• The KIPS Transactions:PartD
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• v.13D no.5 s.108
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• pp.709-716
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• 2006

As the software complexity increases, the development success rate decreases and failure rate increases exponentially. The failure rate related to the software size can be described by a growth function. Based on this phenomenon, this paper estimates the development success and completion rate using the Gompertz growth function. At first, we transformed a software size of numerically suggested $10^n$ into a logarithm and kept the data interval constantly. We tried to derive a functional relationship between the development success rate and the completion rate according to the change of logarithmic software size. However, we could not find a function which can represent this relationship. Therefore, we introduced the failure rate and the cancel rate which are inverse to the development success rate and completion rate, respectively. Then, we indicated the relation between development failure rate and cancel rate based on the change of software size, as a type of growth function. Finally, as we made the Gompertz growth function with the function which describes the cancel rate and the failure rate properly. We could express the actual data suitably. When you apply the growth function model that I suggested, you will be able to get the success rate and completion rate of particular site of software very accurately.

• Korean Journal of Food Science and Technology
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• v.36 no.2
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• pp.349-354
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• 2004

Predictive growth model of Vibrio parahaemolyticus in modified surimi-based imitation crab broth was investigated. Growth curves of V. parahaemolyticus were obtained by measuring cell concentration in culture broth under different conditions ($Initial\;cell\;level,\;1{\times}10^{2},\;1{\times}10^{3},\;and\;1{\times}10^{4}\;colony\;forming\;unit\;(CFU)/mL$; temperature, 15, 25 37, and $40^{\circ}C$; pH 6, 7, and 8) and applying them to Gompertz model. Microbial growth indicators, maximum specific growth rate (k), lag time (LT), and generation time (GT), were calculated from Gompertz model. Maximum specific growth rate (k) of V. parahaemolyticus increased with increasing temperature, reaching maximum rate at $37^{\circ}C$. LT and GT were also the shortest at $37^{\circ}C$. pH and initial cell number did not influence k, LT, and GT values significantly (p>0.05). Polynomial model, $k=a{\cdot}\exp(-0.5{\cdot}((T-T_{max}/b)^{2}+((pH-pH_{max)/c^{2}))$, and square root model, ${\sqrt{k}\;0.06(T-9.55)[1-\exp(0.07(T-49.98))]$, were developed to express combination effects of temperature and pH under each initial cell number using Gauss-Newton Algorism of Sigma plot 7.0 (SPSS Inc.). Relative coefficients between experimental k and k Predicted by polynomial model were 0.966, 0.979, and 0.965, respectively, at initial cell numbers of $1{\times}10^{2},\;1{\times}10^{3},\;and\;1{\times}10^{4}CFU/mL$, while that between experimental k and k Predicted by square root model was 0.977. Results revealed growth of V. parahaemolyticus was mainly affected by temperature, and square root model showing effect of temperature was more credible than polynomial model for prediction of V. parahaemolyticus growth.

• Journal of the Korea Organic Resources Recycling Association
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• v.24 no.1
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• pp.21-29
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• 2016

We mix food waste leachate and sewage sludge by the proportion of 1:9, 3:7 and 5:5. It turns out that they produced 233, 298 and 344 $CH_4{\cdot}mL/g{\cdot}VS$ of methane gas. The result suggests that as the mixing rate of food waste leachate rises, the methane gas productions increases as well. And more methane gas is made when co-digesting sewage sludge and food waste leachate based on the mixing ratio, rather than digesting only sewage sludge alone. Modified Gompertz and Exponential Model describe the BMP test results that show how methane gas are produced from organic waste. According to the test, higher the mixing rate of food waste leachate is, higher the methane gas productions is. The mixing ratio of food waste leachate that produces the largest volume of methane gas is 3:7. Modified Gompertz model and Exponential model describe the test results very well. The correlation values($R^2$) that show how the results of model prediction and experiment are close is 0.92 to 0.98.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.207-215
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• 2021

The number of tourists visiting Nepal has shown rapid growth in recent years, and Nepal is expecting more tourist arrivals in the future. This paper, thus, attempts to analyze the tourist arrivals in Nepal and predict the number of visitors until 2025. This paper has examined the international tourist arrival trend in Nepal using the Gompertz and Logistic growth model. The international tourist arrival data from 1991 to 2018 is used to investigate international tourist arrival trends. The result of the analysis found that the Gompertz model performs a better fit than the Logistic model. The study further forecast the expected tourist arrival below one million (844,319) by 2025. Nevertheless, the government of Nepal has the goal of two million tourists in a year. The present study also discusses system dynamics scenarios for the two million potential visitors within a year. Scenario analysis shows that proper advertisement and positive word-of-mouth will be key factors in achieving a higher number of tourists. The current study could fill the gap of theoretical and empirical forecasting of tourist arrivals in the Nepalese tourism industry. Also, the study findings would be beneficial for government officers, planners and investors, and policy-makers in the Nepalese tourism industry.

• Journal of the Korean GEO-environmental Society
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• v.15 no.9
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• pp.13-18
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• 2014

BMP test was carried out to evaluate the characteristics for co-digestion of night soil and food waste. 6 types of sludge were tested in 30 days which were raw, excess, digested, night soil/septic tank (1:1), food waste (food : dilution water = 1:1), and mixed sludge. Bio gas was produced actively after 2 days, and continued in 2 weeks. Gas generation amount was decreased rapidly after considerable space of time. Especially maximum productivity of gas was shown in 7~8 days. The ultimate methane yields of raw, excess, digested, night soil/septic tank, food waste, and mixed sludge were 64.63, 67.49, 66.45, 72.44, 107.85, and 46.71 mL $CH_4/g$ VS respectively from Modified Gompertz model. The lag growth phase time and maximum specific methane production rate of mixed sludge were 1.88 day and 80.4 mL/day respectively. The methane potential of mixed sludge was higher than individual sludge. So high methane potential was expected by controlling mixing ratio of food waste. Besides stable operation of digestion tank and the solution of oligotrophic problem were possible.

