• Applied Biological Chemistry
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• v.50 no.3
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• pp.192-197
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• 2007

3D-QSARs on the fungicidal activity of N-phenylbenzenesulfonamide and N-phenyl-2-thienylsulfonamide analogues (1-37) against Phytophthora blight (Phytophthora capsici) were studied quantitatively using CoMFA and CoMSIA methods. The statistical results of the optimized CoMFA (2) model ($r^2_{cv.}(q^2)$ = 0.692 & $r^2_{ncv.}$= 0.965) show better predictability and fitness than CoMSIA (2) model ($r^2_{cv.}(q^2)$ = 0.796 & $r^2_{ncv.}$= 0.958). The fungicidal activities according to the information of the optimized CoMFA (2) model were dependent upon the steric and electrostatic fields of the molecules. Therefore, from the contribution contour maps of CoMFA (2) model, it is expected that 63% contribution was caused by the steric bulk of meta-substituent ($R_1$) on the S-phenyl ring. Also, the other contribution level of 32.9% was represented by the positive charged $R_4-group$ ($R_1$) on the N-phenyl ring and para-substituent ($R_1$) on the S-phenyl ring. A series of higher active compounds, $R_1$= 3-decyl substituent ($pred.pI_50$= 5.88) etc. were predicted based on the findings.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1992.05a
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• pp.35-35
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• 1992

산소라디칼-질병모델을 보다 간편한 방법으로 만들어 생화학적 발병 기전을 추구하므로서 신약창출에 필요한 기초자료를 제시하고자 본 실험을 실시하였다. 미세혈관의 혈류이상은 과잉의 활성산소를 생산하게되며 이로 인해 여러 종류의 난치성 질환 및 염증을 수반하는 많은 질병이 발병하게 된다. 일반적으로 흔히 이용되어지는 허혈-재관류모델이 아닌 환경독성물질을 활용한 병태모델을 만들어 이 모델조건에서 활성산소생성과 밀접한 관계를 갖는 잔틴 옥시데이즈(Xanthine Oxidase)의 변화와 과산화지질생성정도를 연관지어 검토하였다. 수은, 구리, 몰리브덴등의 금속이온들은 잔틴 디하이드로저네이즈(Xanthine dehydrogenase)로 부터 옥시데이즈(Oxidase)형으로의 전환을 촉진시켰으며 막독성의 지표로 이용되어지는 과산화지질의 생성도 현저히 증가시켰다. 또한 알데하이드류의 첨가실험에서도 잔틴산화효소의 형전환이 촉진되었으며 첨가한 알데하이드의 탄소수와 수소수 그리고 탄소와 탄소의 결합상태와도 밀접한 관계가 있음이 관찰되었다.

• The Korean Journal of Pesticide Science
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• v.10 no.3
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• pp.157-164
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• 2006

Three dimensional quantitative structure-activity relationships (3D-QSARs) for the fungicidal activities against Rhizoctonia solani (RS) and Phytophthora capsici (PC) by N-phenyl substituents(X) of N-phenylthionocarbamate derivatives were studied quantitatively using comparative molecular field analysis (CoMFA) methodology based on different alignment approaches. Statistical quality of CoMFA models with field fit alignment were slightly higher than that of atom based fit alignment. The optimized CoMFA models (RS: RF2 & PC: PF2) were derived from field fit alignment and combination of CoMFA fields. And the statistical results of the two models showed the best predictability of the fungicidal activities based on the cross-validated value $q^2$ ($r^2_{cv.}$ =RS: 0.557 & PC: 0.676) and non-cross-validated value ($r^2_{ncv.}$ =RS: 0.954 & PC: 0.968), respectively. The selective fungicidal activities between two fungi were dependence upon the electrostatic field of substrate molecule. Therefore, the fungicidal activities from CoMFA contour maps showed that the fungicidal activity will be able to increased according to the modification of X-substituents on the substrate molecules.

