{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T17:09:21Z","timestamp":1793898561461,"version":"4.8.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"content-version":"vor","delay-in-days":31,"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\/s41598-026-41255-2","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T03:06:07Z","timestamp":1771902367000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Efficient industrial point cloud anomaly detection via spatial context aggregation and selective anomalous feature generation"],"prefix":"10.1038","volume":"16","author":[{"given":"Dinh-Cuong","family":"Hoang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phan Xuan","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anh-Nhat","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minhhuy","family":"Le","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ta Huu Anh","family":"Duong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tuan-Minh","family":"Huynh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duc-Manh","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minh-Duc","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duc-Huy","family":"Ngo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minh-Quang","family":"Vu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thu-Uyen","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khanh-Toan","family":"Phan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minh-Quang","family":"Do","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan-Tung","family":"Dinh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Van-Hiep","family":"Duong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Van-Thiep","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"key":"41255_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2025.103138","volume":"65","author":"C Pu","year":"2025","unstructured":"Pu, C. et al. Geometric spatial constraints network for slender and tiny surface defect detection. Adv. Eng. Inform. 65, 103138 (2025).","journal-title":"Adv. Eng. Inform."},{"key":"41255_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2025.117168","volume":"251","author":"X Ma","year":"2025","unstructured":"Ma, X. et al. Stamping part surface crack detection based on machine vision. Measurement 251, 117168 (2025).","journal-title":"Measurement"},{"key":"41255_CR3","first-page":"41","volume":"12","author":"D-C Hoang","year":"2025","unstructured":"Hoang, D.-C. et al. Unsupervised industrial anomaly detection using paired well-lit and low-light images. J. Comput. Des. Eng. 12, 41\u201361 (2025).","journal-title":"J. Comput. Des. Eng."},{"key":"41255_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2025.103936","volume":"69","author":"K Zhang","year":"2026","unstructured":"Zhang, K. et al. Automatic measurement system for aircraft rivet flushness on surfaces empowered by multi-modal large-scale models. Adv. Eng. Inform. 69, 103936 (2026).","journal-title":"Adv. Eng. Inform."},{"key":"41255_CR5","doi-asserted-by":"publisher","first-page":"23894","DOI":"10.1038\/s41598-025-88528-w","volume":"15","author":"S Egodawela","year":"2025","unstructured":"Egodawela, S. et al. Metal loss defect detection and depth estimation using multi-spectral image analysis of cooling excited steel specimen with corrosion. Sci. Rep. 15, 23894 (2025).","journal-title":"Sci. Rep."},{"key":"41255_CR6","doi-asserted-by":"crossref","unstructured":"Hoang, D.-C. et al. Unsupervised visual-to-geometric feature reconstruction for vision-based industrial anomaly detection. IEEE Access. (2025).","DOI":"10.1109\/ACCESS.2025.3525567"},{"key":"41255_CR7","doi-asserted-by":"publisher","first-page":"22860","DOI":"10.1038\/s41598-025-05479-y","volume":"15","author":"X Tong","year":"2025","unstructured":"Tong, X. et al. Dam-faster RCNN: few-shot defect detection method for wood based on dual attention mechanism. Sci. Rep. 15, 22860 (2025).","journal-title":"Sci. Rep."},{"key":"41255_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2025.104309","volume":"25","author":"D-C Hoang","year":"2025","unstructured":"Hoang, D.-C. et al. Image-based anomaly detection in low-light industrial environments with feature enhancement. Results Eng. 25, 104309 (2025).","journal-title":"Results Eng."},{"key":"41255_CR9","doi-asserted-by":"publisher","first-page":"20977","DOI":"10.1038\/s41598-025-08835-0","volume":"15","author":"C Zhang","year":"2025","unstructured":"Zhang, C., Guo, Z. & Li, C. Unsupervised anomaly detection for gearboxes based on the deep convolutional support generative adversarial network. Sci. Rep. 15, 20977 (2025).","journal-title":"Sci. Rep."