강으로부터 해양으로 유입되는 퇴적물은 유역분지의 지질, 지리, 지형, 기후 등의 영향을 반영하며, 육상 유역분 지에서 퇴적물 생성 과정에서 수반되는 화학적 풍화는 대기 중 이산화탄소 농도를 조절하는 데에 중요한 역할을 하는 것으로 알려져 있다. 본 연구에서는 한반도 남해안 낙동강 하구 인근 해저 표층 퇴적물의 지화학 조성을 이용하여 화 학적 풍화의 강도와 퇴적물의 기원지에 분포하는 암석의 조성을 추정하였다. 연구 시료의 화학적 풍화 강도는 중간에 낮은 정도(평균 Chemical Index of Alteration=68)이며 A-CN-K 도표에서 추정한 풍화의 추세는 퇴적물의 기원지 성분 이 평균적인 상부 대륙지각과 매우 유사한 것으로 보인다. 이는 낙동강 유역분지에 분포하는 중생대 화강암류와 경상누 층군 퇴적암이 혼합된 성분을 반영하는 것으로 판단되기 때문에 연구 대상인 퇴적물이 낙동강 하구로부터 유입되는 퇴 적물의 성분을 대표하는 것으로 해석한다. 표층 퇴적물의 희토류원소는 분화된 경희토류-중희토류의 비와 음의 Eu 이상 을 나타내어 상부 대륙지각과 매우 유사하다. 본 연구의 결과를 전 세계 강 하구 퇴적물 자료 및 국내의 큰 강 자료와 비교하였으며, 이를 통하여 지질 및 지형의 잠재적인 영향을 고려할 수 있다.

목적 : 적외선 차단렌즈의 근적외선 차단율과 시감투과율을 제조방식 및 근적외선 차단제 첨가 여부에 따라 조 사하고, 근적외선 차단렌즈의 개발 방향에 대하고 논하였다. 방법 : 시중에서 유통되고 있는 국내외 브랜드의 근적외선 차단렌즈 20종(C-type 10종, M-type 5종, TMtype 5종)을 대상으로 가시광선에서 근적외선에 이르는 380~1,400 nm 영역에서 5 nm 간격으로 투과율을 측정한 후, KS B ISO 13666 표준에 따라 근적외선 차단율과 시감투과율 구하였다. 결과 : 근적외선 차단율은 TM—type이 평균 62.3%로 가장 우수하였지만, 착색렌즈에 미러코팅을 한 관계로 시 감투과율은 평균 20.2%로 매우 낮았다. M—type의 경우는 근적외선 차단제의 첨가로 인한 근적외선 차단효과가 명확하게 나타나기는 했지만, 근적외선 차단제를 첨가하지 않은 C-type의 렌즈의 근적외선 차단율보다 그 성능이 떨어졌고, 시감투과율 또한 낮았다. 근적외선 차단제가 첨가되지 않은 C-type의 렌즈들에서 근적외선 차단율은 우 수하였을 뿐만 아니라, 시감투과율 또한 무반사 렌즈 수준으로 높게 나타났다. 결론 : 최적화된 코팅설계가 적용되면 AR 렌즈 수준의 높은 시감투과율을 보이면서 TM-type의 근적외선 차단 율을 능가하는 우수한 근적외선 차단렌즈를 개발할 수 있다는 점에서 근적외선 차단렌즈의 설계 방향은 C-type이 가장 효과적이라 할 수 있다.

There are growing concerns that the recently implemented Earthquake Early Warning service is overestimating the rapidly provided earthquake magnitudes (M). As a result, the predicted damages unnecessarily activate earthquake protection systems for critical facilities and lifeline infrastructures that are far away. This study is conducted to improve the estimation accuracy of M by incorporating the observed S-wave seismograms in the near source region after removing the site effects of the seismograms in real time by filtering in the time domain. The ensemble of horizontal S-wave spectra from at least five seismograms without site effects is calculated and normalized to a hypocentric target distance (21.54 km) by using the distance attenuation model of Q(f)=348f0.52 and a cross-over distance of 50 km. The natural logarithmic mean of the S-wave ensemble spectra is then fitted to Brune’s source spectrum to obtain the best estimates for M and stress drop (SD) with the fitting weight of 1/standard deviation. The proposed methodology was tested on the 18 recent inland earthquakes in South Korea, and the condition of at least five records for the near-source region is sufficiently fulfilled at an epicentral distance of 30 km. The natural logarithmic standard deviation of the observed S-wave spectra of the ensemble was calculated to be 0.53 using records near the source for 1~10 Hz, compared to 0.42 using whole records. The result shows that the root-mean-square error of M and ln(SD) is approximately 0.17 and 0.6, respectively. This accuracy can provide a confidence interval of 0.4~2.3 of Peak Ground Acceleration values in the distant range.

