KSCE Journal of Civil and Environmental Engineering Research
/
v.26
no.2D
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pp.233-239
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2006
Most research for until at now link travel time were research for mean link travel time calculate or estimate which uses the average of the individual vehicle. however, the link travel time distribution is divided caused by with the impact factor which is various traffic condition, signal operation condition and the road conditional etc. preceding study result for link travel time distribution characteristic showed that the patterns of going through traffic were divided up to 2 in the link travel times. therefore, it will be more accurate to divide up the link travel time into the one involving delay and the other without delay, rather than using the average link travel time in terms of assessing the traffic situation. this study is it analyzed transit hour distribution characteristic and a cause using examine to the variables which give an effect at link travel time distribute using simulation program and determinate link travel time distribute ratio estimation model. to assess the distribution of the link travel times, this research develops the regression model and the fuzzy model. the variables that have high level of correlations in both estimation models are the rest time of green ball and the delay vehicles. these variables were used to construct the methods in the estimation models. The comparison of the two estimation models-fuzzy and regression model- showed that fuzzy model out-competed the regression model in terms of reliability and applicability.
This study reviewed the diagnosis accuracy and evaluation criteria of the fall risk assessment questionnaire that proved validity through factor analysis in previous studies. The purpose of this study was to diagnose high-risk groups and propose personal fall risk profiles for the Korean community-dwelling elderly. The participants of this study were 439 elderly people living in S, U, B, and Y cities Korea (mean age 75.0 ± 5.7 years). Receiver operating characteristic analysis was conducted to review the accuracy of the fall risk assessment and evaluation criteria of the FRA. The results showed that the four sub-factors of the FRA: 'Potential for fall', 'Disease and physical symptoms', 'Environment' and 'Physical function', can effectively diagnose the fall risk of the community-dwelling elderly. The evaluation criteria was presented based on the sensitivity and specificity results. In addition, as a result of analyzing the patterns by the sub factors of the fall risk, the high-risk group accounted for 80% of the elderly who had problems with two or more factors. Therefore, the four sub-factors of FRA can effectively diagnose the fall risk level, and could be present individual fall risk profiles based on the evaluation criteria.
Amid global pandemic of covid-19, Korean government's response has drawn wide attention among social scientists as well as medical studies. The role of Korean state and civil society has attracted particular attention among others. Yet, this paper criticizes extant studies on Korean case which focus on the extensive intervention of the strong state and subjective attitude of Korean citizens in coping with covid-19. The concept of the strong state lacks social scientific specification and subjective citizens do not match with Korean realities. This article argues that Korean state's capacity in collecting and mobilizing digital data may offer better understanding for the successful responses to the pandemic. First, Korean state is the ultimate coordinator in collecting, analyzing and applying big data about the expansion of covid-19 with its huge network of dataveillance. Also, such role has been largely based upon relevant legal framework and well prepared manuals and cooperation with civic actors and companies. In other words, Korean digital dataveillance had demonstrated its transparency and cooperative governance. Second, such dataveillance capacity has deep roots in the long-term development of Korean state's big data management. Korean state has evolved about thirty years while enhancing digital data network within governments, companies and private sectors. Third, the relationship between Korean state's dataveillance and civil society can be characterized as a state centered push model. This model demonstrates highly effective governmental responses to covid-19 crisis but fall short of building social consensus in balancing individual freedom, human rights and effective containment policies. It means communitarian solidarity among citizens has not been a major factor in Korea's successful response yet.
The purpose of this study is to explore the effect of individual difference variables on emotional change after indirect trauma among elderly. After Sewolho Accident in South Korea, we invested emotion of the elderly and collected the same variables from the same sample two months after the accident. In study, we examined how social support, depression, future time perspective, and active aging affect emotion of elderly and which emotions are affected by these four variables. As a result, when compared to the counterpart, those with lower perceived social support, future time perspective, and active aging and higher depression level experienced lower levels of positive emotion and higher level of negative emotion after indirect disaster experience. Overall, we could certify that social support, future time perspective, and active aging functioned as protective factors, whereas depression functioned as a risk factor. Implications and limitations for our findings were discussed.
The purpose of this study was to examine the effects of the job characteristics of the PR practitioner on flourishing and to identify the mediating effect of the meaning of work and job engagement. This study used a survey of 353 PR practitioners from PR firms and the hypothesis was verified by gathering the data of the job characteristics, the meaning of work, the job engagement and the flourishing by conducting hierarchical regression analysis and SPSS Process Macro bootstrapping analysis. The results showed that the job characteristics of the PR practitioner were found to influence the flourishing with the double-mediation effect, the meaning of the work and job engagement. The more PR practitioners regard their work affects their surroundings, the higher the degree of autonomy is, and the more they get feedbacks they engaged more, as they valued their job more thereby experience flourish more. Moreover, when PR practitioners valued their job more, they more engaged in their work and experience flourish. The meaning of work revealed to be the important factor to affect flourish regardless of the job characteristics and the job engagement so that gained the results that PR firms' effort to elevate the meaning of work of PR practitioners has is important. The study findings suggest that PR practitioners' flourishing is manageable in the organization by paying attention to oneself in the aspect of the organization, not leaving in individual areas. Limitaions and implications for future studies were discussed.
