ABSTRACT
BACKGROUND: The hidden curriculum in baccalaureate nursing programs is a means of moral education. Evaluation of the curriculum by students and faculty can increase awareness of its characteristics, which could be useful for planning and further development. OBJECTIVES: This study's aim was to translate the Hidden Curriculum Evaluation Scale in Nursing Education (HCES-N) to Chinese, adapt the scale to the Chinese culture and evaluate its validity and reliability in a sample of undergraduate nursing students. DESIGN: Psychometric assessment of a tool using two cross-sectional surveys. SETTINGS: University-based schools of nursing in seven provinces and cities of China. PARTICIPANTS: Undergraduate nursing students in a baccalaureate program. METHODS: The English version of the HCES-N was translated to Chinese using the Brislin translation model. The test-retest, internal consistency and split-half reliabilities of the HCES-N were examined in a sample of 1016 undergraduate nursing students. Exploratory factor analysis and confirmatory factor analysis were conducted to examine the scale's content validity. RESULTS: The exploratory factor analysis of the final 44-item HCES-N revealed three common factors and a cumulative variance contribution rate of 73.535%. The results of the confirmatory factor analysis showed that the final 44-item, 3-factor model was adequate for the s cale's structure (Chi-square/df = 6.59, RMSEA = 0.074, SRMR = 0.040, CFI = 0.911 and TLI = 0.905). The results confirmed that the Chinese version of HCES-N had good internal consistency (Cronbach α = 0.945); the scale's split-half-reliability was 0.794 and its test-retest reliability after two weeks was 0.894. CONCLUSION: The Chinese version of the HCES-N has good reliability and validity and it can be used to assess the hidden curriculum in baccalaureate nursing programs.
Subject(s)
Education, Nursing, Baccalaureate , Education, Nursing , Students, Nursing , Humans , Education, Nursing, Baccalaureate/methods , Psychometrics/methods , Reproducibility of Results , Cross-Sectional Studies , Surveys and Questionnaires , ChinaABSTRACT
Hyperspectral-image (HSI) restoration plays an essential role in remote sensing image processing. Recently, superpixel segmentation-based the low-rank regularized methods for HSI restoration have shown outstanding performance. However, most of them simply segment the HSI according to its first principal component, which is suboptimal. In this paper, integrating the superpixel segmentation with principal component analysis, we propose a robust superpixel segmentation strategy to better divide the HSI, which can further enhance the low-rank attribute of the HSI. To better employ the low-rank attribute, the weighted nuclear norm by three types of weighting is proposed to efficiently remove the mixed noise in degraded HSI. Experiments conducted on simulated and real HSI data verify the performance of the proposed method for HSI restoration.
ABSTRACT
Oral cancer is one of the most common tumours in the world threatening human life and health. The 5-years survival rate of patients with oral cancer has not been improved significantly for many years. The existing clinical diagnostic methods rarely achieve early diagnosis due to deficiencies such as lack of sensitivity. Most of the patients have progressed to the advanced stages when oral cancer is detected. Unfortunately, the traditional treatment methods are usually ineffective at this stage. Therefore, there is an urgent need for more effective and precise techniques for early diagnosis and effective treatment of oral cancer. In recent decades, nanomedicine has been a novel diagnostic and therapeutic platform for various diseases, especially cancer. The synthesis and application of various nanoagents have emerged at the right moment. Among them, polymer nanoagents have unique advantages, such as good stability, high biosafety and high drug loading, showing great potential in the early accurate diagnosis and treatment of tumours. In this review, we focus on the application of advanced polymeric nanoagents in both the diagnosis and treatment of oral cancer. Then, the future therapy strategies and trends for polymeric nanoagents applied to oral cancer are discussed, with the hope that more advanced nanomedical technology will be applied to oral cancer research and promote the development of stomatology.
ABSTRACT
Compressive sensing (CS) theory asserts that we can reconstruct signals and images with only a small number of samples or measurements. Recent works exploiting the nonlocal similarity have led to better results in various CS studies. To better exploit the nonlocal similarity, in this paper, we propose a non-convex smoothed rank function based model for CS image reconstruction. We also propose an efficient alternating minimization method to solve the proposed model, which reduces a difficult and coupled problem to two tractable subproblems. Experimental results have shown that the proposed method performs better than several existing state-of-the-art CS methods for image reconstruction.
