• Korean Journal of Fisheries and Aquatic Sciences
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• v.3 no.3
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• pp.177-186
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• 1970

The spawning of the abalone, Haliotis discus hannai, was induced In October 1969 by air ex-position for about 30 minutes. At temperatures of from 14.0 to $18.8^{\circ}C$, the youngest trochophore stage was reached within 22 hours after the egg was laid. The trochophore was transformed into the veliger stage within 34 hours after fertilization. For $7\~9$ days after oviposition the veliger floated in sea water and then settled to the bottom. The peristomal shell was secreted along the outer lip of the aperture of the larval shell, and the first respiratory pore appears at about 110 days after fertilization. The shell attained a length of 0.40 mm in 15 days, 1.39 mm in 49 days, 2.14 mm in 110 days, 5.20 mm in 170 days and 10.00 mm in 228 days respectively. Monthly growth rate of the shell length is expressed by the following equation :$L=0.9981\;e^{0.18659M}$ where L is shell length and M is time in month. The density of floating larvae in the culture tank was about 10 larvae per 100 co. The number of larvae attached to a polyethylene collector ($30\times20\;cm$) ranged from 10 to 600. Mortality of the settled larvae on the polyethylene collector was about $87.0\%$ during 170 days following settlement. The culture of Nauicula sp. was made with rough polyethylene collectors hung at three different depths, namely 5 cm, 45 cm and 85 cm. At each depth the highest cell concentration appeared after $15\~17$ days, and the numbers of cells are shown as follows: $$5\;cm\;34.3\times10^4\;Cells/cm^2$$ $$45\;cm\;27.2\times10^4\;Cells/cm^2$$ $$85\;cm\;26.3\times10^4\;Cells/cm^2$$ At temperatures of from 13.0 to $14.3^{\circ}C$, the distance travelled by the larvae (3.0 mm In shell length) averaged 11.36 mm for a Period of 30 days. Their locomation was relatively active between 6 p.m. and 9 p.m., and $52.2\%$ of them moved during this period. When the larvae (2.0 mm in shell length) were kept in water at $0\;to\;\~1.8^{\circ}C$, they moved 1.15cm between 4 p.m. and 8 p.m. and 0.10 cm between midnight and 8 a.m. The relationships between shell length and body weight of the abalone sampled from three different localities are shown as follows: Dolsan-do $W=0.2479\;L^{2.5721}$ Huksan-do $W=0.1001\;L^{3.1021}$ Pohang $W=0.9632\;L^{2.0611}$

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.191-204
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• 2015

According to the '2013 Performance Assessment Report on the Financial Program' from the National Assembly Budget Office, the unfilled recruitment ratio of Software(SW) Developers in South Korea was 25% in the 2012 fiscal year. Moreover, the unfilled recruitment ratio of highly-qualified SW developers reaches almost 80%. This phenomenon is intensified in small and medium enterprises consisting of less than 300 employees. Young job-seekers in South Korea are increasingly avoiding becoming a SW developer and even the current SW developers want to change careers, which hinders the national development of IT industries. The Korean government has recently realized the problem and implemented policies to foster young SW developers. Due to this effort, it has become easier to find young SW developers at the beginning-level. However, it is still hard to recruit highly-qualified SW developers for many IT companies. This is because in order to become a SW developing expert, having a long term experiences are important. Thus, improving job continuity intentions of current SW developers is more important than fostering new SW developers. Therefore, this study surveyed the job continuity intentions of SW developers and analyzed the factors associated with them. As a method, we carried out a survey from September 2014 to October 2014, which was targeted on 130 SW developers who were working in IT industries in South Korea. We gathered the demographic information and characteristics of the respondents, work environments of a SW industry, and social positions for SW developers. Afterward, a regression analysis and a decision tree method were performed to analyze the data. These two methods are widely used data mining techniques, which have explanation ability and are mutually complementary. We first performed a linear regression method to find the important factors assaociated with a job continuity intension of SW developers. The result showed that an 'expected age' to work as a SW developer were the most significant factor associated with the job continuity intention. We supposed that the major cause of this phenomenon is the structural problem of IT industries in South Korea, which requires SW developers to change the work field from developing area to management as they are promoted. Also, a 'motivation' to become a SW developer and a 'personality (introverted tendency)' of a SW developer are highly importantly factors associated with the job continuity intention. Next, the decision tree method was performed to extract the characteristics of highly motivated developers and the low motivated ones. We used well-known C4.5 algorithm for decision tree analysis. The results showed that 'motivation', 'personality', and 'expected age' were also important factors influencing the job continuity intentions, which was similar to the results of the regression analysis. In addition to that, the 'ability to learn' new technology was a crucial factor for the decision rules of job continuity. In other words, a person with high ability to learn new technology tends to work as a SW developer for a longer period of time. The decision rule also showed that a 'social position' of SW developers and a 'prospect' of SW industry were minor factors influencing job continuity intensions. On the other hand, 'type of an employment (regular position/ non-regular position)' and 'type of company (ordering company/ service providing company)' did not affect the job continuity intension in both methods. In this research, we demonstrated the job continuity intentions of SW developers, who were actually working at IT companies in South Korea, and we analyzed the factors associated with them. These results can be used for human resource management in many IT companies when recruiting or fostering highly-qualified SW experts. It can also help to build SW developer fostering policy and to solve the problem of unfilled recruitment of SW Developers in South 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.

