• Food Science of Animal Resources
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• v.40 no.6
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• pp.938-945
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• 2020

A dynamic model was developed to predict the Escherichia coli cell counts in pig trotters at changing temperatures. Five-strain mixture of pathogenic E. coli at 4 Log CFU/g were inoculated to cooked pig trotter samples. The samples were stored at 10℃, 20℃, and 25℃. The cell count data was analyzed with the Baranyi model to compute the maximum specific growth rate (μ_max) (Log CFU/g/h) and lag phase duration (LPD) (h). The kinetic parameters were analyzed using a polynomial equation, and a dynamic model was developed using the kinetic models. The model performance was evaluated using the accuracy factor (A_f), bias factor (B_f), and root mean square error (RMSE). E. coli cell counts increased (p<0.05) in pig trotter samples at all storage temperatures (10℃-25℃). LPD decreased (p<0.05) and μ_max increased (p<0.05) as storage temperature increased. In addition, the value of h₀ was similar at 10℃ and 20℃, implying that the physiological state was similar between 10℃ and 20℃. The secondary models used were appropriate to evaluate the effect of storage temperature on LPD and μ_max. The developed kinetic models showed good performance with RMSE of 0.618, B_f of 1.02, and A_f of 1.08. Also, performance of the dynamic model was appropriate. Thus, the developed dynamic model in this study can be applied to describe the kinetic behavior of E. coli in cooked pig trotters during storage.

• Journal of Korea Technology Innovation Society
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• v.11 no.1
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• pp.72-90
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• 2008

What has been the contribution of industrial innovation to economic growth? Typically, the issue has been approached with growth-accounting methods augmented to include a "stock of knowledge". An independent estimate of the rate of return to R&D is found in order to impute patents granted to the accumulation of knowledge. Griliches(1973) then uses a regression approach to assess the effect of an R&D variable on the computed TFP growth rate. The regression coefficient on the R&D variable would provide an estimate of the social rate of return to R&D. The related studies tend to show high social rates of return to R&D, typically in a range of 20 to 40 % per year. We need to provide multiple equation dynamic system for productivity and innovation in Korean economy in state space form. A wide range of time series models, including the classical linear regression model, can be written and estimated as special cases of a state space specification. State space models have been applied in the econometrics literature to model unobserved variables like productivity. Estimation produces the following results. Considering the goodness of fit, we can see that the evidence is strongly in favor of the range $0.120{\sim}0.135$ for the elasticity of TFP to R&D stock in the period between 1970's and the early 2000's.

• Journal of Korean Society of Forest Science
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• v.83 no.1
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• pp.63-74
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• 1994

One of the essential factors to estimate the stem and stand growth is to correctly portray a stem form (profile). It is also required to numerically approximate a stem form in order to dynamically grasp and represent a stand growth. A whole stem form seems to be a conical form but a stem outline at various positions tapers off differently. Accordingly it is difficult to model a whole stand form with single taper equation. A stem taper equation with different coefficients on each subinterval can be useful tools to accurately portray a stem form. This article presents the derivation method of individual stem taper curve using spline function. It is also in this paper aimed to study how a stand taper curve car, be derived from the population of single stem taper curve in a stand. These taper equations numerically formulated enable to dynamically represent and prognosticate the development process of a stand and prepare the foundation of variety on growth model study and rational forest planning model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.5
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• pp.2067-2077
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• 2011

The recent trend in technology development is characterized as technology convergence, mainly between IT, BT and NT and also more and more industries are starting to use several technologies simultaneously or in a combined way theses days. As a result, the needs on technology interaction analysis is increasing for strategic technology management and policy-making. Responding to the needs, this research deals with technology innovation process in terms of technology competition, particularly focusing on the 17 new growth drivers in 3 areas, which has been announced by the Korean government as a new growth vision for Korean economy, and analyzing their co-evolutionary process. For the analysis, patent data, a representative data on technology innovation, is adopted. Then, Lotka-Volterra Competition model, a model frequently used to describe the dynamism of competitive innovation is applied to the data. The research results are expected to support strategic decision-makings such as effect policy-making or R&D priority-setting, by analyzing the relationship between the 3 areas, the 17 new growth drivers, or the particular technologies in the drivers.

