{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T23:19:41Z","timestamp":1773271181421,"version":"3.50.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s13042-026-03025-4","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T08:20:12Z","timestamp":1773217212000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficientgls-pose: enhancing human pose estimation efficiency and accuracy via global\u2013local collaborative modeling"],"prefix":"10.1007","volume":"17","author":[{"given":"Yongfeng","family":"Qi","sequence":"first","affiliation":[]},{"given":"Yuanzhe","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Mingsen","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Heng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Gaoyang","family":"Dai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,11]]},"reference":[{"issue":"6","key":"3025_CR1","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"key":"3025_CR2","doi-asserted-by":"crossref","unstructured":"Donahue J, Anne\u00a0Hendricks L, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T (2015) Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2625\u20132634","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"3025_CR3","doi-asserted-by":"crossref","unstructured":"Ren Z, Zhou Y, Chen Y, Zhou R, Gao Y (2021) 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","DOI":"10.1109\/FG52635.2021.9666981"},{"key":"3025_CR4","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713\u201313722","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"3025_CR5","doi-asserted-by":"crossref","unstructured":"Liu Z, Hu H, Lin Y, Yao Z, Xie Z, Wei Y, Ning J, Cao Y, Zhang Z, Dong L (2022) Swin transformer v2: Scaling up capacity and resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12009\u201312019","DOI":"10.1109\/CVPR52688.2022.01170"},{"key":"3025_CR6","first-page":"38571","volume":"35","author":"Y Xu","year":"2022","unstructured":"Xu Y, Zhang J, Zhang Q, Tao D (2022) Vitpose: Simple vision transformer baselines for human pose estimation. Adv Neural Inf Process Syst 35:38571\u201338584","journal-title":"Adv Neural Inf Process Syst"},{"issue":"2","key":"3025_CR7","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 (2023) Vitpose++: vision transformer for generic body pose estimation. IEEE Trans Pattern Anal Mach Intell 46(2):1212\u20131230","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3025_CR8","first-page":"7281","volume":"34","author":"Y Yuan","year":"2021","unstructured":"Yuan Y, Fu R, Huang L, Lin W, Zhang C, Chen X, Wang J (2021) Hrformer: high-resolution vision transformer for dense predict. Adv Neural Inf Process Syst 34:7281\u20137293","journal-title":"Adv Neural Inf Process Syst"},{"key":"3025_CR9","doi-asserted-by":"crossref","unstructured":"Yang S, Quan Z, Nie M, Yang W (2021) Transpose: Keypoint localization via transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11802\u201311812","DOI":"10.1109\/ICCV48922.2021.01159"},{"key":"3025_CR10","unstructured":"Ge Z, Liu S, Wang F, Li Z, Sun J (2021) Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430"},{"key":"3025_CR11","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.neunet.2023.11.041","volume":"170","author":"C Liu","year":"2024","unstructured":"Liu C, Wang K, Li Q, Zhao F, Zhao K, Ma H (2024) Powerful-iou: more straightforward and faster bounding box regression loss with a nonmonotonic focusing mechanism. Neural Netw 170:276\u2013284","journal-title":"Neural Netw"},{"key":"3025_CR12","doi-asserted-by":"crossref","unstructured":"Newell A, Yang K, Deng J (2016) Stacked hourglass networks for human pose estimation. