{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T15:48:21Z","timestamp":1784735301247,"version":"3.55.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"33","license":[{"start":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T00:00:00Z","timestamp":1724025600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T00:00:00Z","timestamp":1724025600000},"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":["62172292"],"award-info":[{"award-number":["62172292"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072250"],"award-info":[{"award-number":["62072250"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s00521-024-10295-y","type":"journal-article","created":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T06:57:28Z","timestamp":1724050648000},"page":"20883-20906","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["SEAformer: frequency domain decomposition transformer with signal enhanced for long-term wind power forecasting"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5638-2296","authenticated-orcid":false,"given":"Leiming","family":"Yan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siqi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaopeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianyi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,19]]},"reference":[{"key":"10295_CR1","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1016\/j.egypro.2011.10.103","volume":"12","author":"X Wang","year":"2011","unstructured":"Wang X, Guo P, Huang X (2011) A review of wind power forecasting models. Energy Procedia 12:770\u2013778","journal-title":"Energy Procedia"},{"key":"10295_CR2","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, \u2026 Polosukhin I (2017) Attention is All you Need. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds), Advances in Neural Information Processing Systems (Vol. 30). Curran Associates, Inc."},{"key":"10295_CR3","unstructured":"Kitaev N, Kaiser L, Levskaya A (2020) Reformer: the efficient transformer. In: 8th International conference on learning representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020"},{"key":"10295_CR4","first-page":"22419","volume":"34","author":"H Wu","year":"2021","unstructured":"Wu H, Xu J, Wang J, Long M (2021) Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. Adv Neural Inf Process Syst 34:22419\u201322430","journal-title":"Adv Neural Inf Process Syst"},{"key":"10295_CR5","doi-asserted-by":"crossref","unstructured":"Zhou H, Zhang S, Peng J, Zhang S, Li J, Xiong H, Zhang W (2021) Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI conference on artificial intelligence, vol 35. pp 11106\u201311115","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"10295_CR6","unstructured":"Zhou T, Ma Z, Wen Q, Wang X, Sun L, Jin R (2022) Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In: International conference on machine learning, pp. 27268\u201327286. PMLR"},{"key":"10295_CR7","unstructured":"Kuznetsov V, Mohri M (2015) Learning theory and algorithms for forecasting non-stationary time series. Proceedings of the 28th International Conference on Neural Information Processing Systems - Vol 1. Presented at the Montreal, Canada. Cambridge, MA, USA: MIT Press, pp 541\u2013549"},{"issue":"3","key":"10295_CR8","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","volume":"62","author":"K Dragomiretskiy","year":"2013","unstructured":"Dragomiretskiy K, Zosso D (2013) Variational mode decomposition. IEEE Trans Signal Process 62(3):531\u2013544","journal-title":"IEEE Trans Signal Process"},{"issue":"9","key":"10295_CR9","doi-asserted-by":"publisher","first-page":"3192","DOI":"10.1016\/j.rser.2010.07.001","volume":"14","author":"S Al-Yahyai","year":"2010","unstructured":"Al-Yahyai S, Charabi Y, Gastli A (2010) Review of the use of numerical weather prediction (NWP) models for wind energy assessment. Renew Sustain Energy Rev 14(9):3192\u20133198","journal-title":"Renew Sustain Energy Rev"},{"issue":"8","key":"10295_CR10","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(8):1735\u20131780","journal-title":"Neural Comput"},{"issue":"2","key":"10295_CR11","first-page":"158","volume":"23","author":"GE Box","year":"1974","unstructured":"Box GE, Jenkins GM, MacGregor JF (1974) Some recent advances in forecasting and control. J Roy Stat Soc: Ser C (Appl Stat) 23(2):158\u2013179","journal-title":"J Roy Stat Soc: Ser C (Appl Stat)"},{"key":"10295_CR12","unstructured":"Rangapuram SS, Seeger MW, Gasthaus J, Stella L, Wang Y, Januschowski T (2018) Deep State Space Models for Time Series Forecasting. