Association of Metabolic Syndrome with the Number of Breastfed Children in Postmenopausal Korean Women.

BACKGROUND: Metabolic syndrome is closely related to cardiovascular disease, and the prevalence of metabolic syndrome in postmenopausal women is increasing rapidly. OBJECTIVE: The purpose of this study is to investigate the association between the number of breastfed children and the risk factors for metabolic syndrome in postmenopausal women and to evaluate the association between metabolic syndrome and bone mineral density and body composition variables in postmenopausal women depending on the number of breastfed children Methods: Data from KNHANES V-1 and 2 (2010-2011) were used, and a total of 939 postmenopausal women with 1 to 6 breastfed children aged 65-80 years participated in this study. We divided these women into three groups (group1 with 1-2, group2 with 3-4, group3 with 5-6) depending on the number of breastfed children Results: In the analysis of the associations between metabolic syndrome and its risk factors, highdensity lipoprotein cholesterol was the most negatively strongly associated with group1 (OR=0.103 [0.047-0.225]), triglyceride showed the highest association with group2 (OR=7.760 [3.770-15.97]) and group3 (OR=7.668 [4.102-14.33]). The risk factors of metabolic syndrome except for highdensity lipoprotein cholesterol and triglyceride was not associated with group1. In contrast, all risk factors of metabolic syndrome displayed a high association with group2 and group3. CONCLUSION: The findings of the present study suggest that the number of breastfed children is significantly associated with a more significant number of risk factors of metabolic syndrome in postmenopausal women, and the association between metabolic syndrome and body composition variables may differ depending on the number of breastfed children.

Secure and reliable blockchain-based eBook transaction system for self-published eBook trading.

As eBook readers have expanded on the market, various online eBook markets have arisen as well. Currently, the online eBook market consists of at least publishers and online platform providers and authors, and these actors inevitably incur intermediate costs between them. In this paper, we introduce a blockchain-based eBook market system that enables self-published eBook trading and direct payments from readers to authors without any trusted party; because authors publish themselves and readers purchase directly from authors, neither actor incurs any intermediate costs. However, because of this trustless environment, the validity, ownership and intellectual property of digital contents cannot be verified and protected, and the safety of purchase transactions cannot be ensured. To address these shortcomings, we propose a secure and reliable eBook transaction system that satisfies the following security requirements: (1) verification of the ownership of each eBook, (2) confidentiality of eBook contents, (3) authorization of a right to read a book, (4) authentication of a legitimate purchaser, (5) verification of the validity and integrity of eBook contents, (6) safety of direct purchase transactions, and (7) preventing eBook piracy and illegal distribution. We provide practical cryptographic protocols for the proposed system and analyze the security and simulated performance of the proposed schemes.

Real-Time Traffic Risk Detection Model Using Smart Mobile Device.

Automatically recognizing dangerous situations for a vehicle and quickly sharing this information with nearby vehicles is the most essential technology for road safety. In this paper, we propose a real-time deceleration pattern-based traffic risk detection system using smart mobile devices. Our system detects a dangerous situation through machine learning on the deceleration patterns of a driver by considering the vehicle's headway distance. In order to estimate the vehicle's headway distance, we introduce a practical vehicle detection method that exploits the shadows on the road and the taillights of the vehicle. For deceleration pattern analysis, the proposed system leverages three machine learning models: neural network, random forest, and clustering. Based on these learning models, we propose two types of decision models to make the final decisions on dangerous situations, and suggest three types of improvements to continuously enhance the traffic risk detection model. Finally, we analyze the accuracy of the proposed model based on actual driving data collected by driving on Seoul city roadways and the Gyeongbu expressway. We also propose an optimal solution for traffic risk detection by analyzing the performance between the proposed decision models and the improvement techniques.