{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T03:22:06Z","timestamp":1776136926200,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T00:00:00Z","timestamp":1734998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T00:00:00Z","timestamp":1734998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876189"],"award-info":[{"award-number":["61876189"]}],"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":["61273275"],"award-info":[{"award-number":["61273275"]}],"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":["61703426"],"award-info":[{"award-number":["61703426"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Young Talent Fund of University Association for Science and Technology in Shaanxi, China","award":["20190108"],"award-info":[{"award-number":["20190108"]}]},{"name":"the Innovation Talent Supporting Project of Shaanxi, China","award":["2020KJXX-065"],"award-info":[{"award-number":["2020KJXX-065"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s40747-024-01712-9","type":"journal-article","created":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T17:00:36Z","timestamp":1735059636000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["ImagTIDS: an internet of things intrusion detection framework utilizing GADF imaging encoding and improved Transformer"],"prefix":"10.1007","volume":"11","author":[{"given":"Peng","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yafei","family":"Song","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2785-9539","authenticated-orcid":false,"given":"Xiaodan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiangke","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Xiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,24]]},"reference":[{"key":"1712_CR1","doi-asserted-by":"crossref","unstructured":"Campos EM, Saura PF, Gonz\u00e1lez-Vidal A et al (2022) Evaluating federated learning for intrusion detection in Internet of Things: review and challenges. Comput Netw 203:108661.","DOI":"10.1016\/j.comnet.2021.108661"},{"issue":"9","key":"1712_CR2","doi-asserted-by":"publisher","first-page":"30357","DOI":"10.1016\/j.heliyon.2024.e30357","volume":"10","author":"A Coiduras-Sanagust\u00edn","year":"2024","unstructured":"Coiduras-Sanagust\u00edn A, Manchado-P\u00e9rez E, Garc\u00eda-Hern\u00e1ndez C (2024) Understanding perspectives on personal data privacy in Internet of Things (IoT): a systematic literature review (SLR). Heliyon 10(9):30357","journal-title":"Heliyon"},{"issue":"8","key":"1712_CR3","doi-asserted-by":"publisher","first-page":"3177","DOI":"10.1007\/s40815-023-01563-5","volume":"25","author":"X Song","year":"2023","unstructured":"Song X, Song Y, Stojanovic V et al (2023) Improved dynamic event-triggered security control for T\u2013S fuzzy LPV-PDE systems via pointwise measurements and point control. Int J Fuzzy Syst 25(8):3177\u20133192","journal-title":"Int J Fuzzy Syst"},{"issue":"10","key":"1712_CR4","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1177\/01423312231225782","volume":"46","author":"Y Tao","year":"2024","unstructured":"Tao Y, Tao H, Zhuang Z et al (2024) Quantized iterative learning control of communication-constrained systems with encoding and decoding mechanism. Trans Inst Meas Control 46(10):1943\u20131954","journal-title":"Trans Inst Meas Control"},{"issue":"6","key":"1712_CR5","doi-asserted-by":"publisher","first-page":"7451","DOI":"10.1007\/s40747-023-01135-y","volume":"9","author":"Z Peng","year":"2023","unstructured":"Peng Z, Song X, Song S et al (2023) Hysteresis quantified control for switched reaction\u2013diffusion systems and its application. Complex Intell Syst 9(6):7451\u20137460","journal-title":"Complex Intell Syst"},{"issue":"5","key":"1712_CR6","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1109\/LCOMM.2023.3261423","volume":"27","author":"R Li","year":"2023","unstructured":"Li R, Liao H, An J et al (2023) Intra-class universal adversarial attacks on deep learning-based modulation classifiers. IEEE