• The Journal of the Petrological Society of Korea
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• v.21 no.2
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• pp.129-150
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• 2012

The tectonic evolution of the Central Ogcheon Belt has been newly analyzed in this paper from the detailed geological maps by lithofacies classification, the development processes of geological structures, microstructures, and the time-relationship between deformation and metamorphism in the Ogcheon, Cheongsan, Mungyeong Buunnyeong, Busan areas, Korea and the fossil and radiometric age data of the Ogcheon Supergroup(OSG). The 1st tectonic phase($D^*$) is marked by the rifting of the original Gyeonggi Massif into North Gyeonggi Massif(present Gyeonggi Massif) and South Gyeonggi Massif (Bakdallyeong and Busan gneiss complexes). The Joseon Supergroup(JSG) and the lower unit(quartzose psammitic, pelitic, calcareous and basic rocks) of OSG were deposited in the Ogcheon rift basin during Early Paleozoic time, and the Pyeongan Supergroup(PSG) and its upper unit(conglomerate and pelitic rocks and acidic rocks) appeared in Late Paleozoic time. The 2nd tectonic phase(Ogcheon-Cheongsan phase/Songnim orogeny: D1), which occurred during Late Permian-Middle Triassic age, is characterized by the closing of Ogcheon rift basin(= the coupling of the North and South Gyeonggi Massifs) in the earlier phase(Ogcheon subphase: D1a), and by the coupling of South China block(Gyeonggi Massif and Ogcheon Zone) and North China block(Yeongnam Massif and Taebaksan Zone) in the later phase(Cheongsan subphase: D1b). At the earlier stage of D1a occurred the M1 medium-pressure type metamorphism of OSG related to the growth of coarse biotites, garnets, staurolites. At its later stage, the medium-pressure type metamorphic rocks were exhumed as some nappes with SE-vergence, and the giant-scale sheath fold, regional foliation, stretching lineation were formed in the OSG. At the D1b subphase which occurs under (N)NE-(S)SW compression, the thrusts with NNE- or/and SSW-vergence were formed in the front and rear parts of couple, and the NNE-trending Cheongsan shear zone of dextral strike-slip and the NNE-trending upright folds of the JSG and PSG were also formed in its flank part, and Daedong basin was built in Korean Peninsula. After that, Daedong Group(DG) of the Late Triassic-Early Jurassic was deposited. The 3rd tectonic phase(Honam phase/Daebo orogeny: D2) occurred by the transpression tectonics of NNE-trending Honam dextral strike-slip shearing in Early~Late Jurassic time, and formed the asymmetric crenulated fold in the OSG and the NNE-trending recumbent folds in the JSG and PSG and the thrust faults with ESE-vergence in which pre-Late Triassic Supergroups override DG. The M2 contact metamorphism of andalusite-sillimanite type by the intrusion of Daebo granitoids occurred at the D2 intertectonic phase of Middle Jurassic age. The 4th tectonic phase(Cheongmari phase: D3) occurred under the N-S compression at Early Cretaceous time, and formed the pull-apart Cretaceous sedimentary basins accompanying the NNE-trending sinistral strike-slip shearing. The M3 retrograde metamorphism of OSG associated with the crystallization of chlorite porphyroblasts mainly occurred after the D2. After the D3, the sinistral displacement(Geumgang phase: D4) occurred along the Geumgang fault accompanied with the giant-scale Geumgang drag fold with its parasitic kink folds in the Ogcheon area. These folds are intruded by acidic dykes of Late Cretaceous age.

