{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T11:25:13Z","timestamp":1787225113397,"version":"build-2736575974"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T00:00:00Z","timestamp":1736467200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T00:00:00Z","timestamp":1736467200000},"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 Intell Inf Syst"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s10844-025-00918-8","type":"journal-article","created":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T05:55:09Z","timestamp":1736488509000},"page":"783-805","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["EST transformer: enhanced spatiotemporal representation learning for time series anomaly detection"],"prefix":"10.1007","volume":"63","author":[{"given":"Yao","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianye","family":"Ben","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,10]]},"reference":[{"key":"918_CR1","doi-asserted-by":"publisher","unstructured":"Abdulaal, A., Liu, Z., & Lancewicki, T. (2021). Practical approach to asynchronous multivariate time series anomaly detection and localization. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp 2485\u20132494. https:\/\/doi.org\/10.1145\/3447548.3467174","DOI":"10.1145\/3447548.3467174"},{"issue":"4","key":"918_CR2","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1007\/s10844-023-00829-6","volume":"62","author":"F Angiulli","year":"2024","unstructured":"Angiulli, F., Fassetti, F., & Ferragina, L. (2024). Enhancing anomaly detectors with latentout. Journal of Intelligent Information Systems, 62(4), 905\u2013923. https:\/\/doi.org\/10.1007\/s10844-023-00829-6","journal-title":"Journal of Intelligent Information Systems"},{"key":"918_CR3","doi-asserted-by":"publisher","unstructured":"Audibert, J., Michiardi, P., Guyard, F., et\u00a0al. (2020). Usad: Unsupervised anomaly detection on multivariate time series. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 3395\u20133404. https:\/\/doi.org\/10.1145\/3394486.3403392","DOI":"10.1145\/3394486.3403392"},{"key":"918_CR4","doi-asserted-by":"publisher","unstructured":"Braei, M., & Wagner, S. (2020). Anomaly detection in univariate time-series: A survey on the state-of-the-art. arXiv:2004.00433, https:\/\/doi.org\/10.48550\/arXiv.2004.00433","DOI":"10.48550\/arXiv.2004.00433"},{"key":"918_CR5","doi-asserted-by":"publisher","unstructured":"Breunig, M. M., Kriegel, H. P., Ng, R. T., et\u00a0al. (2000). Lof: identifying density-based local outliers. In: Proceedings of the the ACM SIGMOD International Conference on Management of Data, pp 93\u2013104. https:\/\/doi.org\/10.1145\/342009.335388","DOI":"10.1145\/342009.335388"},{"issue":"2","key":"918_CR6","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s10844-022-00713-9","volume":"59","author":"V Cerqueira","year":"2022","unstructured":"Cerqueira, V., Torgo, L., & Soares, C. (2022). A case study comparing machine learning with statistical methods for time series forecasting: size matters. Journal of Intelligent Information Systems, 59(2), 415\u2013433. https:\/\/doi.org\/10.1007\/s10844-022-00713-9","journal-title":"Journal of Intelligent Information Systems"},{"key":"918_CR7","doi-asserted-by":"publisher","unstructured":"Chen, Z., Chen, D., Zhang, X., et\u00a0al. (2021). Learning graph structures with transformer for multivariate time-series anomaly detection in iot. IEEE Internet of Things Journal, 9(12), 9179\u20139189. https:\/\/doi.org\/10.48550\/arXiv.2104.03466","DOI":"10.48550\/arXiv.2104.03466"},{"issue":"4","key":"918_CR8","doi-asserted-by":"publisher","first-page":"2233","DOI":"10.1109\/TII.2014.2300753","volume":"10","author":"L Da Xu","year":"2014","unstructured":"Da Xu, L., He, W., & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions on Industrial Informatics, 10(4), 2233\u20132243. https:\/\/doi.org\/10.1109\/TII.2014.2300753","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"918_CR9","doi-asserted-by":"publisher","unstructured":"Deng, A., & Hooi, B. (2021). Graph neural network-based anomaly detection in multivariate time series. