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A heat wave countermeasure period was selected and operated to minimize the damage from heat waves in Korea. However, to this date, there are no clear ideas and methodology for selecting a heat wave countermeasure period, resulting in continuous budget expenditure incurred for its preparation and response activities. This requires an efficient selection of countermeasure periods. This study analyzed the time series data trend of heat index using Mann-Kendall and Pettitt test for cities and counties in Gyeongsangbuk-do, and it confirmed the current characteristics of increasing heat waves compared to- those in the past. In addition, K-means cluster analysis was applied to derive the city-wise heat wave characteristics. The cities and counties were classified into three based on the heatwave characteristics and starting point and ending point of each heat wave characteristic representing the years 2004 to 2021 between July 21 to August 11, July 16 to August 15, and July 11 to August 19. It was also confirmed that there was a significant difference from the heat wave countermeasure period (May 20~Sep 30) currently selected. Hence, in the future, economic losses may be minimized in terms of disaster preparedness and response through the selection of the heatwave countermeasure period reflecting temporal and spatial characteristics.

Recently, deep neural networks have greatly improved autonomous driving. However, as a great deal of training data is required, most studies have employed simulators. Generalization of such driving is key in terms of safety. The simulated environments feature only small variations in favorable conditions and thus cannot be used for benchmarking. Therefore, we developed a new open-source (OpenAI Gym-like) off-road environment featuring differently structured forests, plateaus, deserts, and snowfields. The dynamic topographical structures make the off-road environment a very challenging generalization problem. Our off-road environment can precisely evaluate autonomous driving in terms of generalization. Additionally, we proposed an evaluation method based on the success rate of driving tasks, enabling effective driving ability measurement. Furthermore, we evaluate the performance of existing end-to-end driving methods in our off-road environment. The results show that the end-to-end driving methods lack generalization ability and fail to generalize to unseen environments. Our off-road environment can help autonomous driving researchers develop a better, generalizable driving system. Unreal engine-level assets and codes are available at https://github.com/lssac7778/Off-road-Benchmark . We briefly introduce our model in https://www.youtube.com/watch?v=SERSv0TFUwQ&t=44s .

Drought is a slow-onset hazard affecting ecosystems and human society. Although it is difficult to assess the uncertainty associated with drought, it is very important to identify the severity of drought. Using a dynamic naive Bayesian classifier (DNBC), this study combined the strengths of three conventional drought indices, the Standardized Precipitation Index (SPI), the Evaporative Stress Index (ESI), and the Vegetation Health Index (VHI), and developed a DNBC-based drought index (DNBC-DI) to identify overall drought conditions. After comparing recent actual drought events with the drought indices, the drought severity was classified into five states using them: severe wet, moderate wet, normal, moderate drought, and severe drought. We evaluated the performance of the DNBC-DI for representing actual hydrological droughts that occurred since 2000. In this study, the actual hydrological drought was represented by the Streamflow Drought Index (SDI). Our results indicated that the accuracy of the DNBC-DI was 60%, which was higher than SPI (40%), ESI (40%), and VHI (0.41%). Even though in practice, the evaluation of drought is highly dependent on the drought index, this study tried to develop a practical drought index that can be used for comprehensive drought assessment.

Korea has made various efforts to reduce drought damage; however, socio-economic damage has increased in recent years due to climate change, which has led to increasing frequency and intensity of drought. In South Korea, because precipitation is concentrated in the summer, drought damage will be significant in the event of failure of water resources management. Seasonal and regional imbalances in precipitation have contributed to recent extreme droughts in South Korea. In addition, population growth and urbanization have led to increased water use and contributed to water shortage. Drought risk analysis must address multiple contributing factors and comprehensively assess the effects or occurrence of future droughts, which are essential for planning drought mitigation to cope with actual droughts. Drought mitigation depends on the water supply capacity during dry spells. In this study, a dynamic naive Bayesian classifier-based multiple drought index (DNBC-MDI) was developed by combining the strengths of conventional drought indices and water supply capacity. The DNBC-MDI was applied to a bivariate drought frequency analysis to evaluate hydrologic risk of extreme droughts. In addition, future changes of the risk were investigated according to climate change scenarios. As a result, the drought risk had a decreasing trend from the historic period of 1974–2016 to the future period of 2017–2070, then had an increasing trend in the period of 2071–2099.

This paper presents a novel method to prevent blockages by embolic microspheres in catheter channels by using convex air bubbles attached to the channels&rsquo, inner wall surface. The clogging by microspheres can occur by the arching of the microspheres in the catheter. A few studies have been done on reducing the blockage, but their methods are not suitable for use with embolic catheters. In this study, straight catheter channels were fabricated. They had cavities to form convex air bubbles, additionally, a straight channel without the cavities was designed for comparison. Blockage was observed in the straight channel without the cavities, and the blockage arching angle was measured to be 70&deg, while no blockage occurred in the cavity channel with air bubbles, even at a geometrical arching angle of 85&deg, The convex air bubbles have an important role in preventing blockages by microspheres. The slip effect on the air bubble surface and the centrifugal effect make the microspheres drift away from the channel wall. It was observed that as the size of the cavity was increased, the drift distance became larger. Additionally, as more convex air bubbles were formed, the amount of early drift to the center increased. It will be advantageous to design a catheter with large cavities that have a small interval between them.

