Load-balanced route optimization method for accident aboard a ship

An emergency evacuation system is a system that helps people in the space to evacuate safely and quickly from emergencies in the event of an emergency. Such systems are essential as the size of vessels becomes larger and more complex. However, current emergency evacuation systems play only a limited role. For example, evacuation route guidance through placement of real human resources or evacuation route such as direction of emergency exit point which is pointed in one direction only in one place. Relying on human subjective judgment in a dangerous situation can be quite dangerous, and emergency lights and escape routes that always point in the same direction are not able to deal flexibly with risk factors and can expose the public to danger. Furthermore, due to the nature of the ship structure, the initial response is important as the rescue time is delayed rather than the land accident. Therefore, emergency evacuation systems should be more intelligent in increasingly complicated and larger structures, and should be able to quickly identify information on the surrounding situation and suggest an optimal evacuation route. In particular, it is not possible to exclude the possibility that dangerous elements may spread or become dangerous areas in the route where evacuees are passing. Therefore, there is a need for a system that predicts and responds to the near future through sufficient modeling of risk factors. Among various risk factors, risk factors such as fire, smoke, and isolation can be sufficiently collected by using sensors or image processing devices. However, in the case of bottlenecks, it is essential to model the density of the population at the current node, the direction in which people at that location will evacuate, and whether the path of the selected path will accommodate the incoming population. Therefore, we propose a bottleneck modeling method and load balancing based on disaster situation in this paper. The proposed performance is verified by computer simulation.Chapter 1. Introduction 1 1.1 Research background 1 1.2 Research Trends 1 1.3 Research Necessity 3 1.4 Research Summary 3 Chapter 2. Related Theory and Research 4 2.1 Searching Algorithm 4 2.1.1 State Space and Search 4 2.1.2 Blind Search 5 2.1.3 Heuristic Search 7 2.1.4 Algorithm 8 2.1.4.1 Operation Process 9 2.2 Searching System 12 2.2.1 Feeling Factor 12 2.2.2 Risk Predicted Value 14 2.2.3 Evacuation System for accident situation 16 Chapter 3. Proposed Scheme 17 3.1 Graph Search for inside of ship 17 3.2 Modeling of bottleneck 18 3.3 Proposed Route Optimization Algorithm 22 Chapter 4. Simulation and Analysis 23 4.1 Bottleneck occurrence probability 23 4.1.1 Experiment environment and result 23 4.2 Weighted distance according to proposed scheme 25 4.3 Evacuation time according to proposed scheme 27 Chapter 5. Conclusiond 28 References 29Maste

