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During the past decades much progress has been made in the development of computer based methods for parameter and predictive uncertainty estimation of hydrologic models.
For the past 20 years, the development of computer based methods for the identification of optimal designs have been based on a single model, introduced by Walters and Sheble (1993) [1].
The confidence interval of the difference completely excludes the lower margin of inferiority of computer based sessions pre specified (methods section).
Previous studies have shown the value of computer based learning.
Methods of computer modeling based on the data on the primary protein structure or the symbolic amino acid sequence have become an actual alternative to the experimental method of X-ray crystallography for investigating the structure of membrane proteins.
Therefore, methods of computer modeling based on the data on the primary protein structure or the symbolic amino acid sequence have become an actual alternative to the experimental method of X-ray crystallography for studying the structure of membrane proteins [ 3].
This paper describes the development of methods and a computer based system for automated machinability assessment and tool selection for milling.
In contrast to these high-throughput experimental data methods, computer based de novo predictions of alternative splicing events are not well established yet.
The method is called as computer based subtractive manufacturing.
This paper examines two methods of computer-based car styling: texture mapping and direct computer modelling.
This paper compares several interface evaluation methods applied in the case of a computer based learning (CBL) environment, during a longitudinal study performed in three European countries, Greece, Germany, and Holland, and within the framework of an EC funded Leonardo da Vinci program.
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