{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T13:53:35Z","timestamp":1768312415362,"version":"3.49.0"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"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":["J Supercomput"],"DOI":"10.1007\/s11227-025-07654-4","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T14:54:09Z","timestamp":1752764049000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A complementary time-frequency domain approach for short-term photovoltaic power forecasting"],"prefix":"10.1007","volume":"81","author":[{"given":"Wentao","family":"Wang","sequence":"first","affiliation":[]},{"given":"Haiyan","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Ayesha","family":"Ubaid","sequence":"additional","affiliation":[]},{"given":"Jianzhou","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"key":"7654_CR1","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1016\/j.eap.2023.06.044","volume":"79","author":"J Hussain","year":"2023","unstructured":"Hussain J, Lee C-C, Hu D (2023) Maximizing load capacity factor through a carbon-neutral environment via a simulation of carbon peak. Econ Anal Policy 79:746\u2013764","journal-title":"Econ Anal Policy"},{"key":"7654_CR2","doi-asserted-by":"crossref","unstructured":"Liu J, Zang H, Ding T, Cheng L, Wei Z, Sun G (2023) Sky-image-derived deep decomposition for ultra-short-term photovoltaic power forecasting. IEEE Trans Sustain Energy","DOI":"10.1109\/TSTE.2023.3312401"},{"key":"7654_CR3","doi-asserted-by":"crossref","unstructured":"Hokmabad HN, Husev O, Belikov J (2024) Day-ahead solar power forecasting using LightGBM and self-attention based encoder-decoder networks. IEEE Trans Sustain Energy","DOI":"10.1109\/TSTE.2024.3486907"},{"key":"7654_CR4","volume":"45","author":"H Liu","year":"2021","unstructured":"Liu H, Gao Q, Ma P (2021) Photovoltaic generation power prediction research based on high quality context ontology and gated recurrent neural network. Sustain Energy Technol Assess 45:101191","journal-title":"Sustain Energy Technol Assess"},{"key":"7654_CR5","doi-asserted-by":"publisher","first-page":"1490","DOI":"10.1016\/j.egyr.2022.12.076","volume":"9","author":"T Lu","year":"2023","unstructured":"Lu T, Wang C, Cao Y, Chen H (2023) Photovoltaic power prediction under insufficient historical data based on dendrite network and coupled information analysis. Energy Rep 9:1490\u20131500","journal-title":"Energy Rep"},{"key":"7654_CR6","volume":"5","author":"G Zhang","year":"2021","unstructured":"Zhang G, Bai X, Wang Y (2021) Short-time multi-energy load forecasting method based on CNN-Seq2Seq model with attention mechanism. Mach Learn Appl 5:100064","journal-title":"Mach Learn Appl"},{"key":"7654_CR7","doi-asserted-by":"crossref","unstructured":"Lin Y, Koprinska I, Rana M (2020) Temporal convolutional neural networks for solar power forecasting. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN48605.2020.9206713"},{"key":"7654_CR8","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.renene.2021.05.095","volume":"177","author":"A Agga","year":"2021","unstructured":"Agga A, Abbou A, Labbadi M, El Houm Y (2021) Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models. Renew Energy 177:101\u2013112","journal-title":"Renew Energy"},{"key":"7654_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2023.109499","volume":"222","author":"X Qi","year":"2023","unstructured":"Qi X, Chen Q, Zhang J (2023) Short-term prediction of PV power based on fusions of power series and ramp series. Electric Power Syst Res 222:109499","journal-title":"Electric Power Syst Res"},{"key":"7654_CR10","unstructured":"Yi K, Zhang Q, Fan W, Wang S, Wang P, He H, An N, Lian D, Cao L, Niu Z (2024) Frequency-domain MLPs are more effective learners in time series forecasting. Adv Neural Inf Process Syst 36"},{"key":"7654_CR11","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhao S, Song Z, Guo H, Zhang J, Zheng C, Qiang W (2024) Not all frequencies are created equal: towards a dynamic fusion of frequencies in time-series forecasting. In: Proceedings of the 32nd ACM International Conference on Multimedia, pp 4729\u20134737","DOI":"10.1145\/3664647.3681210"},{"key":"7654_CR12","doi-asserted-by":"crossref","unstructured":"Piao X, Chen Z, Murayama T, Matsubara Y, Sakurai Y (2024) Fredformer: frequency debiased transformer for time series forecasting. