{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T08:18:46Z","timestamp":1773562726548,"version":"3.50.1"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:00:00Z","timestamp":1662076800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:00:00Z","timestamp":1662076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001775","name":"University of Technology Sydney","doi-asserted-by":"publisher","award":["189008"],"award-info":[{"award-number":["189008"]}],"id":[{"id":"10.13039\/501100001775","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1007\/s13042-022-01641-4","type":"journal-article","created":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T15:03:21Z","timestamp":1662131001000},"page":"295-311","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Modular transfer learning with transition mismatch compensation for excessive disturbance rejection"],"prefix":"10.1007","volume":"14","author":[{"given":"Tianming","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1677-3633","authenticated-orcid":false,"given":"Wenjie","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Huan","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Dikai","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,2]]},"reference":[{"key":"1641_CR1","doi-asserted-by":"crossref","unstructured":"Abbeel P, Ng AY (2005) Exploration and apprenticeship learning in reinforcement learning. In: Proceedings of the 22nd international conference on Machine learning, pp 1\u20138","DOI":"10.1145\/1102351.1102352"},{"issue":"1","key":"1641_CR2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/0278364919887447","volume":"39","author":"OM Andrychowicz","year":"2020","unstructured":"Andrychowicz OM, Baker B, Chociej M et al (2020) Learning dexterous in-hand manipulation. The International Journal of Robotics Research 39(1):3\u201320","journal-title":"The International Journal of Robotics Research"},{"key":"1641_CR3","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-77899-0","volume-title":"Underwater robots,","author":"G Antonelli","year":"2018","unstructured":"Antonelli G (2018) Underwater robots, vol 123. Springer"},{"key":"1641_CR4","doi-asserted-by":"crossref","unstructured":"Bengio Y, Louradour J, Collobert R, et\u00a0al (2009) Curriculum learning. In: Proceedings of the 26th annual international conference on machine learning, pp 41\u201348","DOI":"10.1145\/1553374.1553380"},{"issue":"4","key":"1641_CR5","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1109\/TEVC.2005.850293","volume":"9","author":"JC Bongard","year":"2005","unstructured":"Bongard JC, Lipson H (2005) Nonlinear system identification using coevolution of models and tests. IEEE Trans Evol Comput 9(4):361\u2013384","journal-title":"IEEE Trans Evol Comput"},{"key":"1641_CR6","unstructured":"Camacho EF, Alba CB (2013) Model predictive control. Springer Science & Business Media"},{"issue":"1","key":"1641_CR7","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana R (1997) Multitask learning. Machine learning 28(1):41\u201375","journal-title":"Multitask learning. Machine learning"},{"key":"1641_CR8","doi-asserted-by":"crossref","unstructured":"Chebotar Y, Handa A, Makoviychuk V, et\u00a0al (2019) Closing the sim-to-real loop: Adapting simulation randomization with real world experience. In: 2019 International Conference on Robotics and Automation (ICRA), IEEE, pp 8973\u20138979","DOI":"10.1109\/ICRA.2019.8793789"},{"issue":"4","key":"1641_CR9","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1109\/41.857974","volume":"47","author":"WH Chen","year":"2000","unstructured":"Chen WH, Ballance DJ, Gawthrop PJ et al (2000) A nonlinear disturbance observer for robotic manipulators. IEEE Trans Industr Electron 47(4):932\u2013938","journal-title":"IEEE Trans Industr Electron"},{"issue":"2","key":"1641_CR10","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1109\/TIE.2015.2478397","volume":"63","author":"WH Chen","year":"2016","unstructured":"Chen WH, Yang J, Guo L et al (2016) Disturbance-observer-based control and related methods-an overview. IEEE Trans