Bee traffic at the hive entrance can be used as an important indicator of foraging activity. We investigated patterns of honeybees and bumblebees near their hives as a basis for calculating bee traffic using the image deep learning. The flight pattern near the hive differed significantly according to bee at entering and leaving the hive. Honeybees mainly showed flight that changed flight direction more than once (69.5%), whereas bumblebees mainly performed straight flight (48.7%) or had a single turn (36.5%) in flight. When bees entered the hive, honeybees primarily showed one-turn or two-turn flight patterns(88.5%), and bumblebees showed a one-turn flight pattern (48.0%). In contrast, when leaving the hive, honeybees primarily showed a straight flight pattern (63.0%), and bumblebees primarily showed a straight or one-turn pattern (90.5%). There was a significant difference in flight speed according to the flight pattern. The speed of straight flight (0.89±0.47 m/s) was 1.5 to 2.1 times faster than flight where direction changed. Therefore, our results can help determine the capturing and recognizing the flying image of bees when calculating bee traffic by image deep learning.

The large copper butterfly Lycaena dispar (Haworth, 1803; Lepidoptera: Lycaenidae) has been categorized as a near-threatened species (NT) in South Korea from 2012 mainly due to limited distribution. In this study, we visited 36 sites spread across all South Korean provinces to verify the distributional range of the species and sequenced mitochondrial COI for 53 individuals from nine sites. We observed L. dispar at 15 sites in six provinces, including the two previously known provinces, indicating a southward range expansion. The in-field monitoring and genetic data collectively suggested that L. dispar does not have a limited distribution nor is it isolated, indicating that it should be reclassified as less vulnerable. Our study demonstrates that the combination of field and genetic data can provide a more reliable assessment of the stability of a species.

As climate change and population growth raise the likelihood of natural disasters, it becomes crucial to comprehend and mitigate these risks in vital infrastructure systems, especially nuclear power plants (NPPs). This research addresses the necessity for evaluating multiple hazards by concentrating on slope failures triggered by earthquakes near NPPs over a timeframe extending up to a return period of 100,000 years. Utilizing a Geographical Information System (GIS) and Monte Carlo Simulation (MCS), the research conducts a comprehensive fragility assessment to predict failure probability under varying ground-shaking conditions. According to the Newmark displacement method, factors such as Peak Ground Acceleration (PGA), slope angle, soil properties, and saturation ratio play significant roles in determining slope safety outcomes. The investigation aims to enhance understanding seismic event repercussions on NPP-adjacent landscapes, providing insights into long-term dynamics and associated risks. Results indicate an increase in slope vulnerability with longer return periods, with distinct instances of slope failures at specific return periods. This analysis not only highlights immediate seismic impacts but also underscores the escalating risk of slope displacement across the extended return period scales, crucial for evaluating long-term stability and associated hazards near nuclear infrastructure.

목적 : 현재 주목받고 있는 스마트 글라스와 같은 증강현실 기술은 배터리, 발열, 착용감 문제 등으로 사용자의 불편을 야기시킨다. 본 연구는 이러한 문제들을 해결하고 사용자 편의성을 극대화할 수 있는 새로운 NED 광학계 를 설계하고자 한다. 방법 : Waveguide 타입과 비구면 구성을 적용한 허상광학계로 NED 광학계를 설계하였다. Code V 소프트웨어 를 사용하였고, 경량화, 높은 시야각, 낮은 왜곡 수차 및 우수한 이미지 품질을 설계 목표로 하여 비순차 광학계를 구성하였다. 결과 : 설계된 광학계는 배율 증가, 포커싱, 수차 감소에 초점을 맞춘 첫 번째 파트와 빛의 손실을 최소화하는 Waveguide 타입의 두 번째 파트로 구성하여 고효율성과 높은 해상도를 나타내었다. 사용자에게 선명하고 편안한 시각적 경험을 보장하고, 컴팩트한 디자인으로 휴대성과 사용자 편의성이 동시에 고려된 광학계를 설계하였다. 결론 : 본 연구에서 개발된 Waveguide 타입과 비구면 광학계는 빛의 손실을 최소화하여 망막까지 효율적으로 전달함으로써 NED 기술의 성능을 크게 향상시켰다. 이는 사용자에게 더 높은 선명도와 생동감 있는 시각적 경험 을 제공하며, 향후 고해상도 광학 응용 분야에 큰 기여를 할 것으로 판단된다.