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, Proceedings, Part VIII 14, pp. 483\u2013499 (2016). Springer","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"3025_CR13","doi-asserted-by":"crossref","unstructured":"Chen Y, Wang Z, Peng Y, Zhang Z, Yu G, Sun J (2018) Cascaded pyramid network for multi-person pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7103\u20137112","DOI":"10.1109\/CVPR.2018.00742"},{"key":"3025_CR14","doi-asserted-by":"crossref","unstructured":"Xiao B, Wu H, Wei Y (2018) Simple baselines for human pose estimation and tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 466\u2013481","DOI":"10.1007\/978-3-030-01231-1_29"},{"key":"3025_CR15","doi-asserted-by":"crossref","unstructured":"Sun K, Xiao B, Liu D, Wang J (2019) 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","DOI":"10.1109\/CVPR.2019.00584"},{"key":"3025_CR16","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"key":"3025_CR17","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28"},{"key":"3025_CR18","doi-asserted-by":"crossref","unstructured":"Pishchulin L, Insafutdinov E, Tang S, Andres B, Andriluka M, Gehler PV, Schiele B (2016) Deepcut: Joint subset partition and labeling for multi person pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4929\u20134937","DOI":"10.1109\/CVPR.2016.533"},{"key":"3025_CR19","unstructured":"Newell A, Huang Z, Deng J (2017) Associative embedding: End-to-end learning for joint detection and grouping. Advances in neural information processing systems 30"},{"key":"3025_CR20","doi-asserted-by":"crossref","unstructured":"Cao Z, Simon T, Wei S-E, Sheikh Y (2017) Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291\u20137299","DOI":"10.1109\/CVPR.2017.143"},{"key":"3025_CR21","doi-asserted-by":"crossref","unstructured":"Kreiss S, Bertoni L, Pifpaf A (2018) Composite fields for human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11977\u201311986","DOI":"10.1109\/CVPR.2019.01225"},{"key":"3025_CR22","doi-asserted-by":"crossref","unstructured":"Cheng B, Xiao B, Wang J, Shi H, Huang TS, Zhang L (2020) 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","DOI":"10.1109\/CVPR42600.2020.00543"},{"key":"3025_CR23","unstructured":"Gu A, Dao T (2023) Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752"},{"key":"3025_CR24","unstructured":"Zhu L, Liao B, Zhang Q, Wang X, Liu W, Wang X (2024) Vision mamba: Efficient visual representation learning with bidirectional state space model. arXiv preprint arXiv:2401.09417"},{"key":"3025_CR25","unstructured":"Tan M, Le QV (2019) Mixconv: Mixed depthwise convolutional kernels. arXiv preprint arXiv:1907.09595"},{"key":"3025_CR26","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114 . PMLR"},{"key":"3025_CR27","doi-asserted-by":"crossref","unstructured":"Wang Y, Li M, Cai H, Chen W-M, Han S (2022) Lite pose: Efficient architecture design for 2d human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13126\u201313136","DOI":"10.1109\/CVPR52688.2022.01278"},{"key":"3025_CR28","doi-asserted-by":"crossref","unstructured":"Luo Z, Wang Z, Huang Y, Wang L, Tan T, Zhou E (2021) 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","DOI":"10.1109\/CVPR46437.2021.01306"},{"key":"3025_CR29","unstructured":"Wang X, Li G, Chen Y, Wen G (2024) Lightweight human pose estimation based on heatmap weighted loss function. Engineering Letters 32(11)"},{"issue":"12","key":"3025_CR30","doi-asserted-by":"publisher","first-page":"3392","DOI":"10.1049\/ipr2.12871","volume":"17","author":"X Lv","year":"2023","unstructured":"Lv X, Hao W, Tian L, Han J, Chen Y, Cai Z (2023) Litedekr: end-to-end lite 2d human pose estimation network. IET Image Proc 17(12):3392\u20133400","journal-title":"IET Image Proc"},{"key":"3025_CR31","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"3025_CR32","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) Eca-net: Efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534\u201311542","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"3025_CR33","unstructured":"Liu Y, Shao Z, Hoffmann N (2021) Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv preprint arXiv:2112.05561"},{"key":"3025_CR34","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"3025_CR35","unstructured":"Lin Z, Garg P, Banerjee A, Magid SA, Sun D, Zhang Y, Van\u00a0Gool L, Wei D, Pfister H (2022)Revisiting rcan: Improved training for image super-resolution. arXiv preprint arXiv:2201.11279"},{"key":"3025_CR36","doi-asserted-by":"crossref","unstructured":"Wang X, Chan KC, Yu K, Dong C, Change\u00a0Loy C (2019) Edvr: Video