• 한국노년학
• /
• v.31 no.3
• /
• pp.653-672
• /
• 2011

The present study empirically confirmed Korean elder's four major pains consisted of poverty, disease, role loss, loneliness and investigated the mediating role of depression between the four major pains and the elder's suicidal thought. To investigate the cause and effect of factors, we conveniently collected 309 samples from 16 Gyungrodangs evenly located in Jeonju and 291 samples, survived the data cleaning such as missing values, outliers, normality and covariance conditions, were analyzed by frequency, factor analysis, reliability, confirmatory factor analysis and structural model analysis. Followed were the selected contributions of the present study. First, the constructs of four major pains such as poverty, disease, role loss, loneliness were predictors of suicidal thought mediated by depression. Second, the elder's poverty, that was the heaviest factor of the four major pain constructs, was a predictor of role loss leading to loneliness. Third, four major pains were predictors of the elder's depression. Note that poverty were not direct but indirect predictor of depression. The present study confirmed the concept of four major pains. Also those who practice in the area of the elderly care should consider the four major pains as well as depression while intervening in the elderly's suicidal thought.

• 한국노년학
• /
• v.32 no.2
• /
• pp.359-376
• /
• 2012

The present study empirically confirmed knowledge of ageing and quality of contact were predictors affecting middle-aged Korean's ageism against the elderly and verified mediating role of intergroup anxiety between not only knowledge of ageing but also quality of contact and ageism. To investigate causalities of factors, we purposively collected 400 samples from 20 Dongs evenly located in Jeonju and 393 samples, survived the data cleaning such as missing values, outliers, normality and covariance conditions, were analyzed by frequency, factor analysis, reliability, confirmatory factor analysis and structural model analysis. Followed were the selected contributions of the present study. First, the knowledge of ageing and quality of contact were predictors of ageism mediated by intergroup anxiety. Second, the knowledge of ageing and quality of contact did not directly affect middle-aged Korean's ageism against the elderly. Third, intergroup anxiety had strong effect on ageism. The contributions suggested increasing knowledge of ageing and providing contact experience to middle-aged Korean as combating strategy against ageism.

• Journal of Korea Entertainment Industry Association
• /
• v.5 no.1
• /
• pp.65-72
• /
• 2011

The major purpose of this study is to investigate the influencing factors toward continuance intention in e-learning services. For this purpose, we introduced Post Acceptance Model(PAM) proposed by Bhattacherjee(2001) as basic analysis framework. Based on the relevant literature reviews, this study posits seven characteristics, that is, contents quality, interactivity, expectation confirmation, perceived ease of use, perceived usefulness, user satisfaction, and continuance intention as key variables to describe the post acceptance behavior in e-learning services. Data have been collected from users who have used e-learning services and the research model and hypotheses were tested through covariance structural model analysis. The results of this study are summarized as follows. First, contents quality, interactivity, and expectation confirmation have positive influence upon perceived usefulness. Second, contents quality, interactivity, expectation confirmation, and perceived ease of use have positive influence upon user satisfaction. Lastly, perceived usefulness have positive effect on the user satisfaction, and perceived usefulness and user satisfaction positively related to continuance intention in e-learning services. The findings have significant implications for e-learning service providers and academic researchers.

• Korean small business review
• /
• v.39 no.3
• /
• pp.57-76
• /
• 2017

While the unemployment rate is rising rapidly due to recent economic recession at home and abroad, university students' reluctance to apply for jobs in Small and Medium Enterprises (SME's) causes instability in manpower supply and demand and social unrest. To provide insights for solving the problem, this study explores how beliefs and attitudes of university students influence their intention to apply for jobs in SME's using Theory of Planned Behavior proposed by Icek Ajzen. This study followed the 2-stage survey methodology suggested by Ajzen. In the first stage of pilot study, a small sample of university students was used to illicit readily accessible behavioral outcomes, normative referents, and control factors. In the second stage of main study, the standard questionnaire was designed and administered and data were collected and analysed using the PLS Structural Equation Modeling (SEM) technique. PLS-SEM was used instead of Covariance Based (CB)- SEM considering the exploratory nature of this study. In overall, the results showed that TPB is very effective in explaining and predicting the university student's intention to apply for jobs in SEM's. Gender turned out to be a significant moderator variable in the relations between intention and its influence factors. Student's scholastic performance showed a negative correlation with intention. More research efforts need to be exerted to better understand university student's job seeking behavior.

