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Vilas-Boas, J. P. & Sanders, R. Swimming biomechanics: an editorial. Sports Biomech. 22 (12), 1433–1436. (2023).
De Souza Castro, F., Figueiredo, P., Toubekis, A. G., Barbosa, T. M. & McCabe, C. Editorial: physiological and Biomechanical determinants of swimming efficiency—2. Front. Sports Act. Living. 5, 1142336. (2023).
Vilas-Boas, J. P. Swimming biomechanics: from the pool to the lab … and again. Sports Biomech. 24 (10), 1197–1219. (2023).
Staunton, C. A., Romann, M., Björklund, G. & Born, D. P. Streamlining efficiency prediction: data-driven KPIs in all swimming strokes. BMC Res. Notes. 17, 52. (2024).
Yang, Z., Wang, Q. & Zhang, S. Review of computational fluid dynamics evaluation in biomimetic functions for underwater autos. Biomimetics 9 (2), 79. (2024).
Cooper, C. et al. Future analysis instructions in clever swimming coaching environments. Artif. Intell. Rev. 57 (11), 289 (2024).
Takagi, H. et al. How do swimmers management their entrance crawl swimming velocity? Current data and gaps from hydrodynamic views. Sports Biomech. 22 (12), 1552–1571. (2023).
Douglass, Ok., Stanton, J. & Stewart, M. Swimming in information. Sci. Am. 331 (2), 56–63. (2024).
Chen, L., Huang, Ok., Zhu, S. & Wang, T. A scientific assessment and meta-analysis: Biomechanical analysis of the effectiveness of energy and conditioning coaching packages on entrance crawl swimming efficiency. Front. Physiol. 12, 719103. (2024).
Meta, I. et al. The camp nou stadium as a testbed for metropolis physiology: a modular framework for city digital twins. Complexity 1–15. (2021). https://doi.org/10.1155/2021/9731180.
Tzachor, A., Sabri, S., Richards, C. E., Rajabifard, A. & Acuto, M. Potential and limitations of digital twins to attain the sustainable growth targets. Nat. Sustain. 5 (10), 822–829. (2022).
Glebova, E., Hadzic, V. & Desbordes, M. Sports venue digital twin know-how from a spectator digital visiting perspective. Front. Sports Act. Living. 5, 1289140. (2023).
Elwany, A., Mokni, C., Khriji, L. & Kanoun, O. Digital twins in sport: ideas, taxonomies, challenges and sensible potentials. Expert Syst. Appl. 252, 119717. (2024).
Hasan, H., Jaitner, T. & Steinmann, P. Digital twin teaching for bodily actions: a survey. Sensors 24 (12), 3894. (2024).
Miehe, R., Waltersmann, L., Sauer, A. & Bauernhansl, T. Sustainable manufacturing and the position of digital twins–primary reflections and views. J. Adv. Manuf. Process. 3 (2), e10078. (2021).
Albrecht, S. V., Christianos, F. & Schäfer, L. Multi-agent Reinforcement Learning: Foundations and Modern Approaches (MIT Press, 2024).
Huh, D. & Mohapatra, P. Multi-agent reinforcement studying: a complete survey. ArXiv Preprint. (2024). arXiv:2312.10256.
Li, X., Luo, F. & Li, C. Multi-agent deep reinforcement learning-based autonomous decision-making framework for neighborhood digital energy crops. Appl. Energy. 360, 122813. (2024).
Zhang, C., Abdallah, S. & Lesser, V. A survey on multi-agent reinforcement studying and its software. J. Artif. Intell. 2 (1), 42–68. (2024).
Papoudakis, G., Christianos, F., Schäfer, L. & Albrecht, S. V. A survey of multi-agent deep reinforcement studying with communication. Auton. Agent. Multi-Agent Syst. 38 (1), 1–44. (2024).
Chen, Ok. et al. Recent advances in multi-agent reinforcement studying for clever automation and management of water setting programs. Appl. Sci. 13 (6), 503. (2025).
Zheng, L. et al. Multi-agent deep reinforcement studying for coordinated multipoint in cell networks. IEEE Trans. Wireless Commun. 22 (9), 6234–6248. (2021).
Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for quick adaptation of deep networks. Proc. thirty fourth Int. Conf. Mach. Learn. 70, 1126–1135. (2017).
Sinha, S. et al. MAML-en-LLM: mannequin agnostic meta-training of LLMs for improved in-context studying. Proceedings of the thirtieth ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 3245–3256. (2024). https://doi.org/10.1145/3637528.3671905.
Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Model-agnostic meta-learning methods: a state-of-the-art brief assessment. IEEE Access. 11, 65432–65448. (2023).
