{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T17:26:02Z","timestamp":1774891562765,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"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":["62277013"],"award-info":[{"award-number":["62277013"]}],"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":["62177040"],"award-info":[{"award-number":["62177040"]}],"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":["U22A2033"],"award-info":[{"award-number":["U22A2033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Social Science Fund Major Project","award":["24&ZD075"],"award-info":[{"award-number":["24&ZD075"]}]},{"name":"Zhejiang Lingyan Project","award":["2026C02A1238"],"award-info":[{"award-number":["2026C02A1238"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LTGG24F020006"],"award-info":[{"award-number":["LTGG24F020006"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LZ22F020015"],"award-info":[{"award-number":["LZ22F020015"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Vis"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s12650-026-01110-y","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T11:44:30Z","timestamp":1771501470000},"page":"391-406","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A hierarchical electricity consumption forecasting visualization system based on multi-scale LSTM-KAN model"],"prefix":"10.1007","volume":"29","author":[{"given":"Hang","family":"Yin","sequence":"first","affiliation":[]},{"given":"Yize","family":"Li","sequence":"additional","affiliation":[]},{"given":"Ning","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Ruiqi","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Ningxin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xiangyang","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yongheng","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4783-6863","authenticated-orcid":false,"given":"Zhiguang","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"1110_CR1","doi-asserted-by":"publisher","first-page":"108148","DOI":"10.1016\/j.patcog.2021.108148","volume":"120","author":"K Bandara","year":"2021","unstructured":"Bandara K, Hewamalage H, Liu Y-H, Kang Y, Bergmeir C (2021) Improving the accuracy of global forecasting models using time series data augmentation. Pattern Recogn 120:108148","journal-title":"Pattern Recogn"},{"key":"1110_CR2","doi-asserted-by":"publisher","first-page":"123060","DOI":"10.1016\/j.energy.2021.123060","volume":"243","author":"D Hadjout","year":"2022","unstructured":"Hadjout D, Torres J, Troncoso A, Sebaa A, Mart\u00ednez-\u00c1lvarez F (2022) Electricity consumption forecasting based on ensemble deep learning with application to the algerian market. Energy 243:123060","journal-title":"Energy"},{"issue":"20","key":"1110_CR3","doi-asserted-by":"publisher","first-page":"6619","DOI":"10.3390\/en14206619","volume":"14","author":"M Zieli\u0144ska-Sitkiewicz","year":"2021","unstructured":"Zieli\u0144ska-Sitkiewicz M, Chrzanowska M, Furma\u0144czyk K, Paczutkowski K (2021) Analysis of electricity consumption in Poland using prediction models and neural networks. Energies 14(20):6619","journal-title":"Energies"},{"issue":"3","key":"1110_CR4","doi-asserted-by":"publisher","first-page":"101647","DOI":"10.1016\/j.gsf.2023.101647","volume":"15","author":"S Sarwar","year":"2024","unstructured":"Sarwar S, Aziz G, Tiwari AK (2024) Implication of machine learning techniques to forecast the electricity price and carbon emission: evidence from a hot region. Geosci Front 15(3):101647","journal-title":"Geosci Front"},{"key":"1110_CR5","doi-asserted-by":"publisher","first-page":"129585","DOI":"10.1016\/j.energy.2023.129585","volume":"287","author":"C Li","year":"2024","unstructured":"Li C, Qi Q (2024) A novel hybrid grey system forecasting model based on seasonal fluctuation characteristics for electricity consumption in primary industry. Energy 287:129585","journal-title":"Energy"},{"key":"1110_CR6","doi-asserted-by":"publisher","first-page":"118791","DOI":"10.1016\/j.energy.2020.118791","volume":"214","author":"S