{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T05:50:00Z","timestamp":1768715400568,"version":"3.49.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T00:00:00Z","timestamp":1745366400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T00:00:00Z","timestamp":1745366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["ZR2021QF134, ZR2023MF084"],"award-info":[{"award-number":["ZR2021QF134, ZR2023MF084"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202268, 62272281, 62341605"],"award-info":[{"award-number":["62202268, 62272281, 62341605"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s13042-025-02642-9","type":"journal-article","created":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T05:05:44Z","timestamp":1745384744000},"page":"6717-6735","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dtla-net: a direct2d transformer with linear angle attention network for multi-organ medical image segmentation"],"prefix":"10.1007","volume":"16","author":[{"given":"Yepeng","family":"Liu","sequence":"first","affiliation":[]},{"given":"Feiyu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jinjie","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Xiaofeng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,23]]},"reference":[{"key":"2642_CR1","doi-asserted-by":"crossref","unstructured":"Xie Y, Zhang J, Xia Y, Shen C (2023) Learning from partially labeled data for multi-organ and tumor segmentation. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2023.3312587"},{"issue":"27","key":"2642_CR2","doi-asserted-by":"publisher","first-page":"6","DOI":"10.4108\/eai.12-4-2021.169184","volume":"7","author":"K Ramesh","year":"2021","unstructured":"Ramesh K, Kumar GK, Swapna K, Datta D, Rajest SS (2021) A review of medical image segmentation algorithms. EAI Endorsed Trans Perv Health Technol 7(27):6\u20136","journal-title":"EAI Endorsed Trans Perv Health Technol"},{"key":"2642_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106666","volume":"125","author":"Z Fu","year":"2023","unstructured":"Fu Z, Li J, Hua Z, Fan L (2023) Deep supervision feature refinement attention network for medical image segmentation. Eng Appl Artif Intell 125:106666","journal-title":"Eng Appl Artif Intell"},{"issue":"10","key":"2642_CR4","doi-asserted-by":"publisher","first-page":"3671","DOI":"10.1007\/s13042-023-01857-y","volume":"14","author":"L Li","year":"2023","unstructured":"Li L, Qin J, Lv L, Cheng M, Wang B, Xia D, Wang S (2023) Icunet++: an inception-cbam network based on unet++ for mr spine image segmentation. Int J Mach Learn Cybern 14(10):3671\u20133683","journal-title":"Int J Mach Learn Cybern"},{"issue":"3","key":"2642_CR5","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1007\/s13042-023-01950-2","volume":"15","author":"H Bao","year":"2024","unstructured":"Bao H, Li Q, Zhu Y (2024) Segmentation-based context-aware enhancement network for medical images. Int J Mach Learn Cybern 15(3):963\u2013983","journal-title":"Int J Mach Learn Cybern"},{"key":"2642_CR6","doi-asserted-by":"crossref","unstructured":"Mandal B (2021) Optimization of quadratic curve fitting from data points using real coded genetic algorithm. In: Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2020, Volume 1, pp. 419\u2013428. Springer","DOI":"10.1007\/978-981-15-9927-9_41"},{"key":"2642_CR7","doi-asserted-by":"crossref","unstructured":"Agarwal R, Ghosal P, Murmu N, Nandi D (2023) Spiking neural network in computer vision: techniques, tools and trends. In: International Conference on Advanced Computational and Communication Paradigms, pp. 201\u2013209. Springer","DOI":"10.1007\/978-981-99-4284-8_16"},{"key":"2642_CR8","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, pp. 234\u2013241. Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2642_CR9","doi-asserted-by":"crossref","unstructured":"Zhou Z, Rahman\u00a0Siddiquee M.M, Tajbakhsh N, Liang J (2018). Unet++: a nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, pp. 3\u201311. Springer","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"2642_CR10","doi-asserted-by":"crossref","unstructured":"Xiao X, Lian S, Luo Z, Li S (2018) Weighted res-unet for high-quality retina vessel segmentation. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 327\u2013331. IEEE","DOI":"10.1109\/ITME.2018.00080"},{"key":"2642_CR11","doi-asserted-by":"crossref","unstructured":"Lou A, Guan S, Loew M (2021) Dc-unet: rethinking the u-net architecture with dual channel efficient cnn for medical image segmentation. In: Medical Imaging 2021: Image Processing, vol. 11596, pp. 758\u2013768. SPIE","DOI":"10.1117\/12.2582338"},{"key":"2642_CR12","doi-asserted-by":"crossref","unstructured":"Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, Han X, Chen Y.-W, Wu J (2020) Unet 3+: a full-scale connected unet for medical image segmentation. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055\u20131059 . IEEE","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"2642_CR13","unstructured":"Alexey D (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv: 2010.11929"},{"key":"2642_CR14","unstructured":"Vaswani A (2017) Attention is all you need. Adv Neural Inform Process Syst"},{"key":"2642_CR15","doi-asserted-by":"crossref","unstructured":"Wang W, Xie E, Li X, Fan D.