{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:48:37Z","timestamp":1772909317957,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:00:00Z","timestamp":1705536000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:00:00Z","timestamp":1705536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100010814","name":"Anhui Provincial Department of Education","doi-asserted-by":"publisher","award":["2022AH050224"],"award-info":[{"award-number":["2022AH050224"]}],"id":[{"id":"10.13039\/501100010814","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010814","name":"Anhui Provincial Department of Education","doi-asserted-by":"publisher","award":["2022AH050224"],"award-info":[{"award-number":["2022AH050224"]}],"id":[{"id":"10.13039\/501100010814","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010814","name":"Anhui Provincial Department of Education","doi-asserted-by":"publisher","award":["2022AH050224"],"award-info":[{"award-number":["2022AH050224"]}],"id":[{"id":"10.13039\/501100010814","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010814","name":"Anhui Provincial Department of Education","doi-asserted-by":"publisher","award":["2022AH050224"],"award-info":[{"award-number":["2022AH050224"]}],"id":[{"id":"10.13039\/501100010814","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s11760-023-02923-2","type":"journal-article","created":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T13:02:06Z","timestamp":1705582926000},"page":"2473-2483","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Human risky behaviour recognition during ladder climbing based on multi-modal feature fusion and adaptive graph convolutional network"],"prefix":"10.1007","volume":"18","author":[{"given":"Wenrui","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donghui","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruifeng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,18]]},"reference":[{"issue":"6","key":"2923_CR1","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1061\/(ASCE)CP.1943-5487.0000279","volume":"27","author":"S Han","year":"2013","unstructured":"Han, S., Lee, S., Pe\u00f1a-Mora, F.: Vision-based detection of unsafe actions of a construction worker: case study of ladder climbing. J. Comput. Civ. Eng. 27(6), 635\u2013644 (2013)","journal-title":"J. Comput. Civ. Eng."},{"key":"2923_CR2","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.autcon.2018.02.018","volume":"91","author":"W Fang","year":"2018","unstructured":"Fang, W., Ding, L., Luo, H., Love, P.E.: Falls from heights: a computer vision-based approach for safety harness detection. Autom. Constr. 91, 53\u201361 (2018)","journal-title":"Autom. Constr."},{"key":"2923_CR3","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.autcon.2018.05.022","volume":"93","author":"Q Fang","year":"2018","unstructured":"Fang, Q., Li, H., Luo, X., Ding, L., Luo, H., Li, C.: Computer vision aided inspection on falling prevention measures for steeplejacks in an aerial environment. Autom. Constr. 93, 148\u2013164 (2018)","journal-title":"Autom. Constr."},{"issue":"2","key":"2923_CR4","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1111\/mice.12579","volume":"36","author":"J Shen","year":"2021","unstructured":"Shen, J., Xiong, X., Li, Y., He, W., Li, P., Zheng, X.: Detecting safety helmet wearing on construction sites with bounding-box regression and deep transfer learning. Comput. Aided Civ. Infrastruct. Eng. 36(2), 180\u2013196 (2021)","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"issue":"2","key":"2923_CR5","doi-asserted-by":"publisher","first-page":"347","DOI":"10.3390\/rs15020347","volume":"15","author":"X Wu","year":"2023","unstructured":"Wu, X., Li, Y., Long, J., Zhang, S., Wan, S., Mei, S.: A remote-vision-based safety helmet and harness monitoring system based on attribute knowledge modeling. Remote Sens. 15(2), 347 (2023)","journal-title":"Remote Sens."},{"key":"2923_CR6","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.autcon.2018.12.014","volume":"99","author":"D Kim","year":"2019","unstructured":"Kim, D., Liu, M., Lee, S., Kamat, V.R.: Remote proximity monitoring between mobile construction resources using camera-mounted UAVs. Autom. Constr. 99, 168\u2013182 (2019)","journal-title":"Autom. Constr."