• 한국노년학
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• v.29 no.3
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• pp.1063-1085
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• 2009

The purposes of this study were to investigate the transition of elderly's welfare service utilization and to examine the factors affecting their utilization as time passed. To solve these research questions, the behavioral model presented by Andersen and Newman(1973) was applied. Using Hallym Aging Panel data consisted of 3 waves from 2003 to 2007, autoregressive modeling and regression analysis were applied for research purposes. The results of this study were as follows; (1) The experiences of welfare service utilization were increasing gradually. The complimentary service for the aged was utilized generally, but leisure service and community service were not used in common. (2) Past experience of service affected service utilization in the following times. (3) The factors affecting older adult's service utilization were different among the types of services. Nonetheless, the factors affecting continuously during the periods were found: age as predisposing factor and area as enabling factor in the complimentary service; area and existence of spouse as enabling factor in leisure service; education as predisposing factor and service cognition as enabling factor in community service. Enabling factor has affected more consistently than other factors. The results showed that special attention should be paid to balanced regional arrangement for welfare resources and the public relation considering the elderly's intellectual level.

• Korean Journal of School Psychology
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• v.9 no.2
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• pp.289-309
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• 2012

This is a longitudinal study which analyzed suppression effects of active/passive coping strategies on academic burnout, using a structural equation modeling. A total of 357 middle school students participated in this study for two waves. In order to measure the levels of students' coping strategies and academic burnout, the Ways of Coping Checklist and the Korean version of Maslach Burnout InventoryStudent Survey(MBI-SS) were used. Latent variables were constructed with standardized residuals computed from a simple linear regression in order to capture the intra-individual changes between two time points. The results of this study are like below. First, the relationship between the change of active coping strategy and the change of passive coping strategy is positively and significantly related with each other. This result indicates that students under stress use various coping strategies simultaneously. Second, significant suppression effects were revealed between the change of active coping strategy and the change of passive coping strategy. That is, when controlling passive coping strategy, the negative relationship between the change of active coping strategy and the change of academic burnout increased. On the other hand, when controlling active coping strategy, the positive relationship between the change of passive coping strategy and the change of academic burnout increased. Based on these results, the value of this study and implications for counseling were discussed.

• Journal of Korean Neurosurgical Society
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• v.67 no.1
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• pp.84-93
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• 2024

Objective : We aimed to examine trends in critically ill neurology-neurosurgery (NNS) patients who were admitted to the intensive care unit (ICU) in South Korea and identify risk factors for in-hospital mortality after ICU admission in NNS patients. Methods : This nationwide population-based retrospective cohort study enrolled adult NNS adult patients admitted to the ICU from 2010 to 2019 extracted from the National Health Insurance Service in South Korea. The critically ill NNS patients were defined as those whose main admission departments were neurology or neurosurgery at ICU admission. The number of ICU admission, age, and total cost for hospitalization from 2010 to 2019 in critically ill NNS patients were examined as trend information. Moreover, multivariable logistic regression modeling was used to identify risk factors for in-hospital mortality among critically ill NNS patients. Results : We included 845474 ICU admission cases for 679376 critically ill NNS patients in South Korea between January 1, 2010 to December 31, 2019. The total number of ICU admissions among NNS patients was 79522 in 2010, which increased to 91502 in 2019. The mean age rose from 62.8 years (standard deviation [SD], 15.6) in 2010 to 66.6 years (SD, 15.2) in 2019, and the average total cost for hospitalization per each patient consistently increased from 6206.1 USD (SD, 5218.5) in 2010 to 10745.4 USD (SD, 10917.4) in 2019. In-hospital mortality occurred in 75455 patients (8.9%). Risk factors strongly associated with increased in-hospital mortality were the usage of mechanical ventilator (adjusted odds ratio [aOR], 19.83; 95% confidence interval [CI], 19.42-20.26; p<0.001), extracorporeal membrane oxygenation (aOR, 3.49; 95% CI, 2.42-5.02; p<0.001), and continuous renal replacement therapy (aOR, 6.47; 95% CI, 6.02-6.96; p<0.001). In addition, direct admission to ICU from the emergency room (aOR, 1.38; 95% CI, 1.36-1.41; p<0.001) and brain cancer as the main diagnosis (aOR, 1.30; 95% CI, 1.22-1.39; p<0.001) are also potential risk factors for increased in-hospital mortality. Conclusion : In South Korea, the number of ICU admissions increased among critically ill NNS patients from 2010 to 2019. The average age and total costs for hospitalization also increased. Some potential risk factors are found to increase in-hospital mortality among critically ill NNS patients.