Commun Lett 27(5):1297\u20131301","journal-title":"IEEE Commun Lett"},{"issue":"5","key":"1712_CR7","doi-asserted-by":"publisher","first-page":"8624","DOI":"10.1109\/JIOT.2023.3320975","volume":"11","author":"W Wang","year":"2023","unstructured":"Wang W, Luo C, An J et al (2023) Semi-supervised RF fingerprinting with consistency-based regularization. IEEE Internet Things J 11(5):8624\u20138636","journal-title":"IEEE Internet Things J"},{"key":"1712_CR8","doi-asserted-by":"crossref","unstructured":"Heidari A, Jabraeil Jamali MA (2023) Internet of Things intrusion detection systems: a comprehensive review and future directions. Clust Comput 26(6):3753\u20133780","DOI":"10.1007\/s10586-022-03776-z"},{"key":"1712_CR9","doi-asserted-by":"publisher","unstructured":"Liu H, An J, Jia X et al (2024) Stacked intelligent metasurfaces for wireless sensing and communication: applications and challenges. https:\/\/doi.org\/10.48550\/arXiv.2407.03566","DOI":"10.48550\/arXiv.2407.03566"},{"key":"1712_CR10","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.1007\/s40815-017-0305-2","volume":"19","author":"N Razmjooy","year":"2017","unstructured":"Razmjooy N, Ramezani M, Ghadimi N (2017) Imperialist competitive algorithm-based optimization of neuro-fuzzy system parameters for automatic red-eye removal. Int J Fuzzy Syst 19:1144\u20131156","journal-title":"Int J Fuzzy Syst"},{"key":"1712_CR11","doi-asserted-by":"publisher","first-page":"105858","DOI":"10.1016\/j.bspc.2023.105858","volume":"90","author":"L Zhang","year":"2024","unstructured":"Zhang L, Zhang J, Gao W, Bai F, Li N, Ghadimi N (2024) A deep learning outline aimed at prompt skin cancer detection utilizing gated recurrent unit networks and improved orca predation algorithm. Biomed Signal Process Control 90:105858","journal-title":"Biomed Signal Process Control"},{"key":"1712_CR12","doi-asserted-by":"publisher","first-page":"106024","DOI":"10.1016\/j.bspc.2024.106024","volume":"91","author":"H Liu","year":"2024","unstructured":"Liu H, Ghadimi N (2024) Hybrid convolutional neural network and Flexible Dwarf Mongoose Optimization Algorithm for strong kidney stone diagnosis. Biomed Signal Process Control 91:106024","journal-title":"Biomed Signal Process Control"},{"key":"1712_CR13","doi-asserted-by":"publisher","unstructured":"Cai X, Li X, Razmjooy N, Ghadimi N (2021) Breast cancer diagnosis by convolutional neural network and advanced thermal exchange optimization algorithm. Comput Math Methods Med 2021:5595180\u20135595193. https:\/\/doi.org\/10.1155\/2021\/5595180","DOI":"10.1155\/2021\/5595180"},{"issue":"1","key":"1712_CR14","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1515\/med-2018-0002","volume":"13","author":"N Razmjooy","year":"2018","unstructured":"Razmjooy N, Sheykhahmad F, Ghadimi N (2018) A hybrid neural network\u2013world cup optimization algorithm for melanoma detection. Open Med 13(1):9\u201316","journal-title":"Open Med"},{"issue":"7","key":"1712_CR15","first-page":"3321","volume":"14","author":"R Aziz","year":"2022","unstructured":"Aziz R, Baluch M, Pate S, Ganie A (2022) LGBM: a machine learning approach for Ethereum fraud detection. Int J Inf Technol 14(7):3321\u20133331","journal-title":"Int J Inf Technol"},{"key":"1712_CR16","doi-asserted-by":"crossref","unstructured":"Rahman R, Singh K, Tomar D, Musheer R (2024) Building resilient digital forensic frameworks for NoSQL database: harnessing the blockchain and quantum technology. In: Sustainable security practices using blockchain, quantum and post-quantum technologies for real time applications. Springer, Berlin, pp 205\u2013238","DOI":"10.1007\/978-981-97-0088-2_11"},{"issue":"8","key":"1712_CR17","doi-asserted-by":"publisher","first-page":"22663","DOI":"10.1007\/s11042-023-16371-0","volume":"83","author":"R Aziz","year":"2024","unstructured":"Aziz R, Hussain A, Sharma P (2024) Cognizable crime rate prediction and analysis under Indian penal code using deep learning with novel optimization approach. Multimed