},{"key":"2923_CR7","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.aei.2018.12.005","volume":"39","author":"W Fang","year":"2019","unstructured":"Fang, W., Zhong, B., Zhao, N., Love, P.E., Luo, H., Xue, J., Xu, S.: A deep learning-based approach for mitigating falls from height with computer vision: convolutional neural network. Adv. Eng. Inform. 39, 170\u2013177 (2019)","journal-title":"Adv. Eng. Inform."},{"issue":"1","key":"2923_CR8","doi-asserted-by":"publisher","first-page":"04022142","DOI":"10.1061\/(ASCE)CO.1943-7862.0002409","volume":"149","author":"X Mei","year":"2023","unstructured":"Mei, X., Zhou, X., Xu, F., Zhang, Z.: Human intrusion detection in static hazardous areas at construction sites: deep learning-based method. J. Constr. Eng. Manag. 149(1), 04022142 (2023)","journal-title":"J. Constr. Eng. Manag."},{"issue":"9","key":"2923_CR9","doi-asserted-by":"publisher","first-page":"2330","DOI":"10.1109\/TMM.2018.2802648","volume":"20","author":"S Zhang","year":"2018","unstructured":"Zhang, S., Yang, Y., Xiao, J., Liu, X., Yang, Y., Xie, D., Zhuang, Y.: Fusing geometric features for skeleton-based action recognition using multilayer LSTM networks. IEEE Trans. Multimedia 20(9), 2330\u20132343 (2018)","journal-title":"IEEE Trans. Multimedia"},{"key":"2923_CR10","doi-asserted-by":"crossref","unstructured":"Si, C., Chen, W., Wang, W., Wang, L., Tan, T.: An attention enhanced graph convolutional lstm network for skeleton-based action recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15\u201320 June 2019, pp. 1227\u20131236 (2019)","DOI":"10.1109\/CVPR.2019.00132"},{"issue":"8","key":"2923_CR11","doi-asserted-by":"publisher","first-page":"1963","DOI":"10.1109\/TPAMI.2019.2896631","volume":"41","author":"P Zhang","year":"2019","unstructured":"Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View adaptive neural networks for high performance skeleton-based human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1963\u20131978 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"2923_CR12","doi-asserted-by":"publisher","first-page":"2206","DOI":"10.1109\/TCSVT.2020.3019293","volume":"31","author":"A Banerjee","year":"2020","unstructured":"Banerjee, A., Singh, P.K., Sarkar, R.: Fuzzy integral-based CNN classifier fusion for 3d skeleton action recognition. IEEE Trans. Circuits Syst. Video Technol. 31(6), 2206\u20132216 (2020)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"2923_CR13","doi-asserted-by":"publisher","first-page":"54078","DOI":"10.1109\/ACCESS.2021.3059650","volume":"9","author":"W Ding","year":"2021","unstructured":"Ding, W., Ding, C., Li, G., Liu, K.: Skeleton-based square grid for human action recognition with 3D convolutional neural network. IEEE Access 9, 54078\u201354089 (2021)","journal-title":"IEEE Access"},{"key":"2923_CR14","unstructured":"Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA, 2\u20137 February 2018, pp. 7444\u20137452 (2018)"},{"key":"2923_CR15","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., Lu, H.: Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15\u201320 June 2019, pp. 12026\u201312035 (2019)","DOI":"10.1109\/CVPR.2019.01230"},{"key":"2923_CR16","doi-asserted-by":"crossref","unstructured":"Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., Tian, Q.: Actional-structural graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15\u201320 June 2019, pp. 3595\u20133603 (2019)","DOI":"10.1109\/CVPR.2019.00371"},{"key":"2923_CR17","doi-asserted-by":"publisher","first-page":"9532","DOI":"10.1109\/TIP.2020.3028207","volume":"29","author":"L Shi","year":"2020","unstructured":"Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Trans. Image Process. 29, 9532\u20139545 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"2923_CR18","unstructured":"Feng, L., Zhao, Y., Zhao, W., Tang, J.: A comparative review of graph convolutional networks for human skeleton-based action recognition. Artif. Intell. Rev. 1\u201331 (2022)"},{"key":"2923_CR19","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21\u201326 July 2017, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"issue":"18","key":"2923_CR20","doi-asserted-by":"publisher","first-page":"2973","DOI":"10.3390\/electronics11182973","volume":"11","author":"Q Zhu","year":"2022","unstructured":"Zhu, Q., Deng, H., Wang, K.: Skeleton action recognition based on temporal gated unit and adaptive graph convolution. Electronics 11(18), 2973 (2022)","journal-title":"Electronics"},{"key":"2923_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103348","volume":"216","author":"T Alsarhan","year":"2022","unstructured":"Alsarhan, T., Ali, U., Lu, H.: Enhanced discriminative graph convolutional network with adaptive temporal modelling for skeleton-based action recognition. Comput. Vis. Image Underst. 