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4027\u20134035. https:\/\/doi.org\/10.48550\/arXiv.2106.06947","DOI":"10.48550\/arXiv.2106.06947"},{"key":"918_CR10","doi-asserted-by":"publisher","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., et\u00a0al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929, https:\/\/doi.org\/10.48550\/arXiv.2010.11929","DOI":"10.48550\/arXiv.2010.11929"},{"issue":"8","key":"918_CR11","doi-asserted-by":"publisher","first-page":"3127","DOI":"10.1109\/TNNLS.2019.2935975","volume":"31","author":"T Ergen","year":"2019","unstructured":"Ergen, T., & Kozat, S. S. (2019). Unsupervised anomaly detection with lstm neural networks. IEEE Transactions on Neural Networks and Learning Systems, 31(8), 3127\u20133141. https:\/\/doi.org\/10.1109\/TNNLS.2019.2935975","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"918_CR12","doi-asserted-by":"publisher","unstructured":"Geiger, A., Liu, D., Alnegheimish, S., et\u00a0al. (2020). Tadgan: Time series anomaly detection using generative adversarial networks. In: Proceedings of the IEEE International Conference on Big Data, pp 33\u201343, https:\/\/doi.org\/10.48550\/arXiv.2009.07769","DOI":"10.48550\/arXiv.2009.07769"},{"key":"918_CR13","doi-asserted-by":"publisher","unstructured":"Ghorbani R, Reinders, M. J., Tax, D. M. (2024). Restad: Reconstruction and similarity based transformer for time series anomaly detection. arXiv:2405.07509https:\/\/doi.org\/10.1109\/MLSP58920.2024.10734755","DOI":"10.1109\/MLSP58920.2024.10734755"},{"key":"918_CR14","doi-asserted-by":"publisher","unstructured":"Gulati, A., Qin, J., Chiu, C. C., et\u00a0al. (2020). Conformer: Convolution-augmented transformer for speech recognition. arXiv:2005.08100https:\/\/doi.org\/10.48550\/arXiv.2005.08100","DOI":"10.48550\/arXiv.2005.08100"},{"issue":"4","key":"918_CR15","doi-asserted-by":"publisher","first-page":"1705","DOI":"10.1109\/TNNLS.2020.3027736","volume":"34","author":"Z He","year":"2020","unstructured":"He, Z., Chen, P., Li, X., et al. (2020). A spatiotemporal deep learning approach for unsupervised anomaly detection in cloud systems. IEEE Transactions on Neural Networks and Learning Systems, 34(4), 1705\u20131719. https:\/\/doi.org\/10.1109\/TNNLS.2020.3027736","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"918_CR16","doi-asserted-by":"publisher","unstructured":"Huang, T., Chen, P., & Li, R. (2022). A semi-supervised vae based active anomaly detection framework in multivariate time series for online systems. In: Proceedings of the ACM Web Conference, pp 1797\u20131806, https:\/\/doi.org\/10.1145\/3485447.3511984","DOI":"10.1145\/3485447.3511984"},{"key":"918_CR17","doi-asserted-by":"publisher","unstructured":"Hundman, K., Constantinou, V., Laporte, C., et\u00a0al. (2018). Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 387\u2013395. https:\/\/doi.org\/10.1145\/3219819.3219845","DOI":"10.1145\/3219819.3219845"},{"key":"918_CR18","doi-asserted-by":"publisher","unstructured":"Hundman, K., Constantinou, V., Laporte, C., et\u00a0al. (2018). Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 387\u2013395. https:\/\/doi.org\/10.1145\/3219819.3219845","DOI":"10.1145\/3219819.3219845"},{"key":"918_CR19","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.neunet.2022.05.024","volume":"153","author":"B Jiang","year":"2022","unstructured":"Jiang, B., Chen, S., Wang, B., et al. (2022). Mglnn: Semi-supervised learning via multiple graph cooperative learning neural networks. Neural Networks, 153, 204\u2013214. https:\/\/doi.org\/10.1016\/j.neunet.2022.05.024","journal-title":"Neural Networks"},{"key":"918_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105964","volume":"120","author":"J Kim","year":"2023","unstructured":"Kim, J., Kang, H., & Kang, P. (2023). Time-series anomaly detection with stacked transformer representations and 1d convolutional network. Engineering Applications of Artificial Intelligence, 120, 105964. https:\/\/doi.org\/10.1016\/j.engappai.2023.105964","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"918_CR21","doi-asserted-by":"publisher","unstructured":"Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv:1412.6980, https:\/\/doi.org\/10.48550\/arXiv.1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"key":"918_CR22","doi-asserted-by":"publisher","unstructured":"Li, L., Yan, J., Wang, H., et\u00a0al. (2020). Anomaly detection of time series with smoothness-inducing sequential variational auto-encoder. IEEE Transactions on Neural Networks and Learning Systems, 32(3), 1177\u20131191. https:\/\/doi.org\/10.48550\/arXiv.2102.01331","DOI":"10.48550\/arXiv.2102.01331"},{"key":"918_CR23","doi-asserted-by":"publisher","unstructured":"Li, Z., Zhao, Y., Han, J., et\u00a0al. (2021). Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp 3220\u20133230. https:\/\/doi.org\/10.1145\/3447548.3467075","DOI":"10.1145\/3447548.3467075"},{"key":"918_CR24","doi-asserted-by":"publisher","unstructured":"Liu, F. T., Ting, K. M., Zhou, Z. H. (2008). Isolation forest. In: Proceedings of the 8th IEEE International Conference on Data Mining, pp 413\u2013422, https:\/\/doi.org\/10.1109\/ICDM.2008.17","DOI":"10.1109\/ICDM.2008.17"},{"key":"918_CR25","doi-asserted-by":"publisher","unstructured":"Liu, Z., Lin, Y., Cao, Y., et\u00a0al. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 10012\u201310022, https:\/\/doi.org\/10.48550\/arXiv.2103.14030","DOI":"10.48550\/arXiv.2103.14030"},{"key":"918_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107443","volume":"108","author":"S Maleki","year":"2021","unstructured":"Maleki, S., Maleki, S., & Jennings, N. R. (2021). Unsupervised anomaly detection with lstm autoencoders using statistical data-filtering. Applied Soft Computing, 108, 107443. https:\/\/doi.org\/10.1016\/j.asoc.2021.107443","journal-title":"Applied Soft Computing"},{"key":"918_CR27","doi-asserted-by":"publisher","unstructured":"Malhotra, P., Vig, L., Shroff, G., et\u00a0al. (2015). Long short term memory networks for anomaly detection in time series. In: Proceedings of the 23rd European Symposium on Artificial Neural Networks, pp.\u00a089. https:\/\/doi.org\/10.1109\/TNNLS.2020.3027736","DOI":"10.1109\/TNNLS.2020.3027736"},{"issue":"2","key":"918_CR28","doi-asserted-by":"publisher","first-page":"2774","DOI":"10.3390\/s150202774","volume":"15","author":"L Mart\u00ed","year":"2015","unstructured":"Mart\u00ed, L., Sanchez-Pi, N., Molina, J. M., et al. (2015). Anomaly detection based on sensor data in petroleum industry applications. Sensors, 15(2), 2774\u20132797. https:\/\/doi.org\/10.3390\/s150202774","journal-title":"Sensors"},{"key":"918_CR29","doi-asserted-by":"publisher","unstructured":"Najari, N., Berlemont, S., Lefebvre, G., et\u00a0al. (2022). Resist: Robust transformer for unsupervised time series anomaly detection. In: International Workshop on Advanced Analytics and Learning on Temporal Data, Springer, pp 66\u201382, https:\/\/doi.org\/10.1007\/978-3-031-24378-3_5","DOI":"10.1007\/978-3-031-24378-3_5"},{"issue":"3","key":"918_CR30","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1198\/TECH.2007.S518","volume":"49","author":"RM Neal","year":"2007","unstructured":"Neal, R. M. (2007). Pattern recognition and machine learning. Technometrics, 49(3), 