{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T18:42:24Z","timestamp":1784745744698,"version":"3.55.0"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"28","license":[{"start":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T00:00:00Z","timestamp":1719964800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T00:00:00Z","timestamp":1719964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s00521-024-10054-z","type":"journal-article","created":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T04:38:32Z","timestamp":1719981512000},"page":"17645-17667","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Spatio-temporal wind speed forecasting with approximate Bayesian uncertainty quantification"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9865-1156","authenticated-orcid":false,"given":"Airton F. Souza","family":"Neto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C\u00e9sar L. C.","family":"Mattos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o P. P.","family":"Gomes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"key":"10054_CR1","doi-asserted-by":"publisher","first-page":"177","DOI":"10.17148\/IJARCCE.2016.51237","volume":"5","author":"G Jain","year":"2016","unstructured":"Jain G, Mallick B (2016) A review on weather forecasting techniques. IJARCCE 5:177\u2013180. https:\/\/doi.org\/10.17148\/IJARCCE.2016.51237","journal-title":"IJARCCE"},{"key":"10054_CR2","unstructured":"Ongoma V (2022) The science of weather forecasting: What it takes and why it\u2019s so hard to get right"},{"key":"10054_CR3","volume-title":"Wind energy explained: theory, design and application","author":"J Manwell","year":"2010","unstructured":"Manwell J, McGowan J, Rogers A (2010) Wind energy explained: theory, design and application, 2nd edn. Wiley, New Jersey","edition":"2"},{"key":"10054_CR4","first-page":"2","volume-title":"Understanding renewable energy systems","author":"V Quaschning","year":"2019","unstructured":"Quaschning V, Hanke T (2019) Understanding renewable energy systems. CRC Press, Boca Raton, p 2"},{"issue":"7","key":"10054_CR5","first-page":"2054","volume":"14","author":"P Pavez","year":"2021","unstructured":"Pavez P, Morales JM, Hern\u00e1ndez JAO, Rodriguez L, Araneda E (2021) Comparative analysis of wind resource characterization methods applied for a potential wind farm site in south Chile. Energies 14(7):2054","journal-title":"Energies"},{"key":"10054_CR6","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/B978-0-08-100504-0.00014-7","volume-title":"Renewable energy forecasting. Woodhead Publishing series in energy","author":"JS Rodrigo","year":"2017","unstructured":"Rodrigo JS, Paredes LF, Girard R, Kariniotakis G, Laquaine K, Stoffels N, Bremen L (2017) The role of predictability in the investment phase of wind farms. In: Kariniotakis G (ed) Renewable energy forecasting. Woodhead Publishing series in energy. Elsevier, Amsterdam, pp 341\u2013357"},{"key":"10054_CR7","first-page":"9","volume":"157","author":"L Chen","year":"2018","unstructured":"Chen L, Yao P, Liu Y, Li G (2018) Price composition and prediction of renewable energy in a free energy market. Energy 157:9\u201317","journal-title":"Energy"},{"key":"10054_CR8","doi-asserted-by":"publisher","DOI":"10.1109\/OAJPE.2020.3029979","author":"T Hong","year":"2020","unstructured":"Hong T, Pinson P, Wang Y, Weron R, Yang D, Zareipour H (2020) Energy forecasting: a review and outlook. IEEE Open Access J Power Energy. https:\/\/doi.org\/10.1109\/OAJPE.2020.3029979","journal-title":"IEEE Open Access J Power Energy"},{"key":"10054_CR9","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1002\/9781119451143","volume-title":"Wind energy handbook 3e","author":"N Jenkins","year":"2021","unstructured":"Jenkins N (2021) Wind energy handbook 3e. Wiley, Hoboken, pp 