• Atmosphere
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• v.31 no.1
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• pp.1-15
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• 2021

High-resolution wind resources maps (maps, here after) with spatial and temporal resolutions of 100 m and 3-hours, respectively, over South Korea have been produced and evaluated for the period from July 2016 to June 2017 using Korea Meteorological Administration (KMA) Post Processing (KMAPP). Evaluation of the 10 m- and 80 m-level wind speed in the new maps (KMAPP-Wind) and the 1.5 km-resolution KMA NWP model, Local Data Assimilation and Prediction System (LDAPS), shows that the new high-resolution maps improves of the LDAPS winds in estimating the 10m wind speed as the new data reduces the mean bias (MBE) and root-mean-square error (RMSE) by 33.3% and 14.3%, respectively. In particular, the result of evaluation of the wind at 80 m which is directly related with power turbine shows that the new maps has significantly smaller error compared to the LDAPS wind. Analyses of the new maps for the seasonal average, maximum wind speed, and the prevailing wind direction shows that the wind resources over South Korea are most abundant during winter, and that the prevailing wind direction is strongly affected by synoptic weather systems except over mountainous regions. Wind speed generally increases with altitude and the proximity to the coast. In conclusion, the evaluation results show that the new maps provides significantly more accurate wind speeds than the lower resolution NWP model output, especially over complex terrains, coastal areas, and the Jeju island where wind-energy resources are most abundant.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.1
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• pp.103-116
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• 2021

Analysis of runoff is substantial for effective water management in the watershed. Runoff occurs by reaction of a watershed to the rainfall and has non-linearity and uncertainty due to the complex relation of weather and watershed factors. ANN (Artificial Neural Network), which learns from the data, is one of the machine learning technique known as a proper model to interpret non-linear data. The performance of ANN is affected by the ANN's structure, the number of hidden layer nodes, learning rate, and activation function. Especially, the activation function has a role to deliver the information entered and decides the way of making output. Therefore, It is important to apply appropriate activation functions according to the problem to solve. In this paper, ANN models were constructed to estimate runoff with different activation functions and each model was compared and evaluated. Sigmoid, Hyperbolic tangent, ReLU (Rectified Linear Unit), ELU (Exponential Linear Unit) functions were applied to the hidden layer, and Identity, ReLU, Softplus functions applied to the output layer. The statistical parameters including coefficient of determination, NSE (Nash and Sutcliffe Efficiency), NSEln (modified NSE), and PBIAS (Percent BIAS) were utilized to evaluate the ANN models. From the result, applications of Hyperbolic tangent function and ELU function to the hidden layer and Identity function to the output layer show competent performance rather than other functions which demonstrated the function selection in the ANN structure can affect the performance of ANN.

• Journal of Korea Water Resources Association
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• v.54 no.3
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• pp.157-166
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• 2021

Forecasting dam inflow based on high reliability is required for efficient dam operation. In this study, deep learning technique, which is one of the data-driven methods and has been used in many fields of research, was manipulated to predict the dam inflow. The Long Short-Term Memory deep learning with Sequence-to-Sequence model (LSTM-s2s), which provides high performance in predicting time-series data, was applied for forecasting inflow of Soyang River dam. Various statistical metrics or evaluation indicators, including correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and error in peak value (PE), were used to evaluate the predictive performance of the model. The result of this study presented that the LSTM-s2s model showed high accuracy in the prediction of dam inflow and also provided good performance for runoff event based runoff prediction. It was found that the deep learning based approach could be used for efficient dam operation for water resource management during wet and dry seasons.

• Journal of Korean Academy of Nursing
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• v.51 no.4
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• pp.489-504
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• 2021

Purpose: This study was investigated the mediating effect of coping behaviors in the relationship between the second victim experiences after patient safety incidents and the nursing practice changes. Methods: A cross-sectional survey was performed using structured questionnaires. Participants were 218 clinical nurses in general tertiary hospitals in South Korea. Data were collected through an online survey and snowball sampling from August 11 to September 6 2020. Data were analyzed using SPSS 23.0 program. A mediation analysis was performed using multiple regression and a simple mediation model applying the PROCESS macro with 95% bias-corrected bootstrap confidence interval. Results: The mean scores of second victim experiences was 3.41/5. Approach coping (β = .55, p < .001) and the avoidant coping (β = - .23, p = .001) showed mediation effects in the relationship between second victim experiences and constructive change in nursing practice. Avoidant coping (β = .29, p < .001) showed a mediation effect in the relationship between second victim experiences and defensive change in nursing practice. Conclusion: Coping behaviors has a mediating effect on the relationship between second victim experiences and nursing practice changes. To ensure that nurses do not experience second victim, medical institutions should have a culture of patient safety that employs a systematic approach rather than blame individuals. They also need to develop strategies that enhance approach coping and reducing avoidant coping to induce nurses' constructive practice changes in clinical nurses in experiencing second victims due to patient safety incidents.