• Journal of Radiation Protection and Research
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• v.21 no.4
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• pp.273-284
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• 1996

Embryos and fetuses are more sensitive to various environmental agents than are adults or children. The biological effects such as intrauterine death and malformation are closely connected with prenatal exposure very various agents. The sensitivity of these embryonic/fetal effects depends on the stage of pregnancy. From the viewpoint of fetal development, embryonic and fetal stages can be divided into three stages : Preimplantation, organogenetic and fetal. Each stage corresponds to 0 to 4.5days, 4.5 to 13.5days, and 13.5days of gestation in mice, respectively. Many studies on the biologcal effects of mice irradiated by ${\gamma}-rays$ at various stages during organogenesis and fetal period have been performed. Based on these results, the dose-effect and dose-response relationships in malformations, intrauterine death, or retardation of the physical growth have been practically modeled by the ICRP(International Commission on Radiological Protection) and other international bodies for radiation protection. Many experimental studies on mice have made it clear that mice embryos in the preimplantation period have a higher sensitivity to radiation for lethal effects than the embryos/fetuses on other prenatal periods. However, no eratogenic effects of radiation at preimplantation stages of mice have been described in many textbooks. It has been believed that 'all or none action results' for radiation of mice during the preimplantation period were applied. The teratogenic and lethal effects during the preimplantation stage are one of the most important problems from the viewpoint of radiological protection, since the preimplantation stage is the period when the pregnancy itself is not noticed by a pregnant woman. There are many physical or chemical agents which affect embryos/fetuses in the environment. It is assumed that each agents indirectly effects a human. Then, a safety criterion on each agent is determined independently. The pregnant ICR mice on 2, 48, 72 or 96 hours post-conception (hpc), at which are preimplantation stage of embryos, were irradiated whole body Cesium-gamma radiation at doses of 0.1, 0.25, 0.5, 1.5, and 2.5 Gy with dose rate of 0.2 Gy/min. In the embryos from the fetuses from the mice irradiated at various period in preimplantation, embryonic/fetal mortalities, incidence of external gross malformation, fetal body weight and sex ratio were observed at day 18 of gestation. The sensitivity of embryonic mortalities in the mice irradiated at the stage of preimplantation were higher than those in the mice irradiated at the stage of organogenesis. And the more sensitive periods of preimplantation stage for embryonic death were 2 and 48 hpc, at which embryos were one cell and 4 to 7 cell stage, respectively. Many types of the external gross malformations such as exencephaly, cleft palate and anophthalmia were observed in the fetuses from the mice irradiated at 2, 72 and 96 hpc. However, no malformations were observed in the mice irradiated at 48 hpc, at which stage the embryos were about 6 cell stage precompacted embryos. So far, it is believed that the embryos on preimplantation stage are not susceptible to teratogens such as radiation and chemical agents. In this study, the sensitivity for external malformations in the fetuses from the mice irradiated at preimplantation were higher than those in the fetuses on stage of organogenesis.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Horticultural Science & Technology
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• v.32 no.4
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• pp.499-506
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• 2014