216, 103348 (2022)","journal-title":"Comput. Vis. Image Underst."},{"issue":"7","key":"2923_CR22","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.3390\/electronics12071711","volume":"12","author":"S-B Zhou","year":"2023","unstructured":"Zhou, S.-B., Chen, R.-R., Jiang, X.-Q., Pan, F.: 2s-GATCN: two-stream graph attentional convolutional networks for skeleton-based action recognition. Electronics 12(7), 1711 (2023)","journal-title":"Electronics"},{"key":"2923_CR23","doi-asserted-by":"crossref","unstructured":"Shahroudy, A., Liu, J., Ng, T.-T., Wang, G.: NTU RGB+D: a large scale dataset for 3d human activity analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27\u201330 June 2016, pp. 1010\u20131019 (2016)","DOI":"10.1109\/CVPR.2016.115"},{"key":"2923_CR24","first-page":"3200","volume":"45","author":"Z Sun","year":"2022","unstructured":"Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., Liu, J.: Human action recognition from various data modalities: a review. IEEE Trans. Pattern Anal. Mach. Intell. 45, 3200\u20133225 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"17","key":"2923_CR25","doi-asserted-by":"publisher","first-page":"19157","DOI":"10.1109\/JSEN.2021.3089705","volume":"21","author":"X Weiyao","year":"2021","unstructured":"Weiyao, X., Muqing, W., Min, Z., Ting, X.: Fusion of skeleton and RGB features for RGB-D human action recognition. IEEE Sens. J. 21(17), 19157\u201319164 (2021)","journal-title":"IEEE Sens. J."},{"key":"2923_CR26","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.biosystemseng.2022.03.006","volume":"218","author":"Z Li","year":"2022","unstructured":"Li, Z., Zhang, Q., Lv, S., Han, M., Jiang, M., Song, H.: Fusion of RGB, optical flow and skeleton features for the detection of lameness in dairy cows. Biosyst. Eng. 218, 62\u201377 (2022)","journal-title":"Biosyst. Eng."},{"key":"2923_CR27","doi-asserted-by":"crossref","unstructured":"Abavisani, M., Joze, H.R.V., Patel, V.M.: Improving the performance of unimodal dynamic hand-gesture recognition with multimodal training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15\u201320 June 2019, pp. 1165\u20131174 (2019)","DOI":"10.1109\/CVPR.2019.00126"},{"issue":"5","key":"2923_CR28","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.1109\/TCSVT.2020.3015051","volume":"31","author":"Y-F Song","year":"2020","unstructured":"Song, Y.-F., Zhang, Z., Shan, C., Wang, L.: Richly activated graph convolutional network for robust skeleton-based action recognition. IEEE Trans. Circuits Syst. Video Technol. 31(5), 1915\u20131925 (2020)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"2923_CR29","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-R\u00faa, J.-M., Vielzeuf, V., Pateux, S., Baccouche, M., Jurie, F.: MFAS: multimodal fusion architecture search. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15\u201320 June 2019, pp. 6966\u20136975 (2019)","DOI":"10.1109\/CVPR.2019.00713"},{"key":"2923_CR30","doi-asserted-by":"crossref","unstructured":"Das, S., Sharma, S., Dai, R., Bremond, F., Thonnat, M.: VPN: learning video-pose embedding for activities of daily living. In: Proceedings of the Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, pp. 72\u201390 (2020)","DOI":"10.1007\/978-3-030-58545-7_5"},{"key":"2923_CR31","doi-asserted-by":"crossref","unstructured":"Duan, H., Zhao, Y., Chen, K., Lin, D., Dai, B.: Revisiting skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18\u201324 June 2022, pp. 2969\u20132978 (2022)","DOI":"10.1109\/CVPR52688.2022.00298"},{"issue":"12","key":"2923_CR32","doi-asserted-by":"publisher","first-page":"9236","DOI":"10.1109\/TPAMI.2021.3125995","volume":"44","author":"X Liang","year":"2021","unstructured":"Liang, X., Qian, Y., Guo, Q., Cheng, H., Liang, J.: AF: an association-based fusion method for multi-modal classification. IEEE Trans. Pattern Anal. Mach. Intell. 