Search Results (6)

This study examines the long-term climate predictability in the Seomjin River basin using statistical methods, and explores the effects of incorporating the duration of climate indices as predictors. A multiple linear regression model is employed, utilizing 44 climate indices as predictors, including global climate patterns and local meteorological factors specific to the area. The analysis focuses on teleconnections between the target variables and climate indices, considering the value of each index, not only for the corresponding month, but also for an average value over a duration of 2 and 3 months. The correlation analysis reveals that considering the duration of climate indices allows for the inclusion of predictors with higher correlation, leading to improved forecasting accuracy. The goodness of fit analysis, which compares predicted mean values with observed values on a monthly basis, indicates that neither precipitation nor temperature is significantly affected by the duration. However, the tercile hit rate analysis, comparing the results with historical data, shows a 34.7% hit rate for precipitation, both before and after, reflecting the duration of indices. Notably, for long lead times (10–12 months), the hit rate improves after incorporating the duration. In contrast, for temperature, the tercile hit rate is higher before considering the duration. Nonetheless, both precipitation and temperature exhibit hit rates higher than the baseline probability of 33.3%, affirming the reliability of long-term forecasts in the Seomjin River basin. Incorporating the duration of climate indices enhances the selection of predictors with higher correlation, resulting in a notable impact on long-lead precipitation forecasting. However, since temperature demonstrates little irregularity and displays a consistent pattern according to the month and season, the effect of considering the duration is relatively insignificant compared to precipitation. Future research will explore the decrease in hit rate due to reflecting the duration in temperature by extending the analysis to other regions. Full article

Objectives: Healthcare workers in hospitals (HHCWs), a notably increasing workforce, face various occupational hazards. A high incidence of cancer among HHCWs has been observed; however, the cancer incidence status among HHCWs in South Korea is yet to be studied. This study aimed to assess cancer incidence among HHCWs in South Korea. Methods: We constructed a retrospective cohort of HHCWs using National Health Insurance claims data, including cancer incidence status and vital status, from 2007 to 2015. Those who had worked in hospitals for at least three years were defined as HHCWs. Standardized incidence ratios (SIRs) for all cancer types and standardized mortality ratios were calculated. Results: A total of 107,646 HHCWs were followed up, and the total follow-up duration was 905,503 person-years. Compared to the total workers, female HHCWs showed significantly higher SIR for all cancers (observed cases = 1480; SIR = 1.25; 95% confidence interval [CI] = 1.06–1.47). The incidence of breast cancer among female HHCWs was significantly higher compared to that among total workers (observed cases = 376; SIR = 1.21; 95% CI = 1.09–1.36). Conclusions: Our findings indicate that female HHCWs have an elevated probability of developing cancer, which suggests that occupational risk factors such as night-shift work, anti-neoplastic medications, stressful jobs, and ionizing radiation should be assessed. Further investigation and occupational environment improvement activities are required. Full article

A series of material tests were performed on cryogenic metallic materials meant for liquid hydrogen storage tanks using a 20 K tensile cryostat and an electrochemical hydrogen-charging apparatus. Mechanical evaluation of the electrochemically hydrogen-charged specimens was performed in a tensile cryostat using helium gas at ambient temperature and cryogenic temperature (20 K). The tensile cryostat was equipped with a vacuum jacket and a G-M cryocooler with gaseous helium. Furthermore, the cathodic electrolysis cell used for charging the specimens was adopted for internal hydrogen conditions with a reflux condenser and heating mantle to increase hydrogen diffusivity. The target materials were austenite stainless steel and aluminum alloy, which are suitable for liquefied natural gas and gaseous hydrogen environments. No significant change in the yield strength and flow stress of the hydrogen-charged specimen up to 20% strain was observed. However, changes in tensile strength and elongation were observed thereafter. Electrochemical hydrogen charging of stainless steel leads to a high concentration of hydrogen on the surface of the specimen. The resulting surface cracks reduced the flow stress. The 20 K tensile test showed discontinuous yielding in the austenitic stainless steel with an abrupt increase in temperature. The mechanical performance of the aluminum alloys improved in terms of strength and elongation. Changes in the mechanical performance and relative area reduction were observed for all the metallic materials at 300 K and 20 K. Full article

