{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T15:46:23Z","timestamp":1782834383573,"version":"3.54.5"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"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":["Vis Comput"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s00371-025-04273-1","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T10:19:09Z","timestamp":1765880349000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing plant leaf disease classification through self-attention super-resolution GAN and dual attention model"],"prefix":"10.1007","volume":"42","author":[{"given":"V.","family":"Sasikala","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Venkatramaphanikumar","family":"Sistla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepak Chowdary","family":"Edara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Venkata Krishna Kishore","family":"Kolli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"issue":"June","key":"4273_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106279","volume":"187","author":"A Abbas","year":"2021","unstructured":"Abbas, A., Jain, S., Gour, M., Vankudothu, S.: Tomato plant disease detection using transfer learning with C-GAN synthetic images. Comput. Electron. Agric. 187(June), 106279 (2021). https:\/\/doi.org\/10.1016\/j.compag.2021.106279","journal-title":"Comput. Electron. Agric."},{"key":"4273_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy13061524","author":"A Abbas","year":"2023","unstructured":"Abbas, A., Zhang, Z., Zheng, H., Alami, M.M., Alrefaei, A.F., Abbas, Q., Naqvi, S.A.H., Rao, M.J., Mosa, W.F.A., Abbas, Q., Hussain, A., Hassan, M.Z., Zhou, L.: Drones in plant disease assessment, efficient monitoring, and detection: a way forward to smart agriculture. Agronomy (2023). https:\/\/doi.org\/10.3390\/agronomy13061524","journal-title":"Agronomy"},{"key":"4273_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2022.100083","volume":"3","author":"A Ahmad","year":"2023","unstructured":"Ahmad, A., Saraswat, D., El Gamal, A.: A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools. Smart Agric. Technol. 3, 100083 (2023). https:\/\/doi.org\/10.1016\/j.atech.2022.100083","journal-title":"Smart Agric. Technol."},{"key":"4273_CR4","unstructured":"Alexey D., et al.: \u201cAn image is worth 16X16 words: transformers for image recognition at scale. In: ICLR 2021\u20149th International Conference on Learning Representations (2021)"},{"key":"4273_CR5","doi-asserted-by":"publisher","DOI":"10.1186\/s13007-025-01332-5","volume":"21","author":"TT Baiju","year":"2025","unstructured":"Baiju, T.T., Joseph, T.J., Thomas, M.: Robust CRW crop leaf disease detection and classification using GAN and rotation loss. Plant Methods 21, 01332 (2025). https:\/\/doi.org\/10.1186\/s13007-025-01332-5","journal-title":"Plant Methods"},{"issue":"7","key":"4273_CR6","doi-asserted-by":"publisher","first-page":"617","DOI":"10.3390\/agriculture11070617","volume":"11","author":"P Bansal","year":"2021","unstructured":"Bansal, P., Kumar, R., Kumar, S.: Disease detection in Apple leaves using deep convolutional neural network. Agriculture 11(7), 617 (2021). https:\/\/doi.org\/10.3390\/agriculture11070617","journal-title":"Agriculture"},{"key":"4273_CR7","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy14020327","author":"U Barman","year":"2024","unstructured":"Barman, U., Sarma, P., Rahman, M., Deka, V., Lahkar, S., Sharma, V., Saikia, M.J.: ViT-SmartAgri: vision transformer and smartphone-based plant disease detection for smart agriculture. Agronomy (2024). https:\/\/doi.org\/10.3390\/agronomy14020327","journal-title":"Agronomy"},{"issue":"December","key":"4273_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fpls.2020.583438","volume":"11","author":"L Bi","year":"2020","unstructured":"Bi, L., Hu, G.: Improving image-based plant disease classification with generative adversarial network under limited training set. Front. Plant Sci. 11(December), 1\u201312 (2020). https:\/\/doi.org\/10.3389\/fpls.2020.583438","journal-title":"Front. Plant Sci."