• Journal of Life Science
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• v.29 no.8
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• pp.861-870
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• 2019

This study was undertaken to establish optimum medium compositions for cost-effective mannitol production by Leuconostoc mesenteroides SRCM201425 isolated from kimchi. L. mesenteroides SRCM21425 from kimchi was selected for efficient mannitol production based on fructose analysis and identified by its 16S rRNA gene sequence, as well as by carbohydrate fermentation pattern analysis. To enhance mannitol production by L. mesenteroides SRCM201425, the effects of carbon, nitrogen, and mineral sources on mannitol production were first determined using Plackett-Burman design (PBD). The effects of 11 variables on mannitol production were investigated of which three variables, fructose, sucrose, and peptone, were selected. In the second step, each concentration of fructose, sucrose, and peptone was optimized using a central composite design (CCD) and response surface analysis. The predicted concentrations of fructose, sucrose, and peptone were 38.68 g/l, 30 g/l, and 39.67 g/l, respectively. The mathematical response model was reliable, with a coefficient of determination of $R^2=0.9185$. Mannitol production increased 20-fold as compared with the MRS medium, corresponding to a mannitol yield 97.46% when compared to MRS supplemented with 100 g/l of fructose in flask system. Furthermore, the production in the optimized medium was cost-effective. The findings of this study can be expected to be useful in biological production for catalytic hydrogenation causing byproduct and additional production costs.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.9
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• pp.955-960
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• 2008

We investigated and estimated at the characteristics of decomposition and mineralization of benomyl using a design of experiment(DOE) based on the general factorial design in an E-beam process, and also the main factors(variables) with benomyl concentration(X$_1$) and E-beam irradiation(X$_2$) which consisted of 5 levels in each factor was set up to estimate the prediction model and the optimization conditions. At frist, the benomyl in all treatment combinations except 17 and 18 trials was almost degraded and the difference in the decomposition of benomyl in the 3 blocks was not significant(p > 0.05, one-way ANOVA). However, the % of benomyl mineralization was 46%(block 1), 36.7%(block 2) and 22%(block 3) and showed the significant difference of the % that between each block(p < 0.05). The linear regression equations of benomyl mineralization in each block were also estimated as followed; block 1(Y$_1$ = 0.024X$_1$ + 34.1(R$^2$ = 0.929)), block 2(Y$_2$ = 0.026X$_2$ + 23.1(R$^2$ = 0.976)) and block 3(Y$_3$ = 0.034X$_3$ + 6.2(R$^2$ = 0.98)). The normality of benomyl mineralization obtained from Anderson-Darling test in all treatment conditions was satisfied(p > 0.05). The results of prediction model and optimization point using the canonical analysis in order to obtain the optimal operation conditions were Y = 39.96 - 9.36X$_1$ + 0.03X$_2$ - 10.67X$_1{^2}$ - 0.001X$_2{^2}$ + 0.011X$_1$X$_2$(R$^2$ = 96.3%, Adjusted R$^2$ = 94.8%) and 57.3% at 0.55 mg/L and 950 Gy, respectively. A Microtox test using V. fischeri showed that the toxicity, expressed as the inhibition(%), was reduced almost completely after an E-beam irradiation, whereas the inhibition(%) for 0.5 mg/L, 1 mg/L and 1.5 mg/L was 10.25%, 20.14% and 26.2% in the initial reactions in the absence of an E-beam illumination.

• KDI Journal of Economic Policy
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• v.12 no.1
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• pp.127-148
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• 1990

