{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:55:18Z","timestamp":1789106118065,"version":"build-2803163510"},"reference-count":47,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2018ZX10201002"],"award-info":[{"award-number":["2018ZX10201002"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2017YFA0505500"],"award-info":[{"award-number":["2017YFA0505500"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2021YFF1201200"],"award-info":[{"award-number":["2021YFF1201200"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["31930022"],"award-info":[{"award-number":["31930022"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["12131020"],"award-info":[{"award-number":["12131020"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["12026608"],"award-info":[{"award-number":["12026608"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["XDB38040400"],"award-info":[{"award-number":["XDB38040400"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2022YFS0048"],"award-info":[{"award-number":["2022YFS0048"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of China","award":["2018ZX10201002"],"award-info":[{"award-number":["2018ZX10201002"]}]},{"name":"Natural Science Foundation of China","award":["2017YFA0505500"],"award-info":[{"award-number":["2017YFA0505500"]}]},{"name":"Natural Science Foundation of China","award":["2021YFF1201200"],"award-info":[{"award-number":["2021YFF1201200"]}]},{"name":"Natural Science Foundation of China","award":["31930022"],"award-info":[{"award-number":["31930022"]}]},{"name":"Natural Science Foundation of China","award":["12131020"],"award-info":[{"award-number":["12131020"]}]},{"name":"Natural Science Foundation of China","award":["12026608"],"award-info":[{"award-number":["12026608"]}]},{"name":"Natural Science Foundation of China","award":["XDB38040400"],"award-info":[{"award-number":["XDB38040400"]}]},{"name":"Natural Science Foundation of China","award":["2022YFS0048"],"award-info":[{"award-number":["2022YFS0048"]}]},{"name":"Strategic Priority Project of Chinese Academy of Sciences","award":["2018ZX10201002"],"award-info":[{"award-number":["2018ZX10201002"]}]},{"name":"Strategic Priority Project of Chinese Academy of Sciences","award":["2017YFA0505500"],"award-info":[{"award-number":["2017YFA0505500"]}]},{"name":"Strategic Priority Project of Chinese Academy of Sciences","award":["2021YFF1201200"],"award-info":[{"award-number":["2021YFF1201200"]}]},{"name":"Strategic Priority Project of Chinese Academy of Sciences","award":["31930022"],"award-info":[{"award-number":["31930022"]}]},{"name":"Strategic Priority Project of Chinese Academy of Sciences","award":["12131020"],"award-info":[{"award-number":["12131020"]}]},{"name":"Strategic Priority Project of Chinese Academy of Sciences","award":["12026608"],"award-info":[{"award-number":["12026608"]}]},{"name":"Strategic Priority Project of Chinese Academy of Sciences","award":["XDB38040400"],"award-info":[{"award-number":["XDB38040400"]}]},{"name":"Strategic Priority Project of Chinese Academy of Sciences","award":["2022YFS0048"],"award-info":[{"award-number":["2022YFS0048"]}]},{"name":"Sichuan Science and Technology Program","award":["2018ZX10201002"],"award-info":[{"award-number":["2018ZX10201002"]}]},{"name":"Sichuan Science and Technology Program","award":["2017YFA0505500"],"award-info":[{"award-number":["2017YFA0505500"]}]},{"name":"Sichuan Science and Technology Program","award":["2021YFF1201200"],"award-info":[{"award-number":["2021YFF1201200"]}]},{"name":"Sichuan Science and Technology Program","award":["31930022"],"award-info":[{"award-number":["31930022"]}]},{"name":"Sichuan Science and Technology Program","award":["12131020"],"award-info":[{"award-number":["12131020"]}]},{"name":"Sichuan Science and Technology Program","award":["12026608"],"award-info":[{"award-number":["12026608"]}]},{"name":"Sichuan Science and Technology Program","award":["XDB38040400"],"award-info":[{"award-number":["XDB38040400"]}]},{"name":"Sichuan Science and Technology Program","award":["2022YFS0048"],"award-info":[{"award-number":["2022YFS0048"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Predicting high-dimensional short-term time-series is a difficult task due to the lack of sufficient information and the curse of dimensionality. To overcome these problems, this study proposes a novel spatiotemporal transformer neural network (STNN) for efficient prediction of short-term time-series with three major features. Firstly, the STNN can accurately and robustly predict a high-dimensional short-term time-series in a multi-step-ahead manner by exploiting high-dimensional\/spatial information based on the spatiotemporal information (STI) transformation equation. Secondly, the continuous attention mechanism makes the prediction results more accurate than those of previous studies. Thirdly, we developed continuous spatial self-attention, temporal self-attention, and transformation attention mechanisms to create a bridge between effective spatial information and future temporal evolution information. Fourthly, we show that the STNN model can reconstruct the phase space of the dynamical system, which is explored in the time-series prediction. The experimental results demonstrate that the STNN significantly outperforms the existing methods on various benchmarks and real-world systems in the multi-step-ahead prediction of a short-term time-series.