{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T11:04:43Z","timestamp":1779793483324,"version":"3.53.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"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":["52302416"],"award-info":[{"award-number":["52302416"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hubei Provincial Natural Science Foundation of China","award":["2024AFD406, 2025AFD751"],"award-info":[{"award-number":["2024AFD406, 2025AFD751"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s10489-026-07255-5","type":"journal-article","created":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:47:48Z","timestamp":1777704468000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust minimax multi-agent deep deterministic policy gradient for reward uncertainty"],"prefix":"10.1007","volume":"56","author":[{"given":"Daicheng","family":"Song","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiming","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixuan","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoxuan","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwen","family":"Chong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4888-6045","authenticated-orcid":false,"given":"Li","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,2]]},"reference":[{"issue":"11","key":"7255_CR1","doi-asserted-by":"publisher","first-page":"1238","DOI":"10.1177\/0278364913495440","volume":"32","author":"J Kober","year":"2013","unstructured":"Kober J, Bagnell JA, Peters J (2013) RL in robotics: a survey. Int J Robot Res 32(11):1238\u20131274. https:\/\/doi.org\/10.1177\/0278364913495440","journal-title":"Int J Robot Res"},{"issue":"21","key":"7255_CR2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.aar6089","volume":"3","author":"M Andrychowicz","year":"2018","unstructured":"OpenAI, Andrychowicz M, Baker B, Chociej M, J\u00f3zefowicz R, McGrew B, Pachocki J, Petron A, Plappert M, Powell G, Ray A, Schneider J, Sidor S, Tobin J, Welinder P, Weng L, Zaremba W (2018) Learning dexterous in-hand manipulation. Sci Robot 3(21):eaar6089. https:\/\/doi.org\/10.1126\/scirobotics.aar6089","journal-title":"Sci Robot"},{"issue":"19","key":"7255_CR3","doi-asserted-by":"publisher","first-page":"70","DOI":"10.2352\/ei.2017.19.19.autonomous-119","volume":"2017","author":"A El Sallab","year":"2017","unstructured":"El Sallab A, Abdou M, Perot E, Yogamani S (2017) Deep RL framework for autonomous driving. Electron Imaging 2017(19):70\u201376. https:\/\/doi.org\/10.2352\/ei.2017.19.19.autonomous-119","journal-title":"Electron Imaging"},{"key":"7255_CR4","unstructured":"Shalev-Shwartz S, Shammah S, Shashua A (2016) Safe, multi-agent RL for autonomous driving. arXiv preprint arXiv:1610.03295 [cs.AI, cs.RO, stat.ML]."},{"key":"7255_CR5","doi-asserted-by":"publisher","unstructured":"Balaji B, Mallya S, Genc S, Gupta S, Dirac L, Khare V, Roy G, Sun T, Tao Y, Townsend B, Calleja E, Muralidhara S, Karuppasamy D (2020) DeepRacer: Autonomous racing platform for experimentation with sim2real RL. In: IEEE International Conference on Robotics and Automation. IEEE, pp 2746\u20132754. https:\/\/doi.org\/10.1109\/ICRA40945.2020.9196700","DOI":"10.1109\/ICRA40945.2020.9196700"},{"issue":"6443","key":"7255_CR6","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1126\/science.aau6249","volume":"364","author":"C Berner","year":"2019","unstructured":"OpenAI, Berner C, Brockman G, Chan B, Cheung V, D\u02dbebiak P, Dennison C, Farhi D, Fischer Q, Hashme S, Hesse C, J\u00f3zefowicz R, Gray S, Olsson C, Pachocki J, Petrov M, Pinto