{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:07:29Z","timestamp":1772644049856,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The Key Science and Technology Program of Henan Province","award":["Grant No.252102221007"],"award-info":[{"award-number":["Grant No.252102221007"]}]},{"name":"Doctoral Start-up Fund Project of Shanghai Jianqiao University","award":["Grant No.BBYQ202444"],"award-info":[{"award-number":["Grant No.BBYQ202444"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s00371-025-04274-0","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T14:15:06Z","timestamp":1765289706000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced sodium rod detection and distribution using a YOLOv5s-SNet2-CBAM lightweight network"],"prefix":"10.1007","volume":"42","author":[{"given":"Haoju","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guiqin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,9]]},"reference":[{"key":"4274_CR1","doi-asserted-by":"publisher","first-page":"1700274","DOI":"10.1002\/aenm.201700274","volume":"7","author":"WJ Li","year":"2017","unstructured":"Li, W.J., Han, C., Wang, W., et al.: Commercial prospects of existing cathode materials for sodium ion storage. Adv. Energy Mater. 7, 1700274 (2017). https:\/\/doi.org\/10.1002\/aenm.201700274","journal-title":"Adv. Energy Mater."},{"key":"4274_CR2","doi-asserted-by":"publisher","first-page":"e464","DOI":"10.1002\/cey2.464","volume":"6","author":"Y Gao","year":"2024","unstructured":"Gao, Y., Zhang, H., Peng, J., et al.: A 30-year overview of sodium-ion batteries. Carbon Energy 6, e464 (2024). https:\/\/doi.org\/10.1002\/cey2.464","journal-title":"Carbon Energy"},{"key":"4274_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/jphot.2022.3228013","volume":"15","author":"Y Li","year":"2023","unstructured":"Li, Y., Liu, W., Li, L., et al.: Vision-based target detection and positioning approach for underwater robots. IEEE Photonics J. 15, 1\u201312 (2023). https:\/\/doi.org\/10.1109\/jphot.2022.3228013","journal-title":"IEEE Photonics J."},{"key":"4274_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/1729881418808991","volume":"15","author":"D Ji","year":"2018","unstructured":"Ji, D., Li, H., Chen, C.-W., et al.: Visual detection and feature recognition of underwater target using a novel model-based method. Int. J. Adv. Robot. Syst. 15, 1\u201310 (2018). https:\/\/doi.org\/10.1177\/1729881418808991","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"4274_CR5","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/s11227-024-06852-w","volume":"81","author":"S Xiao","year":"2025","unstructured":"Xiao, S., Liu, J., Pan, Z., et al.: LiteYOLO-GHG: a lightweight YOLOv8-based algorithm for transformer bushing fault detection. J. Supercomput. 81, 365 (2025). https:\/\/doi.org\/10.1007\/s11227-024-06852-w","journal-title":"J. Supercomput."},{"key":"4274_CR6","doi-asserted-by":"publisher","unstructured":"Redmon, J., Divvala, S., Girshick, R., & Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016). https:\/\/doi.org\/10.1109\/cvpr.2016.91","DOI":"10.1109\/cvpr.2016.91"},{"key":"4274_CR7","doi-asserted-by":"publisher","first-page":"120845","DOI":"10.1016\/j.eswa.2023.120845","volume":"231","author":"MH Hamzenejadi","year":"2023","unstructured":"Hamzenejadi, M.H., Mohseni, H.: Fine-tuned YOLOv5 for real-time vehicle detection in UAV imagery: architectural improvements and performance boost. Expert Syst. Appl. 231, 120845 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.120845","journal-title":"Expert Syst. Appl."},{"key":"4274_CR8","doi-asserted-by":"publisher","first-page":"085202","DOI":"10.1088\/1361-6501\/ad42c5","volume":"35","author":"F Hao","year":"2024","unstructured":"Hao, F., Zhang, T., He, G., et al.: CaSnLi-YOLO: construction site multi-target detection method based on improved YOLOv5s. Meas. Sci. Technol. 35, 085202 (2024). https:\/\/doi.org\/10.1088\/1361-6501\/ad42c5","journal-title":"Meas. Sci. Technol."},{"key":"4274_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/15589250241232153","volume":"19","author":"X Kang","year":"2024","unstructured":"Kang, X.: Research on fabric defect detection method based on lightweight network. J. Eng. Fibers Fabr. 19, 1\u201316 (2024). https:\/\/doi.org\/10.1177\/15589250241232153","journal-title":"J. Eng. Fibers Fabr."