{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:50:38Z","timestamp":1766069438005,"version":"3.48.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","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":[[2025,12,18]],"date-time":"2025-12-18T00:00:00Z","timestamp":1766016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Computing"],"DOI":"10.1007\/s10791-025-09849-y","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:45:50Z","timestamp":1766069150000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Human in the loop reinforcement learning framework for adaptive phishing detection"],"prefix":"10.1007","volume":"28","author":[{"given":"Sohail","family":"Khan","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,18]]},"reference":[{"key":"9849_CR1","doi-asserted-by":"publisher","first-page":"11895","DOI":"10.1109\/ACCESS.2021.3051633","volume":"9","author":"Z Wang","year":"2021","unstructured":"Wang Z, Zhu H, Sun L. Social engineering in cybersecurity: effect mechanisms, human vulnerabilities and attack methods. IEEE Access. 2021;9:11895\u2013910.","journal-title":"IEEE Access"},{"key":"9849_CR2","doi-asserted-by":"publisher","first-page":"39325","DOI":"10.1109\/ACCESS.2022.3162594","volume":"10","author":"W Syafitri","year":"2022","unstructured":"Syafitri W, Shukur Z, Asma\u2019Mokhtar U, Sulaiman R, Ibrahim MA. Social engineering attacks prevention: a systematic literature review. IEEE Access. 2022;10:39325\u201343.","journal-title":"IEEE Access"},{"issue":"12","key":"9849_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-024-10973-2","volume":"57","author":"M Schmitt","year":"2024","unstructured":"Schmitt M, Flechais I. Digital deception: generative artificial intelligence in social engineering and phishing. Artif Intell Rev. 2024;57(12):1\u201323.","journal-title":"Artif Intell Rev"},{"key":"9849_CR4","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.neunet.2022.03.037","volume":"152","author":"Y Matsuo","year":"2022","unstructured":"Matsuo Y, LeCun Y, Sahani M, Precup D, Silver D, Sugiyama M, et al. Deep learning, reinforcement learning, and world models. Neural Netw. 2022;152:267\u201375.","journal-title":"Neural Netw"},{"issue":"2","key":"9849_CR5","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1007\/s10462-021-09996-w","volume":"55","author":"S Gronauer","year":"2022","unstructured":"Gronauer S, Diepold K. Multi-agent deep reinforcement learning: a survey. Artif Intell Rev. 2022;55(2):895\u2013943.","journal-title":"Artif Intell Rev"},{"issue":"1","key":"9849_CR6","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/JAS.2023.123843","volume":"11","author":"D Wang","year":"2023","unstructured":"Wang D, Gao N, Liu D, Li J, Lewis FL. Recent progress in reinforcement learning and adaptive dynamic programming for advanced control applications. IEEE\/CAA J Autom Sinica. 2023;11(1):18\u201336.","journal-title":"IEEE\/CAA J Autom Sinica"},{"issue":"3","key":"9849_CR7","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1049\/ise2.12107","volume":"17","author":"FM Zennaro","year":"2023","unstructured":"Zennaro FM, Erd\u0151di L. Modelling penetration testing with reinforcement learning using capture-the-flag challenges: trade-offs between model-free learning and a priori knowledge. IET Inf Secur. 2023;17(3):441\u201357.","journal-title":"IET Inf Secur"},{"issue":"9","key":"9849_CR8","first-page":"17","volume":"6","author":"S Agrawal","year":"2023","unstructured":"Agrawal S. Mitigating cross-site request forgery (CSRF) attacks using reinforcement learning and predictive analytics. Appl Res Artif Intell Cloud Comput. 2023;6(9):17\u201330.","journal-title":"Appl Res Artif Intell Cloud Comput"},{"issue":"4","key":"9849_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3502868","volume":"31","author":"A Romdhana","year":"2022","unstructured":"Romdhana A, Merlo A, Ceccato M, Tonella P. Deep reinforcement learning for black-box testing of android apps. ACM Trans Softw Engin Methodol (TOSEM). 