A Milgrom-Roberts style signalling model of limit pricing is developed to analyze the possibility and the scope of limit pricing in general, noncooperative oligopolies. The model contains multiple incumbent firms facing a potential entrant and assumes an information asymmetry between incombents and the potential entrant about the market demand. There are two periods in the model. In period 1, n incumbent firms simultaneously and noncooperatively choose quantities. At the end of period 1, the potential entrant observes the market price and makes an entry decision. In period 2, depending on the entry decision of the entrant, n' or (n+1) firms choose quantities again before the game terminates. Since the choice of incumbent firms in period 1 depends on their information about demand, the market price in period 1 conveys information about the market demand. Thus, there is a systematic link between the market price and the profitability of entry. Using Bayes-Nash equilibrium as the solution concept, we find that there exist some demand conditions under which incumbent firms will limit price. In symmetric equilibria, incumbent firms each produce an output that is greater than the Cournot output and induce a price that is below the Cournot price. In doing so, each incumbent firm refrains from maximizing short-run profit and supplies a public good that is entry deterrence. The reason that entry is deterred by such a reduced price is that it conveys information about the demand of the industry that is unfavorable to the entrant. This establishes the possibility of limit pricing by noncooperative oligopolists in a setting that is fully rational, and also generalizes the result of Milgrom and Roberts to general oligopolies, confirming Bain's intuition. Limit pricing by incumbents explained above can be interpreted as a form of credible collusion in which each firm voluntarily deviates from myopic optimization in order to deter entry using their superior information. This type of implicit collusion differs from Folk-theorem type collusions in many ways and suggests that a collusion can be a credible one even in finite games as long as there is information asymmetry. Another important result is that as the number of incumbent firms approaches infinity, or as the industry approaches a competitive one, the probability that limit pricing occurs converges to zero and the probability of entry converges to that under complete information. This limit result confirms the intuition that as the number of agents sharing the same private information increases, the value of the private information decreases, and the probability that the information gets revealed increases. This limit result also supports the conventional belief that there is no entry problem in a competitive market. Considering the fact that limit pricing is generally believed to occur at an early stage of an industry and the fact that many industries in Korea are oligopolies in their infant stages, the theoretical results of this paper suggest that we should pay attention to the possibility of implicit collusion by incumbent firms aimed at deterring new entry using superior information. The long-term loss to the Korean economy from limit pricing can be very large if the industry in question is a part of the world market and the domestic potential entrant whose entry is deterred could .have developed into a competitor in the world market. In this case, the long-term loss to the Korean economy should include the lost opportunity in the world market in addition to the domestic long-run welfare loss.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.221-232
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• 2016

Models to estimate solar radiation have been used because solar radiation is measured at a smaller number of weather stations than other variables including temperature and rainfall. For example, solar radiation has been estimated using the Angstrom-Prescott (AP) model that depends on two coefficients obtained empirically at a specific site ($AP_{Choi}$) or for a climate zone ($AP_{Frere}$). The objective of this study was to identify the coefficients of the AP model for reliable estimation of solar radiation under a wide range of spatial and temporal conditions. A global optimization was performed for a range of AP coefficients to identify the values of $AP_{max}$ that resulted in the greatest degree of agreement at each of 20 sites for a given month during 30 years. The degree of agreement was assessed using the value of Concordance Correlation Coefficient (CCC). When $AP_{Frere}$ was used to estimate solar radiation, the values of CCC were relatively high for conditions under which crop growth simulation would be performed, e.g., at rural sites during summer. The statistics for $AP_{Frere}$ were greater than those for $AP_{Choi}$ although $AP_{Frere}$ had the smaller statistics than $AP_{max}$ did. The variation of CCC values was small over a wide range of AP coefficients when those statistics were summarized by site. $AP_{Frere}$ was included in each range of AP coefficients that resulted in reasonable accuracy of solar radiation estimates by site, year, and month. These results suggested that $AP_{Frere}$ would be useful to provide estimates of solar radiation as an input to crop models in Korea. Further studies would be merited to examine feasibility of using $AP_{Frere}$ to obtain gridded estimates of solar radiation at a high spatial resolution under a complex terrain in Korea.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.617-628
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• 2022

A ground support pattern should be designed by properly integrating various support materials in accordance with the rock mass grade when constructing a tunnel, and a technical decision must be made in this process by professionals with vast construction experiences. However, designing supports at the early stage of tunnel design, such as feasibility study or basic design, may be very challenging due to the short timeline, insufficient budget, and deficiency of field data. Meanwhile, the design of the support pattern can be performed more quickly and reliably by utilizing the machine learning technique and the accumulated design data with the rapid increase in tunnel construction in South Korea. Therefore, in this study, the design data and ground exploration data of 48 road tunnels in South Korea were inspected, and data about 19 items, including eight input items (rock type, resistivity, depth, tunnel length, safety index by tunnel length, safety index by rick index, tunnel type, tunnel area) and 11 output items (rock mass grade, two items for shotcrete, three items for rock bolt, three items for steel support, two items for concrete lining), were collected to automatically determine the rock mass class and the support pattern. Three machine learning models (S1, A1, A2) were developed using two machine learning algorithms (SVR, ANN) and organized data. As a result, the A2 model, which applied different loss functions according to the output data format, showed the best performance. This study confirms the potential of support pattern design using machine learning, and it is expected that it will be able to improve the design model by continuously using the model in the actual design, compensating for its shortcomings, and improving its usability.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.2
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• pp.165-171
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• 2013