},{"key":"41255_CR10","doi-asserted-by":"publisher","first-page":"19649","DOI":"10.1038\/s41598-025-04811-w","volume":"15","author":"H Song","year":"2025","unstructured":"Song, H. Rstd-yolov7: a steel surface defect detection based on improved yolov7. Sci. Rep. 15, 19649 (2025).","journal-title":"Sci. Rep."},{"key":"41255_CR11","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D. & Steger, C. Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 4183\u20134192 (2020).","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"41255_CR12","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.neucom.2020.11.018","volume":"424","author":"Y Shi","year":"2021","unstructured":"Shi, Y., Yang, J. & Qi, Z. DFR: Deep feature reconstruction for unsupervised anomaly segmentation. Neurocomputing 424, 9\u201322 (2021).","journal-title":"Neurocomputing"},{"key":"41255_CR13","doi-asserted-by":"crossref","unstructured":"Roth, K. et al. Towards total recall in industrial anomaly detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 14318\u201314328 (2022).","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"41255_CR14","doi-asserted-by":"crossref","unstructured":"Hoang, D.-C. et al. Accurate industrial anomaly detection with efficient multimodal fusion. Array 100512 (2025).","DOI":"10.1016\/j.array.2025.100512"},{"key":"41255_CR15","doi-asserted-by":"crossref","unstructured":"Horwitz, E. & Hoshen, Y. Back to the feature: classical 3d features are (almost) all you need for 3d anomaly detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2968\u20132977 (2023).","DOI":"10.1109\/CVPRW59228.2023.00298"},{"key":"41255_CR16","doi-asserted-by":"crossref","unstructured":"Bergmann, P. & Sattlegger, D. Anomaly detection in 3d point clouds using deep geometric descriptors. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2613\u20132623 (2023).","DOI":"10.1109\/WACV56688.2023.00264"},{"key":"41255_CR17","first-page":"30402","volume":"36","author":"J Liu","year":"2023","unstructured":"Liu, J. et al. Real3D-AD: A dataset of point cloud anomaly detection. Adv. Neural. Inf. Process. Syst. 36, 30402\u201330415 (2023).","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"41255_CR18","doi-asserted-by":"crossref","unstructured":"Zhu, H. et al. Towards high-resolution 3d anomaly detection via group-level feature contrastive learning. In Proceedings of the 32nd ACM International Conference on Multimedia, 4680\u20134689 (2024).","DOI":"10.1145\/3664647.3680919"},{"key":"41255_CR19","doi-asserted-by":"crossref","unstructured":"Li, W. et al. Towards scalable 3d anomaly detection and localization: A benchmark via 3d anomaly synthesis and a self-supervised learning network. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 22207\u201322216 (2024).","DOI":"10.1109\/CVPR52733.2024.02096"},{"key":"41255_CR20","doi-asserted-by":"crossref","unstructured":"Zhou, Z. et al. R3D-AD: Reconstruction via diffusion for 3d anomaly detection. In European Conference on Computer Vision, 91\u2013107 (Springer, 2024).","DOI":"10.1007\/978-3-031-72764-1_6"},{"key":"41255_CR21","doi-asserted-by":"crossref","unstructured":"Venkataramanan, S., Peng, K.-C., Singh, R. V. & Mahalanobis, A. Attention guided anomaly localization in images. In European Conference on Computer Vision, 485\u2013503 (Springer, 2020).","DOI":"10.1007\/978-3-030-58520-4_29"},{"key":"41255_CR22","doi-asserted-by":"crossref","unstructured":"Ding, C., Pang, G. & Shen, C. Catching both gray and black swans: Open-set supervised anomaly detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 7388\u20137398 (2022).","DOI":"10.1109\/CVPR52688.2022.00724"},{"key":"41255_CR23","doi-asserted-by":"crossref","unstructured":"Chadha, G. S., Rabbani, A. & Schwung, A. Comparison of semi-supervised deep neural networks for anomaly detection in industrial processes. In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), vol. 1, 214\u2013219 (IEEE, 2019).","DOI":"10.1109\/INDIN41052.2019.8972172"},{"key":"41255_CR24","doi-asserted-by":"crossref","unstructured":"Chu, W.-H. & Kitani, K. M. Neural batch sampling with reinforcement learning for semi-supervised anomaly detection. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXVI 16, 751\u2013766 (Springer, 2020).","DOI":"10.1007\/978-3-030-58574-7_45"},{"key":"41255_CR25","doi-asserted-by":"crossref","unstructured":"Gu, Z. et al. Remembering normality: Memory-guided knowledge distillation for unsupervised anomaly detection. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 16401\u201316409 (2023).","DOI":"10.1109\/ICCV51070.2023.01503"},{"key":"41255_CR26","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1109\/TBDATA.2024.3350539","volume":"10","author":"G Tong","year":"2024","unstructured":"Tong, G., Li, Q. & Song, Y. Enhanced multi-scale features mutual mapping fusion based on reverse knowledge distillation for industrial anomaly detection and localization. IEEE Trans. Big Data 10, 498\u2013513 (2024).","journal-title":"IEEE Trans. Big Data"},{"key":"41255_CR27","doi-asserted-by":"crossref","unstructured":"Hou, J. et al. Divide-and-Assemble: Learning block-wise memory for unsupervised anomaly detection. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 8791\u20138800 (2021).","DOI":"10.1109\/ICCV48922.2021.00867"},{"key":"41255_CR28","doi-asserted-by":"crossref","unstructured":"Zavrtanik, V., Kristan, M. & Sko\u010daj, D. DRAEM-a discriminatively trained reconstruction embedding for surface anomaly detection. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 8330\u20138339 (2021).","DOI":"10.1109\/ICCV48922.2021.00822"},{"key":"41255_CR29","doi-asserted-by":"crossref","unstructured":"Zavrtanik, V., Kristan, M. & Sko\u010daj, D. DSR-a dual subspace re-projection network for surface anomaly detection. In European Conference on Computer Vision, 539\u2013554 (Springer, 2022).","DOI":"10.1007\/978-3-031-19821-2_31"},{"key":"41255_CR30","doi-asserted-by":"crossref","unstructured":"Fang, Z. et al. FastRecon: Few-shot industrial anomaly detection via fast feature reconstruction. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 17481\u201317490 (2023).","DOI":"10.1109\/ICCV51070.2023.01603"},{"key":"41255_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, X., Xu, M. & Zhou, X. RealNet: A feature selection network with realistic synthetic anomaly for anomaly detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 16699\u201316708 (2024).","DOI":"10.1109\/CVPR52733.2024.01580"},{"key":"41255_CR32","doi-asserted-by":"crossref","unstructured":"Yan, X., Zhang, H., Xu, X., Hu, X. & Heng, P.-A. Learning semantic context from normal samples for unsupervised anomaly detection. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, 3110\u20133118 (2021).","DOI":"10.1609\/aaai.v35i4.16420"},{"key":"41255_CR33","unstructured":"Song, J., Kong, K., Park, Y.-I., Kim, S.-G. & Kang, S.-J. AnoSeg: Anomaly segmentation network using self-supervised learning. arXiv preprint arXiv:2110.03396 (2021)."},{"key":"41255_CR34","doi-asserted-by":"publisher","first-page":"4327","DOI":"10.1109\/TIP.2023.3293772","volume":"32","author":"Y Liang","year":"2023","unstructured":"Liang, Y. et al. Omni-frequency channel-selection representations for unsupervised anomaly detection. IEEE Trans. Image Process. 32, 4327\u20134340 (2023).","journal-title":"IEEE Trans. Image Process."},{"key":"41255_CR35","doi-asserted-by":"publisher","first-page":"2250030","DOI":"10.1142\/S0129065722500307","volume":"32","author":"A De Nardin","year":"2022","unstructured":"De Nardin, A., Mishra, P., Foresti, G. L. & Piciarelli, C. Masked transformer for image anomaly localization. Int. J. Neural Syst. 32, 2250030 (2022).","journal-title":"Int. J. Neural Syst."},{"key":"41255_CR36","doi-asserted-by":"crossref","unstructured":"Yao, X., Li, R., Qian, Z., Luo, Y. & Zhang, C. Focus the discrepancy: Intra-and inter-correlation learning for image anomaly detection. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 6803\u20136813 (2023).","DOI":"10.1109\/ICCV51070.2023.00626"},{"key":"41255_CR37","doi-asserted-by":"crossref","unstructured":"Luo, W., Yao, H., Yu, W. & Li, Z. AMI-Net: Adaptive mask inpainting network for industrial anomaly detection and localization. IEEE Trans. Autom. Sci. Eng. (2024).","DOI":"10.1109\/TASE.2024.3368142"},{"key":"41255_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, X. et al. Unsupervised surface anomaly detection with diffusion probabilistic model. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 6782\u20136791 (2023).","DOI":"10.1109\/ICCV51070.2023.00624"},{"key":"41255_CR39","doi-asserted-by":"crossref","unstructured":"Lu, F., Yao, X., Fu, C.-W. & Jia, J. Removing anomalies as noises for industrial defect localization. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 16166\u201316175 (2023).","DOI":"10.1109\/ICCV51070.2023.01481"},{"key":"41255_CR40","doi-asserted-by":"crossref","unstructured":"Tebbe, J. & Tayyub, J. Dynamic addition of noise in a diffusion model for anomaly detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 3940\u20133949 (2024).","DOI":"10.1109\/CVPRW63382.2024.00398"},{"key":"41255_CR41","doi-asserted-by":"crossref","unstructured":"Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M. H. & Rabiee, H. R. Multiresolution knowledge distillation for anomaly detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 14902\u201314912 (2021).","DOI":"10.1109\/CVPR46437.2021.01466"},{"key":"41255_CR42","doi-asserted-by":"publisher","first-page":"2330","DOI":"10.1109\/TII.2022.3182385","volume":"19","author":"Q Wan","year":"2022","unstructured":"Wan, Q., Gao, L., Li, X. & Wen, L. Unsupervised image anomaly detection and segmentation based on pretrained feature mapping. IEEE Trans. Industr. Inf. 19, 2330\u20132339 (2022).","journal-title":"IEEE Trans. Industr. Inf."