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R (eds), Advances in Neural Information Processing Systems (Vol. 31). Curran Associates, Inc."},{"issue":"8","key":"10295_CR13","doi-asserted-by":"publisher","first-page":"5251","DOI":"10.3390\/en7085251","volume":"7","author":"MG De Giorgi","year":"2014","unstructured":"De Giorgi MG, Campilongo S, Ficarella A, Congedo PM (2014) Comparison between wind power prediction models based on wavelet decomposition with least-squares support vector machine (LS-SVM) and artificial neural network (ANN). Energies 7(8):5251\u20135272","journal-title":"Energies"},{"issue":"4","key":"10295_CR14","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/S0169-2070(00)00066-2","volume":"16","author":"V Assimakopoulos","year":"2000","unstructured":"Assimakopoulos V, Nikolopoulos K (2000) The theta model: a decomposition approach to forecasting. Int J Forecast 16(4):521\u2013530","journal-title":"Int J Forecast"},{"issue":"1","key":"10295_CR15","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.ijforecast.2003.09.015","volume":"20","author":"CC Holt","year":"2004","unstructured":"Holt CC (2004) Forecasting seasonals and trends by exponentially weighted moving averages. Int J Forecast 20(1):5\u201310","journal-title":"Int J Forecast"},{"issue":"1","key":"10295_CR16","first-page":"3","volume":"6","author":"RB Cleveland","year":"1990","unstructured":"Cleveland RB, Cleveland WS, McRae JE, Terpenning I (1990) STL: a seasonal-trend decomposition. J Off Stat 6(1):3\u201373","journal-title":"J Off Stat"},{"issue":"355","key":"10295_CR17","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1080\/01621459.1976.10481532","volume":"71","author":"WP Cleveland","year":"1976","unstructured":"Cleveland WP, Tiao GC (1976) Decomposition of seasonal time series: a model for the census x\u201311 program. J Am Stat Assoc 71(355):581\u2013587","journal-title":"J Am Stat Assoc"},{"key":"10295_CR18","unstructured":"Woo G, Liu C, Sahoo D, Kumar A, Hoi SCH (2022) Cost: contrastive learning of disentangled seasonal-trend representations for time series forecasting. In: The tenth international conference on learning representations, ICLR 2022, Virtual Event, April 25-29, 2022"},{"issue":"4","key":"10295_CR19","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1016\/j.ijforecast.2009.07.001","volume":"26","author":"E McKenzie","year":"2010","unstructured":"McKenzie E, Gardner ES Jr (2010) Damped trend exponential smoothing: a modelling viewpoint. Int J Forecast 26(4):661\u2013665","journal-title":"Int J Forecast"},{"key":"10295_CR20","doi-asserted-by":"crossref","unstructured":"Wen Q, Gao J, Song X, Sun L, Xu H, Zhu S (2019) Robuststl: a robust seasonal-trend decomposition algorithm for long time series. In: Proceedings of the AAAI conference on artificial intelligence, vol 33. pp 5409\u20135416","DOI":"10.1609\/aaai.v33i01.33015409"},{"key":"10295_CR21","doi-asserted-by":"crossref","unstructured":"Huang S, Yan C, Qu Y (2023) Deep learning model-transformer based wind power forecasting approach. In: Frontiers in Energy Research","DOI":"10.3389\/fenrg.2022.1055683"},{"key":"10295_CR22","doi-asserted-by":"crossref","unstructured":"Fu X, Gao F, Wu J, Wei X, Duan F (2019) Spatiotemporal attention networks for wind power forecasting. In: 2019 International conference on data mining workshops (ICDMW), pp 149\u2013154","DOI":"10.1109\/ICDMW.2019.00032"},{"key":"10295_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.127678","volume":"278","author":"EGS Nascimento","year":"2023","unstructured":"Nascimento EGS, de Melo TAC, Moreira DM (2023) A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy. Energy 278:127678","journal-title":"Energy"},{"key":"10295_CR24","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1016\/j.egyr.2023.12.030","volume":"11","author":"S Mo","year":"2024","unstructured":"Mo S, Wang H, Li B, Xue Z, Fan S, Liu X (2024) Powerformer: a temporal-based transformer model for wind power forecasting. Energy Rep 11:736\u2013744","journal-title":"Energy Rep"},{"key":"10295_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.129753","volume":"288","author":"S Wang","year":"2024","unstructured":"Wang S, Shi J, Yang W, Yin Q (2024) High and low frequency wind power prediction based on transformer and BiGRU-attention. Energy 288:129753","journal-title":"Energy"},{"key":"10295_CR26","doi-asserted-by":"crossref","unstructured":"Deng B, Wu Y, Liu S, Xu Z (2022) Wind speed forecasting for wind power