366\u2013366. https:\/\/doi.org\/10.1198\/TECH.2007.S518","journal-title":"Technometrics"},{"issue":"3","key":"918_CR31","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.1109\/LRA.2018.2801475","volume":"3","author":"D Park","year":"2018","unstructured":"Park, D., Hoshi, Y., & Kemp, C. C. (2018). A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder. IEEE Robotics and Automation Letters, 3(3), 1544\u20131551. https:\/\/doi.org\/10.1109\/LRA.2018.2801475","journal-title":"IEEE Robotics and Automation Letters"},{"key":"918_CR32","doi-asserted-by":"publisher","unstructured":"Paszke, A., Gross, S., Massa, F., et\u00a0al. (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 32, 8026. https:\/\/doi.org\/10.48550\/arXiv.1912.01703","DOI":"10.48550\/arXiv.1912.01703"},{"issue":"1","key":"918_CR33","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A. C., et al. (2008). The graph neural network model. IEEE Transactions on Neural Networks, 20(1), 61\u201380. https:\/\/doi.org\/10.1109\/TNN.2008.2005605","journal-title":"IEEE Transactions on Neural Networks"},{"key":"918_CR34","doi-asserted-by":"publisher","unstructured":"Sch\u00f6lkopf, B., Williamson, R. C., Smola, A., et\u00a0al. (1999). Support vector method for novelty detection. In: Advances in Neural Information Processing Systems, vol\u00a012. MIT Press. https:\/\/doi.org\/10.5555\/3009657.3009740","DOI":"10.5555\/3009657.3009740"},{"key":"918_CR35","first-page":"13016","volume":"33","author":"L Shen","year":"2020","unstructured":"Shen, L., Li, Z., & Kwok, J. (2020). Timeseries anomaly detection using temporal hierarchical one-class network. Advances in Neural Information Processing Systems, 33, 13016\u201313026.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"918_CR36","doi-asserted-by":"publisher","unstructured":"Shin, Y., Lee, S., Tariq, S., et\u00a0al. (2020). Itad: integrative tensor-based anomaly detection system for reducing false positives of satellite systems. In: Proceedings of the 29th ACM International Conference on Information & Knowledge, pp. 2733\u20132740. https:\/\/doi.org\/10.1145\/3340531.3412716","DOI":"10.1145\/3340531.3412716"},{"key":"918_CR37","doi-asserted-by":"publisher","unstructured":"Su, Y., Zhao, Y., Niu, C., et\u00a0al. (2019). Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2828\u20132837. https:\/\/doi.org\/10.1145\/3292500.3330672","DOI":"10.1145\/3292500.3330672"},{"key":"918_CR38","doi-asserted-by":"publisher","unstructured":"Tariq, S., Lee, S., Shin, Y., et\u00a0al. (2019). Detecting anomalies in space using multivariate convolutional lstm with mixtures of probabilistic pca. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2123\u20132133. https:\/\/doi.org\/10.1145\/3292500.3330776","DOI":"10.1145\/3292500.3330776"},{"key":"918_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107751","volume":"112","author":"M Thill","year":"2021","unstructured":"Thill, M., Konen, W., Wang, H., et al. (2021). Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing, 112, 107751. https:\/\/doi.org\/10.1016\/j.asoc.2021.107751","journal-title":"Applied Soft Computing"},{"issue":"1","key":"918_CR40","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/s10844-020-00609-6","volume":"56","author":"HTT Thuy","year":"2021","unstructured":"Thuy, H. T. T., Anh, D. T., & Chau, V. T. N. (2021). Efficient segmentation-based methods for anomaly detection in static and streaming time series under dynamic time warping. Journal of Intelligent Information Systems, 56(1), 121\u2013146. https:\/\/doi.org\/10.1007\/s10844-020-00609-6","journal-title":"Journal of Intelligent Information Systems"},{"key":"918_CR41","doi-asserted-by":"publisher","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et\u00a0al. (2017). Attention is all you need. Advances in Neural Information Processing Systems 