},{"key":"41255_CR43","doi-asserted-by":"crossref","unstructured":"Defard, T., Setkov, A., Loesch, A. & Audigier, R. PaDiM: a patch distribution modeling framework for anomaly detection and localization. In International Conference on Pattern Recognition, 475\u2013489 (Springer, 2021).","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"41255_CR44","doi-asserted-by":"crossref","unstructured":"Bae, J., Lee, J.-H. & Kim, S. PNI: Industrial anomaly detection using position and neighborhood information. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, 6373\u20136383 (2023).","DOI":"10.1109\/ICCV51070.2023.00586"},{"key":"41255_CR45","doi-asserted-by":"crossref","unstructured":"Zuo, Z., Wu, Z., Chen, B. & Zhong, X. A reconstruction-based feature adaptation for anomaly detection with self-supervised multi-scale aggregation. In ICASSP 2024\u20132024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5840\u20135844 (IEEE, 2024).","DOI":"10.1109\/ICASSP48485.2024.10446766"},{"key":"41255_CR46","doi-asserted-by":"crossref","unstructured":"Hyun, J. et al. ReConPatch: Contrastive patch representation learning for industrial anomaly detection. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2052\u20132061 (2024).","DOI":"10.1109\/WACV57701.2024.00205"},{"key":"41255_CR47","doi-asserted-by":"crossref","unstructured":"Li, C.-L., Sohn, K., Yoon, J. & Pfister, T. CutPaste: Self-supervised learning for anomaly detection and localization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 9664\u20139674 (2021).","DOI":"10.1109\/CVPR46437.2021.00954"},{"key":"41255_CR48","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhou, Y., Xu, Y. & Wang, Z. SimpleNet: A simple network for image anomaly detection and localization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 20402\u201320411 (2023).","DOI":"10.1109\/CVPR52729.2023.01954"},{"key":"41255_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110761","volume":"156","author":"Y Cao","year":"2024","unstructured":"Cao, Y., Xu, X. & Shen, W. Complementary pseudo multimodal feature for point cloud anomaly detection. Pattern Recogn. 156, 110761 (2024).","journal-title":"Pattern Recogn."},{"key":"41255_CR50","doi-asserted-by":"crossref","unstructured":"Wang, Y. et al. Multimodal industrial anomaly detection via hybrid fusion. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 8032\u20138041 (2023).","DOI":"10.1109\/CVPR52729.2023.00776"},{"key":"41255_CR51","doi-asserted-by":"crossref","unstructured":"Rudolph, M., Wehrbein, T., Rosenhahn, B. & Wandt, B. Asymmetric student-teacher networks for industrial anomaly detection. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2592\u20132602 (2023).","DOI":"10.1109\/WACV56688.2023.00262"},{"key":"41255_CR52","doi-asserted-by":"crossref","unstructured":"Pang, Y. et al. Masked autoencoders for point cloud self-supervised learning. In European Conference on Computer Vision, 604\u2013621 (Springer, 2022).","DOI":"10.1007\/978-3-031-20086-1_35"},{"key":"41255_CR53","unstructured":"Chang, A. X. et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012 (2015)."},{"key":"41255_CR54","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1561\/2200000073","volume":"11","author":"G Peyr\u00e9","year":"2019","unstructured":"Peyr\u00e9, G. et al. Computational optimal transport: With applications to data science. Found. Trends Mach. Learn. 11, 355\u2013607 (2019).","journal-title":"Found. Trends Mach. Learn."},{"key":"41255_CR55","doi-asserted-by":"crossref","unstructured":"Rusu, R. B., Blodow, N. & Beetz, M. Fast point feature histograms (FPFH) for 3d registration. In 2009 IEEE International Conference on Robotics and Automation, 3212\u20133217 (IEEE, 2009).","DOI":"10.1109\/ROBOT.2009.5152473"},{"key":"41255_CR56","doi-asserted-by":"crossref","unstructured":"Yu, X. et al. Point-BERT: Pre-training 3d point cloud transformers with masked point modeling. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 19313\u201319322 (2022).","DOI":"10.1109\/CVPR52688.2022.01871"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-41255-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-41255-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-41255-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T10:08:07Z","timestamp":1774606087000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-41255-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,24]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["41255"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-41255-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,24]]},"assertion":[{"value":"15 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 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":"10309"}}