},{"issue":"1","key":"4273_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/s12145-024-01339-x","volume":"17","author":"W Boulila","year":"2024","unstructured":"Boulila, W.: Performer-attention-guided few-shot learning model for plant disease classification. Earth Sci. Inform. 17(1), 01339 (2024). https:\/\/doi.org\/10.1007\/s12145-024-01339-x","journal-title":"Earth Sci. Inform."},{"key":"4273_CR10","unstructured":"Cap, Q.H., Tani, H., Uga, H., Kagiwada, S., Iyatomi, H. (n.d.). Super-Resolution for Practical Automated Plant Disease Diagnosis System"},{"issue":"PC","key":"4273_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108307","volume":"133","author":"H Catal Reis","year":"2024","unstructured":"Catal Reis, H., Turk, V.: Potato leaf disease detection with a novel deep learning model based on depthwise separable convolution and transformer networks. Eng. Appl. Artif. Intell. 133(PC), 108307 (2024). https:\/\/doi.org\/10.1016\/j.engappai.2024.108307","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4273_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/J.CVIU.2020.103038","volume":"200","author":"V Chudasama","year":"2020","unstructured":"Chudasama, V., Upla, K.: E-ProSRNet: an enhanced progressive single image super-resolution approach. Comput. Vis. Image Underst. 200, 103038 (2020). https:\/\/doi.org\/10.1016\/J.CVIU.2020.103038","journal-title":"Comput. Vis. Image Underst."},{"key":"4273_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e29802","author":"S Duhan","year":"2024","unstructured":"Duhan, S., Gulia, P., Gill, N.S., Shukla, P.K., Khan, S.B., Almusharraf, A., Alkhaldi, N.: Investigating attention mechanisms for plant disease identification in challenging environments. Heliyon (2024). https:\/\/doi.org\/10.1016\/j.heliyon.2024.e29802","journal-title":"Heliyon"},{"key":"4273_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/s22197384","author":"E Elizar","year":"2022","unstructured":"Elizar, E., Zulkifley, M.A., Muharar, R., Zaman, M.H.M., Mustaza, S.M.: A review on multiscale-deep-learning applications. Sensors (2022). https:\/\/doi.org\/10.3390\/s22197384","journal-title":"Sensors"},{"key":"4273_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.107938","volume":"170","author":"Y Fu","year":"2024","unstructured":"Fu, Y., Liu, J., Shi, J.: Tsca-net: transformer-based spatial-channel attention segmentation network for medical images. Comput. Biol. Med. 170, 107938 (2024). https:\/\/doi.org\/10.1016\/j.compbiomed.2024.107938","journal-title":"Comput. Biol. Med."},{"key":"4273_CR16","doi-asserted-by":"publisher","unstructured":"Gosai, D., Kaka, B., Garg, D., Patel, R., Ganatra, A.: Plant disease detection and classification using machine learning algorithm. In: 2022 International Conference for Advancement in Technology, ICONAT 2022 (2022). https:\/\/doi.org\/10.1109\/ICONAT53423.2022.9726036","DOI":"10.1109\/ICONAT53423.2022.9726036"},{"issue":"7","key":"4273_CR17","doi-asserted-by":"publisher","first-page":"3751","DOI":"10.3390\/s23073751","volume":"23","author":"S Hossain","year":"2023","unstructured":"Hossain, S., Tanzim Reza, M., Chakrabarty, A., Jung, Y.J.: Aggregating different scales of attention on feature variants for tomato leaf disease diagnosis from image data: a transformer driven study. Sensors 23(7), 3751 (2023)","journal-title":"Sensors"},{"issue":"6","key":"4273_CR18","doi-asserted-by":"publisher","first-page":"3570","DOI":"10.1002\/JSFA.13241","volume":"104","author":"X Hu","year":"2024","unstructured":"Hu, X., Li, X., Huang, Z., Chen, Q., Lin, S.: Detecting tea tree pests in complex backgrounds using a hybrid architecture guided by transformers and multi-scale attention mechanism. J. Sci. Food Agric. 