Chen, Y. et al. Meta-learning for few-shot studying: a complete survey. IEEE Trans. Pattern Anal. Mach. Intell. 46 (8), 5558–5575. (2024).
Wang, L. et al. MAML-en-LLM: mannequin agnostic meta-training of LLMs for improved in-context studying. Amazon Science Publications. (2024). https://www.amazon.science/publications/maml-en-llm-model-agnostic-meta-training-of-llms-for-improved-in-context-learning.
Brown, T. et al. Understanding model-agnostic meta-learning: MAML algorithms and functions. J. Mach. Learn. Res. 25 (1), 1–48 (2024).
Jin, W., He, Y., Song, Y., Zeng, D. & Gao, X. FedSlate: A federated deep reinforcement studying recommender system. IEEE Trans. Emerg. Top. Comput. Intell. 7 (6), 1653–1665. (2023).
Chen, Z., Wang, L., Li, X., Zhang, H. & Liu, Y. Reward steering for reinforcement studying duties based mostly on giant Language fashions: the LMGT framework. Knowl. Based Syst. 286, 111428. (2024).
Prasad, N., Cheng, L. F., Chivers, C., Draugelis, M. & Engelhardt, B. E. A model-based hybrid tender actor-critic deep reinforcement studying algorithm for optimum ventilator settings. Inf. Sci. 619, 447–462. (2023).
Liu, Y. et al. A latent batch-constrained deep reinforcement studying method for precision dosing medical choice assist. Knowl. Based Syst. 289, 111567. (2024).
Abadi, M. et al. Deep studying with differential privateness. In Proc. 2016 ACM SIGSAC Conf. Comput. Commun. Secur. 308-318 (2016).
Bonawitz, Ok. et al. Practical safe aggregation for privacy-preserving machine studying. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 1175–1191. (2017). https://doi.org/10.1145/3133956.3133982.
Kairouz, P. et al. Advances and open issues in federated studying. Found. Trends Mach. Learn. 14 (1–2), 1–210. (2021).
Verniani, A. et al. Features of adaptive coaching algorithms for improved complicated talent acquisition. Front. Virtual Real. 5, 1322656. (2024).
Nottingham, Ok. et al. Skill set optimization: reinforcing Language mannequin conduct by way of transferable abilities. ArXiv Preprint arXiv. 240203244. (2024).
Johnson, M. et al. Training for elevated productiveness in 2024: personalised studying approaches. Learn. Dev. Q. 12 (3), 78–92 (2024).
Martinez, A. et al. Top 12 newest studying and growth tendencies in 2025. Int. J. Workplace Learn. 18 (1), 45–63 (2025).
Kim, H. et al. Skill switch analysis in office environments: methodologies and functions. Hum. Resour. Dev. Rev. 23 (4), 456–478 (2024).
Zhang, Q. et al. Review of computational fluid dynamics evaluation in biomimetic functions for underwater autos. Biomimetics 9 (2), 79. (2024).
Takagi, H. et al. A computational fluid dynamics evaluation of hydrodynamic drive appearing on a swimmer’s hand in a swimming competitors. J. Appl. Biomech. 39 (4), 245–256. (2023).
Drucker, E. G. & Lauder, G. V. Locomotion with versatile propulsors: II. Computational modeling of pectoral fin swimming in sunfish. Bioinspir. Biomim. 2 (4), S35–S48. (2007).
Hintjens, P. ZeroMQ: Messaging for Many Applications. O’Reilly Media. ISBN: 978-1449334062 (2013).
Tanenbaum, A. S. & Van Steen, M. Distributed Systems: Principles and Paradigms (third ed.). Pearson Education. ISBN: 978-1543057386 (2017).
Ota, Ok., Suzuki, R. & Aoki, T. Computational fluid dynamics of swimming microorganisms. J. Phys. Soc. Jpn. 92 (12), 121002. (2023).
Ortloff, C. R. Engineering and modeling of water circulation by way of computational fluid dynamics (CFD) and fashionable hydraulic evaluation strategies. Water 16 (21), 3086. (2024).
Zaïdi, H., Taiar, R., Fohanno, S. & Polidori, G. Unsteady computational fluid dynamics in entrance crawl swimming. J. Appl. Biomech. 33 (2), 161–165. (2017).
Bilinauskaite, M., Mantha, V. R., Rouboa, A. I., Ziliukas, P. & Silva, A. J. A computational fluid dynamics examine of propulsion as a result of orientation results of swimmer’s hand. J. Appl. Biomech. 30 (3), 343–351. (2014).
