Welcome to research revolution in national sports science?

Doctor of science, рrofessor M.P. Shestakov1
Doctor of science, docent T.G. Fomichenko1
1Federal Scientific Center of Physical Culture and Sports (VNIIFK), Moscow

Objective of the study was to analyze the present situation and developmental prospects of the national sports training theory and practice.
Methods and structure of the study. For the last three decades, the ongoing modeling projects have been implemented in different timeframes and domains, for example:
1) Small molecules: organic and inorganic compounds modeled using molecular mechanics codes to understand their repertoire and degrees of freedom [12];
2) Biological macromolecules: RNA, DNA and protein molecules may now be modeled using molecular dynamics technologies – e.g. ribosome and RNA polymerase models available in high resolution;
3) Cellular models: molecular-genetic systemic mechanisms of bodily adaptation under extreme stressors;
4) Biomechanical models including the cardiovascular system model, respiratory system model, skeletal geometry model, neuromuscular control model for locomotion, etc.
5) CNS is the key system in the bodily systems hierarchy, and this is why subject to new models is the motor sills control and learning systems, with every skill controlled by specific neuromodulatory brain mechanisms. Conclusion. Further progress of the modern computational technologies applicable in the sports science may be described by a few progress vectors. Of special importance are the efforts to create adequate data mining toolkits to analyze bodily states in the context of the newly discovered biological regularities. In the near future we expect a few breakthroughs in the hardware upgrade domain with implantable special-purpose microprocessors and new technologies to grow special artificial receptors using modern bionanomaterials inside bodily organs.

Keywords: scientific revolution, modeling, sports science, digitalization.

References

  1. Bernshtein NA Essays on physiology of movements and physiology of activity / N. A. Bernstein. – M .: Medicine, 1966. – 49 p.
  2. Kuhn T. The structure of scientific revolutions. – M.: AST, 2020. – 320 p.
  3. Simon G. Science of the artificial / Per. from English – M.: Mir, 1972. – 147 p.
  4. Seluyanov V.N. Modeling in the theory of sport (physical training of athletes) / VN Seluyanov: Uch. allowance. – M.: GTsOLIFK, 1991. – 58 p.
  5. Seluyanov V.N. Preparation of a middle distance runner / V.N. Seluyanov. – M.: SportAkademPress., 2001. – 104 p.
  6. Tarasov V.B. From Artificial Intelligence to Artificial Life: New Directions in the Sciences of Artificial // News of Artificial Intelligence. 1995, no. 4, p. 93-118.
  7. Decree of the President of the Russian Federation of December 1, 2016 No. 642 (2016) On the Strategy of Scientific and Technological Development of the Russian Federation / Grant. http://www.garant.ru/products/ipo/prime/ doc / 71451998.
  8. Shestakov M.P. Artificial intelligence in sports science of the XXI century // Theory and practice of physical. culture. 2000, no. 7.
  9. Clark D. E. Rapid calculation of the polar molecular surface area and its application to the prediction of transport phenomena //J. Pharmaceutical Sciences, vol. 88, pp. 807-814, 2000.
  10. Friston, K. (2009) The free-energy principle: A rough guide to the brain? Trends in Cognitive Sciences 13 (7): 293-301.
  11. Hansson T., Oostenbrink C., van Gunsteren W. F, Molecular dynamics simulations // Current Opinion in Structural Biology, vol. 12, pp. 190-196, 2002.
  12. Karplus M. and McCammon A. Molecular dynamics simulations of biomolecules // Nature Structural Biology, vol. 9, pp. 646–652, 2002.
  13. Shestakov M. and Balsevish V. (2020) Features of the Use of Genetic Infor-mation in the Training of Highly Qualified Athletes / In Athletes: From Performance Analysis to Injury Prevention, Nova Science Pub Inc. P.1-21.
  14. Thelen D. G., Anderson F. C., and Delp S. L. Generating forward dynamic simulations of movement using computed muscle control, J. Biomech., Vol. 36, pp. 321–328, 2003.
  15. Tomita M. Whole-cell simulation: A grand challenge of the 21st century, Trends in Biotechnology, vol. 19, no. 6, pp. 205-210, 2001.
  16. Wolpert, D. M., Miall, R. C., Kawato, M., 1998. Internal models in the cerebellum. Trends Cogn. Sci. 2, pp.