30. https:\/\/doi.org\/10.31219\/osf.io\/5p2hu","DOI":"10.31219\/osf.io\/5p2hu"},{"key":"918_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2022.110791","volume":"191","author":"X Wang","year":"2022","unstructured":"Wang, X., Pi, D., Zhang, X., et al. (2022). Variational transformer-based anomaly detection approach for multivariate time series. Measurement, 191, 110791. https:\/\/doi.org\/10.1016\/j.measurement.2022.110791","journal-title":"Measurement"},{"issue":"12","key":"918_CR43","doi-asserted-by":"publisher","first-page":"9214","DOI":"10.1109\/JIOT.2021.3094295","volume":"9","author":"Y Wu","year":"2021","unstructured":"Wu, Y., Dai, H. N., & Tang, H. (2021). Graph neural networks for anomaly detection in industrial internet of things. IEEE Internet of Things Journal, 9(12), 9214\u20139231. https:\/\/doi.org\/10.1109\/JIOT.2021.3094295","journal-title":"IEEE Internet of Things Journal"},{"key":"918_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111928","volume":"296","author":"W Xiong","year":"2024","unstructured":"Xiong, W., Wang, P., Sun, X., et al. (2024). Siet: Spatial information enhanced transformer for multivariate time series anomaly detection. Knowledge-Based Systems, 296, 111928. https:\/\/doi.org\/10.1016\/j.knosys.2024.111928","journal-title":"Knowledge-Based Systems"},{"key":"918_CR45","doi-asserted-by":"publisher","unstructured":"Xu, J., Wu, H., Wang, J., et\u00a0al. (2021). Anomaly transformer: Time series anomaly detection with association discrepancy. arXiv:2110.02642, https:\/\/doi.org\/10.48550\/arXiv.2110.02642","DOI":"10.48550\/arXiv.2110.02642"},{"key":"918_CR46","doi-asserted-by":"publisher","unstructured":"Zhang, H., Xia, Y., Yan, T., et\u00a0al. (2021). Unsupervised anomaly detection in multivariate time series through transformer-based variational autoencoder. In: 2021 33rd Chinese Control and Decision Conference (CCDC), IEEE, pp. 281\u2013286. https:\/\/doi.org\/10.1109\/CCDC52312.2021.9601669","DOI":"10.1109\/CCDC52312.2021.9601669"},{"key":"918_CR47","doi-asserted-by":"publisher","unstructured":"Zhang, M., Li, T., Shi, H., et\u00a0al. (2019). A decomposition approach for urban anomaly detection across spatiotemporal data. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 6043\u20136049. https:\/\/doi.org\/10.24963\/ijcai.2019\/837","DOI":"10.24963\/ijcai.2019\/837"},{"key":"918_CR48","doi-asserted-by":"publisher","unstructured":"Zhou, B., Liu, S., Hooi, B., et\u00a0al. (2019). Beatgan: Anomalous rhythm detection using adversarially generated time series. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 4433\u20134439, https:\/\/doi.org\/10.24963\/ijcai.2019\/616","DOI":"10.24963\/ijcai.2019\/616"},{"key":"918_CR49","doi-asserted-by":"publisher","unstructured":"Zong, B., Song, Q., Min, M. R., et\u00a0al. (2018). Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: Proceedings of the International Conference on Learning Representations. https:\/\/doi.org\/10.1109\/TAES.2017.2671247","DOI":"10.1109\/TAES.2017.2671247"}],"container-title":["Journal of Intelligent Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-025-00918-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10844-025-00918-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-025-00918-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T04:28:33Z","timestamp":1748060913000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10844-025-00918-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,10]]},"references-count":49,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["918"],"URL":"https:\/\/doi.org\/10.1007\/s10844-025-00918-8","relation":{},"ISSN":["0925-9902","1573-7675"],"issn-type":[{"value":"0925-9902","type":"print"},{"value":"1573-7675","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,10]]},"assertion":[{"value":"3 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}