{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T23:55:04Z","timestamp":1781654104887,"version":"3.54.5"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":56,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-50189-8","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T15:48:05Z","timestamp":1776872885000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal deep learning for anomaly detection in urban infrastructure networks: improving the resilience of public management systems"],"prefix":"10.1038","volume":"16","author":[{"given":"Lin","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingbo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"issue":"2","key":"50189_CR1","first-page":"157","volume":"16","author":"Z Gao","year":"2024","unstructured":"Gao, Z. et al. Utilizing angle-based outlier detection method with sliding window mechanism to identify real-time crash risk. J. Transp. Saf. Secur. 16 (2), 157\u2013174 (2024).","journal-title":"J. Transp. Saf. Secur."},{"key":"50189_CR2","unstructured":"Yang Tao. Power equipment fault diagnosis based on improved double AlexNet neural network and random forest. (Donghua University, 2021)."},{"key":"50189_CR3","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Gao, Y. & Zhao, Z. Research on operation and anomaly detection of smart power grid based on information technology using CNN+ Bidirectional LSTM. Informatica, 49 (7). (2025).","DOI":"10.31449\/inf.v49i7.7037"},{"key":"50189_CR4","doi-asserted-by":"crossref","unstructured":"Jeon, Y. et al. Multimodal AI-based water pipeline data accumulation and leakage prediction research using image and ultrasound data[C]\/\/Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2024. SPIE, 12949, 81\u201385. (2024).","DOI":"10.1117\/12.3007717"},{"key":"50189_CR5","unstructured":"Ikeda, Y. et al. Anomaly detection and interpretation using multimodal autoencoder and sparse optimization. arXiv (2018)."},{"issue":"3","key":"50189_CR6","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1109\/JSTSP.2017.2679538","volume":"11","author":"Z Liu","year":"2017","unstructured":"Liu, Z. et al. Heterogeneous sensor data fusion by deep multimodal encoding. IEEE J. Selec. Topics Signal Process. 11 (3), 479\u2013491 (2017).","journal-title":"IEEE J. Selec. Topics Signal Process."},{"key":"50189_CR7","unstructured":"Cho, M. et al. Cocoon: Robust multi-modal perception with uncertainty-aware sensor fusion. arXiv (2024)."},{"key":"50189_CR8","doi-asserted-by":"crossref","unstructured":"Zhu, B. et al. Spatio-temporal feature attention fusion for fault detection in complex industrial processes. In 2024 IEEE International Symposium on Product Compliance Engineering-Asia (ISPCE-ASIA). 1\u20138. (IEEE, 2024).","DOI":"10.1109\/ISPCE-ASIA64773.2024.10756266"},{"key":"50189_CR9","doi-asserted-by":"publisher","first-page":"106400","DOI":"10.1016\/j.neunet.2024.106400","volume":"178","author":"J Liu","year":"2024","unstructured":"Liu, J., Duan, Z. & Liu, H. A grid fault diagnosis framework based on adaptive integrated decomposition and cross-modal attention fusion. Neural Netw. 178, 106400 (2024).","journal-title":"Neural Netw."},{"key":"50189_CR10","doi-asserted-by":"publisher","first-page":"45","DOI":"10.54254\/2755-2721\/111\/2024CH0105","volume":"111","author":"B Liang","year":"2024","unstructured":"Liang, B. Traffic flow prediction with strong dynamic spatiotemporal dependence using a hybrid model of transformer and TCN. Appl. Comput. Eng. 111, 45\u201353 (2024).","journal-title":"Appl. Comput. Eng."},{"key":"50189_CR11","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s42162-024-00465-6","volume":"8","author":"R Xu","year":"2025","unstructured":"Xu, R. & Zhang, J. Intelligent information systems for power grid fault analysis by computer communication technology. Energy Inf. 8, 10. https:\/\/doi.org\/10.1186\/s42162-024-00465-6 (2025).","journal-title":"Energy Inf."},{"key":"50189_CR12","unstructured":"Das, S. et al. MMPrune4U: Regularizing multimodal feature distortion in weight pruning for deep neural network compression. (2024)."},{"key":"50189_CR13","unstructured":"Guo, Y. et al. Construction of multi-modal data-driven security situational awareness framework. Library Information Work 68 (24): 4\u201314. (2024)."},{"key":"50189_CR14","doi-asserted-by":"crossref","unstructured":"Nagamani, G. M. & Kumar, C. K. Design of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning. Heliyon, 10(24). (2024).","DOI":"10.1016\/j.heliyon.2024.e41071"},{"key":"50189_CR15","doi-asserted-by":"crossref","unstructured":"Kayode, B. F. et al. Temporal-spatial attention network (TSAN) for DoS attack detection in network traffic. arXiv (2025).","DOI":"10.1109\/ICMLT65785.2025.11193363"},{"key":"50189_CR16","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1109\/TMM.2023.3267882","volume":"26","author":"Z Yuan","year":"2023","unstructured":"Yuan, Z. et al. Noise imitation based adversarial training for robust multimodal sentiment analysis. IEEE Trans. Multimedia. 26, 529\u2013539 (2023).","journal-title":"IEEE Trans. Multimedia"},{"key":"50189_CR17","doi-asserted-by":"publisher","unstructured":"Zhiyuan Wang, L. et al. Iterative knowledge distillation and pruning for model compression in unsupervised domain adaptation. Pattern Recogn. 164, 111512 https:\/\/doi.org\/10.1016\/j.patcog.2025.111512 (2025).","DOI":"10.1016\/j.patcog.2025.111512"},{"issue":"1","key":"50189_CR18","doi-asserted-by":"publisher","first-page":"2692","DOI":"10.1038\/s41598-025-85822-5","volume":"15","author":"SB Veesam","year":"2025","unstructured":"Veesam, S. B. et al. Design of an integrated model with temporal graph attention and transformer-augmented RNNs for enhanced anomaly detection. Sci. Rep. 15 (1), 2692 (2025).","journal-title":"Sci. Rep."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-50189-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-50189-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-50189-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T23:22:18Z","timestamp":1781652138000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-50189-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,22]]},"references-count":18,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["50189"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-50189-8","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,22]]},"assertion":[{"value":"8 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2026","order":3,"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"}}],"article-number":"18775"}}