• Journal of Korea Entertainment Industry Association
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• v.14 no.4
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• pp.321-331
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• 2020

This study was conducted to investigate communication competence, clinical competence and experience of handover which influence self-efficacy of handover among nursing students. The study design was a descriptive survey. A total of 255 students were recruited from nursing departments in G-city. Structured questionnaire was self-administrated from June to September, 2019. The collected data were analyzed using t-test, ANOVA, Pearson's coefficient and stepwise multiple regression. As results of the study, communication competence 57.3, clinical competence 69.8 and self-efficacy of handover was 33.8. Self-efficacy of handover had significant differences in gender(F=4.60, p<.001), age(F=16.72, p<.001), grade(t=-6.39, p<.001), satisfaction of clinical practice(F=3.68, p=.027), education experience about handover(t=26.44, p<.001), experience of handover(t=4.84, p<.001), fear of handover(F=16.97, p<.001), and handover importance of patient's safety(F=6.42, p=.002). Self-efficacy of handover had significant positive correlations with communication competence(r=.249, p<.001) and clinical competence(r=.426, p<.001). In multiple regression analysis, fear of handover(β=-.294, p<.001), clinical competence(β=.252, p<.001), grade(β=.191, p=.001), experience of handover(β=.185, p<.001), gender(β=.150, p=.003), and education experience about handover(β=.126, p=.017) were significant factors of self-efficacy of handover explaining 40.0%(F=29.26, p<.001) of the variables. In conclusion, to enhance self-efficacy of handover for nursing students, it is necessary to develop educational program for increasing experiences of handover, education experience about handover, and clinical competence.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.83-92
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• 2023

This study was established to investigate the site environment of mixed forests in Korea and to estimate the growth and yield of stands using national forest resources inventory data. The growth of mixed forests was derived by applying the Chapman-Richards model with diameter at breast height (DBH), height, and cross-sectional area at breast height (BA), and the yield of mixed forests was derived by applying stepwise regression analysis with factors such as cross-sectional area at breast height, site index (SI), age, and standing tree density per ha. Mixed forests were found to be growing in various locations. By climate zone, more than half of them were distributed in the temperate central region. By altitude, about 62% were distributed at 101-400 m. The fitness indexes (FI) for the growth model of mixed forests, which is the independent variable of stand age, were 0.32 for the DBH estimation, 0.22 for the height estimation, and 0.18 for the basal area at breast height estimation, which were somewhat low. However, considering the graph and residual between the estimated and measured values of the estimation equation, the use of this estimation model is not expected to cause any particular problems. The yield prediction model of mixed forests was derived as follows: Stand volume =-162.6859+6.3434 ∙ BA+9.9214 ∙ SI+0.7271 ∙ Age, which is a step- by-step input of basal area at breast height (BA), site index (SI), and age among several growth factors, and the determination coefficient (R²) of the equation was about 96%. Using our optimal growth and yield prediction model, a makeshift stand yield table was created. This table of mixed forests was also used to derive the rotation of the highest production in volume.

• Korean Journal of Heritage: History & Science
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• v.55 no.4
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• pp.278-294
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• 2022

To date, cultural property education has seen rapid quantitative growth due to national and personal needs. However, qualitative growth is lacking. The objectives of cultural property education have not been established, and therefore, even its identity is not clear. The most pressing issue at present in cultural property education is to first set objectives. This study aimed to analyze the objectives of current cultural property education, identify the problems, and set new objectives to meet significant national and personal needs in terms of education. The problems with the objectives of current cultural property education are that the persons interested in the education do not understand the concept of the education objectives clearly and that the objectives do not contain much actual content of the education. Also, the objectives of the education do not take into account the dynamic competencies and interests of the learners and do not satisfy the changes of the times. To solve these problems, new cultural property education, called 'U.V.E.C.,' was offerred. U.V.E.C. education is aimed at understanding cultural properties, recognizing their value, and enjoying them, and at creating culture. The objectives of U.V.E.C. cultural property education were set such that they can be modified flexibly in a learner-centric way with clear and practical format and contents. Based on this direction, stepwise objectives were set including overall objectives, detailed objectives, and practice objectives, and objective cases of each step were proposed. Considering the generality of the education and the distinct characteristics of the cultural properties, the U.V.E.C. education objectives took into account the diversity of behavioral objectives, clearness in statements, the objectives of problem solving, the initiative of learners and openness for expression outcomes. The U.V.E.C. objectives are clear and specific so that teachers can enhance their pedagogical efficiency and learners are able to develop interesting and diversified competencies. In addition, it is expected that the U.V.E.C. objectives will significantly affect objective setting for education on cultural properties which have not been studied widely. Further systemic and specific studies on the contents and methods of the U.V.E.C. education would help to change the overall education on cultural properties and position the field as a new academic area.