HPO, Raiman J, Salimans T, Schlatter J, Schneider J, Sidor S, Sutskever I, Tang J, Wolski F, Zhan S (2019) Dota 2 with large scale deep RL. Science 364(6443):859\u2013865. https:\/\/doi.org\/10.1126\/science.aau6249","journal-title":"Science"},{"issue":"7782","key":"7255_CR7","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1038\/s41586-019-1724-z","volume":"575","author":"O Vinyals","year":"2019","unstructured":"Vinyals O, Babuschkin I, Czarnecki WMC, Mathieu M, Dudzik A, Chung J, Choi DH, Powell R, Ewalds T, Georgiev P et al (2019) Grandmaster level in StarCraft II using multi-agent RL. Nature 575(7782):350\u2013354. https:\/\/doi.org\/10.1038\/s41586-019-1724-z","journal-title":"Nature"},{"key":"7255_CR8","doi-asserted-by":"publisher","unstructured":"Littman ML (1994) Markov games as a framework for multi-agent RL. In: International Conference on Machine Learning. Morgan Kaufmann, pp 157\u2013163. https:\/\/doi.org\/10.5555\/304182.304214","DOI":"10.5555\/304182.304214"},{"key":"7255_CR9","doi-asserted-by":"publisher","unstructured":"Shapley LS (1953) Stochastic games. Proc Natl Acad Sci 39(10):1095\u20131100. https:\/\/doi.org\/10.1073\/pnas.39.10.1095","DOI":"10.1073\/pnas.39.10.1095"},{"key":"7255_CR10","first-page":"6379","volume-title":"Advances in Neural Information Processing Systems","author":"R Lowe","year":"2017","unstructured":"Lowe R, Wu Y, Tamar A, Harb J, Abbeel P, Mordatch I (2017) Multiagent actor-critic for mixed cooperative-competitive environments. Advances in Neural Information Processing Systems, vol 30. Curran Associates Inc., Red Hook, NY, USA, pp 6379\u20136390"},{"key":"7255_CR11","doi-asserted-by":"publisher","unstructured":"Foerster JN, Farquhar G, Afouras T, Nardelli N, Whiteson S (2018) Counterfactual multi-agent policy gradients. In: Thirty-Second AAAI Conference on Artificial Intelligence. AAAI Press, pp 2974\u20132982. https:\/\/doi.org\/10.1609\/aaai.v32i1.11846","DOI":"10.1609\/aaai.v32i1.11846"},{"key":"7255_CR12","unstructured":"Zhang K, Yang Z, Liu H, Zhang T, Ba\u015far T (2018) Fully decentralized multi-agent RL with networked agents. In: International Conference on Machine Learning. PMLR, pp 5867\u20135876. https:\/\/proceedings.mlr.press\/v80\/zhang18h.html"},{"key":"7255_CR13","doi-asserted-by":"publisher","unstructured":"Foerster JN, Farquhar G, Afouras T, Nardelli N, Whiteson S (2018) Counterfactual multi-agent policy gradients. In: Thirty-Second AAAI Conference on Artificial Intelligence, Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI Press, pp 2974\u20132982. https:\/\/doi.org\/10.1609\/aaai.v32i1.11846","DOI":"10.1609\/aaai.v32i1.11846"},{"key":"7255_CR14","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1007\/978-3-319-68488-6_5","volume-title":"Autonomous Agents and Multi-Agent Systems, Lecture Notes in Computer Science","author":"JK Gupta","year":"2017","unstructured":"Gupta JK, Egorov M, Kochenderfer MK (2017) Cooperative multi-agent control using deep RL. In: Sukthankar G, Rodriguez-Aguilar JA (eds) Autonomous Agents and Multi-Agent Systems, Lecture Notes in Computer Science, vol 10642. Springer, Cham, pp 66\u201383. https:\/\/doi.org\/10.1007\/978-3-319-68488-6_5"},{"issue":"2","key":"7255_CR15","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1287\/moor.1040.0139","volume":"30","author":"GN Iyengar","year":"2005","unstructured":"Iyengar GN (2005) Robust dynamic programming. Math Oper Res 30(2):257\u2013280. https:\/\/doi.org\/10.1287\/moor.1040.0139","journal-title":"Math Oper