104(6), 3570\u20133584 (2024). https:\/\/doi.org\/10.1002\/JSFA.13241","journal-title":"J. Sci. Food Agric."},{"key":"4273_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/J.PATCOG.2021.107846","volume":"115","author":"Y Jin","year":"2021","unstructured":"Jin, Y., Zhang, Y., Cen, Y., Li, Y., Mladenovic, V., Voronin, V.: Pedestrian detection with super-resolution reconstruction for low-quality images. Pattern Recognit. 115, 107846 (2021). https:\/\/doi.org\/10.1016\/J.PATCOG.2021.107846","journal-title":"Pattern Recognit."},{"key":"4273_CR20","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/1790171","author":"S Kumar","year":"2021","unstructured":"Kumar, S., Jain, A., Shukla, A.P., Singh, S., Raja, R., Rani, S., Harshitha, G., Alzain, M.A., Masud, M.: A comparative analysis of machine learning algorithms for detection of organic and nonorganic cotton diseases. Math. Probl. Eng. (2021). https:\/\/doi.org\/10.1155\/2021\/1790171","journal-title":"Math. Probl. Eng."},{"key":"4273_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-10950-9","volume":"57","author":"A Kumar","year":"2024","unstructured":"Kumar, A., Singh, S., Kumar, M., Rani, S.: Zero-shot plant disease classification with semantic attributes and pre-trained models. Artif. Intell. Rev. 57, 10950 (2024). https:\/\/doi.org\/10.1007\/s10462-024-10950-9","journal-title":"Artif. Intell. Rev."},{"key":"4273_CR22","doi-asserted-by":"publisher","first-page":"1256773","DOI":"10.3389\/fpls.2023.1256773","volume":"14","author":"G Li","year":"2023","unstructured":"Li, G., Wang, Y., Zhao, Q., Yuan, P., Chang, B.: PMVT: a lightweight vision transformer for plant disease identification on mobile devices. Front. Plant Sci. 14, 1256773 (2023)","journal-title":"Front. Plant Sci."},{"key":"4273_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s11633-024-1393-8","author":"Y Liu","year":"2024","unstructured":"Liu, Y., Wu, Y.-H., Sun, G., Zhang, L., Chhatkuli, A., Van Gool, L.: Vision transformers with hierarchical attention. Mach. Intell. Res. (2024). https:\/\/doi.org\/10.1007\/s11633-024-1393-8","journal-title":"Mach. Intell. Res."},{"issue":"30","key":"4273_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ifacol.2019.12.406","volume":"52","author":"CY Lu","year":"2019","unstructured":"Lu, C.Y., Arcega Rustia, D.J., Lin, T.T.: Generative adversarial network based image augmentation for insect pest classification enhancement. IFAC-PapersOnLine 52(30), 1\u20135 (2019). https:\/\/doi.org\/10.1016\/j.ifacol.2019.12.406","journal-title":"IFAC-PapersOnLine"},{"issue":"11","key":"4273_CR25","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1007\/s10462-024-10944-7","volume":"57","author":"I Pacal","year":"2024","unstructured":"Pacal, I., Kunduracioglu, I., Alma, M.H., Deveci, M., Kadry, S., Nedoma, J., et al.: A systematic review of deep learning techniques for plant diseases. Artif. Intell. Rev. 57(11), 304 (2024)","journal-title":"Artif. Intell. Rev."},{"issue":"3","key":"4273_CR26","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1111\/ELE.14412","volume":"27","author":"TS Priyadarshana","year":"2024","unstructured":"Priyadarshana, T.S., Martin, E.A., Sirami, C., Woodcock, B.A., Goodale, E., Mart\u00ednez-N\u00fa\u00f1ez, C., Lee, M.B., Pagani-N\u00fa\u00f1ez, E., Raderschall, C.A., Brotons, L., Rege, A., Ouin, A., Tscharntke, T., Slade, E.M.: Crop and landscape heterogeneity increase biodiversity in agricultural landscapes: a global review and meta-analysis. Ecol. Lett. 27(3), 234 (2024). https:\/\/doi.org\/10.1111\/ELE.14412","journal-title":"Ecol. Lett."