Due to the difficulty in considering dynamic load in the view point of a computer resource and computing time, it is common that external load is assumed as ideal static loads. However, structural analysis under static load cannot guarantee the safety of design of the structures under dynamic loadings. Recently, the systematic method to construct equivalent static load from the given dynamic load has been proposed. Previous study has calculated equivalent static load through the optimization procedure under displacement constraints. However, previously reported works to distribute equivalent static load were based on ad-hoc methods. Improper selection of equivalent static loading positions may results in unreliable prediction of structural design. The present study proposes the selection method of the proper locations of equivalent static loads to dynamically applied loads when we consider transient dynamic structural problems. Moreover, it is appropriate to take into account the stress constraint as well as displacement constraint condition for the safety design. But the previously reported studies of equivalent static load design methods considered only displacement constraint conditions but not stress constraint conditions. In the present study we consider not only displacement constraint but also stress constraint conditions. Through a few numerical examples, the efficiency and reliability of proposed scheme is verified by comparison of the equivalent stress between equivalent static loading and dynamic loading.

• Journal of the Korean GEO-environmental Society
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• v.13 no.11
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• pp.37-42
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• 2012

This study investigated the use of Geobacter lovleyi with TBOS(Tetrabutoxysilane) for TCE(Trichloroethylene) dechlorination. The TCE dechlorination by Geobacter lovleiy was mathematically described as the independent variables such as initial concentration of TCE, protein mass of Geobacter lovleyi and initial concentration of TOBS, and these were modeled by the use of response surface methodology(RSM). These experiments were carried out as a Box-Behnken Design(BBD) consisting of 15 experiments. The application of RSM yielded the following equation, which is empirical relationship for the dechlorination efficiency($Y_1$, %) of TCE and first order kinetic constant of TCE($Y_2,\;d^{-1}$) by independent variables in coded unit : $Y_1=-11.50X_1$(initial concentration of TCE) + $4.25X_2$(protein mass as Geobacter lovleyi injected mass) - $4.75X_3$(initial concentration of TBOS) - ${6.58X_1}^2$ - ${8.58X_2}^2$ + 93.67, $Y_2=-10.92X_1+5.06X_2-4.89X_3-{4.93X_3}^2-2.19X_1X_2+2.54X_1X_3-2.19X_2X_3+16.71$. In this case, the value of the adjusted determination coefficient(adjusted $R^2$= 0.975 and 0.934) were closed to 1, showing a high significance of the model. Statistical results showed the order of TCE dechlorination at experimental factors to be initial TCE concentration > initial TBOS concentration > protein mass, but the interaction effects were non-significant.

• Journal of Microbiology and Biotechnology
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• v.21 no.6
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• pp.637-645
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• 2011

Production of recombinant proteins by excretory expression has many advantages over intracellular expression in Escherichia coli. Hyperexpression of a secretory exoglucanase, Exg, of Cellulomonas fimi was previously shown to saturate the SecYEG pathway and result in dramatic cell death of E. coli. In this study, we demonstrated that overexpression of the PspA in the JM101(pM1VegGcexL-pspA) strain enhanced excretion of Exg to 1.65 U/ml using shake-flask cultivation, which was 80% higher than the highest yield previously obtained from the optimized JM101(pM1VegGcexL) strain. A much higher excreted Exg activity of 4.5 U/ml was further achieved with high cell density cultivation using rich media. Furthermore, we showed that the PspA overexpression strain enjoyed an elevated critical value (CV), which was defined as the largest quotient between the intracellular unprocessed precursor and its secreted mature counterpart that was still tolerable by the host cells prior to the onset of cell death, improving from the previously determined CV of 20/80 to the currently achieved CV of 45/55 for Exg. The results suggested that the PspA overexpression strain might tolerate a higher level of precursor Exg making use of the SecYEG pathway for secretion. The reduced lethal effect might be attributable to the overexpressed PspA, which was postulated to be able to reduce membrane depolarization and damage. Our findings introduce a novel strategy of the combined application of metabolic engineering and construct optimization to the attainment of the best possible E. coli producers for secretory/excretory production of recombinant proteins, using Exg as the model protein.

• Korean Journal of Environmental Biology
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• v.35 no.2
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• pp.207-214
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• 2017

There is little information about the seed longevity of wild plants, although seed bank storage is an important tool for biodiversity conservation. This study was conducted to predict the seed viability equation of Agastache rugosa. The A. rugosa seeds were stored at moisture contents ranging from 2.7 to 12.5%, and temperatures between 10 and $50^{\circ}C$. Viability data were fitted to the seed viability equation in a one step and two step approach. The A. rugosa seeds showed orthodox seed storage behaviour. The viability constants were $K_E=6.9297$, $C_W=4.2551$ $C_H=0.0329$, and $C_Q=0.00048$. The P85 of A. rugosa seeds was predicted to 152 years under standard seed bank conditions. The P85 predicted by seed viability equation can be used as basic information for optimization of seed storage processes.