As the environment changes, the stationarity assumption in hydrological analysis has become questionable. If nonstationarity of an observed time series is not fully considered when handling climate change scenarios, the outcomes of statistical analyses would be invalid in practice. This study established bivariate time-varying copula models for risk analysis based on the generalized additive models in location, scale, and shape (GAMLSS) theory to develop the nonstationary joint drought management index (JDMI). Two kinds of daily streamflow data from the Soyang River basin were used, one is that observed during 1976&ndash, 2005, and the other is that simulated for the period 2011&ndash, 2099 from 26 climate change scenarios. The JDMI quantified the multi-index of reliability and vulnerability of hydrological drought, both of which cause damage to the hydrosystem. Hydrological drought was defined as the low-flow events that occur when streamflow is equal to or less than Q80 calculated from observed data, allowing future drought risk to be assessed and compared with the past. Then, reliability and vulnerability were estimated based on the duration and magnitude of the events, respectively. As a result, the JDMI provided the expected duration and magnitude quantities of drought or water deficit.

This paper presents a fabrication method of hollow polymeric nanocapsules (HPNCs). The HPNCs were examined to reduce light trapping in an organic light emitting diodes (OLED) device by increasing the refractive index contrast. They were continuously fabricated by the sequential process of self-assembly and photo-crosslinking of an amphiphilic block copolymer of SBR-b-PEGMA, poly(styrene-r-butadiene)-b-poly(poly(ethylene glycol) methyl ether methacrylate) in a flow-focusing microfluidic device. After the photo-crosslinking process, the produced HPNCs have a higher resistance to water and organic solvents, which is applicable to the fabrication process of optical devices. The morphology and hollow structure of the produced nanocapsules were determined by transmission electron microscopy (TEM), scanning electron microscopy (SEM), and atomic force microscopy (AFM). Also, their size control was examined by varying the ratio of inlet flow rates and the morphological difference was studied by changing the polymer concentration. The size was measured by dynamic light scattering (DLS). The refractive index of the layer with and without the HPNCs was measured, and a lower refractive index was obtained in the HPNCs-dispersed layer. In future work, the light extraction efficiency of the HPNCs-dispersed OLED will be examined.

This paper challenges the production of the protein nanoparticles using the conjugation of Ni2+ complexed nitrilotriacetic acid end-functionalized polystyrene (Ni-NTA-PS) and histidine tagged GFP (His-GFP) hybrid. The microfluidic synthesis of the protein nanoparticle with the advantages of a uniform size, a fast reaction, and a precise control of preparation conditions is examined. The self-assembly occurs on the interfacial surface of the multi-laminated laminar flow stably formed in the microchannel. The clogging of the produced protein nanoparticles on the channel surface is solved by adding a retarding inlet channel. The size and shape of the produced protein nanoparticles are measured by the analysis of transmission electron microscopy (TEM) and scanning electron microscope (SEM) images, and the attachment of the protein is visualized with a green fluorescent image. Future research includes the encapsulation of vaccines and the coating of antigens on the protein surface.

Recently, 3-dimensional in vitro cell cultures have gained much attention in biomedical sciences because of the closer relevance between in vitro cell cultures and in vivo environments. This paper presents a microfluidic perfusion 3D cell culture system with consistent control of long-term culture conditions to mimic an in vivo microenvironment. It consists of two sudden expansion reservoirs to trap incoming air bubbles, gradient generators to provide a linear concentration, and microchannel mixers. Specifically, the air bubbles disturb a flow in the microfluidic channel resulting in the instability of the perfusion cell culture conditions. For long-term stable operation, the sudden expansion reservoir is designed to trap air bubbles by using buoyancy before they enter the culture system. The performance of the developed microfluidic perfusion 3D cell culture system was examined experimentally and compared with analytical results. Finally, it was applied to test the cytotoxicity of cells infected with Ewing's sarcoma. Cell death was observed for different concentrations of H2O2. For future work, the developed microfluidic perfusion 3D cell culture system can be used to examine the behavior of cells treated with various drugs and concentrations for high-throughput drug screening.

It is essential to predict drought properties precisely using robust drought measures representing anomaly of all possible drought-driving factors. This study quantified the comprehensive multi-variables based future drought projection, which is lacking in precedent studies carried out for South Korea. The analyses were based on historical (1985-2014) and future (2015-2100) climatic data generated from regional climate model (HadGEM-3R) under the Representative Concentration Pathway (RCP 8.5) scenario. The overall results indicated that winter season showed more signposts of severe drought followed by spring. The temporal analyses resulted an increase in numbers of drought events from the historical segment (1985-2014) to first segment (2015-2040) of future followed by a slightly decrease from the first segment to the second segment (2041-2070) and then again increase afterward. Furthermore, it was found that most of the projected annual droughts would occur in 2035 to 2060, and 2078 to 2100.