Summary
Many chemical and biological experiments involve multiple treatment factors and often it is convenient to fit a non‐linear model in these factors. This non‐linear model can be mechanistic, empirical or a hybrid of the two. Motivated by experiments in chemical engineering, we focus on D‐optimal designs for multifactor non‐linear response surfaces in general. To find and study optimal designs, we first implement conventional point and co‐ordinate exchange algorithms. Next, we develop a novel multiphase optimization method to construct D‐optimal designs with improved properties. The benefits of this method are demonstrated by application to two experiments involving non‐linear regression models. The designs obtained are shown to be considerably more informative than designs obtained by using traditional design optimality algorithms.
Biochemical mechanism studies often assume statistical models derived from Michaelis–Menten kinetics, which are used to approximate initial reaction rate data given the concentration level of a single substrate. In experiments dealing with industrial applications, however, there are typically a wide range of kinetic profiles where more than one factor is controlled. We focus on optimal design of such experiments requiring the use of multifactor hybrid nonlinear models, which presents a considerable computational challenge. We examine three different candidate models and search for tailor-made D- or weighted-A-optimal designs that can ensure the efficiency of nonlinear least squares estimation. We also study a compound design criterion for discriminating between two candidate models, which we recommend for design of advanced kinetic studies.
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Under the national vocational education reform, HVC (hereinafter referred to as HVC) begin to implement the “double high program”, which is an important measure to promote the rapid development of vocational education. Among them, the improvement of teachers’ scientific research and innovation ability is an important part of the “double high program”, which is a solid foundation for the quality of personnel training. The 21st century is the era of big data, which requires HVC to improve teachers’ scientific research and innovation ability based on big data analysis. At present, there are many problems in HVC, which need us to improve teachers’ comprehensive ability. Firstly, this paper analyzes the basic principle of big data mining technology. Then, it puts forward the factors that restrict the development of teachers’ scientific research and innovation ability. Finally, this paper puts forward some suggestions, which can better build a higher vocational college research and innovation ability promotion system.
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