production based on frequency-enhanced transformer. In: 2022 4th International conference on machine learning, big data and business intelligence (MLBDBI), pp 151\u2013155","DOI":"10.1109\/MLBDBI58171.2022.00036"},{"issue":"18","key":"10295_CR27","doi-asserted-by":"publisher","first-page":"6642","DOI":"10.3390\/en15186642","volume":"15","author":"X Huang","year":"2022","unstructured":"Huang X, Jiang A (2022) Wind power generation forecast based on multi-step informer network. Energies 15(18):6642","journal-title":"Energies"},{"key":"10295_CR28","unstructured":"Li S, Jin X, Xuan Y, Zhou X, Chen W, Wang Y-X, Yan X (2019) Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in Neural Information Processing Systems, 32"},{"key":"10295_CR29","unstructured":"Beltagy I, Peters ME, Cohan A (2020) Longformer: the long-document transformer. arXiv preprint arXiv:2004.05150"},{"key":"10295_CR30","unstructured":"Choromanski KM, Likhosherstov V, Dohan D, Song X, Gane A, Sarl\u00f3s T, Hawkins P, Davis JQ, Mohiuddin A, Kaiser L, Belanger DB, Colwell LJ, Weller A (2021) Rethinking attention with performers. In: 9th International conference on learning representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021"},{"key":"10295_CR31","unstructured":"Wang S, Li BZ, Khabsa M, Fang H, Ma H (2020) Linformer: self-attention with linear complexity. arXiv preprint arXiv:2006.04768"},{"key":"10295_CR32","doi-asserted-by":"crossref","unstructured":"Gulati, A., Qin, J., Chiu, C., Parmar, N., Zhang, Y., Yu, J., Han, W., Wang, S., Zhang, Z., Wu, Y., Pang, R.: Conformer: convolution-augmented transformer for speech recognition. In: Interspeech 2020, 21st Annual conference of the international speech communication association, virtual event, Shanghai, China, 25-29 October 2020, pp 5036\u20135040 (2020)","DOI":"10.21437\/Interspeech.2020-3015"},{"key":"10295_CR33","volume-title":"Time series analysis: forecasting and control","author":"GE Box","year":"2015","unstructured":"Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. John Wiley & Sons, Hoboken"},{"key":"10295_CR34","unstructured":"Wen R, Torkkola K, Narayanaswamy B, Madeka D (2017) A multi-horizon quantile recurrent forecaster. arXiv:Machine Learning"},{"key":"10295_CR35","unstructured":"Yu R, Zheng S, Anandkumar A, Yue Y (2017) Long-term forecasting using higher order tensor rnns. arXiv preprint arXiv:1711.00073"},{"key":"10295_CR36","doi-asserted-by":"crossref","unstructured":"Cho K, Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1724\u20131734. Association for Computational Linguistics, Doha, Qatar","DOI":"10.3115\/v1\/D14-1179"},{"issue":"3","key":"10295_CR37","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1016\/j.ijforecast.2019.07.001","volume":"36","author":"D Salinas","year":"2020","unstructured":"Salinas D, Flunkert V, Gasthaus J, Januschowski T (2020) Deepar: probabilistic forecasting with autoregressive recurrent networks. Int J Forecast 36(3):1181\u20131191","journal-title":"Int J Forecast"},{"key":"10295_CR38","doi-asserted-by":"crossref","unstructured":"Qin Y, Song D, Chen H, Cheng W, Jiang G, Cottrell GW (2017) A dual-stage attention-based recurrent neural network for time series prediction. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, pp 2627\u20132633","DOI":"10.24963\/ijcai.2017\/366"},{"key":"10295_CR39","doi-asserted-by":"crossref","unstructured":"Lai G, Chang W-C, Yang Y, Liu H (2018) Modeling long-and short-term temporal patterns with deep neural networks. In: The 41st International ACM SIGIR conference on research & development in information retrieval, pp 95\u2013104","DOI":"10.1145\/3209978.3210006"},{"key":"10295_CR40","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1007\/s10994-019-05815-0","volume":"108","author":"S-Y Shih","year":"2019","unstructured":"Shih S-Y, Sun F-K, Lee H-Y (2019) Temporal pattern attention for multivariate time series forecasting. Mach Learn 108:1421\u20131441","journal-title":"Mach Learn"},{"key":"10295_CR41","unstructured":"Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. CoRR arxiv:abs\/1803.01271"},{"key":"10295_CR42","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, vol 1 (Long and Short Papers), Minneapolis, Minnesota, pp 4171\u20134186"},{"key":"10295_CR43","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10295_CR44","first-page":"980","volume":"34","author":"Y Rao","year":"2021","unstructured":"Rao Y, Zhao W, Zhu Z, Lu J, Zhou J (2021) Global filter networks for image classification. Adv Neural Inf Process Syst 34:980\u2013993","journal-title":"Adv Neural Inf Process Syst"},{"key":"10295_CR45","doi-asserted-by":"crossref","unstructured":"Zhu Z, Soricut R (2021) H-transformer-1D: Fast one-dimensional hierarchical attention for sequences. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (Volume 1: Long Papers), Online, pp 3801\u20133815","DOI":"10.18653\/v1\/2021.acl-long.294"},{"issue":"1","key":"10295_CR46","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1214\/ss\/1177011077","volume":"8","author":"D Bertsimas","year":"1993","unstructured":"Bertsimas D, Tsitsiklis J (1993) Simulated Annealing. Stat Sci 8(1):10\u201315. https:\/\/doi.org\/10.1214\/ss\/1177011077","journal-title":"Stat Sci"},{"key":"10295_CR47","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/B978-0-12-821986-7.00013-5","volume-title":"Nature-inspired optimization algorithms","author":"X-S Yang","year":"2021","unstructured":"Yang X-S (2021) Chapter 6 - genetic algorithms. In: Yang X-S (ed) Nature-inspired optimization algorithms, second edition. Academic Press, Amsterdam, pp 91\u2013100. https:\/\/doi.org\/10.1016\/B978-0-12-821986-7.00013-5","edition":"second edition"},{"key":"10295_CR48","doi-asserted-by":"publisher","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN\u201995 - international conference on neural networks, vol 4. pp 1942\u201319484. https:\/\/doi.org\/10.1109\/ICNN.1995.488968","DOI":"10.1109\/ICNN.1995.488968"},{"issue":"4","key":"10295_CR49","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1109\/MCI.2006.329691","volume":"1","author":"M Dorigo","year":"2006","unstructured":"Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28\u201339. https:\/\/doi.org\/10.1109\/MCI.2006.329691","journal-title":"IEEE Comput Intell Mag"},{"key":"10295_CR50","doi-asserted-by":"publisher","unstructured":"Zefan C, Xiaodong Y (2017) Cuckoo search algorithm with deep search. In: 2017 3rd IEEE international conference on computer and communications (ICCC), pp 2241\u20132246. https:\/\/doi.org\/10.1109\/CompComm.2017.8322934","DOI":"10.1109\/CompComm.2017.8322934"},{"key":"10295_CR51","unstructured":"Zhou J, Lu X, Xiao Y, Su J, Lyu J, Ma Y, Dou D (2022) SDWPF: a dataset for spatial dynamic wind power forecasting challenge at KDD cup 2022. CoRR arXiv:abs\/2208.04360"},{"key":"10295_CR52","doi-asserted-by":"crossref","unstructured":"Paparrizos J, Gravano L (2015) k-shape: efficient and accurate clustering of time series. In: Proceedings of the 2015 ACM SIGMOD international conference on management of data","DOI":"10.1145\/2723372.2737793"},{"key":"10295_CR53","unstructured":"Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings"},{"key":"10295_CR54","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, et al (2019) Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32"},{"key":"10295_CR55","unstructured":"Zeng A, Chen M-H, Zhang L, Xu Q (2022) Are transformers effective for time series forecasting? In: AAAI conference on artificial intelligence. https:\/\/api.semanticscholar.org\/CorpusID:249097444"},{"key":"10295_CR56","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","journal-title":"Neural Comput"},{"key":"10295_CR57","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1109\/TSTE.2019.2926147","volume":"11","author":"C Li","year":"2020","unstructured":"Li C, Tang G, Xue X, Saeed A, Hu X (2020) Short-term wind speed interval prediction based on ensemble GRU model. IEEE Trans Sustain Energy 11:1370\u20131380","journal-title":"IEEE Trans Sustain Energy"},{"key":"10295_CR58","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","volume":"50","author":"GP Zhang","year":"2003","unstructured":"Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159\u2013175","journal-title":"Neurocomputing"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10295-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10295-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10295-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T16:04:21Z","timestamp":1727539461000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10295-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,19]]},"references-count":58,"journal-issue":{"issue":"33","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["10295"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10295-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,19]]},"assertion":[{"value":"9 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 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":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}