Res"},{"issue":"5","key":"7255_CR16","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1287\/opre.1050.0225","volume":"53","author":"A Nilim","year":"2005","unstructured":"Nilim A, El Ghaoui L (2005) Robust control of Markov decision processes with uncertain transition matrices. Oper Res 53(5):780\u2013798. https:\/\/doi.org\/10.1287\/opre.1050.0225","journal-title":"Oper Res"},{"issue":"1","key":"7255_CR17","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1287\/moor.1120.0589","volume":"38","author":"W Wiesemann","year":"2013","unstructured":"Wiesemann W, Kuhn D, Rustem B (2013) Robust Markov decision processes. Math Oper Res 38(1):153\u2013183. https:\/\/doi.org\/10.1287\/moor.1120.0589","journal-title":"Math Oper Res"},{"key":"7255_CR18","unstructured":"Lim SH, Xu H, Mannor S (2013) RL in robust Markov decision processes. Advances in Neural Information Processing Systems, vol 26. Curran Associates Inc., pp 701\u2013709"},{"key":"7255_CR19","unstructured":"Derman E, Mankowitz DJ, Mann TA, Mannor S (2018) Soft-robust actor-critic policy-gradient. arXiv preprint arXiv:1803.04848 [cs.LG]."},{"key":"7255_CR20","unstructured":"Mankowitz DJ, Levine N, Jeong R, Abdolmaleki A, Springenberg JT, Mann T, Hester T, Riedmiller M (2019) Robust RL for continuous control with model misspecification. arXiv preprint arXiv:1906.07516 [cs.LG, eess.SY]."},{"issue":"2","key":"7255_CR21","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1162\/0899766053328923","volume":"17","author":"J Morimoto","year":"2005","unstructured":"Morimoto J, Doya K (2005) Robust RL. Neural Comput 17(2):335\u2013359. https:\/\/doi.org\/10.1162\/0899766053328923","journal-title":"Neural Comput"},{"key":"7255_CR22","unstructured":"Pinto L, Davidson J, Sukthankar R, Gupta A (2017) Robust adversarial RL. In: International Conference on Machine Learning, vol 70. JMLR.org, pp 2817\u20132826. https:\/\/jmlr.org\/proceedings\/papers\/v70\/pinto17a.html"},{"key":"7255_CR23","unstructured":"Tessler C, Efroni Y, Mannor S (2019) Action robust RL and applications in continuous control. arXiv preprint arXiv:1901.09184 [cs.LG. eess.SY]"},{"issue":"2","key":"7255_CR24","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1287\/opre.1100.0891","volume":"59","author":"E Karde\u00b8s","year":"2011","unstructured":"Karde\u00b8s E, Ord\u00f3\u00f1ez F, Hall RW (2011) Discounted robust stochastic games and an application to queueing control. Oper Res 59(2):365\u2013382. https:\/\/doi.org\/10.1287\/opre.1100.0891","journal-title":"Oper Res"},{"issue":"Nov","key":"7255_CR25","first-page":"1039","volume":"4","author":"J Hu","year":"2003","unstructured":"Hu J, Wellman MP (2003) Nash Q-learning for general-sum stochastic games. J Mach Learn Res 4(Nov):1039\u20131069. https:\/\/jmlr.org\/papers\/v4\/hu03a.html","journal-title":"J Mach Learn Res"},{"issue":"1","key":"7255_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2422085.2422086","volume":"60","author":"TDH Hansen","year":"2013","unstructured":"Hansen TDH, Miltersen PB, Zwick U (2013) Strategy iteration is strongly polynomial for 2-player turn-based stochastic games with a constant discount factor. J ACM 60(1):1\u201316. https:\/\/doi.org\/10.1145\/2422085.2422086","journal-title":"J ACM"},{"key":"7255_CR27","unstructured":"Zhang K, Kakade SM, Ba\u015far T, Yang LF (2020) Model-based multiagent RL in zero-sum Markov games with near-optimal sample complexity. arXiv preprint arXiv:2007.07461 [cs.LG, math.OC]."