Guefano","year":"2021","unstructured":"Guefano S, Tamba JG, Azong TEW, Monkam L (2021) Forecast of electricity consumption in the Cameroonian residential sector by grey and vector autoregressive models. Energy 214:118791","journal-title":"Energy"},{"issue":"6","key":"1110_CR7","first-page":"279","volume":"12","author":"SJ Parre\u00f1o","year":"2022","unstructured":"Parre\u00f1o SJ (2022) Forecasting electricity consumption in the Philippines using Arima models. Int J Mach Learn Comput 12(6):279\u2013285","journal-title":"Int J Mach Learn Comput"},{"issue":"11","key":"1110_CR8","first-page":"1629","volume":"35","author":"W Yang","year":"2023","unstructured":"Yang W, Chen C, Zhu J, Li L, Liu P, Liu S (2023) A survey of visual analytics research for improving training data quality. J Comput-Aided Design & Comput Graph 35(11):1629\u20131642","journal-title":"J Comput-Aided Design & Comput Graph"},{"key":"1110_CR9","doi-asserted-by":"publisher","first-page":"115917","DOI":"10.1016\/j.eswa.2021.115917","volume":"187","author":"PC Albuquerque","year":"2022","unstructured":"Albuquerque PC, Cajueiro DO, Rossi MD (2022) Machine learning models for forecasting power electricity consumption using a high dimensional dataset. Expert Syst Appl 187:115917","journal-title":"Expert Syst Appl"},{"key":"1110_CR10","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1016\/j.apenergy.2019.05.103","volume":"250","author":"P Shine","year":"2019","unstructured":"Shine P, Scully T, Upton J, Murphy MD (2019) Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine. Appl Energy 250:1110\u20131119","journal-title":"Appl Energy"},{"issue":"5","key":"1110_CR11","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1109\/THMS.2023.3296692","volume":"53","author":"Z Zhou","year":"2023","unstructured":"Zhou Z, Zheng F, Wen J, Chen Y, Li X, Liu Y, Wang Y, Chen W (2023) A user-driven sampling model for large-scale geographical point data visualization via convolutional neural networks. IEEE Trans Hum Mach Syst 53(5):885\u2013894","journal-title":"IEEE Trans Hum Mach Syst"},{"issue":"11","key":"1110_CR12","doi-asserted-by":"publisher","first-page":"4062","DOI":"10.3390\/s22114062","volume":"22","author":"S Mahjoub","year":"2022","unstructured":"Mahjoub S, Chrifi-Alaoui L, Marhic B, Delahoche L (2022) Predicting energy consumption using LSTM, multi-layer Gru and drop-Gru neural networks. Sensors 22(11):4062","journal-title":"Sensors"},{"issue":"14","key":"1110_CR13","doi-asserted-by":"publisher","first-page":"4993","DOI":"10.3390\/en15144993","volume":"15","author":"A L\u2019Heureux","year":"2022","unstructured":"L\u2019Heureux A, Grolinger K, Capretz MA (2022) Transformer-based model for electrical load forecasting. Energies 15(14):4993","journal-title":"Energies"},{"key":"1110_CR14","doi-asserted-by":"publisher","first-page":"15200","DOI":"10.1111\/cgf.15200","volume":"43","author":"Y Liu","year":"2024","unstructured":"Liu Y, Ma Y, Shi Q, Wen J, Zheng W, Yue X, Ye H, Chen W, Meng Y, Zhou Z (2024) Ebpvis: visual analytics of economic behavior patterns in a virtual experimental environment. In Computer Graphics Forum 43:15200","journal-title":"In Computer Graphics Forum"},{"issue":"2","key":"1110_CR15","doi-asserted-by":"publisher","first-page":"1354","DOI":"10.1109\/TVCG.2024.3361001","volume":"31","author":"Z Zhou","year":"2025","unstructured":"Zhou Z, Ye L, Cai L, Wang L, Wang Y, Wang Y, Chen W, Wang Y (2025) Conceptthread: Visualizing threaded concepts in MOOC videos. IEEE Trans Vis Comput Graph 31(2):1354\u20131370","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"1110_CR16","doi-asserted-by":"crossref","unstructured":"Zhou Z, Wang H, Zhao Z, Zheng F, Wang Y, Chen W, Wang Y (2024) Chartkg: A knowledge-graph-based representation for chart images. IEEE Trans Visual Comput Graphics","DOI":"10.1109\/TVCG.2024.3476508"},{"issue":"4","key":"1110_CR17","doi-asserted-by":"publisher","first-page":"1292","DOI":"10.1109\/59.801887","volume":"14","author":"AJ Urdaneta","year":"1999","unstructured":"Urdaneta AJ, Gomez