Résumé
PURPOSE: The aim of this study was to investigate and identify the influence of nurses' followership types and ego-resilience on job embeddedness. METHODS: Self-reported questionnaires were distributed to 546 nurses working at hospitals with 400 to 700 beds in Seoul and Gangwon Province, and 520 sincere questionnaires were analyzed using SPSS / WIN ver 23.0 program. RESULTS: The job embeddedness, ego-resilience and followership types of the participants showed significant correlation with each other. In hierarchical multiple regression analysis, nurses' followership types and ego-resilience were identified as predictors of job embeddedness (Adj. R2=0.34, p < .001). CONCLUSION: Findings of this study indicate that nurses need to develop their followership and ego-resilience to increase job embeddedness. Future studies should explore ways to improve followership and ego-resilience.
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SéoulRésumé
The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis has been carried out, some of which produced commercial systems. However, there still remains much room for improving the accuracy of chromosome classification. In this paper, We propose an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of multi-step multi-layer neural network(MMANN). We reconstructed chromosome image to improve the chromosome classification accuracy and extracted three morphological features parameters such as centromeric index(C.1.), relative length ratio(R.L.), and relative area ratio(R.A.). This Parameters employed as input in neural network by preprocessing twenty human chromosome images. The experiment results show that the chromosome classification error is reduced much more than that of the other classification methods.