• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.253-263
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• 2018

This paper examined the U-shape curvilinear relationship between team level conflict management and individual level turnover intention by using exit-voice theory, bandwagon effect, and social loafing theory. In addition to the non-linear relationship between team-level conflict management and individual-level turnover intentions, we also examined how trust in leaders has a moderating effect on this relationship. The samples were collected from a South Korean manufacturing company with 331 team members from 48 teams and items were measured twice to avoid common method biases. The intercepts-as-outcomes model of hierarchical linear modelling was conducted to verify the hypothesis. Results supported the cross-level curvilinear hypothesis which indicated that employees' turnover intention sharply decreased if the activeness of group conflict management was small and increases slightly, but this tendency moderated as activeness increases. After passing the lowest point, their turnover intention increased in the end. However, the moderation effect of trust in leader on this relationship was not statistically significant and hypothesis 2 was rejected. This paper explained the effects of group dynamics of conflict management on individual turnover intention. Such evidence may elucidate the importance of managing the social loafing behavior on conflict management process. This paper examined the sequential, multi-level, and curvilinear relationship between conflict management and turnover intention. Organizations and managers will benefit from avoiding the human resource loss by managing the conflict management process.

• Tuberculosis and Respiratory Diseases
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• v.59 no.4
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• pp.368-373
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• 2005

Background : Bronchial anthracofibrosis usually manifest as a form of obstructive airway disease, and can be accompanied by parenchymal diseases such as pneumonia, and pulmonary tuberculosis. This study investigated the ventilatory dynamics according to the severity of bronchial stenosis in patients with bronchial anthracofibrosis. Method : One hundred and thirteen patients with bronchial anthracofibrosis that was confirmed by bronchoscopy and who had undergone a pulmonary function test were enrolled in this study group. The correlation coefficients between the pulmonary functional parameters and the number of lobes with bronchial stenosis were investigated. Results : The incidence of ventilatory dysfunction was 56(49.6%) for obstructive, 8(7.1%) for restrictive, 2(1.8%) for mixed, and 47(41.6%) for a normal pattern. The $FEV_1/FVC$, $FEF_{25{\sim}75%}$, $FEF_{25%}$, $FEF_{50%}$, $FEF_{75%}$, and PEF showed a significant negative correlation (p<0.05) and the Raw had a significant positive correlation with the number of lobes with bronchial stenosis(p<0.001). Conclusion : These findings suggest that the most common abnormality of the ventilatory function in bronchial anthracofibrosis is an obstructive pattern with a small airway dysfunction according to the severity of bronchial stenosis.

• Korean Journal of Ecology and Environment
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• v.36 no.3 s.104
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• pp.286-294
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• 2003

This study was attempted to understand seasonal dynamics of phyto- and zooplankton communities in shallow, eutrophic Lake llgam and to compare them with the PEG (Plankton Ecology Group) model. Seasonal succession pattern of phytoplankton community was similar to PEG model as Chlorophyceae and Baciliphyceae increase during spring and autumn fellowed by increase of Cyanophyceae. However, based on the cell density and biomass, a dominant phytoplankton community differed with PEG model: Cyanophyceae had been a dominant community throughout a year, except for ice-cover period during which Chlorophyceae was a dominant group. In spring, when ice melted and dissolved nutrients in water column increased, the increase of Chlorophyceae occurred: when nutrients (DIN and DIP) rapidly decreased, Cyanophyceae increase occurred. Microcystis, Oscillatoria, Lyngbya, Merismopedia were maior dominant species of Cyanophyceae and their cell density and/or biomass was the highest in October 2000 (12.9${\pm}$5.8${\times}10^5$ cells/ml, 3.5${\pm}$0.9${\times}10^3{\mu}gC/l$). Cyanophyceae biomass showed positive relationship with chlorophyll a ($r^2$ = 0.71,P< 0.001) and TP concentration ($r^2$ = 0.62, P< 0.001). Small-sized rotifers such as Keratella cochlearis, increased between March and May when Chlorophyceae increased. Both high standing crop of copepods and cladocerans, such as Diaphanosoma brachyrum and Bosmina longirostris occurred between June and September accompanied with the increase of Dinophyceae and Bacillariophyceae. There was no evidence that clear-water phase was caused by zooplankton grazing. The diversity and evenness index of phyto- and/or zooplankton increased with chlorophyll a concentration. These results suggest zooplankton grazing and limiting nutrient deficiency could lead to change of phytoplankton biomass, but not the phytoplankton community in Lake llgam.