Industr Electron 63(2):1083\u20131095","journal-title":"IEEE Trans Industr Electron"},{"key":"1641_CR11","unstructured":"Chen X, Hu J, Jin C, et\u00a0al (2021) Understanding domain randomization for sim-to-real transfer. arXiv preprint arXiv:2110.03239"},{"key":"1641_CR12","unstructured":"Deisenroth M, Rasmussen CE (2011) Pilco: A model-based and data-efficient approach to policy search. In: Proceedings of the 28th International Conference on machine learning (ICML-11), pp 465\u2013472"},{"key":"1641_CR13","doi-asserted-by":"crossref","unstructured":"Deisenroth MP, Neumann G, Peters J, et\u00a0al (2013) A survey on policy search for robotics. Foundations and Trends\u00ae in Robotics 2(1\u20132):1\u2013142","DOI":"10.1561\/2300000021"},{"key":"1641_CR14","unstructured":"Duan Y, Schulman J, Chen X, et\u00a0al (2016) Rl2: Fast reinforcement learning via slow reinforcement learning. arXiv preprint arXiv:1611.02779"},{"key":"1641_CR15","unstructured":"Fakoor R, Chaudhari P, Soatto S, et\u00a0al (2019) Meta-q-learning. arXiv preprint arXiv:1910.00125"},{"key":"1641_CR16","unstructured":"Feinberg V, Wan A, Stoica I, et\u00a0al (2018) Model-based value estimation for efficient model-free reinforcement learning. arXiv preprint arXiv:1803.00101"},{"issue":"1","key":"1641_CR17","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1109\/LRA.2016.2531792","volume":"2","author":"DC Fern\u00e1ndez","year":"2016","unstructured":"Fern\u00e1ndez DC, Hollinger GA (2016) Model predictive control for underwater robots in ocean waves. IEEE Robotics and Automation letters 2(1):88\u201395","journal-title":"IEEE Robotics and Automation letters"},{"key":"1641_CR18","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, JMLR. org, pp 1126\u20131135"},{"key":"1641_CR19","doi-asserted-by":"crossref","unstructured":"Fu J, Levine S, Abbeel P (2016) One-shot learning of manipulation skills with online dynamics adaptation and neural network priors. In: 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp 4019\u20134026","DOI":"10.1109\/IROS.2016.7759592"},{"key":"1641_CR20","unstructured":"Fujimoto S, Hoof H, Meger D (2018) Addressing function approximation error in actor-critic methods. In: International conference on machine learning, PMLR, pp 1587\u20131596"},{"issue":"1","key":"1641_CR21","first-page":"3","volume":"230","author":"H Gao","year":"2016","unstructured":"Gao H, Cai Y (2016) Nonlinear disturbance observer-based model predictive control for a generic hypersonic vehicle. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 230(1):3\u201312","journal-title":"Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering"},{"issue":"4","key":"1641_CR22","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1016\/j.isatra.2013.09.012","volume":"53","author":"Z Gao","year":"2014","unstructured":"Gao Z (2014) On the centrality of disturbance rejection in automatic control. ISA Trans 53(4):850\u2013857","journal-title":"ISA Trans"},{"key":"1641_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-84996-513-2","volume-title":"Block-oriented nonlinear system identification,","author":"F Giri","year":"2010","unstructured":"Giri F, Bai EW (2010) Block-oriented nonlinear system identification, vol 1. Springer"},{"key":"1641_CR24","unstructured":"Gleave A, Dennis M, Wild C, et\u00a0al (2019) Adversarial policies: Attacking deep reinforcement learning. arXiv preprint arXiv:1905.10615"},{"key":"1641_CR25","volume-title":"Technology and applications of autonomous underwater vehicles,","author":"G Griffiths","year":"2002","unstructured":"Griffiths G (2002) Technology and applications of autonomous underwater vehicles, vol 2. CRC Press"},{"key":"1641_CR26","unstructured":"Haarnoja T, Zhou A, Abbeel P, et\u00a0al (2018) Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In: International conference on machine learning, PMLR, pp 1861\u20131870"},{"key":"1641_CR27","unstructured":"Hausknecht M, Stone P (2015) Deep recurrent q-learning for partially observable mdps. In: 2015 AAAI Fall Symposium Series"},{"key":"1641_CR28","unstructured":"Heess N, Hunt JJ, Lillicrap TP, et\u00a0al (2015) Memory-based control with recurrent neural networks. arXiv preprint arXiv:1512.04455"},{"key":"1641_CR29","doi-asserted-by":"crossref","unstructured":"Hessel M, Modayil J, Van\u00a0Hasselt H, et\u00a0al (2018) Rainbow: Combining improvements in deep reinforcement learning. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11796"},{"key":"1641_CR30","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.artint.2015.05.002","volume":"247","author":"T Hester","year":"2017","unstructured":"Hester T, Stone P (2017) Intrinsically motivated model learning for developing curious robots. Artif Intell 247:170\u2013186","journal-title":"Artif Intell"},{"key":"1641_CR31","unstructured":"Huang B, Feng F, Lu C, et\u00a0al (2021) Adarl: What, where, and how to adapt in transfer reinforcement learning. arXiv preprint arXiv:2107.02729"},{"key":"1641_CR32","unstructured":"Igl M, Zintgraf L, Le TA, et\u00a0al (2018) Deep variational reinforcement learning for pomdps. In: International Conference on Machine Learning, PMLR, pp 2117\u20132126"},{"key":"1641_CR33","unstructured":"Jiang Y, Li C, Dai W, et\u00a0al (2021) Monotonic robust policy optimization with model discrepancy. In: International Conference on Machine Learning, PMLR, pp 4951\u20134960"},{"issue":"1\u20132","key":"1641_CR34","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/S0004-3702(98)00023-X","volume":"101","author":"LP Kaelbling","year":"1998","unstructured":"Kaelbling LP, Littman ML, Cassandra AR (1998) Planning and acting in partially observable stochastic domains. Artif Intell 101(1\u20132):99\u2013134","journal-title":"Artif Intell"},{"key":"1641_CR35","doi-asserted-by":"crossref","unstructured":"Kontes GD, Scherer DD, Nisslbeck T, et\u00a0al (2020) High-speed collision avoidance using deep reinforcement learning and domain randomization for autonomous vehicles. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), IEEE, pp 1\u20138","DOI":"10.1109\/ITSC45102.2020.9294396"},{"issue":"3","key":"1641_CR36","doi-asserted-by":"publisher","first-page":"2471","DOI":"10.1109\/LRA.2018.2800106","volume":"3","author":"I Koryakovskiy","year":"2018","unstructured":"Koryakovskiy I, Kudruss M, Vallery H et al (2018) Model-plant mismatch compensation using reinforcement learning. IEEE Robotics and Automation Letters 3(3):2471\u20132477","journal-title":"IEEE Robotics and Automation Letters"},{"key":"1641_CR37","unstructured":"Lennart L (1999) System identification: theory for the user. PTR Prentice Hall, Upper Saddle River, NJ pp 1\u201314"},{"key":"1641_CR38","unstructured":"Liu Q, Yu T, Bai Y, et\u00a0al (2021) A sharp analysis of model-based reinforcement learning with self-play. In: International Conference on Machine Learning, PMLR, pp 7001\u20137010"},{"issue":"7540","key":"1641_CR39","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529","journal-title":"Nature"},{"key":"1641_CR40","unstructured":"Mnih V, Badia AP, Mirza M, et\u00a0al (2016) Asynchronous methods for deep reinforcement learning. In: International conference on machine learning, pp 1928\u20131937"},{"key":"1641_CR41","unstructured":"Nagabandi A, Clavera I, Liu S, et\u00a0al (2018a) Learning to adapt in dynamic, real-world environments through meta-reinforcement learning. arXiv preprint arXiv:1803.11347"},{"key":"1641_CR42","doi-asserted-by":"crossref","unstructured":"Nagabandi A, Kahn G, Fearing RS, et\u00a0al (2018b) Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning. In: Robotics and Automation (ICRA), 2018 IEEE International Conference on, IEEE, pp 7579\u20137586","DOI":"10.1109\/ICRA.2018.8463189"},{"key":"1641_CR43","doi-asserted-by":"crossref","unstructured":"Niu H, Hu J, Cui Z, et\u00a0al (2021) Dr2l: Surfacing corner cases to robustify autonomous driving via domain randomization reinforcement learning. In: The 5th International Conference on Computer Science and Application Engineering, pp 1\u20138","DOI":"10.1145\/3487075.3487177"},{"key":"1641_CR44","unstructured":"Packer C, Gao K, Kos J, et\u00a0al (2018) Assessing generalization in deep reinforcement learning. arXiv preprint arXiv:1810.12282"},{"key":"1641_CR45","unstructured":"Pattanaik A, Tang Z, Liu S, et\u00a0al (2017) Robust deep reinforcement learning with adversarial attacks. arXiv preprint arXiv:1712.03632"},{"key":"1641_CR46","doi-asserted-by":"crossref","unstructured":"Peng XB, Andrychowicz M, Zaremba W, et\u00a0al (2018) Sim-to-real transfer of robotic control with dynamics randomization. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp 1\u20138","DOI":"10.1109\/ICRA.2018.8460528"},{"issue":"1","key":"1641_CR47","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3390\/robotics9010008","volume":"9","author":"R Polvara","year":"2020","unstructured":"Polvara R, Patacchiola M, Hanheide M et al (2020) Sim-to-real quadrotor landing via sequential deep q-networks and domain randomization. Robotics 9(1):8","journal-title":"Robotics"},{"key":"1641_CR48","unstructured":"Rajeswaran A, Ghotra S, Ravindran B, et\u00a0al (2016) Epopt: Learning robust neural network policies using model ensembles. arXiv preprint arXiv:1610.01283"},{"key":"1641_CR49","unstructured":"Rakelly K, Zhou A, Quillen D, et\u00a0al (2019) Efficient off-policy meta-reinforcement learning via probabilistic context variables. arXiv preprint arXiv:1903.08254"},{"key":"1641_CR50","unstructured":"Schaul T, Quan J, Antonoglou I, et\u00a0al (2015) Prioritized experience replay. arXiv preprint arXiv:1511.05952"},{"key":"1641_CR51","doi-asserted-by":"crossref","unstructured":"Scheiderer C, Dorndorf N, Meisen T (2021) Effects of domain randomization on simulation-to-reality transfer of reinforcement learning policies for industrial robots. In: Advances in Artificial Intelligence and Applied Cognitive Computing. Springer, p 157\u2013169","DOI":"10.1007\/978-3-030-70296-0_13"},{"key":"1641_CR52","unstructured":"Schulman J, Levine S, Abbeel P, et\u00a0al (2015) Trust region policy optimization. In: International Conference on Machine Learning, pp 1889\u20131897"},{"key":"1641_CR53","unstructured":"Schulman J, Wolski F, Dhariwal P, et\u00a0al (2017) Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347"},{"issue":"1","key":"1641_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10458-012-9200-2","volume":"27","author":"G Shani","year":"2013","unstructured":"Shani G, Pineau J, Kaplow R (2013) A survey of point-based pomdp solvers. Auton Agent Multi-Agent Syst 27(1):1\u201351","journal-title":"Auton Agent Multi-Agent Syst"},{"key":"1641_CR55","unstructured":"Silver D, Hubert T, Schrittwieser J, et\u00a0al (2017) Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv preprint arXiv:1712.01815"},{"key":"1641_CR56","doi-asserted-by":"crossref","unstructured":"Singh A, Yang L, Hartikainen K, et\u00a0al (2019) End-to-end robotic reinforcement learning without reward engineering. arXiv preprint arXiv:1904.07854","DOI":"10.15607\/RSS.2019.XV.073"},{"key":"1641_CR57","unstructured":"Sorokin I, Seleznev A, Pavlov M, et\u00a0al (2015) Deep attention recurrent q-network. arXiv preprint arXiv:1512.01693"},{"key":"1641_CR58","unstructured":"Sutton RS, Barto AG (2018) Reinforcement learning: An introduction. MIT press"},{"key":"1641_CR59","doi-asserted-by":"crossref","unstructured":"Tan J, Zhang T, Coumans E, et\u00a0al (2018) Sim-to-real: Learning agile locomotion for quadruped robots. arXiv preprint arXiv:1804.10332","DOI":"10.15607\/RSS.2018.XIV.010"},{"key":"1641_CR60","unstructured":"Taylor ME, Stone P (2009) Transfer learning for reinforcement learning domains: A survey. Journal of Machine Learning Research 10(Jul):1633\u20131685"},{"key":"1641_CR61","unstructured":"Tessler C, Efroni Y, Mannor S (2019) Action robust reinforcement learning and applications in continuous control. In: International Conference on Machine Learning, PMLR, pp 6215\u20136224"},{"key":"1641_CR62","unstructured":"Wang JX, Kurth-Nelson Z, Tirumala D, et\u00a0al (2016) Learning to reinforcement learn. arXiv preprint arXiv:1611.05763"},{"key":"1641_CR63","doi-asserted-by":"crossref","unstructured":"Wang T, Lu W, Liu D (2018a) A case study: Modeling of a passive flexible link on a floating