637\u2013663"},{"key":"10054_CR10","doi-asserted-by":"publisher","DOI":"10.3390\/en10030317","author":"CB Hasager","year":"2017","unstructured":"Hasager CB, Nygaard NG, Volker PJH, Karagali I, Andersen SJ, Badger J (2017) Wind farm wake: the 2016 horns rev photo case. Energies. https:\/\/doi.org\/10.3390\/en10030317","journal-title":"Energies"},{"key":"10054_CR11","unstructured":"NOAA (2021) Global forecast system, national centers for environmental prediction, national oceanic and atmospheric administration. NOAA, United States. [Online]. https:\/\/www.ncei.noaa.gov"},{"key":"10054_CR12","unstructured":"ECMWF (2021) ERA5 reanalysis dataset, European Centre for medium-range weather forecasts, United Kingdom. [Online]. https:\/\/www.ecmwf.int"},{"key":"10054_CR13","doi-asserted-by":"publisher","DOI":"10.1201\/9780429258480","volume-title":"Bayesian data analysis","author":"A Gelman","year":"2003","unstructured":"Gelman A, Carlin JB, Stern HS, Rubin DB (2003) Bayesian data analysis. Chapman & Hall\/CRC, London"},{"key":"10054_CR14","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Massachussets"},{"key":"10054_CR15","unstructured":"Izmailov P, Vikram S, Hoffman MD, Wilson AGG (2021) What are Bayesian neural network posteriors really like? In: International conference on machine learning, PMLR. pp 4629\u20134640"},{"key":"10054_CR16","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1007\/s10462-023-10562-9","volume":"56","author":"J Gawlikowski","year":"2021","unstructured":"Gawlikowski J, Tassi CRN, Ali M, Lee J, Humt M, Feng J, Kruspe A, Triebel R, Jung P, Roscher R, Shahzad M, Yang W, Bamler R, Zhu XX (2021) A survey of uncertainty in deep neural networks. Artif Intel Rev 56:1513","journal-title":"Artif Intel Rev"},{"key":"10054_CR17","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","volume":"76","author":"M Abdar","year":"2021","unstructured":"Abdar M, Pourpanah F, Hussain S, Rezazadegan D, Liu L, Ghavamzadeh M, Fieguth P, Cao X, Khosravi A, Acharya UR, Makarenkov V, Nahavandi S (2021) A review of uncertainty quantification in deep learning: techniques, applications and challenges. Inf Fusion 76:243\u2013297. https:\/\/doi.org\/10.1016\/j.inffus.2021.05.008","journal-title":"Inf Fusion"},{"key":"10054_CR18","unstructured":"Gal Y, Ghahramani Z (2016) Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International conference on machine learning, pp. 1050\u20131059. MIT Press, New York, US"},{"key":"10054_CR19","unstructured":"Maddox WJ, Izmailov P, Garipov T, Vetrov DP, Wilson AG (2019) A simple baseline for Bayesian uncertainty in deep learning. In: Advances in neural information processing systems, pp. 13153\u201313164. MIT Press, Massachussets"},{"key":"10054_CR20","first-page":"4697","volume":"33","author":"A Wilson","year":"2020","unstructured":"Wilson A, Izmailov P (2020) Bayesian deep learning and a probabilistic perspective of generalization. Adv Neural Inf Process Syst 33:4697","journal-title":"Adv Neural Inf Process Syst"},{"key":"10054_CR21","unstructured":"Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in neural information processing systems, pp. 6402\u20136413. Neural Information Processing Systems (NIPS), California, US"},{"issue":"477","key":"10054_CR22","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1198\/016214506000001437","volume":"102","author":"T Gneiting","year":"2007","unstructured":"Gneiting T, Raftery AE (2007) Strictly proper scoring rules, prediction, and estimation. J Am Stat Assoc 102(477):359\u2013378","journal-title":"J Am Stat Assoc"},{"issue":"1","key":"10054_CR23","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","volume":"25","author":"A Esteva","year":"2019","unstructured":"Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J (2019) A guide to deep learning in healthcare. Nat Med 25(1):24\u201329","journal-title":"Nat