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Rycroft is interested in developing new computational methods to model physical and biological systems.
I believe that this distinction between the beauty of mathematical structures and the usefulness of mathematics to model physical and social phenomena is a false dichotomy.
Fractional calculus has been used to model physical and engineering processes that are found to be best described by fractional differential equations.
Fractional calculus has been used to model physical and engineering processes, which are found to be best described by fractional differential equations [1].
Within this model, physical and social environments are identified as intermediate factors that mediate relationships between fundamental factors and more proximate factors such as physical activity and dietary practices that ultimately influence anthropomorphic and physiologic indicators of cardiovascular risk.
Fractional calculus has been used to model physical and engineering processes that are found to be best described by fractional differential equations (FDEs) (see[1, 2, 3] and references therein).
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During habitat suitability modeling, physical and human factors were considered based on literature evidences and field observation.
The PDEs are widely used in science and engineering to model physical systems and processes.
Additionally, compared with the other three general models, i.e. BP neural model, physical model and CK model, the multiple linear regression model is more reasonable and effective.
Such robots would learn from mental rehearsals in simulations that would model physical, cultural, and psychological factors.
Here, we propose a new approach to model physical phenomena and track their evolution by taking advantage of the recent developments of pattern recognition for nonlinear functional learning.
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