• 대한조선학회 특별논문집
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• 대한조선학회 2006년도 특별논문집
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• pp.89-96
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• 2006

The objective of this paper is to find out a reasonable method for docking analysis of ships. The characteristics of reaction force distribution under docking condition are investigated by carrying out parametric study. To assess the allowable reaction force on keel block from structural strength point of view, two kinds of structural assessment methods are proposed in accordance with expected collapse pattern. In order to verify the proposed method, linear buckling and elasto-plastic large deflection analyses of typical double bottom structure are carried out and the results are compared.

• 통합자연과학논문집
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• 제15권4호
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• pp.179-186
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• 2022

The c-Jun N-terminal kinase (JNK3) play major role in neurodegenerative diseases like Alzheimer's disease, Parkinson's disease, cerebral ischemia and other Central Nervous System disorders. Since JNK3 is primarily stated in the brain and stimulated by stress-stimuli, this situation is conceivable that inhibiting JNK3 could be a possible treatment for the mechanisms underlying neurodegenerative diseases. In this study drugs from Zinc15 database were screened to identify the JNK3 inhibitors by Molecular docking and Density functional theory approach. Molecular docking was done by Autodock vina and the ligands were selected based on the binding affinity. Our results identified top ten novel ligands as potential inhibitors against JNK3. Molecular docking revealed that Venetoclax, Fosaprepitant and Avapritinib exhibited better binding affinity and interacting with proposed binding site residues of JNK3. Density functional theory was used to compute the values for energy gap, lowest unoccupied molecular orbital (LUMO), and highest occupied molecular orbital (HOMO). The results of Density functional theory study showed that Venetoclax, Fosaprepitant and Avapritinib serves as a lead compound for the development of JNK3 small molecule inhibitors.

• 한국항해항만학회지
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• 제48권2호
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• pp.116-124
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• 2024

Automatic docking of small planing ship is a critical aspect of maritime operations, requiring accurate prediction of motion states to ensure safe and efficient maneuvers. This study investigates the use of Artificial Neural Network (ANN) to predict motion state of a small planing ship to enhance navigation automation in port environments. To achieve this, simulation tests were conducted to control a small planing ship while docking at various heading angles in calm water and in waves. Comprehensive analysis of the ANN-based predictive model was conducted by training and validation using data from various docking situations to improve its ability to accurately capture motion characteristics of a small planing ship. The trained ANN model was used to predict the motion state of the small planning ship based on any initial motion state. Results showed that the small planing ship could dock smoothly in both calm water and waves conditions, confirming the accuracy and reliability of the proposed method for prediction. Moreover, the ANN-based prediction model can adjust the dynamic model of the small planing ship to adapt in real-time and enhance the robustness of an automatic positioning system. This study contributes to the ongoing development of automated navigation systems and facilitates safer and more efficient maritime transport operations.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2005년도 추계학술대회
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• pp.211-220
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• 2005

• 대한안전경영과학회지
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• 제15권2호
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• pp.167-184
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• 2013

A cross docking operation involves multiple inbound trucks that deliver items from suppliers to a distribution center and multiple outbound trucks that ship items from the distribution center to customers. Based on customer demands, an inbound truck may have its items transferred to multiple outbound trucks. Similarly, an outbound truck can receive its consignments from multiple inbound trucks. The objective of this study is to find the best truck spotting sequence for both inbound and outbound trucks in order to minimize total operation time of the cross docking system under the condition that multiple visits to the dock by a truck to unload or load its consignments is allowed. The allocations of the items from inbound trucks to outbound trucks are determined simultaneously with the spotting sequences of both the inbound and outbound trucks.

