• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.836-846
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• 2018

The purpose of this study was to evaluate nursing educational objectives achievement, critical thinking disposition, and clinical competence and their relationships in nurses within five years after graduation. Subjects were 82 nurses who graduated one nursing college within five years and 68 managers in nursing unit of hospital. Questions were given to nurses. They reported their achievement of educational objectives of the nursing college, critical thinking disposition and clinical competence. Nursing unit managers received the same questions to evaluate nurses working at their unit. Data were collected from January to march 2014 and analyzed by descriptive statistics, t-test, ANOVA, Pearson correlation and multiple regression with SPSS/WIN 23.0 program. Mean scores for achievement of educational objectives, critical thinking disposition, and clinical competence were $3.60{\pm}0.60$, $3.46{\pm}0.28$, and $4.17{\pm}0.56$, respectively. Educational objectives achievement showed significant correlations with critical thinking disposition (r = 0.52, p <. 001) and clinical competence (r = 0.52, p < .001). Regression analysis showed that critical thinking disposition (${\beta}=.30$, p = .018) and clinical competence (${\beta}=.26$, p = .029) were significant factors for predicting educational objectives achievement. Manager's evaluation score for educational objectives achievement and clinical competence was higher than graduated nurses' report (p < .001). This could be used as a feed- back to reset educational objectives and upgrade the curriculum. These study results could be used to, establish strategies to improve educational objectives achievement.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.2
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• pp.130-137
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• 2021

This study evaluated newly introduced, commercial super sweet corn hybrids (Zea mays L.) for their potential as breeding materials. Agronomic traits were measured and variance components were obtained using a linear mixed model to estimate the heritability. The trials were carried out in 2018 at two locations (Haenam and Oksan in South Korea). All traits had low heritability, except for mid tasseling and silking days. These traits with low heritability mostly had low genetic variance component estimate. In case of ear height ratio, significant genotype by location appeared to be responsible for low genetic variance, which in turn led to low heritability. Low heritability estimates from the trials with commercial hybrids were perhaps because those hybrids were highly improved for commercial success. Hence, this does not necessarily point to them having poor potential as breeding materials. To overcome low heritability, significant genotype by environment interaction, and achieve high selection efficiency, intermating among hybrids is recommended to create new recombinants before inbred line development.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.13-23
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• 2022

In this study we leveraged a probing method to evaluate how a pre-trained L2 LSTM language model represents sentences with relative and coordinate clauses. The probing experiment employed adapted models based on the pre-trained L2 language models to trace the syntactic properties of sentence embedding vector representations. The dataset for probing was automatically generated using several templates related to different sentence structures. To classify the syntactic properties of sentences for each probing task, we measured the adaptation effects of the language models using syntactic priming. We performed linear mixed-effects model analyses to analyze the relation between adaptation effects in a complex statistical manner and reveal how the L2 language models represent syntactic features for English sentences. When the L2 language models were compared with the baseline L1 Gulordava language models, the analogous results were found for each probing task. In addition, it was confirmed that the L2 language models contain syntactic features of relative and coordinate clauses hierarchically in the sentence embedding representations.

• Journal of the Microelectronics and Packaging Society
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• v.4 no.1
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• pp.1-12
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• 1997