In this study, artificial neural network (ANN) models were constructed to predict the rainfall during May and June for the Han River basin, South Korea. This was achieved using the lagged global climate indices and historical rainfall data. Monte-Carlo cross-validation and aggregation (MCCVA) was applied to create an ensemble of forecasts. The input-output patterns were randomly divided into training, validation, and test datasets. This was done 100 times to achieve diverse data splitting. In each data splitting, ANN training was repeated 100 times using randomly assigned initial weight vectors of the network to construct 10,000 prediction ensembles and estimate their prediction uncertainty interval. The optimal ANN model that was used to forecast the monthly rainfall in May had 11 input variables of the lagged climate indices such as the Arctic Oscillation (AO), East Atlantic/Western Russia Pattern (EAWR), Polar/Eurasia Pattern (POL), Quasi-Biennial Oscillation (QBO), Sahel Precipitation Index (SPI), and Western Pacific Index (WP). The ensemble of the rainfall forecasts exhibited the values of the averaged root mean squared error (RMSE) of 27.4, 33.6, and 39.5 mm, and the averaged correlation coefficient (CC) of 0.809, 0.725, and 0.641 for the training, validation, and test sets, respectively. The estimated uncertainty band has covered 58.5% of observed rainfall data with an average band width of 50.0 mm, exhibiting acceptable results. The ANN forecasting model for June has 9 input variables, which differed from May, of the Atlantic Meridional Mode (AMM), East Pacific/North Pacific Oscillation (EPNP), North Atlantic Oscillation (NAO), Scandinavia Pattern (SCAND), Equatorial Eastern Pacific SLP (SLP_EEP), and POL. The averaged RMSE values are 39.5, 46.1, and 62.1 mm, and the averaged CC values are 0.853, 0.771, and 0.683 for the training, validation, and test sets, respectively. The estimated uncertainty band for June rainfall forecasts generally has a coverage of 67.9% with an average band width of 83.0 mm. It can be concluded that the neural network with MCCVA enables us to provide acceptable medium-term rainfall forecasts and define the prediction uncertainty interval. Full article

In this study, long-term precipitation forecasting models capable of reflecting constantly changing climate characteristics and providing forecasts for up to 12 months in advance were developed using lagged correlations with global and local climate indices. These models were applied to predict monthly precipitation in the Han River basin, South Korea. Based on the lead month of forecast, 10 climate indices with high correlations were selected and combined to construct four-variable multiple regression models for monthly precipitation forecasting. The forecast results for the analytical period (2010–2019) showed that predictability was low for some summer seasons but satisfactory for other seasons and long periods. In the goodness-of-fit test results, the Nash–Sutcliffe efficiency (0.48–0.57) and the ratio of the root mean square error to the standard deviation of the observation (0.66–0.72) were evaluated to be satisfactory while the percent bias (9.4–15.5%) was evaluated to be between very good and good. Due to the nature of the statistical models, however, the predictability is highly likely to be reduced if climate phenomena that are different from the statistical characteristics of the past appear in the forecast targets or predictors. The forecast results were also presented as tercile probability information (below normal, normal, above normal) through a comparison with the observation data of the past 30 years. The results are expected to be utilized as useful forecast information in practice if the predictability for some periods is improved. Full article

This study develops a late spring-early summer rainfall forecasting model using an artificial neural network (ANN) for the Geum River Basin in South Korea. After identifying the lagged correlation between climate indices and the rainfall amount in May and June, 11 significant input variables were selected for the preliminary ANN structure. From quantification of the relative importance of the input variables, the lagged climate indices of East Atlantic Pattern (EA), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), East Pacific/North Pacific Oscillation (EP/NP), and Tropical Northern Atlantic Index (TNA) were identified as significant predictors and were used to construct a much simpler ANN model. The final best ANN model, with five input variables, showed acceptable performance with relative root mean square errors of 25.84%, 32.72%, and 34.75% for training, validation, and testing data sets, respectively. The hit score, which is the number of hit years divided by the total number of years, was more than 60%, which indicates that the ANN model successfully predicts rainfall in the study area. The developed ANN model, incorporated with lagged global climate indices, could allow for more timely and flexible management of water resources and better preparation against potential droughts in the study region. Full article