This study examines Korean representation of the biotechnology and psychological factors which can influence lay people's perception and attitude about biotechnology. Korean college students(N=433) and lay adults(N=90) whom had college education participated in the study. Participants of the study 1 were asked to list words which comes to mind when associate with the biotechnology in broad sense, and several specific applications in health, medicines, agriculture and research. Participants of the study 2 were asked to list possible benefits and costs of biotechnology and their specific applications. In study 3, Participants responded the questionnaires about perceptions and attitudes of biotechnology. Korean people associated the biotechnology with its costs or risks and benefits. Korean college students mainly got the informations of the biotechnology from TV, newspapers, or internet. They trusted the scientist group and NGO group on their judgements about the assessment of risk and benefit of the biotechnology. College students showed the positive attitude with the applications in medicines and negative attitude with the applications in agriculture and public using of individual's genetic information. The radicalism, sensitivity in behavioral activation system, and trust/cynicism were to be found as a significant influencing factor for interest/knowledge and behavioral intention in related with biotechnology. Finally, more extensive knowledge of biotechnology did not lead to greater acceptance of it.
Background: Depressive disorders can be categorized into daily depression and clinical depression. The experience of depressive disorder can increase health care utilization due to decreased treatment compliance and somatization. On the other hand, the clinical depression group may also experience social prejudice associated with the illness, which can limit their access to health care utilization. In terms of the significance of health care utilization as a factor in individual and social issues, this study aims to compare the health care utilization of the clinical depression group with that of the non-depressed group and the daily depression group. Methods: The analysis utilized the inverse probability of treatment weighting based on the generalized propensity score. Results: As a result of the analysis, clinical depression and daily depression were higher among women, low-income groups, individuals with low education levels, and so forth. The clinical depression group was also higher among individuals who were not economically active, did not have private health insurance, or had multiple chronic diseases. The number of outpatient department visits in the depression group was significantly higher than in the non-depressed group. In addition, the number of outpatient department visits for the clinical depression group was significantly higher than that for the daily depression group. Outpatient medical expenses were higher in the depression group than in the non-depressed group, and there was no significant difference between the clinical depression group and the daily depression group. Conclusion: Health care utilization was higher in the depression group than the non-depressed group, it was also higher in the clinical depression group than the daily depression group.
In this study, we developed a sustainability education program employing a project-based learning strategy for prospective teachers and investigated its effectiveness. A total of 23 senior students from a university of education participated in the study. The investigation involved a pretest on their pro-environmental behavior and attitudes, followed by a five-week implementation of the program, during which students individually engaged in energy-saving projects. Following the program, a post-test, which used the same questionnaire as the pretest, was administered. In addition, we conducted individual interviews with nine students who actively engaged in the projects. We analyzed the interview contents, portfolios, and reports; identified sub-concepts related to the program's effectiveness and its causes; and then organized them into subcategories. Then, we extracted recurring relationships among the subcategories to formulate a tentative explanatory model. The results indicate that the program positively impacted students' pro-environmental behavior and values/attitudes. Notably, the students' "sense of achievement gained through success" emerged as a significant factor influencing their pro-environmental behavior. Furthermore, some causes were found to indirectly affect pro-environmental behavior through pro-environmental values and attitudes.
In the newly encountered economy caused by the Corona virus Disease-19, remote transaction becomes a new normal that disrupt traditional economic order. In the middle of the disruption, mobile tech is placed and remote finance on mobile is highly noticed and considered as an infra-tech to support the new ecology, In mobile finance. remote payment is becoming the most common service and personal identification on it is critical to operate the new service. There are various means of remotely identifying a person. Recently the use of biometric information is increasing. In this study, a correlation analysis was conducted on factors that effects to biometrics usage and the intention to use in remote personal identification. Variables for critical factor in the remote identification were classified into 4 groups such as Performance expectancy, Effort expectancy, Social influence, and Security. The empirical analysis based on the Unified Theory of Acceptance and Use of Technology (UTAUT) was conducted. The relationship between variables and the intention to use is also analyzed. In the study, stepwise regression analysis was conducted four times in which variables were adjusted in individual stage. As a result, the analysis suggests that performance expectancy, effort expectancy, social influence, security have positive effects for intention to use. Additionally, effort expectancy and security have moderating effects to intention to use depends on biometric authentication experience. The analysis has shown positive effect of variables grouped on the intention to use them. It also suggests that the intention to use biometric authentication will quickly grow. This study is expected to make a contribution to utilize and improve the use of biometric information in mobile payment.
With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.
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