To ensure the safety of disposal facilities for radioactive waste, it is essential to quantitatively evaluate the performance of the waste disposal facilities by using safety assessment models. This paper addresses the development of the safety assessment model for the underground silo of Wolseong Low-and Immediate-Level Waste (LILW) disposal facility in Korea. As the simulated result, the nuclides diffused from the waste were kept inside the silo without the leakage of those while the integrity of the concrete is maintained. After the degradation of concrete, radionuclides migrate in the same direction as the groundwater flow by mainly advection mechanism. The release of radionuclides has a positive linear relationship with a half-life in the range of medium half-life. Additionally, the solidified waste form delays and reduces the migration of radionuclides through the interaction between the nuclides and the solidified medium. Herein, the phenomenon of this delay was implemented with the mass transfer coefficient of the flux node at numerical modeling. The solidification effects, which are delaying and reducing the leakage of nuclides, were maintained the integrity of the nuclides. This effect was decreased by increasing the half-life and the mass transfer coefficient of radionuclides.

Over the years, in the field of safety assessment of geological disposal system, system-level models have been widely employed, primarily due to considerations of computational efficiency and convenience. However, system-level models have their limitations when it comes to phenomenologically simulating the complex processes occurring within disposal systems, particularly when attempting to account for the coupled processes in the near-field. Therefore, this study investigates a machine learning-based methodology for incorporating phenomenological insights into system-level safety assessment models without compromising computational efficiency. The machine learning application targeted the calculation of waste degradation rates and the estimation of radionuclide flux around the deposition holes. To develop machine learning models for both degradation rates and radionuclide flux, key influencing factors or input parameters need to be identified. Subsequently, process models capable of computing degradation rates and radionuclide flux will be established. To facilitate the generation of machine learning data encompassing a wide range of input parameter combinations, Latin-hypercube sampling will be applied. Based on the predefined scenarios and input parameters, the machine learning models will generate time-series data for the degradation rates and radionuclide flux. The time-series data can subsequently be applied to the system-level safety assessment model as a time table format. The methodology presented in this study is expected to contribute to the enhancement of system-level safety assessment models when applied.

Long-term climate and surface environment changes can influence the geological subsurface environment evolution. In this context, a fluid flow pathway developing and connection possibility can be increased between the near-surface zone and deep depth underground. Thus, it is necessary to identify and prepare for the overall fluid flow at the entire geological system to minimize uncertainty on the spent nuclear fuel (SNF) disposal safety. The fluid flow outside the subsurface environment is initially penetrated through the surface and then the unsaturated area. Thus, the previously proved reports, POSIVA in Finland, suggested that sequential research about the fluid infiltration experiment (INEX) and the investigation is necessary. Characterizing the unsaturated zone can help predict changes and ensure the safety of SNFs according to geological long-term evolution. For example, the INEX test was conducted at the upper part of ONKALO, about 50 to 100 m depth, to understand the geochemical evolution of the groundwater through the unsaturated zone, to evaluate the main flow of groundwater that can approach the SNF disposal reservoir, and to estimate the decreasing progress of the buffering capacity along the pathway through the deep geological disposal. In the present study, a preliminary test was performed in the UNsaturated-zone In-situ Test (UNIT) facility near the KAERI underground research tunnel to design and establish a methodology for infiltration experiments consistent with the regional characteristics. The results represented the methodological application is possible for characterizing unsaturated-zone to perform infiltration experiments. The scale of the experiment will be expanded sequentially, and continuous research will be conducted for the next application.