},{"key":"4274_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2014.81","author":"R Girshick","year":"2014","unstructured":"Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (2014). https:\/\/doi.org\/10.1109\/cvpr.2014.81","journal-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit."},{"key":"4274_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/ius54386.2022.9957216","author":"R Girshick","year":"2015","unstructured":"Girshick, R.: Fast r-cnn. Proc. IEEE Int. Conf. Comput. Vis. (2015). https:\/\/doi.org\/10.1109\/ius54386.2022.9957216","journal-title":"Proc. IEEE Int. Conf. Comput. Vis."},{"issue":"6","key":"4274_CR12","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/tpami.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., et al.: Faster R-CNN:towards realtime objectdetectionwith region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017). https:\/\/doi.org\/10.1109\/tpami.2016.2577031","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4274_CR13","doi-asserted-by":"publisher","unstructured":"Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: Proceedings of the 30th International Conference on Neural Information Processing Systems (2016) https:\/\/doi.org\/10.48550\/arXiv.1605.06409","DOI":"10.48550\/arXiv.1605.06409"},{"key":"4274_CR14","doi-asserted-by":"publisher","unstructured":"He, K., Gkioxari, G., Dollar, P., et al.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 2980\u20132988 (2017) https:\/\/doi.org\/10.1109\/ICCV.2017.322","DOI":"10.1109\/ICCV.2017.322"},{"key":"4274_CR15","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/jproc.2023.3238524","volume":"111","author":"Z Zou","year":"2023","unstructured":"Zou, Z., Chen, K., Shi, Z., et al.: Object detection in 20 years: a survey. Proc. IEEE 111, 257\u2013276 (2023). https:\/\/doi.org\/10.1109\/jproc.2023.3238524","journal-title":"Proc. IEEE"},{"key":"4274_CR16","doi-asserted-by":"crossref","unstructured":"Liu,W., Anguelov, D., Erhan, D. et al.: SSD: single shot multiBox detector. In: Amsterdam: European Conference on Computer Vision, pp. 21\u201337 (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"4274_CR17","doi-asserted-by":"publisher","first-page":"015404","DOI":"10.1088\/1361-6501\/acfab1","volume":"35","author":"Y Yang","year":"2023","unstructured":"Yang, Y., Li, D., Guo, Y., et al.: Research on coal gangue recognition method based on XBS-YOLOv5s. Meas. Sci. Technol. 35, 015404 (2023). https:\/\/doi.org\/10.1088\/1361-6501\/acfab1","journal-title":"Meas. Sci. Technol."},{"key":"4274_CR18","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"TY Lin","year":"2020","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., et al.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42, 318\u2013327 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2018.2858826","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4274_CR19","doi-asserted-by":"publisher","unstructured":"Tan, M., Pang, R., Le, Q.V.: EfficientDet: scalable and efficient object detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 10778\u201310787 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01079","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"4274_CR20","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1109\/jstars.2023.3234161","volume":"16","author":"W Lu","year":"2023","unstructured":"Lu, W., Lan, C., Niu, C., et al.: A CNN-Transformer hybrid model based on cswin transformer for UAV image object detection. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 16, 1211\u20131231 (2023). https:\/\/doi.org\/10.1109\/jstars.2023.3234161","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"4274_CR21","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1016\/j.ins.2023.02.045","volume":"630","author":"Y Xu","year":"2023","unstructured":"Xu, Y., Feng, Z., Zhou, X., et al.: Attention-based neural networks for trust evaluation in online social networks. Inf. Sci. 630, 507\u2013522 (2023). https:\/\/doi.org\/10.1016\/j.ins.2023.02.045","journal-title":"Inf. Sci."},{"key":"4274_CR22","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/s13042-024-02262-9","volume":"16","author":"Y Xie","year":"2024","unstructured":"Xie, Y., Hong, C., Zhuang, W., et al.: HOGFormer: high-order graph convolution transformer for 3D human pose estimation. Int. J. Mach. Learn. Cybern. 