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To solve the mathematical models, a two-stage strategy is proposed to search for feasible solutions to unit commitment.
In this paper, based on two mathematical models, a set of optimal anticancer drug regimens is designed.
Considering the complexity of analytical solutions of mathematical models, a comprehensive numerical investigation was established and a detailed comparison was carried out between the conventional and optimized PDMs.
To further enhance the accuracy of the mathematical models, a compensatory machining process, which eliminates residual profile on the revolving surface, is also presented.
Based on these mathematical models, a MOPSO algorithm is then introduced to optimize the design variables by comprehensively considering thermal efficiency, temperature uniformity and structural strength of the RHCM mold.
Here, we derive two novel mathematical models, a stochastic agent-based model and an integro-differential equation model, each of which describes the growth of cancer cells as a dynamic transition between proliferative and quiescent states.
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Artificial neural networks are a rough mathematical model, a draft, inspired by the little we know about the brain.
In "Mathematical modeling", a description of the proposed model is provided.
Based on this mathematical model, a design algorithmic procedure is presented.
With this energy inclusion in the mathematical model, a low complexity solution cannot be achieved.
For simplifying the construction of the mathematical model, a method called 'cascaded analysis model' is proposed.
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