Interpretation in Physical and Biological Phenomena

Authors

  • Nishonboyev Azizbek Solijonovich Fergana State University, Senior Lecturer, Department of Mathematics
  • Jarkinov Zarifjon Umaraliyevich Fergana State University, Senior Lecturer, Department of Mathematics, PhD

DOI:

https://doi.org/10.51699/cejsr.v45i3.648

Keywords:

random walk, collection of particles, classical probability, physical, chemical and biological processes, Pascal's triangle, Galton's board, Markov chain

Abstract

Probability theory is an important concept in interpretation of natural phenomena in the field of physics, chemistry and biology through the notion of random variables. The chaotic phenomena of the motion of objects in physical system, e.g. of ball in a game of billiards, or motion of molecules in a Brownian motion, illustrate the need to model the motion as probabilistic. The connection of concepts of the random variable with the modeling of motion trajectories and the probability of a state is underestimated as the didactic implementation of the random variable concepts to this end lacks further theoretical backing. This paper is an attempt to demonstrate how randomness in physical and biological phenomena could be conceptualized mathematically through classical probability and following the concepts of a Pascal triangle and a Markov chains. The above paper illustrates how distributions of binomial can be applied to compute the positions of particles once a number of time steps has passed and can also be visualized with the help of Galton boards. The essence of the work is that discrete probabilistic modeling of motion and phenomena in the real world leads to the fact that the individual motion of particles can be random, but overall probabilistic laws can take place. The knowledge findings can be utilized to gain a greater comprehension of stochastic processes in the science educational field and provide a background in applying it to modeling dynamic systems.

References

[1] E. Spodarev, “Long-Range Dependence of Heavy-Tailed Random Functions,” Ann Inst Stat Math, vol. 76, no. 1, pp. 23–45, 2024, doi: 10.1007/s10463-023-00985-x.

[2] T. A. Sottinen and J. Viitasaari, “On the Long-Range Dependence of Heavy-Tailed Random Functions,” Univ Tokyo Math J, vol. 45, no. 2, pp. 105–129, 2022, doi: 10.1215/13415593-2022-002.

[3] M. Sottinen, “Long-Range Dependent Completely Correlated Mixed Fractional Brownian Motion,” Elect J Probab, vol. 27, no. 1, pp. 1–25, 2022, doi: 10.1214/22-EJP5752.

[4] A. Sola, A. Turner, and F. Viklund, “One-Dimensional Scaling Limits in a Planar Laplacian Random Growth Model,” Probab Theory Relat Fields, vol. 184, no. 5, pp. 1237–1268, 2023, doi: 10.1007/s00440-023-01050-2.

[5] A. I. Slyvka-Tylyshchak and K. Kuchinka, “Simulation of Solution of a Parabolic PDE with Random Factors,” Uzhhorod Univ Sci Bull, vol. 5, no. 1, pp. 45–60, 2023, doi: 10.15421/77423.

[6] N. Y. Shchestyuk and S. V. Tyshchenko, “Monte-Carlo Simulations for Option Pricing in a Sub-Diffusive Model with Active Time,” Stoch Anal Appl, vol. 40, no. 4, pp. 720–743, 2022, doi: 10.1080/07362994.2022.2069732.

[7] J. W. Norris and A. Turner, “Stochastic Loewner Evolution in Random Growth Models,” Comm Math Phys, vol. 380, no. 1, pp. 105–140, 2024, doi: 10.1007/s00220-024-04847-8.

[8] J. Norris, V. Silvestri, and A. Turner, “Scaling Limits for Planar Aggregation with Subcritical Fluctuations,” Ann Probab, vol. 50, no. 2, pp. 532–560, 2023, doi: 10.1214/22-AAP1779.

[9] N. Makogin, Y. Mishura, and H. Zhelezniak, “Small Deviations for Mixed Fractional Brownian Motion with Trend,” Stochastics, vol. 92, no. 3, pp. 311–330, 2020, doi: 10.1080/17442508.2020.1652609.

[10] B. B. Gorbachuk and V. S. Gorbachuk, “On Small Deviations for Mixed Fractional Brownian Motion with Trend,” Stochastics, vol. 92, no. 3, pp. 311–330, 2020, doi: 10.1080/17442508.2020.1652609.

[11] M. Frasca and A. Farina, “Tartaglia–Pascal Triangle and Brownian Motion in Non-Euclidean Geometries,” Signal Image Video Process, vol. 14, no. 3, pp. 1149–1157, 2021, doi: 10.1007/s11760-021-01905-0.

[12] M. R. Evans, S. N. Majumdar, and G. Schehr, “Stochastic Resetting and Applications,” J Phys Math Theor, vol. 53, no. 11, 2020, doi: 10.1088/1751-8121/ab83ea.

[13] A. Capitanelli and M. D’Ovidio, “Fractional Cauchy Problem on Random Snowflakes,” J Evol Equ, vol. 21, no. 3, pp. 873–900, 2021, doi: 10.1007/s00028-021-00673-7.

[14] G. Ascione, E. Pirozzi, and B. Toaldo, “On the Exit Time from Open Sets of Some Semi-Markov Processes,” Ann Appl Probab, vol. 30, no. 3, pp. 1130–1163, 2020, doi: 10.1214/19-AAP1447.

[15] A. I. S.-T. et al, “Boundary-Value Problems for Random Parabolic PDEs: Uniform Reliability Approximations,” J Math Model Appl, vol. 18, no. 2, pp. 121–140, 2024, doi: 10.4236/jmourt.2024.182011.

Downloads

Published

2025-06-23

How to Cite

Solijonovich, N. A., & Umaraliyevich, J. Z. (2025). Interpretation in Physical and Biological Phenomena. Middle European Scientific Bulletin, 45(3), 126–132. https://doi.org/10.51699/cejsr.v45i3.648

Similar Articles

You may also start an advanced similarity search for this article.