• Journal of The Korean Association For Science Education
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• v.37 no.1
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• pp.155-168
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• 2017

This study investigates how elementary students used cognitive-emotional rebuttals in the context of modeling activities, especially on how their emotional and cognitive processes lead them to use rebuttals in terms of epistemic affect. Twenty-five fifth grade elementary students participated in the study as part of their science class. During the course of their sixth periods, students constructed a human respiratory system model through continuous discussion. The research results showed that elementary students used an elaboration-oriented rebuttal, a defence-oriented rebuttal, and a blame-oriented rebuttal in their modeling activity. The elaboration-oriented rebuttal interspersed with negative epistemic affect was used to elaborate on a student's explanation, and a negative epistemic affect was elicited from their cognitive discrepancy. On the other hand, defence-oriented rebuttal and blame-oriented rebuttal entangled with negative epistemic affect were used to defeat the students rather than help rigor evaluation of students' explanation, and the negative epistemic affect was elicited from the other students' undesirable behavior. These results suggest that students' rebuttals can be elicited by epistemic dynamics related to the epistemic affect. The study shows that if negative epistemic affect were elicited from the other students' naive or false explanations, such an emotion is natural in terms of model construction, and the model can be further developed through the acceptance of the elaboration-oriented rebuttals by students' emotion regulation. In addition, we suggest that negative emotions aroused from the worsening of relationships during small group modeling activities are difficult to regulate and can have negative effects on students' cooperative model construction.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.16 no.4
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• pp.180-195
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• 2011

A balanced trophic model for Sinjido marine ecosystem was constructed using ECOPATH model and data obtained 1994 in the region. The model integrates available information on biomass and food spectrum, and analyses ecosystem properties, dynamics of the main species populations and the key trophic pathways of the system, and then compares these results with those of other marine environments. The model comprises 17 groups of benthic algae, phytoplankton, zooplankton, gastropoda, polychaeta, bivalvia, echinodermata, crustacean, cephalopoda, goby, flatfish, rays and skates, croaker, blenny, conger, flatheads, and detritus. The model shows trophic levels of 1.0~4.0 from primary producers and detritus to top predator as flathead group. The model estimates total biomass(B) of 0.1 $kgWW/m^2$, total net primary production(PP) of 1.6 $kgWW/m^2/yr$, total system throughput(TST) of 3.4 $kgWW/m^2/yr$ and TST's components of consumption 7%, exports 43%, respiratory flows 4% and flows into detritus 46%. The model also calculates PP/TR of 0.012, PP/B of 0.015, omnivory index(OI) of 0.12, Fin's cycling index(FCI) of 0.7%, Fin's mean path length(MPL) of2.11, ascendancy(A) of 4.1 $kgWW/m^2/yr$ bits, development capacity(C) of 8.2 $kgWW/m^2/yr$ bits and A/C of 51%. In particular this study focuses the analysis of mixed trophic impacts and describes the indirect impact of a groupb upon another through mediating one based on 4 types. A large proportion of total export in TST means higher exchange rate in the study region than in semi enclosed basins, which seems by strong tidal currents along the channels between islands, called Sinjido, Choyakdo and Saengildo. Among ecosystem theory and cycling indices, B, TST, PP/TR, FCI, MPL and OI are shown low, indicating the system is not fully mature according to Odum's theory. Additionally, high A/C reveals the maximum capacity of the region is small. To sum up, the study region has high exports of trophic flow and low capacity to develop, and reaches a development stage in the moment. This is a pilot research applied to the Sinjido in terms of trophic flow and food web system such that it may be helpful for comparison and management of the ecosystem in the future.