ObjectiveThe main purpose of this study was to explore the association between early fundamental motor skills and executive function in preschool children.MethodsA total of 394 young children were evaluated. The FMS and EF were evaluated using the Test of Gross Motor Development-2 and the NIH Toolbox Cognition Battery, respectively.ResultsTotal FMS score was moderately and positively correlated with total EF score and was a significant predictor of total EF score. Specifically, locomotor skills were significant predictors of inhibition control, working memory, and (...) cognitive flexibility, while object control skills were only significant predictors of inhibition control.ConclusionFMS were significantly and positively correlated with EF and were significant predictors of EF. Early childhood policymakers, preschool teachers, and researchers should take these connections seriously and implement appropriate complex motor intervention programs in future teaching to stimulate the development of both motor and higher-order cognitive skills in preschool children. (shrink)

With the increasing scale of e-commerce, the complexity of image content makes commodity image classification face great challenges. Image feature extraction often determines the quality of the final classification results. At present, the image feature extraction part mainly includes the underlying visual feature and the intermediate semantic feature. The intermediate semantics of the image acts as a bridge between the underlying features and the advanced semantics of the image, which can make up for the semantic gap to a certain extent (...) and has strong robustness. As a typical intermediate semantic representation method, the bag-of-visual-words model has received extensive attention in image classification. However, the traditional BoVW model loses the location information of local features, and its local feature descriptors mainly focus on the texture shape information of local regions but lack the expression of color information. Therefore, in this paper, the improved bag-of-visual-words model is presented, which contains three aspects of improvement: multiscale local region extraction; local feature description by speeded up robust features and color vector angle histogram ; and diagonal concentric rectangular pattern. Experimental results show that the three aspects of improvement to the BoVW model are complementary, while compared with the traditional BoVW and the BoVW adopting SURF + SPM, the classification accuracy of the improved BoVW is increased by 3.60% and 2.33%, respectively. (shrink)