• Korea Journal of Hospital Management
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• v.22 no.1
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• pp.40-50
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• 2017

The purpose of this study is to develop the KPIs(Key Performance Indices) needed to improve management and strategy in the dental clinic based on the four perspectives of BSC(Balanced Scorecard). The questionnaire was conducted on 52 dentists approved by Dental Managment Research Committee in Seoul National University as a panel. Using the Delphi technique, the top five KPIs for each point of perspective in BSC were extracted from KPI pools. In the third survey, the top five KPIs of all points were compared with each other through AHP(Analytic Hierarchy Process) method, and priority and overall importance rankings were calculated. The biggest difference in the three level AHP results was the customer perspective took priority to others. In the second survey, the financial perspective, which was number one, was pushed back. The overall significance of KPIs was in the order of customer, internal process, finance, learning and growth perspective, with the exception of medical profits (5th of 20) and new patient growth (10th of 20). We were able to overcome the limitations of the Delphi Technique with the AHP method. In general, the financial perspective in BSC is known to be the most important, but we conclude that the customer perspective is more important through the pairwise comparison survey. In the current dental service market, which is a long-term recession, excessive competition, customer satisfaction and customer relationship management seem to be the first goal to pursue in dental clinic.

• The Korean Journal of Food And Nutrition
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• v.21 no.1
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• pp.28-34
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• 2008

This study was performed in an effort to standardize the quality of fresh-cut products. Here, consumers' perceptions of fresh-cut products were surveyed to determine the quality factors in preparing quality standards for fresh-cut products. According to the surveys, freshness was the most important factor for consumers when choosing fresh-cut products at the market. Secondary indicators of good quality, i.e., "sensory qualities," were determined to be vibrant color, clean taste, nice feel, neat presentation, etc., as well as knowledge of whether it was organically grown. Off-odors decreased a product's marketability. Likewise, the more wilted or brown it appeared, the less a product was desired. From the results, the quality control indices for fresh-cut products were derived as freshness, discoloration, off-flavor, uniformity, prohibition of adulteration by alien substances, packaging and labeling. The quality of fresh-cut lettuce samples was measured against three storage temperatures: $20^{\circ}C,\;10^{\circ}C$ and $5^{\circ}C$. Sensory quality evaluations revealed the following: produce stored at $20^{\circ}C$ lost its marketability in less than one day; produce stored at $10^{\circ}C$ lost its marketability in less than two days; on the other hand, produced stored at $5^{\circ}C$, maintained its marketability for six days. At these respective points, browning started to occur around cut areas, and increased gradually once it began. Vitamin C content decreased with storage time, but storage at $5^{\circ}C$ maintained vitamin content the longest duration. Finally, further examinations were performed on the sensory qualities of fresh-cut lettuce samples at four levels of increased browning. Up to the third level, the product score for marketability was 5.6.

• Journal of Pharmacopuncture
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• v.23 no.3
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• pp.165-172
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• 2020

Objectives: In the past few years, herbal medicines have gained popularity over synthetic drugs because of their natural source and minimal side effects which has led to a tremendous growth of phytopharmaceuticals usage. With the development of nanotechnology, it provides alternative approaches to overcome several limitations using nano-formulations. In spite of considerable quantity of antianemic preparations with different iron forms available, currently additives are used and represented in modern pharmaceutical market. Iron deficiency anemia is a major global public health problem which particularly affects pregnant women, children and elderly persons. The situation is complicated because of disadvantages and drug side effects from existing antianemic medicines. There is a great demand for the development of new antianemic preparations. Green synthesis of iron oxide nanoparticles, possess high potential in this field. Methods: Our study focuses on developing green synthesis of iron oxide nanoparticles (IONPs) of 10-50 nm with spherical shape where different dosages were used -1 mg/kg, 10 mg/kg and 100 mg/kg for exposure in Wistar albino female rats for 28 days. The toxicity was assessed using various parameters such as measurements of the rat body and organ mass, hematology, biochemical evaluation and histopathological examinations. Results: No significant differences were observed in body and organ weights. Hematological indices also indicated no significant differences whereas biochemical factors showed increase in levels of direct bilirubin and globulin of medium as well as high dose and SGPT levels were increased only in high dose. The major organs (heart, kidney and liver) showed histopathological alterations in 10 and 100 mg/kg whereas brain showed only in 100 mg/kg. Conclusion: The toxicity of IONPs was found to be more significant when the concentration was increased; however, low doses can be used for further investigation as an antianemic preparation.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.730-736
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• 2011

In this paper, we designed the models for pattern classification which can reflect the latest trend in time series. It has been shown that fusion models based on statistical and AI methods are superior to traditional ones for the pattern classification model supporting decision making. Especially, the hit rates of pattern classification models combined with fuzzy theory are relatively increased. The statistical SVM models combined with fuzzy membership function, or the models combining neural network and FCM has shown good performance. BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, and Regression Analysis were used for pattern classification models in the experiments of this paper. The economical indices DB with time series properties of the financial market(Korea, KOSPI200 DB) and the electrocardiogram DB of arrhythmia patients in hospital emergencies(USA, MIT-BIH DB) were used for data base.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1065-1074
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• 2011

During several decades, many researchers in the field of finance have studied Value at Risk (VaR) to measure the market risk. VaR indicates the worst loss over a target horizon such that there is a low, pre-specified probability that the actual loss will be larger (Jorion, 2006, p.106). In this paper, we compare conditional copula method with two conventional VaR forecasting methods based on simple moving average and exponentially weighted moving average for measuring the risk of the portfolio, consisting of two domestic stock indices. Through real data analysis, we conclude that the conditional copula method can improve the accuracy of portfolio VaR forecasting in the presence of high kurtosis and strong correlation in the data.