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp 2400\u20132410","DOI":"10.1145\/3637528.3671928"},{"key":"7654_CR13","doi-asserted-by":"crossref","unstructured":"Fei J, Yi K, Fan W, Zhang Q, Niu Z (2025) Amplifier: Bringing attention to neglected low-energy components in time series forecasting. In: The 39th Annual AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v39i11.33267"},{"key":"7654_CR14","doi-asserted-by":"crossref","unstructured":"Zhang Z, Chen Y, Zhang D, Qian Y, Wang H (2023) CTFNet: long-sequence time-series forecasting based on convolution and time\u2013frequency analysis. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2023.3294064"},{"key":"7654_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106334","volume":"176","author":"Y Chen","year":"2024","unstructured":"Chen Y, Liu S, Yang J, Jing H, Zhao W, Yang G (2024) A joint time-frequency domain transformer for multivariate time series forecasting. Neural Netw 176:106334","journal-title":"Neural Netw"},{"key":"7654_CR16","doi-asserted-by":"crossref","unstructured":"Qiu X, Wu X, Lin Y, Guo C, Hu J, Yang B (2025) Duet: dual clustering enhanced multivariate time series forecasting. In: Proceedings of the 31th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","DOI":"10.1145\/3690624.3709325"},{"key":"7654_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2024.141690","volume":"448","author":"C Yu","year":"2024","unstructured":"Yu C, Qiao J, Chen C, Yu C, Mi X (2024) TFEformer: a new temporal frequency ensemble transformer for day-ahead photovoltaic power prediction. J Clean Prod 448:141690","journal-title":"J Clean Prod"},{"key":"7654_CR18","unstructured":"Rahaman N, Baratin A, Arpit D, Draxler F, Lin M, Hamprecht F, Bengio Y, Courville A (2019) On the spectral bias of neural networks. In: International Conference on Machine Learning. PMLR, pp 5301\u20135310"},{"key":"7654_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.116239","volume":"283","author":"MJ Mayer","year":"2021","unstructured":"Mayer MJ, Gr\u00f3f G (2021) Extensive comparison of physical models for photovoltaic power forecasting. Appl Energy 283:116239","journal-title":"Appl Energy"},{"key":"7654_CR20","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.renene.2022.05.056","volume":"195","author":"M Paulescu","year":"2022","unstructured":"Paulescu M, Stefu N, Dughir C, Sabadus A, Calinoiu D, Badescu V (2022) A simple but accurate two-state model for nowcasting PV power. Renew Energy 195:322\u2013330","journal-title":"Renew Energy"},{"key":"7654_CR21","doi-asserted-by":"crossref","unstructured":"Zhou X, Pang C, Zeng X, Jiang L, Chen Y (2023) A short-term power prediction method based on temporal convolutional network in virtual power plant photovoltaic system. IEEE Trans Instrum Meas","DOI":"10.1109\/TIM.2023.3301904"},{"issue":"10","key":"7654_CR22","doi-asserted-by":"publisher","first-page":"1772","DOI":"10.1016\/j.solener.2009.05.016","volume":"83","author":"P Bacher","year":"2009","unstructured":"Bacher P, Madsen H, Nielsen HA (2009) Online short-term solar power forecasting. Sol Energy 83(10):1772\u20131783","journal-title":"Sol Energy"},{"key":"7654_CR23","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.solener.2016.06.069","volume":"136","author":"J