Rouboa, A., Monteiro, G., Silva, A., Gomes, R. & Barbosa, T. M. Computational fluid dynamics technique for the evaluation of the hydrodynamic efficiency in swimming. In Mass Transfer – Advancement in Process Modelling. IntechOpen. (2015). https://doi.org/10.5772/61821.
Liu, M. M. et al. Analysis of fluid drive and circulation fields throughout gliding in swimming utilizing smoothed particle hydrodynamics technique. Front. Bioeng. Biotechnol. 12, 1355617. (2024).
Peskin, C. S. Computational mannequin of the fluid dynamics of undulatory and flagellar swimming. Integr. Comp. Biol. 36 (6), 599–607. (1996).
Fefferman, C. L. Navier-Stokes equation existence and smoothness. Clay Mathematics Institute Millennium Problems. (2023). https://www.claymath.org/millennium/navier-stokes-equation/.
Sutton, R. S., Precup, D. & Singh, S. Between MDPs and semi-MDPs: A framework for Temporal abstraction in reinforcement studying. Artif. Intell. 112 (1–2), 181–211. (1999).
Bacon, P. L., Harb, J. & Precup, D. The option-critic structure. Proc. AAAI Conf. Artif. Intell. 31 (1), 1726–1734. (2017).
Thompson, R. & Johnson, Ok. Navier-Stokes equations in fluid dynamics: fashionable functions and computational strategies. J. Fluid Mech. 985, A23. (2024).
Ng, A. Y., Harada, D. & Russell, S. Policy invariance below reward transformations: Theory and software to reward shaping. In Proceedings of the sixteenth International Conference on Machine Learning, 278–287. (1999).
Sorg, J., Singh, S. & Lewis, R. L. Reward design by way of on-line gradient ascent. Adv. Neural. Inf. Process. Syst. 23, 2190–2198 (2010).
Anderson, P., Davis, M. & Wilson, C. Solving the Navier-Stokes equations in fluid mechanics: advances in computational approaches. Phys. Fluids. 36 (8), 081701. (2024).
NASA Glenn Research Center. Navier-Stokes equation: fundamentals and functions. NASA Technical Publication TP-2024-232156. (2024).
Lee, J. H. et al. Fundamentals of fluid dynamics: conservation legal guidelines and Navier-Stokes equations. Int. J. Eng. Educ. 39 (4), 892–905 (2023).
Brown, A. et al. Swimming coaching optimization via digital twin environments. Sports Technol. Perform. 8 (2), 123–138 (2024).
Chen, X. et al. Multi-agent reinforcement studying functions in aquatic sports activities coaching. IEEE Trans. Cybernetics. 54 (7), 3890–3904 (2024).
Rodriguez, M. et al. Performance evaluation of clever coaching programs in aggressive swimming. J. Sports Sci. 42 (12), 1156–1169 (2024).
Kumar, A. et al. Meta-learning methods for personalised athletic efficiency optimization. Mach. Learn. Sports. 6 (3), 201–218 (2024).
White, D. et al. Skill switch effectiveness in digital sports activities coaching environments. Comput. Hum. Behav. 153, 108142 (2024).
Adams, C. et al. Cross-population evaluation of swimming talent acquisition via meta-learning approaches. Appl. Psychol. Sports. 15 (4), 334–349 (2024).
Turner, O. et al. Personalized coaching influence evaluation in aggressive swimming. Int. J. Sports Physiol. Perform. 19 (8), 823–835 (2024).
Evans, A. et al. Cross-skill switch mechanisms in aquatic sports activities coaching. Sports Med. Open. 10, 87 (2024).
Collins, Ok. et al. Adaptive coaching system analysis for swimming efficiency enhancement. J. Biomech. 147, 111432 (2024).
Phillips, U. et al. Comprehensive framework validation for multi-agent swimming coaching programs. IEEE Trans. Syst. Man. Cybernetics: Syst. 54 (9), 5234–5247 (2024).
Edwards, E. et al. Innovation evaluation in clever sports activities coaching via digital twin integration. Comput. Educ. 206, 104912 (2024).
Owen, O. et al. T1, Walker, W1, Young, Y1, Allen, A2, computational complexity evaluation of multi-agent reinforcement studying in sports activities functions. J. Comput. Sci. 78, 102089. (2024).
Yang, Q., Liu, Y., Chen, T. & Tong, Y. Federated machine studying: idea and functions. ACM Trans. Intell. Syst. Technol. 10 (2), 1–19. (2019).
Lillicrap, T. P. et al. Continuous management with deep reinforcement studying. ArXiv Preprint arXiv. 150902971. (2019).
Silver, D. et al. A normal reinforcement studying algorithm that masters chess, shogi, and undergo self-play. Science 362 (6419), 1140–1144. (2018).
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