Med"},{"key":"10054_CR24","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877\u20131901","journal-title":"Adv Neural Inf Process Syst"},{"key":"10054_CR25","doi-asserted-by":"publisher","unstructured":"Soniya Paul S, Singh L (2015) A review on advances in deep learning. IEEE workshop on computational intelligence: theories, applications and future directions (WCI), 1\u20136 https:\/\/doi.org\/10.1109\/WCI.2015.7495514","DOI":"10.1109\/WCI.2015.7495514"},{"key":"10054_CR26","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamar\u00eda J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8:53. https:\/\/doi.org\/10.1186\/s40537-021-00444-8","journal-title":"J Big Data"},{"issue":"3","key":"10054_CR27","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1162\/neco.1992.4.3.448","volume":"4","author":"DJC MacKay","year":"1992","unstructured":"MacKay DJC (1992) A practical Bayesian framework for backpropagation networks. Neural Comput 4(3):448\u2013472","journal-title":"Neural Comput"},{"key":"10054_CR28","volume-title":"Bayesian methods for adaptive models","author":"DJC MacKay","year":"1992","unstructured":"MacKay DJC (1992) Bayesian methods for adaptive models. California Institute of Technology, Pasadena"},{"issue":"3","key":"10054_CR29","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1088\/0954-898X_6_3_011","volume":"6","author":"DJC MacKay","year":"1995","unstructured":"MacKay DJC (1995) Probable networks and plausible predictions\u2014a review of practical Bayesian methods for supervised neural networks. Netw Comput Neural Syst 6(3):469\u2013505. https:\/\/doi.org\/10.1088\/0954-898X_6_3_011","journal-title":"Netw Comput Neural Syst"},{"key":"10054_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-0745-0","volume-title":"Bayesian learning for neural networks","author":"RM Neal","year":"1996","unstructured":"Neal RM (1996) Bayesian learning for neural networks. Springer, Berlin"},{"issue":"3","key":"10054_CR31","first-page":"327","volume":"50","author":"J Smith","year":"2020","unstructured":"Smith J, Johnson L (2020) Bayesian inference with Laplace approximations. J Stat Comput Simul 50(3):327\u2013345","journal-title":"J Stat Comput Simul"},{"key":"10054_CR32","volume-title":"Advances in neural information processing systems","author":"E Daxberger","year":"2021","unstructured":"Daxberger E, Kristiadi A, Immer A, Eschenhagen R, Bauer M, Hennig P (2021) Laplace redux\u2014effortless Bayesian deep learning. In: Beygelzimer A, Dauphin Y, Liang P, Vaughan JW (eds) Advances in neural information processing systems. MIT Press, Cambridge"},{"key":"10054_CR33","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.neucom.2021.07.054","volume":"461","author":"J Morales","year":"2021","unstructured":"Morales J, Yu W (2021) Improving neural network\u2019s performance using Bayesian inference. Neurocomputing 461:319\u2013326","journal-title":"Neurocomputing"},{"issue":"56","key":"10054_CR34","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(56):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"10054_CR35","unstructured":"Damianou A, Lawrence ND (2013). Deep gaussian processes. Artificial intelligence and statistics, 207\u2013215 PMLR"},{"key":"10054_CR36","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2015) Explaining and harnessing adversarial examples"},{"key":"10054_CR37","first-page":"33646","volume":"35","author":"T Abe","year":"2022","unstructured":"Abe T, Buchanan EK, Pleiss G, Zemel R, Cunningham JP (2022) Deep ensembles work, but are they necessary? Adv Neural Inf Process Syst 35:33646\u201333660","journal-title":"Adv Neural Inf Process Syst"},{"key":"10054_CR38","unstructured":"Hoffmann L, Elster C (2021) Deep ensembles from a Bayesian perspective. arXiv:2105.13283"},{"key":"10054_CR39","unstructured":"Izmailo, P, Podoprikhin D, Garipov T, Vetrov D. Wilson AG (2018) Averaging weights leads to wider optima and better generalization. In: Conference on uncertainty in artificial intelligence, 876\u2013885 Association For Uncertainty in Artificial Intelligence (AUAI)"},{"key":"10054_CR40","first-page":"8024","volume":"32","author":"A Paszke","year":"2019","unstructured":"...Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) Pytorch: an imperative style, high-performance deep learning library. Adva Neural Inf Process Syst 32:8024\u20138035","journal-title":"Adva Neural Inf Process Syst"},{"key":"10054_CR41","doi-asserted-by":"publisher","first-page":"114259","DOI":"10.1016\/j.apenergy.2019.114259","volume":"260","author":"Y Liu","year":"2020","unstructured":"Liu Y, Qin H, Zhang Z, Pei S, Jiang Z, Feng Z, Zhou J (2020) Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model. Appl Energy 260:114259. https:\/\/doi.org\/10.1016\/j.apenergy.2019.114259","journal-title":"Appl Energy"},{"key":"10054_CR42","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.apenergy.2019.01.010","volume":"238","author":"R Yu","year":"2019","unstructured":"Yu R, Liu Z, Li X, Lu W, Ma D, Yu M, Wang J, Li B (2019) Scene learning: deep convolutional networks for wind power prediction by embedding turbines into grid space. Appl Energy 238:249\u2013257. https:\/\/doi.org\/10.1016\/j.apenergy.2019.01.010","journal-title":"Appl Energy"},{"key":"10054_CR43","unstructured":"Ghaderi A, Sanandaji BM (2022) Deep forecast: Deep learning-based spatio-temporal forecasting. J Artif Intell"},{"key":"10054_CR44","doi-asserted-by":"publisher","DOI":"10.3390\/en11040705","author":"Q Zhu","year":"2018","unstructured":"Zhu Q, Chen J, Zhu L, Duan X, Liu Y (2018) Wind speed prediction with spatio-temporal correlation: a deep learning approach. Energies. https:\/\/doi.org\/10.3390\/en11040705","journal-title":"Energies"},{"issue":"4","key":"10054_CR45","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1109\/TPWRS.2014.2299767","volume":"29","author":"M He","year":"2014","unstructured":"He M, Yang L, Zhang J, Vittal V (2014) A spatio-temporal analysis approach for short-term forecast of wind farm generation. IEEE Trans Power Syst 29(4):1611\u20131622. https:\/\/doi.org\/10.1109\/TPWRS.2014.2299767","journal-title":"IEEE Trans Power Syst"},{"key":"10054_CR46","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-32483-x","author":"L Espeholt","year":"2022","unstructured":"Espeholt L, Agrawal S, Sonderby C, Kumar M, Heek J, Bromberg C, Gazen C, Carver R, Andrychowicz M, Hickey J, Bell A (2022) Deep learning for twelve hour precipitation forecasts. Nature. https:\/\/doi.org\/10.1038\/s41467-022-32483-x","journal-title":"Nature"},{"key":"10054_CR47","doi-asserted-by":"publisher","unstructured":"Wang B, Lu J, Yan Z, Luo H, Li T, Zheng Y, Zhang G (2019) Deep uncertainty quantification: a machine learning approach for weather forecasting. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining. KDD \u201919, pp. 2087\u20132095. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3292500.3330704","DOI":"10.1145\/3292500.3330704"},{"issue":"9","key":"10054_CR48","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1038\/s42254-022-00498-4","volume":"4","author":"Y Gal","year":"2022","unstructured":"Gal Y, Koumoutsakos P, Lanusse F, Louppe G, Papadimitriou C (2022) Bayesian uncertainty quantification for machine-learned models in physics. Nat Rev Phys 4(9):573\u2013577. https:\/\/doi.org\/10.1038\/s42254-022-00498-4","journal-title":"Nat Rev Phys"},{"key":"10054_CR49","doi-asserted-by":"crossref","unstructured":"Mukhoti J, Kirsch A, Amersfoort J, Torr PHS, Gal Y (2022) Deep deterministic uncertainty: a simple baseline","DOI":"10.1109\/CVPR52729.2023.02336"},{"key":"10054_CR50","unstructured":"Pinson P, Kariniotakis G, Nielsen HA, Nielsen TS, Madsen H (2006) Properties of quantile and interval forecasts of wind generation and their evaluation. In: Proceedings of the