-P, Song K, Liang D, Lu T, Luo P, Shao L (2021) Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 568\u2013578","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"2642_CR16","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":"2642_CR17","doi-asserted-by":"crossref","unstructured":"Tu Z, Talebi H, Zhang H, Yang F, Milanfar P, Bovik A, Li Y (2022) Maxvit: multi-axis vision transformer. In: European Conference on Computer Vision, pp. 459\u2013479. Springer","DOI":"10.1007\/978-3-031-20053-3_27"},{"key":"2642_CR18","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306"},{"key":"2642_CR19","doi-asserted-by":"crossref","unstructured":"Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M (2022) Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205\u2013218. Springer","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"2642_CR20","doi-asserted-by":"crossref","unstructured":"Zhang Y, Liu H, Hu Q (2021). Transfuse: fusing transformers and cnns for medical image segmentation. In: Medical Image Computing and Computer Assisted intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24, pp. 14\u201324. Springer","DOI":"10.1007\/978-3-030-87193-2_2"},{"key":"2642_CR21","doi-asserted-by":"crossref","unstructured":"Lin X, Yan Z, Deng X, Zheng C, Yu L (2023) Convformer: plug-and-play cnn-style transformers for improving medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 642\u2013651. Springer","DOI":"10.1007\/978-3-031-43901-8_61"},{"key":"2642_CR22","unstructured":"Kayalibay B, Jensen G, Smagt P (2017) Cnn-based segmentation of medical imaging data. arXiv preprint arXiv:1701.03056"},{"key":"2642_CR23","unstructured":"Dai J, Li Y, He K, Sun J (2016) R-fcn: object detection via region-based fully convolutional networks. Adv Neural Inform Process Syst 29"},{"issue":"12","key":"2642_CR24","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2642_CR25","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","volume":"162","author":"FI Diakogiannis","year":"2020","unstructured":"Diakogiannis FI, Waldner F, Caccetta P, Wu C (2020) Resunet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J Photogram Remote Sens 162:94\u2013114","journal-title":"ISPRS J Photogram Remote Sens"},{"key":"2642_CR26","doi-asserted-by":"crossref","unstructured":"Alom M.Z, Hasan M, Yakopcic C, Taha TM, Asari VK (2018) Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint arXiv:1802.06955","DOI":"10.1109\/NAECON.2018.8556686"},{"issue":"10","key":"2642_CR27","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R et al (2014) The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans Med Imaging 34(10):1993\u20132024","journal-title":"IEEE Trans Med Imaging"},{"key":"2642_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102863","volume":"88","author":"Z Mirikharaji","year":"2023","unstructured":"Mirikharaji Z, Abhishek K, Bissoto A, Barata C, Avila S, Valle E, Celebi ME, Hamarneh G (2023) A survey on deep learning for skin lesion segmentation. Med Image Anal 88:102863","journal-title":"Med Image Anal"},{"key":"2642_CR29","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ejmp.2021.05.003","volume":"85","author":"Y Fu","year":"2021","unstructured":"Fu Y, Lei Y, Wang T, Curran WJ, Liu T, Yang X (2021) A review of deep learning based methods for medical image multi-organ segmentation. Phys Med 85:107\u2013122","journal-title":"Phys Med"},{"issue":"8","key":"2642_CR30","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1109\/TMI.2018.2806309","volume":"37","author":"E Gibson","year":"2018","unstructured":"Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, Davidson B, Pereira SP, Clarkson MJ, Barratt DC (2018) Automatic multi-organ segmentation on abdominal ct with dense v-networks. IEEE Trans Med Imaging 37(8):1822\u20131834","journal-title":"IEEE Trans Med Imaging"},{"key":"2642_CR31","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","volume":"452","author":"Z Niu","year":"2021","unstructured":"Niu Z, Zhong G, Yu H (2021) A review on the attention mechanism of deep learning. Neurocomputing 452:48\u201362","journal-title":"Neurocomputing"},{"key":"2642_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121708","volume":"238","author":"Y Liu","year":"2024","unstructured":"Liu Y, Huang S, Tian X, Zhang F, Zhao F, Zhang C (2024) A stock series prediction model based on variational mode decomposition and dual-channel attention network. Expert Syst Appl 238:121708","journal-title":"Expert Syst Appl"},{"key":"2642_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111473","volume":"288","author":"S Huang","year":"2024","unstructured":"Huang S, Liu Y (2024) Fl-net: a multi-scale cross-decomposition network with frequency external attention for long-term time series forecasting. Knowl Based Syst 288:111473","journal-title":"Knowl Based Syst"},{"key":"2642_CR34","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","volume":"53","author":"J Schlemper","year":"2019","unstructured":"Schlemper J, Oktay O, Schaap M, Heinrich M, Kainz B, Glocker B, Rueckert D (2019) Attention gated networks: learning to leverage salient regions in medical images. Med Image Anal 53:197\u2013207","journal-title":"Med Image