Tools Appl 83(8):22663\u201322700","journal-title":"Multimed Tools Appl"},{"key":"1712_CR18","doi-asserted-by":"crossref","unstructured":"Ali S, Hassan M, Granelli F et al (2024) Optimizing multi-tier cellular networks with deep learning for 6G consumer electronics communications. IEEE Trans Consumer Electron 70(1):627\u2013634","DOI":"10.1109\/TCE.2024.3357794"},{"key":"1712_CR19","doi-asserted-by":"crossref","unstructured":"Hassan M, Javed R, Granelli F et al (2023) Intelligent transportation systems in smart city: a systematic survey. In: 2023 international conference on robotics and automation in industry (ICRAI), Peshawar, Pakistan, pp 1\u20139","DOI":"10.1109\/ICRAI57502.2023.10089543"},{"key":"1712_CR20","first-page":"3","volume":"111","author":"M Hassan","year":"2022","unstructured":"Hassan M, Ali S, Imad M, Bibi S (2022) New advancements in cybersecurity: a comprehensive survey. Big Data Anal Comput Intell Cybersecur 111:3\u201317","journal-title":"Big Data Anal Comput Intell Cybersecur"},{"key":"1712_CR21","doi-asserted-by":"publisher","first-page":"59353","DOI":"10.1109\/ACCESS.2021.3073408","volume":"9","author":"N Mishra","year":"2021","unstructured":"Mishra N, Pandya S (2021) Internet of things applications, security challenges, attacks, intrusion detection, and future visions: a systematic review. IEEE Access 9:59353\u201359377","journal-title":"IEEE Access"},{"key":"1712_CR22","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.comnet.2019.01.023","volume":"151","author":"KA da Costa","year":"2019","unstructured":"da Costa KA, Papa JP, Lisboa CO et al (2019) Internet of Things: a survey on machine learning-based intrusion detection approaches. Comput Netw 151:147\u2013157","journal-title":"Comput Netw"},{"issue":"4","key":"1712_CR23","doi-asserted-by":"publisher","first-page":"2479","DOI":"10.3390\/app13042479","volume":"13","author":"JB Awotunde","year":"2023","unstructured":"Awotunde JB, Folorunso SO, Imoize AL et al (2023) An ensemble tree-based model for intrusion detection in industrial Internet of Things networks. Appl Sci 13(4):2479","journal-title":"Appl Sci"},{"key":"1712_CR24","doi-asserted-by":"publisher","first-page":"102994","DOI":"10.1016\/j.scs.2021.102994","volume":"72","author":"N Moustafa","year":"2021","unstructured":"Moustafa N (2021) A new distributed architecture for evaluating AI-based security systems at the edge: network TON_IoT datasets. Sustain Cities Soc 72:102994","journal-title":"Sustain Cities Soc"},{"issue":"21","key":"1712_CR25","doi-asserted-by":"publisher","first-page":"8085","DOI":"10.3390\/s22218085","volume":"22","author":"H Benaddi","year":"2022","unstructured":"Benaddi H, Jouhari M, Ibrahimi K et al (2022) Anomaly detection in industrial IoT using distributional reinforcement learning and generative adversarial networks. Sensors 22(21):8085","journal-title":"Sensors"},{"issue":"12","key":"1712_CR26","doi-asserted-by":"publisher","first-page":"9444","DOI":"10.1109\/JIOT.2021.3126811","volume":"9","author":"A Jamalipour","year":"2021","unstructured":"Jamalipour A, Murali S (2021) A taxonomy of machine-learning-based intrusion detection systems for the internet of things: a survey. IEEE Internet Things J 9(12):9444\u20139466","journal-title":"IEEE Internet Things J"},{"key":"1712_CR27","doi-asserted-by":"crossref","unstructured":"Albulayhi K, Abu Al-Haija Q, Alsuhibany SA et al (2022) IoT intrusion detection using machine learning with a novel high performing feature selection method, vol 12(10), p 5015","DOI":"10.3390\/app12105015"},{"issue":"4","key":"1712_CR28","doi-asserted-by":"publisher","first-page":"3603","DOI":"10.1007\/s11277-022-09726-7","volume":"125","author":"PJ Prakash","year":"2022","unstructured":"Prakash PJ, Lalitha B (2022) Optimized ensemble classifier based network intrusion detection system for RPL based Internet of Things. Wireless Pers Commun 125(4):3603\u20133626","journal-title":"Wireless Pers Commun"},{"key":"1712_CR29","doi-asserted-by":"crossref","unstructured":"Le