• The Journal of the Korea Contents Association
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• v.6 no.6
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• pp.26-32
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• 2006

Predictions on stock prices have been a hot issue in stock market as people get more interested in stock investments. Assuming that the stock price is moving by a trend in a specific pattern, we believe that a model can be derived from past data to describe the change of the price. The best model can help predict the future stock price. In this paper, our model derivation is based on automata over temporal data to which the model is explicable. We use Bayesian Information Criterion(BIC) to determine the best number of states of the model. We confirm the validity of Bayesian Information Criterion and apply it to building models over stock price indices. The model derived for predicting daily stock price are compared with real price. The comparisons show the predictions have been found to be successful over the data sets we chose.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.5
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• pp.556-563
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• 2015

Chinese cabbage is one of the most important vegetables in Korea and a target crop for market stabilization as well. Remote sensing has long been used as a tool to extract plant growth, cultivated area and yield information for many crops, but little research has been conducted on Chinese cabbage. This study refers to the derivation of simple Chinese cabbage growth prediction equation by using RapidEye derived vegetation index. Daesan-myeon area in Gochang-gun, Jeollabuk-do, Korea is one of main producing district of Chinese cabbage. RapidEye multi-spectral imagery was taken on the Daesan-myeon five times from early September to late October during the Chinese cabbage growing season. Meanwhile, field reflectance spectra and five plant growth parameters, including plant height (P.H.), plant diameter (P.D.), leaf height (L.H.), leaf length (L.L.) and leaf number (L.N.), were measured for about 20 plants (ten plants per plot) for each ground survey. The normalized difference vegetation index (NDVI) for each of the 20 plants was measured using an active plant growth sensor (Crop $Circle^{TM}$) at the same time. The results of correlation analysis between the vegetation indices and Chinese cabbage growth data showed that NDVI was the most suited for monitoring the L.H. (r=0.958~0.978), L.L. (r=0.950~0.971), P.H. (r=0.887~0.982), P.D. (r=0.855~0.932) and L.N. (r=0.718~0.968). Retrieval equations were developed for estimating Chinese cabbage growth parameters using NDVI. These results obtained using the NDVI is effective provided a basis for establishing retrieval algorithm for the biophysical properties of Chinese cabbage. These results will also be useful in determining the RapidEye multi-spectral imagery necessary to estimate parameters of Chinese cabbage.

• International Area Studies Review
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• v.14 no.1
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• pp.77-97
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• 2010

Recently, countries on the Caspian Sea were had heavily interested due to instability of international resource market. These countries having been developed basing on energy exports, especially Kazakhstan have drastically grown during a decades. However economy, heavily relied on the exports of energy, is influenced on fluctuation in the international energy price as well as sometimes exposed at Dutch disease. These days, Kazakhstan, increased trade and investment with Korea, has been on the rise as new supplier for energy. Therefore, economic change in Kazakhstan can be an important issue. In this paper, we analyze relations among oil price, interest rate, and real exchange rate during sample period from January 1999 to December 2008 expanding Balasa-Samuelson model. Empirical results present that oil price, interest rate, and real exchange rate mutually keep their balance. Eventually, we find out Kazakhstan has exposed at Dutch disease since oil price and interest rate have negative impacts on real exchange rate respectively.

• Journal of Industrial Convergence
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• v.20 no.2
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• pp.11-19
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• 2022

This study examines the role of economic uncertainty in stock price determination in the domestic stock market. To this end, we analyzed the relationship between economic uncertainty indices at home and abroad (USA, China) and stock returns for non-financial companies in Korea from January 2000 to 2017. For the analysis model, the 3-factor model of Fama and French (1992) and the 5-factor model including momentum and liquidity were used. As a result of the analysis, a portfolio with a high beta of economic uncertainty showed higher stock returns than a portfolio with a low beta. This was the same as the US analysis result. Also, the analysis results using the US uncertainty index were more significant than the regression analysis results using the Korean economic uncertainty index.

• Korea Trade Review
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• v.41 no.2
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• pp.97-121
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• 2016

This study evaluates industrial competitiveness of 34 industries in the 41 countries in terms of several trade indices such as value-added RCA(VRCA) and value added intra-industry trade(VIIT). Conclusions are as follows: First, China is still showing week evidence to replace or overtake Korea in terms of VRCA. Second, it is not supportive of the assertion that the gap between Japan and South Korea had widened in the 1995-2011 period. Third, Korea's exports pattern in the manufacturing sector has shifted from the one featured by developing countries(re-exportation of final goods produced using imported intermediate inputs) to that of the developed (exportation of intermediate goods). According to dynamic panel analysis regressing the RCA gap on the IIT gap, intermediate-goods RCA and the market share gap, the estimated coefficient of the gap between value-added IIT and gross IIT is 0.253 and statistically significant at the 2% level. This implies that increases in IIT or intermediate-goods trade to sustain the global competitiveness are the main reasons for the gap between value-added RCA and gross RCA.