European wind energy conference and exhibition (October 2015), 2\u20136"},{"key":"10054_CR51","volume-title":"Statistical inference","author":"G Casella","year":"2002","unstructured":"Casella G, Berger RL (2002) Statistical inference. Duxbury Press, Belmont"},{"key":"10054_CR52","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM (2006) Pattern recognition and machine learning. Springer, New York"},{"key":"10054_CR53","volume-title":"Gaussian processes for machine learning","author":"CK Williams","year":"2006","unstructured":"Williams CK, Rasmussen CE (2006) Gaussian processes for machine learning, vol 2. MIT Press, Cambridge"},{"key":"10054_CR54","volume-title":"Bayesian inference on complicated data","author":"MY Wang","year":"2020","unstructured":"Wang MY, Park T (2020) A brief tour of Bayesian sampling methods. In: Tang N (ed) Bayesian inference on complicated data. IntechOpen, Rijeka"},{"key":"10054_CR55","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1201\/b10905-6","volume-title":"Handbook of Markov chain Monte Carlo","author":"RM Neal","year":"2011","unstructured":"Neal RM (2011) MCMC using Hamiltonian dynamics. In: Brooks S, Gelman A, Jones G, Meng X-L (eds) Handbook of Markov chain Monte Carlo. CRC Press, Boca Raton, pp 113\u2013162"},{"key":"10054_CR56","doi-asserted-by":"publisher","unstructured":"Nix DA, Weigend AS (1994) Estimating the mean and variance of the target probability distribution. IEEE International conference on neural networks (ICNN) 1:55\u2013601. https:\/\/doi.org\/10.1109\/ICNN.1994.374138","DOI":"10.1109\/ICNN.1994.374138"},{"key":"10054_CR57","unstructured":"Kuleshov V, Fenner N, Ermon S (2018) Accurate uncertainties for deep learning using calibrated regression. In: International conference on machine learning, pp. 2796\u20132804. PMLR"},{"issue":"2","key":"10054_CR58","doi-asserted-by":"publisher","first-page":"025006","DOI":"10.1088\/2632-2153\/ab7e1a","volume":"1","author":"K Tran","year":"2020","unstructured":"Tran K, Neiswanger W, Yoon J, Zhang Q, Xing E, Ulissi ZW (2020) Methods for comparing uncertainty quantifications for material property predictions. Mach Learn Sci Technol 1(2):025006","journal-title":"Mach Learn Sci Technol"},{"key":"10054_CR59","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"10054_CR60","unstructured":"Shi X, Chen Z, Wang H, Yeung D-Y, Wong W-K, Woo W-C (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. Advances in neural information processing systems. 28"},{"key":"10054_CR61","unstructured":"Daneels A (1999) What is SCADA? In: International conference on accelerator and large experimental physics control systems"},{"key":"10054_CR62","unstructured":"Dumoulin V, Visin F (2016) A guide to convolution arithmetic for deep learning"},{"key":"10054_CR63","unstructured":"Hernandez-Lobato JM, Adams R (2015) Probabilistic backpropagation for scalable learning of Bayesian neural networks. pp 1861\u20131869. PMLR"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10054-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10054-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10054-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T15:17:56Z","timestamp":1726672676000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10054-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,3]]},"references-count":63,"journal-issue":{"issue":"28","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["10054"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10054-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,3]]},"assertion":[{"value":"13 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors are not financially supported by any research funding other than the Federal University of Cear\u00e1; therefore, there is no conflict of interest applicable for this matter.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}