• Journal of Animal Science and Technology
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• v.45 no.5
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• pp.711-724
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• 2003

Some growth curve models were used to fit individual growth of 1,083 Hanwoo cows born from 1970 to 2001 in Daekwanryeong branch, National Livestock Research Institute(NLRI). The effects of year-season of birth and age of dam were analyzed. In analysis of variance for growth curve parameters, the effects of birth year-season were significant for mature weight(A), growth ratio(b) and maturing rate(k)(P〈.01). The effects of age of dam were significant for growth ratio(b) but not significant for mature weight(A) and maturing rate(k). The linear term of the covariate of age at the final weights was significant for the A(P〈.01) and k(P〈.01) of Gompertz model, von Bertalanffy model and Logistic model. For the growth curve parameters fitted on individual data using Gompertz model, von Bertalanffy model and Logistic model, resulting the linear contrasts(fall-spring), Least square means of A in three nonlinear models were higher cows born at fall and A of Logistic model was significant(P〈.05) between the seasons. According to the results of the least square means of growth curve parameters by age of dam, least square means of mature weight(A) in Gompertz model was largest in 6 year and smallest estimating for 3 and 8 years of age of dam. The growth ratio(b) was largest in 2 year of age of dam and smallest estimating in 8 year. The A and k were not different by age of dam(p〉.05), On the other hand, the b was different by age of dam(p〈.01). The estimate of A in von Bertalanffy model was largest in 6 year and smallest in 8 and 9 years of age of dam. The b was largest in 2 year and tend to decline as age of dam increased. The A and k were not different by age of dam(p〉.05), On the other hand, the b was highly significant by age of dam(p〈.01).

• Journal of Food Hygiene and Safety
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• v.26 no.2
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• pp.120-124
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• 2011

Predictive mathematical models were developed for predicting the kinetics of growth of Listeria monocytogenes in smoked salmon, which is the popular ready-to-eat foods in the world, as a function of temperature (4, 10, 20 and $30^{\circ}C$). At these storage temperature, the primary growth curve fit well ($r^2$=0.989~0.996) to a Gompertz equation to obtain specific growth rate (SGR) and lag time (LT). The Polynomial model for natural logarithm transformation of the SGR and LT as a function of temperature was obtained by nonlinear regression (Prism, version 4.0, GraphPad Software). Results indicate L. monocytogenes growth was affected by temperature mainly, and SGR model equation is $365.3-31.94^*Temperature+0.6661^*Temperature^{\wedge^2}$ and LT model equation is $0.1162-0.01674^*Temperature+0.0009303^*Temperature{\wedge^2}$. As storage temperature decreased $30^{\circ}C$ to $4^{\circ}C$, SGR decreased and LT increased respectively. Polynomial model was identified as appropriate secondary model for SGR and LT on the basis of most statistical indices such as bias factor (1.01 by SGR, 1.55 by LT) and accuracy factor (1.03 by SGR, 1.58 by LT).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.131-138
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• 2023

This study started with questioning the fact that in the assessmentof technology, which has taken place every two years since 2010, the technology gap in the most technologically advanced countries was evaluated as 4-5 years in each evaluation. To interrogate this question, regression estimation was performed using the Gompertz model based on time series data for technology level evaluation. As a result, it would take 17 years for high-speed rail vehicle technology to reach the level of 95 % of the country with the highest technology, and 72 years to reach the level of 100 %. Recognizing the technology gap is important in establishing a technology catchup strategy. A collaborative technology catchup strategy is the best strategy for moving to an original technology development stage while competing with large global leaders without much domestic market demand. This can occur regardless of where Korea is located in the technology catchup stage.

• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.658-664
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• 2021

This study aimed to develop accurate status site index curves for C. japonica in Gyeongsang-do that reflect the regional characteristics. The development of high-growth models in Chapman-Richards, Schumacher, and Gompertz for 552 C. japonica growing in Gyeongsang-do. The Gompertz growth function is the most suitable for developing site index curves. The comparative test was analyzed using the F test at a significance level of 5% and the graph. As a result, compared with the national site index curves and site index curves under base age in Jeolla-do, the p-value was 0.05 or higher, and there was no statistically significant difference. The p-value was 0.05 or lower compared with site index curves over stand age in Jeolla-do, indicating a statistically significant difference. Therefore, it was determined that site index curves for C. japonica in Gyeongsang-do can be applied to the national site index curves and site index curves under base age in Jeolla-do, but not to site index curves over base age in Jeolla-do. Hence, based on the results of the study, it is possible to provide basic data on the forest management system for C. japonica in Gyeongsang-do and systematic and reasonable management through high field application reflecting regional characteristics.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.205-211
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• 2020

The method of predicting the future may be predicted by technical characteristics or technical performance. Therefore, technology prediction is used in the field of strategic research that can produce economic and social benefits. In this study, we predicted the future market through the study of how to predict the future with these technical characteristics. The future prediction method was studied through the prediction of the time when the market occupied according to the demand of special product. For forecasting market demand, we proposed the future forecasting model through comparison of representative quantitative analysis methods such as CAGR model, BASS model, Logistic model and Gompertz Growth Curve. This study combines Rogers' theory of innovation diffusion to predict when products will spread to the market. As a result of the research, we developed a methodology to predict when a particular product will mature in the future market through the spread of various factors for the special product to occupy the market. However, there are limitations in reducing errors in expert judgment to predict the market.