<\/jats:p>","DOI":"10.3390\/e24111651","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T02:28:58Z","timestamp":1668479338000},"page":"1651","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Spatiotemporal Transformer Neural Network for Time-Series Forecasting"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0670-6755","authenticated-orcid":false,"given":"Yujie","family":"You","sequence":"first","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3708-1727","authenticated-orcid":false,"given":"Le","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu 610065, China"},{"name":"Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China"},{"name":"Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Tao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suran","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu 610065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3960-0068","authenticated-orcid":false,"given":"Luonan","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China"},{"name":"State Key Laboratory of Cell Biology, Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China"},{"name":"Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai 519031, China"},{"name":"West China Biomedical Big Data Center, Med-X Center for Informatics, West China Hospital, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1093\/bioinformatics\/btz542","article-title":"Revealing dynamic regulations and the related key proteins of myeloma-initiating cells by integrating experimental data into a systems biological model","volume":"37","author":"Zhang","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1109\/TCBB.2021.3049617","article-title":"2019nCoVAS: Developing the Web Service for Epidemic Transmission Prediction, Genome Analysis, and Psychological Stress Assessment for 2019-nCoV","volume":"18","author":"Xiao","year":"2021","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhu, Y., and Shasha, D.E. (2002). StatStream: Statistical monitoring of thousands of data streams in real time. VLDB\u201902: Proceedings of the 28th International Conference on Very Large Databases, Morgan Kaufmann.","DOI":"10.1016\/B978-155860869-6\/50039-1"},{"key":"ref_4","first-page":"15008","article-title":"Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network","volume":"35","author":"Zhang","year":"2021","journal-title":"Proc. Conf. AAAI Artif. Intell."},{"key":"ref_5","first-page":"5","article-title":"NASA\u2019s Modern Era Retrospective-analysis for Research and Applications (MERRA)","volume":"4","author":"Bosilovich","year":"2006","journal-title":"U.S. CLIVAR Var."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lai, G., Chang, W.-C., Yang, Y., and Liu, H. (2018, January 8\u201312). Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks. Proceedings of the SIGIR \u201818: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor, MI, USA.","DOI":"10.1145\/3209978.3210006"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"E9994","DOI":"10.1073\/pnas.1802987115","article-title":"PNAS Plus: Randomly distributed embedding making short-term high-dimensional data predictable","volume":"115","author":"Ma","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/0169-2070(90)90008-Y","article-title":"Bootstrap prediction intervals for autoregressions","volume":"6","author":"Masarotto","year":"1990","journal-title":"Int. J. Forecast."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1080\/01621459.1970.10481180","article-title":"Distribution of residual autocorrelations in autoregressive-integrated moving average time series models","volume":"65","author":"Box","year":"1970","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ma, X., Zhang, Y., and Wang, Y. (2015, January 2\u20135). Performance evaluation of kernel functions based on grid search for support vector regression. Proceedings of the 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Beijing, China.","DOI":"10.1109\/ICCIS.2015.7274635"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1109\/JSEN.2014.2356501","article-title":"Sensor Data Fusion by Support Vector Regression Methodology\u2014A Comparative Study","volume":"15","author":"Shamshirband","year":"2014","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1186\/s12859-020-3454-5","article-title":"Exploring the dynamics and interplay of human papillomavirus and cervical tumorigenesis by integrating biological data into a mathematical model","volume":"21","author":"Wu","year":"2020","journal-title":"BMC Bioinform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.neucom.2020.10.118","article-title":"Denoising of MR and CT images using cascaded multi-supervision convolutional neural networks with progressive training","volume":"469","author":"Song","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1007\/s11390-021-0861-7","article-title":"Robust Needle Localization and Enhancement Algorithm for Ultrasound by Deep Learning and Beam Steering Methods","volume":"36","author":"Gao","year":"2021","journal-title":"J. Comput. Sci. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.eng.2019.06.008","article-title":"A Brief Review of Artificial Intelligence Applications and Algorithms for Psychiatric Disorders","volume":"6","author":"Liu","year":"2019","journal-title":"Engineering"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"033056","DOI":"10.1103\/PhysRevResearch.1.033056","article-title":"Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius","volume":"1","author":"Jiang","year":"2019","journal-title":"Phys. Rev. Res."},{"key":"ref_17","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."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103143","DOI":"10.1063\/1.5118725","article-title":"Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing","volume":"29","author":"Haluszczynski","year":"2019","journal-title":"Chaos"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1093\/nsr\/nwaa025","article-title":"Predicting future dynamics from short-term time series using an Anticipated Learning Machine","volume":"7","author":"Chen","year":"2020","journal-title":"Natl. Sci. Rev."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1007\/BF01053745","article-title":"Embedology","volume":"65","author":"Sauer","year":"1991","journal-title":"J. Stat. Phys."},{"key":"ref_21","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. arXiv."