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The growing significance of software upgrades is one of the most intriguing and pertinent market trends of the last few years. Fierce competition on a worldwide scale combined with the ever-changing environment has rendered software products outdated. During a program’s early stages, increasing efforts are made to increase overall performance before its inherent performance limit is reached. To characterize the process of awareness, appraisal, and decision-making, mathematical models with step structures are presented. The first step is to present a system of ordinary differential equations that includes both the awareness and decision-making stages. Furthermore, it is demonstrated that if the adoption rate is strongly nonlinear, then although there exists a stable equilibrium, it is not a global attractor. It is shown that the system has bifurcation points. The direction of equilibrium bifurcation is also explored.
Słowa kluczowe
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Tom
Numer
Strony
31-52
Opis fizyczny
Twórcy
autor
- Department of Mathematics, Lovely Professional University, Phagwara, Punjab 144 411, India
autor
- Department of Mathematics, Lovely Professional University, Phagwara, Punjab 144 411, India, am.arunavamajumder@gmail.com
autor
- Department of Mathematics, Shri Jagdishprasad Jhabarmal Tibrewala University, Jhunjhunu, Rajasthan 333 001, India
autor
- School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India 144 411
Bibliografia
- Bayram, M., Partal, T., and Orucova Buyukoz, G. Numerical methods for simulation of stochastic differential equations. Advances in Difference Equations 2018 (2018), 17.
- Chen, K.-J., and Huang, C.-Y. Using modified diffusion models for reliability estimation of open source software. IEEE Access 11 (2023), 51631–51646.
- Fadwa, B., El Maroufy, H., and Mousse, H. A. Simulation and parametric inference of a mixed effects model with stochastic differential equations using the fokker-planck equation solution. In Numerical Modeling and Computer Simulation (Rijeka, 2020), D. M. Cvetkovi´c and G. A. Birajdar, Eds., IntechOpen, pp. 91–112.
- Heydari, M., Avazzadeh, Z., Navabpour, H., and Loghmani, G. B. Numerical solution of fredholm integral equations of the second kind by using integral mean value theorem II. High dimensional problems. Applied Mathematical Modelling 37, 1-2 (2013), 432–442.
- Jafari-Asl, J., Ben Seghier, M. E. A., Ohadi, S., Dong, Y., and Plevris, V. A comparative study on the efficiency of reliability methods for the probabilistic analysis of local scour at a bridge pier in clay-sand-mixed sediments. Modelling 2, 1 (2021), 63–77.
- Karatzas, I., and Ruf, J. Pathwise solvability of stochastic integral equations with generalized drift and non-smooth dispersion functions. Annales de l’Institut Henri Poincaré, Probabilités et Statistiques 52, 2 (2016), 915–938.
- Kumar, A., and Garg, D. Reliability technology theory and application. LAP Lambert Academic Publishing, 2019.
- Kumar, A., Garg, D., and Goel, P. Mathematical modeling and behavioral analysis of a washing unit in paper mill. International Journal of System Assurance Engineering and Management 10, 6 (2019), 1639–1645.
- Kumar, A., Garg, D., and Goel, P. Sensitivity analysis of a cold standby system with priority for preventive maintenance. Journal of Advance and Scholarly Researches in Allied Education 16, 4 (2019), 253–258.
- Kumar, A., Garg, D., and Goel, P. Behaviour analysis of a bread making system. International Journal of Statistics and Applied Mathematics 3, 6 (2018), 56–61.
- Kumari, S., Khurana, P., Singla, S., and Kumar, A. Solution of constrained problems using particle swarm optimiziation. International Journal of System Assurance Engineering and Management 13, 4 (2022), 1688–1695.
- Kuznetsov, D. F. Stochastic differential equations: theory and practice of numerical solution. With programs on MATLAB. Differential Equations and Control Processes 2018, 4 (2018), 1–1073 (in Russian).
- Mohammadi, F. Efficient Galerkin solution of stochastic fractional differential equations using second kind Chebyshev wavelets. Boletim da Sociedade Paranaense de Matematica 35, 1 (2017), 195–215.
- Nayak, S. Numerical solution of fuzzy stochastic Volterra-Fredholm integral equation with imprecisely defined parameters. In Recent Trends in Wave Mechanics and Vibrations: Select Proceedings of WMVC 2018 (Singapore, 2020), S. Chakraverty and Paritosh Biswas, Eds., Springer, pp. 107–117.
- Parvin, A. J., Beruvides, M. G., and Tercero-Gómez, V. G. In situ technological innovation diffusion rate accuracy assessment. Systems 10, 2 (2022), 25.
- Rihan, F. A., Rajivganthi, C., and Muthukumar, P. Fractional stochastic differential equations with hilfer fractional derivative: Poisson jumps and optimal control. Discrete Dynamics in Nature and Society 2017 (2017), 394528.
- Shaikh, S. L. Some applications of the new integral transform for partial differential equations. Mathematical Journal of Interdisciplinary Sciences 7, 1 (2018), 45–49.
- Shiralashetti, S. C., and Lamani, L. Haar wavelet based numerical method for the solution of multidimensional stochastic integral equations. International Journal of Applied Engineering Research 14, 10 (2019), 2507–2521.
- Singh, V. V., and Gahlot, M. Reliability analysis of (n) clients system under star topology and copula linguistic approach. International Journal of Computational Systems Engineering 6, 3 (2021), 123–133.
- Tsai, S.-C., and Chen, C.-H. Exploring the innovation diffusion of big data robo-advisor. Applied System Innovation 5, 1 (2022), 15.
- Zarei, E., and Noeiaghdam, S. Solving generalized Abel’s integral equations of the first and second kinds via Taylor-collocation method, 2018. Working paper version available from arXiv: https://arxiv.org/abs/1804.08571.
- Zhang, J. The existence of strong solutions for a class of stochastic differential equations. International Journal of Differential Equations 2018 (2018), 059694.
Typ dokumentu
Bibliografia
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Identyfikator YADDA
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