본 연구에서는 용액성장법에 의해 양자 입자로 구성된 CdS 박막을 슬라이드 유리기 판위에 성장시키고 이들의 구조적 광학적 특성에 대하여 연구하였고 이들 결과를 토대로 용 액성장법으로 성장된 CdS 박막의 양자 사이즈 효과에 대하여 연구하였다. 성장시간은 1, 3, 10, 20분이었고 성장온도는 75$^{\circ}C$였다. X-선 회절 분석결과 본 연구에서 합성된 CdS 박막은 hexagonal상의 결정구조를 갖는 것으로 나타났고 성장시간에 따라 막의 투께는 61~195nm, 입자사이즈는 8.5~22.5nm로 나타났다. 광에너지 변화에 따른 투과도 측정결과 본 연구의 CdS 시료는 성장시간에 따라 에너지 밴드갭이 2.43~2.51 eV로 나타나서 벌크 CdS의 에너 지 밴드갭인 2.42 ev보다 높은 에너지 밴드갭을 갖게 되어 양자 사이즈 효과에 의한 blue shift 현상이 용액성장법에 의해 합성된 CdS 시료에도 존재한다는 것이 밝혀졌다 그리고 이 같은 용액성장법으로 성장된 CdS에 대해 최초로 수행된 Raman 산란 실험결과 이성장방법 으로 성장된 CdS에는 1TO, E2, 1LO 포논 모드가 존재함을 알수 있었고 또한 입자 사이즈 감소에 의한 1LO포논 모드의저주파수 shift 현상 즉 포논 모드의 softening 현상이 있음이 밝혀졌고 softening은 최대6.2%까지 발생하였다. 이와같은 높은 softening은 본연구의 CdS 박막 내 양자 입자의 입도가 작은것에 기인하는 것으로 밝혀졌다. 또한 본 CdS 시료의 양 자 사이즈 효과의 결과로 1TO 포논도 나타났는데 이 1TO 포논과 E2 포논의 Raman shift 는 성장시간 즉 막의 두께와는 무관한 것으로 나타났다.행렬모형(二重比例行列模型)을 이용하여, 산업구조의 변화로 인한 직업별 인력수요 변화가 충분히 고려되도록 하였다. 전망의 결과에 따르면 향후 우리 경제는 지식기반경제(knowledge-based economy)로 이행하고 있다고 볼 수 있다. 우선 산업구조면에서 지식집약적산업으로의 구조조정이 일어나게 되고 이에 따라 산업별 취업구조에서도 고기술산업의 취업준비중이 급속히 증가하게 된다. 직업별 취업분포에 있어서도 전문기술직 행정관리직 등의 고숙련 사무직의 비중은 크게 증가하는 반면 생산관련직과 농림어업직의 비중은 감소하게 된다. 이처럼 경제가 지식집약화되어 감에 따라 고학력자에 대한 수요는 지속적으로 증가하지만 현재 적절한 인력양성과 공급이 이루어지지 않고 있어 향후 기술이나 기능에 따른 수급부일정(需給不一政)(skill mismatch)현상이 매우 심해질 것으로 보인다. 따라서 앞으로의 인력정책에서 가장 주안점을 두어야 할 부분은 첨단기술산업과 관련된 인력의 양성에 있다고 하겠다.2시간까지 LPDG용액은 MEC용액보다 비교적 나은 회복을 보였고 재관류 3일과 7일의 폐기능 평가에서 두 용액 모두에서 폐기능의 점차적 소실을 보였으며 이는 병리조직검사에서 보듯이 폐혐에 의한 외적인 요소라고 생각되며 따라서 LPDG용액은 허혈재관류손상 방지 및 급성폐렴 등 염증을 잘 관리한다면 20시간 이상 LPDG용액의 안전한 폐보존의 가능성 을 얻을 수 있었다.ic 형태로 외래유전자가 발현되었지만 대조구에서 87.0% (26/30개) 배반포기가 $\beta$-Gal 활력을 보인 반면, G418 처리구에서는 모든 배반포기가 $\beta$-Gal 활력을 보였다 (P<0.05). 그러나 대조구 및 G418 처리구의 ICM

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.339-350
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• 2022

The purpose of this study was to develop and investigate the validity and reliability of the Korean Version of Nurse's Job Crafting Scale. The Korean version of Job Crafting was translated and reverse-translated, and its content validity was verified by experts. Statistics were processed using SPSS/WIN 21.0 and AMOS 21.0 programs through self-report questionnaires for 151 nurses. Exploratory factor analysis and confirmatory actor analysis were performed to verify construct validity, and model fit, concentrated validity, and discriminant validity were confirmed through the analysis results.To verify the criterion validity, correlations with each domain were obtained using the calling scale. For reliability verification, the internal consistency reliability coefficient was calculated and confirmed. Reliability of all 20 job crafting tools was Cronbach's α = .93, with .91 for factor 1 (Increase in structural work resources, 5 questions) and .87 for factor 2 (Increase in structural work resources, 5 questions). The factor 3 (Increase in social work resources, 5 questions) was .83. The factor 4 (Increasing challenging business needs, 5 items) was .87, which was satisfactory for the reliability of internal consistency, and the Korean Version of Nurse's Job Crafting Scale was found to be an applicable tool. This study shows that the Korean Version of the Nurse's Job Crafting Scale is a valid and reliable instrument to assess nurses in Korea.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.360-373
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• 2017

