Extreme Storm Surge Return Period Prediction Using Tidal Gauge Data and Estimation of Damage to Structures from Storm-Induced Wind Speed in South Korea

Global warming, which is one of the most serious consequence of climate change, can be expected to have different effects on the atmosphere, the ocean, icebergs, etc. Global warming has also brought secondary consequences into nature and human society directly. The most negative effect among the several effects of global warming is the rising sea level related to the large typhoons which can cause flooding on low-level land, coastal invasion, sea water flow into rivers and underground water, rising river level, and fluctuation of sea tides. It is crucial to recognize surge level and its return period more accurately to prevent loss of human life and property damage caused by typhoons. This study researches two topics. The first purpose of this study is to develop a statistical model to predict the return period of the storm surge water related to typhoon Maemi, 2003 in South Korea. To estimate the return period of the typhoon, clustered separated peaks-over-threshold simulation (CSPS) has been used and Weibull distribution is used for the peak storm surge height’s fitting. The estimated return period of typhoon Maemi’s peak total water level is 389.11 years (95% confidence interval 342.27 - 476.2 years). The second aim is related to the fragility curves with the loss data caused by typhoons. Although previous studies have developed various methods to mitigate damages from typhoons, the extent of financial loss has not been investigated enough. In this research, an insurance company provides their loss data caused by the wind speed of typhoon Maemi in 2003. The loss data is very important in evaluating the extent of the damages. In this study, the damage ratio in the loss dataset has been used as the main indicator to investigate the extent of the damages. The damage ratio is calculated by dividing the direct loss by the insured amount. In addition, this study investigates the fragility curves of properties to estimate the damage from typhoon Maemi in 2003. The damage ratios and storm induced wind speeds are used as the main factor for constructing fragility curves to predict the levels of damage of the properties. The geographical information system (GIS) has been applied to produce properties’ spatial wind speeds from the typhoon. With the damage ratios, wind speeds and GIS spatial data, this study constructs the fragility curves with four different damage levels (Level I - Level IV). The findings and results of this study can be basic new references for governments, the engineering industry, and the insurance industry to develop new polices and strategies to cope with climate change

Efficacy and Safety of Human Placental Extract Solution on Fatigue: A Double-Blind, Randomized, Placebo-Controlled Study

Introduction. Fatigue is a common symptom, but only a few effective treatments are available. This study was conducted to assess the efficacy and safety of the human placental extract solution, which has been known to have a fatigue recovery effect. Methods. A total of 315 subjects were randomly assigned to three groups: group 1 (with Unicenta solution administration), group 2 (with exclusively human placental extract administration, excluding other ingredients from the Unicenta solution), and the placebo group. Subsequently, solutions were administered for four weeks. Results. The fatigue recovery rate was 71.00% in group 1, 71.72% in group 2, and 44.21% in the placebo group, which show statistically significant differences between the group 1 and the placebo group (P value = 0.0002), and between group 2 and the placebo group (P value = 0.0001). Conclusion. The human placental extract solution was effective in the improvement of fatigue

Effect of Apneic Oxygenation via Nasal Prong on Gas Exchange and Vital Signs during Fiberoptic Intubation under General Anesthesia

A clinical study for the evaluation of the effect of apneic oxygenation by nasal prong during fiberoptic orotracheal intubation on gas exchange and vital signs has been done on the patients who received tympanomastoidectomy (ASA c1assfication 1 and 2, aged from 20 to 40). Among them, 22 patients were selected whose fiberoptic intubation lasted more than 3 but less than 4 minutes, to observe the changes of Pa02, PaC02, HR, and MAP. 11 patients who underwent fiberoptic orotracheal intubation in apneic state without oxygen administration (Group I) showed similar increases in vital signs to the other 11 patients who received apneic oxygenation (Group II). PaC02 increased more in Group I than in Group II, which was not statistically significant. The differences of Pa02 at 1 and 2 minutes between two groups after removal of oxygen mask and beginning of fiberoptic intubation, were not statistically significant but Group II showed a significantly lesser degree of decrease in Pa02 at 3 minutes. We might say that apneic oxygenation during fiberoptic intubation under general anesthesia is useful because it could delay the onset of hypoxia, thereby provide extra time for intubation. Therefore we could attempt intubation up to 3 minutes on the fully relaxed patient, if we give oxygen via nasal prong

A study of deep learning algorithm usage in predicting building loss ratio due to typhoons: the case of southern part of the Korean Peninsula

The goal of this study is to suggest an approach to predict building loss due to typhoons using a deep learning algorithm. Due to the influence of climate change, the frequency and severity of typhoons gradually increase and cause exponential destruction of building. Therefore, related industries and the government are focusing their efforts on research and model development to quantify precisely the damage caused by typhoons. However, advancement in the accuracy of prediction is still needed, and the introduction of new technology, obtained due to the fourth revolution, is necessary. Therefore, this study proposed a framework for developing a model based on a deep neural network (DNN) algorithm for predicting losses to buildings caused by typhoons. The developed DNN model was tested and verified by calculating mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2). In addition, to further verify the robustness of the model, the applicability of the framework proposed in this study was verified through comparative verification with the conventional multi-regression model. The results and framework of this study will contribute to the present understanding by suggesting a deep learning method to predict the loss of buildings due to typhoons. It will also provide management strategies to related workers such as insurance companies and facility managers