{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T09:16:49Z","timestamp":1771233409350,"version":"3.50.1"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T00:00:00Z","timestamp":1766016000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"vor","delay-in-days":26,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Anhui Provincial Department of Education\u2019s Natural Science Foundation Major Project, China","award":["2023AH040275"],"award-info":[{"award-number":["2023AH040275"]}]},{"name":"the\u00a0Key\u00a0Discipline\u00a0Construction\u00a0Project\u00a0of\u00a0Anhui\u00a0Science\u00a0and Technology\u00a0University,China","award":["XK-XJGY002"],"award-info":[{"award-number":["XK-XJGY002"]}]},{"name":"Anhui Provincial Department of Education\u2019s Natural Science Foundation Key Projects, China","award":["2023AH051866"],"award-info":[{"award-number":["2023AH051866"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s40747-025-02203-1","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T17:16:54Z","timestamp":1766078214000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["IRG-ResNet: distillation model for corn disease recognition"],"prefix":"10.1007","volume":"12","author":[{"given":"Shaoqiu","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lujie","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haitao","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,18]]},"reference":[{"key":"2203_CR1","unstructured":"National Bureau of Statistics of China (2024) China statistical yearbook 2024 [EB\/OL]. [2025-11-29]. https:\/\/www.stats.gov.cn\/sj\/ndsj\/2024\/indexch.htm"},{"issue":"7","key":"2203_CR2","volume":"13","author":"SA Shah","year":"2023","unstructured":"Shah SA, Lakho GM, Keerio HA et al (2023) Application of drone surveillance for advance agriculture monitoring by android application using convolution neural network. Agronomy 13(7):1764","journal-title":"Agronomy"},{"key":"2203_CR3","doi-asserted-by":"crossref","first-page":"85900","DOI":"10.1109\/ACCESS.2023.3301504","volume":"11","author":"RM Saleem","year":"2023","unstructured":"Saleem RM, Bashir RN, Faheem M et al (2023) Internet of things based weekly crop pest prediction by using deep neural network. IEEE Access 11:85900\u201385913","journal-title":"IEEE Access"},{"key":"2203_CR4","doi-asserted-by":"crossref","first-page":"145813","DOI":"10.1109\/ACCESS.2023.3346299","volume":"11","author":"MN Mowla","year":"2023","unstructured":"Mowla MN, Mowla N, Shah AFMS et al (2023) Internet of Things and wireless sensor networks for smart agriculture applications: a survey. IEEE Access 11:145813\u2013145852","journal-title":"IEEE Access"},{"key":"2203_CR5","doi-asserted-by":"crossref","unstructured":"Bhatia S, Jaffery ZA, Mehfuz S (2023) A comparative study of wireless communication protocols for use in smart farming framework development. In: 2023 3rd international conference on intelligent communication and computational techniques (ICCT). IEEE, Jaipur, pp 1\u20137","DOI":"10.1109\/ICCT56969.2023.10075696"},{"key":"2203_CR6","volume":"3","author":"M Wakchaure","year":"2023","unstructured":"Wakchaure M, Patle BK, Mahindrakar AK (2023) Application of AI techniques and robotics in agriculture: a review. Artif Intell Life Sci 3:100057","journal-title":"Artif Intell Life Sci"},{"issue":"8","key":"2203_CR7","doi-asserted-by":"crossref","DOI":"10.3390\/machines11080774","volume":"11","author":"V Balaska","year":"2023","unstructured":"Balaska V, Adamidou Z, Vryzas Z et al (2023) Sustainable crop protection via robotics and artificial intelligence solutions. Machines 11(8):774","journal-title":"Machines"},{"issue":"5","key":"2203_CR8","volume":"23","author":"X Zou","year":"2023","unstructured":"Zou X, Liu W, Huo Z et al (2023) Current status and prospects of research on sensor fault diagnosis of agricultural internet of things. Sensors (Basel) 23(5):2528","journal-title":"Sensors (Basel)"},{"key":"2203_CR9","doi-asserted-by":"crossref","DOI":"10.3389\/978-2-8325-1321-7","volume":"14","author":"L Zhao","year":"2023","unstructured":"Zhao L, Zhou H, Xu L et al (2023) Parameter optimization of the spiral fertiliser discharger for mango orchards based on the