JF, Sorrentino E, Flores L, Diaz R (1999) A hybrid genetic algorithm for optimal reactive power planning based upon successive linear programming. IEEE Trans Power Syst 14(4):1292\u20131298","journal-title":"IEEE Trans Power Syst"},{"key":"1110_CR18","doi-asserted-by":"crossref","unstructured":"Jin W, Chen L, Zheng S, Jiang Y, Li Y, Chen H (2024) A novel bi-level vsc-dc transmission expansion planning method of VSC-DC for power system flexibility and stability enhancement. Energy Eng 121(11)","DOI":"10.32604\/ee.2024.054068"},{"key":"1110_CR19","doi-asserted-by":"publisher","first-page":"121025","DOI":"10.1016\/j.energy.2021.121025","volume":"232","author":"ML Merlin","year":"2021","unstructured":"Merlin ML, Chen Y (2021) Analysis of the factors affecting electricity consumption in dr congo using fully modified ordinary least square (fmols), dynamic ordinary least square (dols) and canonical cointegrating regression (ccr) estimation approach. Energy 232:121025","journal-title":"Energy"},{"key":"1110_CR20","doi-asserted-by":"crossref","unstructured":"Ye Y, Koch SF, Ye X (2025) The effect of temperature on household hourly electricity consumption: Evidence from South Africa. Energy, 134925","DOI":"10.1016\/j.energy.2025.134925"},{"issue":"1","key":"1110_CR21","first-page":"4234206","volume":"2018","author":"H Cui","year":"2018","unstructured":"Cui H, Wu R, Zhao T (2018) Dynamic decomposition analysis and forecasting of energy consumption in Shanxi province based on var and gm (1, 1) models. Math Probl Eng 2018(1):4234206","journal-title":"Math Probl Eng"},{"key":"1110_CR22","doi-asserted-by":"publisher","first-page":"123978","DOI":"10.1016\/j.eswa.2024.123978","volume":"251","author":"H Gu","year":"2024","unstructured":"Gu H, Chen Y, Wu L (2024) A new grey adaptive integrated model for forecasting renewable electricity production. Expert Syst Appl 251:123978","journal-title":"Expert Syst Appl"},{"key":"1110_CR23","doi-asserted-by":"publisher","first-page":"111119","DOI":"10.1016\/j.epsr.2024.111119","volume":"238","author":"X He","year":"2025","unstructured":"He X, Zhao W, Gao Z, Zhang L, Zhang Q, Li X (2025) Short-term load forecasting by Gru neural network and DDPG algorithm for adaptive optimization of hyperparameters. Electric Power Systems Research 238:111119","journal-title":"Electric Power Systems Research"},{"key":"1110_CR24","doi-asserted-by":"publisher","first-page":"126361","DOI":"10.1016\/j.eswa.2024.126361","volume":"268","author":"P Du","year":"2025","unstructured":"Du P, Ye Y, Wu H, Wang J (2025) Study on deterministic and interval forecasting of electricity load based on multi-objective whale optimization algorithm and transformer model. Expert Syst Appl 268:126361","journal-title":"Expert Syst Appl"},{"issue":"2","key":"1110_CR25","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1109\/TPWRS.2005.846054","volume":"20","author":"AJ Conejo","year":"2005","unstructured":"Conejo AJ, Plazas MA, Espinola R, Molina AB (2005) Day-ahead electricity price forecasting using the wavelet transform and Arima models. IEEE Trans Power Syst 20(2):1035\u20131042","journal-title":"IEEE Trans Power Syst"},{"key":"1110_CR26","doi-asserted-by":"crossref","unstructured":"Latifah AL, Shabrina A, Wahyuni IN, Sadikin R (2019) Evaluation of random forest model for forest fire prediction based on climatology over borneo. In: 2019 International Conference on Computer, Control, Informatics and Its Applications (IC3INA), pp. 4\u20138","DOI":"10.1109\/IC3INA48034.2019.8949588"},{"key":"1110_CR27","doi-asserted-by":"crossref","unstructured":"Peng L, Zhang Q (2024) Research on sailboat price prediction based on ls-svm prediction algorithm and xgboost algorithm. In: 2024 International Conference on Computers, Information Processing and Advanced Education (CIPAE), pp. 160\u2013164","DOI":"10.1109\/CIPAE64326.2024.00034"},{"key":"1110_CR28","doi-asserted-by":"publisher","first-page":"2011359","DOI":"10.1007\/s11704-025-50947-3","volume":"20","author":"MA Klxxk-L","year":"2026","unstructured":"Klxxk-L MA (2026) Deep