{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:28:18Z","timestamp":1770748098993,"version":"3.49.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T00:00:00Z","timestamp":1624665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T00:00:00Z","timestamp":1624665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971182"],"award-info":[{"award-number":["61971182"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771191"],"award-info":[{"award-number":["61771191"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011496","name":"Science and Technology Bureau, Changsha","doi-asserted-by":"publisher","award":["CSKJ2020-12"],"award-info":[{"award-number":["CSKJ2020-12"]}],"id":[{"id":"10.13039\/501100011496","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011496","name":"Science and Technology Bureau, Changsha","doi-asserted-by":"publisher","award":["CSKJ2019-08"],"award-info":[{"award-number":["CSKJ2019-08"]}],"id":[{"id":"10.13039\/501100011496","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s00521-021-06234-w","type":"journal-article","created":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T09:02:31Z","timestamp":1624698151000},"page":"14653-14665","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Aggregated decentralized down-sampling-based ResNet for smart healthcare systems"],"prefix":"10.1007","volume":"35","author":[{"given":"Zhiwen","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3574-2675","authenticated-orcid":false,"given":"Ziji","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaonan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xun","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keping","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alireza","family":"Jolfaei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,26]]},"reference":[{"issue":"11","key":"6234_CR1","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc IEEE"},{"key":"6234_CR2","doi-asserted-by":"publisher","unstructured":"Szegedy C, Wei L, Yangqing J, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015:1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"6234_CR3","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Computer Science. https:\/\/arxiv.org\/abs\/1409.1556"},{"key":"6234_CR4","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016:770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"6234_CR5","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: 31st AAAI Conference on Artificial Intelligence 4278\u20134284","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"6234_CR6","doi-asserted-by":"publisher","unstructured":"Haque MF, Lim H, Kang D (2019) Object detection based on VGG with ResNet network. In: 2019 International Conference on Electronics, Information, and Communication (ICEIC) 1\u20133. https:\/\/doi.org\/10.23919\/ELINFOCOM.2019.8706476","DOI":"10.23919\/ELINFOCOM.2019.8706476"},{"key":"6234_CR7","doi-asserted-by":"publisher","unstructured":"Lu X, Kang X, Nishide S, Ren F (2019) Object detection based on SSD-ResNet. In: 2019 IEEE 6th International Conference on Cloud Computing and Intelligence Systems (CCIS) 89\u201392. https:\/\/doi.org\/10.1109\/CCIS48116.2019.9073753","DOI":"10.1109\/CCIS48116.2019.9073753"},{"key":"6234_CR8","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3042504","author":"K Yu","year":"2020","unstructured":"Yu K, Lin L, Alazab M et al (2020) Deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in a 5G-enabled intelligent transportation system. IEEE Trans Intell Transp Syst. https:\/\/doi.org\/10.1109\/TITS.2020.3042504","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6234_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3030856","author":"L Zhen","year":"2020","unstructured":"Zhen L, Bashir AK, Yu K et al (2020) Energy-efficient random access for LEO satellite-assisted 6g internet of remote things. IEEE Internet Things J. https:\/\/doi.org\/10.1109\/JIOT.2020.3030856","journal-title":"IEEE Internet Things J"},{"key":"6234_CR10","doi-asserted-by":"publisher","unstructured":"Jung H, Choi M, Jung J, Lee J, Kwon S, Jung WY (2017) ResNet-based vehicle classification and localization in traffic surveillance systems. