• Korean Journal of Poultry Science
• /
• v.43 no.1
• /
• pp.1-14
• /
• 2016

Prediction of growth patterns of commercial chicken strains is important. It can provide visual assessment of growth as function of time and prediction body weight (BW) at a specific age. The aim of current study is to compare the three nonlinear functions (i.e., Logistic, Gompertz, and von Betalanffy) for modeling the growth of twenty five commercial Korean native chicken (KNC) strains reared under a battery cage system until 32 weeks of age and to evaluate the three models with regard to their ability to describe the relationship between BW and age. A clear difference in growth pattern among 25 strains were observed and classified in to the groups according to their growth patterns. The highest and lowest estimated values for asymptotic body weight (C) for 3H and 5W were given by von Bertalanffy and Logistic model 4629.7 g for 2197.8 g respectively. The highest estimated parameter for maturating rate (b) was given by Logistic model 0.249 corresponds to the 2F and lowest in von Bertalanffy model 0.094 for 4Y. According to the coefficient of determination ($R^2$) and mean square of error (MSE), Gompertz and von Bertalanffy models were suitable to describe the growth of Korean native chicken. Moreover, von Bertalannfy model was well described the most of KNC growth with biologically meaningful parameter compared to Gompertz model.

• International Journal of Computer Science & Network Security
• /
• v.21 no.1
• /
• pp.192-200
• /
• 2021

In the mid of the December 2019, the virus has been started to spread from China namely Corona virus. It causes fatalities globally and WHO has been declared as pandemic in the whole world. There are different methods which can fit such types of values which obtain peak and get flattened by the time. The main aim of the paper is to find the best or nearly appropriate modeling of such data. The three different models has been deployed for the fitting of the data of Coronavirus confirmed patients in Pakistan till the date of 20^th November 2020. In this paper, we have conducted analysis based on data obtained from National Institute of Health (NIH) Islamabad and produced a forecast of COVID-19 confirmed cases as well as the number of deaths and recoveries in Pakistan using the Logistic model, Gompertz model and Auto-Regressive Integrated Moving Average Model (ARIMA) model. The fitted models revealed high exponential growth in the number of confirmed cases, deaths and recoveries in Pakistan.

• Korean Journal of Food Science and Technology
• /
• v.36 no.2
• /
• pp.349-354
• /
• 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 Animal Science and Technology
• /
• v.45 no.4
• /
• pp.509-516
• /
• 2003

Growth curves were estimated for 1083 female Korean cattle raised in Daekwanryeong branch, National Livestock Research Institute (NLRI). Comparisons were made among various growth curve models for goodness of fit for the growth of the cows. Estimated growth curve functions were $W_t=370.2e^{-2.208e^{-0.00327t}$ for Gompertz model, for von Bertalanffy model, and $W_t=341.2(1+5.652e^{-0.00524t})^{-1}$ for Logistic model. Ages at inflection estimated from Gompertz model, von Bertalanffy model and Logistic model were 242.2 days, 191.5 days, and 330.5 days respectively, body weight at inflection were 136kg, 115kg, and 170kg, and daily gain at inflection were 0.445kg, 0.451kg, and 0.446kg. The predicted weights by ages from Gompertz model, von Bertalanffy model, and Logistic model were onsistently overestimated at birth weight and underestimated at 36 month weight. The von Bertalanffy model which had a variable point of inflection fit the data best.

• Korea journal of population studies
• /
• v.16 no.2
• /
• pp.65-83
• /
• 1993

The purpose of this study is estimate limits of Korean life expectancy at birth by 'Gompertz growth curse Model', 'Cause-Elimination Model' and Multidimensional models of Senescencee and Mortality'. Data used in Gompertz curve were obtained from all life tables published from 1905 to 1990 in Korea, and life expectancies at birth of eighteen groups were selected at five-year interval in consideration of time-series changes. Data used in Cause-Elimination Model are 'Cause of Death statistics in 1991' published in 1992 by National Bureau of Statistics of Korea and 'life table of 1989' published in 1990 by National Bureau of Statistics, Economic Planning Board of Korea. The materials are all classifiable death data, 119, 253 cases of male and 82, 420 cases of female, which is from 1991 Causes of Death statistics. The cases of death analyzed belong to one of 8 categories; i.e., Infectious and Parasitic Diseases(001-139; with notation of Infectious Diseases), Malignant Neoplasms(140-208), Hypertensive Diseases(401-405), Ischemic Heart Dieases and Diseases of Pulmonary Circulation and Other Forms of Heart Diseases(410-429;with notation of Heart Disease), Cerebrovascular Diseases(430-438), Chronic Liver Diseases and Cirrhosis(571; with notation of Liver Diseases), Injury and Poisoning(800-999) and all other disease. Data used in 'Multidimensional models of senescence and mortality' were life table of 1989 published by National Bureau of statistics, Economic Planning Board of Korea and life table of 1970, 1978-79, 1983, 1985 and 1987. The major findings may be summarised as follows: 1. Estimate equations of Gompertz growth curve using life expectancy at birth during the 1905-1990 period are as the following. Male : y = 88.047697 $\times$ $0.199690^{0.903381x}$ Female : y = 95.632828 $\times$ $0.199690^{0.903381x}$ Limits of life expectancy at birth, which were estimated by Gompertz growth curve, are 88.05 for male and 95.63 for female. 2. The effect on life expectancy at birth eliminationg all causes death is 14.04 years(for male) and 10.86 years(for female). Astonishingly, eliminating the malignant neoplasms increase life expectancy at birth by 2.85 years for male 2.03 years for female in 1991. In table 8 we show the effect on life expectancy at birth of separately eliminating each of the 8 categorical causes of death. The theoretical limit to life expectancy by Cause-Elimination Model is 80.96 for male and 85.82 for female. 3. If the same rate of delay [0.376 year(male), 0.435 year(femable) per calendar year] continued, then life expectancy at birth would reach 74.82(male) years and 84, 10(female) years in 2010. With 14.04-years(male) and 10.86-years(female) effect attributable in 2010 would be 88.86 years(male) and 94.96(femable) years. 4. 'Multidimensional models of senescence and death' permits calculations of the value of the attribution coefficient (B), percent of loss per year of physiologic function. The results of Ro and B during the 1970-1989 period are listed in table 9. Estimate of limit to Korean life expectancy at birth by 'Multidimensional models of senescence and death' is 99.47 years for male and 104.74 years for female in 1989.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2012.10a
• /
• pp.953-954
• /
• 2012

• Journal of Applied Reliability
• /
• v.16 no.2
• /
• pp.162-170
• /
• 2016

Purpose: The purpose of this study was to analyze the failure modes of an auto transfer switch (ATS), determine the most common failure mechanisms, and iterate the design to improve reliability. Methods: We carried out failure mode and effect analysis (FMEA) to determine the failure modes and mechanisms. We identified the parts or modules that required improvement via two-stage quality function deployment based on FMEA, and improvements to reliability were monitored using the Gomperz growth model. Results: The main failure modes of the ATS were damage to, and deformation of, the stator / movable element due to repetitive movements. Five iterations of design modification were carried out, and the mean time to failure (MTTF) increased to 14,539 cycles, corresponding to 85% of the target MTTF. The Gompertz growth model indicates that the 10th iteration of design modification is expected to achieve the target MTTF. Conclusion: We improved the reliability of mechanical parts via failure mode analysis, and characterized the iterative improvements in the MTTF using the Gompertz growth model.

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

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

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