Antonanzas","year":"2016","unstructured":"Antonanzas J, Osorio N, Escobar R, Urraca R, Martinez-de-Pison FJ, Antonanzas-Torres F (2016) Review of photovoltaic power forecasting. Sol Energy 136:78\u2013111","journal-title":"Sol Energy"},{"key":"7654_CR24","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1016\/j.matpr.2020.08.449","volume":"39","author":"S Das","year":"2021","unstructured":"Das S (2021) Short term forecasting of solar radiation and power output of 89.6 kwp solar PV power plant. Mater Today Proc 39:1959\u20131969","journal-title":"Mater Today Proc"},{"key":"7654_CR25","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.renene.2013.11.067","volume":"66","author":"Y Li","year":"2014","unstructured":"Li Y, Su Y, Shu L (2014) An ARMAX model for forecasting the power output of a grid connected photovoltaic system. Renew Energy 66:78\u201389","journal-title":"Renew Energy"},{"key":"7654_CR26","doi-asserted-by":"crossref","unstructured":"Kushwaha V, Pindoriya NM (2017) Very short-term solar PV generation forecast using SARIMA model: a case study. In: 2017 7th International Conference on Power Systems (ICPS). IEEE, pp 430\u2013435","DOI":"10.1109\/ICPES.2017.8387332"},{"issue":"4","key":"7654_CR27","doi-asserted-by":"publisher","first-page":"4967","DOI":"10.1109\/JSYST.2019.2962971","volume":"14","author":"G Li","year":"2020","unstructured":"Li G, Li Y, Roozitalab F (2020) Midterm load forecasting: a multistep approach based on phase space reconstruction and support vector machine. IEEE Syst J 14(4):4967\u20134977","journal-title":"IEEE Syst J"},{"issue":"2","key":"7654_CR28","doi-asserted-by":"publisher","first-page":"4010","DOI":"10.1109\/TPWRS.2023.3284559","volume":"39","author":"L Cheng","year":"2023","unstructured":"Cheng L, Zang H, Trivedi A, Srinivasan D, Ding T, Wei Z, Sun G (2023) Prediction of non-stationary multi-head cloud motion vectors for intra-hourly satellite-derived solar power forecasting. IEEE Trans Power Syst 39(2):4010\u20134019","journal-title":"IEEE Trans Power Syst"},{"issue":"3","key":"7654_CR29","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1109\/TIA.2012.2190816","volume":"48","author":"J Shi","year":"2012","unstructured":"Shi J, Lee W-J, Liu Y, Yang Y, Wang P (2012) Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Trans Ind Appl 48(3):1064\u20131069","journal-title":"IEEE Trans Ind Appl"},{"key":"7654_CR30","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.renene.2023.03.029","volume":"208","author":"J Zhu","year":"2023","unstructured":"Zhu J, Li M, Luo L, Zhang B, Cui M, Yu L (2023) Short-term PV power forecast methodology based on multi-scale fluctuation characteristics extraction. Renew Energy 208:141\u2013151","journal-title":"Renew Energy"},{"key":"7654_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.127807","volume":"278","author":"C Scott","year":"2023","unstructured":"Scott C, Ahsan M, Albarbar A (2023) Machine learning for forecasting a photovoltaic (PV) generation system. Energy 278:127807","journal-title":"Energy"},{"key":"7654_CR32","doi-asserted-by":"crossref","unstructured":"Gensler A, Henze J, Sick B, Raabe N (2016) Deep learning for solar power forecasting-an approach using autoencoder and LSTM neural networks. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp 002858\u2013002865","DOI":"10.1109\/SMC.2016.7844673"},{"key":"7654_CR33","doi-asserted-by":"crossref","unstructured":"Hochreiter S (1997) Long short-term memory. Neural Computation. MIT-Press","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"7654_CR34","doi-asserted-by":"crossref","unstructured":"Cho K (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. preprint at arXiv:1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"7654_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.124661","volume":"256","author":"Y Dai","year":"2022","unstructured":"Dai