restoration with enhanced deformable convolutional networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 0\u20130","DOI":"10.1109\/CVPRW.2019.00247"},{"key":"3025_CR37","doi-asserted-by":"crossref","unstructured":"Apostolidis E, Balaouras G, Mezaris V, Patras I (2021) Combining global and local attention with positional encoding for video summarization. In: 2021 IEEE International Symposium on Multimedia (ISM), pp. 226\u2013234 . IEEE","DOI":"10.1109\/ISM52913.2021.00045"},{"key":"3025_CR38","doi-asserted-by":"crossref","unstructured":"Xia Z, Pan X, Song S, Li LE, Huang G (2023) Dat++: Spatially dynamic vision transformer with deformable attention. arXiv preprint arXiv:2309.01430","DOI":"10.1109\/CVPR52688.2022.00475"},{"issue":"12","key":"3025_CR39","doi-asserted-by":"publisher","first-page":"6142","DOI":"10.1007\/s11263-024-02173-w","volume":"132","author":"L Zhang","year":"2024","unstructured":"Zhang L, Lu J, Zheng S, Zhao X, Zhu X, Fu Y, Xiang T, Feng J, Torr PH (2024) Vision transformers: from semantic segmentation to dense prediction. Int J Comput Vis 132(12):6142\u20136162","journal-title":"Int J Comput Vis"},{"issue":"8","key":"3025_CR40","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s10462-025-11250-6","volume":"58","author":"Y Ding","year":"2025","unstructured":"Ding Y, Wang X, Yuan H, Qu M, Jian X (2025) Decoupling feature-driven and multimodal fusion attention for clothing-changing person re-identification. Artif Intell Rev 58(8):241","journal-title":"Artif Intell Rev"},{"issue":"5","key":"3025_CR41","doi-asserted-by":"publisher","first-page":"4337","DOI":"10.1007\/s11760-024-03076-6","volume":"18","author":"Y Ding","year":"2024","unstructured":"Ding Y, Mao R, Du G, Zhang L (2024) Clothes-eraser: clothing-aware controllable disentanglement for clothes-changing person re-identification. SIViP 18(5):4337\u20134348","journal-title":"SIViP"},{"key":"3025_CR42","doi-asserted-by":"crossref","unstructured":"Ding Y, Wu Y, Wu C, Qu M, Zhang L (2025) Person parsing-driven and text-guided for cloth-changing person re-identification. IEEE Internet of Things Journal","DOI":"10.1109\/JIOT.2025.3581183"},{"issue":"8","key":"3025_CR43","doi-asserted-by":"publisher","first-page":"8574","DOI":"10.1109\/TCYB.2021.3095305","volume":"52","author":"Z Zheng","year":"2021","unstructured":"Zheng Z, Wang P, Ren D, Liu W, Ye R, Hu Q, Zuo W (2021) Enhancing geometric factors in model learning and inference for object detection and instance segmentation. IEEE Trans Cybernet 52(8):8574\u20138586","journal-title":"IEEE Trans Cybernet"},{"key":"3025_CR44","doi-asserted-by":"crossref","unstructured":"Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759\u20138768","DOI":"10.1109\/CVPR.2018.00913"},{"key":"3025_CR45","doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le QV (2020) Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781\u201310790","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"3025_CR46","unstructured":"Glenn J (2023) Yolov8:object detection A cutting-edge real-time framework, [EB\/OL]. https:\/\/github.com\/ultralytics\/ultralytics\/tree\/v8.2.50.\/"},{"key":"3025_CR47","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","volume":"506","author":"Y-F Zhang","year":"2022","unstructured":"Zhang Y-F, Ren W, Zhang Z, Jia Z, Wang L, Tan T (2022) Focal and efficient iou loss for accurate bounding box regression. Neurocomputing 506:146\u2013157","journal-title":"Neurocomputing"},{"key":"3025_CR48","doi-asserted-by":"crossref","unstructured":"Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S (2019) Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 658\u2013666","DOI":"10.1109\/CVPR.2019.00075"},{"key":"3025_CR49","unstructured":"Gevorgyan Z (2022) Siou loss: More powerful learning for bounding box regression. arXiv preprint arXiv:2205.12740"},{"key":"3025_CR50","doi-asserted-by":"crossref","unstructured":"Papandreou G, Zhu T, Kanazawa N, Toshev A, Tompson J, Bregler C, Murphy K (2017) Towards accurate multi-person pose estimation in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4903\u20134911","DOI":"10.1109\/CVPR.2017.395"},{"key":"3025_CR51","doi-asserted-by":"crossref","unstructured":"Sun X, Xiao B, Wei F, Liang S, Wei Y (2018) Integral human pose regression. