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Recent experience with the strongest earthquakes greater than magnitude 5.4 in Korea leads to public interest in the safety and resilience of critical infrastructures. There are many nuclear power plants near the epicenters of the earthquakes. Nuclear power plants are essential infrastructures that provide stable and enough energy for human life, and simultaneously, control systems for the safety and security of nuclear power plants are critical due to the risk of nuclear accidents on public health and the environment. The nuclear area uses probabilistic risk assessment to estimate the risk of structures, systems, and components in nuclear power plants, and the evaluation of the fragility curve is a key process for probabilistic risk assessment. The challenges of a seismic fragility analysis lie in estimating the influence of various uncertainties in material, geometry, and earthquake and improving the existing fragility analysis methods, which require time-consuming nonlinear time history analysis. Thus, this paper conducts a multivariate seismic fragility analysis using surrogate models for reinforced concrete squat shear walls and proposes a simplified closed form equation for a chosen surrogate seismic demand model. The surrogate models are trained and validated by several approaches: response surface method, support vector machine, Gaussian process regression, and neural network. In addition, a correlation analysis is used to evaluate the relative importance of the variables to the seismic demand to simplify the surrogate model further. Finally, simplified surrogate models based on the importance of the variables are proposed as closed form of polynomials, and the performance of these models on the fragility analysis is evaluated. [ABSTRACT FROM AUTHOR]

Nurses' turnover intention has increased since the COVID-19 pandemic. Emotional labor is reportedly high among nurses in Korea, and a positive psychological capital can help reduce turnover intention. This cross-sectional study investigated the factors influencing turnover intention in nurses during the COVID-19 pandemic. Survey data were collected from 155 nurses caring for patients with COVID-19 at a university hospital in South Korea in March 2022. Self-reported and paper-based questionnaires on emotional labor, positive psychological capital, and turnover intention were employed. The mean values for emotional labor, positive psychological capital, and turnover intention were 54.52/80, 58.03/90, and 38.92/50, respectively, and 77.8% of nurses reported planning to quit working. Turnover intention correlated with emotional labor (r = 0.17; p = 0.041) and had no significant relationship with positive psychological capital. Sex (β = 0.24; p = 0.003) and emotional labor (β = 0.18; p = 0.019) affected turnover intention, with 9% explanatory power. Female nurses caring for patients with COVID-19 had a higher turnover intention than male nurses. Finally, the higher the emotional labor of nurses caring for patients with COVID-19, the higher the turnover intention. To reduce the turnover intention of nurses, hospitals need to help them enhance their emotional labor and positive psychological capital by establishing measures such as emotional coaching programs or psychological capital interventions. [ABSTRACT FROM AUTHOR]

Influenced by cultural background, economic development, social system, education system, and other factors, there is still a big gap between Chinese institutions and developed countries in flute teaching, even with our neighbors, South Korea and Japan. Under the influence of cultural background, economic development, social system, and educational system, there is still a very big gap between Chinese colleges and universities and developed countries in flute teaching, even with our neighbors, South Korea and Japan. Because of its local perception and weight-sharing structure, the convolutional neural network is closer to the biological neural network in the real world. The weight-sharing structure reduces the complexity of the neural network, which can avoid the complexity of feature extraction and classification process in data reconstruction. This paper studies the analysis and optimization of flute playing and teaching system based on a convolutional neural network. By applying local perception field and parameter sharing in a convolutional neural network at the same time and adding multiple filters, it can not only effectively reduce the number of parameters but also extract features layer by layer. In the process of convolution, the parameters of the characteristic map obtained by each layer decrease layer by layer, but the number increases gradually. Based on the analysis of the problems faced by the flute performance teaching, this paper puts forward the corresponding solutions in order to promote the flute performance teaching in China to achieve better results. [ABSTRACT FROM AUTHOR]