• 대한안전경영과학회지
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• 제4권3호
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• pp.79-93
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• 2002

크로스도킹이란 물류와 분배의 개념으로서 창고나 분배 센터에서 물품이 재고로 남겨짐이 없이 곧바로 하역 창구에서 적재 창구로 이동되어지는 것을 말한다 창고시설 이나 운영 조건이나 운영 전략에 의해서 다양한 크로스도킹 모델이 생성될 수 있다. 본 연구에서 고려되어지는 크로스도킹 모델은 하나의 독립된 하역 창구와 독립된 적재 창구를 가정하고 있다. 또한 하역 트럭에 적재되어 있는 물품이나 적재 트럭에 실려질 물품의 수와 종류는 사전에 알려져 있다는 것을 가정한다. 또한 고려되어진 창고나 분배창구는 단지 하나의 하역 창구와 하나의 적재 창구를 가지고 있다고 가정된다. 본 연구의 목적은 고려되어진 크로스도킹 시스템의 총 운영 시간을 최소화하기 위한 하역 트럭과 적재 트럭의 일정 계획을 찾는데 있다.

• 통합자연과학논문집
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• 제8권3호
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• pp.209-213
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• 2015

Influenza A virus (H1N1) causes and spreads infectious diseases and becomes a major health threat in humans. Among the subtypes of influenza virus, neuraminidase (NA) plays an important role in viral life cycle and becomes an attractive therapeutic target. Currently two NA inhibitors namely Zanamivir and Oseltamivir are available for treating infectious diseases. Recently five naturally derived polyphenols extracted from Phellinus baumii was reported as inhibitors against NA. Molecular docking is powerful tool in computer aided drug designing which aids in exploring and elucidating the properties of the molecules from their 3D structure. Hence, in the present study, molecular docking was carried out on reported polyphenols isolated from ethanolic extract of fruiting bodies of Phellinus baumii. The objective of this work was to study the interaction and to propose the binding mode of these compounds within the binding site of H1N1 neuraminidase. The results showed these compounds had better binding energy and H-bond interactions with the important active site residues of the receptor which authenticate these compounds contributes to inhibitory activity of neuraminidase to treat influenza infection.

• Bulletin of the Korean Chemical Society
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• 제33권5호
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• pp.1597-1602
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• 2012

In this study, we determined the 3D-structure of Arabidopsis thaliana KAPAS by homology modeling. We then investigated the binding mode of compounds obtained from in-house library using computational docking methods. From the flexible docking study, we achieved high dock scores for the active compounds denoted in this study as compound $\mathbf{3}$ and compound $\mathbf{4}$. Thus, we highlight the flexibility of specific residues, Lys 312 and Phe 172, when used in active sites.

• 통합자연과학논문집
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• 제11권2호
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• pp.121-129
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• 2018

CXCR6 is a major target in drug design as it is a determinant receptor in many diseases like AIDS, Type I Diabetes, some cancer types, atherosclerosis, tumor formation, liver disease and steatohepatitis. In this study, we propose the active site residues of CXCR6 molecule. We employed homology modelling and molecular docking approach to generate the 3D structure for CXCR6 and to explore its interaction between the antagonists and agonists. 3D models were generated using 14 different templates having high sequence identity with CXCR6. Surflex docking studies using pyridine and pyrimidine derivatives enabled the analysis of the binding site and finding of the important residues involved in binding. 3D structure of CXCL16, a natural ligand for CXCR6, was modelled using PHYRE and protein - protein docking was performed using ClusPro. The residues which were found to be crucial in interaction with the ligand are THR110, PHE113, TYR114, GLN160, GLN195, CYS251 and SER255. This study can be used as a guide for therapeutic studies of human CXCR6.

• 통합자연과학논문집
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• 제10권2호
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• pp.105-109
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• 2017

Urotensin-2 receptor (UTS2R) is the most potent vasoconstrictor and plays a major role in the pathophysiology of various cardiovascular diseases and becomes a potential target for human pharmacotherapy. Hence, we have performed molecular docking of six antagonists with different inhibitory activity against UTS2R into its binding site. The binding mode of these antagonists was obtained using Surflex dock program interfaced in Sybyl-X2.0. The residues such as GLN278, THR304, TYR305, THR300, LEU299, CYS302, ASP47, TYR100 and THR304 are found in interaction between UTS2R and its antagonists. This study could be useful for identifying and analyzing the important residues involved in binding site of UTS2R receptor.