• Journal of The Korean Association For Science Education
• /
• v.23 no.1
• /
• pp.35-46
• /
• 2003

Problem solving ability, having been thought as one of the most important goals of science education is also a primary task for the current education. Indeed, the students' problem solving ability has shown almost no actual progress, despite our long accumulated science education. Under this circumstances, cooperative learning, a way to grow students' positive inter-dependence and problem solving ability in the basis of their active participation and discussion, was proposed as an effective teaching method. But, results have not consistently shown the advantage of cooperative learning over traditional learning for promoting academic achievement in science. Studies have consistently shown greater effectiveness on affective aspects. But, relatively few have focused on biology in Korea. The purpose of this study was to examine the effects of cooperative learning on the achievement and attitude of high school biology students. The pretest-posttest control group design was applied. The sample consisted of 50 11th-grade female students in experimental group(cooperative learning Student Team Achievement Division model) and 50 students in control group(traditional learning). Students in both groups recieved identical content instruction on the unit 'II. Methabolism'. These groups were treated for 13 hours during 5 weeks. Achievement data were collected using a 24-item multiple-choice test(content validity= .85). Science attitude was measured by an instrument which adapted by Kim In Hee(1994). The instrument(Cronbach $\alpha$=.89) included 40 items in four subscales: attitude toward science, social meaning of science, attitude toward science class, and scientific attitude. An analysis of covariance (ANCOVA) was used as the data analysis procedure. For the achievement data, no significant difference exists between the cooperative and traditional groups (p> .05). But, cooperative learning was effective in low-ability students(p < .05). For the science learning attitude data, cooperative learning was more effective than the traditional one(p< .05). Students in the cooperative group acheived better than those in traditional one especially in the subscale of attitude toward science class. There was no meaningful difference of the two methods in both high and average ability students, while cooperative learning was more effective than the traditional one in low ability students(p<.05).

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.22 no.4
• /
• pp.327-339
• /
• 2020

Transpiration is the movement of water into the atmosphere through leaf stomata of plant, and it accounts for more than half of evapotranspiration from the land surface. The measurements of transpiration could be conducted in various ways including eddy covariance and water balance method etc. However, the transpiration measurements of individual trees are necessary to quantify and compare the water use of each species and individual component within stands. For the measurement of the transpiration by individual tree, the thermometric methods such as heat dissipation and heat pulse methods are widely used. However, it is difficult and labor consuming to maintain the transpiration measurements of individual trees in a wide range area and especially for long-term experiment. Therefore, the sharing of sapflow data through database should be useful to promote the studies on transpiration and water balance for large spatial scale. In this paper, we present sap flow database, which have Granier type sap flux data from 18 Korean pine (Pinus koraiensis) since 2011 and 16 (Quercus aliena) since 2013 in Mt.Taehwa Seoul National University forest and 18 needle fir (Abies holophylla), seven (Quercus serrata), three (Carpinus laxiflora and C. cordata each since 2013 in Gwangneung. In addition, the database includes the sapling transpiration of nine species (Prunus sargentii, Larix kaempferii, Quercus accutisima, Pinus densiflora, Fraxinus rhynchophylla, Chamecypans obtuse, P. koraiensis, Betulla platyphylla, A. holophylla, Pinus thunbergii), which were measured using heat pulse method since 2018. We believe this is the first database to share the sapflux data in Rep. of Korea, and we wish our database to be used by other researchers and contribute a variety of researches in this field.

• Tuberculosis and Respiratory Diseases
• /
• v.44 no.6
• /
• pp.1296-1307
• /
• 1997

Background : Erdosteine is a thiol derivative developed for the treatement of chronic obstructive bronchitis, including acute infective exacerbation of chronic bronchitis. Erdosteine has mucomodulating and antioxidant properties and especially exhibits excellent gastrointestinal tolerability. Methods : The study was conducted as a prospective evaluation, with 2 comparative groups orally treated with erdosteine 300mg (bid.) or ambroxol 30mg (b.i.d.) for 7 days and the design of trial was double-blind. The treatments have been assigned randomly to patients (n=80) with acute or chronic bronchitis. The primary end-point used to determine efficacy in this study was subjective symptoms including expectorating frequence, expectoration volume, expectorating difficulty, expectoration viscosity, cough intensity and dyspnea. The secondary end-points of efficacy was the result of arterial blood gas analysis and pulmonary function test. Safety was evaluated with adverse drug reactions and laboratory tests monitoring. 61 patients was included in the efficacy analysis, due to the fact that 19 patients drop-out for different reasons. The obtained values have been analyzed with paired Hest., ANOVA test., multivariate $t^2$-test, repeated measures analysis of covariance, two sample t-test, loglinear-logit model analysis, Fisher's exact test. Results : 1) There was no significant difference on demographic data and vital signs between erdosteine and ambroxol treated groups. 2) The comparison between erdosteine and ambroxol treated groups showed no significant difference in improvement of each symptom in spite of the more favorable efficacy obtained with erdosteine. No difference on the contrary was observed for arterial blood gas analysis and pulmonary function test. 3) As safety is concerned, no clinical significant changes in laboratory test and symptom were induced in erdosteine and ambroxol treated group and two patients in ambroxol treated group drop-out for adverse reactions in symptom. 4) In the evaluation of final clinical efficacy, erdosteine improved more effectively patient's overall symptoms {very good effect (11/31), good effect (12/31), moderate effect (6/31), no effect (2/31), aggravation (0/31)} than ambroxol {very good effect (6/30), good effect (14/30), moderate effect (5/30), no effect (4/30), aggravation (2/30)}. And the probability of symptomatic improvement by erdosteine compared to ambroxol was 2.5 times. (p<0.05). Conclusion : This study showed that erdosteine was clinically effective and safe drug for treatment of acute and chronic bronchitis.

