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Sumin Oh,Yang-Hyun Baek,Sungju Jung,Sumin Yoon,Byeonggeun Kang,Su-hyang Han,Gaeul Park,Je Yeong Ko,Sang-Young Han,Jin-Sook Jeong,Jin-Han Cho,Young-Hoon Roh,Sung-Wook Lee,Gi-Bok Choi,Yong Sun Lee,Won K 대한간학회 2024 Clinical and Molecular Hepatology(대한간학회지) Vol.30 No.2
Background/Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression. Methods: Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD. Results: After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort. Conclusions: We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.
A Solution Procedure for Emergency Logistics Problem in Disaster Scene
Sumin Han,Jinwoo Park,정한일 대한산업공학회 2018 Industrial Engineeering & Management Systems Vol.17 No.2
In 2000s, the frequency of natural disasters occurrence has increased continuously. Due to abnormal climate change, natural disasters such as torrential downpours and typhoons have been occurring more frequently. Therefore, need for research on an effective disaster response system is emphasized. Nonetheless, the existing disaster response systems are not yet efficient. This study deals with the distributed emer-gency vehicle routing problem for emergency logistics in disaster situations. This problem has different characteristics from the general vehicle route problem. This study suggests a solution procedure that consists of two parts. The one is to generate an emergency vehicle rout-ing plan for individual vehicle and the other is to coordinate those plans for headquarter. The algorithm for generating a routing plan is made up optimization technique using the integer programming and a metaheuristic using the genetic algorithm. And for coordinating, a priority based heuristic and k-means clustering are proposed. The performance of the suggested algorithm is empirically proven to be efficient in both solution quality and search time in various prob-lem size
한수민(Sumin Han),정한일(Hanil Jeong),김기동(Kidong Kim),박진우(Jinwoo Park) 한국경영과학회 2016 經營 科學 Vol.33 No.1
In 2000s, incidence of natural disaster is increasing continuously. Therefore, the necessity of research on the effective disaster response is emphasized. Korea is not safe from natural disaster. Natural disasters like torrential downpours, typhoons have occurred more frequently than before. In addition disasters like droughts and MERS has also occurred. Therefore, needs for effective systems and algorithms to respond disaster are increased. This study covers the vehicle routing problem for effective logistics in disaster situations caused by natural disasters. The emergency vehicle route problem has different property from the general vehicle route problem. It has the property of the importance of deadline, the uncertain and dynamic demand information, and the uncertainty in information transfer. In this study, a solution that focused on the importance of deadline. In this study, the heuristic solution using the genetic algorithm are suggested. Finally the simulation experiment which reflects the actual environment are conducted to verify the performance of the solution.
자연 재해로 인한 피해 경감을 위한 구호 물류 거점 계획에 대한 연구
한수민(Sumin Han),정한일(Hanil Jeong),박진우(Jinwoo Park) 한국전자거래학회 2018 한국전자거래학회지 Vol.23 No.3
최근 들어 자연재해의 빈도 및 강도가 늘어나고 있으며, 이에 따른 피해 역시 늘어남에 따라, 재난 현장에 대한 대응이 점차 중요해지고 있다. 본 연구에서는 피해를 경감하기 위한 구호 물류에서 큰 비중을 차지하는 부분인 물류 거점의 위치와 규모를 선정하기 위한 연구를 수행하였다. 구호 물류에서의 거점은 물자의 비축을 수행하는 일반 물류 거점의 기능 외에도 이재민 수용, 부상자 응급 처치 등 다양한 기능을 겸비해야 하며, 거점의 위치를 선정하는 기준 역시 거리 외의 거점의 신뢰도 및 주변 환경의 위험성을 고려하여야 한다. 본 연구에서는 이를 위하여 IoT 센서를 이용하여 정보를 파악하기 위한 체계를 제안하였다. 또한 재난물류 거점문제는 다양한 수요에 대응하기 위한 기능의 배치에 대한 고려 역시 포함하여야 한다. 이를 풀이하기 위하여, 혼합정수계획 모델에 기반을 둔 알고리즘을 제시하였고, 재해 발생 시나리오를 고려한 시뮬레이션 실험을 통하여 모델의 성능을 검증하였다. Recently, the magnitude and frequency of the natural disaster have been increased, the damage has become severer. The importance of disaster response system to relieve the damage has arised continuously. This study has tried to develop the algorithm to solve the facility location and size problem in emergency logistics. A facility in the emergency logistics has various roles in victim care, casualty treatment, relief resource management and relief vehicle assistance. Moreover, the location of facility in emergency logistics has to consider the safety and reliability. To gather these information, information management system with IoT sensors are suggested. The location problem in this study also covers various features to response various demands in disaster. To solve this problem, this study suggested MIP based algorithm. Scenario based simulation experiments are conducted to verify the performance suggested algorithm.