• Journal of Nutrition and Health
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• v.56 no.3
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• pp.264-276
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• 2023

Purpose: Food environmental factors related to food insecurity affect household food intake in several socio-ecological aspects. This study explores the relationship between food environment factors and food insecurity in households with married immigrant women. Methods: From November 2018 to February 2020, a survey was conducted enrolling 249 married immigrant women residing in the metropolitan areas of South Korea. In the final analysis, 229 subjects were divided into 2 groups classified as food security (n = 154) and food insecurity (n = 75), as assessed by the score of food security. Three aspects of food environments were measured: built·natural, political·economic, and socio-cultural Results: Food environments were significantly different between food security and food insecurity groups, as follows: the number of foods market and their distance from the home and food status for the last week at home in the built·natural domain; monthly cost of food purchase and experience for food assistance in the political·economic domain; total score of social support, parenting, and cooking skills in the socio-cultural domain. A stepwise multivariate linear regression model showed a negative association between the food insecurity score with social support from family and food inventory status in the last week. After adjusting for confounders, a positive association was obtained between the experience of a food support program. The final regression model explains about 30% of the relationship obtained in the three food environment domains and food insecurity (p < 0.001). Conclusion: Not only economic factors, which are common determinants of household food insecurity, but socio-cultural factors such as social support also affect household food insecurity. Therefore, plans for implementing a food assistance program to improve food insecurity for households with immigrant women should consider financial support as well as other comprehensive aspects, including socio-cultural domain such as social support from family and community.

• 한국노년학
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• v.31 no.1
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• pp.33-47
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• 2011

The purpose of this study was to examine the moderating and mediating effects of social support and social activity on the relationship between depression and quality of life in elderly women living alone. Subjects were 129 elderly living alone at K city in C province, from June to July, 2010. The data was analyzed using the SPSS program for descriptive statistics, Pearson's correlation coefficient and stepwise multiple regression. The degree of depression of elderly living alone was above the average(2.65), and that of quality of life was average(2.80). The correlated factors of quality of life among elderly women living alone included depression(r=-.745, p=.004), social support(r=.544, p=.000), leisure activity(r=.480, p=.024), and economic activity(r=.711, p=.001). Social support was an important mediator between the depression and quality of life in elderly women living alone. The moderating effects of social support and social activity between depression and quality of life in elderly women living alone were not significant. This study suggests that social support considered in enhancing the quality of life programs designed for elderly living alone. Further research needs to be done to refine moderating and mediating effects of social support, social activity including leisure activity, economic activity and volunteer activity.

• Tunnel and Underground Space
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• v.34 no.4
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• pp.374-392
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• 2024

In order to successfully select a site for deep geological disposal of high-level radioactive waste, it is important to perform the stepwise approach along with the systematic selection and survey of evaluation parameters of geological environmental characteristics suitable for the domestic geological environment. In this study, we evaluated the characteristics of hydraulic conductivity, which is considered the most important evaluation parameter in the field of hydrogeology, targeting a deep-depth rock aquifer where actual disposal facilities are expected to be located. In particular, for the first time in Korea, we obtained in-situ pressure-flow data by directly conducting hydraulic tests in boreholes at depths ranging from 500 m to 750 m in various rock types distributed in Korea (granite/volcanic rock/gneiss/mudstone). And we derived hydraulic conductivity values by rock types and depth using verified analytical methods. For this purpose, precision hydraulic testing equipment developed in-house through this study was used, and detailed investigation procedures based on standard test methods were applied to field tests. As a result of the analysis, the average hydraulic conductivity value was found to be in the range of 10^-9 m/s in all granite/volcanic rock/gneiss areas. In the mudstone area, an average hydraulic conductivity value of 10^-11 m/s was derived, which was about 100 times (2 orders of magnitude) lower than that of the fractured rock aquifers. Moreover, permeability tended to slightly decrease with depth in fractured rock aquifers (granite and volcanic rock areas) containing many rock fractures. The gneiss area tended to have large local differences in permeability according to the composition of the stratum and the development of fracture zones rather than depth. In mudstone areas with weak fracture development, there was no significant variation in rock permeability according to depth. The hydraulic conductivity results by various rock types and depth presented in this study are expected to be utilized in building a foundational database for the site selection, design, and construction of disposal facilities in Korea.

• Journal of Korean Society of Forest Science
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• v.84 no.4
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• pp.465-478
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• 1995

