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Studying the frequency and impulse characteristics of grounding grids is of great reference value for the optimal design of the substation. As the frequency increasing, the variation trend of the grounding resistance under different conditions is not same, which is affected by many factors. Therefore, this study focuses on the influence of grounding grid sizes, sparsity, soil resistivity, and grounding material on frequency and impulse characteristics. Based on these factors, the corresponding value of the grounding resistances at different frequencies and the impulse resistances are obtained by simulation. The trends of grounding resistances under different factors are analysed by using the dispersion mechanism. The simulation results show that the impulse grounding resistance will decrease with the increase of the grounding grid sizes and the conductor radius, or will decrease with the decrease of the spacing and the soil resistivity. With the increasing in frequency, the inductance effect of grounding resistance is strengthened, and the effect of soil resistivity and grounding grid parameters on grounding resistance become remarkable. [ABSTRACT FROM AUTHOR]

The present longitudinal study investigated the predictive power of preschool linguistic skills and early family factors on children's comprehensive literacy skills at the end of primary school in 262 Chinese children. The results indicated that a substantial (20–34%) share of variance of 5th grade (age 11) literacy skills in Chinese could be explained by early family factors (age 3) and linguistic skills (age 3–age 5). Family socioeconomic status and parent-child reading tuition were associated with different literacy measures. A differential pattern of prediction was also observed among different literacy skills. Furthermore, path analyses indicated that the relationships between early family factors and literacy skills at age 11 were mediated by specific linguistic and cognitive skills at preschool. [ABSTRACT FROM AUTHOR]

Reading comprehension is a crucial reading skill for learning and putatively contains 2 key components: reading decoding and linguistic comprehension. Current understanding of the neural mechanism underlying these reading comprehension components is lacking, and whether and how neuroanatomical features can be used to predict these 2 skills remain largely unexplored. In the present study, we analyzed a large sample from the Human Connectome Project (HCP) dataset and successfully built multivariate predictive models for these 2 skills using whole-brain gray matter volume features. The results showed that these models effectively captured individual differences in these 2 skills and were able to significantly predict these components of reading comprehension for unseen individuals. The strict cross-validation using the HCP cohort and another independent cohort of children demonstrated the model generalizability. The identified gray matter regions contributing to the skill prediction consisted of a wide range of regions covering the putative reading, cerebellum, and subcortical systems. Interestingly, there were gender differences in the predictive models, with the female-specific model overestimating the males' abilities. Moreover, the identified contributing gray matter regions for the female-specific and male-specific models exhibited considerable differences, supporting a gender-dependent neuroanatomical substrate for reading comprehension.