},{"key":"ref_22","unstructured":"Azencot, O., Erichson, N.B., Lin, V., and Mahoney, M.W. (2020). Forecasting Sequential Data Using Consistent Koopman Autoencoders. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4950","DOI":"10.1038\/s41467-018-07210-0","article-title":"Deep learning for universal linear embeddings of nonlinear dynamics","volume":"9","author":"Lusch","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_24","unstructured":"Wu, N., Green, B., Ben, X., and O\u2019Banion, S. (2020). Deep transformer models for time series forecasting: The influenza prevalence case. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3932","DOI":"10.1073\/pnas.1517384113","article-title":"Discovering governing equations from data by sparse identification of nonlinear dynamical systems","volume":"113","author":"Brunton","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1103\/PhysRevLett.45.712","article-title":"Geometry from a Time Series","volume":"45","author":"Packard","year":"1980","journal-title":"Phys. Rev. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4568","DOI":"10.1038\/s41467-020-18381-0","article-title":"Autoreservoir computing for multistep ahead prediction based on the spatiotemporal information transformation","volume":"11","author":"Chen","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1007\/s10994-019-05815-0","article-title":"Temporal pattern attention for multivariate time series forecasting","volume":"108","author":"Shih","year":"2019","journal-title":"Mach. Learn."},{"key":"ref_29","first-page":"3104","article-title":"Sequence to Sequence Learning with Neural Networks","volume":"27","author":"Sutskever","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_30","unstructured":"Smale, S. (1974). Differential Equations, Dynamical Systems, and Linear Algebra, Academic Press."},{"key":"ref_31","unstructured":"Greydanus, S., Dzamba, M., and Yosinski, J. (2019). Hamiltonian Neural Networks. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"121107","DOI":"10.1063\/1.5128231","article-title":"On learning Hamiltonian systems from data","volume":"29","author":"Bertalan","year":"2019","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01612888","article-title":"A generalized Lorenz system","volume":"60","author":"Curry","year":"1978","journal-title":"Commun. Math. Phys."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Takeishi, N., Kawahara, Y., and Yairi, T. (2017, January 4\u20139). Learning Koopman invariant subspaces for dynamic mode decomposition. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA.","DOI":"10.1109\/ICIP.2017.8296769"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/TCST.2018.2844362","article-title":"Robust Calibration of High Dimension Nonlinear Dynamical Models for Omics Data: An Application in Cancer Systems Biology","volume":"28","author":"Bianconi","year":"2018","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1089\/omi.2009.0040","article-title":"A Network Biology Study on Circadian Rhythm by Integrating Various Omics Data","volume":"13","author":"Wang","year":"2009","journal-title":"OMICS A J. Integr. Biol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1140\/epjst\/e2015-50090-2","article-title":"Predicting ramps by integrating different sorts of information","volume":"225","author":"Hirata","year":"2016","journal-title":"Eur. Phys. J. Sp\u00e9c. Top."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.energy.2018.01.177","article-title":"Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM","volume":"148","author":"Qing","year":"2018","journal-title":"Energy"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., and Zhu, Z. (2017). Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. arXiv.","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref_40","first-page":"59","article-title":"Thirteen ways to look at the correlation coefficient","volume":"42","author":"Nicewander","year":"1988","journal-title":"Am. Stat."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1002\/ijc.33860","article-title":"A disease network-based deep learning approach for characterizing melanoma","volume":"150","author":"Lai","year":"2021","journal-title":"Int. J. Cancer"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1093\/bib\/bbz134","article-title":"CpG-island-based annotation and analysis of human housekeeping genes","volume":"22","author":"Zhang","year":"2021","journal-title":"Brief. Bioinform."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, L., Bai, W., Yuan, N., and Du, Z. (2019). Comprehensively benchmarking applications for detecting copy number variation. PLoS Comput. Biol., 15.","DOI":"10.1371\/journal.pcbi.1007367"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance","volume":"30","author":"Willmott","year":"2005","journal-title":"Clim. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3624","DOI":"10.1093\/bioinformatics\/bty392","article-title":"Lineage-associated underrepresented permutations (LAUPs) of mammalian genomic sequences based on a Jellyfish-based LAUPs analysis application (JBLA)","volume":"34","author":"Zhang","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3092","DOI":"10.1016\/j.apsb.2021.05.032","article-title":"MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction","volume":"11","author":"Zhang","year":"2021","journal-title":"Acta Pharm. Sin. B"},{"key":"ref_47","unstructured":"Glantz, S. (1990). Primer of Applied Regression & Analysis of Variance, McGraw-Hill, Inc."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/11\/1651\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:17:50Z","timestamp":1760145470000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/11\/1651"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,14]]},"references-count":47,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["e24111651"],"URL":"https:\/\/doi.org\/10.3390\/e24111651","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,14]]}}}