This study aims to propose an effective intelligent model for predicting entrepreneurial intention, which can provide a reasonable reference for the formulation of talent training programs and the guidance of entrepreneurial intention of students. The prediction model is mainly based on the kernel extreme learning machine (KELM) optimized by the improved Harris hawk's optimizer (HHO). In order to obtain better parameters and feature subsets, the Gaussian barebone (GB) strategy is introduced to improve the HHO algorithm, so as to strengthen the optimization ability for tuning parameters of KELM and identifying the compact feature subsets. Then, an optimal KELM model (GBHHO-KELM) is established according to the obtained optimal parameters and feature subsets to predict the entrepreneurial intention of students. In the experiment, GBHHO is compared with the other nine well-known methods in 30 CEC 2014 benchmark problems. The experimental findings suggest that the proposed GBHHO method is significantly superior to the existing methods in most problems. At the same time, GBHHO-KELM is compared with other machine learning methods in the prediction of entrepreneurial intention. The experimental results indicate that the proposed GBHHO-KELM can achieve better classification performance and higher stability in accordance with the four metrics. Therefore, we can conclude that the GBHHO-KELM model is expected to be an effective tool for the prediction of entrepreneurial intention.

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Background GATA-binding protein 4 (GATA4) is the critical regulator in gonadal development and its mutation has been reported related with 46,XY disorder of sex development (DSD). Here, we found the two Chinese cases with 46,XY DSD carried the GATA4 mutation. Physical examinations, B-ultrasound and Karyotype analysis were performed and confirmed the two patients with 46,XY DSD. Results Sequencing were performed and the heterozygous mutation p.Gly375Arg in GATA4 gene was identified in the 2 cases with 46,XY DSD. Their mother was identified carrying the p.Gly375Arg mutation in GATA4 protein. However, their father and litter sister without 46,XY DSD didn’t be found carrying the p.Gly375Arg mutation in GATA4 gene. Conclusion This is the first report that the case with 46,XY DSD carried the mutation Gly375Arg in the GATA4 gene. Our

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