discrete element method and genetic algorithm. Front Plant Sci 14:1169091","journal-title":"Front Plant Sci"},{"issue":"6","key":"2203_CR10","doi-asserted-by":"crossref","DOI":"10.3390\/agronomy13061534","volume":"13","author":"YN Kontos","year":"2023","unstructured":"Kontos YN, Rompis I, Karpouzos D (2023) Optimal pollution control and pump-and-fertilize strategies in a nitro-polluted aquifer, using genetic algorithms and modflow. Agronomy 13(6):1534","journal-title":"Agronomy"},{"issue":"18","key":"2203_CR11","doi-asserted-by":"crossref","first-page":"13874","DOI":"10.3390\/su151813874","volume":"15","author":"B Li","year":"2023","unstructured":"Li B, Muhammad S, Hira K et al (2023) Sustainable smart agriculture farming for cotton crop: a fuzzy logic rule based methodology. Sustainability 15(18):13874","journal-title":"Sustainability"},{"issue":"1","key":"2203_CR12","first-page":"1","volume":"5","author":"TY Lee","year":"2023","unstructured":"Lee TY, Reza MN, Chung SO et al (2023) Application of fuzzy logics for smart agriculture: a review. Precis Agric 5(1):1\u201320","journal-title":"Precis Agric"},{"issue":"1","key":"2203_CR13","first-page":"27","volume":"8","author":"LC Ngugi","year":"2021","unstructured":"Ngugi LC, Abelwahab M, Abo-Zahhad M (2021) Recent advances in image processing techniques for automated leaf pest and disease recognition\u2014a review. Inf Process Agric 8(1):27\u201351","journal-title":"Inf Process Agric"},{"key":"2203_CR14","unstructured":"Brock A, De S, Smith SL et al (2021) High-performance large-scale image recognition without normalization. In: Proceedings of the 38th international conference on machine learning. PMLR, Vienna, pp 1059\u20131071"},{"key":"2203_CR15","doi-asserted-by":"crossref","unstructured":"Ge F, Ding Y, Zhang Y et al (2024) A review of deep learning based target detection algorithms. In: International conference on the efficiency and performance engineering network. Springer, Cham, pp 88\u201399","DOI":"10.1007\/978-3-031-73407-6_9"},{"key":"2203_CR16","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1109\/TIP.2022.3199107","volume":"32","author":"B Li","year":"2022","unstructured":"Li B, Xiao C, Wang L et al (2022) Dense nested attention network for infrared small target detection. IEEE Trans Image Process 32:1745\u20131758","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"2203_CR17","volume":"22","author":"HE Kim","year":"2022","unstructured":"Kim HE, Cosa-Linan A, Santhanam N et al (2022) Transfer learning for medical image classification: a literature review. BMC Med Imaging 22(1):69","journal-title":"BMC Med Imaging"},{"issue":"22","key":"2203_CR18","volume":"13","author":"L Chen","year":"2021","unstructured":"Chen L, Li S, Bai Q et al (2021) Review of image classification algorithms based on convolutional neural networks. Remote Sens 13(22):4712","journal-title":"Remote Sens"},{"issue":"3","key":"2203_CR19","first-page":"61","volume":"2","author":"F Paulo","year":"2020","unstructured":"Paulo F, Zhao Z, Jithin M et al (2020) Distinguishing volunteer corn from soybean at seedling stage using images and machine learning. Smart Agric 2(3):61","journal-title":"Smart Agric"},{"key":"2203_CR20","doi-asserted-by":"crossref","unstructured":"Yumang A, Magwili G, Montoya SKC et al (2020) Determination of shelled corn damages using colored image edge detection with convolutional neural network. In: 2020 IEEE 12th international conference on humanoid, nanotechnology, information technology, communication and control, environment, and management (HNICEM). IEEE, pp 1\u20136","DOI":"10.1109\/HNICEM51456.2020.9400023"},{"issue":"4","key":"2203_CR21","doi-asserted-by":"crossref","first-page":"446","DOI":"10.62411\/jcta.10459","volume":"1","author":"RK Rachman","year":"2024","unstructured":"Rachman RK, Setiadi DRIM, Susanto A et al (2024) Enhanced vision transformer and transfer learning approach to improve rice disease recognition. J Comput Theor Appl 1(4):446\u2013460","journal-title":"J Comput Theor Appl"},{"issue":"2","key":"2203_CR22","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1109\/TAFE.2024.3406544","volume":"2","author":"MA Rahman","year":"2024","unstructured":"Rahman