초기 파킨슨병에서 향후 보행동결 발생의 예측인자들: 임상적, 도파민 운반체 영상 및 뇌척수액 지표들

학위논문(석사)--서울대학교 대학원 :의과대학 의학과,2020. 2. 전범석.Objective The aim of this study was to determine whether dopamine transporter (DAT) imaging and cerebrospinal fluid (CSF) parameters can be used as a predictor of freezing of gait (FOG) in patients with early Parkinsons disease (PD). In addition, we further investigated the predictive value of clinical, DAT imaging and CSF markers for the development of FOG both separately and in combination. Methods This cohort study using the Parkinsons Progression Markers Initiative data included a total of 393 early PD patients without FOG. Demographic and clinical data, DAT imaging results, and CSF marker levels including β-amyloid 1-42 (Aβ42), α-synuclein, total tau, phosphorylated tau181, and the calculated ratio of Aβ42 to total tau were collected at baseline. The FOG data up to 4 years of follow-up were included. The development of FOG was defined to be present if the score was 1 or greater either for the Movement Disorder Society Unified Parkinsons Disease Rating Scale (MDS-UPDRS) item 2.13 or item 3.11 at any point during the follow-up period. Cox regression models were conducted to identify the factors predictive of FOG. Based on these results, we constructed a predictive model for the development of FOG. Results During a median follow-up of 4.0 years (mean 3.0 years), 136 patients developed FOG, and its cumulative incidence was 17, 21, 28, and 37% at 1-, 2-, 3- and 4-year follow-up, respectively. Among DAT imaging and CSF markers, caudate DAT uptake (hazard ratio [HR] 0.581; 95% confidence interval [CI] 0.408−0.827; p=0.003) and CSF Aβ42 (HR 0.997; 95% CI 0.996−0.999; p=0.009) were predictive of FOG. Postural instability gait difficulty (PIGD) score (HR 1.494; 95% CI 1.282−1.741; p<0.001) and, to a lesser extent, male sex (HR 1.512; 95% CI 1.007−2.271; p=0.046), MDS-UPDRS motor score (HR 1.022; 95% CI 1.000−1.045; p=0.046), and Montreal Cognitive Assessment score (HR 0.927; 95% CI 0.860−0.995; p=0.035) were also related to the development of FOG. The combined model integrating the PIGD score, caudate DAT uptake, and CSF Aβ42 achieved a better prediction accuracy (area under the curve 0.755; 95% CI 0.700−0.810) than any factor alone. Conclusions This study found striatal DAT uptake and CSF Aβ42 as predictors of FOG in patients with early PD. Furthermore, FOG development within 4 years after PD diagnosis can be predicted with acceptable accuracy using our risk model.서론 도파민운반체 영상 및 뇌척수액 지표들이 초기 파킨슨병 환자에서 보행동결의 예측인자일 수 있는지를 알아보고 임상지표와의 조합을 통해 향후 보행동결 발생을 어느 정도 예측할 수 있는지를 분석하였다. 방법 Parkinson's progression markers initiative (PPMI) 연구는 초기 파킨슨병 환자에서 향후 증상들의 진행을 예측하는 바이오마커를 발견하고 검증하기 위해 고안된 국제다기관 코호트 연구이다. 본 연구는 PPMI 데이터를 이용하였고 4년까지 경과관찰한 데이터를 포함하였다. 초기 파킨슨병 환자들 중 보행동결이 없는 393명이 연구에 포함되었다. 보행동결의 발생은 Movement Disorders Society Unified Parkinsons Disease Rating Scale (MDS-UPDRS) 항목 2.13　또는 항목 3.11의 답이 1점 이상인 경우로 정의하였다. 콕스 회귀모델을 수행하여 보행동결의 예측 인자를 확인하였고 이러한 결과를 바탕으로 예측 모델을 개발하였다. 결과 평균 3.0년 (중앙값 4.0년)간의 추적관찰 동안 136명의 환자들에서 보행동결이 발생하였고 보행동결의 누적발생률은 1, 2, 3, 4년 경과관찰에서 각각 17, 21, 28 및 37%였다. 도파민운반체 영상 및 뇌척수액 지표들 중 미상핵 도파민 운반체 섭취 감소 (위험비 0.581; 95% 신뢰구간 0.408-0.827; p=0.003) 및 베타아밀로이드 1-42 (위험비 0.997; 95% 신뢰구간 0.996-0.999; p=0.009)가 보행동결의 발생과 관련이 있었다. 이외에 보행장애-자세불안 점수 (p<0.001), 남성 (p=0.046), MDS-UPDRS 운동 점수 (p=0.046) 및 몬트리올 인지평가 점수 (p=0.035)가 보행동결을 예측하였다. 보행장애-자세불안 점수, 미상핵 도파민 운반체 섭취 감소 및 뇌척수액 베타아밀로이드 1-42를 조합한 예측모델의 area under curve는 0.755 (95% 신뢰구간 0.700–0.810)로 측정되었다. 결론 이 연구는 초기 파킨슨병 환자에서 선조체의 도파민결핍 및 뇌척수액 베타아밀로이드 1-42가 향후 보행동결 발생의 예측인자인 것을 확인하였다. 또한 우리의 위험모델은 파킨슨병 진단 후 4 년 이내에 보행동결 발생을 수용 가능한 정도의 정확도로 예측하였다.Introduction 1 Materials and Methods 3 Results 7 Discussion 10 Conclusion 13 References 14 Tables and Figures 18 Acknowledgement 23 Abstract (in Korean) 24Maste

AIMP2-DX2 유전자의 2번 엑손 접합 변형이 급성 골수성 백혈병 및 기타 암종에서 가지는 임상적 의미

학위논문 (석사)-- 서울대학교 대학원 : 의학과, 2017. 2. 윤성수.Aminoacyl tRNA synthetase complex-interacting multifunctional protein 2 (AIMP2) is a potent tumor suppressor. An exon-2 depleted splicing variant ofAIMP2 (AIMP2-DX2) is responsible for tumorigenesis by compromising the tumor suppressive activity of AIMP2. This study aimed to investigate the role ofAIMP2-DX2 over diverse cancers using whole transcriptome data in The Cancer Genome Atlas (TCGA), and International Cancer Genome Consortium (ICGC) database. A total of 753 samples were analyzed for the presence of AIMP2-DX2 and its prognostic role in various cancers. AIMP2-DX2 was universally expressed to varying degrees, with a prognostic implication in several cancers. In acute myemyeloid leukemia (AML), AIMP2-DX2/AIMP2 ratio was strongly correlated with major cancer signaling pathways, and had a tendency toward exhibiting poor prognosis (Log rank P=0.16). We validated the prognostic implication of AIMP2-DX2 using AML patient samples. For 51 AML patients, overall survival (OS) and progression-free survival (PFS) of AIMP2-DX2 positive patients were significantly inferior to that of AIMP2-DX2 negative patients (for OS: hazard ratio [HR] 2.4795% confidence interval [CI] 1.14–5.34P=0.022for PFS: HR 2.5995% CI 1.32–5.11P=0.006). Collectively, AIMP2-DX2 may be a novel biomarker and a potential therapeutic target for AML.I. Introduction 1 II. Materials and Methods 3 III. Results 9 IV. Discussion 24 V. Conclusions 28 VI. References 29 Supplementary materials 34 Supplementary Figures 34 Supplementary Tables 51 국문 초록 54Maste