learning for time series forecasting: a survey of recent advances. Front Comp Sci 20:2011359","journal-title":"Front Comp Sci"},{"key":"1110_CR29","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1016\/j.egyr.2022.07.139","volume":"8","author":"I Amalou","year":"2022","unstructured":"Amalou I, Mouhni N, Abdali A (2022) Multivariate time series prediction by RNN architectures for energy consumption forecasting. Energy Rep 8:1084\u20131091","journal-title":"Energy Rep"},{"key":"1110_CR30","doi-asserted-by":"crossref","unstructured":"Xu Y, Hong W, Noori M, Altabey WA, Silik A, Farhan NS (2024) Big model strategy for bridge structural health monitoring based on data-driven, adaptive method and convolutional neural network (cnn) group. Struct Durab & Health Monitoring (SDHM) 18(6)","DOI":"10.32604\/sdhm.2024.053763"},{"issue":"12","key":"1110_CR31","doi-asserted-by":"publisher","first-page":"13902","DOI":"10.1109\/TCYB.2021.3121312","volume":"52","author":"W Zheng","year":"2021","unstructured":"Zheng W, Chen G (2021) An accurate Gru-based power time-series prediction approach with selective state updating and stochastic optimization. IEEE Transactions on Cybernetics 52(12):13902\u201313914","journal-title":"IEEE Transactions on Cybernetics"},{"key":"1110_CR32","first-page":"11968","volume":"39","author":"Y Kong","year":"2025","unstructured":"Kong Y, Wang Z, Nie Y, Zhou T, Zohren S, Liang Y, Sun P, Wen Q (2025) Unlocking the power of LSTM for long term time series forecasting. Procee AAAI Conf Artif Intell 39:11968\u201311976","journal-title":"Procee AAAI Conf Artif Intell"},{"key":"1110_CR33","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.procir.2024.01.012","volume":"122","author":"E Boos","year":"2024","unstructured":"Boos E, Zimmermann J, Wiemer H, Ihlenfeldt S (2024) Investigation of alternative attention modules in transformer models for remaining useful life predictions: Addressing challenges in high-frequency time-series data. Procedia CIRP 122:85\u201390","journal-title":"Procedia CIRP"},{"key":"1110_CR34","first-page":"11106","volume":"35","author":"H Zhou","year":"2021","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. Proceed AAAI Conf Artif Intell 35:11106\u201311115","journal-title":"Proceed AAAI Conf Artif Intell"},{"issue":"3","key":"1110_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3719207","volume":"16","author":"C Chang","year":"2025","unstructured":"Chang C, Wang W-Y, Peng W-C, Chen T-F (2025) Llm4ts: Aligning pre-trained llms as data-efficient time-series forecasters. ACM Trans Intell Syst Technol 16(3):1\u201320","journal-title":"ACM Trans Intell Syst Technol"},{"issue":"2","key":"1110_CR36","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.visinf.2024.04.004","volume":"8","author":"T Munz-K\u00f6rner","year":"2024","unstructured":"Munz-K\u00f6rner T, Weiskopf D (2024) Exploring visual quality of multidimensional time series projections. Visual Inf 8(2):27\u201342","journal-title":"Visual Inf"},{"issue":"2","key":"1110_CR37","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1109\/TVCG.2017.2653106","volume":"24","author":"Y Wang","year":"2017","unstructured":"Wang Y, Han F, Zhu L, Deussen O, Chen B (2017) Line graph or scatter plot? automatic selection of methods for visualizing trends in time series. IEEE Trans Visual Comput Graphics 24(2):1141\u20131154","journal-title":"IEEE Trans Visual Comput Graphics"},{"issue":"1","key":"1110_CR38","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1109\/TVCG.2023.3327162","volume":"30","author":"Z Deng","year":"2023","unstructured":"Deng Z, Chen S, Schreck T, Deng D, Tang T, Xu M, Weng D, Wu Y (2023) Visualizing large-scale spatial time series with geochron. IEEE Trans Visual Comput Graphics 30(1):1194\u20131204","journal-title":"IEEE Trans Visual Comput Graphics"},{"issue":"1","key":"1110_CR39","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1109\/TVCG.2015.2467751","volume":"22","author":"J Walker","year":"2015","unstructured":"Walker J, Borgo R, Jones MW (2015) Timenotes: a study on effective chart