44(12), 9236\u20139254 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2923_CR33","doi-asserted-by":"crossref","unstructured":"Gavrilyuk, K., Sanford, R., Javan, M., Snoek, C.G.: Actor-transformers for group activity recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13\u201319 June 2020, pp. 839\u2013848 (2020)","DOI":"10.1109\/CVPR42600.2020.00092"},{"issue":"6","key":"2923_CR34","doi-asserted-by":"publisher","first-page":"04018042","DOI":"10.1061\/(ASCE)CO.1943-7862.0001497","volume":"144","author":"H Guo","year":"2018","unstructured":"Guo, H., Yu, Y., Ding, Q., Skitmore, M.: Image-and-skeleton-based parameterized approach to real-time identification of construction workers\u2019 unsafe behaviors. J. Constr. Eng. Manag. 144(6), 04018042 (2018)","journal-title":"J. Constr. Eng. Manag."},{"key":"2923_CR35","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.autcon.2017.05.002","volume":"82","author":"Y Yu","year":"2017","unstructured":"Yu, Y., Guo, H., Ding, Q., Li, H., Skitmore, M.: An experimental study of real-time identification of construction workers\u2019 unsafe behaviors. Autom. Constr. 82, 193\u2013206 (2017)","journal-title":"Autom. Constr."},{"key":"2923_CR36","doi-asserted-by":"publisher","first-page":"36725","DOI":"10.1109\/ACCESS.2022.3164676","volume":"10","author":"S Anjum","year":"2022","unstructured":"Anjum, S., Khan, N., Khalid, R., Khan, M., Lee, D., Park, C.: Fall prevention from ladders utilizing a deep learning-based height assessment method. IEEE Access 10, 36725\u201336742 (2022)","journal-title":"IEEE Access"},{"key":"2923_CR37","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.autcon.2017.11.002","volume":"86","author":"L Ding","year":"2018","unstructured":"Ding, L., Fang, W., Luo, H., Love, P.E., Zhong, B., Ouyang, X.: A deep hybrid learning model to detect unsafe behavior: integrating convolution neural networks and long short-term memory. Autom. Constr. 86, 118\u2013124 (2018)","journal-title":"Autom. Constr."},{"issue":"4","key":"2923_CR38","first-page":"30","volume":"32","author":"L Yao","year":"2022","unstructured":"Yao, L., Shuangjian, J.: Application of ST-GCN in unsafe action identification of construction workers. China Saf. Sci. J. 32(4), 30 (2022)","journal-title":"China Saf. Sci. J."},{"key":"2923_CR39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27\u201330 June 2016, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2923_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, X., Xu, C., Tao, D.: Context aware graph convolution for skeleton-based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27\u201330 June 2016, pp. 770\u2013778 (2020)","DOI":"10.1109\/CVPR42600.2020.01434"},{"issue":"7","key":"2923_CR41","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.3390\/electronics12071711","volume":"12","author":"S-B Zhou","year":"2023","unstructured":"Zhou, S.-B., Chen, R.-R., Jiang, X.-Q., Pan, F.: 2s-GATCN: two-stream graph attentional convolutional networks for skeleton-based action recognition. Electronics 12(7), 1711 (2023)","journal-title":"Electronics"},{"issue":"1","key":"2923_CR42","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","volume":"43","author":"Z Cao","year":"2021","unstructured":"Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans. Pattern Anal. Mach. Intell. 43(1), 172\u2013186 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2923_CR43","doi-asserted-by":"publisher","first-page":"2963","DOI":"10.1109\/TIP.2021.3056895","volume":"30","author":"C Bian","year":"2021","unstructured":"Bian, C., Feng, W., Wan, L., Wang, S.: Structural knowledge distillation for efficient skeleton-based action recognition. IEEE Trans. Image Process. 30, 2963\u20132976 (2021)","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"2923_CR44","doi-asserted-by":"publisher","first-page":"1250","DOI":"10.1109\/TCSVT.2021.3077512","volume":"32","author":"H Wu","year":"2021","unstructured":"Wu, H., Ma, X., Li, Y.: Spatiotemporal multimodal learning with 3D CNNs for video action recognition. IEEE Trans. Circuits Syst. Video Technol. 32(3), 1250\u20131261 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02923-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-023-02923-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02923-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T20:05:19Z","timestamp":1710878719000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-023-02923-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,18]]},"references-count":44,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["2923"],"URL":"https:\/\/doi.org\/10.1007\/s11760-023-02923-2","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,18]]},"assertion":[{"value":"10 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}