In mega cities such as Seoul in South Korea, it is very important to protect the cities from surface flooding even for a short time period due to the enormous economic damage. That is why stormwater pipe networks are commonly applied to mega cities with large impervious areas to drain runoff from the city. Therefore, the stormwater pipe networks in urban catchments should be carefully designed for quick and efficient runoff removal. In this study, the structures of different stormwater pipe networks were evaluated based on the relationship between peak rainfall and runoff in the urban catchments in South Korea. More than 400 historical rainfall events from five urban catchments were used to develop respective linear regression models for estimating peak runoff for different pipe network structures. The developed regression models exhibited greater than 0.9 in determination coefficients and demonstrated overall the broader ranges in peak runoff with the greater rainfall amount, especially when the pipe networks were branched. This implies that the effect of pipe network structures on runoff is more profound in the branched networks whose runoff water flow is one-directional and thus tends to concentrate to the catchment outlet. In the case of the looped networks in which runoff paths are multiple, rainfall runoff can be routed to several alternative water paths depending on rainfall events resulting in the reduced peak runoff. The structures of pipe networks can be measured in drainage density which is defined as the ratio of total pipe length to catchment area. As a result, the range of the estimated runoff at the 95% confidence level increased as the drainage density increased, which implies increased uncertainty with the looped networks which commonly involve more pipe installation for unit area as compared to the branched. However, the looped networks with multiple water paths can reduce the time to drain rainfall from the catchments and thus the 95% confidence interval becomes narrow, which means greater reliability in peak runoff estimation. It would therefore be favorable to adopt looped stormwater pipe networks within an affordable budget and the complexity of pipe networks needs to be counted to reduce urban flood risk.

Recently, chemotherapy and radiotherapy are known to directly affect some immunosuppressive barriers within a tumor microenviroment. We used cyclophosphamide (CTX), which is known to enhance the immune response by suppressing CD4+CD25+ regulatory T cells (Treg cells) when used at a low dose, as a chemotherapeutic agent to provide a synergic effect in the irradiation and dendritic cells (DC) combination therapy. Some previous studies observed that a single-dose CTX treatment significantly reduced the number of Treg cells in 3-5 days, however, the reduced Treg cells increased rapidly after 5 days. To overcome the disadvantages of a single-dose CTX, we used 30 mg/kg dose of CTX, which was treated intraperitoneally to mice 3 days before every immature DC (iDC) injection (known as "metronomic schedule CTX"). Irradiation was applied at a dose of 10 Gy to the tumor on the right thigh by a linear accelerator. Then, iDC was intratumorally injected into the irradiated tumor site. Growth of a distant tumor on the right and left flank was suppressed by an injection of iDC into the irradiated tumor, and this effect was increased by the metronomic schedule CTX. Also, combinations treated with the metronomic schedule CTX and ionizing radiation (IR)/iDC, showed the longest survival time compared with other groups. This antitumor immune response of IR/iDC was improved by metronomic schedule CTX and this result was associated with decreasing the proportion of CD4+CD25+ Treg cells and increasing the number of tumor-specific interferon-γ-secreting T cells. Our results demonstrated that metronomic schedule CTX improves the antitumor effect of immunization with an injection of DC s into the irradiated tumor.

As infrastructure and populations are highly condensed in megacities, urban flood management has become a significant issue because of the potentially severe loss of lives and properties. In the megacities, rainfall from the catchment must be discharged throughout the stormwater pipe networks of which the travel time is less than one hour because of the high impervious rate. For a more accurate calculation of runoff from the urban catchment, hourly or even sub-hourly (minute) rainfall data must be applied. However, the available data often fail to meet the hydrologic system requirements. Many studies have been conducted to disaggregate time-series data while preserving distributional statistics from observed data. The K-nearest neighbor resampling (KNNR) method is a useful application of the nonparametric disaggregation technique. However, it is not easy to apply in the disaggregation of daily rainfall data into hourly while preserving statistical properties and boundary continuity. Therefore, in this study, three-day rainfall patterns were proposed to improve reproducible ability of statistics. Disaggregated rainfall was resampled only from a group having the same three-day rainfall patterns. To show the applicability of the proposed disaggregation method, probability distribution and L-moment statistics were compared. The proposed KNNR method with three-day rainfall patterns reproduced better the characteristics of rainfall event such as event duration, inter-event time, and toral amount of rainfall event. To calculate runoff from urban catchment, rainfall event is more important than hourly rainfall depth itself. Therefore, the proposed stochastic disaggregation method is useful to hydrologic analysis, particularly in rainfall disaggregation.

As the heavy snow storm occurrence increases due to the climate change, the demage caused by snowstorm also increases. Therefore, in this study, RCP climate change scenario 4.5 and 8.5 are applied for the frequency analysis of future probable fresh snow days and probable maximum fresh snow depth. Artificial Neural Network (ANN) models are constructed for the frequency analysis. Data in the input layer of ANN are the minimum, maximum, and average temperature and precipitation data for the simulation of the fresh snow days. Another ANN model has also the minimum, maximum, and average temperature and precipitation data for the simulation of the maximum fresh snow depth. Learning of ANN model used two types of data. The first uses total 74 gauging stations data and the second model uses each gauging station data separately. The model efficiency of the first model is higher than the second. Quantile mapping is applied to remove the discrepancy of the climate change data and to generate the outlier data from the model. As the result, probable fresh snow days and probable maximum fresh snow depth tend to decrease over the entire Korean Peninsula. Decreasing tendency in Kangwon province is noticeable. In Kyungsan province, maximum fresh snow depth decreases but fresh snow days increases.

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