platform for intervention tasks. In: 2018 13th World Congress on Intelligent Control and Automation (WCICA), IEEE, pp 187\u2013193","DOI":"10.1109\/WCICA.2018.8630398"},{"key":"1641_CR64","unstructured":"Wang T, Lu W, Liu D (2018b) Excessive disturbance rejection control of autonomous underwater vehicle using reinforcement learning. In: Australasian Conference on Robotics and Automation"},{"key":"1641_CR65","doi-asserted-by":"crossref","unstructured":"Wang T, Lu W, Yan Z, et\u00a0al (2019a) Dob-net: Actively rejecting unknown excessive time-varying disturbances. arXiv preprint arXiv:1907.04514","DOI":"10.1109\/ICRA40945.2020.9196641"},{"key":"1641_CR66","unstructured":"Wang Y, He H, Tan X (2019b) Robust reinforcement learning in pomdps with incomplete and noisy observations. arXiv preprint arXiv:1902.05795"},{"key":"1641_CR67","doi-asserted-by":"crossref","unstructured":"Woolfrey J, Liu D, Carmichael M (2016) Kinematic control of an autonomous underwater vehicle-manipulator system (auvms) using autoregressive prediction of vehicle motion and model predictive control. In: Robotics and Automation (ICRA), 2016 IEEE International Conference on, IEEE, pp 4591\u20134596","DOI":"10.1109\/ICRA.2016.7487660"},{"key":"1641_CR68","unstructured":"Wu Y, Mansimov E, Grosse RB, et\u00a0al (2017) Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation. Advances in neural information processing systems 30"},{"key":"1641_CR69","unstructured":"Wulfmeier M, Posner I, Abbeel P (2017) Mutual alignment transfer learning. In: Conference on Robot Learning, PMLR, pp 281\u2013290"},{"issue":"12","key":"1641_CR70","doi-asserted-by":"publisher","first-page":"2203","DOI":"10.1109\/9.895559","volume":"45","author":"LL Xie","year":"2000","unstructured":"Xie LL, Guo L (2000) How much uncertainty can be dealt with by feedback? IEEE Trans Autom Control 45(12):2203\u20132217","journal-title":"IEEE Trans Autom Control"},{"key":"1641_CR71","unstructured":"Yu T, Quillen D, He Z, et\u00a0al (2020) Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning. In: Conference on Robot Learning, PMLR, pp 1094\u20131100"},{"key":"1641_CR72","unstructured":"Yu T, Kumar A, Rafailov R, et\u00a0al (2021) Combo: Conservative offline model-based policy optimization. Advances in Neural Information Processing Systems 34"},{"key":"1641_CR73","doi-asserted-by":"publisher","unstructured":"Yu W, Liu CK, Turk G (2017) Preparing for the unknown: Learning a universal policy with online system identification. In: Proceedings of Robotics: Science and Systems, Cambridge, Massachusetts, https:\/\/doi.org\/10.15607\/RSS.2017.XIII.048","DOI":"10.15607\/RSS.2017.XIII.048"},{"key":"1641_CR74","unstructured":"Zarchan P, Musoff H (2013) Fundamentals of Kalman filtering: a practical approach. American Institute of Aeronautics and Astronautics, Inc"},{"key":"1641_CR75","doi-asserted-by":"crossref","unstructured":"Zhao W, Queralta JP, Westerlund T (2020) Sim-to-real transfer in deep reinforcement learning for robotics: a survey. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp 737\u2013744","DOI":"10.1109\/SSCI47803.2020.9308468"},{"key":"1641_CR76","unstructured":"Zintgraf L, Shiarlis K, Igl M, et\u00a0al (2020) Varibad: A very good method for bayes-adaptive deep rl via meta-learning. arXiv preprint arXiv:1910.08348"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01641-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01641-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01641-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T04:10:44Z","timestamp":1674101444000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01641-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,2]]},"references-count":76,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["1641"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01641-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,2]]},"assertion":[{"value":"5 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2022","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 have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}