Sentence examples for mathematical modeling techniques from inspiring English sources

Mathematical modeling techniques are now an integral component of researchers' efforts to find better ways to reduce the burden of cancer.

Scientists have introduced mathematical modeling techniques to predict the release of nutrients from the CRFs to elucidate fundamental understanding of the dynamics of the release processes and to design new CRFs in a shorter time and with relatively lower cost.

Mathematical modeling techniques also vary in their physiological assumptions.

The procedure combines structural bioinformatics tools and mathematical modeling techniques that allow the reconstruction of biochemical networks.

The strategy that has been outlined enables mathematical modeling techniques to inform bioreactor design based on the oxygen requirements of the cell type.

With the advent of new imaging equipment, image-processing methods, and mathematical modeling techniques, the importance of trabecular microarchitecture and cortical ultrastructure in the context of bone strength and fracture risk has received considerable attention.

Emilie Dressaire, from Trinity College in Hartford, Connecticut, US, and her colleagues used high-speed filming and mathematical modelling techniques to show how shitake and oyster mushrooms release water vapour when they drop their spores.

With the recent development in farm technologies and mathematical modelling techniques supported by computer-based simulations, new methodologies in research are available to improve animal traction tillage implements.

It is shown theoretically using elementary mechanics and applied mathematical modelling techniques that for cisplatin to be accepted, the carbon nanotube must have a radius of at least 4.785 Å, and that the maximum suction energy occurs when the carbon nanotube radius is 5.27 Å.

Tobacco control researchers have recently become more interested in systems science methods and mathematical modelling techniques as a means to understand how complex inter-relationships among various factors translate into population-level summaries of tobacco use prevalence and its associated medical and social costs.

The mathematical modeling technique, namely softcomputing modeling approach, is explored and used as a platform for development and validated with generated random data.