T-T-H, Oktian YE, Kim H (2022) XGBoost for imbalanced multiclass classification-based industrial internet of things intrusion detection systems. Sustainability 14(14):8707","DOI":"10.3390\/su14148707"},{"issue":"2","key":"1712_CR30","doi-asserted-by":"publisher","first-page":"1801","DOI":"10.32604\/cmc.2021.018466","volume":"69","author":"N Islam","year":"2021","unstructured":"Islam N, Farhin F, Sultana I et al (2021) Towards machine learning based intrusion detection in IoT networks. Comput Mater Continua 69(2):1801\u20131821","journal-title":"Comput Mater Continua"},{"key":"1712_CR31","doi-asserted-by":"publisher","first-page":"107810","DOI":"10.1016\/j.compeleceng.2022.107810","volume":"99","author":"T Saba","year":"2022","unstructured":"Saba T, Rehman A, Sadad T et al (2022) Anomaly-based intrusion detection system for IoT networks through deep learning model. Comput Electr Eng 99:107810","journal-title":"Comput Electr Eng"},{"key":"1712_CR32","doi-asserted-by":"crossref","unstructured":"Latif S, Huma EZ, Jamal SS et al (2021) Intrusion detection framework for the internet of things using a dense random neural network. IEEE Trans Ind Inform 18(9):6435\u20136444","DOI":"10.1109\/TII.2021.3130248"},{"key":"1712_CR33","doi-asserted-by":"crossref","unstructured":"Abd Elaziz M, Al-Qaness MAA, Dahou A et al (2023) Intrusion detection approach for cloud and IoT environments using deep learning and Capuchin search algorithm. Adv Eng Softw 176:103402","DOI":"10.1016\/j.advengsoft.2022.103402"},{"issue":"14","key":"1712_CR34","doi-asserted-by":"publisher","first-page":"242","DOI":"10.54254\/2755-2721\/4\/20230459","volume":"59","author":"X Li","year":"2023","unstructured":"Li X, Zhao G, Wang Y, Ning K (2023) Improved internet of things intrusion detection model for CNN and RNN. Comput Eng Appl 59(14):242\u2013250","journal-title":"Comput Eng Appl"},{"issue":"9","key":"1712_CR35","doi-asserted-by":"publisher","first-page":"2100","DOI":"10.3390\/electronics12092100","volume":"12","author":"M Wang","year":"2023","unstructured":"Wang M, Yang N, Weng N (2023) Securing a smart home with a transformer-based IoT intrusion detection system. Electronics 12(9):2100","journal-title":"Electronics"},{"key":"1712_CR36","doi-asserted-by":"publisher","first-page":"109982","DOI":"10.1016\/j.comnet.2023.109982","volume":"235","author":"S Wang","year":"2023","unstructured":"Wang S, Xu W, Liu Y (2023) Res-TranBiLSTM: an intelligent approach for intrusion detection in the Internet of Things. Comput Netw 235:109982","journal-title":"Comput Netw"},{"issue":"12","key":"1712_CR37","doi-asserted-by":"publisher","first-page":"9960","DOI":"10.1109\/JIOT.2021.3119055","volume":"9","author":"R Zhao","year":"2021","unstructured":"Zhao R, Gui G, Xue Z et al (2021) A novel intrusion detection method based on lightweight neural network for internet of things. IEEE Internet Things J 9(12):9960\u20139972","journal-title":"IEEE Internet Things J"},{"issue":"4","key":"1712_CR38","doi-asserted-by":"publisher","first-page":"3094","DOI":"10.1109\/JIOT.2021.3112159","volume":"10","author":"Y Wu","year":"2021","unstructured":"Wu Y, Nie L, Wang S et al (2021) Intelligent intrusion detection for internet of things security: a deep convolutional generative adversarial network-enabled approach. IEEE Internet Things J 10(4):3094\u20133106","journal-title":"IEEE Internet Things J"},{"key":"1712_CR39","doi-asserted-by":"publisher","first-page":"92041","DOI":"10.1109\/ACCESS.2023.3307429","volume":"11","author":"S Li","year":"2023","unstructured":"Li S, Chai G, Wang Y et al (2023) CRSF: an intrusion detection framework for industrial internet of things based on pretrained CNN2D-RNN and SVM. IEEE Access 11:92041\u201399205","journal-title":"IEEE Access"},{"key":"1712_CR40","doi-asserted-by":"publisher","first-page":"142206","DOI":"10.1109\/ACCESS.2021.3120626","volume":"9","author":"A Gad","year":"2021","unstructured":"Gad A, Nashat A, Barkat T (2021) Intrusion