본 연구는 국내 주요 수계 중 만경강, 섬진강, 남해권 수계, 탐진강, 영산강, 동진강과 연결된 농수로 어류의 종 조성과 어류 길드 특성, 군집 특성을 분석하였다. 2017년 9월부터 2018년 8월까지 4회에 걸쳐 현지 조사를 시행한 결과, 어류는 농수로에서 6목 11과 53종 3,633개체가 출현하였고 본류에서 9목 15과 57종 7,108개체가 출현했다. 농수로의 우점종과 아우점종은 칼납자루 (Tanakia koreensis)와 납자루 (T. lanceolata)로 나타났으며, 납자루아과의 어종이 우점하는 지점이 10지점 중 6지점으로 나타났다. 이는 담수성 이매패류의 서식에 기인한다고 사료된다. 그리고 환경부 지정 멸종위기 야생생물 I급 임실납자루 (T. somjinensis)가 섬진강 수계의 농수로에서 출현했고, 멸종위기 야생생물 II급 큰줄납자루 (Acheilognathus majusculus)와 꺽저기 (Coreoperca kawamebari)가 각각 섬진강 수계, 섬진강과 탐진강 수계의 농수로에서 출현했다. 지점별로 어류 길드 분석 결과, 대체로 농수로가 본류보다 낮은 민감종의 비율을 나타냈다. 군집 분석 결과 하상 구조가 복잡하게 나타난 지점에서 비교적 안정적인 군집을 보였으며, 환경적 변수에 따른 분석 결과 하상 구조가 복잡한 농수로 그룹이 높은 다양도 지수와 낮은 우점도 지수를 나타냈다. 결과적으로 본 연구는 농수로의 어류 서식 특성을 나타내고, 향후 연구와 관리의 필요성을 제시했다.

In this study, real-time monitoring of air quality using a real-time mobile monitoring system was conducted to identify the emission characteristics and current status of air pollutants and odorous substances that are mainly generated in domestic dyeing industrial areas and to trace the pollutant sources. The concentration of toluene in the industrial area was detected up to 926.4 ppb, which was 3 to 4 times higher than that of other industrial areas. The concentration of methylethylketone was 124.7 ppb and the concentration of dichloromethane was 129.5 ppb. Acrolein concentration was highest at E point at 521.6 ppb, methanol concentration was highest at D point at 208.8 ppb, and acetone concentration was highest at M and N points at 549.3 ppb. The most frequently detected concentration of pollutants in the air quality monitoring results in the industrial area was, in descending order, toluene > methanol > acrolein > dichloromethane > acetone, which was similar to the chemical emissions used in the industrial area by the Pollutant Release and Transfer Register data. The concentration of odorous substances measured in real time was compared with the concentration of minimum detection, and the concentration of hydrogen sulfide was about 10 times higher than the concentration of minimum detection at A point, which was judged to be the main odorous cause of A point. In the future, if the real-time mobile measurement system is constructed to automatically connect wind direction/wind speed, PRTR (Pollutant Release and Transfer Register) data and SEMS (Stack Emission Management System) data, etc., it was judged that more accurate monitoring could be performed.

Reliability analysis of the components frequently starts with the data that manufacturer provides. If enough failure data are collected from the field operations, the reliability should be recomputed and updated on the basis of the field failure data. However, when the failure time record for a component contains only a few observations, all statistical methodologies are limited. In this case, where the failure records for multiple number of identical components are available, a valid alternative is combining all the data from each component into one data set with enough sample size and utilizing the useful information in the censored data. The ROK Navy has been operating multiple Patrol Killer Guided missiles (PKGs) for several years. The Korea Multi-Function Control Console (KMFCC) is one of key components in PKG combat system. The maintenance record for the KMFCC contains less than ten failure observations and a censored datum. This paper proposes a Bayesian approach with a Dirichlet mixture model to estimate failure time density for KMFCC. Trends test for each component record indicated that null hypothesis, that failure occurrence is renewal process, is not rejected. Since the KMFCCs have been functioning under different operating environment, the failure time distribution may be a composition of a number of unknown distributions, i.e. a mixture distribution, rather than a single distribution. The Dirichlet mixture model was coded as probabilistic programming in Python using PyMC3. Then Markov Chain Monte Carlo (MCMC) sampling technique employed in PyMC3 probabilistically estimated the parameters’ posterior distribution through the Dirichlet mixture model. The simulation results revealed that the mixture models provide superior fits to the combined data set over single models.

In this study, we investigate the relationship between the peeling behavior of the backsheet of a photovoltaic(PV) module and its surface temperature in order facilitate removal of the backsheet from the PV module. At low temperatures, the backsheet does not peel off whereas, at high temperatures, part of the backsheet remains on the surface of the PV module after the peeling process. The backsheet material remaining on the surface of the PV module is confirmed by X-ray diffraction(XRD) analysis to be poly-ethylene(PE). Differential scanning calorimetry(DSC) is also performed to investigate the interfacial characteristics of the layers of the PV module. In particular, DSC provides the melting temperature(Tm) of laminated ethylene vinyl acetate(EVA) and of the backsheet on the PV module. It is found that the backsheet does not peel off below the Tm of ethylene of EVA, while the PE layer of the backsheet remains on the surface of the PV module above the Tm of the PE. Thus, the backsheet is best removed at a temperature between the Tm of ethylene and that of PE layer.