16, 599\u2013610 (2024). https:\/\/doi.org\/10.1007\/s13042-024-02262-9","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"4274_CR23","doi-asserted-by":"publisher","first-page":"9454","DOI":"10.1109\/TPAMI.2023.3243048","volume":"45","author":"Z Peng","year":"2023","unstructured":"Peng, Z., Guo, Z., Huang, W., et al.: Conformer: local features coupling global representations for recognition and detection. IEEE Trans. Pattern Anal. Mach. Intell. 45, 9454\u20139468 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2023.3243048","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4274_CR24","doi-asserted-by":"publisher","first-page":"3952","DOI":"10.1109\/tii.2018.2884211","volume":"15","author":"C Hong","year":"2019","unstructured":"Hong, C., Yu, J., Zhang, J., et al.: Multimodal face-pose estimation with multitask manifold deep learning. IEEE Trans. Ind. Inform. 15, 3952\u20133961 (2019). https:\/\/doi.org\/10.1109\/tii.2018.2884211","journal-title":"IEEE Trans. Ind. Inform."},{"key":"4274_CR25","doi-asserted-by":"publisher","first-page":"103224","DOI":"10.1016\/j.cviu.2021.103224","volume":"208-209","author":"C Hong","year":"2021","unstructured":"Hong, C., Chen, L., Liang, Y., et al.: Stacked capsule graph autoencoders for geometry-aware 3D head pose estimation. Comput. Vis. Image Underst. 208\u2013209, 103224 (2021). https:\/\/doi.org\/10.1016\/j.cviu.2021.103224","journal-title":"Comput. Vis. Image Underst."},{"key":"4274_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-025-04130-1","author":"S Zhao","year":"2025","unstructured":"Zhao, S., Wang, Y., Huo, Z., et al.: Lightweight and real-time semantic segmentation network via multi-scale dilated convolutions. Vis. Comput. (2025). https:\/\/doi.org\/10.1007\/s00371-025-04130-1","journal-title":"Vis. Comput."},{"key":"4274_CR27","doi-asserted-by":"publisher","first-page":"7123","DOI":"10.1007\/s00371-024-03796-3","volume":"41","author":"W Pan","year":"2025","unstructured":"Pan, W., Yang, Z.: A lightweight enhanced YOLOv8 algorithm for detecting small objects in UAV aerial photography. Vis. Comput. 41, 7123\u20137139 (2025). https:\/\/doi.org\/10.1007\/s00371-024-03796-3","journal-title":"Vis. Comput."},{"key":"4274_CR28","doi-asserted-by":"publisher","unstructured":"Lu, H., Liu, Z., Zhang, M.: Distilling the knowledge in object detection with adaptive balance. In: 2022 16th IEEE International Conference on Signal Processing (ICSP), Beijing, pp. 272\u2013275 (2022) https:\/\/doi.org\/10.1109\/ICSP56322.2022.9965214","DOI":"10.1109\/ICSP56322.2022.9965214"},{"key":"4274_CR29","doi-asserted-by":"publisher","unstructured":"Yang, Z., Li, Z., Jiang, X., et al.: Focal and global knowledge distillation for detectors. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, pp. 4633\u20134642 (2022) https:\/\/doi.org\/10.1109\/CVPR52688.2022.00460","DOI":"10.1109\/CVPR52688.2022.00460"},{"key":"4274_CR30","doi-asserted-by":"publisher","first-page":"4047","DOI":"10.1007\/s00371-024-03645-3","volume":"41","author":"L-A Tran","year":"2024","unstructured":"Tran, L.-A., Park, D.-C.: Lightweight image dehazing networks based on soft knowledge distillation. Vis. Comput. 41, 4047\u20134066 (2024). https:\/\/doi.org\/10.1007\/s00371-024-03645-3","journal-title":"Vis. Comput."},{"key":"4274_CR31","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/s11554-023-01305-8","volume":"20","author":"X Wen","year":"2023","unstructured":"Wen, X., Li, B., Wang, X., et al.: A Swin transformer-functionalized lightweight YOLOv5s for real-time coal\u2013gangue detection. J. Real-Time Image Process. 20, 47 (2023). https:\/\/doi.org\/10.1007\/s11554-023-01305-8","journal-title":"J. Real-Time Image Process."},{"key":"4274_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-025-04108-z","author":"C Chen","year":"2025","unstructured":"Chen, C., Yu, C., Cai, S.: Advancing landslide recognition through multi-dimensional feature fusion and transformer architectures. Vis. Comput. (2025). https:\/\/doi.org\/10.1007\/s00371-025-04108-z","journal-title":"Vis. Comput."},{"key":"4274_CR33","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s11227-024-06739-w","volume":"81","author":"Y Xie","year":"2025","unstructured":"Xie, Y., Zhao, Y.: Lightweight improved YOLOv5 algorithm for PCB defect detection. J. Supercomput. 