• Journal of Christian Education in Korea
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• v.78
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• pp.213-229
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• 2024

Purpose of Study : The purpose of this study is to verify whether self-control has a mediating effect on the influence of grit on college students' self-efficacy, which has a significant impact on academic achievement. Research content and methods : In order to verify the influence of grit and self-control on college students' self-efficacy, this study first selected measurement tools for self-efficacy, grit, and self-control and created an online questionnaire. Next, a survey was conducted on 128 students at University A in Chungcheongnam-do. Descriptive statistical analysis and bivariate correlation analysis were performed on the collected data to verify the normality of the data and multicollinearity between factors. In addition, multiple regression analysis was used to verify the influence of grit and self-control on self-efficacy. Next, the effect of grit on self-efficacy was analyzed using structural equation modeling to verify whether self-control mediates it. As a result of the analysis, overall self-efficacy was influenced by the reliability of self-control and academic passion of grit. Self-confidence, a sub-factor of self-efficacy, was influenced by reliability of self-control and academic passion of grit. Self-regulation efficacy was influenced by the reliability of self-control and academic persistence of grit. Preference for task difficulty was influenced by grit, maintenance of academic interest, and self-control. Next, self-control was found to mediate the effect of grit on self-efficacy. Conclusion and Recommendations : This study explored the effects of grit and self-control on college students' self-efficacy. As a result, grit and self-control had a positive effect on self-efficacy. Additionally, self-control was found to mediate the effect of grit on self-efficacy. This study proposes to support grit and self-control in order to support successful academic achievement of college students.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.2
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• pp.63-75
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• 2013

The Inner Port Phase 2 area of the Pyeongtaek-Dangjin Port is enclosed by a total of three permeable sea-walls, and the disposal site to the east of the Inner Port Phase 2 is also enclosed by two permeable sea-walls. The maximum tidal range measured in the Inner Port Phase 2 and in the disposal site in May 2010 is 4.70 and 2.32 m, respectively. It reaches up to 54 and 27%, respectively of 8.74 m measured simultaneously in the exterior. Regression formulas between the difference of hydraulic head and the rate of interior water volume change, are induced. A three-dimensional numerical hydrodynamic model for the Asan Bay is constructed incorporating a module to compute water discharge through the permeable sea-walls at each computation time step by employing the formulas. Hydrodynamics for the period from 13th to 27th May, 2010 is simulated by driving forces of real-time reconstructed tide with major five constituents($M_2$, $S_2$, $K_1$, $O_1$ and $N_2$) and freshwater discharges from Asan, Sapkyo, Namyang and Seokmoon Sea dikes. The skill scores of modeled mean high waters, mean sea levels and mean low waters are excellent to be 96 to 100% in the interior of permeable sea-walls. Compared with the results of simulation to obstruct the flow through the permeable sea-walls, the maximum current speed increases by 0.05 to 0.10 m/s along the main channel and by 0.1 to 0.2 m/s locally in the exterior of the Outer Sea-wall of Inner Port. The maximum bottom shear stress is also intensified by 0.1 to 0.4 $N/m^2$ in the main channel and by more than 0.4 $N/m^2$ locally around the arched Outer Sea-wall. The module developed to compute the flow through impermeable seawalls can be practically applied to simulate and predict the advection and dispersion of materials, the erosion or deposion of sediments, and the local scouring around coastal structures where large-scale permeable sea-walls are maintained.

• Journal of the Korean Society of Child Welfare
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• no.59
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• pp.209-234
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• 2017

This study viewed schools as a cause of students dropping out and posited that dropping out of high school would vary depending on the characteristics and influencing factors of the school from which students were dropping out. Therefore, focusing on schools, we longitudinally investigated the change patterns of school dropout across high schools in the country, and the types of changes in dropping out of high school. In addition, we predicted the general characteristics of schools according to the type of school students were dropping out from, looked at the changes in the major factors (i.e., school violence and school counseling) affecting school dropout, and reviewed schools' long-term efforts and outcomes in relation to school dropout. For this purpose, KERIS EDSS's "Secondary School Information Disclosure Data" were used. The final model included data collected five years20122016) from high schools across the country. The results were as follows. First, in order to examine the longitudinal change patterns of dropping out of high schools, a latent growth models analysis was conducted, and it revealed that, as time passed, the dropout rate decreased. Second, growth mixture modeling was used to explore types according to the change patterns of the school students were dropping out from. The results showed three types: the "remaining in school" type, the "gradually decreasing school dropout" type, and the "increasing school dropping out". Third, the multinomial logistic regression was conducted to predict the general characteristics of schools by type. The results showed that public schools, vocational schools, and schools with a large number of students who have below the basic levels in Korean, English and mathematics were more likely to belong to the "increasing school dropout" type. Further, the larger the total number of students, the higher the probability of belonging to the "remaining in school" type or the "gradually decreasing school dropout" type. Lastly, growth mixture modeling was used to analyze the trend of school violence and school counseling according to the three types. The focus was on the "gradually decreasing school dropout" type. In the case of the "gradually decreasing school dropout" type, it was found that as time passed, the number of school violence cases and the number of offenders gradually decreased. In addition, in terms of change in school counseling the results revealed that the number of placement of professional counselors in schools increased every year and peer counseling was continuously promoted, which may account for the "gradually decreasing school dropout" type.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.09a
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• pp.125-153
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• 2005