This research was conducted to develop root media containing ground and aged pine bark (GAPB) and ground and raw pine bark (GRPB). After analysis of physico chemical properties, the pine barks were blended with peat moss (PM) or coir dust (CD) in various ratios to formulate 12 root media. Then, two out of 12 root media were chosen based on the physical properties for further experiments. The pre-planting nutrient charge fertilizers (PNCF) were incorporated into two root media and chemical properties were analysed again. The total porosity (TP), container capacity (CC), and air-filled porosity (AFP) of GAPB were 78.7%. 39.4%, and 38.3%, respectively, while those of GRPB were 74.7%, 41.2%, and 33.4%, respectively. The percentage of easily available water (EAW, from CC to 4.90 kPa tension) and buffering water (BW, 4.91-9.81 kPa tension) in GAPB were 12.7% and 8.5%, respectively, which were a little lower than the 13.5% and 8.8% in GRPB. The pH and EC were not different significantly, but cation exchange capacity was different between the two pine barks (GAPB: pH 5.26, EC $0.61dS{\cdot}m^{-1}$, CEC $15.7meq{\cdot}100g^{-1}$; GRPB: pH 5.19, EC $0.32dS{\cdot}m^{-1}$, CEC $9.32meq{\cdot}100g^{-1}$). The concentrations of exchangeable cations in GAPB were Ca 0.32, K 0.05, Mg 0.27 and $0.12cmol+{\cdot}kg^{-1}$, whereas those in GRPB were Ca 0.28, K 0.08, Mg 0.25 and $0.09cmol+{\cdot}kg^{-1}$. The concentrations of $PO_4$-P, $NH_4$-N and $NO_3$-N were 485.8, 0.62 and $0.91mg{\cdot}L^{-1}$ in GAPB and 578, 1.00 and $0.82mg{\cdot}L^{-1}$ in GRPB, respectively, when those were analyzed in the solution of the saturated paste. The TP, CC and AFP in the two selected media were 89.3 and 76.3, and 13.0% in PM+GAPB (8:2, v/v) and 88.2, 68.2 and 20.0% in CD+GRPB (8:2), respectively. The pHs and ECs were 3.8 and $0.24dS{\cdot}m^{-1}$ in PM+GAPB which were a little lower than 5.8 and $0.65dS{\cdot}m^{-1}$ in CD+GRPB. However, the pHs analysed before and after incorporation of PNCF in the two root media did not show large differences. This is because the solubility of dolomitic lime is very low, and the pH it is expected to rise gradually when crops are cultivated int he root media. The information obtained in this study should facilitate effective formulation of root media containing pine bark.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.26 no.3
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• pp.221-229
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• 1990

The horizontal distribution of squid gill-net fishing ground in the North Pacific Ocean was examined within the main fishing season, May to October, during 1986~1989. Data of sea surface temperature were selected from Technical Reports of National Fisheries Research Development Agency of Korea, Data Records of Hokkaido University, Deep-sea Training Reports of Korea Fishing Training centre, Fishing Operation Reports of Daelim Fisheries Co., Ltd., Oyang Fisheries Co., Ltd. and Dong-won Industrial Co., Ltd.. Data of catch were also collected from Deep-sea Training Reports of Korea Fishing Training Centre and Fishing Operation Report of three fisheries companies in Korea. The fishing ground was segmented in every 1 degree of latitude from $34^{\circ}N$ to $46^{\circ}N$ and 2 degree of longitude from $144^{\circ}E$ to $162^{\circ}W.$ The distribution and centeroid of fishing ground, fished and optimum surface temperature, catch per unit effort (CPUE) in the fishing ground were computed, based on the above data. The resulted obtained can be summarized as follows: 1. Range of fishing ground can be estimated as $35^{\circ}~40^{\circ}N,$ $178^{\circ}~166^{\circ}W$ in May, $36^{\circ}~41^{\circ}N,$ $178^{\circ}E~166^{\circ}W$ in June, $38^{\circ}~44^{\circ}N,$ $170^{\circ}E~170^{\circ}W$ in July, $39^{\circ}~44^{\circ}N,$ $144^{\circ}~180^{\circ}E$ in August, $39^{\circ}~44^{\circ}N,$ $144^{\circ}~170^{\circ}E$ in September and $40^{\circ}~44^{\circ}N,$ $144^{\circ}~154^{\circ}E$ in October. 2. Fishing ground in May, June and October is similarly distributed along longitude and latitude, but the range of the former is larger than that of the latter in July, August and September. Monthly centeroids of fishing sectors is estimated as #3888 in May, #3884 in June, #4078 in July, #4154 in August, #4146 in September and #4044 in October respectively. 3. Fished temperature and optimum and temperature are estimated as $14.0~18.5^{\circ}C$ and $15.0~16.0^{\circ}C$ in May, $13.5~18.5^{\circ}C$ and $14.5~16.0^{\circ}C$ in June, $14.0~20.0^{\circ}C$ and $14.5^{\circ}C,$ $19.0^{\circ}C$ in July, $16.0~21.5^{\circ}C$ and $18.0~20.0^{\circ}C$ in August, $14.5~22.0^{\circ}C$ and $17.0~18.5^{\circ}C$ in September, $14.0~18.0^{\circ}C$ and $16.0~17.0^{\circ}C$ in October. 4. Monthly mean CPUE which corresponds to the net weight of catch(kg) divided by the sheet number of operated gillnets is calcuted as 3.2, 4.5, 4.3, 5.1, 6.4 and 5.8 kg/sheet respectively. 5. Considering the monitoring program of the squid gill-net fishery in the North Pacific Ocean during 1989~1990, set by the Korean Government, 12 sectors may be restricted out of 21 fishing sectors in May, 7 out of 24 in June, 4 out of 25 in July. They are free from restriction hereafter August.

