Chung-Ang University

About: Chung-Ang University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Population & Thin film. The organization has 13381 authors who have published 26978 publications receiving 416735 citations. The organization is also known as: CAU & Chung.

Current trends and future projections of hip fracture in South Korea using nationwide claims data.

Abstract: Incidence of hip fracture increased in Korean populations over age 50 between 2008 and 2012, and the number of fractures was predicted to increase by 1.4 times by 2025. This is important information for public health planning. The purposes of this study were to evaluate the trends in the incidence and mortality of hip fracture between 2008 and 2012 and predict the number of hip fractures in Korea through 2025 using nationwide claims data. The data managed by the National Health Insurance Service were used to identify the hip fractures in patients aged >50 years between 2008 and 2012. Projections of hip fractures were conducted using the Poisson distribution from 2016 to 2025 in Korea. The incidence of hip fractures (per 100,000) increased by 14.1 % over the 5 years of the study, by 15.8 % in women and 10.9 % in men; the older age group showed a steep rise and shift in the incidence from 2008 to 2012. The cumulative mortality rates at 1 year after hip fractures were 17.2 % (3575/20,849) in 2008 and 16.0 % (4547/28,426) in 2012. Overall standardized mortality ratios (SMRs) for hip fracture were higher in men (11.93) than in women (11.22) and were higher than those in the general population in all age groups. In 2016, the total number of hip fractures was estimated to increase an overall of 1.4 times by 2025. The incidence of hip fracture continues to increase, and the related mortality is still high, although it has decreased over time. The socioeconomic burden of hip fracture is expected to increase in Korea along with the increased estimated number of fractures. Nationwide strategies should include attempts to reduce the future socioeconomic burdens of hip fractures.

Effects of time perspective and self-control on procrastination and Internet addiction.

Abstract: Background and aimsCollege students experiencing stress show tendencies to procrastinate and can develop Internet addiction problems. This study investigated the structural relationship between time perspective and self-control on procrastination and Internet addiction. MethodsCollege students (N = 377) residing in South Korea completed the following questionnaires: the Pathological Internet Use Behavior Symptom Scale for Adults, the Zimbardo Time Perspective Inventory, the Self-Control Rating Scale, and the Aitken Procrastination Inventory. The sample variance–covariance matrix was analyzed using AMOS 20.0. ResultsTime perspective had a direct effect on self-control and an indirect effect on Internet use and procrastination. In addition, self-control affected procrastination and Internet use. ConclusionsIndividuals with a present-oriented time perspective tend to evidence poorer self-control, increasing the likelihood of procrastination and Internet addiction. Individuals with a future-oriented time pers...

Auction-based Charging Scheduling with Deep Learning Framework for Multi-Drone Networks

Abstract: State-of-the-art drone technologies have severe flight time limitations due to weight constraints, which inevitably lead to a relatively small amount of available energy. Therefore, frequent battery replacement or recharging is necessary in applications such as delivery, exploration, or support to the wireless infrastructure. Mobile charging stations (i.e., mobile stations with charging equipment) for outdoor ad-hoc battery charging is one of the feasible solutions to address this issue. However, the ability of these platforms to charge the drones is limited in terms of the number and charging time. This paper designs an auction-based mechanism to control the charging schedule in multi-drone setting. In this paper, charging time slots are auctioned, and their assignment is determined by a bidding process. The main challenge in developing this framework is the lack of prior knowledge on the distribution of the number of drones participating in the auction. Based on optimal second-price-auction, the proposed formulation, then, relies on deep learning algorithms to learn such distribution online. Numerical results from extensive simulations show that the proposed deep learning-based approach provides effective battery charging control in multi-drone scenarios.