It has been widely known that the seismic piezo-cone penetration test (SCPTU) is one of the most useful techniques for investigating the geotechnical characteristics including dynamic soil properties. As the practical applications in Korea, SCPTU was carried out at two sites in Busan and four sites in Incheon, which are mainly composed of alluvial or marine soil deposits. From the SCPTU waveform data obtained from the testing sites, the first arrival times of shear waves were and the corresponding time differences with depth were determined using the cross-over method, and the shear wave velocity profiles (VS) were derived based on the refracted ray path method based on Snell's law and similar to the trend of cone tip resistance (qt) profiles. In Incheon area, the testing depths of SCPTU were deeper than those of conventional down-hole seismic tests. Moreover, for the application of the conventional CPTU to earthquake engineering practices, the correlations between VS and CPTU data were deduced based on the SCPTU results. For the empirical evaluation of VS for all soils together with clays and sands which are classified unambiguously in this study by the soil behavior type classification Index (IC), the authors suggested the VS-CPTU data correlations expressed as a function of four parameters, qt, fs, $\sigma$, v0 and Bq, determined by multiple statistical regression modeling. Despite the incompatible strain levels of the down-hole seismic test during SCPTU and the conventional CPTU, it is shown that the VS-CPTU data correlations for all soils clays and sands suggested in this study is applicable to the preliminary estimation of VS for the Korean deposits and is more reliable than the previous correlations proposed by other researchers.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.136-152
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• 2017

The thermodynamic models, PC-SAFT (Perturbed-Chain Statistical Associated Fluid Theory) state equation and the Two-model approach liquid activity coefficient model NRTL (Non Random Two Liquid) + Henry + Peng-Robinson, for modeling the Rectisol process using methanol aqueous solution as the $CO_2$ removal solvent were compared. In addition, to determine the new binary interaction parameters of the PC-SAFT state equations and the Henry's constant of the two-model approach, absorption equilibrium experiments between carbon dioxide and methanol at 273.25K and 262.35K were carried out and regression analysis was performed. The accuracy of the newly determined parameters was verified through the regression results of the experimental data. These model equations and validated parameters were used to model the carbon dioxide removal process. In the case of using the two-model approach, the methanol solvent flow rate required to remove 99.00% of $CO_2$ was estimated to be approximately 43.72% higher, the cooling water consumption in the distillation tower was 39.22% higher, and the steam consumption was 43.09% higher than that using PC-SAFT EOS. In conclusion, the Rectisol process operating under high pressure was designed to be larger than that using the PC-SAFT state equation when modeled using the liquid activity coefficient model equation with Henry's relation. For this reason, if the quantity of low-solubility gas components dissolved in a liquid at a constant temperature is proportional to the partial pressure of the gas phase, the carbon dioxide with high solubility in methanol does not predict the absorption characteristics between methanol and carbon dioxide.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.61 no.1
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• pp.70-77
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• 2016

The object of this study was to evaluate the effects of climatic elements on potato yield and create a model for estimating the potato yield. We used 35 yield data of Sumi variety produced in mulching cultivation from 17 regions over 11 years. According to the results, some climatic elements showed significant level of correlation coefficient with marketable yield of potato. Totally 22 items of climatic elements appeared to be significant. Especially precipitation for 20 days after planting (Prec_1 & 2), relative humidity during 11~20 days after planting (RH_2), precipitation for 20 days before harvest (Prec_9 & 10), sunshine hours during 50~41 days before harvest (SH_6) and 20 days before harvest (SH_9 & 10), and days of rain during 10 days before harvest (DR_10) were highly significant in quadratic regression analysis. 22 items of predicted yield ($Y_i=aX_i{^2}+bX_i+c$) were induced from the 22 items of climatic elements (step 1). The correlations between the predicted yields and marketable yield were stepwised using SPSS, statistical program, and we selected a model (step 2), in which 4 items of independent variables ($Y_i$) were used. Subsequently the $Y_i$ were replaced with the equation in step 1, $aX_i{^2}+bX_i+c$. Finally we derived the model to predict the marketable yield of potato as below. $$Y=-336{\times}DR_-10^2+854{\times}DR_-10-0.422{\times}Prec_-9^2+43.3{\times}Prec_-9\\-0.0414{\times}RH_-2^2+46.2{\times}RH_-2-0.0102{\times}Prec_-2^2-7.00{\times}Prec_-2-10039$$.