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The health status of LBC is problematic. Food preferences, personality type and parenting styles should be taken into account when measures are developed to improve the health of these children.

The identification for haploid seeds is an important process in maize haploid breeding. Thanks to the diffuse transmission (DT) technology of near-infrared (NIR) spectroscopy, maize haploid seeds can be selected automatically using NIR spectrum features. However, the NIR spectra of maize seeds contain a large number of redundant features and noise that will degrade the identification performance. We resolved this problem by designing a low dimension and uniform space of seed spectrum features to improve the collected spectra. The zero-phase component analysis (ZCA) method was utilized to uniform the feature space and the partial least squares regression (PLSR) was employed to design the low dimension space. Then, by using the classifier of back propagation neural network (BPNN), a high qualitative identification method was developed for selecting maize haploid seeds. The study results demonstrate that the average accuracy of the proposed method is outstanding (96.16%) with a minor standard deviation (SD) compared with other methods. Therefore, our proposed method is potentially useful for automatically identifying maize haploid seeds.

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As a method of function approximation, polynomial fitting has always been the main research hotspot in mathematical modeling. In many disciplines such as computer, physics, biology, neural networks have been widely used, and most of the applications have been transformed into fitting problems using neural networks. One of the main reasons that neural networks can be widely used is that it has a certain sense of universal approximation. In order to fit the polynomial, this paper constructs a three-layer feedforward neural network, uses Taylor series as the activation function, and determines the number of hidden layer neurons according to the order of the polynomial and the dimensions of the input variables. For explicit polynomial fitting, this paper uses non-linear functions as the objective function, and compares the fitting effects under different orders of polynomials. For the fitting of implicit polynomial curves, the current popular polynomial fitting algorithms are compared and analyzed. Experiments have proved that the algorithm used in this paper is suitable for both explicit polynomial fitting and implicit polynomial fitting. The algorithm is relatively simple, practical, easy to calculate, and can efficiently achieve the fitting goal. At the same time, the computational complexity is relatively low, which has certain application value.

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