• Journal of Microbiology and Biotechnology
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• v.16 no.6
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• pp.993-998
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• 2006

We earlier suggested a hierarchical regulatory network using defined modeling symbols and weights in order to improve the flux balance analysis (FBA) with regulatory events that were represented by if-then rules and Boolean logic. In the present study, the simulation results of the models, which were developed and improved from the previou model by incorporating a hierarchical regulatory network into the FBA, were compared with the experimental outcome of an aerobic batch growth of E. coli on glucose and tryptophan. From the experimental result, a diauxic growth curve was observed, reflecting growth resumption, when tryptophan was used as an alternativee after the supply of glucose was exhausted. The model parameters, the initial concentration of substrates (0.92 mM glucose and 1 mM tryptophan), cell density (0.0086 g biomass/1), the maximal uptake rates of substrates (5.4 mmol glucose/g DCW h and 1.32 mmol tryptophan/g DCW h), and lag time (0.32 h) were derived from the experimental data for more accurate prediction. The simulation results agreed with the experimental outcome of the temporal profiles of cell density and glucose, and tryptophan concentrations.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.5
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• pp.606-612
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• 2005

Individual production traits, such as reproduction and mortality rates, are partial measures, but may be used to evaluate the performance of different systems by comparing the rate of flock growth and potential offtake. The productivity of two existing sheep production systems, one extensive, one intensive, was compared with an alternative semi-intensive system. The future flock sizes, offtakes and structures were predicted based on the age structure of the flock and age-specific reproduction, mortality and growth rates. The measurements were illustrated with reference to growth of a sheep flock of different age and sex categories. The flock was in a socalled dynamic situation. During the dry period, the digestible organic matter intake of the animals in the intensive system and both extensive and semi-extensive systems was 36 and 20.1 g kg$^{-0.75}$ d$^{-1}$, respectively. During the cold period, the digestible organic matter intake of the animals in extensive, intensive and semi-extensive systems was 34, 34.5 and 41 g kg$^{-0.75}$ d$^{-1}$, respectively. During the dry period, the animals in the both extensive and semi-intensive systems lost in body weight at a rate of 19 g per day, but the rate of gain in body weight of the animals in intensive system was 57 g per day. During the cold period, the animals in extensive, intensive and semiintensive systems gained in body weight at rates of 56, 67 and 97 g per day, respectively. The higher gain of animals during the cold period in the semi-intensive system was related to a sustained higher intake of low-quality roughage and more efficient use of the available feed. Compared to the intensive system, the annual concentrate input of the semi-intensive system was about 48% lower for each livestock unit. The productivity of the semi-intensive system was higher than that of the extensive system.

• The Journal of Fisheries Business Administration
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• v.46 no.3
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• pp.103-118
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• 2015

The purpose of this paper is to discuss possibilities of applying growth curve models, such as Logistic, Log-Logistic, Log-Normal, Gompertz and Weibull, to three specific technology areas of Fishery Science and Technology in the process of measuring their technology level between Korea and countries with the state-of-the art level. Technology areas of hazard control of organism, environment restoration, and fish cluster detect were selected for this study. Expert panel survey was conducted to construct relevant panel data for years of 2013, 2016, and a future time of approaching the theoretical maximum technology level. The size of data was 70, 70 and 40 respectively. First finding is that estimation of shape and location parameters of each model was statistically significant, and lack-of-fit test using estimated parameters was statistically rejected for each model, meaning all models were good enough to apply for measuring technology levels. Second, three models other than Pearl and Gompertz seemed very appropriate to apply despite the fact that previous case studies have used only Gompertz and Pearl. This study suggests that Weibull model would be a very valid candidate for the purpose. Third, fish cluster detect technology level is relatively higher for both Korea and a country with the state-of-the-art among three areas as of 2013. However, all three areas seem to be approaching their limits(highest technology level point) until 2020 for countries with the state-of-the-art. This implies that Korea might have to speed up her research activities in order to catch up them prior to 2020. Final suggestion is that future study may better apply various and more appropriate models respectively considering each technology characteristics and other factors.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.2
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• pp.121-125
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• 2004