2022;31(4):1\u201329.","journal-title":"ACM Trans Softw Engin Methodol (TOSEM)"},{"key":"9849_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114386","volume":"168","author":"I Tariq","year":"2021","unstructured":"Tariq I, Sindhu MA, Abbasi RA, Khattak AS, Maqbool O, Siddiqui GF. Resolving cross-site scripting attacks through genetic algorithm and reinforcement learning. Expert Syst Appl. 2021;168:114386.","journal-title":"Expert Syst Appl"},{"issue":"12","key":"9849_CR11","doi-asserted-by":"publisher","first-page":"6042","DOI":"10.3390\/app12126042","volume":"12","author":"MA Siddiqi","year":"2022","unstructured":"Siddiqi MA, Pak W, Siddiqi MA. A study on the psychology of social engineering-based cyberattacks and existing countermeasures. Appl Sci. 2022;12(12):6042.","journal-title":"Appl Sci"},{"key":"9849_CR12","doi-asserted-by":"publisher","first-page":"85094","DOI":"10.1109\/ACCESS.2020.2992807","volume":"8","author":"Z Wang","year":"2020","unstructured":"Wang Z, Sun L, Zhu H. Defining social engineering in cybersecurity. IEEE Access. 2020;8:85094\u2013115.","journal-title":"IEEE Access"},{"key":"9849_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107363","volume":"94","author":"Y Al-Hamar","year":"2021","unstructured":"Al-Hamar Y, Kolivand H, Tajdini M, Saba T, Ramachandran V. Enterprise credential spear-phishing attack detection. Comput Electr Eng. 2021;94:107363.","journal-title":"Comput Electr Eng"},{"key":"9849_CR14","doi-asserted-by":"crossref","unstructured":"Vanitha J, Mallika C, Hema A, Parkavi K, Shree KS, Senthilkumar S. Detection system of whaling attack using deep learning techniques. In: 2024 2nd international conference on self sustainable artificial intelligence systems (ICSSAS). IEEE: New York; 2024. p. 493\u2013498.","DOI":"10.1109\/ICSSAS64001.2024.10760478"},{"key":"9849_CR15","doi-asserted-by":"publisher","DOI":"10.3389\/fcomp.2021.563060","volume":"3","author":"Z Alkhalil","year":"2021","unstructured":"Alkhalil Z, Hewage C, Nawaf L, Khan I. Phishing attacks: a recent comprehensive study and a new anatomy. Front Comput Sci. 2021;3:563060.","journal-title":"Front Comput Sci"},{"key":"9849_CR16","doi-asserted-by":"publisher","first-page":"72224","DOI":"10.1109\/ACCESS.2024.3403197","volume":"12","author":"M Zaoui","year":"2024","unstructured":"Zaoui M, Yousra B, Yassine S, Yassine M, Karim O. A comprehensive taxonomy of social engineering attacks and defense mechanisms: toward effective mitigation strategies. IEEE Access. 2024;12:72224\u201341.","journal-title":"IEEE Access"},{"key":"9849_CR17","doi-asserted-by":"publisher","DOI":"10.32604\/csse.2023.038917","author":"R Yang","year":"2023","unstructured":"Yang R, Zheng K, Wang X, Wu B, Wu C. Social engineering attack-defense strategies based on reinforcement learning. Comput Syst Sci Eng. 2023. https:\/\/doi.org\/10.32604\/csse.2023.038917.","journal-title":"Comput Syst Sci Eng"},{"issue":"1","key":"9849_CR18","doi-asserted-by":"publisher","DOI":"10.1080\/08839514.2022.2037254","volume":"36","author":"B Guembe","year":"2022","unstructured":"Guembe B, Azeta A, Misra S, Osamor VC, Fernandez-Sanz L, Pospelova V. The emerging threat of ai-driven cyber attacks: a review. Appl Artif Intell. 2022;36(1):2037254.","journal-title":"Appl Artif Intell"},{"issue":"4","key":"9849_CR19","first-page":"57","volume":"3","author":"MI Khan","year":"2024","unstructured":"Khan MI, Arif A, Khan ARA. AI\u2019s revolutionary role in cyber defense and social engineering. Int J Multidiscip Sci Arts. 2024;3(4):57\u201366.","journal-title":"Int J Multidiscip Sci Arts"},{"issue":"1","key":"9849_CR20","first-page":"72","volume":"30","author":"H Faotu","year":"2025","unstructured":"Faotu H, Asheshemi ON, et al. Human vulnerabilities in cybersecurity: Analyzing social engineering attacks and AI-driven machine learning countermeasures. J Sci Technol. 