{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T04:15:57Z","timestamp":1767845757954,"version":"3.49.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T00:00:00Z","timestamp":1718064000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T00:00:00Z","timestamp":1718064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62172212"],"award-info":[{"award-number":["62172212"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s10586-024-04608-y","type":"journal-article","created":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T14:26:30Z","timestamp":1718115990000},"page":"12323-12340","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["MDA-YOLO Person: a 2D human pose estimation model based on YOLO detection framework"],"prefix":"10.1007","volume":"27","author":[{"given":"Chengang","family":"Dong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhao","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liyan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,11]]},"reference":[{"key":"4608_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.127125","volume":"570","author":"M Xu","year":"2024","unstructured":"Xu, M., Wang, Y., Xu, B., Zhang, J., Ren, J., Huang, Z., Poslad, S., Xu, P.: A critical analysis of image-based camera pose estimation techniques. Neurocomputing 570, 127125 (2024)","journal-title":"Neurocomputing"},{"issue":"3","key":"4608_CR2","doi-asserted-by":"publisher","first-page":"7135","DOI":"10.1007\/s11042-023-15633-1","volume":"83","author":"R Ghosh","year":"2024","unstructured":"Ghosh, R.: Product identification in retail stores by combining faster R-CNN and recurrent neural network. Multimedia Tools Appl. 83(3), 7135\u20137158 (2024)","journal-title":"Multimedia Tools Appl."},{"key":"4608_CR3","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems 30 (2017)"},{"key":"4608_CR4","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"4608_CR5","doi-asserted-by":"crossref","unstructured":"Liu, H., Chen, Q., Tan, Z., Liu, J.-J., Wang, J., Su, X., Li, X., Yao, K., Han, J., Ding, E., et al.: Group pose: a simple baseline for end-to-end multi-person pose estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15029\u201315038 (2023)","DOI":"10.1109\/ICCV51070.2023.01380"},{"issue":"06","key":"4608_CR6","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., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(06), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4608_CR7","doi-asserted-by":"crossref","unstructured":"Luo, Z., Wang, Z., Huang, Y., Wang, L., Tan, T., Zhou, E.: Rethinking the heatmap regression for bottom-up human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13264\u201313273 (2021)","DOI":"10.1109\/CVPR46437.2021.01306"},{"key":"4608_CR8","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/978-1-4842-6168-2_11","volume-title":"Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization","author":"B Koonce","year":"2021","unstructured":"Koonce, B., Koonce, B.: Mobilenetv3. In: Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization, pp. 125\u2013144. Apress, Berkeley (2021)"},{"key":"4608_CR9","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., Sun, J.: RepVGG: making VGG-style convnets great again. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13733\u201313742 (2021)","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"4608_CR10","unstructured":"Xu, S., Wang, X., Lv, W., Chang, Q., Cui, C., Deng, K., Wang, G., Dang, Q., Wei, S., Du, Y., et al.: PP-YOLOE: an evolved version of YOLO. arXiv preprint arXiv:2203.16250 (2022)"},{"key":"4608_CR11","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021)"},{"key":"4608_CR12","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464\u20137475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"4608_CR13","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"4608_CR14","doi-asserted-by":"publisher","first-page":"3048","DOI":"10.1109\/TMM.2021.3068576","volume":"23","author":"X Ma","year":"2021","unstructured":"Ma, X., Guo, J., Sansom, A., McGuire, M., Kalaani, A., Chen, Q., Tang, S., Yang, Q., Fu, S.: Spatial pyramid attention for deep convolutional neural networks. IEEE Trans. Multimedia 23, 3048\u20133058 (2021)","journal-title":"IEEE Trans. Multimedia"},{"key":"4608_CR15","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: Scaled-YOLOv4: scaling cross stage partial network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13029\u201313038 (2021)","DOI":"10.1109\/CVPR46437.2021.01283"},{"key":"4608_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107338","volume":"127","author":"X Dong","year":"2024","unstructured":"Dong, X., Wang, X., Li, B., Wang, H., Chen, G., Cai, M.: YH-pose: human pose estimation in complex coal mine scenarios. Eng. Appl. Artif. Intell. 127, 107338 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4608_CR17","doi-asserted-by":"crossref","unstructured":"Maji, D., Nagori, S., Mathew, M., Poddar, D.: YOLO-pose: Enhancing yolo for multi person pose estimation using object keypoint similarity loss. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2637\u20132646 (2022)","DOI":"10.1109\/CVPRW56347.2022.00297"},{"issue":"9","key":"4608_CR18","doi-asserted-by":"publisher","first-page":"2639","DOI":"10.1007\/s11263-021-01482-8","volume":"129","author":"J Zhang","year":"2021","unstructured":"Zhang, J., Chen, Z., Tao, D.: Towards high performance human keypoint detection. Int. J. Comput. Vis. 129(9), 2639\u20132662 (2021)","journal-title":"Int. J. Comput. Vis."},{"key":"4608_CR19","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, C., Zhu, H., Mao, Y., Fang, H.-S., Lu, C.: CrowdPose: efficient crowded scenes pose estimation and a new benchmark. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10863\u201310872 (2019)","DOI":"10.1109\/CVPR.2019.01112"},{"key":"4608_CR20","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., et al.: YOLOv6: a single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976 (2022)"},{"key":"4608_CR21","unstructured":"Zhang, H., Ouyang, H., Liu, S., Qi, X., Shen, X., Yang, R., Jia, J.: Human pose estimation with spatial contextual information. arXiv preprint arXiv:1901.01760 (2019)"},{"key":"4608_CR22","unstructured":"Bertasius, G., Feichtenhofer, C., Tran, D., Shi, J., Torresani, L.: Learning temporal pose estimation from sparsely-labeled videos. In: Advances in neural information processing systems 32 (2019)"},{"key":"4608_CR23","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5693\u20135703 (2019)","DOI":"10.1109\/CVPR.2019.00584"},{"key":"4608_CR24","first-page":"38571","volume":"35","author":"Y Xu","year":"2022","unstructured":"Xu, Y., Zhang, J., Zhang, Q., Tao, D.: ViTPose: simple vision transformer baselines for human pose estimation. Adv. Neural Inf. Process. Syst. 35, 38571\u201338584 (2022)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"4608_CR25","doi-asserted-by":"publisher","first-page":"1212","DOI":"10.1109\/TPAMI.2023.3330016","volume":"46","author":"Y Xu","year":"2023","unstructured":"Xu, Y., Zhang, J., Zhang, Q., Tao, D.: ViTPose++: vision transformer for generic body pose estimation. IEEE Trans. Pattern Anal. Mach. Intell. 46, 1212\u20131230 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4608_CR26","doi-asserted-by":"crossref","unstructured":"Osokin, D.: Real-time 2D multi-person pose estimation on CPU: lightweight openpose. arXiv preprint arXiv:1811.12004 (2018)","DOI":"10.5220\/0007555407440748"},{"key":"4608_CR27","doi-asserted-by":"crossref","unstructured":"Cheng, B., Xiao, B., Wang, J., Shi, H., Huang, T.S., Zhang, L.: HigherHRNet: scale-aware representation learning for bottom-up human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5386\u20135395 (2020)","DOI":"10.1109\/CVPR42600.2020.00543"},{"key":"4608_CR28","doi-asserted-by":"crossref","unstructured":"Bras\u00f3, G., Kister, N., Leal-Taix\u00e9, L.: The center of attention: center-keypoint grouping via attention for multi-person pose estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11853\u201311863 (2021)","DOI":"10.1109\/ICCV48922.2021.01164"},{"key":"4608_CR29","doi-asserted-by":"crossref","unstructured":"Geng, Z., Sun, K., Xiao, B., Zhang, Z., Wang, J.: Bottom-up human pose estimation via disentangled keypoint regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14676\u201314686 (2021)","DOI":"10.1109\/CVPR46437.2021.01444"},{"key":"4608_CR30","unstructured":"Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)"},{"key":"4608_CR31","doi-asserted-by":"crossref","unstructured":"Walawalkar, D., Shen, Z., Liu, Z., Savvides, M.: Attentive CutMix: an enhanced data augmentation approach for deep learning based image classification. arXiv preprint arXiv:2003.13048 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053994"},{"key":"4608_CR32","doi-asserted-by":"crossref","unstructured":"Guo, H.: Nonlinear Mixup: out-of-manifold data augmentation for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 4044\u20134051 (2020)","DOI":"10.1609\/aaai.v34i04.5822"},{"key":"4608_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Single-shot refinement neural network for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4203\u20134212 (2018)","DOI":"10.1109\/CVPR.2018.00442"},{"key":"4608_CR34","doi-asserted-by":"crossref","unstructured":"Li, S., Yang, L., Huang, J., Hua, X.-S., Zhang, L.: Dynamic anchor feature selection for single-shot object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6609\u20136618 (2019)","DOI":"10.1109\/ICCV.2019.00671"},{"issue":"17","key":"4608_CR35","doi-asserted-by":"publisher","first-page":"14881","DOI":"10.1007\/s00521-022-07264-8","volume":"34","author":"F Xu","year":"2022","unstructured":"Xu, F., Wang, H., Sun, X., Fu, X.: Refined marine object detector with attention-based spatial pyramid pooling networks and bidirectional feature fusion strategy. Neural Comput. Appl. 34(17), 14881\u201314894 (2022)","journal-title":"Neural Comput. Appl."},{"key":"4608_CR36","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"4608_CR37","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"4608_CR38","doi-asserted-by":"crossref","unstructured":"Christlein, V., Spranger, L., Seuret, M., Nicolaou, A., Kr\u00e1l, P., Maier, A.: Deep generalized max pooling. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1090\u20131096. IEEE (2019)","DOI":"10.1109\/ICDAR.2019.00177"},{"key":"4608_CR39","doi-asserted-by":"crossref","unstructured":"Moskvyak, O., Maire, F., Dayoub, F., Baktashmotlagh, M.: Keypoint-aligned embeddings for image retrieval and re-identification. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 676\u2013685 (2021)","DOI":"10.1109\/WACV48630.2021.00072"},{"key":"4608_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, F., Zhu, X., Dai, H., Ye, M., Zhu, C.: Distribution-aware coordinate representation for human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7093\u20137102 (2020)","DOI":"10.1109\/CVPR42600.2020.00712"},{"key":"4608_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11704-019-8266-2","volume":"14","author":"G Hua","year":"2020","unstructured":"Hua, G., Li, L., Liu, S.: Multipath affinage stacked-hourglass networks for human pose estimation. Front. Comput. Sci. 14, 1\u201312 (2020)","journal-title":"Front. Comput. Sci."},{"key":"4608_CR42","doi-asserted-by":"crossref","unstructured":"McNally, W., Vats, K., Wong, A., McPhee, J.: Rethinking keypoint representations: modeling keypoints and poses as objects for multi-person human pose estimation. In: European Conference on Computer Vision, pp. 37\u201354. Springer (2022)","DOI":"10.1007\/978-3-031-20068-7_3"},{"key":"4608_CR43","doi-asserted-by":"crossref","unstructured":"Shi, D., Wei, X., Li, L., Ren, Y., Tan, W.: End-to-end multi-person pose estimation with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11069\u201311078 (2022)","DOI":"10.1109\/CVPR52688.2022.01079"},{"key":"4608_CR44","unstructured":"Yang, J., Zeng, A., Liu, S., Li, F., Zhang, R., Zhang, L.: Explicit box detection unifies end-to-end multi-person pose estimation. arXiv preprint arXiv:2302.01593 (2023)"},{"key":"4608_CR45","doi-asserted-by":"crossref","unstructured":"Jeon, H.-J., Lang, S., Vogel, C., Behrens, R.: An integrated real-time monocular human pose & shape estimation pipeline for edge devices. In: 2023 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1\u20136 (2023). IEEE","DOI":"10.1109\/ROBIO58561.2023.10354994"},{"key":"4608_CR46","first-page":"12464","volume":"35","author":"Y Xiao","year":"2022","unstructured":"Xiao, Y., Su, K., Wang, X., Yu, D., Jin, L., He, M., Yuan, Z.: QueryPose: sparse multi-person pose regression via spatial-aware part-level query. Adv. Neural Inf. Process. Syst. 35, 12464\u201312477 (2022)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"4608_CR47","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., Zhao, Q.: TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2778\u20132788 (2021)","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"4608_CR48","doi-asserted-by":"crossref","unstructured":"Ren, Z., Zhou, Y., Chen, Y., Zhou, R., Gao, Y.: Efficient human pose estimation by maximizing fusion and high-level spatial attention. In: 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), pp. 01\u201306. IEEE (2021)","DOI":"10.1109\/FG52635.2021.9666981"},{"key":"4608_CR49","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713\u201313722 (2021)","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"4608_CR50","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"4608_CR51","unstructured":"Yu, Z., Huang, H., Chen, W., Su, Y., Liu, Y., Wang, X.: YOLO-FaceV2: a scale and occlusion aware face detector. arXiv preprint arXiv:2208.02019 (2022)"},{"key":"4608_CR52","doi-asserted-by":"crossref","unstructured":"Chen, J., Mai, H., Luo, L., Chen, X., Wu, K.: Effective feature fusion network in BIFPN for small object detection. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 699\u2013703 (2021). IEEE","DOI":"10.1109\/ICIP42928.2021.9506347"},{"key":"4608_CR53","doi-asserted-by":"crossref","unstructured":"Dai, X., Chen, Y., Yang, J., Zhang, P., Yuan, L., Zhang, L.: Dynamic DETR: end-to-end object detection with dynamic attention. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2988\u20132997 (2021)","DOI":"10.1109\/ICCV48922.2021.00298"},{"key":"4608_CR54","doi-asserted-by":"crossref","unstructured":"Chen, W., Zhao, Q., Liu, J., Wang, Z., Liu, Y., Yao, M.: Improved YOLO-pose crowd pose estimation. In: Proceedings of the 2023 6th International Conference on Signal Processing and Machine Learning, pp. 201\u2013206 (2023)","DOI":"10.1145\/3614008.3614040"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04608-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-04608-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04608-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T21:51:29Z","timestamp":1727301089000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-04608-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,11]]},"references-count":54,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["4608"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-04608-y","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,11]]},"assertion":[{"value":"22 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 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":"We have conducted a thorough assessment of both financial and non-financial affiliations that could potentially create a conflict of interest with the research presented. We unequivocally declare that no conflict of interest have been identified that could in any way introduce bias or influence the outcomes of our study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}