• Journal of Intelligence and Information Systems
• /
• v.22 no.2
• /
• pp.81-96
• /
• 2016

As the internet has become widespread and easy to access everywhere, it is common for people to search information via online search engines such as Google and Naver in everyday life. Recent studies have used online search volume of specific keyword as a measure of the internet users' attention in order to predict disease outbreaks such as flu and cancer, an unemployment rate, and an index of a nation's economic condition, and etc. For stock traders, web search is also one of major information resources to obtain data about individual stock items. Therefore, search volume of a stock item can reflect the amount of investors' attention on it. The investor attention has been regarded as a crucial factor influencing on stock price but it has been measured by indirect proxies such as market capitalization, trading volume, advertising expense, and etc. It has been theoretically and empirically proved that an increase of investors' attention on a stock item brings temporary increase of the stock price and the price recovers in the long run. Recent development of internet environment enables to measure the investor attention directly by the internet search volume of individual stock item, which has been used to show the attention-induced price pressure. Previous studies focus mainly on Dow Jones and NASDAQ market in the United States. In this paper, we investigate the relationship between the individual investors' attention measured by the internet search volumes and stock price changes of individual stock items in the KOSDAQ market in Korea, where the proportion of the trades by individual investors are about 90% of the total. In addition, we examine the difference between industries in the influence of investors' attention on stock return. The internet search volume of stocks were gathered from "Naver Trend" service weekly between January 2007 and June 2015. The regression model with the error term with AR(1) covariance structure is used to analyze the data since the weekly prices in a stock item are systematically correlated. The market capitalization, trading volume, the increment of trading volume, and the month in which each trade occurs are included in the model as control variables. The fitted model shows that an abnormal increase of search volume of a stock item has a positive influence on the stock return and the amount of the influence varies among the industry. The stock items in IT software, construction, and distribution industries have shown to be more influenced by the abnormally large internet search volume than the average across the industries. On the other hand, the stock items in IT hardware, manufacturing, entertainment, finance, and communication industries are less influenced by the abnormal search volume than the average. In order to verify price pressure caused by investors' attention in KOSDAQ, the stock return of the current week is modelled using the abnormal search volume observed one to four weeks ahead. On average, the abnormally large increment of the search volume increased the stock return of the current week and one week later, and it decreased the stock return in two and three weeks later. There is no significant relationship with the stock return after 4 weeks. This relationship differs among the industries. An abnormal search volume brings particularly severe price reversal on the stocks in the IT software industry, which are often to be targets of irrational investments by individual investors. An abnormal search volume caused less severe price reversal on the stocks in the manufacturing and IT hardware industries than on average across the industries. The price reversal was not observed in the communication, finance, entertainment, and transportation industries, which are known to be influenced largely by macro-economic factors such as oil price and currency exchange rate. The result of this study can be utilized to construct an intelligent trading system based on the big data gathered from web search engines, social network services, and internet communities. Particularly, the difference of price reversal effect between industries may provide useful information to make a portfolio and build an investment strategy.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.135-149
• /
• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.20 no.1
• /
• pp.88-100
• /
• 2018

Methane emissions from rice paddies are the largest source of greenhouse gases in the agricultural sector, but there are significant regional differences depending on the surrounding conditions and cultivation practices. To visualize these differences and to analyze their causes and characteristics, the methane emissions from each administrative district in South Korea were calculated according to the IPCC guidelines using the data from the 2010 Agriculture, Forestry and Fisheries Census, and then the results were mapped by using the ArcGIS. The nationwide average of methane emissions per unit area was $380{\pm}74kg\;CH_4\;ha^{-1}\;yr^{-1}$. The western region showed a trend toward higher values than the eastern region. One of the major causes resulting in such regional differences was the $SF_o$ (scaling factor associated with the application of organic matter), where the number of cultivation days played an important role to either offset or deepen the differences. Comparison of our results against the actual methane emissions data observed by eddy covariance flux measurement in the three KoFlux rice paddy sites in Gimje, Haenam and Cheorwon showed some differences but encouraging results with a difference of 10 % or less depending on the sites and years. Using the updated GWP (global warming potential) value of 28, the national total methane emission in 2010 was estimated to be $8,742,000tons\;CO_2eq$ - 13% lower than that of the National Greenhouse Gas Inventory Report (i.e., $10,048,000tons\;CO_2eq$). The administrative districts-based map of methane emissions developed in this study can help identify the regional differences, and the analysis of their key controlling factors will provide important scientific basis for the practical policy makings for methane mitigation.