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2017:934\u2013940. https:\/\/doi.org\/10.1109\/CVPRW.2017.129","DOI":"10.1109\/CVPRW.2017.129"},{"key":"6234_CR11","doi-asserted-by":"publisher","unstructured":"Hu X, Dai G, Ge Y, Ning Z, Liu Y A (2018) Simplified deep residual network for citywide crowd flows prediction. In: 2018 14th International Conference on Semantics, Knowledge and Grids (SKG) 60\u201367. https:\/\/doi.org\/10.1109\/SKG.2018.00016","DOI":"10.1109\/SKG.2018.00016"},{"key":"6234_CR12","doi-asserted-by":"publisher","unstructured":"Lu Z, Lu J, Ge Q, Zhan T (2019) Multi-object detection method based on YOLO and ResNet hybrid networks. In: 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM) 827-832. https:\/\/doi.org\/10.1109\/ICARM.2019.8833671","DOI":"10.1109\/ICARM.2019.8833671"},{"key":"6234_CR13","doi-asserted-by":"publisher","first-page":"108152","DOI":"10.1109\/ACCESS.2019.2931922","volume":"7","author":"X Ou","year":"2019","unstructured":"Ou X, Yan P, Zhang Y, Tu B, Zhang G, Wu J, Li W (2019) Moving object detection method via ResNet-18 with encoder-decoder structure in complex scenes. IEEE Access 7:108152\u2013108160. https:\/\/doi.org\/10.1109\/ACCESS.2019.2931922","journal-title":"IEEE Access"},{"key":"6234_CR14","doi-asserted-by":"publisher","unstructured":"Li Y, Ding Z, Zhang C, Wang Y, Chen J (2019) SAR ship detection based on resnet and transfer learning. In: IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium 1188\u20131191. https:\/\/doi.org\/10.1109\/IGARSS.2019.8900290","DOI":"10.1109\/IGARSS.2019.8900290"},{"key":"6234_CR15","doi-asserted-by":"publisher","unstructured":"Rezende E, Ruppert G, Carvalho T, Ramos F, Geus Pd (2017) Malicious software classification using transfer learning of ResNet-50 deep neural network. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) 1011\u20131014. https:\/\/doi.org\/10.1109\/ICMLA.2017.00-19","DOI":"10.1109\/ICMLA.2017.00-19"},{"issue":"21","key":"6234_CR16","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1049\/el.2019.2225","volume":"55","author":"WZ Yu","year":"2019","unstructured":"Yu WZ, Wen YM, Huang XQ (2019) ResNet-based Trojan detection methodology for protected ICs. Electron Lett 55(21):1116\u20131118. https:\/\/doi.org\/10.1049\/el.2019.2225","journal-title":"Electron Lett"},{"key":"6234_CR17","doi-asserted-by":"publisher","unstructured":"Oztel I (2020) Human detection system using different depths of the Resnet-50 in Faster R-CNN. In: 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) 1\u20135. https:\/\/doi.org\/10.1109\/ISMSIT50672.2020.9255109","DOI":"10.1109\/ISMSIT50672.2020.9255109"},{"key":"6234_CR18","doi-asserted-by":"publisher","unstructured":"Atliha V, \u0160e\u0161ok D, (2020) Comparison of VGG and ResNet used as encoders for image captioning. In: 2020 IEEE Open Conference of Electrical. Electronic and Information Sciences (eStream) 1\u20134. https:\/\/doi.org\/10.1109\/eStream50540.2020.9108880","DOI":"10.1109\/eStream50540.2020.9108880"},{"key":"6234_CR19","doi-asserted-by":"publisher","first-page":"3100","DOI":"10.1109\/ICIP.2017.8296853","volume":"2017","author":"Z Cui","year":"2017","unstructured":"Cui Z, Zhang Q, Geng S, Niu X, Yang J, Qiao Y (2017) Semantic segmentation with multi-path refinement and pyramid pooling dilated-resnet. IEEE Int Conf Image Process (ICIP) 2017:3100\u20133104. https:\/\/doi.org\/10.1109\/ICIP.2017.8296853","journal-title":"IEEE Int Conf Image Process (ICIP)"},{"key":"6234_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2020.3033149","author":"K Li","year":"2020","unstructured":"Li K, Ma Z, Xu L, Chen Y, Ma Y, Wu W, Wang F, Liu Z (2020) Depthwise separable ResNet in the MAP framework for hyperspectral image classification. IEEE Geosci Remote Sens Lett. https:\/\/doi.org\/10.1109\/LGRS.2020.3033149","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"6234_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/CICT48419.2019.9066215","volume":"2019","author":"PR Jeyaraj","year":"2019","unstructured":"Jeyaraj PR, Nadar ERS, Panigrahi BK (2019) ResNet convolution neural network based hyperspectral imagery classification for accurate cancerous region detection. IEEE Conf Inform Commun Technol 2019:1\u20136. https:\/\/doi.org\/10.1109\/CICT48419.2019.9066215","journal-title":"IEEE Conf Inform Commun