detection system using machine learning for vehicular ad hoc networks based on ToN-IoT dataset. IEEE Access 9:142206\u2013142217","journal-title":"IEEE Access"},{"issue":"1","key":"1712_CR41","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1109\/TII.2022.3164770","volume":"19","author":"ST Mehedi","year":"2022","unstructured":"Mehedi ST, Anwar A, Rahman Z et al (2022) Dependable intrusion detection system for IoT: a deep transfer learning based approach. IEEE Trans Industr Inf 19(1):1006\u20131017","journal-title":"IEEE Trans Industr Inf"},{"issue":"1","key":"1712_CR42","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1109\/TITS.2022.3188671","volume":"24","author":"A Oseni","year":"2022","unstructured":"Oseni A, Moustafa N, Creech G et al (2022) An explainable deep learning framework for resilient intrusion detection in IoT-enabled transportation networks. IEEE Trans Intell Transp Syst 24(1):1000\u20131014","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"3","key":"1712_CR43","doi-asserted-by":"publisher","first-page":"2523","DOI":"10.1109\/TITS.2021.3119968","volume":"23","author":"M Abdel-Basset","year":"2021","unstructured":"Abdel-Basset M, Moustafa N, Hawash H et al (2021) Federated intrusion detection in blockchain-based smart transportation systems. IEEE Trans Intell Transp Syst 23(3):2523\u20132537","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1712_CR44","doi-asserted-by":"crossref","unstructured":"Zerveas G, Jayaraman S, Patel D et al (2021) A transformer-based framework for multivariate time series representation learning. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. virtual event. Association for Computing Machinery, Singapore, pp 2114\u20132124","DOI":"10.1145\/3447548.3467401"},{"issue":"10","key":"1712_CR45","first-page":"8112","volume":"34","author":"J Ahmad","year":"2022","unstructured":"Ahmad J, Shah SA, Latif S et al (2022) DRaNN_PSO: a deep random neural network with particle swarm optimization for intrusion detection in the industrial internet of things. J King Saud Univ Comput Inform Sci 34(10):8112\u20138121","journal-title":"J King Saud Univ Comput Inform Sci"},{"key":"1712_CR46","unstructured":"Pahl M, Aubet F (2018) All eyes on you: distributed multi-dimensional IoT microservice anomaly detection. In: 2018 14th international conference on network and service management (CNSM), Rome, Italy, pp 72\u201380"},{"key":"1712_CR47","doi-asserted-by":"publisher","first-page":"100059","DOI":"10.1016\/j.iot.2019.100059","volume":"7","author":"M Hasan","year":"2019","unstructured":"Hasan M, Islam M, Islam I et al (2019) Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Int Things 7:100059","journal-title":"Int Things"},{"key":"1712_CR48","doi-asserted-by":"publisher","first-page":"55595","DOI":"10.1109\/ACCESS.2021.3071766","volume":"9","author":"ZE Huma","year":"2021","unstructured":"Huma ZE, Latif S, Ahmad J et al (2021) A hybrid deep random neural network for cyberattack detection in the industrial Internet of Things. IEEE Access 9:55595\u201355605","journal-title":"IEEE Access"},{"key":"1712_CR49","doi-asserted-by":"publisher","unstructured":"Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. https:\/\/doi.org\/10.48550\/arXiv.1803.01271","DOI":"10.48550\/arXiv.1803.01271"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01712-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01712-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01712-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T20:18:45Z","timestamp":1738268325000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01712-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,24]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1712"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01712-9","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,24]]},"assertion":[{"value":"18 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2024","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"93"}}