• The Korean Journal of Pesticide Science
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• v.11 no.1
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• pp.8-17
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• 2007

Three-dimensional quantitative structure-activity relationships (3D-QSARs) on the fungicidal activity against damping-off (Pythium ultimum) with N-phenylbenzenesulfonamide and N-phenyl-2-thienylsulfonamide analogues (1-34) were studied quantitatively using CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indeces analysis) methodologies. On the whole, the statistical qualities of CoMSIA models with field fit alignment (FF1-FF5) were slightly higher than that of atom based fit alignment (AF1-AF5) but, the deviations of statistical quality between two alignments in case of CoMFA models were slightly lower. The statistical results of CoMFA and CoMSIA model showed that the optimized CoMSIA model (FF1: $r_{cv.}^2\;(q^2)=0.674$ & $r_{ncv.}^2=0.964$) for damping-off is better predictability and fitness for fungicidal activities than CoMFA model (AF5: $r_{cv.}^2\;(q^2)=0.616$ & $r_{ncv.}^2=0.930$). The fungicidal activities according to the information of the CoMSIA (FF1) model were dependence upon the electrostatic and hydrophobic field of the N-phenylbenzene sulfonamide analogues. Therefore, from the results of graphical analyses on the contour maps with CoMSIA (FF3) model, it is expected that the characters of R4-substituent on the N-phenyl ring as hydrophobic and hydrogen bond acceptor will be contributed to the fungicidal activity against damping-off.

• The Korean Journal of Pesticide Science
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• v.12 no.2
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• pp.111-117
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• 2008

The comparative molecular field analysis (CoMFA) for the fungicidal activity with N-phenylbenzenesulfonamide analogues (1-45) against gray mold (Botriyts cinerea) were studied quantitatively. The statistical values of CoMFA models had much better predictability and fitness than those of comparative molecular similarity indices analysis (CoMSIA) models. The statistical values of the optimized CoMFA I model were predictablity, $r^2_{cv.}(or\;q^2)=0.457$ and correlation coefficient, $r^2_{ncv.}=0.959$, and their fungicidal activity was dependent on the steric field (52%) and electrostatic field (35.6%) of the substrate molecules. And also, it was found that the optimized CoMFA I model with the sensitivity to perturbation ($d_q^{2'}/dr^2_{yy'}=0.898$) and prediction ($q^2=0.346$ & SDEP=0.614) produced by a progressive scrambling analysis was not dependent on chance correlation. From the results of graphical analyses on the contour maps with the optimized CoMFA I model, it is expected that the $R_3$ and $R_4$-substituents on the N-phenyl ring as steric favor group and para-substituents ($R_1$) on the S-phenyl ring as steric disfavor group will contribute to the fungicidal activity. Therefore, the optimized CoMFA I model should be applicable to the prediction of the fungicidal activities against gray mold.

• Clean Technology
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• v.25 no.3
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• pp.206-222
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• 2019

Recently, various multiphase flows have been developed, and among them some models have been commercialized. However, most of them have been developed based on a pressure-based approach; therefore, various numerical difficulties were involved inherently. Accordingly, in order to overcome these numerical difficulties, a multiphase flow model, MultiPhaSe flow (MPS), following a fractional-flow based approach was developed. In this study, by combining a contaminant transport module describing an enhanced dissolution effect of a surfactant with MPS, a MultiPhaSe flow and TranSport (MPSTS) model was developed. The developed model was verified using the analytical solution of Clement. The MPSTS model can simulate the process of surfactant enhanced aquifer remediation including interphase mass transfer and contaminant transport in multiphase flow by using the coupled particle tracking method and Lagrangian-Eulerian method. In this study, a surfactant was used in a non aqueous phase liquid (NAPL) contaminated area, and the effect of hydro-geological heterogeneity in the layered media on remediation efficiency was studied using the developed model. According to the numerical simulation, when hydraulic conductivity in a lower layer is 10 times, 20 times, and 50 times larger than that in an upper layer, the concentration of dissolved diesel in the lower layer is much higher than that in the upper layer because the surfactant moves faster along the lower layer owing to preferential flow; thus, the surfactant enhances dissolution of residual non aqueous phase liquid in the lower layer.