Anal"},{"key":"2642_CR35","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"2642_CR36","doi-asserted-by":"crossref","unstructured":"Agarwal R, Chowdhury A, Chatterjee R.K, Chel H, Murmu C, Murmu N, Nandi D (2024) Deep quasi-recurrent self-attention with dual encoder-decoder in biomedical ct image segmentation. IEEE J Biomed Health Inform","DOI":"10.1109\/JBHI.2024.3447689"},{"key":"2642_CR37","doi-asserted-by":"crossref","unstructured":"Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24, pp. 78\u201388. Springer","DOI":"10.1007\/978-3-030-87193-2_8"},{"key":"2642_CR38","doi-asserted-by":"crossref","unstructured":"Pan X, Ge C, Lu R, Song S, Chen G, Huang Z, Huang G (2022) On the integration of self-attention and convolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 815\u2013825","DOI":"10.1109\/CVPR52688.2022.00089"},{"key":"2642_CR39","doi-asserted-by":"crossref","unstructured":"Xia Z, Pan X, Song S, Li L.E, Huang G (2022) Vision transformer with deformable attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4794\u20134803","DOI":"10.1109\/CVPR52688.2022.00475"},{"key":"2642_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108464","volume":"257","author":"R Agarwal","year":"2024","unstructured":"Agarwal R, Ghosal P, Sadhu AK, Murmu N, Nandi D (2024) Multi-scale dual-channel feature embedding decoder for biomedical image segmentation. Comput Methods Prog Biomed 257:108464","journal-title":"Comput Methods Prog Biomed"},{"key":"2642_CR41","doi-asserted-by":"crossref","unstructured":"Wang H, Cao P, Wang J, Zaiane OR (2022) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 2441\u20132449","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"2642_CR42","unstructured":"Ioffe S (2015). Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167"},{"key":"2642_CR43","unstructured":"Landman B, Xu Z, Igelsias J, Styner M, Langerak T, Klein A (2015). Miccai multi-atlas labeling beyond the cranial vault\u2013workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault-Workshop Challenge, vol. 5, p. 12"},{"issue":"11","key":"2642_CR44","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng P-A, Cetin I, Lekadir K, Camara O, Ballester MAG et al (2018) Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging 37(11):2514\u20132525","journal-title":"IEEE Trans Med Imaging"},{"key":"2642_CR45","unstructured":"Loshchilov I (2017) Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101"},{"key":"2642_CR46","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571 . IEEE","DOI":"10.1109\/3DV.2016.79"},{"key":"2642_CR47","doi-asserted-by":"crossref","unstructured":"Fu S, Lu Y, Wang Y, Zhou Y, Shen W, Fishman E, Yuille A (2020) Domain adaptive relational reasoning for 3d multi-organ segmentation. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part I 23, pp. 656\u2013666. Springer","DOI":"10.1007\/978-3-030-59710-8_64"},{"key":"2642_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JTEHM.2022.3210047","volume":"10","author":"Y Zhao","year":"2022","unstructured":"Zhao Y, Li J, Hua Z (2022) Mpsht: multiple progressive sampling hybrid model multi-organ segmentation. IEEE J Transl Eng Health Med 10:1\u20139","journal-title":"IEEE J Transl Eng Health Med"},{"key":"2642_CR49","doi-asserted-by":"crossref","unstructured":"Azad R, Heidari M, Shariatnia M, Aghdam EK, Karimijafarbigloo S, Adeli E, Merhof D (2022) Transdeeplab: convolution-free transformer-based deeplab v3+ for medical image segmentation. In: International Workshop on PRedictive Intelligence in MEdicine, pp. 91\u2013102. Springer","DOI":"10.1007\/978-3-031-16919-9_9"},{"key":"2642_CR50","unstructured":"Huang X, Deng Z, Li D, Yuan X (2021) Missformer: an effective medical image segmentation transformer. arXiv preprint arXiv:2109.07162"},{"key":"2642_CR51","doi-asserted-by":"crossref","unstructured":"Heidari M, Kazerouni A, Soltany M, Azad R, Aghdam E.K, Cohen-Adad J, Merhof D (2023) Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6202\u20136212","DOI":"10.1109\/WACV56688.2023.00614"},{"key":"2642_CR52","doi-asserted-by":"crossref","unstructured":"Rahman M.M, Marculescu R (2023) Medical image segmentation via cascaded attention decoding. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6222\u20136231","DOI":"10.1109\/WACV56688.2023.00616"},{"key":"2642_CR53","unstructured":"Azad R, Jia Y, Aghdam E.K, Cohen-Adad J, Merhof D (2023) Enhancing medical image segmentation with transception: A multi-scale feature fusion approach. arXiv preprint arXiv:2301.10847"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02642-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02642-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02642-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T11:01:05Z","timestamp":1757156465000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02642-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,23]]},"references-count":53,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["2642"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02642-9","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,23]]},"assertion":[{"value":"12 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2025","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"}}]}}