},{"key":"7255_CR28","unstructured":"Zhang K, Yang Z, Ba\u015far T (2019) Policy optimization provably converges to Nash equilibria in zero-sum linear quadratic games. Advances in Neural Information Processing Systems, vol 32. Curran Associates Inc., pp 11602\u201311614"},{"key":"7255_CR29","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780195128956.001.0001","volume-title":"An introduction to game theory","author":"MJ Osborne","year":"2004","unstructured":"Osborne MJ, Rubinstein A (2004) An introduction to game theory, vol 3. Oxford University Press, New York. https:\/\/doi.org\/10.1093\/acprof:oso\/9780195128956.001.0001"},{"key":"7255_CR30","unstructured":"Swaminathan B, Vaishali R, Subashri TS (2020) Analysis of minimax algorithm using tic-tac-toe"},{"key":"7255_CR31","doi-asserted-by":"publisher","unstructured":"Foerster J, Chen RY, Al-Shedivat M, Whiteson S, Abbeel P, Mordatch I (2018) Learning with opponent learning awareness. In: International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, pp 122\u2013130. https:\/\/doi.org\/10.5555\/3237383.3237400","DOI":"10.5555\/3237383.3237400"},{"key":"7255_CR32","doi-asserted-by":"crossref","unstructured":"Grau-Moya J, Leibfried F, Bou-Ammar H (2018) Balancing two-player stochastic games with soft q-learning. arXiv preprint arXiv:1802.03442 [cs.LG].","DOI":"10.24963\/ijcai.2018\/37"},{"key":"7255_CR33","unstructured":"Gao C, Mueller M, Hayward R (2018) Adversarial policy gradient for alternating Markov games"},{"key":"7255_CR34","unstructured":"Maniyar MP, Mondal PLA, Bhatnagar A (2024) S A cubic-regularized policy Newton algorithm for RL. In: International Conference on Artificial Intelligence and Statistics, vol 238. Proceedings of Machine Learning Research, pp 4708\u20134716. https:\/\/proceedings.mlr.press\/v238\/maniyar24a.html"},{"key":"7255_CR35","doi-asserted-by":"publisher","unstructured":"Li S, Wu Y, Cui X, Dong H, Fang F, Russell S (2019) Robust multi-agent RL via minimax deep deterministic policy gradient. In: AAAI Conference on Artificial Intelligence, vol 33. AAAI Press, pp 4213\u20134220. https:\/\/doi.org\/10.1609\/aaai.v33i01.33014213","DOI":"10.1609\/aaai.v33i01.33014213"},{"key":"7255_CR36","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1412.6572","author":"IJ Goodfellow","year":"2014","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv preprint. https:\/\/doi.org\/10.48550\/arXiv.1412.6572. arXiv:1412.6572 [cs.CV]","journal-title":"arXiv preprint"},{"key":"7255_CR37","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1902.03442","author":"M Uricar","year":"2019","unstructured":"Uricar M, Krizek P, Hurych D, Sobh I, Yogamani S, Denny P (2019) Yes, we GAN: Applying adversarial techniques for autonomous driving. arXiv preprint arXiv:1902 03442 [cs CV cs RO]. https:\/\/doi.org\/10.48550\/arXiv.1902.03442","journal-title":"arXiv preprint arXiv:1902 03442 [cs CV cs RO]"},{"key":"7255_CR38","doi-asserted-by":"publisher","unstructured":"Spooner T, Savani R (2021) Robust market making via adversarial RL. In: International Joint Conference on Artificial Intelligence (IJCAI\u201920). IJCAI Press, pp 4590\u20134596. https:\/\/doi.org\/10.24963\/ijcai.2020\/633","DOI":"10.24963\/ijcai.2020\/633"},{"issue":"7587","key":"7255_CR39","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver D, Huang A, Maddison CJ, Guez A, Sifre G, Van Den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M et al (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484\u2013489. https:\/\/doi.org\/10.1038\/nature16961","journal-title":"Nature"},{"key":"7255_CR40","doi-asserted-by":"publisher","unstructured":"Zhang C, Wu Z, Li Z, Xu H, Xue Z, Qian R (2024) Multi-agent RL-based UAV swarm confrontation: Integrating QMIX algorithm with artificial potential field method. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, Kuching, Malaysia, pp 161\u2013166. https:\/\/doi.org\/10.1109\/SMC54092.2024.10832089","DOI":"10.1109\/SMC54092.2024.10832089"},{"key":"7255_CR41","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s10462-011-9256-2","volume":"38","author":"Y Duan","year":"2012","unstructured":"Duan Y, Cui BX, Xu XH (2012) A multi-agent RL approach to robot soccer. Artif Intell Rev 38:193\u2013211. https:\/\/doi.org\/10.1007\/s10462-011-9256-2","journal-title":"Artif Intell Rev"},{"key":"7255_CR42","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1703.03400","author":"C Finn","year":"2017","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. arXiv preprint. arXiv:1703.03400 [cs.LG, stat.ML] https:\/\/doi.org\/10.48550\/arXiv.1703.03400","journal-title":"arXiv preprint"},{"key":"7255_CR43","unstructured":"Zhang K, Sun T, Tao Y, Genc S, Mallya S, Ba\u015far T (2020) Robust multi-agent RL with model uncertainty. In: Neural Information Processing Systems (NIPS \u201820). Curran Associates Inc., Red Hook, NY, USA, pp 10571\u201310583. https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/887c0901b5607201318720250e915b9a-Abstract.html"},{"key":"7255_CR44","unstructured":"Sutton RS, McAllester DA, Singh SP, Mansour Y (2000) Policy gradient methods for RL with function approximation. In: Advances in Neural Information Processing Systems, vol 13. MIT Press,pp 1057\u20131063. https:\/\/proceedings.neurips.cc\/paper\/2000\/hash\/4b86abe48d358ecf194c56c66844286-Abstract.html"},{"key":"7255_CR45","unstructured":"Silver D, Lever G, Heess N, Degris T, Wierstra D, Riedmiller M (2014) Deterministic policy gradient algorithms. In: International Conference on Machine Learning. PMLR, pp 387\u2013395. https:\/\/proceedings.mlr.press\/v32\/silver14.html"},{"key":"7255_CR46","unstructured":"Konda VR, Tsitsiklis JN (2000) Actor-critic algorithms. In: Advances in Neural Information Processing Systems, vol 13. MIT Press, pp 1008\u20131014. https:\/\/proceedings.neurips.cc\/paper\/2000\/hash\/6449f44a102fde848669f0206871d80-Abstract.html"},{"issue":"4","key":"7255_CR47","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1137\/S0363012901389073","volume":"42","author":"VR Konda","year":"2003","unstructured":"Konda VR, Tsitsiklis JN (2003) On actor-critic algorithms. SIAM J Control Optim 42(4):1143\u20131166. https:\/\/doi.org\/10.1137\/S0363012901389073","journal-title":"SIAM J Control Optim"},{"issue":"32","key":"7255_CR48","first-page":"1","volume":"25","author":"Y Zhong","year":"2024","unstructured":"Zhong Y, Grudzien Kuba J, Feng X, Hu S, Ji J, Yang Y (2024) Heterogeneous-Agent Reinforcement Learning. J Mach Learn Res 25(32):1\u201367. http:\/\/jmlr.org\/papers\/v25\/23-0488.html","journal-title":"J Mach Learn Res"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07255-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-026-07255-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07255-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T10:43:25Z","timestamp":1779792205000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-026-07255-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":48,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["7255"],"URL":"https:\/\/doi.org\/10.1007\/s10489-026-07255-5","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"16 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2026","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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"239"}}