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The inverse approach is computationally efficient in aerodynamic design as the desired target performance distribution is prespecified. However, it has some significant limitations that prevent it from achieving full efficiency. First, the iterative procedure should be repeated whenever the specified target distribution changes. Target distribution optimization can be performed to clarify the ambiguity in specifying this distribution, but several additional problems arise in this process such as loss of the representation capacity due to parameterization of the distribution, excessive constraints for a realistic distribution, inaccuracy of quantities of interest due to theoretical/empirical predictions, and the impossibility of explicitly imposing geometric constraints. To deal with these issues, a novel inverse design optimization framework with a two-step deep learning approach is proposed. A variational autoencoder and multi-layer perceptron are used to generate a realistic target distribution and predict the quantities of interest and shape parameters from the generated distribution, respectively. Then, target distribution optimization is performed as the inverse design optimization. The proposed framework applies active learning and transfer learning techniques to improve accuracy and efficiency. Finally, the framework is validated through aerodynamic shape optimizations of the wind turbine airfoil. Their results show that this framework is accurate, efficient, and flexible to be applied to other inverse design engineering applications.

Tuberculosis (TB) infection is a common occupational risk for health workers (HWs) and poses a threat to the patients under their care and to other HWs. Hence, the development of a prevention strategy is crucial. We conducted a study to understand the status and risk factors of TB infection among HWs. The existing literature was searched for all published reports from 1 August 2010 to 31 December 2018, related to TB among HWs according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The inclusion criteria were: (1) study participants working in a health care facility; (2) TB cases diagnosed by medical professionals; (3) original research articles; and (4) English reports in a peer-reviewed journal. We finally included 61 studies from 642 articles searched initially. The TB infection rate in HWs was higher than that of the general population. Based on 39 studies, the prevalence of TB in HWs (tuberculin skin test positive) was 29.94%. In contrast, the global burden of latent TB infection was 23.0% (95% uncertainty interval: 20.4%–26.4%) in 2014. The risk factors of TB among HWs were aging, long duration of employment, nursing professionals, lack of Bacillus Calmette-Guerin vaccination, and low body mass index. HWs have an increased risk for TB infection, which can cause secondary infections in patients or other HWs. An effective prevention strategy must be developed to enable early diagnosis and prompt treatment.

Purpose: The aim of this study was to test the validity and reliability of the Korean version of the Quality of Nursing Work Life (QNWL-K) scale. The scale measures the reported quality of the nurse' work life among Korean nurses. Methods: The QNWL-K was developed through forward-backward translation techniques. An internal consistency reliability and construct validity using exploratory analysis were conducted using SPSS/WIN (21.0). Survey data were collected from 309 nurses who worked in two tertiary hospitals, four general hospitals, and two hospitals in Seoul and Gyeonggi, South Korea. Results: Factor analysis results of the revised QWNL-K demonstrated that it has a four-factor structure (work context, support systems for home/work life, work design, and staffing) that supports construct validity. Factor loadings of the 36 items ranged from .30~.77. The QNWL-K showed reliable internal consistency from Cronbach's ⍺ for the total scale of .93. Conclusion: The findings support that the QNWL-K has satisfactory construct validity and is a reliable measure of nursing work life in Korea.

In simple optimization problem, direct searching methods are most accurate and practical enough. However, for more complicated problem which contains many design variables and demands high computational costs, surrogate model methods are recommendable instead of direct searching methods. In this case, surrogate models should have reliability for not only accuracy of the optimum value but also globalness of the solution. In this paper, the Kriging method was used to construct surrogate model for finding aerodynamically improved three dimensional single stage turbine. At first, nozzle was optimized coupled with base rotor blade. And then rotor was optimized with the optimized nozzle vane in order. Kriging method is well known for its good describability of nonlinear design space. For this reason, Kriging method is appropriate for describing the turbine design space, which has complicated physical phenomena and demands many design variables for finding optimum three dimensional blade shapes. To construct airfoil shape, Prichard topology was used. The blade was divided into 3 sections and each section has 9 design variables. Considering computational cost, some design variables were picked up by using sensitivity analysis. For selecting experimental point, D-optimal method, which scatters each experimental points to have maximum dispersion, was used. Model validation was done by comparing estimated values of random points by Kriging model with evaluated values by computation. The constructed surrogate model was refined repeatedly until it reaches convergence criteria, by supplying additional experimental points. When the surrogate model satisfies the reliability condition and developed enough, finding optimum point and its validation was followed by. If any variable was located on the boundary of design space, the design space was shifted in order to avoid the boundary of the design space. This process was also repeated until finding appropriate design space. As a result, the optimized design has more complicated blade shapes than that of the baseline design but has higher aerodynamic efficiency than the baseline turbine stage.