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Neonicotinoid계 토양처리제의 흡수이행에 따른 엽상잔류독성과 생물학적 지표를 이용한 꿀벌 영향 평가
Honeybee toxicity test was conducted to evaluate a toxic effects of residues on foliage after planting hole application of systemic insecticide, neonicotinoids, with a high toxicity to honeybee(Apis mellifera) . Also, biological effects of the pesticide were investigated in honeybee exposed to the low concentration by measuring the Acetylcholinesterase(AChE) activity and DNA damage. In this study, the three neonicotinoid pesticides(dinotefuran GR, imidacloprid GR and clothianidin GR) were applied in the planting hole at transplanting of tomato, pepper and cucumber. The residue analysis of pesticides and the honeybee mortalities on foliage were investigated with time course. Although some differences in kinds of tested crops and pesticides, the honeybee mortality and the residual change on foliage showed the much similarity. It was confirmed that neonicotinoid insecticides applied to soil can be affect honeybee mortality on foliage by absorption and translocation through the roots without any direct exposure. The biological effect of honeybee was assessed and compared with the AChE assay and comet assay for DNA damage in survival honeybees after pesticide exposure at the level of LD50 and NOEC. The activity of AChE was increased in honeybee exposed to pesticides and DNA damage was significantly different from control in the treatment of low concentration without mortality. In conclusion, a test method for foliage toxicity to honeybee with soil application pesticides and the measurement of AChE and DNA damage of honeybee also were proposed for the evaluation of biological effects at non-lethal concentration of pesticides.
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In this paper, Artificial Neural Network(ANN) is applied to automatic berthing control for a ship. ANN is suitable for a maneuvering such as ship's berthing, because it can describe non-linearity of the system. Multi-layer perceptron which has more than one hidden layer between input layer and output layer is applied to ANN. Using a back-propagation algorithm with teaching data, we trained ANN to get a minimal error between output value and desired one. For the automatic berthing control of a containership, we introduced low speed maneuvering mathematical models. The berthing control with the structure of 8 input layer units in ANN is compared to 6 input layer units. From the simulation results, the berthing conditions are satisfied, even though the berthing paths are different. 두 ANN에 의한 선박의 자동접안을 수행하였으며 그 결과를 요약하면 다음과 같다. 1. 입력층-은닉층-출력층의 8-8-2 구조의 ANN과 6-6-2 구조의 ANN의 제어 성능을 비교하였다. 8-8-2 ANN에서 입력층 성분 중 좌표계 성분 한 쌍(, )을 제거한 6-6-2 ANN을 구성하였다. 이로 인하여 조타 제어에 영향을 주었으며 두 ANN의 접안 경로의 선택이 선박의 초기 조건에 따라 차이가 나게 되었다. 2. 선박의 초기 위치에 따라, 8-8-2 ANN은 가 목표지점에 가까울수록, 6-6-2 ANN은 가 목표지점에서 멀어질수록 목표지점에 도착하였을 때 선박이 계류시설과 좀 더 평행하도록 제어를 하였다. 3. 두 ANN의 접안 경로의 선택에 차이가 나지만 접안 조건을 만족하는 제어를 하였다. 따라서 6-6-2 ANN에 의한 선박의 접안 제어가 가능하였다. 4. 선수의 방향이 초기에 목표지점을 향하여 있다면 ANN에 의하여 훈련되지 않은 영역에서 접안을 시도하여도 제어가 가능하였다. 5. 6-6-2 ANN에서 모든 입력층의 성분들이 ANN의 출력층 성분에 영향을 미치지만, 출력층의 성분에 가장 많은 영향을 미치는 요소는 와 의 위치성분이다.
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Numerical approach for American knock-in barrier option using adaptive mesh method
In this paper, we consider the tree models as numerical methods for the American knock-in barrier option. Even if they have some difficulties when they are applied to evaluate the value of American barrier option, Adaptive Mesh Model derived by Figlewski and Gao(1999) can resolve these problems. We employ adaptive mesh model to the American knock-in barrier option and we compare the estimated price of American knock-in barrier option using Adaptive Mesh Model with that of Monte Carlo simulation using antithetic variates.
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