ObjectivesTo examine the relationship between stress, social support, and empathy among medical students.MethodsWe evaluated the relationships between stress and empathy, and social support and empathy among medical students. The respondents completed a question-naire including demographic information, the Jefferson Scale of Empathy, the Perceived Stress Scale, and the Multidimensional Scale of Perceived Social Support. Corre-lation and linear regression analyses were conducted, along with sub-analyses according to gender, admission system, and study year.ResultsIn total, 2,692 questionnaires were analysed. Empathy and social support positively correlated, and empathy and stress negatively correlated. Similar correla-tion patterns were detected in the sub-analyses; the correla-tion between empathy and stress among female students was negligible. In the regression model, stress and social support predicted empathy among all the samples. In the sub-analysis, stress was not a significant predictor among female and first-year students.ConclusionsStress and social support were significant predictors of empathy among all the students. Medical educators should provide means to foster resilience against stress or stress alleviation, and to ameliorate social support, so as to increase or maintain empathy in the long term. Furthermore, stress management should be emphasised, particularly among female and first-year students.

Photovoltaic cells have recently attracted considerable attention for indoor energy harvesting for low-power-consumption electronic products due to the rapid growth of the Internet of Things (IoT). The IoT platform is being developed with a vision of connecting a variety of wireless electronic devices, such as sensors, household products, and personal data storage devices, which will be able to sense and communicate with their internal states or the external environment. A self-sustainable power source is required to power such devices under low light indoor environments. Inorganic photovoltaic cells show excellent device performance under 1 Sun illumination and dominate the market for outdoor applications. However, their performance is limited for indoor applications with low incident light intensities as they exhibit low photo-voltage. Among the emerging photovoltaic technologies, organic photovoltaics have unique advantages, including solution processibility, flexibility, and lightweight tailorable design; hence, they are considered the best solution for indoor light harvesting applications due to their high photo-voltage, strong absorption of UV-visible wavelengths, and a spectral response similar to that emitted by modern indoor lighting systems. In this review article, we discuss the factors affecting device performance of different photovoltaic technologies under low incident light intensities or indoor conditions and provide a comprehensive analysis of future opportunities for enhancing indoor performance of the photovoltaic devices. Furthermore, we discuss some of the results of semi-transparent organic solar cell which operated under complex environmental conditions like low illumination, incident light angle etc. Based on the results, one can suggest that semi-transparent organic solar cell is a more suitable case for progressive indoor solar cell. After highlighting the factors that limit indoor device performance of photovoltaic cells, we discuss potential applications of IoT devices powered by organic photovoltaic cells in indoor lighting environments.

No abstract

No abstract

Sea surface winds and coastal winds, which have a significant influence on the ocean environment, are very difficult to predict. Although most planetary boundary layer (PBL) parameterizations have demonstrated the capability to represent many meteorological phenomena, little attention has been paid to the precise prediction of winds at the lowest PBL level. In this study, the ability to simulate sea winds of two widely used mesoscale models, fifth-generation mesoscale model (MM5) and weather research and forecasting model (WRF), were compared. In addition, PBL sensitivity experiments were performed using Medium-Range Forecasts (MRF), Eta, Blackadar, Yonsei University (YSU), and Mellor-Yamada-Janjic (MYJ) during Typhoon Ewiniar in 2006 to investigate the optimal PBL parameterizations for predicting sea winds accurately. The horizontal distributions of winds were analyzed to discover the spatial features. The time-series analysis of wind speed from five sensitivity experimental cases was compared by correlation analysis with surface observations. For the verification of sea surface winds, QuikSCAT satellite 10-m daily mean wind data were used in root-mean-square error (RMSE) and bias error (BE) analysis. The MRF PBL using MM5 produced relatively smaller wind speeds, whereas YSU and MYJ using WRF produced relatively greater wind speeds. The hourly surface observations revealed increasingly strong winds after 0300 UTC, July 10, with most of the experiments reproducing observations reliably. YSU and MYJ using WRF showed the best agreements with observations. However, MRF using MM5 demonstrated underestimated winds. The conclusions from the correlation analysis and the RMSE and BE analysis were compatible with the above-mentioned results. However, some shortcomings were identified in the improvements of wind prediction. The data assimilation of topographical data and asynoptic observations along coast lines and satellite data in sparsely observed ocean areas should make it possible to improve the accuracy of sea surface wind predictions.