{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:02:00Z","timestamp":1757617320345,"version":"3.44.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"27","license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-20431-4","type":"journal-article","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T03:10:59Z","timestamp":1733109059000},"page":"32237-32259","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FA-MSVNet: multi-scale and multi-view feature aggregation methods for stereo 3D reconstruction"],"prefix":"10.1007","volume":"84","author":[{"given":"Yao","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqi","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen-Liang","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"20431_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2021.102073","volume":"69","author":"B Lu","year":"2021","unstructured":"Lu B, He Y, Wang H (2021) Stereo disparity optimization with depth change constraint based on a continuous video. Displays 69:102073","journal-title":"Displays"},{"key":"20431_CR2","doi-asserted-by":"publisher","first-page":"2106","DOI":"10.1109\/TIP.2022.3150297","volume":"31","author":"C Sui","year":"2022","unstructured":"Sui C, He K, Lyu C, Liu Y-H (2022) Accurate 3d reconstruction of dynamic objects by spatial-temporal multiplexing and motion-induced error elimination. IEEE Trans Image Process 31:2106\u20132121","journal-title":"IEEE Trans Image Process"},{"key":"20431_CR3","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.displa.2019.07.002","volume":"59","author":"C Yildirim","year":"2019","unstructured":"Yildirim C (2019) Cybersickness during vr gaming undermines game enjoyment: a mediation model. Displays 59:35\u201343","journal-title":"Displays"},{"key":"20431_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.displa.2019.02.001","volume":"57","author":"H Kang","year":"2019","unstructured":"Kang H, Ko J, Park H, Hong H (2019) Effect of outside view on attentiveness in using see-through type augmented reality device. Displays 57:1\u20136","journal-title":"Displays"},{"issue":"23","key":"20431_CR5","doi-asserted-by":"publisher","first-page":"35651","DOI":"10.1007\/s11042-023-14706-5","volume":"82","author":"Z Hongjin","year":"2023","unstructured":"Hongjin Z, Hui W, Gang M (2023) A new stereo matching energy model based on image local features. Multimed Tools Appl 82(23):35651\u201335684","journal-title":"Multimed Tools Appl"},{"key":"20431_CR6","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems. NIPS\u201917. Curran Associates Inc., Red Hook, NY, USA, pp 6000\u20136010"},{"key":"20431_CR7","doi-asserted-by":"crossref","unstructured":"Pan X, Xia Z, Song S, Li LE, Huang G (2021) 3d object detection with pointformer. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7463\u20137472","DOI":"10.1109\/CVPR46437.2021.00738"},{"key":"20431_CR8","doi-asserted-by":"crossref","unstructured":"Ranftl R, Bochkovskiy A, Koltun V (2021) Vision transformers for dense prediction. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 12179\u201312188","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"20431_CR9","doi-asserted-by":"crossref","unstructured":"Ding Y, Yuan W, Zhu Q, Zhang H, Liu X, Wang Y, Liu X (2022) Transmvsnet: global context-aware multi-view stereo network with transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8585\u20138594","DOI":"10.1109\/CVPR52688.2022.00839"},{"key":"20431_CR10","doi-asserted-by":"crossref","unstructured":"Wang X, Zhu Z, Huang G, Qin F, Ye Y, He Y, Chi X, Wang X (2022) Mvster: epipolar transformer for efficient multi-view stereo. In: European conference on computer vision. Springer, pp 573\u2013591","DOI":"10.1007\/978-3-031-19821-2_33"},{"key":"20431_CR11","first-page":"8564","volume":"35","author":"J Liao","year":"2022","unstructured":"Liao J, Ding Y, Shavit Y, Huang D, Ren S, Guo J, Feng W, Zhang K (2022) Wt-mvsnet: window-based transformers for multi-view stereo. Adv Neural Inf Process Syst 35:8564\u20138576","journal-title":"Adv Neural Inf Process Syst"},{"issue":"19","key":"20431_CR12","doi-asserted-by":"publisher","first-page":"7659","DOI":"10.3390\/s22197659","volume":"22","author":"R Jia","year":"2022","unstructured":"Jia R, Chen X, Cui J, Hu Z (2022) Mvs-t: a coarse-to-fine multi-view stereo network with transformer for low-resolution images 3d reconstruction. Sensors 22(19):7659","journal-title":"Sensors"},{"key":"20431_CR13","doi-asserted-by":"crossref","unstructured":"Yao Y, Luo Z, Li S, Fang T, Quan L (2018) Mvsnet: depth inference for unstructured multi-view stereo. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 767\u2013783","DOI":"10.1007\/978-3-030-01237-3_47"},{"key":"20431_CR14","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"20431_CR15","doi-asserted-by":"crossref","unstructured":"Gu X, Fan Z, Zhu S, Dai Z, Tan F, Tan P (2020) Cascade cost volume for high-resolution multi-view stereo and stereo matching. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2495\u20132504","DOI":"10.1109\/CVPR42600.2020.00257"},{"key":"20431_CR16","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"20431_CR17","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s11263-016-0902-9","volume":"120","author":"H Aan\u00e6s","year":"2016","unstructured":"Aan\u00e6s H, Jensen RR, Vogiatzis G, Tola E, Dahl AB (2016) Large-scale data for multiple-view stereopsis. Int J Comput Vision 120:153\u2013168","journal-title":"Int J Comput Vision"},{"issue":"4","key":"20431_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3072959.3073599","volume":"36","author":"A Knapitsch","year":"2017","unstructured":"Knapitsch A, Park J, Zhou Q-Y, Koltun V (2017) Tanks and temples: benchmarking large-scale scene reconstruction. ACM Trans Graphics (ToG) 36(4):1\u201313","journal-title":"ACM Trans Graphics (ToG)"},{"key":"20431_CR19","doi-asserted-by":"crossref","unstructured":"Liu J, Ji S (2020) A novel recurrent encoder-decoder structure for large-scale multi-view stereo reconstruction from an open aerial dataset. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6050\u20136059","DOI":"10.1109\/CVPR42600.2020.00609"},{"key":"20431_CR20","doi-asserted-by":"crossref","unstructured":"Ji M, Gall J, Zheng H, Liu Y, Fang L (2017) Surfacenet: an end-to-end 3d neural network for multiview stereopsis. In: Proceedings of the IEEE international conference on computer vision, pp 2307\u20132315","DOI":"10.1109\/ICCV.2017.253"},{"key":"20431_CR21","unstructured":"Kar A, H\u00e4ne C, Malik J (2017) Learning a multi-view stereo machine. In: Proceedings of the 31st international conference on neural information processing systems. NIPS\u201917. Curran Associates Inc., Red Hook, NY, USA, pp 364\u2013375"},{"key":"20431_CR22","doi-asserted-by":"crossref","unstructured":"Kendall A, Martirosyan H, Dasgupta S, Henry P, Kennedy R, Bachrach A, Bry A (2017) End-to-end learning of geometry and context for deep stereo regression. In: Proceedings of the IEEE international conference on computer vision, pp 66\u201375","DOI":"10.1109\/ICCV.2017.17"},{"key":"20431_CR23","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, pp. 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"4","key":"20431_CR24","doi-asserted-by":"publisher","first-page":"10247","DOI":"10.1007\/s11042-023-15362-5","volume":"83","author":"H Sun","year":"2023","unstructured":"Sun H, Han J, Pang Y, Li X (2023) Supervised biadjacency networks for stereo matching. Multimed Tools Appl 83(4):10247\u201310272","journal-title":"Multimed Tools Appl"},{"key":"20431_CR25","doi-asserted-by":"publisher","first-page":"35825","DOI":"10.1007\/s11042-023-16721-y","volume":"83","author":"H Dogan","year":"2023","unstructured":"Dogan H (2023) A higher performance shape from focus strategy based on unsupervised deep learning for 3d shape reconstruction. Multim Tools Appl 83:35825\u201335848","journal-title":"Multim Tools Appl"},{"issue":"9","key":"20431_CR26","doi-asserted-by":"publisher","first-page":"12961","DOI":"10.1007\/s11042-022-12579-8","volume":"81","author":"J Wang","year":"2022","unstructured":"Wang J, Peng C, Li M, Li Y, Du S (2022) The study of stereo matching optimization based on multi-baseline trinocular model. Multimed Tools Appl 81(9):12961\u201312972","journal-title":"Multimed Tools Appl"},{"key":"20431_CR27","doi-asserted-by":"crossref","unstructured":"Yang J, Mao W, Alvarez JM, Liu M (2020) Cost volume pyramid based depth inference for multi-view stereo. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4877\u20134886","DOI":"10.1109\/CVPR42600.2020.00493"},{"key":"20431_CR28","doi-asserted-by":"crossref","unstructured":"Cheng S, Xu Z, Zhu S, Li Z, Li LE, Ramamoorthi R, Su H (2020) Deep stereo using adaptive thin volume representation with uncertainty awareness. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2524\u20132534","DOI":"10.1109\/CVPR42600.2020.00260"},{"key":"20431_CR29","doi-asserted-by":"crossref","unstructured":"Li Z, Liu X, Drenkow N, Ding A, Creighton FX, Taylor RH, Unberath M (2021) Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6197\u20136206","DOI":"10.1109\/ICCV48922.2021.00614"},{"key":"20431_CR30","doi-asserted-by":"crossref","unstructured":"Wang F, Galliani S, Vogel C, Speciale P, Pollefeys M (2021) Patchmatchnet: learned multi-view patchmatch stereo. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14194\u201314203","DOI":"10.1109\/CVPR46437.2021.01397"},{"key":"20431_CR31","doi-asserted-by":"crossref","unstructured":"Yao Y, Luo Z, Li S, Shen T, Fang T, Quan L (2019) Recurrent mvsnet for high-resolution multi-view stereo depth inference. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5525\u20135534","DOI":"10.1109\/CVPR.2019.00567"},{"key":"20431_CR32","doi-asserted-by":"crossref","unstructured":"Yan J, Wei Z, Yi H, Ding M, Zhang R, Chen Y, Wang G, Tai Y-W (2020) Dense hybrid recurrent multi-view stereo net with dynamic consistency checking. In: European conference on computer vision. Springer, pp 674\u2013689","DOI":"10.1007\/978-3-030-58548-8_39"},{"key":"20431_CR33","doi-asserted-by":"crossref","unstructured":"Wei Z, Zhu Q, Min C, Chen Y, Wang G (2021) Aa-rmvsnet: adaptive aggregation recurrent multi-view stereo network. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6187\u20136196","DOI":"10.1109\/ICCV48922.2021.00613"},{"key":"20431_CR34","doi-asserted-by":"publisher","first-page":"4008","DOI":"10.1109\/TIP.2021.3068645","volume":"30","author":"Y Chen","year":"2021","unstructured":"Chen Y, Tu Z, Kang D, Chen R, Bao L, Zhang Z, Yuan J (2021) Joint hand-object 3d reconstruction from a single image with cross-branch feature fusion. IEEE Trans Image Process 30:4008\u20134021","journal-title":"IEEE Trans Image Process"},{"issue":"26","key":"20431_CR35","doi-asserted-by":"publisher","first-page":"40987","DOI":"10.1007\/s11042-023-15183-6","volume":"82","author":"G Yang","year":"2023","unstructured":"Yang G, Liao Y (2023) An improved binocular stereo matching algorithm based on aanet. Multimed Tools Appl 82(26):40987\u201341003","journal-title":"Multimed Tools Appl"},{"key":"20431_CR36","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"20431_CR37","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European conference on computer vision. Springer, pp 213\u2013229","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"20431_CR38","doi-asserted-by":"crossref","unstructured":"Sun J, Shen Z, Wang Y, Bao H, Zhou X (2021) Loftr: detector-free local feature matching with transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8922\u20138931","DOI":"10.1109\/CVPR46437.2021.00881"},{"key":"20431_CR39","doi-asserted-by":"crossref","unstructured":"Chen H, Wang Y, Guo T, Xu C, Deng Y, Liu Z, Ma S, Xu C, Xu C, Gao W (2021) Pre-trained image processing transformer. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12299\u201312310","DOI":"10.1109\/CVPR46437.2021.01212"},{"key":"20431_CR40","doi-asserted-by":"crossref","unstructured":"Sarlin P-E, DeTone D, Malisiewicz T, Rabinovich A (2020) Superglue: learning feature matching with graph neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4938\u20134947","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"20431_CR41","doi-asserted-by":"publisher","first-page":"1120","DOI":"10.1109\/TIP.2023.3240834","volume":"32","author":"Y Wang","year":"2023","unstructured":"Wang Y, Zhao Q, Gan Y, Xia Z (2023) Joint-confidence-guided multi-task learning for 3d reconstruction and understanding from monocular camera. IEEE Trans Image Process 32:1120\u20131133","journal-title":"IEEE Trans Image Process"},{"key":"20431_CR42","doi-asserted-by":"publisher","first-page":"5793","DOI":"10.1109\/TIP.2021.3087397","volume":"30","author":"Z Ruan","year":"2021","unstructured":"Ruan Z, Zou C, Wu L, Wu G, Wang L (2021) Sadrnet: self-aligned dual face regression networks for robust 3d dense face alignment and reconstruction. IEEE Trans Image Process 30:5793\u20135806","journal-title":"IEEE Trans Image Process"},{"key":"20431_CR43","doi-asserted-by":"crossref","unstructured":"Zhang X, Hu Y, Wang H, Cao X, Zhang B (2021) Long-range attention network for multi-view stereo. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 3782\u20133791","DOI":"10.1109\/WACV48630.2021.00383"},{"key":"20431_CR44","unstructured":"Zhu J, Peng B, Li W, Shen H, Zhang Z, Lei J (2021) Multi-view stereo with transformer. arXiv preprint arXiv:2112.00336"},{"key":"20431_CR45","doi-asserted-by":"crossref","unstructured":"Ma X, Gong Y, Wang Q, Huang J, Chen L, Yu F (2021) Epp-mvsnet: epipolar-assembling based depth prediction for multi-view stereo. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 5732\u20135740","DOI":"10.1109\/ICCV48922.2021.00568"},{"key":"20431_CR46","doi-asserted-by":"publisher","first-page":"6831","DOI":"10.1109\/TIP.2022.3215024","volume":"31","author":"X Jia","year":"2022","unstructured":"Jia X, Yang S, Wang Y, Zhang J, Peng Y, Chen S (2022) Dual-view 3d reconstruction via learning correspondence and dependency of point cloud regions. IEEE Trans Image Process 31:6831\u20136846","journal-title":"IEEE Trans Image Process"},{"key":"20431_CR47","doi-asserted-by":"crossref","unstructured":"Yao Y, Luo Z, Li S, Zhang J, Ren Y, Zhou L, Fang T, Quan L (2020) Blendedmvs: a large-scale dataset for generalized multi-view stereo networks. In: 2020 IEEE\/CVF conference on Computer Vision and Pattern Recognition (CVPR), pp 1787\u20131796","DOI":"10.1109\/CVPR42600.2020.00186"},{"issue":"8","key":"20431_CR48","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1109\/TPAMI.2009.161","volume":"32","author":"Y Furukawa","year":"2010","unstructured":"Furukawa Y, Ponce J (2010) Accurate, dense, and robust multiview stereopsis. IEEE Trans Pattern Anal Mach Intell 32(8):1362\u20131376","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"20431_CR49","doi-asserted-by":"crossref","unstructured":"Galliani S, Lasinger K, Schindler K (2015) Massively parallel multiview stereopsis by surface normal diffusion. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp 873\u2013881","DOI":"10.1109\/ICCV.2015.106"},{"key":"20431_CR50","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/978-3-319-46487-9_31","volume-title":"Computer vision - ECCV 2016","author":"JL Sch\u00f6nberger","year":"2016","unstructured":"Sch\u00f6nberger JL, Zheng E, Frahm J-M, Pollefeys M (2016) Pixelwise view selection for unstructured multi-view stereo. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision - ECCV 2016. Springer, Cham, pp 501\u2013518"},{"key":"20431_CR51","doi-asserted-by":"crossref","unstructured":"Chen R, Han S, Xu J, Su H (2019) Point-based multi-view stereo network. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp 1538\u20131547","DOI":"10.1109\/ICCV.2019.00162"},{"key":"20431_CR52","doi-asserted-by":"crossref","unstructured":"Zhang J, Yao Y, Li S, Luo Z, Fang T (2020) Visibility-aware multi-view stereo network. arXiv preprint arXiv:2008.07928","DOI":"10.5244\/C.34.109"},{"key":"20431_CR53","doi-asserted-by":"crossref","unstructured":"Ling S, Li J, Ding L, Wang N (2024) Multi-view jujube tree trunks stereo reconstruction based on uav remote sensing imaging acquisition system. Appl Sci 14(4)","DOI":"10.3390\/app14041364"},{"key":"20431_CR54","doi-asserted-by":"crossref","unstructured":"Zhang X, Yang F, Chang M, Qin X (2023) Mg-mvsnet: multiple granularities feature fusion network for multi-view stereo. Neurocomputing 528:35\u201347","DOI":"10.1016\/j.neucom.2023.01.062"},{"key":"20431_CR55","doi-asserted-by":"crossref","unstructured":"Liu L, Zhang F, Su W, Qi Y, Tao W (2023) Geometric prior-guided self-supervised learning for multi-view stereo. Remote Sens 15(8)","DOI":"10.3390\/rs15082109"},{"key":"20431_CR56","doi-asserted-by":"crossref","unstructured":"Darmon F, Bascle B, Devaux J-C, Monasse P, Aubry M (2021) Deep multi-view stereo gone wild. In: 2021 International conference on 3D Vision (3DV), pp 484\u2013493","DOI":"10.1109\/3DV53792.2021.00058"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20431-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-20431-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20431-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T01:24:06Z","timestamp":1757121846000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-20431-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"references-count":56,"journal-issue":{"issue":"27","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["20431"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-20431-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"30 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 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":"Not applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest\/Competing interests"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}