2025;30(1):72\u201384.","journal-title":"J Sci Technol"},{"issue":"1","key":"9849_CR21","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/6356152","volume":"2022","author":"B Prabhu Kavin","year":"2022","unstructured":"Prabhu Kavin B, Karki S, Hemalatha S, Singh D, Vijayalakshmi R, Thangamani M, et al. Machine learning-based secure data acquisition for fake accounts detection in future mobile communication networks. Wirel Commun Mob Comput. 2022;2022(1):6356152.","journal-title":"Wirel Commun Mob Comput"},{"key":"9849_CR22","doi-asserted-by":"crossref","unstructured":"Naqvi B, Perova K, Farooq A, Makhdoom I, Oyedeji S. Porras J. Mitigation strategies against the phishing attacks: a systematic literature review. Comput Sec; 2023. 132: 103387.","DOI":"10.1016\/j.cose.2023.103387"},{"issue":"2","key":"9849_CR23","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1007\/s10462-024-11055-z","volume":"58","author":"S Kavya","year":"2024","unstructured":"Kavya S, Sumathi D. Staying ahead of phishers: a review of recent advances and emerging methodologies in phishing detection. Artif Intell Rev. 2024;58(2):50.","journal-title":"Artif Intell Rev"},{"key":"9849_CR24","first-page":"331","volume":"2","author":"ML Puterman","year":"1990","unstructured":"Puterman ML. Markov decision processes. Handb Oper Res Manag Sci. 1990;2:331\u2013434.","journal-title":"Handb Oper Res Manag Sci"},{"issue":"1","key":"9849_CR25","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1023\/A:1018064306595","volume":"22","author":"S Mahadevan","year":"1996","unstructured":"Mahadevan S. Average reward reinforcement learning: foundations, algorithms, and empirical results. Mach Learn. 1996;22(1):159\u201395.","journal-title":"Mach Learn"},{"key":"9849_CR26","first-page":"279","volume":"8","author":"CJ Watkins","year":"1992","unstructured":"Watkins CJ, Dayan P. Q-learning. Mach Learn. 1992;8:279\u201392.","journal-title":"Mach Learn"},{"issue":"3","key":"9849_CR27","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1145\/203330.203343","volume":"38","author":"G Tesauro","year":"1995","unstructured":"Tesauro G, et al. Temporal difference learning and TD-Gammon. Commun ACM. 1995;38(3):58\u201368.","journal-title":"Commun ACM"},{"issue":"3\u20134","key":"9849_CR28","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1561\/2200000071","volume":"11","author":"V Fran\u00e7ois-Lavet","year":"2018","unstructured":"Fran\u00e7ois-Lavet V, Henderson P, Islam R, Bellemare MG, Pineau J, et al. An introduction to deep reinforcement learning. Foundations and Trends\u00ae in Machine Learning. 2018;11(3\u20134):219\u2013354.","journal-title":"Foundations and Trends\u00ae in Machine Learning."},{"key":"9849_CR29","unstructured":"Fedus W, Ramachandran P, Agarwal R, Bengio Y, Larochelle H, Rowland M, et\u00a0al. Revisiting fundamentals of experience replay. In: International conference on machine learning. PMLR; 2020. p. 3061\u20133071."},{"key":"9849_CR30","doi-asserted-by":"crossref","unstructured":"Buzzega P, Boschini M, Porrello A, Calderara S. Rethinking experience replay: a bag of tricks for continual learning. In: 2020 25th International Conference on Pattern Recognition (ICPR). IEEE; 2021. p. 2180\u20132187.","DOI":"10.1109\/ICPR48806.2021.9412614"},{"key":"9849_CR31","first-page":"1264","volume":"33","author":"Y Liu","year":"2020","unstructured":"Liu Y, Swaminathan A, Agarwal A, Brunskill E. Provably good batch off-policy reinforcement learning without great exploration. Adv Neural Inf Process Syst. 2020;33:1264\u201374.","journal-title":"Adv Neural Inf Process Syst"},{"key":"9849_CR32","unstructured":"Sankararaman KA, De S, Xu Z, Huang WR, Goldstein T. The impact of neural network overparameterization on gradient confusion and stochastic gradient descent. In: International conference on machine learning. PMLR; 2020. p. 8469\u20138479."