• The Journal of Small Business Innovation
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• v.19 no.4
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• pp.19-39
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• 2016

With increasing adoption of smart products and complexity, companies have shifted their strategies from stand alone and competitive strategies to business ecosystem oriented and cooperative strategies. The win-win growth of business refers to corporate efforts undertaken by companies to pursue the healthiness of business between conglomerates and partnering companies such as suppliers for mutual prosperity and a long-term corporate soundness based on their business ecosystem and cooperative strategies. This study is designed to validate a theoretical proposition that the win-win growth strategy of Samsung Electronics and cooperative efforts among companies can create a healthy business ecosystem, based on results of case studies and surveys. In this study, a level of global market access of small and mid-sized companies is adopted as the key achievement index. The foreign market entry is considered as one of vulnerabilities in the ecosystem of small and mid-sized enterprises (SMEs). For SMEs, the global market access based on the research and development (R&D) has become the critical component in the process of transforming them into global small giants. The results of case studies and surveys are analyzed mainly based on a model of a virtuous cycle of Creativity, Opportunity, Productivity, and Proactivity (the COPP model) that features the characteristics of the healthiness of a business ecosystem. In the COPP model, a virtuous circle of profits made by the first three factors and Proactivity, which is the manifestation of entrepreneurship that proactively invests and reacts to the changing business environment of the future, enhances the healthiness of a given business ecosystem. With the application of the COPP model, this study finds major achievements of the win-win growth of Samsung Electronics as follows. First, Opportunity plays a role as a parameter in the relations of Creativity, Productivity, and creating profits. Namely, as companies export more (with more Opportunity), they are more likely to link their R&D efforts to Productivity and profitability. However, companies that do not export tend to fail to link their R&D investment to profitability. Second, this study finds that companies with huge investment on R&D for the future, which is the result of Proactivity, tend to hold a large number of patents (Creativity). And companies with significant numbers of patents tend to be large exporters as well (Opportunity), and companies with a large amount of exports tend to record high profitability (Productivity and profitability), and thus forms the virtuous cycle of the COPP model. In addition, to access global markets for sustainable growth, SMEs need to build and strengthen their competitiveness. This study concludes that companies with a high level of proactivity to invest for the future can create a virtuous circle of Creativity, Opportunity, Productivity, and Proactivity, thereby providing a strategic implication that SMEs should invest time and resources in forming such a virtuous cycle which is a sure way for the SMEs to grow into global small giants.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.3
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• pp.425-430
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• 2014

Foods contain various nutrients such as carbohydrates, protein, oil, vitamins, and minerals. Among them, carbohydrates, protein, and oil are the main constituents of foods. Usually, these constituents are analyzed by the Kjeldahl and Soxhlet method and so on. However, these analytical methods are complex, costly, and time-consuming. Thus, this study aimed to rapidly and effectively analyze carbohydrate, protein, and oil contents with near-infrared reflectance spectroscopy (NIRS). A total of 517 food samples were measured within the wavelength range of 400 to 2,500 nm. Exactly 412 food calibration samples and 162 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of carbohydrates, the most accurate equation was obtained under 1, 4, 5, 1 (1st derivative, 4 nm gap, 5 points smoothing, and 1 point second smoothing) math treatment conditions using the weighted MSC (multiplicative scatter correction) scatter correction method with MPLS (modified partial least square) regression. In the case of protein and oil, the best equation were obtained under 2, 5, 5, 3 and 1, 1, 1, 1 conditions, respectively, using standard MSC and standard normal variate only scatter correction methods with MPLS regression. Calibrations of these NIRS equations showed a very high coefficient of determination in calibration ($R^2$: carbohydrates, 0.971; protein, 0.974; oil, 0.937) and low standard error of calibration (carbohydrates, 4.066; protein, 1.080; oil, 1.890). Optimal equation conditions were applied to a validation set of 162 samples. Validation results of these NIRS equations showed a very high coefficient of determination in prediction ($r^2$: carbohydrates, 0.987; protein, 0.970; oil, 0.947) and low standard error of prediction (carbohydrates, 2.515; protein, 1.144; oil, 1.370). Therefore, these NIRS equations can be applicable for determination of carbohydrates, proteins, and oil contents in various foods.