MA, Khan AA, Hasan MM et al (2024) Deep learning modeling for potato breed recognition. IEEE Trans Agrifood Electron 2(2):419\u2013427","journal-title":"IEEE Trans Agrifood Electron"},{"key":"2203_CR23","volume":"187","author":"C Chen","year":"2021","unstructured":"Chen C, Zhu W, Norton T (2021) Behaviour recognition of pigs and cattle: journey from computer vision to deep learning. Comput Electron Agric 187:106255","journal-title":"Comput Electron Agric"},{"key":"2203_CR24","volume":"102","author":"X Zhang","year":"2024","unstructured":"Zhang X, Xuan C, Ma Y et al (2024) Lightweight model-based sheep face recognition via face image recording channel. J Anim Sci 102:skae066","journal-title":"J Anim Sci"},{"key":"2203_CR25","doi-asserted-by":"crossref","unstructured":"Ghosh H, Rahat IS, Pattanayak RM et al (2023) Innovative approaches in tomato leaf disease recognition using deep learning. In: 2023 6th international conference on recent trends in advance computing (ICRTAC). IEEE, Chennai, pp 86\u201396","DOI":"10.1109\/ICRTAC59277.2023.10480762"},{"issue":"9","key":"2203_CR26","volume":"11","author":"Y Wang","year":"2022","unstructured":"Wang Y, Tao J, Gao H (2022) Corn disease recognition based on attention mechanism network. Axioms 11(9):480","journal-title":"Axioms"},{"issue":"3","key":"2203_CR27","first-page":"853","volume":"20","author":"S Mathulaprangsan","year":"2021","unstructured":"Mathulaprangsan S, Patarapuwadol S, Lanthong K et al (2021) Rice disease recognition using effective deep neural networks. J Web Eng 20(3):853\u2013878","journal-title":"J Web Eng"},{"key":"2203_CR28","doi-asserted-by":"crossref","unstructured":"Ghosh H, Rahat IS, Lenka R et al (2023) Benchmarking ML and DL models for mango leaf disease detection: a comparative analysis. In: International conference on applied machine learning and data analytics. Springer, Cham, pp 97\u2013110","DOI":"10.1007\/978-3-031-55486-5_8"},{"key":"2203_CR29","first-page":"23","volume":"6","author":"AS Paymode","year":"2022","unstructured":"Paymode AS, Malode VB (2022) Transfer learning for multi-crop leaf disease image classification using convolutional neural network VGG. Artif Intell Agric 6:23\u201333","journal-title":"Artif Intell Agric"},{"key":"2203_CR30","volume":"11","author":"X Xie","year":"2020","unstructured":"Xie X, Ma Y, Liu B et al (2020) A deep-learning-based real-time detector for grape leaf diseases using improved convolutional neural networks. Front Plant Sci 11:751","journal-title":"Front Plant Sci"},{"issue":"6","key":"2203_CR31","first-page":"1","volume":"10","author":"H Ghosh","year":"2023","unstructured":"Ghosh H, Rahat IS, Shaik K et al (2023) Potato leaf disease recognition and prediction using convolutional neural networks. EAI Endors Trans Scalable Inf Syst 10(6):1\u20138","journal-title":"EAI Endors Trans Scalable Inf Syst"},{"issue":"20","key":"2203_CR32","doi-asserted-by":"crossref","first-page":"10278","DOI":"10.3390\/app122010278","volume":"12","author":"A Ksibi","year":"2022","unstructured":"Ksibi A, Ayadi M, Soufiene BO et al (2022) MobiRes-net: a hybrid deep learning model for detecting and classifying olive leaf diseases. Appl Sci 12(20):10278","journal-title":"Appl Sci"},{"issue":"1","key":"2203_CR33","doi-asserted-by":"crossref","DOI":"10.1007\/s43621-025-01149-1","volume":"6","author":"H Ghosh","year":"2025","unstructured":"Ghosh H, Rahat IS, Emon MMR et al (2025) Advanced neural network architectures for tomato leaf disease diagnosis in precision agriculture. Discov Sustain 6(1):312","journal-title":"Discov Sustain"},{"issue":"6","key":"2203_CR34","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou J, Yu B, Maybank SJ et al (2021) Knowledge distillation: a survey. Int J Comput Vis 129(6):1789\u20131819","journal-title":"Int J Comput Vis"},{"key":"2203_CR35","volume":"572","author":"J Zhang","year":"2024","unstructured":"Zhang J, Shi Y, Yang J et al (2024) KD-SCFNet: towards more accurate and lightweight salient object detection via knowledge distillation. Neurocomputing 572:127206","journal-title":"Neurocomputing"},{"key":"2203_CR36","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/TIP.2020.3035028","volume":"30","author":"G