• Journal of Korean Medicine Rehabilitation
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• v.32 no.3
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• pp.77-84
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• 2022

Objectives This study is designed to identify the effectiveness of herbal medicine for osteoporosis in rheumatoid arthritis. Methods We will investigate 10 databases, 4 Korean databases (KoreaMed, KMBASE, Koreanstudies Information Service System [KISS], ScienceOn) and 6 of abroad (PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure [CNKI], WanFang, Citation Information by NII [CiNii]) without publication date, language limitation for clinical study of herbal medicine for osteoporosis in rheumatoid arthritis. Type, dose, duration, frequency of herb medicine will be analyzed. Results Randomized controlled trials about herbal medicine or herb extract for osteoporosis in rheumatoid arthritis should be included in the study. Cochrane's risk of bias tools will be used to assess quality of the study. Mean differences or standardized mean differences of 95% confidence intervals will calculated and data synthesis will be conducted using Review Manager (RevMan, ver.5.3; The Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen, Denmark). Conclusions It is expected to provide basic data for the active use of herb medicine by systematically synthesizing and analyzing the actual situation, effectiveness, and safety of herb medicine for osteoporosis in rheumatoid arthritis.

• Journal of The Korean Astronomical Society
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• v.55 no.5
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• pp.149-172
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• 2022

We present a new method which constructs an Hɪ super-profile of a galaxy which is based on profile decomposition analysis. The decomposed velocity profiles of an Hɪ data cube with an optimal number of Gaussian components are co-added after being aligned in velocity with respect to their centroid velocities. This is compared to the previous approach where no prior profile decomposition is made for the velocity profiles being stacked. The S/N improved super-profile is useful for deriving the galaxy's global Hɪ properties like velocity dispersion and mass from observations which do not provide sufficient surface brightness sensitivity for the galaxy. As a practical test, we apply our new method to 64 high-resolution Hɪ data cubes of nearby galaxies in the local Universe which are taken from THINGS and LITTLE THINGS. In addition, we also construct two additional Hɪ super-profiles of the sample galaxies using symmetric and all velocity profiles of the cubes whose centroid velocities are determined from Hermite h3 polynomial fitting, respectively. We find that the Hɪ super-profiles constructed using the new method have narrower cores and broader wings in shape than the other two super-profiles. This is mainly due to the effect of either asymmetric velocity profiles' central velocity bias or the removal of asymmetric velocity profiles in the previous methods on the resulting Hɪ super-profiles. We discuss how the shapes (𝜎_n/𝜎_b, A_n/A_b, and A_n/A_tot) of the new Hɪ super-profiles which are measured from a double Gaussian fit are correlated with star formation rates of the sample galaxies and are compared with those of the other two super-profiles.

• Atmosphere
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• v.31 no.5
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• pp.637-656
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• 2021

The United States has been known as the world's major producer of crops such as wheat, corn, and soybeans. Therefore, using meteorological long-term forecast data to project reliable crop yields in the United States is important for planning domestic food policies. The current study is part of an effort to improve the seasonal predictability of regional-scale precipitation across the United States for estimating crop production in the country. For the purpose, a dynamic downscaling method using Weather Research and Forecasting (WRF) model is utilized. The WRF simulation covers the crop-growing period (March to October) during 2000-2020. The initial and lateral boundary conditions of WRF are derived from the Pusan National University Coupled General Circulation Model (PNU CGCM), a participant model of Asia-Pacific Economic Cooperation Climate Center (APCC) Long-Term Multi-Model Ensemble Prediction System. For bias correction of downscaled daily precipitation, empirical quantile mapping (EQM) is applied. The downscaled data set without and with correction are called WRF_UC and WRF_C, respectively. In terms of mean precipitation, the EQM effectively reduces the wet biases over most of the United States and improves the spatial correlation coefficient with observation. The daily precipitation of WRF_C shows the better performance in terms of frequency and extreme precipitation intensity compared to WRF_UC. In addition, WRF_C shows a more reasonable performance in predicting drought frequency according to intensity than WRF_UC.

