김지범 교수

OBJECTIVE: Faced with aging societies, there is an immense need to better understand the nature of volunteering outside advanced Western industrial countries. As a case of a rapidly aging society, we identify robust factors associated with elderly volunteering in Korea in terms of a resource framework. METHODS: Data were derived from the Social Statistics Survey conducted by the Korea National Statistical Office in 1999 (N = 7,135) and 2003 (N = 8,371). We first determined overall and age-relate

This article explores whether public attitudes vary between environmental issues. We focus on climate change caused by global warming, and compare it with other environmental issues. We find significant differences in the attitude toward climate change vis-à-vis other environmental issues between respondents’ educational attainments and between politician partisanships. We argue that the relative complexity of the climate issue compared to other environmental problems may be a reason for this va

ocial-science research has been transformed over the last generation by the advent and expansion of the general social surveys (GSS). The GSS model of research has created a infrastructure for the social sciences designed to address the interests and research agenda of scholars and their students; cover a wide range of topics; utilize reliable, valid, and generalizable measurement; and provide data both across nations and across time. This design in turn has generated widespread analysis and not

Previous studies of the determinants of living arrangements for immigrant elderly have focused on cultural tradition or individual characteristics, without addressing community-level structural factors. This study examined the living arrangements of Korean American elderly. The authors hypothesized that the probability of Korean American elderly living independently is associated with community characteristics, specifically the availability of subsidized housing near Korean ethnic communities. U

SUMMARY We propose a multiobjective mesh optimization framework for mesh quality improvement and mesh untangling. Our framework combines two or more competing objective functions into a single objective function to be solved using one of various multiobjective optimization methods. Methods within our framework are able to optimize various aspects of the mesh such as the element shape, element size, associated PDE interpolation error, and number of inverted elements, but the improvement is not li

The fundamental properties of long-range (LoRa) performance have been revealed by previous research, but advanced issues remain unresolved. This paper tackles three technical challenges that are confronted when establishing a LoRa network on a smart energy campus testbed in Korea. First, the communication range of LoRa in a combined indoor and outdoor environment has yet to be determined. To address this problem, this study builds a LoRa testbed from which we measure the propagation properties o

Our results highlight the importance of living arrangements to older men's suicidal ideation. We discuss gender differences in the implications of living arrangements to suicidal ideation within the context of Confucian culture.

Abstract Several studies have attempted to solve traveling salesman problems (TSPs) using various deep learning techniques. Among them, Transformer-based models show state-of-the-art performance even for large-scale Traveling Salesman Problems (TSPs). However, they are based on fully-connected attention models and suffer from large computational complexity and GPU memory usage. Our work is the first CNN-Transformer model based on a CNN embedding layer and partial self-attention for TSP. Our CNN-

Korea's religious context is not simple. According to the 2005 Korean Census, the Korean population consists of 23 percent Buddhists, 18 percent Protestants, and 11 percent Catholics, with 47 percent nonreligious. To accurately describe Korean religion in recent periods, we have used 1985, 1995, and 2005 Korean Censuses. We found that Korean people became more religious from 1985 to 1995, but that change was stalled from 1995 to 2005. The percentages of Buddhists and Protestants exhibited little

Optimizing techniques for discovering molecular structures with desired properties is crucial in artificial intelligence (AI)-based drug discovery. Combining deep generative models with reinforcement learning has emerged as an effective strategy for generating molecules with specific properties. Despite its potential, this approach is ineffective in exploring the vast chemical space and optimizing particular chemical properties. To overcome these limitations, we present Mol-AIR, a reinforcement

Poorly-shaped and/or inverted elements negatively affect numerical simulation accuracy and efficiency. Most current approaches consider mesh quality improvement and mesh untangling problems as numerical optimization problems and solve them using nonlinear optimization. However, these optimization-based approaches require users to set and solve complex numerical optimization problems with no guarantee that the output meshes are valid meshes with good element qualities. Therefore, this paper propo