{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T23:11:43Z","timestamp":1784761903046,"version":"3.55.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62067002"],"award-info":[{"award-number":["62067002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62062033"],"award-info":[{"award-number":["62062033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"The Science and Technology Project of Transportation Department of Jiangxi Province","award":["2022X0040"],"award-info":[{"award-number":["2022X0040"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s10586-024-04326-5","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T14:02:36Z","timestamp":1709560956000},"page":"6591-6608","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MLANet: multi-level attention network with multi-scale feature fusion for crowd counting"],"prefix":"10.1007","volume":"27","author":[{"given":"Liyan","family":"Xiong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijuan","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhida","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,4]]},"reference":[{"key":"4326_CR1","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.\u00a0589\u2013597 (2016)","DOI":"10.1109\/CVPR.2016.70"},{"key":"4326_CR2","doi-asserted-by":"crossref","unstructured":"Yuhong Li,\u00a0Xiaofan Zhang,\u00a0Deming Chen.: CSRNet:\u00a0Dilated\u00a0Convolutional\u00a0Neural\u00a0Networks\u00a0for\u00a0Understanding\u00a0the\u00a0Highly\u00a0Congested\u00a0Scenes.In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091\u20131100 (2018)","DOI":"10.1109\/CVPR.2018.00120"},{"key":"4326_CR3","doi-asserted-by":"crossref","unstructured":"Sam, D.B., Surya, S., Babu, R.V.: Switching convolutional neural network for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.\u00a04031\u20134039 IEEE (2017)","DOI":"10.1109\/CVPR.2017.429"},{"key":"4326_CR4","doi-asserted-by":"crossref","unstructured":"Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision, pp.\u00a0734\u2013750 (2018)","DOI":"10.1007\/978-3-030-01228-1_45"},{"key":"4326_CR5","unstructured":"Weizhe Liu,\u00a0Mathieu Salzmann,\u00a0Pascal Fua.: Context-Aware\u00a0Crowd\u00a0Counting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099\u20135108 (2019)"},{"key":"4326_CR6","doi-asserted-by":"crossref","unstructured":"Liu, L., Qiu, Z., Li, G., Liu, S., Ouyang, W., and Lin, L.: Crowd counting with deep structured scale integration network. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1774\u20131783 (2019)","DOI":"10.1109\/ICCV.2019.00186"},{"key":"4326_CR7","doi-asserted-by":"crossref","unstructured":"Zhang, A., Y ue, L., Shen, J., Zhu, F., Zhen, X., Cao, X., and Shao, L.: Attentional neural fields for crowd counting. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5713\u20135722 (2019)","DOI":"10.1109\/ICCV.2019.00581"},{"issue":"8","key":"4326_CR8","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TPAMI.2014.2300479","volume":"36","author":"P Doll\u00e1r","year":"2014","unstructured":"Doll\u00e1r, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532\u20131545 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"4326_CR9","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1109\/TPAMI.2009.204","volume":"32","author":"Z Lin","year":"2010","unstructured":"Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 604\u2013618 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"4326_CR10","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1109\/TPAMI.2011.155","volume":"34","author":"P Dollar","year":"2011","unstructured":"Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: An evaluation of the state of the art Mach. IEEE Trans. Pat. Anal. Mach. Intell. 34(4), 743\u2013761 (2011)","journal-title":"IEEE Trans. Pat. Anal. Mach. Intell."},{"key":"4326_CR11","doi-asserted-by":"crossref","unstructured":"Chan, A.B., Liang, Z.-S.J., Vasconcelos, N.: Privacy preserving crowd monitoring: counting people without people models or tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.\u00a01\u20137 (2008)","DOI":"10.1109\/CVPR.2008.4587569"},{"key":"4326_CR12","doi-asserted-by":"crossref","unstructured":"Chan, A. B., V asconcelos, N.: Bayesian poisson regression for crowd counting. In: Proceedings of the IEEE 12th International Conference on Computer Vision, pp. 545\u2013551 (2009)","DOI":"10.1109\/ICCV.2009.5459191"},{"issue":"1","key":"4326_CR13","doi-asserted-by":"publisher","first-page":"231240","DOI":"10.1155\/2010\/231240","volume":"2010","author":"D Conte","year":"2010","unstructured":"Conte, D., Foggia, P., Percannella, G., Tufano, F., Vento, M.: A method for counting moving people in video surveillance videos. EURASIP J. Adv. Signal Process. 