Ding","year":"2020","unstructured":"Ding G, Zhang S, Jia Z et al (2020) Where to prune: using LSTM to guide data-dependent soft pruning. IEEE Trans Image Process 30:293\u2013304","journal-title":"IEEE Trans Image Process"},{"key":"2203_CR37","volume":"122","author":"JH Tan","year":"2022","unstructured":"Tan JH, Chan CS, Chuah JH (2022) End-to-end supermask pruning: learning to prune image captioning models. Pattern Recognit 122:108366","journal-title":"Pattern Recognit"},{"key":"2203_CR38","first-page":"28699","volume":"36","author":"E Michaud","year":"2023","unstructured":"Michaud E, Liu Z, Girit U et al (2023) The quantization model of neural scaling. Adv Neural Inf Process Syst 36:28699\u201328722","journal-title":"Adv Neural Inf Process Syst"},{"key":"2203_CR39","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.neucom.2021.07.045","volume":"461","author":"T Liang","year":"2021","unstructured":"Liang T, Glossner J, Wang L et al (2021) Pruning and quantization for deep neural network acceleration: a survey. Neurocomputing 461:370\u2013403","journal-title":"Neurocomputing"},{"key":"2203_CR40","doi-asserted-by":"crossref","first-page":"27546","DOI":"10.1109\/ACCESS.2023.3258360","volume":"11","author":"R Sayed","year":"2023","unstructured":"Sayed R, Azmi H, Shawkey H et al (2023) A systematic literature review on binary neural networks. IEEE Access 11:27546\u201327578","journal-title":"IEEE Access"},{"key":"2203_CR41","doi-asserted-by":"crossref","first-page":"62307","DOI":"10.1109\/ACCESS.2023.3286730","volume":"11","author":"KM Hosny","year":"2023","unstructured":"Hosny KM, El-Hady WM, Samy FM et al (2023) Multi-class classification of plant leaf diseases using feature fusion of deep convolutional neural network and local binary pattern. IEEE Access 11:62307\u201362317","journal-title":"IEEE Access"},{"key":"2203_CR42","first-page":"253","volume":"55","author":"P Yuan","year":"2024","unstructured":"Yuan P, Ouyang L, Zhai Z et al (2024) Lightweight identification of rice diseases based on improved ECA and MobileNetV3Small. Trans Chin Soc Agric Mach 55:253\u2013262","journal-title":"Trans Chin Soc Agric Mach"},{"key":"2203_CR43","volume":"27","author":"S Zhu","year":"2024","unstructured":"Zhu S, Gao H (2024) Mc-shufflenetv2: a lightweight model for maize disease recognition. Egypt Inform J 27:100503","journal-title":"Egypt Inform J"},{"key":"2203_CR44","doi-asserted-by":"crossref","DOI":"10.3389\/fcomp.2024.1279810","volume":"6","author":"P Tharani Pavithra","year":"2024","unstructured":"Tharani Pavithra P, Baranidharan B (2024) Osps-micronet: a distilled knowledge micro-CNN network for detecting rice diseases. Front Comput Sci 6:1279810","journal-title":"Front Comput Sci"},{"issue":"102","key":"2203_CR45","first-page":"515","volume":"10","author":"CJ Entuni","year":"2023","unstructured":"Entuni CJ, Zulcaffle TMA, Ping KH (2023) Classification of capsicum leaf disease from a complex cluster of leaves using an improved multiple layers ShuffleNet CNN model. Int J Adv Technol Eng Explor 10(102):515","journal-title":"Int J Adv Technol Eng Explor"},{"issue":"1","key":"2203_CR46","volume":"17","author":"N Yao","year":"2021","unstructured":"Yao N, Ni F, Wang Z et al (2021) L2MXception: an improved Xception network for classification of peach diseases. Plant Methods 17(1):36","journal-title":"Plant Methods"},{"key":"2203_CR47","doi-asserted-by":"crossref","first-page":"4501","DOI":"10.32604\/cmc.2023.038446","volume":"5","author":"Y Zhou","year":"2023","unstructured":"Zhou Y, Fu C, Zhai Y et al (2023) Identification of rice leaf disease using improved ShuffleNet V2. Comput Mater Continua 5:4501\u20134517","journal-title":"Comput Mater Continua"},{"issue":"1","key":"2203_CR48","doi-asserted-by":"crossref","DOI":"10.1186\/s40537-024-00972-z","volume":"11","author":"P Theerthagiri","year":"2024","unstructured":"Theerthagiri P, Ruby AU, Chandran JGC et al (2024) Deep SqueezeNet learning model for diagnosis and prediction of maize leaf diseases. J Big Data 11(1):112","journal-title":"J Big Data"},{"issue":"1","key":"2203_CR49","volume":"1693","author":"J Liu","year":"2020","unstructured":"Liu J, Wang M, Bao L et al (2020) Efficientnet based recognition of maize diseases by leaf image classification. J Phys Conf Ser 1693(1):012148","journal-title":"J Phys Conf Ser"},{"issue":"3","key":"2203_CR50","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1007\/s41348-022-00601-y","volume":"129","author":"BT Hanh","year":"2022","unstructured":"Hanh BT, Van Manh H, Nguyen NV (2022) Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification. J Plant Dis Prot 129(3):623\u2013634","journal-title":"J Plant Dis Prot"},{"issue":"2","key":"2203_CR51","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s41348-023-00707-x","volume":"130","author":"M Gehlot","year":"2023","unstructured":"Gehlot M, Gandhi GC (2023) \u201cEffiNet-TS\u201d: a deep interpretable architecture using EfficientNet for plant disease detection and visualization. J Plant Dis Prot 130(2):413\u2013430","journal-title":"J Plant Dis Prot"},{"key":"2203_CR52","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY et al (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":"2203_CR53","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neunet.2017.12.012","volume":"107","author":"S Elfwing","year":"2018","unstructured":"Elfwing S, Uchibe E, Doya K (2018) Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Netw 107:3\u201311","journal-title":"Neural Netw"},{"issue":"9","key":"2203_CR54","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S et al (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904\u20131916","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2203_CR55","doi-asserted-by":"crossref","unstructured":"Liu Y, Cao J, Li B et al (2019) Knowledge distillation via instance relationship graph. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7096\u20137104","DOI":"10.1109\/CVPR.2019.00726"},{"issue":"1","key":"2203_CR56","doi-asserted-by":"crossref","first-page":"14","DOI":"10.25299\/itjrd.2023.13904","volume":"8","author":"M Fadhilla","year":"2023","unstructured":"Fadhilla M, Suryani D, Labellapansa A et al (2023) Corn leaf diseases recognition based on convolutional neural network. IT J Res Dev (ITJRD) 8(1):14\u201321","journal-title":"IT J Res Dev (ITJRD)"},{"key":"2203_CR57","doi-asserted-by":"crossref","first-page":"4211","DOI":"10.1007\/s11540-025-09914-5","volume":"68","author":"Z Wu","year":"2025","unstructured":"Wu Z, Su L (2025) Potato-ShuffleNetV2: a field potato disease identification model based on the improved ShuffleNetV2. Potato Res 68:4211\u20134236","journal-title":"Potato Res"},{"key":"2203_CR58","volume":"11","author":"B Liu","year":"2020","unstructured":"Liu B, Ding Z, Tian L et al (2020) Grape leaf disease identification using improved deep convolutional neural networks. Front Plant Sci 11:1082","journal-title":"Front Plant Sci"},{"key":"2203_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2025.101433","author":"Y Zhang","year":"2025","unstructured":"Zhang Y, Xu Y, Xu T et al (2025) RSD-YOLO: an improved YOLOv7-tiny framework for oat disease severity identification with integration of ReXNet and decoupled head. Smart Agric Technol. https:\/\/doi.org\/10.1016\/j.atech.2025.101433","journal-title":"Smart Agric Technol"},{"issue":"27","key":"2203_CR60","first-page":"1","volume":"8","author":"H Zhang","year":"2025","unstructured":"Zhang H, Dai C, Ren J et al (2025) Research on grape disease recognition method based on GhostNetV2 improved YOLO v8 model. J Agric Mach 8(27):1\u201311","journal-title":"Smart Agric Technol"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-02203-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02203-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02203-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T08:24:23Z","timestamp":1771230263000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-02203-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,18]]},"references-count":60,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["2203"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-02203-1","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,18]]},"assertion":[{"value":"15 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 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":"75"}}