In plants, nitrogen is the major component for growth and development. Leaf growth is based on the division, elongation and maturation of cells, which are used for making of epidermis, mesophyll, bundle sheath, xylem, phloem and so on. Dynamics of these tissues with respect to nitrogen are required for better understanding. This experiment was conducted to evaluate effect of nitrogen on the elongation of epidermal and guard cell of two rice (Oryza sativa L.) varieties, Seoanbyeo and Dasanbyeo on May 2000 at Chungbuk national university in Cheongju. After transplaning the 20-day-old seedlings into a/5000 pots, the main characteristics related with cell elongation were investigated and evaluated. A maximum. leaf length reached at 7 or 8 days after emerging from the collar, and also the leaf elongation rates were greatly affected by the increase of N application rate. The initial and final cell length were about $17\mu\textrm{m}$ and $130\mu\textrm{m}$, respectively. Cell divisions occurred within 1.0mm from leaf base. With die higher nitrogen application rate of 22 kg-N $10\textrm{a}^{-1}$, cell division per hour was greater 1.5 to 1.9 and 1.2 to 1.3 fold as compared to the N application rate of 0 and 11 kg-N $10\textrm{a}^{-1}$, respectively. Cell enlargement of epidermal and guard cell under higher N application rate (22kg-N $10\textrm{a}^{-1}$) was finished within about 20 (Seoanbyeo) and 15 hours (Dasanbyeo), while it took much time, about 30 hours.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.2
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• pp.1073-1083
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• 2015

As an empirical study focused on how transactive memory concept influence the four processes of knowledge growth, this study aimed at explaining the function of transactive memory in the dynamic process of knowledge growth. To verify the research model, this study carried out a path analysis of 130 team memebers and as a result, part of the team transactive memory measurements does affect the knowledge growth. Consequentially, transactive memory especially based on team level, is relevant with knowledge growth. According to team level, this study targeted at the team members, and by adopting the team transactive memory concept in Cognitive Psycology, theoretically explained and analysed how to approach personal knowledge in internal organizations. To accelerate the knowledge process, the work should be modified collaboratively by trusting the team members' duty relations more than specialized knowledge. Furthermore, managers had better assign team members the work where they can make the most of their personal knowledge, and this study presented that the whole team performance could be improved by doing that.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.2
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• pp.91-100
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• 2018

Small and medium enterprises (SMEs) endeavor to overcome the adverse resource conditions and secure competitive advantage through technological innovation capability. Prior studies have focused on the overall or specific dimensions of technological innovation capability, and examined their performance impact. However, there has been less scholarly attention on the dynamic characteristics such as the relative importance of technological innovation capability or its performance impact at the different growth stages of a firm. In this vein, this study investigates the relationship between SME innovation capability and innovation performance at each growth stages of a firm. Based on the empirical analysis of manufacturing SMEs in Korea, we found that all dimensions of technological innovation capability had positive effects on innovation performance. However, each dimension of technological innovation capability had different effect on innovation performance by the growth stages. The planning capability can improve innovation performance at the growth and maturity stages. Manufacturing capability can have positive effect on innovation performance at the maturity stage. Both of new product development capability and commercialization capability contribute to innovation performance at all of the growth stages. This study suggests the guidelines for enhancing technological innovation capability at the different growth stages of SMEs. It also provides policy implications for the design and operation of growth-stage specific programs. Finally, the limitations of the research and future research directions are presented.