The BTO-a projects is the types, which has a demand risk among the type of PPP projects in Korea. When demand risk is realized, private investor encounters financial difficulties due to lower revenue than its expectation and the government may also have a problem in stable infrastructure operation. In this regards, the government has applied various risk sharing policies in response to demand risk. However, the amount of government's risk sharing is the government's contingent liabilities as a result of demand uncertainty, and it fails to be quantified by the conventional NPV method of expressing in the text of the concession agreement. The purpose of this study is to estimate the value of investment risk sharing by the government considering the demand risk in the profit sharing system (BTO-a) introduced in 2015 as one of the demand risk sharing policy. The investment risk sharing will take the form of options in finance. Private investors have the right to claim subsidies from the government when their revenue declines, while the government has the obligation to pay subsidies under certain conditions. In this study, we have established a methodology for estimating the value of investment risk sharing by using the Black - Scholes option pricing model and examined the appropriateness of the results through case studies. As a result of the analysis, the value of investment risk sharing is estimated to be 12 billion won, which is about 4% of the investment cost of the private investment. In other words, it can be seen that the government will invest 12 billion won in financial support by sharing the investment risk. The option value when assuming the traffic volume risk as a random variable from the case studies is derived as an average of 12.2 billion won and a standard deviation of 3.67 billion won. As a result of the cumulative distribution, the option value of the 90% probability interval will be determined within the range of 6.9 to 18.8 billion won. The method proposed in this study is expected to help government and private investors understand the better risk analysis and economic value of better for investment risk sharing under the uncertainty of future demand.

• Korea Real Estate Review
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• v.28 no.1
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• pp.79-90
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• 2018

The purpose of this study was to estimate the pricing factors of residential lands in new cities by estimating the pricing model of residential lands. For this purpose, hedonic equations for each quantile of the conditional distribution of land prices were estimated using quantile regression methods and the sale price date of Jangyu New Town in Gimhae. In this study, a quantile regression method that models the relation between a set of explanatory variables and each quantile of land price was adopted. As a result, the differences in the effects of the characteristics by price quantile were confirmed. The number of years that elapsed after the completion of land construction is the quadratic effect in the model because its impact may give rise to a non-linear price pattern. Age appears to decrease the price until certain years after the construction, and increases the price afterward. In the estimation of the quantile regression, land age appears to have a statistically significant impact on land price at the traditional level, and the turning point appears to be shorter for the low quantiles than for the higher quantiles. The positive effects of the use of land for commercial and residential purposes were found to be the biggest. Land demand is preferred if there are more than two roads on the ground. In this case, the amount of sunshine will improve. It appears that the shape of a square wave is preferred to a free-looking land. This is because the square land is favorable for development. The variables of the land used for commercial and residential purposes have a greater impact on low-priced residential lands. This is because such lands tend to be mostly used for rental housing and have different characteristics from residential houses. Residential land prices have different characteristics depending on the price level, and it is necessary to consider this in the evaluation of the collateral value and the drafting of real estate policy.

• Journal of the Korean Orthopaedic Association
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• v.54 no.5
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• pp.427-434
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• 2019

Purpose: This study evaluated the long term clinical and radiographic results and the survival rates of unicompartmental knee arthroplasty (UKA). In addition, the factors affecting the survival of the procedure were analyzed and the survival curve was compared according to the affecting factors. Materials and Methods: Ninety-nine cases of UKA performed between December 1982 and January 1996 were involved: 10 cases with Modular II, 44 cases with Microloc, and 45 cases with Allegretto prostheses. The mean follow-up period was 16.5 years. Clinically, the hospital for special surgery (HSS) scoring system and the range of motion (ROM) were evaluated. Radiographically, the femorotibial angle (FTA) was measured. The survival rate was analyzed using the Kaplan-Meier method. Cox regression analysis was used to identify the factors affecting the survival according to age, sex, body mass index, preoperative diagnosis, and type of implant. The Kaplan-Meier survival curves were compared according to the factors affecting the survival of UKA. Results: The overall average HSS score and ROM was 57.7 and 134.3° preoperatively, 92.7 and 138.4° at 1 year postoperatively, and 79.1 and 138.4° at the last follow-up (p<0.001, respectively). The overall average FTA was varus 0.8° preoperatively, valgus 4.1° at postoperative 2 weeks, and valgus 3.0° at the last follow-up. The overall 5-, 10-, 15- and 20-year survival rates were 91.8%, 82.9%, 71.0%, and 67.0%, respectively. The factors affecting the survival were the age and type of implant. The risk of the failure decreased with age (hazard ratio=0.933). The Microloc group was more hazardous than the other prostheses (hazard ratio=0.202, 0.430, respectively). The survival curve in the patients below 60 years of age was significantly lower than those of the patients over 60 years of age (p=0.003); the survival curve of the Microloc group was lower compared to the Modular II and Allegretto groups (p=0.025). Conclusion: The long-term clinical and radiographic results and survival of UKA using old fixed bearing prostheses were satisfactory. The selection of appropriate patient and prosthesis will be important for the long term survival of the UKA procedure.