Y, Wang Y, Leng M, Yang X, Zhou Q (2022) Lowess smoothing and random forest based GRU model: a short-term photovoltaic power generation forecasting method. Energy 256:124661","journal-title":"Energy"},{"issue":"4","key":"7654_CR36","doi-asserted-by":"publisher","first-page":"3282","DOI":"10.1109\/TIA.2021.3073652","volume":"57","author":"J Yan","year":"2021","unstructured":"Yan J, Hu L, Zhen Z, Wang F, Qiu G, Li Y, Yao L, Shafie-khah M, Catal\u00e3o JP (2021) Frequency-domain decomposition and deep learning based solar PV power ultra-short-term forecasting model. IEEE Trans Ind Appl 57(4):3282\u20133295","journal-title":"IEEE Trans Ind Appl"},{"key":"7654_CR37","doi-asserted-by":"crossref","unstructured":"Zhou X, Liu Y, Qi L, Xu X, Dou W, Zhang X, Zhang Y, Zhou X (2024) GLFNet: Global and local frequency-domain network for long-term time series forecasting. In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pp 3527\u20133536","DOI":"10.1145\/3627673.3679579"},{"key":"7654_CR38","volume-title":"Fundamentals of solar cells: photovoltaic solar energy conversion","author":"A Fahrenbruch","year":"2012","unstructured":"Fahrenbruch A, Bube R (2012) Fundamentals of solar cells: photovoltaic solar energy conversion. Elsevier"},{"issue":"6391","key":"7654_CR39","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1126\/science.aan3256","volume":"360","author":"M-M Yang","year":"2018","unstructured":"Yang M-M, Kim DJ, Alexe M (2018) Flexo-photovoltaic effect. Science 360(6391):904\u2013907","journal-title":"Science"},{"key":"7654_CR40","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. In: International Conference on Learning Representations 2013 (ICLR 2013), Scottsdale"},{"key":"7654_CR41","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":"7654_CR42","unstructured":"Yang J, Li C, Zhang P, Dai X, Xiao B, Yuan L, Go, J (2021) Focal self-attention for local-global interactions in vision transformers. Preprint at arXiv:2107.00641"},{"key":"7654_CR43","first-page":"14541","volume":"35","author":"Z Pan","year":"2022","unstructured":"Pan Z, Cai J, Zhuang B (2022) Fast vision transformers with Hilo attention. Adv Neural Inf Process Syst 35:14541\u201314554","journal-title":"Adv Neural Inf Process Syst"},{"key":"7654_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.131071","volume":"295","author":"X Wang","year":"2024","unstructured":"Wang X, Ma W (2024) A hybrid deep learning model with an optimal strategy based on improved VMD and transformer for short-term photovoltaic power forecasting. Energy 295:131071","journal-title":"Energy"},{"key":"7654_CR45","doi-asserted-by":"publisher","first-page":"105939","DOI":"10.1109\/ACCESS.2021.3099169","volume":"9","author":"P Jia","year":"2021","unstructured":"Jia P, Zhang H, Liu X, Gong X (2021) Short-term photovoltaic power forecasting based on VMD and ISSA-GRU. IEEE Access 9:105939\u2013105950","journal-title":"IEEE Access"},{"key":"7654_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2024.114479","volume":"200","author":"J Kim","year":"2024","unstructured":"Kim J, Obregon J, Park H, Jung J-Y (2024) Multi-step photovoltaic power forecasting using transformer and recurrent neural networks. Renew Sustain Energy Rev 200:114479","journal-title":"Renew Sustain Energy Rev"},{"key":"7654_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.133495","volume":"312","author":"R Chen","year":"2024","unstructured":"Chen R, Liu G, Cao Y, Xiao G, Tang J (2024) CGAformer: multi-scale feature transformer with MLP architecture for short-term photovoltaic power forecasting. Energy 312:133495","journal-title":"Energy"},{"key":"7654_CR48","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.solener.2021.09.050","volume":"230","author":"T Yao","year":"2021","unstructured":"Yao T, Wang J, Wu H, Zhang P, Li S, Wang Y, Chi X, Shi M (2021) A