• Journal of Chest Surgery
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• v.55 no.6
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• pp.442-451
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• 2022

Background: This meta-analysis was conducted to evaluate the effect of fractional flow reserve (FFR) on clinical outcomes after coronary artery bypass grafting (CABG). Methods: Five online databases were searched for studies that (1) enrolled patients who underwent isolated CABG or CABG with aortic valve replacement and (2) demonstrated the effect of an FFR-guided strategy on major adverse cardiac events (MACE) after surgery based on a randomized controlled trial or adjusted analysis. MACE included cardiac death, acute myocardial infarction (MI), and repeated revascularization. The primary outcomes were all MACE outcomes and a composite of all-cause death and MI, and the secondary outcomes were the individual MACE outcomes. Publication bias was assessed using a funnel plot and the Egger test. Results: Six articles (3 randomized and 3 non-randomized studies: n=1,027) were selected. MACE data were extracted from 4 studies. The pooled analyses showed that the risk of MACE was not significantly different between patients who underwent FFR-guided CABG and those who underwent angiography-guided CABG (hazard ratio [HR], 0.80; 95% CI, 0.57-1.12). However, the risk of the composite of death or MI was significantly lower in patients undergoing FFR-guided CABG (HR, 0.62; 95% CI, 0.41-0.94). The individual MACE outcomes were not significantly different between FFR-guided and angiography-guided CABG. Conclusion: FFR-guided CABG might be beneficial in terms of the composite outcome of death or MI compared with angiography-guided CABG although data are limited.

• Journal of Society of Preventive Korean Medicine
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• v.26 no.2
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• pp.11-24
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• 2022

Objectives : This study was designed to investigate the effect of Banhahubak-tang on gastroesophageal reflux disease(GERD) through a systematic review and meta-analysis of randomized controlled trials(RCTs). Methods : RCTs using Banhahubak-tang on GERD was searched in databases such as EMBASE, PubMed, MEDLINE, CENTRAL, CNKI, KISS, RISS, ScienceON, and OASIS. RCTs published up to October 8th, 2021 were included. Meta-analysis was performed by synthesizing outcome data, including Total Effectiveness Rate (TER), Reflux Symptom Index(RSI), Reflux Finding Score(RFS), and Incidence of Adverse Reactions. RevMan 5.4 software was used for data analysis. The Cochrane collaboration bias risk assessment scale was used to evaluate the methodological quality of the included studies. Results : Ten RCTs met the inclusion criteria. The total effective rate was the most commonly used outcome measure. The meta-analysis revealed that the TER in the experimental group was higher than that of the control group(N=2, RR:1.22, 95% CI:1.09 to 1.36, P=0.0004, I²=0%)(N=6, RR:1.22, 95% CI:1.14 to 1.32, P<0.00001, I²=0%)(N=8, RR:1.22, 95% CI: 1.14 to 1.30, P<0.00001, I²=0%). On the other hand, RSI(N=2, MD : -4.29, 95% CI: -4.71 to -3.86, I²=94%), RFS(N=2, MD : -3.28, 95% CI: -3.71 to -2.85, I²=96%), and Incidence of Adverse Reactions(N=5, RR: 0.32, 95% CI: 0.17 to 0.61, I²=0%) in the experimental group were lower than that of the control group. Conclusion : Treatment with Banhahubak-tang was found to be effective on GERD. However the results might be biased because of the poor quality and small sample size of the included RCTs.

• Journal of Korea Port Economic Association
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• v.37 no.3
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• pp.1-17
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• 2021

Forecasting the daily volume of container is important in many aspects of port operation. In this article, we utilized a machine-learning algorithm based on decision tree to predict future container throughput of Busan port. Accurate volume forecasting improves operational efficiency and service levels by reducing costs and shipowner latency. We showed that our method is capable of accurately and reliably predicting container throughput in short-term(days). Forecasting accuracy was improved by more than 22% over time series methods(ARIMA). We also demonstrated that the current method is assumption-free and not prone to human bias. We expect that such method could be useful in a broad range of fields.