81, 261 (2025). https:\/\/doi.org\/10.1007\/s11227-024-06739-w","journal-title":"J. Supercomput."},{"key":"4274_CR34","doi-asserted-by":"publisher","first-page":"4821","DOI":"10.1007\/s11760-024-03118-z","volume":"18","author":"K Cai","year":"2024","unstructured":"Cai, K., Yang, J., Ren, J., et al.: A lightweight algorithm for small traffic sign detection based on improved YOLOv5s. Signal Image Video Process. 18, 4821\u20134829 (2024). https:\/\/doi.org\/10.1007\/s11760-024-03118-z","journal-title":"Signal Image Video Process."},{"key":"4274_CR35","doi-asserted-by":"publisher","unstructured":"Zhang, X., Zhou, X., Lin, M., et al.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, pp. 6848\u20136856 (2018) https:\/\/doi.org\/10.1109\/CVPR.2018.00716","DOI":"10.1109\/CVPR.2018.00716"},{"key":"4274_CR36","doi-asserted-by":"publisher","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., et al.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Computer Vision \u2013 ECCV 2018, pp. 122\u2013138. https:\/\/doi.org\/10.1007\/978-3-030-01264-9_8(2018)","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"4274_CR37","doi-asserted-by":"publisher","unstructured":"Han, K., Wang, Y., Tian, Q., et al.: GhostNet: more features from cheap operations. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1577\u20131586 (2020) https:\/\/doi.org\/10.1109\/CVPR42600.2020.00165","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"4274_CR38","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: efficient convolutional neural networks for mobile vision applications (2017) https:\/\/arxiv.org\/abs\/1704.04861"},{"key":"4274_CR39","doi-asserted-by":"publisher","first-page":"3129","DOI":"10.1007\/s00371-024-03591-0","volume":"41","author":"F Li","year":"2024","unstructured":"Li, F., Yang, Z., Gui, Y.: SES-yolov5: small object graphics detection and visualization applications. Vis. Comput. 41, 3129\u20133142 (2024). https:\/\/doi.org\/10.1007\/s00371-024-03591-0","journal-title":"Vis. Comput."},{"key":"4274_CR40","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/tmm.2021.3120873","volume":"25","author":"X Lin","year":"2023","unstructured":"Lin, X., Sun, S., Huang, W., et al.: EAPT: efficient attention pyramid transformer for image processing. IEEE Trans. Multimed. 25, 50\u201361 (2023). https:\/\/doi.org\/10.1109\/tmm.2021.3120873","journal-title":"IEEE Trans. Multimed."},{"key":"4274_CR41","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1109\/joe.2023.3290987","volume":"48","author":"T Zhou","year":"2023","unstructured":"Zhou, T., Wang, Y., Zhang, L., et al.: Underwater multitarget tracking method based on threshold segmentation. IEEE J. Ocean. Eng. 48, 1255\u20131269 (2023). https:\/\/doi.org\/10.1109\/joe.2023.3290987","journal-title":"IEEE J. Ocean. Eng."},{"key":"4274_CR42","doi-asserted-by":"publisher","first-page":"113326","DOI":"10.1109\/access.2021.3104515","volume":"9","author":"I Zeger","year":"2021","unstructured":"Zeger, I., Grgic, S., Vukovic, J., et al.: Grayscale image colorization methods: overview and evaluation. IEEE Access 9, 113326\u2013113346 (2021). https:\/\/doi.org\/10.1109\/access.2021.3104515","journal-title":"IEEE Access"},{"key":"4274_CR43","doi-asserted-by":"publisher","first-page":"104960","DOI":"10.1016\/j.engappai.2022.104960","volume":"113","author":"G Ma","year":"2022","unstructured":"Ma, G., Yue, X.: An improved whale optimization algorithm based on multilevel threshold image segmentation using the Otsu method. Eng. Appl. Artif. Intell. 113, 104960 (2022). https:\/\/doi.org\/10.1016\/j.engappai.2022.104960","journal-title":"Eng. Appl. Artif. Intell."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04274-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-04274-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04274-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T13:04:41Z","timestamp":1772629481000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-04274-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,9]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["4274"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-04274-0","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,9]]},"assertion":[{"value":"11 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2025","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":"30"}}