshausen","year":"2006","journal-title":"J. Mach. Learn. Res."},{"year":"2011","series-title":"Estimating conditional quantiles with the help of the pinball loss","author":"Steinwart","key":"10.1016\/j.neucom.2024.128434_b57"},{"key":"10.1016\/j.neucom.2024.128434_b58","doi-asserted-by":"crossref","DOI":"10.1016\/j.epsr.2020.106636","article-title":"Forecasting conditional extreme quantiles for wind energy","volume":"190","author":"Gon\u00e7alves","year":"2021","journal-title":"Electr. Power Syst. Res."},{"key":"10.1016\/j.neucom.2024.128434_b59","series-title":"International Conference on Artificial Intelligence and Statistics","first-page":"4346","article-title":"Adaptive, distribution-free prediction intervals for deep networks","author":"Kivaranovic","year":"2020"},{"year":"2022","series-title":"Improved conformalized quantile regression","author":"Sousa","key":"10.1016\/j.neucom.2024.128434_b60"},{"year":"2023","series-title":"Conformal prediction with conditional guarantees","author":"Gibbs","key":"10.1016\/j.neucom.2024.128434_b61"},{"key":"10.1016\/j.neucom.2024.128434_b62","series-title":"Conference on Learning Theory","first-page":"732","article-title":"Exact and robust conformal inference methods for predictive machine learning with dependent data","author":"Chernozhukov","year":"2018"},{"key":"10.1016\/j.neucom.2024.128434_b63","first-page":"6216","article-title":"Conformal time-series forecasting","volume":"34","author":"Stankeviciute","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2024.128434_b64","first-page":"4138","article-title":"Predictive inference is free with the jackknife+-after-bootstrap","volume":"33","author":"Kim","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2024.128434_b65","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"10.1016\/j.neucom.2024.128434_b66","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"year":"2023","series-title":"NN5 forecasting competion dataset","key":"10.1016\/j.neucom.2024.128434_b67"},{"issue":"366","key":"10.1016\/j.neucom.2024.128434_b68","doi-asserted-by":"crossref","first-page":"427","DOI":"10.2307\/2286348","article-title":"Distribution of the estimators for autoregressive time series with a unit root","volume":"74","author":"Dickey","year":"1979","journal-title":"J. Amer. Statist. Assoc."},{"issue":"4","key":"10.1016\/j.neucom.2024.128434_b69","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.2307\/1912517","article-title":"Likelihood ratio statistics for autoregressive time series with a unit root","volume":"49","author":"Dickey","year":"1981","journal-title":"Econometrica"},{"key":"10.1016\/j.neucom.2024.128434_b70","first-page":"5","article-title":"Multilayer perceptron tutorial","volume":"4","author":"Noriega","year":"2005","journal-title":"School Comput. Staffordshire University"},{"year":"2014","series-title":"Adam: A method for stochastic optimization","author":"Kingma","key":"10.1016\/j.neucom.2024.128434_b71"},{"year":"2015","series-title":"TensorFlow: Large-scale machine learning on heterogeneous systems","author":"Abadi","key":"10.1016\/j.neucom.2024.128434_b72"},{"year":"2017","series-title":"pmdarima: ARIMA estimators for Python","author":"Smith","key":"10.1016\/j.neucom.2024.128434_b73"},{"key":"10.1016\/j.neucom.2024.128434_b74","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.apenergy.2018.02.165","article-title":"Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data","volume":"218","author":"Shepero","year":"2018","journal-title":"Appl. Energy"},{"issue":"2","key":"10.1016\/j.neucom.2024.128434_b75","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1109\/TSTE.2016.2606488","article-title":"Modeling uncertainty in tidal current forecast using prediction interval-based SVR","volume":"8","author":"Kavousi-Fard","year":"2016","journal-title":"IEEE Trans. Sustain. Energy"},{"issue":"337","key":"10.1016\/j.neucom.2024.128434_b76","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1080\/01621459.1972.10481224","article-title":"A decision-theoretic approach to interval estimation","volume":"67","author":"Winkler","year":"1972","journal-title":"J. Amer. Statist. Assoc."},{"year":"2020","series-title":"Exchangeability, conformal prediction, and rank tests","author":"Kuchibhotla","key":"10.1016\/j.neucom.2024.128434_b77"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231224012050?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231224012050?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T05:39:42Z","timestamp":1725860382000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231224012050"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":77,"alternative-id":["S0925231224012050"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2024.128434","relation":{},"ISSN":["0925-2312"],"issn-type":[{"type":"print","value":"0925-2312"}],"subject":[],"published":{"date-parts":[[2024,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2024.128434","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"128434"}}