2010(1), 231240 (2010)","journal-title":"EURASIP J. Adv. Signal Process."},{"issue":"2","key":"4326_CR14","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/s11263-006-0027-7","volume":"75","author":"B Wu","year":"2007","unstructured":"Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. Int. J. Comput. Vision 75(2), 247\u2013266 (2007)","journal-title":"Int. J. Comput. Vision"},{"key":"4326_CR15","doi-asserted-by":"crossref","unstructured":"Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid CNNs. In: Proceedings of the IEEE International Conference on Computer Vision, pp.\u00a01861\u20131870 (2017)","DOI":"10.1109\/ICCV.2017.206"},{"key":"4326_CR16","doi-asserted-by":"crossref","unstructured":"Sindagi, V.A., Patel, V.M.: Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. In: Proceedings of the 14th EEE International Conference on Advanced Video and Signal Based Surveillance pp. 1\u20136 (2017)","DOI":"10.1109\/AVSS.2017.8078491"},{"issue":"4","key":"4326_CR17","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1109\/TITS.2011.2132759","volume":"12","author":"J Zhang","year":"2011","unstructured":"Zhang, J., Tan, B., Sha, F., He, L.: Predicting pedestrian counts in crowded scenes with rich and high-dimensional features. IEEE Trans. Intell. Transp. Syst. 12(4), 1037\u20131046 (2011)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"4326_CR18","doi-asserted-by":"crossref","unstructured":"Liu, J., Gao, C., Meng, D., Hauptmann, A.G.: Decidenet: counting varying density crowds through attention guided detection and density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197\u20135206 (2018)","DOI":"10.1109\/CVPR.2018.00545"},{"key":"4326_CR19","doi-asserted-by":"crossref","unstructured":"Boominathan, L., Kruthiventi, S. S., and Babu, R. V.: Crowdnet: A deep convolutional network for dense crowd counting. In: Proceedings of the 24th ACM international conference on Multimedia, pp. 640\u2013644 (2016)","DOI":"10.1145\/2964284.2967300"},{"key":"4326_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2019.08.018","volume":"363","author":"J Gao","year":"2019","unstructured":"Gao, J., Wang, Qi., Yuan, Y.: SCAR: Spatial-\/channel-wise attention regression networks for crowd counting. Neurocomputing 363, 1\u20138 (2019)","journal-title":"Neurocomputing"},{"key":"4326_CR21","doi-asserted-by":"crossref","unstructured":"Zhang L., Shi M. and Chen Q.: Crowd Counting via Scale-Adaptive Convolutional Neural Network. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, pp. 1113\u20131121 (2018)","DOI":"10.1109\/WACV.2018.00127"},{"key":"4326_CR22","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.neucom.2018.10.058","volume":"329","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Zhou, C., Chang, F., Kot, A.C.: Multi-resolution attention convolutional neural network for crowd counting. Neurocomputing 329, 144\u2013152 (2019)","journal-title":"Neurocomputing"},{"key":"4326_CR23","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1007\/s11760-021-01903-8","volume":"15","author":"P Li","year":"2021","unstructured":"Li, P., Zhang, M., Wan, J., Jiang, M.: Multi-scale guided attention network for crowd counting. Signal Image Video Process 15, 1663\u20131670 (2021)","journal-title":"Signal Image Video Process"},{"issue":"8","key":"4326_CR24","doi-asserted-by":"publisher","first-page":"4776","DOI":"10.1109\/TITS.2020.2983475","volume":"22","author":"X Ding","year":"2020","unstructured":"Ding, X., He, F., Lin, Z., Wang, Y., Guo, H., Huang, Y.: Crowd density estimation using fusion of multi-layer features. IEEE Transact. Intell. Transport. Syst. 22(8), 4776\u20134787 (2020)","journal-title":"IEEE Transact. Intell. Transport. Syst."},{"key":"4326_CR25","doi-asserted-by":"crossref","unstructured":"Guo, D., Li, K., Zha, Z.-J., and Wang, M..: DADNet: Dilated-Attention-Deformable ConvNet for Crowd Counting. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 1823\u20131832 (2019)","DOI":"10.1145\/3343031.3350881"},{"key":"4326_CR26","doi-asserted-by":"crossref","unstructured":"Marsden, M., McGuinness, K.; Little, S. and E. O\u2019Connor, N.:\u00a0Fully Convolutional Crowd Counting on Highly Congested Scenes. VISIGRAPP 27\u201333 (2017)","DOI":"10.5220\/0006097300270033"},{"key":"4326_CR27","doi-asserted-by":"crossref","unstructured":"H. Idrees, I. Saleemi, C. Seibert and M. Shah: Multi-source Multi-scale Counting in Extremely Dense Crowd Images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547\u20132554 (2013)","DOI":"10.1109\/CVPR.2013.329"},{"key":"4326_CR28","unstructured":"Zhang C., Li H., Wang X., Yang X.: Cross-scene crowd counting via deep convolutional neural networks.\u00a0In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 833\u2013841 (2015)"},{"key":"4326_CR29","doi-asserted-by":"crossref","unstructured":"Ding X., Lin Z., He F., Wang Y. and Huang Y.: A Deeply-Recursive Convolutional Network For Crowd Counting. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1942\u20131946 (2018)","DOI":"10.1109\/ICASSP.2018.8461772"},{"key":"4326_CR30","doi-asserted-by":"publisher","first-page":"13929","DOI":"10.1007\/s11042-022-13920-x","volume":"82","author":"L Xiong","year":"2023","unstructured":"Xiong, L., Yi, H., Huang, X., et al.: An efficient multi-scale contextual feature fusion network for counting crowds with varying densities and scales. Multimed Tools Appl 82, 13929\u201313949 (2023)","journal-title":"Multimed Tools Appl"},{"key":"4326_CR31","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.neucom.2020.04.139","volume":"404","author":"S Wang","year":"2020","unstructured":"Wang, S., Lu, Y., Zhou, T., et al.: SCLNet: spatial context learning network for congested crowd counting. Neurocomputing 404, 227\u2013239 (2020)","journal-title":"Neurocomputing"},{"issue":"10","key":"4326_CR32","doi-asserted-by":"publisher","first-page":"3486","DOI":"10.1109\/TCSVT.2019.2919139","volume":"30","author":"J Gao","year":"2019","unstructured":"Gao, J., Wang, Q., Li, X.: PCC-net: perspective crowd counting via spatial convolutional network. IEEE T. Circ. Syst Vid. 30(10), 3486\u20133498 (2019)","journal-title":"IEEE T. Circ. Syst Vid."},{"key":"4326_CR33","doi-asserted-by":"crossref","unstructured":"Jiang, X., Zhang, L., Xu, M., Zhang, T., Lv, P ., Zhou, B., & Pang, Y.: Attention scaling for crowd counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4706\u20134715 (2020)","DOI":"10.1109\/CVPR42600.2020.00476"},{"key":"4326_CR34","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.neucom.2019.03.065","volume":"350","author":"J Ma","year":"2019","unstructured":"Ma, J., Dai, Y., Tan, Y.P.: Atrous convolutions spatial pyramid network for crowd counting and density estimation. Neurocomputing 350, 91\u2013101 (2019)","journal-title":"Neurocomputing"},{"key":"4326_CR35","doi-asserted-by":"publisher","first-page":"10472","DOI":"10.1007\/s10489-022-03967-6","volume":"53","author":"L Liang","year":"2023","unstructured":"Liang, L., Zhao, H., Zhou, F., et al.: PDDNet: lightweight congested crowd counting via pyramid depth-wise dilated convolution. Appl. Intell. 53, 10472\u201310484 (2023). https:\/\/doi.org\/10.1007\/s10489-022-03967-6","journal-title":"Appl. Intell."},{"key":"4326_CR36","doi-asserted-by":"crossref","unstructured":"Jiang X., Xiao Z., Zhang B. et al.: Crowd counting and density estimation by trellis encoder\u2013decoder networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6133\u20136142 (2019)","DOI":"10.1109\/CVPR.2019.00629"},{"key":"4326_CR37","doi-asserted-by":"crossref","unstructured":"Shi M., Yang Z., Xu C., Chen Q.: Revisiting perspective information for efficient crowd counting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7279\u20137288 (2019)","DOI":"10.1109\/CVPR.2019.00745"},{"key":"4326_CR38","doi-asserted-by":"crossref","unstructured":"Rong L., Li C.: Coarse-and fine-grained attention network with background-aware loss for crowd density map estimation. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, pp. 3674\u20133683 (2021)","DOI":"10.1109\/WACV48630.2021.00372"},{"key":"4326_CR39","doi-asserted-by":"crossref","unstructured":"Wang J., Jiang W., Ma L., Liu W., Xu Y.: Bidirectional attentive fusion with context gating for dense video captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7190\u20137198 (2018)","DOI":"10.1109\/CVPR.2018.00751"},{"key":"4326_CR40","unstructured":"Kingma DP, Ba J: Adam: a method for stochastic optimization. arXiv preprint arXiv: 1412.6980(2014)"},{"key":"4326_CR41","doi-asserted-by":"publisher","first-page":"3259","DOI":"10.1007\/s00530-023-01194-8","volume":"29","author":"L Xiong","year":"2023","unstructured":"Xiong, L., Li, Z., Huang, X., et al.: TFA-CNN: an efficient method for dealing with crowding and noise problems in crowd counting. Multimed. Syst. 29, 3259\u20133276 (2023)","journal-title":"Multimed. Syst."