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Energy models are valuable mathematical tools based on the systems approach.
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Rough set feature selection approach has proven to be an efficient mathematical tool based on upper and lower approximation.
The aim of this work was to design an mathematical tool based on documented data, that would predict the risk of metastatic involvement for each nodal station.
The aim of this study was to design a mathematical tool based on documented data to predict the risk of metastatic involvement for each nodal station.
The work presented is focused on the development of an innovative mathematical tool based on the particle-in-cell method (PIC) for the simulation of the soot distribution inside a single channel of a diesel particulate filter.
The last two decades have seen tremendous activity in the development of new mathematical and computational tools based on multiscale ideas (wavelet).
The mathematical tools are based on the kernel estimators technique.
Within the context of decision-support tools based on mathematical programming techniques, this contribution presents a procedure for constraints identification and classification.
Quantitative design tools based on mathematical programming may play a relevant role in driving the decision-making progress on well-advised investments in the energy sector.
This paper proposes a new method, a Self-adaptive Mathematical Expression Model (SMEM), to describe volumetric errors of machine tools based on Non-Uniform Rational B-Spline (NURBS).
The G2SBC Database provides systems level queries (enabling a top down approach), tools based on PPIs network (e.g. the shortest paths) and a mathematical models section.
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