• Membrane Journal
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• v.24 no.3
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• pp.249-257
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• 2014

This experiment was carried out for a laboratory scale activated sludge bioreactor equipped with submerged flat sheet membrane using the synthetic wastewater. The membrane system for the activated sludge solution of MLSS 5,000 mg/L was operated with constant permeate flux by continuously permeating and periodically 10 minute-permeating/2 minute-resting modes, respectively. The transmembrane pressure was measured as the permeate flux increased from 10 to $25L/m^2{\cdot}hr$ under the constant air flowrate 0.25 L/min. Also, the complete blocking, standard blocking, intermediate blocking, incompressible cake and linear compressible cake fouling models were retrofitted for the experimental data in order to determine the state of the membrane fouling. Because the transmembrane pressure fluctuated as a pulse shape for every period of 10 minute-permeating/2-minute resting mode, the membrane fouling models were separately applied for the maximum and minimum connecting lines. The linear compressible cake fouling model for the activated sludge cakes was the best fitted with the experimental results from the above five models.

• Korean Chemical Engineering Research
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• v.48 no.6
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• pp.784-790
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• 2010

VOCs such as acetone, benzene, toluene, ethylbenzene were adsorbed in a fixed-bed adsorber using zeolite synthesized from coal fly ash and 4 kinds of activated carbon at 101.3 kPa. The adsorber was operated batchwise with the charge of 5 g adsorbent to obtain the breakthrough curve of VOCs. Experiments were carried out at $40^{\circ}C$, nitrogen flow rate of $70cm^3/min$ and sparger temperature of $30^{\circ}C$. The deactivation model was tested for these curves by combining the adsorption of VOCs and the deactivation of adsorbent particles. The observed values of the adsorption rate constant and the deactivation rate constant were evaluated through analysis of the experimental breakthrough data using a nonlinear least square technique. The experimental breakthrough data were fitted very well to the deactivation model than the adsorption isotherm models in the literature. Also, adsorption capacities of adsorbents were obtained from the breakthrough curve to observe the correlation between adsorption capacity and the physical properties of VOCs.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.85-92
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• 2020

Deep learning shows outstanding performance in image and video analysis, such as object classification, object detection and semantic segmentation. In this paper, it is analyzed that the performances of deep learning models can be affected by characteristics of train dataset. It is proposed as a method for selecting activation function and optimization algorithm of deep learning to classify facial expression. Classification performances are compared and analyzed by applying various algorithms of each component of deep learning model for CK+, MMI, and KDEF datasets. As results of simulation, it is shown that genetic algorithm can be an effective solution for optimizing components of deep learning model.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.247-248
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• 2008

본 논문에서는 TYPE-2 퍼지 추론 기반의 RBF 뉴럴 네트워크(TYPE-2 Radial Basis Function Neural Network, T2RBFNN)를 설계하고 PSO(Particle Swarm Optimization) 알고리즘을 이용하여 모델의 파라미터를 동정한다. 제안된 모델의 은닉층은 TYPE-2 가우시안 활성 함수로 구성되며, 출력층은 Interval set 형태의 연결가중치를 갖는다. 여기에서 규칙 전반부 활성함수의 중심 선택은 C-means 클러스터링 알고리즘을 이용하고, 규칙 후반부 Interval set 형태의 연결가중치 결정에는 경사 하강법(Gradient descent method)을 이용한 오류 역전파 알고리즘을 사용하여 학습한다. 또한, 최적의 모델을 설계하기 위한 학습율 및 활성함수의 활성화 영역 결정에는 입자 군집 최적화(PSO; Particle Swarm Optimization) 알고리즘으로 동조한다. 마지막으로, 제안된 모델의 평가를 위하여 모의 데이터 집합(Synthetic dadaset)을 적용하고 근사화 및 일반화 능력에 대하여 토의한다.