},{"issue":"6","key":"4326_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-021-3445-y","volume":"65","author":"D Liang","year":"2022","unstructured":"Liang, D., Chen, X., Wei, Xu., Zhou, Yu., Bai, X.: TransCrowd: weakly-supervised crowd counting with transformers. Sci. China Inf. Sci. 65(6), 1\u201314 (2022)","journal-title":"Sci. China Inf. Sci."},{"key":"4326_CR43","unstructured":"Ma, Y.: Inception-based\u00a0crowd\u00a0counting\u00a0-\u00a0being\u00a0fast\u00a0while\u00a0remaining\u00a0accurate. arXiv https:\/\/arxiv.org\/abs\/2210.09796v1 (2022)"},{"issue":"3","key":"4326_CR44","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s00371-021-02383-0","volume":"39","author":"Z Li","year":"2023","unstructured":"Li, Z., Shuhua, Lu., Dong, Y., Guo, J.: MSFFA: a multi-scale feature fusion and attention mechanism network for crowd counting. Vis. Comput. 39(3), 1045\u20131056 (2023)","journal-title":"Vis. Comput."},{"key":"4326_CR45","doi-asserted-by":"publisher","first-page":"9285","DOI":"10.1007\/s10489-022-03954-x","volume":"53","author":"S Aldhaheri","year":"2023","unstructured":"Aldhaheri, S., Alotaibi, R., Alzahrani, B., et al.: MACC net: multi-task attention crowd counting network. Appl. Intell. 53, 9285\u20139297 (2023). https:\/\/doi.org\/10.1007\/s10489-022-03954-x","journal-title":"Appl. Intell."},{"key":"4326_CR46","doi-asserted-by":"publisher","first-page":"21891","DOI":"10.1007\/s10489-023-04641-1","volume":"53","author":"D Wu","year":"2023","unstructured":"Wu, D., Fan, Z., Yi, S.: Crowd counting based on multi-level multi-scale feature. Appl. Intell. 53, 21891\u201321901 (2023). https:\/\/doi.org\/10.1007\/s10489-023-04641-1","journal-title":"Appl. Intell."},{"key":"4326_CR47","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s00371-021-02313-0","volume":"39","author":"L Zhang","year":"2023","unstructured":"Zhang, L., Yan, L., Zhang, M., et al.: T2CNN: a novel method for crowd counting via two-task convolutional neural network. Vis. Comput. 39, 73\u201385 (2023)","journal-title":"Vis. Comput."},{"key":"4326_CR48","doi-asserted-by":"publisher","first-page":"15436","DOI":"10.1007\/s10489-022-03263-3","volume":"52","author":"Y Shi","year":"2022","unstructured":"Shi, Y., Sang, J., Wu, Z., et al.: MGSNet: a multi-scale and gated spatial attention network for crowd counting. Appl. Intell. 52, 15436\u201315446 (2022)","journal-title":"Appl. Intell."},{"key":"4326_CR49","doi-asserted-by":"publisher","first-page":"e902","DOI":"10.7717\/peerj-cs.902","volume":"8","author":"P Li","year":"2022","unstructured":"Li, P., Zhang, M., Wan, J., Jiang, M.: DMPNet: densely connected multi-scale pyramid networks for crowd counting. PeerJ Comput. Sci. 8, e902 (2022)","journal-title":"PeerJ Comput. Sci."},{"key":"4326_CR50","doi-asserted-by":"publisher","first-page":"2671","DOI":"10.1007\/s00371-022-02485-3","volume":"39","author":"B Li","year":"2023","unstructured":"Li, B., Zhang, Y., Xu, H., et al.: CCST: crowd counting with swin transformer. Vis. Comput. 39, 2671\u20132682 (2023). https:\/\/doi.org\/10.1007\/s00371-022-02485-3","journal-title":"Vis. Comput."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04326-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-04326-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04326-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T19:44:00Z","timestamp":1731527040000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-04326-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,4]]},"references-count":50,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["4326"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-04326-5","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,4]]},"assertion":[{"value":"21 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2024","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 that there are no competing interests related to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}