},{"key":"9849_CR33","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1706.03741","author":"PF Christiano","year":"2017","unstructured":"Christiano PF, Leike J, Brown T, Martic M, Legg S, Amodei D. Deep reinforcement learning from human preferences. Adv Neurl Inform Process Syst. 2017. https:\/\/doi.org\/10.48550\/arXiv.1706.03741.","journal-title":"Adv Neurl Inform Process Syst"},{"key":"9849_CR34","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/s10994-012-5313-8","volume":"89","author":"J F\u00fcrnkranz","year":"2012","unstructured":"F\u00fcrnkranz J, H\u00fcllermeier E, Cheng W, Park SH. Preference-based reinforcement learning: a formal framework and a policy iteration algorithm. Mach Learn. 2012;89:123\u201356.","journal-title":"Mach Learn"},{"key":"9849_CR35","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1007\/978-0-387-30164-8_417","volume-title":"Encyclopedia of machine learning","author":"P Abbeel","year":"2011","unstructured":"Abbeel P, Ng AY. Inverse reinforcement learning. In: Abbeel P, Ng AY, editors. Encyclopedia of machine learning. Cham: Springer; 2011. p. 554\u20138."},{"key":"9849_CR36","unstructured":"Casper S, Davies X, Shi C, Gilbert TK, Scheurer J, Rando J, et\u00a0al. Open problems and fundamental limitations of reinforcement learning from human feedback. arXiv preprint arXiv:2307.15217. 2023."},{"key":"9849_CR37","first-page":"5539","volume":"36","author":"Y Ge","year":"2024","unstructured":"Ge Y, Hua W, Mei K, Tan J, Xu S, Li Z, et al. Openagi: when llm meets domain experts. Adv Neurl Inform Process Syst. 2024;36:5539\u201368.","journal-title":"Adv Neurl Inform Process Syst"},{"key":"9849_CR38","doi-asserted-by":"crossref","unstructured":"Song F, Yu B, Li M, Yu H, Huang F, Li Y, et\u00a0al. Preference ranking optimization for human alignment. In: Proceedings of the AAAI conference on artificial intelligence. vol.\u00a038. 2024. p. 18990\u201318998.","DOI":"10.1609\/aaai.v38i17.29865"},{"key":"9849_CR39","doi-asserted-by":"publisher","unstructured":"Prasad A, Chandra S. PhiUSIIL Phishing URL (Website). UCI Machine Learning Repository. https:\/\/archive.ics.uci.edu\/dataset\/967\/phiusiil+phishing+url+dataset, https:\/\/doi.org\/10.1016\/j.cose.2023.103545.\u00a0","DOI":"10.1016\/j.cose.2023.103545"},{"issue":"2","key":"9849_CR40","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1007\/s10207-023-00778-9","volume":"23","author":"S Ariyadasa","year":"2024","unstructured":"Ariyadasa S, Fernando S, Fernando S. SmartiPhish: a reinforcement learning-based intelligent anti-phishing solution to detect spoofed website attacks. Int J Inf Secur. 2024;23(2):1055\u201376.","journal-title":"Int J Inf Secur"},{"issue":"2","key":"9849_CR41","doi-asserted-by":"publisher","first-page":"26","DOI":"10.3390\/jcp5020026","volume":"5","author":"H Jabbar","year":"2025","unstructured":"Jabbar H, Al-Janabi S. AI-driven phishing detection: enhancing cybersecurity with reinforcement learning. J Cybersecur Privacy. 2025;5(2):26.","journal-title":"J Cybersecur Privacy"},{"issue":"6","key":"9849_CR42","doi-asserted-by":"publisher","first-page":"118","DOI":"10.3390\/computers12060118","volume":"12","author":"A Maci","year":"2023","unstructured":"Maci A, Santorsola A, Coscia A, Iannacone A. Unbalanced web phishing classification through deep reinforcement learning. Computers. 2023;12(6):118.","journal-title":"Computers"},{"issue":"6","key":"9849_CR43","first-page":"61","volume":"3","author":"MF Ansari","year":"2022","unstructured":"Ansari MF, Sharma PK, Dash B. Prevention of phishing attacks using AI-based Cybersecurity Awareness Training. Prevention. 2022;3(6):61\u201372.","journal-title":"Prevention"},{"key":"9849_CR44","doi-asserted-by":"crossref","unstructured":"Ghillani D. Deep learning and artificial intelligence framework to improve the cyber security. Authorea Preprints. 2022.","DOI":"10.22541\/au.166379475.54266021\/v1"},{"key":"9849_CR45","doi-asserted-by":"crossref","unstructured":"Ghanem MC, Wojtczak D, Kheddar H, Benkhelifa E, Nepomuceno EG, Kerrache CA. Leveraging reinforcement learning for an efficient windows registry analysis during cyber incident response. Preprints. 