photovoltaic power output dataset: multi-source photovoltaic power output dataset with python toolkit. Sol Energy 230:122\u2013130","journal-title":"Sol Energy"},{"key":"7654_CR49","doi-asserted-by":"crossref","unstructured":"Yang J, He H, Zhao X, Wang J, Yao T, Cao H, Wan M (2023) Day-ahead PV power forecasting model based on fine-grained temporal attention and cloud-coverage spatial attention. IEEE Trans Sustain Energy","DOI":"10.1109\/TSTE.2023.3326887"},{"key":"7654_CR50","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.solener.2021.05.095","volume":"224","author":"Y Nie","year":"2021","unstructured":"Nie Y, Zamzam AS, Brandt A (2021) Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks. Sol Energy 224:341\u2013354","journal-title":"Sol Energy"},{"key":"7654_CR51","unstructured":"Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. Preprint at arXiv:1803.01271"},{"key":"7654_CR52","unstructured":"Liu S, Yu H, Liao C, Li J, Lin W, Liu AX, Dustdar S (2022) Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting. In: International Conference on Learning Representations"},{"key":"7654_CR53","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. PMLR, pp 27268\u201327286"},{"key":"7654_CR54","unstructured":"Liu Y, Hu T, Zhang H, Wu H, Wang S, Ma L, Long M (2024) iTransformer: inverted transformers are effective for time series forecasting. In: The 12th International Conference on Learning Representations"},{"issue":"1","key":"7654_CR55","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1109\/TSTE.2022.3206240","volume":"14","author":"J Li","year":"2022","unstructured":"Li J, Zhang C, Sun B (2022) Two-stage hybrid deep learning with strong adaptability for detailed day-ahead photovoltaic power forecasting. IEEE Trans Sustain Energy 14(1):193\u2013205","journal-title":"IEEE Trans Sustain Energy"},{"key":"7654_CR56","doi-asserted-by":"crossref","unstructured":"Zhong S, Wang X, Xu B, Wu H, Ding M (2023) Hybrid network model based on data enhancement for short-term power prediction of new PV plants. J Mod Power Syst Clean Energy","DOI":"10.35833\/MPCE.2022.000759"},{"key":"7654_CR57","doi-asserted-by":"crossref","unstructured":"Zhao P, Hu W, Cao D, Huang R, Wu X, Huang Q, Chen Z (2024) Causal mechanism-enabled zero-label learning for power generation forecasting of newly-built PV sites. IEEE Trans Sustain Energy","DOI":"10.1109\/TSTE.2024.3459415"},{"issue":"4","key":"7654_CR58","doi-asserted-by":"publisher","first-page":"5659","DOI":"10.1109\/TPWRS.2023.3345328","volume":"39","author":"P Zhao","year":"2023","unstructured":"Zhao P, Cao D, Hu W, Huang Y, Hao M, Huang Q, Chen Z (2023) Geometric loss-enabled complex neural network for multi-energy load forecasting in integrated energy systems. IEEE Trans Power Syst 39(4):5659\u20135671","journal-title":"IEEE Trans Power Syst"},{"key":"7654_CR59","doi-asserted-by":"crossref","unstructured":"Zhao P, Hu W, Cao D, Zhang Z, Huang Y, Dai L, Chen Z (2024) Probabilistic multienergy load forecasting based on hybrid attention-enabled transformer network and gaussian process-aided residual learning. IEEE Trans Ind Inform","DOI":"10.1109\/TII.2024.3366946"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07654-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07654-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07654-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T13:08:27Z","timestamp":1757250507000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07654-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,17]]},"references-count":59,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["7654"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07654-4","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,17]]},"assertion":[{"value":"2 July 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"1165"}}