},{"key":"4273_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/J.CMPB.2022.106995","volume":"225","author":"D Qiu","year":"2022","unstructured":"Qiu, D., Cheng, Y., Wang, X.: Improved generative adversarial network for retinal image super-resolution. Comput. Methods Programs Biomed. 225, 106995 (2022). https:\/\/doi.org\/10.1016\/J.CMPB.2022.106995","journal-title":"Comput. Methods Programs Biomed."},{"key":"4273_CR28","doi-asserted-by":"publisher","unstructured":"Rukhsar, Upadhyay, S.K.: Deep transfer learning-based rice leaves disease diagnosis and classification model using inceptionV3. In: Proceedings of International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2022, pp. 493\u2013499 (2022). https:\/\/doi.org\/10.1109\/CISES54857.2022.9844374","DOI":"10.1109\/CISES54857.2022.9844374"},{"key":"4273_CR29","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C. (n.d.). MobileNetV2: Inverted Residuals and Linear Bottlenecks"},{"key":"4273_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110534","author":"C Sarkar","year":"2023","unstructured":"Sarkar, C., Gupta, D., Gupta, U., Hazarika, B.B.: Leaf disease detection using machine learning and deep learning: review and challenges. Appl. Soft Comput. (2023). https:\/\/doi.org\/10.1016\/j.asoc.2023.110534","journal-title":"Appl. Soft Comput."},{"key":"4273_CR31","doi-asserted-by":"publisher","DOI":"10.1186\/s12870-025-06289-0","volume":"25","author":"R Srinivasan","year":"2025","unstructured":"Srinivasan, R., Rajalakshmi, P., Kumar, S.: Sugarcane leaf disease classification using EfficientNet-B7 and DenseNet201 architectures. BMC Plant Biol. 25, 06289 (2025). https:\/\/doi.org\/10.1186\/s12870-025-06289-0","journal-title":"BMC Plant Biol."},{"key":"4273_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-10944-7","volume":"57","author":"MK Sunil","year":"2024","unstructured":"Sunil, M.K., Kumar, M., Singh, S., Patel, A.: Deep learning techniques for plant diseases: a comprehensive review. Artif. Intell. Rev. 57, 10944 (2024). https:\/\/doi.org\/10.1007\/s10462-024-10944-7","journal-title":"Artif. Intell. Rev."},{"key":"4273_CR33","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2017.01190","author":"JR Ubbens","year":"2017","unstructured":"Ubbens, J.R., Stavness, I.: Deep plant phenomics: a deep learning platform for complex plant phenotyping tasks. Front. Plant Sci. (2017). https:\/\/doi.org\/10.3389\/fpls.2017.01190","journal-title":"Front. Plant Sci."},{"key":"4273_CR34","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., et al.: Attention is all you need. In: Advances in neural information processing systems, vol. 30 (2017)"},{"issue":"1","key":"4273_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/plants11010024","volume":"11","author":"SA Wagle","year":"2022","unstructured":"Wagle, S.A., Harikrishnan, R., Ali, S.H.M., Faseehuddin, M.: Classification of plant leaves using new compact convolutional neural network models. Plants 11(1), 1\u201325 (2022). https:\/\/doi.org\/10.3390\/plants11010024","journal-title":"Plants"},{"key":"4273_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/J.PATCOG.2021.108206","volume":"121","author":"L Wang","year":"2022","unstructured":"Wang, L., Yoon, K.J.: Semi-supervised student-teacher learning for single image super-resolution. Pattern Recognit. 121, 108206 (2022). https:\/\/doi.org\/10.1016\/J.PATCOG.2021.108206","journal-title":"Pattern Recognit."},{"key":"4273_CR37","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-030-11021-5_5","volume":"11133","author":"X Wang","year":"2019","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., Loy, C.C.: ESRGAN: enhanced super-resolution generative adversarial networks. Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform.) 11133, 63\u201379 (2019). https:\/\/doi.org\/10.1007\/978-3-030-11021-5_5","journal-title":"Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform.)"},{"issue":"4","key":"4273_CR38","doi-asserted-by":"publisher","first-page":"5427","DOI":"10.1007\/s40747-024-01445-9","volume":"10","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Wang, W., Li, Y., Jia, Y., Xu, Y., Ling, Y., Ma, J.: An attention mechanism module with spatial perception and channel information interaction. Complex Intell. Syst. 10(4), 5427\u20135444 (2024). https:\/\/doi.org\/10.1007\/s40747-024-01445-9","journal-title":"Complex Intell. Syst."},{"key":"4273_CR39","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/J.NEUCOM.2019.03.106","volume":"398","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Jiang, K., Yi, P., Han, Z., He, Z.: Ultra-dense GAN for satellite imagery super-resolution. Neurocomputing 398, 328\u2013337 (2020). https:\/\/doi.org\/10.1016\/J.NEUCOM.2019.03.106","journal-title":"Neurocomputing"},{"key":"4273_CR40","doi-asserted-by":"publisher","DOI":"10.22214\/ijraset.2021.37482","author":"AD Wilson","year":"2021","unstructured":"Wilson, A.D.: Detection and classification of plant diseases by image processing. Int. J. Res. Appl. Sci. Eng. Technol. (2021). https:\/\/doi.org\/10.22214\/ijraset.2021.37482","journal-title":"Int. J. Res. Appl. Sci. Eng. Technol."},{"issue":"March","key":"4273_CR41","doi-asserted-by":"publisher","first-page":"21176","DOI":"10.1109\/ACCESS.2023.3251098","volume":"11","author":"Z Zhang","year":"2023","unstructured":"Zhang, Z., Gao, Q., Liu, L., He, Y.: A high-quality rice leaf disease image data augmentation method based on a dual GAN. IEEE Access 11(March), 21176\u201321191 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3251098","journal-title":"IEEE Access"},{"issue":"3","key":"4273_CR42","doi-asserted-by":"publisher","first-page":"1817","DOI":"10.1109\/TCBB.2021.3056683","volume":"19","author":"Y Zhao","year":"2022","unstructured":"Zhao, Y., Chen, Z., Gao, X., Song, W., Xiong, Q., Hu, J., Zhang, Z.: Plant disease detection using generated leaves based on DoubleGAN. IEEE\/ACM Trans. Comput. Biol. Bioinform. 19(3), 1817\u20131826 (2022). https:\/\/doi.org\/10.1109\/TCBB.2021.3056683","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"4273_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/J.SIGPRO.2019.107251","volume":"166","author":"D Zhou","year":"2020","unstructured":"Zhou, D., Duan, R., Zhao, L., Chai, X.: Single image super-resolution reconstruction based on multi-scale feature mapping adversarial network. Signal Process. 166, 107251 (2020). https:\/\/doi.org\/10.1016\/J.SIGPRO.2019.107251","journal-title":"Signal Process."},{"key":"4273_CR44","doi-asserted-by":"publisher","DOI":"10.1007\/s44163-024-00150-3","volume":"4","author":"Y Zhou","year":"2024","unstructured":"Zhou, Y., Wang, X., Liu, H., Chen, T.: Improved data augmentation using object-based style transfer for plant disease image classification. Intell. Comput. 4, 00150 (2024). https:\/\/doi.org\/10.1007\/s44163-024-00150-3","journal-title":"Intell. Comput."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04273-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-04273-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04273-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T13:04:43Z","timestamp":1772629483000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-04273-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,16]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["4273"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-04273-1","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,16]]},"assertion":[{"value":"8 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"71"}}