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In recent years, various mathematical methods have been developed to meet this task.
Classical mathematical methods have failed in handling nonconvexities and nonsmoothness in optimization problems.
Many classical mathematical methods have failed in handling nonconvexities and nonsmoothness in reliability redundancy optimization problems.
To deal with the equation, various mathematical methods have been proposed for obtaining exact and approximate analytic solutions.
So, a large number of effective mathematical methods have been proposed to solve KP equations [14, 15, 16, 17, 18, 19, 20, 21].
Many mathematical methods have been developed for this purpose, while little research has been done to guide us in designing experiments which are relatively efficient for uncertainty minimization.
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Embedding biological principles into mathematical methods has yielded valuable tools for scientists and engineers.
Although this field is in its infancy, the appropriate use of mathematical methods has considerable potential for helping to take tissue engineering from experimental concept to clinical reality.
A detailed description of the mathematical methods has been published (Smallbone et al, 2006).
The lack of desire or skill of health care managers to use modelling and simulation, particularly mathematical methods has been highlighted as one of the practical challenges [ 3, 7, 11, 12, 21].
In particular, the prediction of non-specific brain tissue binding has been in focus, for which in silico, in vitro, or bio-mathematical methods have been applied [6]-[9].
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