A study on measurements of local ice pressure for ice breaking research vessel “ARAON” at the Amundsen Sea

ABSTRACT:In this study, a local ice pressure prediction has been conducted by using measured data from two ice breaking tests that was conducted for a relatively big ice floe at Amundsen Sea in the Antarctica from January 31 to March 30 2012. The symmetry of load was considered by attaching strain gauges on the same sites inside the shell plating of ship at the port and the starboard sides in the bow thrust room. Using measured strain data, after the ice pressure was converted by the influence coefficient method and the direct method, the two values were found to be similar

Dynamical Volatilities for Yen-Dollar Exchange Rates

We study the continuous time random walk theory from financial tick data of the yen-dollar exchange rate transacted at the Japanese financial market. The dynamical behavior of returns and volatilities in this case is particularly treated at the long-time limit. We find that the volatility for prices shows a power-law with anomalous scaling exponent κ = 0.96 (one minute) and 0.86 (ten minutes), and that our behavior occurs in the subdiffusive process. Our result presented will be compared with that of recent numerical calculations

Projection of Cancer Incidence and Mortality From 2020 to 2035 in the Korean Population Aged 20 Years and Older

Objectives: This study aimed to identify the current patterns of cancer incidence and estimate the projected cancer incidence and mortality between 2020 and 2035 in Korea. Methods: Data on cancer incidence cases were extracted from the Korean Statistical Information Service from 2000 to 2017, and data on cancer-related deaths were extracted from the National Cancer Center from 2000 to 2018. Cancer cases and deaths were classified according to the International Classification of Diseases, 10th edition. For the current patterns of cancer incidence, age-standardized incidence rates (ASIRs) and age-standardized mortality rates were investigated using the 2000 mid-year estimated population aged over 20 years and older. A joinpoint regression model was used to determine the 2020 to 2035 trends in cancer. Results: Overall, cancer cases were predicted to increase from 265 299 in 2020 to 474 085 in 2035 (growth rate: 1.8%). The greatest increase in the ASIR was projected for prostate cancer among male (7.84 vs. 189.53 per 100 000 people) and breast cancer among female (34.17 vs. 238.45 per 100 000 people) from 2000 to 2035. Overall cancer deaths were projected to increase from 81 717 in 2020 to 95 845 in 2035 (average annual growth rate: 1.2%). Although most cancer mortality rates were projected to decrease, those of breast, pancreatic, and ovarian cancer among female were projected to increase until 2035. Conclusions: These up-to-date projections of cancer incidence and mortality in the Korean population may be a significant resource for implementing cancer-related regulations or developing cancer treatments

New integer optimization models and an approximate dynamic programming algorithm for the lot-sizing and scheduling problem with sequence-dependent setups

© 2021 Elsevier B.V.In this paper, we propose new integer optimization models for the lot-sizing and scheduling problem with sequence-dependent setups, based on the general lot-sizing and scheduling problem. To incorporate setup crossover and carryover, we first propose a standard model that straightforwardly adapts a formulation technique from the literature. Then, as the main contribution, we propose a novel optimization model that incorporates the notion of time flow. We derive a family of valid inequalities with which to compare the tightness of the models’ linear programming relaxations. In addition, we provide an approximate dynamic programming algorithm that estimates the value of a state using its lower and upper bounds. Then, we conduct computational experiments to demonstrate the competitiveness of the proposed models and the solution algorithm. The test results show that the newly proposed time-flow model has considerable advantages compared with the standard model in terms of tightness and solvability. The proposed algorithm also shows computational benefits over the standard mixed integer programming solver.N

A reinforcement learning approach for multi-fleet aircraft recovery under airline disruption

An airline scheduler plans flight schedules with efficient resource utilization. However, unpredictable airline disruptions, such as temporary closures of an airports, cause schedule perturbations. Therefore, recovering disrupted flight schedules is essential for airlines. Many previous studies have relied on copies of flight arcs, which could affect the quality of solutions, and have not addressed the key measure of airlines' on-time performance as their objective. To fill these research gaps, we propose Q-learning and Double Q-learning algorithms using the reinforcement learning approach for aircraft recovery to support airline operations. We present an artificial environment of daily flight schedules and the Markov decision process for aircraft recovery. The proposed approach is first compared with existing algorithms on the benchmark instance. In comparison with other algorithms, the developed Q-learning and Double Q-learning algorithms obtain high-quality solutions within the proper computation time. To verify that the proposed approach can be applicable to a real-world case and can adapt to realistic conditions, we employ a domestic flight schedule from one of the airlines in South Korea. We evaluate the reinforcement learning approach on a set of experiments carried out on real-world data. Computational experiments show that reinforcement learning algorithms recover disrupted flight schedules effectively, and that our approaches flexibly adapt to various objectives and realistic conditions. (c) 2022 Elsevier B.V. All rights reserved.N