2025.","DOI":"10.36227\/techrxiv.173626850.08639272\/v2"},{"key":"9849_CR46","doi-asserted-by":"crossref","unstructured":"Alemayehu M, Ghanem MC, Ouazzane K, Kheddar H, Lacerda MJ. A systematic analysis on the use of AI techniques in industrial IoT DDoS attacks detection, mitigation and prevention. Preprints. 2025.","DOI":"10.36227\/techrxiv.174495047.75842155\/v1"},{"key":"9849_CR47","unstructured":"Dunsin D, Ghanem MC, Palmieri EA, Kheddar H, Habchi Y. MalVol-25: a diverse, labelled and detailed volatile memory dataset for malware detection and response testing and validation. In: Proceedings of the 8th IEEE conference on cloud and internet of things (CIoT 2025). London, UK; 2025. 2025. 29\u201331."},{"key":"9849_CR48","doi-asserted-by":"crossref","unstructured":"Chatterjee M, Namin AS, Detecting phishing websites through deep reinforcement learning. In,. IEEE 43rd annual computer software and applications conference (COMPSAC). vol. 2. IEEE. 2019;2019:227\u201332.","DOI":"10.1109\/COMPSAC.2019.10211"},{"key":"9849_CR49","first-page":"04010","volume-title":"E3S web of conferences","author":"P Dinesh","year":"2023","unstructured":"Dinesh P, Mukesh M, Navaneethan B, Sabeenian R, Paramasivam M, Manjunathan A. Identification of phishing attacks using machine learning algorithm. In: Dinesh P, Mukesh M, Navaneethan B, Sabeenian R, editors. E3S web of conferences, vol. 399. London: EDP Sciences; 2023. p. 04010."},{"key":"9849_CR50","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3317692","author":"AN Njoya","year":"2023","unstructured":"Njoya AN, Ngongag VLT, Tchakount\u00e9 F, Atemkeng M, Fachkha C. Characterizing mobile money phishing using reinforcement learning. IEEE Access. 2023. https:\/\/doi.org\/10.1109\/ACCESS.2023.3317692.","journal-title":"IEEE Access"},{"key":"9849_CR51","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.dss.2018.01.001","volume":"107","author":"S Smadi","year":"2018","unstructured":"Smadi S, Aslam N, Zhang L. Detection of online phishing email using dynamic evolving neural network based on reinforcement learning. Decis Support Syst. 2018;107:88\u2013102.","journal-title":"Decis Support Syst"},{"key":"9849_CR52","doi-asserted-by":"publisher","DOI":"10.1109\/TCDS.2024.3377445","author":"TR McIntosh","year":"2024","unstructured":"McIntosh TR, Susnjak T, Liu T, Watters P, Halgamuge MN. The inadequacy of reinforcement learning from human feedback-radicalizing large language models via semantic vulnerabilities. IEEE Trans Cogn Dev Syst. 2024. https:\/\/doi.org\/10.1109\/TCDS.2024.3377445.","journal-title":"IEEE Trans Cogn Dev Syst"}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09849-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-025-09849-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09849-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:45:53Z","timestamp":1766069153000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-025-09849-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,18]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["9849"],"URL":"https:\/\/doi.org\/10.1007\/s10791-025-09849-y","relation":{},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,18]]},"assertion":[{"value":"25 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 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":"This research was carried out in accordance with the Effat University Research and Ethical Guidelines and received approval from the Research Ethics Institutional Review Board (REIRB) of Effat University.\u00a0Informed consent was obtained from all expert raters who provided feedback during training; no personal identifiers were collected.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"I confirm that manuscript contains no individual person\u2019s data in any form; consent to publish is therefore not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"311"}}