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 529\u2013545","DOI":"10.1007\/978-3-030-01231-1_33"},{"key":"3025_CR52","doi-asserted-by":"crossref","unstructured":"Zhang F, Zhu X, Dai H, Ye M, Zhu C (2020) Distribution-aware coordinate representation for human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7093\u20137102","DOI":"10.1109\/CVPR42600.2020.00712"},{"key":"3025_CR53","doi-asserted-by":"crossref","unstructured":"Liu H, Liu F, Fan X, Huang D (2021) Polarized self-attention: Towards high-quality pixel-wise regression. arXiv preprint arXiv:2107.00782","DOI":"10.1016\/j.neucom.2022.07.054"},{"issue":"8","key":"3025_CR54","doi-asserted-by":"publisher","first-page":"3429","DOI":"10.1007\/s00371-023-02953-4","volume":"39","author":"S Li","year":"2023","unstructured":"Li S, Dai J, Chen Z, Pan J (2023) A lightweight pose estimation network with multi-scale receptive field. Vis Comput 39(8):3429\u20133440","journal-title":"Vis Comput"},{"issue":"6","key":"3025_CR55","doi-asserted-by":"publisher","first-page":"3025","DOI":"10.3390\/app15063025","volume":"15","author":"H Tu","year":"2025","unstructured":"Tu H, Qiu Z, Yang K, Tan X, Zheng X (2025) Hp-yolo: aA lightweight real-time human pose estimation method. Appl Sci 15(6):3025","journal-title":"Appl Sci"},{"key":"3025_CR56","doi-asserted-by":"crossref","unstructured":"Sun X, Chen X (2025) Human pose estimation method based on grsa-yolov8n pose. In: Proceedings of the 2025 4th International Conference on Cyber Security, Artificial Intelligence and the Digital Economy, pp. 399\u2013403","DOI":"10.1145\/3729706.3729769"},{"key":"3025_CR57","doi-asserted-by":"crossref","unstructured":"Geng Z, Sun K, Xiao B, Zhang Z, Wang J (2021) 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","DOI":"10.1109\/CVPR46437.2021.01444"},{"key":"3025_CR58","doi-asserted-by":"crossref","unstructured":"Lan G, Wu Y, Hao Q (2024) Dir-bhrnet: A lightweight network for real-time vision-based multiperson pose estimation on smartphones. IEEE Transactions on Industrial Informatics","DOI":"10.1109\/TII.2024.3421511"},{"issue":"3","key":"3025_CR59","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1007\/s00521-024-10676-3","volume":"37","author":"G Han","year":"2025","unstructured":"Han G, Song C, Wang S, Wang H, Chen E, Wang G (2025) Occluded human pose estimation based on limb joint augmentation. Neural Comput Appl 37(3):1241\u20131253","journal-title":"Neural Comput Appl"},{"issue":"2","key":"3025_CR60","first-page":"189","volume":"48","author":"X Shi","year":"2025","unstructured":"Shi X, Zhang H (2025) Light-weight human pose estimation network based on enhanced feature fusion method. Electric Measure Technol 48(2):189\u2013198","journal-title":"Electric Measure Technol"},{"issue":"4","key":"3025_CR61","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1007\/s11554-021-01132-9","volume":"18","author":"C Neff","year":"2021","unstructured":"Neff C, Sheth A, Furgurson S, Middleton J, Tabkhi H (2021) Efficienthrnet: efficient and scalable high-resolution networks for real-time multi-person 2d human pose estimation. J Real-Time Image Proc 18(4):1037\u20131049","journal-title":"J Real-Time Image Proc"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03025-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03025-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03025-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T08:20:26Z","timestamp":1773217226000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03025-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,11]]},"references-count":61,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["3025"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03025-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,11]]},"assertion":[{"value":"26 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declare no Conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"190"}}