Combined model based on optimized multi-variable grey model and multiple linear regression

The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.

DEAP1 encodes a fasciclin-like arabinogalactan protein required for male fertility in rice

Arabinogalactan proteins(AGPs)are widely distributed in plant cells.Fasciclin-like AGPs(FLAs)belong to a subclass of AGPs that play important roles in plant growth and development.However,little is known about the biological functions of rice FLA.Herein,we report the identification of a male-sterile mutant of DEFECTIVE EXINE AND APERTURE PATTERNING1(DEAP1)in rice.The deap1 mutant anthers produced aberrant pollen grains with defective exine formation and a flattened aperture annulus and exhibited slightly delayed tapetum degradation.DEAP1 encodes a plasma membrane-associated member of groupⅢplant FLAs and is specifically and temporally expressed in reproductive cells and the tapetum layer during male development.Gene expression studies revealed reduced transcript accumulation of genes related to exine formation,aperture patterning,and tapetum development in deap1 mutants.Moreover,DEAP1 may interact with two rice D6 PROTEIN KINASE-LIKE3 s(OsD6PKL3s),homologs of a known Arabidopsis aperture protein,to affect rice pollen aperture development.Our findings suggested that DEAP1 is involved in male reproductive development and may affect exine formation and aperture patterning,thereby providing new insights into the molecular functions of plant FLAs in male fertility.

The Novel Triangle MGM(1,m,N)Model and Its Applications

The MGM(1,m,N)model is an effective grey multi-variate forecasting model that considers multiple system characteristic sequences affected by multiplefactors.Nevertheless,it isregularly inaccurate in the application This is because the model requires a strong correlation between the system characteristic sequences That reduces the applicability of the model.To solve this problem,this paper proposes a novel multi-variate grey model.This model does not require a certain correlation between system characteristic sequences and has higher applicability Through numerical integration,a two-point trapezoidal formula,and a recursive method,thetime-response expressions ofthetwo model forms are obtained Some properties of the proposed model are further discussed Finally,the validity of the proposed model is evaluated by using two real cases related to China's invention patent development.Theresultsshow that the novel models outperformother models inbothsimulation and prediction applications.