Background. Occupational stress is a phenomenon affecting people worldwide. Investigating occupational stress among immigrant worker populations will unravel some of the intricacies of this condition and its psychological effects on this population. Aim. This paper conceptually examined occupational stress within the context of immigrant workers' mental health and offer an operational definition to aid nurse researchers, educators, and practitioners in assessing and managing patients and developing culturally appropriate interventions for this population. Design. Walker and Avant's eight-step concept analysis is used as an organizing framework. Data Source. MEDLINE, CINAHL, OVID, PubMed, and APA Psych Info. Review Methods. Keywords job stress, immigrant work stress, occupational stress scale, immigrant work-related stress, and mental health were used. The search yielded 142 articles; 17 were selected based on the effect of work stress on mental health. Results. This analysis found that occupational stress can be attributed to communication problems, alienation, discrimination, and barriers to work-life balance can cause negative consequences among immigrants. An operational definition is also provided. Conclusion. There is a growing need to examine closely and differentiate between occupational and acculturative stress to navigate a more profound understanding of how these conditions negatively complement each other. [ABSTRACT FROM AUTHOR]

Local roads have numerous blind spots caused by complex geometry, obstacles, and narrow width. Thus, conventional proactive countermeasures, such as passive traffic signs and convex mirrors, have not always been effective in preventing local road collisions. In this paper, we present a novel proactive two-step approach for traffic safety on local roads, comprised of detection of pedestrian-to-vehicle and vehicle-to-vehicle collision risks and warning systems. First, using video surveillance and radars to eliminate blind spots, the system detects road objects, predicts their trajectories and reachable areas, and identifies a potential risk situation. Second, it provides road users such as vehicles and pedestrians with warnings through LED variable message signs, which allows them to react effectively in risky situations. We have applied the system to two local road sites in South Korea, including a university campus in Seoul City and an apartment complex in Daejeon City. The detecting system has been validated using a confusion matrix. We have assessed the warning effect through a before-and-after study and found that the proposed system contributed to the improvement of traffic safety at the case study site in that traffic conflicts decreased by 55–62%. [ABSTRACT FROM AUTHOR]

Passive transit-signal priority (TSP) is an effective approach to improve the operational efficiency of transit systems on arterial roads. This paper presents an integrated TSP model that benefits both transit vehicles and other road users. The proposed model uses the random arrival characteristics of transit vehicles related to their stochastic dwell times at near-side stops. Thus, it enables not only two-way bandwidth maximization within subsystems but also the combined optimization of adjacent subsystems. To minimize any negative impact on side-street traffic, this study, unlike previous research, considers the influential area by the introduction of TSP. Case studies on 15 intersections in Sejong, Korea, verify the superior performance of the proposed model for both transit and general vehicles. [ABSTRACT FROM AUTHOR]

Summary: Cables are among the most important structural components of cable‐stayed bridges; however, their vibrations due to the wind and vehicles significantly affect the serviceability of bridges. To overcome the expensive and time‐ and effort‐intensive installation of sensors in measuring cable vibrations, computer‐vision‐based displacement methods have been introduced. Even with the improved accuracy and convenience of vision‐based displacement methods, there are two main issues in their application in measuring cable vibrations: the manual selection of the region of interest (ROI) and loss of feature points using traditional tracking algorithms. To address these, this paper proposes a new specialized method to measure the structural displacement of cables. The proposed method automatically selects the ROI using a convolutional neural network and tracks the feature points in the cable more robustly using a modified Kanade–Lucas–Tomasi (KLT) algorithm. The automatic cable ROI selection network (Cable‐ROI Net) predicted the cable with an intersection over union of 89.59%. Meanwhile, modified KLT (Uni‐KLT) successfully measured the cable displacement with a root mean square error of 1.077 pixels, the conventional KLT failed to track the feature points. The on‐site validation test at a cable‐stayed bridge in Korea demonstrated the ability of the proposed method to automatically select the ROI and successfully measure the cable vibration for the entire duration of 30 min, whereas the traditional method required manual ROI selection and lost all feature points four times throughout the test. Therefore, the proposed method is expected to improve the current methods for measuring cable vibrations. [ABSTRACT FROM AUTHOR]