visualization and interaction techniques for time-series data. IEEE Trans Visual Comput Graphics 22(1):549\u2013558","journal-title":"IEEE Trans Visual Comput Graphics"},{"issue":"7","key":"1110_CR40","doi-asserted-by":"publisher","first-page":"2563","DOI":"10.1109\/TVCG.2020.3038446","volume":"28","author":"J Bok","year":"2020","unstructured":"Bok J, Kim B, Seo J (2020) Augmenting parallel coordinates plots with color-coded stacked histograms. IEEE Trans Visual Comput Graphics 28(7):2563\u20132576","journal-title":"IEEE Trans Visual Comput Graphics"},{"issue":"2","key":"1110_CR41","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1109\/TVCG.2020.3030398","volume":"27","author":"E Cakmak","year":"2020","unstructured":"Cakmak E, Schlegel U, J\u00e4ckle D, Keim D, Schreck T (2020) Multiscale snapshots: Visual analysis of temporal summaries in dynamic graphs. IEEE Trans Visual Comput Graphics 27(2):517\u2013527","journal-title":"IEEE Trans Visual Comput Graphics"},{"issue":"12","key":"1110_CR42","doi-asserted-by":"publisher","first-page":"2669","DOI":"10.1109\/TVCG.2012.253","volume":"18","author":"C Shi","year":"2012","unstructured":"Shi C, Cui W, Liu S, Xu P, Chen W, Qu H (2012) Rankexplorer: Visualization of ranking changes in large time series data. IEEE Trans Visual Comput Graphics 18(12):2669\u20132678","journal-title":"IEEE Trans Visual Comput Graphics"},{"issue":"1","key":"1110_CR43","first-page":"1161","volume":"30","author":"D Braun","year":"2023","unstructured":"Braun D, Borgo R, Sondag M, Landesberger T (2023) Reclaiming the horizon: Novel visualization designs for time-series data with large value ranges. IEEE Trans Visual Comput Graphics 30(1):1161\u20131171","journal-title":"IEEE Trans Visual Comput Graphics"},{"key":"1110_CR44","doi-asserted-by":"crossref","unstructured":"Lange D, Judson-Torres R, Zangle TA, Lex A (2024) Aardvark: Composite visualizations of trees, time-series, and images. IEEE Trans Visual Comput Graphics","DOI":"10.31219\/osf.io\/cdbm6"},{"key":"1110_CR45","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1007\/s11063-019-10185-8","volume":"51","author":"Y Liu","year":"2020","unstructured":"Liu Y, Mu Y, Chen K, Li Y, Guo J (2020) Daily activity feature selection in smart homes based on Pearson correlation coefficient. Neural Process Lett 51:1771\u20131787","journal-title":"Neural Process Lett"},{"key":"1110_CR46","doi-asserted-by":"crossref","unstructured":"Graves A, Graves A (2012) Long short-term memory. Supervised sequence labelling with recurrent neural networks","DOI":"10.1007\/978-3-642-24797-2"},{"key":"1110_CR47","unstructured":"Liu Z, Wang Y, Vaidya S, Ruehle F, Halverson J, Solja\u010di\u0107 M, Hou TY, Tegmark M (2024) Kan: Kolmogorov-arnold networks. arXiv preprint arXiv:2404.19756"},{"key":"1110_CR48","doi-asserted-by":"publisher","first-page":"111426","DOI":"10.1016\/j.asoc.2024.111426","volume":"155","author":"Q Li","year":"2024","unstructured":"Li Q, Ji Y, Zhu M, Zhu X, Sun L (2024) Unsupervised feature selection using chronological fitting with shapley additive explanation (shap) for industrial time-series anomaly detection. Appl Soft Comput 155:111426","journal-title":"Appl Soft Comput"}],"container-title":["Journal of Visualization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12650-026-01110-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12650-026-01110-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12650-026-01110-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T16:44:25Z","timestamp":1774889065000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12650-026-01110-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,19]]},"references-count":48,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["1110"],"URL":"https:\/\/doi.org\/10.1007\/s12650-026-01110-y","relation":{},"ISSN":["1343-8875","1875-8975"],"issn-type":[{"value":"1343-8875","type":"print"},{"value":"1875-8975","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,19]]},"assertion":[{"value":"20 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}