Technol"},{"key":"6234_CR22","doi-asserted-by":"publisher","unstructured":"Gouda N, Amudha J (2020) Skin cancer classification using ResNet. In: 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) 536\u2013541. https:\/\/doi.org\/10.1109\/ICCCA49541.2020.9250855","DOI":"10.1109\/ICCCA49541.2020.9250855"},{"key":"6234_CR23","doi-asserted-by":"publisher","first-page":"0945","DOI":"10.1109\/ICCSP.2019.8697909","volume":"2019","author":"ASB Reddy","year":"2019","unstructured":"Reddy ASB, Juliet DS (2019) Transfer learning with ResNet-50 for malaria cell-image classification. Int Conf Commun Signal Process (ICCSP) 2019:0945\u20130949. https:\/\/doi.org\/10.1109\/ICCSP.2019.8697909","journal-title":"Int Conf Commun Signal Process (ICCSP)"},{"issue":"9","key":"6234_CR24","doi-asserted-by":"publisher","first-page":"2535","DOI":"10.1109\/JBHI.2020.2990529","volume":"24","author":"G Yang","year":"2020","unstructured":"Yang G, Pang Z et al (2020) Homecare robotic systems for healthcare 4.0: visions and enabling technologies. IEEE J Biomed Health Inform 24(9):2535\u20132549. https:\/\/doi.org\/10.1109\/JBHI.2020.2990529","journal-title":"IEEE J Biomed Health Inform"},{"key":"6234_CR25","unstructured":"Yu K, Tan L, Lin L, Chen X, Zhang and Sato T  (2011) Deep learning empowered breast cancer auxiliary diagnosis for 5GB remote E-Health. IEEE Wireless Communications"},{"issue":"3","key":"6234_CR26","doi-asserted-by":"publisher","first-page":"4005","DOI":"10.1109\/JIOT.2018.2876279","volume":"6","author":"X Chen","year":"2019","unstructured":"Chen X, Zhang H, Wu C, Mao S, Ji Y, Bennis M (2019) Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning. IEEE Internet Things 6(3):4005\u20134018. https:\/\/doi.org\/10.1109\/JIOT.2018.2876279","journal-title":"IEEE Internet Things"},{"key":"6234_CR27","doi-asserted-by":"publisher","unstructured":"Zahisham Z, Lee CP, Lim KM (2020) Food recognition with ResNet-50. In: 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) 1\u20135. https:\/\/doi.org\/10.1109\/IICAIET49801.2020.9257825","DOI":"10.1109\/IICAIET49801.2020.9257825"},{"key":"6234_CR28","doi-asserted-by":"publisher","unstructured":"Song X, Chen K, Cao Z (2020) ResNet-based image classification of railway shelling defect. In: 2020 39th Chinese Control Conference (CCC) 6589\u20136593. https:\/\/doi.org\/10.23919\/CCC50068.2020.9189112","DOI":"10.23919\/CCC50068.2020.9189112"},{"key":"6234_CR29","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1109\/ICESC48915.2020.9155585","volume":"2020","author":"V Kumar","year":"2020","unstructured":"Kumar V, Arora H, Harsh Sisodia J (2020) ResNet-based approach for detection and classification of plant leaf diseases. Int Conf Electron Sustain Commun Syst (ICESC) 2020:495\u2013502. https:\/\/doi.org\/10.1109\/ICESC48915.2020.9155585","journal-title":"Int Conf Electron Sustain Commun Syst (ICESC)"},{"key":"6234_CR30","doi-asserted-by":"publisher","first-page":"115287","DOI":"10.1109\/ACCESS.2020.3001237","volume":"8","author":"W Hu","year":"2020","unstructured":"Hu W, Fan J, Du Y, Li B, Xiong N, Bekkering E (2020) MDFC-ResNet: an agricultural IoT system to accurately recognize crop diseases. IEEE Access 8:115287\u2013115298. https:\/\/doi.org\/10.1109\/ACCESS.2020.3001237","journal-title":"IEEE Access"},{"key":"6234_CR31","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1109\/ICACSIS.2018.8618235","volume":"2018","author":"NM Firdaus","year":"2018","unstructured":"Firdaus NM, Chahyati D, Fanany MI (2018) Tourist attractions classification using ResNet. Int Conf Adv Comput Sci Inform Syst (ICACSIS) 2018:429\u2013433. https:\/\/doi.org\/10.1109\/ICACSIS.2018.8618235","journal-title":"Int Conf Adv Comput Sci Inform Syst (ICACSIS)"},{"key":"6234_CR32","doi-asserted-by":"publisher","unstructured":"Zhu X, Jiang Y, Yang S, Wang X, Li W, Fu P, Wang H, Luo Z (2017) Deep residual text detection network for scene text. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) 807\u2013812. https:\/\/doi.org\/10.1109\/ICDAR.2017.137","DOI":"10.1109\/ICDAR.2017.137"},{"key":"6234_CR33","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1109\/HPBDIS.2019.8735469","volume":"2019","author":"Z Huang","year":"2019","unstructured":"Huang Z, Zhang Q (2019) Skew correction of handwritten chinese character based on ResNet. Int Conf High Perform Big Data Intell Syst (HPBD&IS) 2019:223\u2013227. https:\/\/doi.org\/10.1109\/HPBDIS.2019.8735469","journal-title":"Int Conf High Perform Big Data Intell Syst (HPBD&IS)"},{"issue":"2","key":"6234_CR34","doi-asserted-by":"publisher","first-page":"2087","DOI":"10.32604\/cmc.2020.014220","volume":"66","author":"J Zhang","year":"2020","unstructured":"Zhang J, Yu K, Wen Z et al (2020) 3D reconstruction for motion blurred images using deep learning-based intelligent systems. CMC Comput Mater Contin 66(2):2087\u20132104. https:\/\/doi.org\/10.32604\/cmc.2020.014220","journal-title":"CMC Comput Mater Contin"},{"issue":"2","key":"6234_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3234463","volume":"19","author":"L He","year":"2019","unstructured":"He L, Ota K, Dong MX (2019) Deep reinforcement scheduling for mobile crowdsensing in fog computing. Acm Trans Internet Technol 19(2):1\u201318","journal-title":"Acm Trans Internet Technol"},{"key":"6234_CR36","doi-asserted-by":"publisher","first-page":"2268","DOI":"10.1109\/TWC.2019.2963667","volume":"19","author":"X Chen","year":"2020","unstructured":"Chen X, Wu C, Chen T et al (2020) Age of information aware radio resource management in vehicular networks: a proactive deep reinforcement learning perspective. IEEE Trans Wirel Commun 19:2268\u20132281. https:\/\/doi.org\/10.1109\/TWC.2019.2963667","journal-title":"IEEE Trans Wirel Commun"},{"key":"6234_CR37","doi-asserted-by":"crossref","unstructured":"Chen X, Wu C , Liu Z et al (2020) Computation offloading in beyond 5g networks: a distributed learning framework and applications. IEEE Wireless Commun. arXiv.org\/abs\/2007.08001","DOI":"10.1109\/MWC.001.2000296"},{"issue":"1","key":"6234_CR38","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1109\/tnse.2018.2825144","volume":"7","author":"FX Wang","year":"2020","unstructured":"Wang FX, Gong W, Liu JC et al (2020) Channel selective activity recognition with WiFi: a deep learning approach exploring wideband information. IEEE Trans Netw Sci Eng 7(1):181\u2013192. https:\/\/doi.org\/10.1109\/tnse.2018.2825144","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"6234_CR39","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.sigpro.2020.107454","volume":"170","author":"FY Li","year":"2020","unstructured":"Li FY, Wu K, Qin C et al (2020) Anti-compression JPEG steganography over repetitive compression networks. Signal Process 170:12. https:\/\/doi.org\/10.1016\/j.sigpro.2020.107454","journal-title":"Signal Process"},{"key":"6234_CR40","unstructured":"Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, (2009)"},{"key":"6234_CR41","first-page":"558","volume":"2019","author":"T He","year":"2019","unstructured":"He T, Zhang Z, Zhang H, Zhang Z, Xie J, Li M (2019) Bag of tricks for image classification with convolutional neural networks. IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR) 2019:558\u2013567","journal-title":"IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"6234_CR42","unstructured":"https:\/\/odir2019.grand-challenge.org\/dataset\/"},{"key":"6234_CR43","doi-asserted-by":"publisher","first-page":"5987","DOI":"10.1109\/CVPR.2017.634","volume":"2017","author":"S Xie","year":"2017","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. IEEE Conf Comput Vis Pattern Recognit (CVPR) 2017:5987\u20135995. https:\/\/doi.org\/10.1109\/CVPR.2017.634","journal-title":"IEEE Conf Comput Vis Pattern Recognit (CVPR)"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06234-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06234-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06234-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T09:52:07Z","timestamp":1685699527000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06234-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,26]]},"references-count":43,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["6234"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06234-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,26]]},"assertion":[{"value":"22 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2021","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 declared that they have no conflicts of interest to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}