The objective of this study was to investigate the impacts of large-scale timber harvesting on the environment of a mature hardwood forest. To achieve the objective, the effects of harvesting on forest environmental factors were analyzed quantitatively using the field data measured in the study sites of Seoul National University Research Forests [(Mt.) Paekunsan] for two years(1993-1994) following timber harvesting. The field data include information on vegetation, soil mesofauna, physicochemical characteristics of soil, surface water runoff, water quality in the stream, and hillslope erosion. For comparison, field data for each environmental factor were collected in forest areas disturbed by logging and undisturbed, separately. The results of this study were as follows : The diversity of vegetational species increased in the harvested sites. However, the similarity index value of species between harvested and non-harvested sites was close to each other. Soil bulk density and soil hardness were increased after timber harvesting, respectively. The level of organic matter, total-N, avail $P_2O_5$, CEC($K^+$, $Na^+$, $Ca^{{+}{+}}$, $Mg^{{+}{+}}$) in the harvested area were found decreased. While the population of Colembola spp., and Acari spp. among soil mesofauna in harvested sites increased by two to seven times compared to those of non-harvested sites during the first year, the rates of increment decreased in the second year. However, those members of soil mesofauna in harvested sites were still higher than those of non-harvested sites in the second year. The results of statistical analysis using the stepwise regression method indicated that the diversity of soil mesofauna were significantly affected by soil moisture, soil bulk density, $Mg^{{+}{+}}$, CEC, and soil temperature at soil depth of 5(0~10)cm in the order of importance. The amount of surface water runoff on harvested sites was larger than that of non-harvested sites by 28% in the first year and 24.5% in the second year after timber harvesting. The level of BOD, COD, and pH in the stream water on the harvested sites reached at the level of the domestic use for drinking in the first and second year after timber harvesting. Such heavy metals as Cd, Pb, Cu, and organic P were not found. Moreover, the level of eight factors of domestic use for drinking water designated by the Ministry of Health and Welfare of Korea were within the level of the first class in the quality of drinking water standard. The study also showed that the amount of hillslope erosion in harvested sites was 4.77 ton/ha/yr in the first year after timber harvesting. In the second year, the amount decreased rapidly to 1.0 ton/ha/yr. The impact of logging on hillslope erosion in the harvested sites was larger than that in non-harvested sites by seven times in the first year and two times in the second year. The above results indicate that the large-scale timber harvesting cause significant changes in the environmental factors. However, the results are based on only two-year field observation. We should take more field observation and analyses to increase understandings on the impacts of timber harvesting on environmental changes. With the understandings, we might be able to improve the technology of timber harvesting operations to reduce the environmental impacts of large-scale timber harvesting.

• Economic and Environmental Geology
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• v.40 no.2 s.183
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• pp.191-207
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• 2007

A paleomagnetic investigation for the Cretaceous rocks in the Buyeo and Hampyeong Basins, located out of the Gyeongsang Basin, was carried out in order to elucidate the paleomagnetic directions in conjunction with the formation of the basins. Typical stepwise thermal demagnetization and measurement methods were used to determine the directions of characteristic remanent magnetizations (ChRMs). The mean direction of the sedimentary rocks from the Buyeo Basin after bedding correction $(D/I=356.5^{\circ}/61.5^{\circ},\;k=39.3\;\alpha_{95}=7.4^{\circ})$, is more dispersed than that before bedding correction $(D/I=356.5^{\circ}/61.5^{\circ},\;k=39.3\;\alpha_{95}=7.4^{\circ})$, which suggests that the rocks in the Buyeo Basin were remagnetized. However, the statistics and dispersion of the ChRM directions after bedding correction are still acceptable and the paleomagnetic pole position after tilt correction $(Lat./Long.=69.3^{\circ}N/186.7^{\circ}E,\;K=11.6\;A_{95}=14.0^{\circ})$ is closer to that of the Late Cretaceous pole of the Korean Peninsula. More detailed study is needed to confirm the nature of the remagnetization in the Buyeo Basin. On the other hand, the paleomagnetic pole before bedding correction $(Lat./Long.=81.6^{\circ}N/106.9^{\circ}E,\;K=25.1\;A_{95}=9.3^{\circ})$ is positioned near the paleogene pole of the Eurasian APWP. The mean ChRM direction of the sedimentary rocks from the Hampyeong Basin after bedding correction is $D/I=32.5^{\circ}/55.4^{\circ},\;(k=35.6,\;\alpha_{95}=8.7^{\circ})$. It is more clustered than that before bedding correction $D/I=18.3^{\circ}/62.5^{\circ},\;k=14.1,\;\alpha_{95}=14.2^{\circ})$, indicating that the ChRM was acquired before tilting of the strata. The paleomagnetic pole position of the Cretaceous sedimentary rocks in the Hampyeong Basin, averaged out of site pole positions calculated from the tilt-corrected ChRMs, is $Lat./Long.=63.9^{\circ}N/202.7^{\circ}E,\;(K=21.3,\;A_{95}=7.6^{\circ})$, similar to the Late Cretaceous paleomagnetic pole of the Korean Peninsula $(Lat./Long.=70.9^{\circ}N/215.4^{\circ}E,\;A_{95}=5.3^{\circ})$, suggesting that the Hampyeong Basin has been stable since the Late Cretaceous period. One normal and two reversed ChRM directions are revealed through the measurements of the volcanic rocks from the Hampyeong Basin. Although these normal and reversed directions are not exactly antipodal, it is interpreted that the normal direction is the representative primary direction of the volcanic rocks of the Hampyeong Basin and the mixed polarity is the records of geomagnetic field at the time of the formation of the volcanic rocks. Paleomagnetic poles are at $Lat./Long.=70.2^{\circ}N/199.5^{\circ}E,\;(K=18.1,\;A_{95}=9.6^{\circ})$ for the normal direction, and $Lat./Long.=65.5^{\circ}S/251.3^{\circ}E,\;(K=7.1,\;A_{95}=20.7^{\circ})$ for the reversed direction. Compared with the representative pole positions of the Cretaceous period of the Korean Peninsula, it is concluded that the age of the volcanic rocks in the Hampyeong Basin is of the Late Cretaceous.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.