• Title/Summary/Keyword: Technology growth model

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Growth Characteristics of Enterobacter sakazakii Used to Develop a Predictive Model

  • Seo, Kyo-Young;Heo, Sun-Kyung;Bae, Dong-Ho;Oh, Deog-Hwan;Ha, Sang-Do
    • Food Science and Biotechnology
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    • 제17권3호
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    • pp.642-650
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    • 2008
  • A mathematical model was developed for predicting the growth rate of Enterobacter sakazakii in tryptic soy broth medium as a function of the combined effects of temperature (5, 10, 20, 30, and $40^{\circ}C$), pH (4, 5, 6, 7, 8, 9, and 10), and the NaCl concentration (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10%). With all experimental variables, the primary models showed a good fit ($R^2=0.8965$ to 0.9994) to a modified Gompertz equation to obtain growth rates. The secondary model was 'In specific growth $rate=-0.38116+(0.01281^*Temp)+(0.07993^*pH)+(0.00618^*NaCl)+(-0.00018^*Temp^2)+(-0.00551^*pH^2)+(-0.00093^*NaCl^2)+(0.00013^*Temp*pH)+(-0.00038^*Temp*NaCl)+(-0.00023^*pH^*NaCl)$'. This model is thought to be appropriate for predicting growth rates on the basis of a correlation coefficient (r) 0.9579, a coefficient of determination ($R^2$) 0.91, a mean square error 0.026, a bias factor 1.03, and an accuracy factor 1.13. Our secondary model provided reliable predictions of growth rates for E. sakazakii in broth with the combined effects of temperature, NaCl concentration, and pH.

Development of Predictive Mathematical Model for the Growth Kinetics of Staphylococcus aureus by Response Surface Model

  • Seo, Kyo-Young;Heo, Sun-Kyung;Lee, Chan;Chung, Duck-Hwa;Kim, Min-Gon;Lee, Kyu-Ho;Kim, Keun-Sung;Bahk, Gyung-Jin;Bae, Dong-Ho;Kim, Kwang-Yup;Kim, Cheorl-Ho;Ha, Sang-Do
    • Journal of Microbiology and Biotechnology
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    • 제17권9호
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    • pp.1437-1444
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    • 2007
  • A response surface model was developed for predicting the growth rates of Staphylococcus aureus in tryptic soy broth (TSB) medium as a function of combined effects of temperature, pH, and NaCl. The TSB containing six different concentrations of NaCl (0, 2, 4, 6, 8, and 10%) was adjusted to an initial of six different pH levels (pH 4, 5, 6, 7, 8, 9, and 10) and incubated at 10, 20, 30, and $40^{\circ}C$. In all experimental variables, the primary growth curves were well ($r^2=0.9000$ to 0.9975) fitted to a Gompertz equation to obtain growth rates. The secondary response surface model for natural logarithm transformations of growth rates as a function of combined effects of temperature, pH, and NaCl was obtained by SAS's general linear analysis. The predicted growth rates of the S. aureus were generally decreased by basic (pH 9-10) or acidic (pH 5-6) conditions and higher NaCl concentrations. The response surface model was identified as an appropriate secondary model for growth rates on the basis of correlation coefficient (r=0.9703), determination coefficient ($r^2=0.9415$), mean square error (MSE=0.0185), bias factor ($B_f=1.0216$), and accuracy factor ($A_f=1.2583$). Therefore, the developed secondary model proved reliable for predictions of the combined effect of temperature, NaCl, and pH on growth rates for S. aureus in TSB medium.

확산 모형을 이용한 줄기 세포 기술의 수명 주기 분석 : 연구 단계를 중심으로 (Life Cycle Analysis of Stem Cell Technology Based on Diffusion Model : Focused on the Research Stage)

  • 장인영;홍정식;김태구
    • 대한산업공학회지
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    • 제41권5호
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    • pp.488-498
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    • 2015
  • Research on stem cells can be divided into three categories : pluripotent stem cell, adult stem cell, and induced pluripotent stem cell. Technology life cycle (TLC) on research stage is analyzed for the three stem cell categories based on diffusion model. Three diffusion models-logistic, Bass, and Bass model with integration constant (BMIC)-are applied to the number of articles related to each stem cell category in SCOPUS lists. Two different parameter estimation methods is used for each of logistic and Bass model. Results show that (1) the current year, 2015, lies in growth period at pluripotent stem cell and adult stem cell, and lies in growth period or maturity period at induced pluripotent stem cell. (2) Model fitness is the highest at BMIC model. (3) Imitation effect works best at the research area of induced pluripotent stem cell.

재결정 및 결정립 성장거동을 기초한 조직예측 모델에 대한 변수 결정방법 (Determination of Material Parameters for Microstructure Prediction Model Based on Recystallization and Grain Growth Behaviors)

  • 염종택;김정한;홍재근;박노광
    • 한국소성가공학회:학술대회논문집
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    • 한국소성가공학회 2009년도 춘계학술대회 논문집
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    • pp.270-273
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    • 2009
  • This work describes a method of determining material parameters included in recrystallization and grain growth models. Focus is on the recrystallization and grain growth models of Ni-Fe base superalloy, Alloy 718. High temperature compression tests at different strain, strain rate and temperature conditions were chosen to determine the material parameters of dynamic recrystallization model. The critical strain and dynamically recrystallized grain size and fraction at various process variables were quantitated with the microstructual analysis and strain-stress relationships of the compression tests. Besides, isothermal heat treatments were utilized to fit the material constants included in the grain growth model. Verification of the determined material parameters is carried out by comparing the measured data obtained from other compression tests.

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A combined stochastic diffusion and mean-field model for grain growth

  • Zheng, Y.G.;Zhang, H.W.;Chen, Z.
    • Interaction and multiscale mechanics
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    • 제1권3호
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    • pp.369-379
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    • 2008
  • A combined stochastic diffusion and mean-field model is developed for a systematic study of the grain growth in a pure single-phase polycrystalline material. A corresponding Fokker-Planck continuity equation is formulated, and the interplay/competition of stochastic and curvature-driven mechanisms is investigated. Finite difference results show that the stochastic diffusion coefficient has a strong effect on the growth of small grains in the early stage in both two-dimensional columnar and three-dimensional grain systems, and the corresponding growth exponents are ~0.33 and ~0.25, respectively. With the increase in grain size, the deterministic curvature-driven mechanism becomes dominant and the growth exponent is close to 0.5. The transition ranges between these two mechanisms are about 2-26 and 2-15 nm with boundary energy of 0.01-1 J $m^{-2}$ in two- and three-dimensional systems, respectively. The grain size distribution of a three-dimensional system changes dramatically with increasing time, while it changes a little in a two-dimensional system. The grain size distribution from the combined model is consistent with experimental data available.

공적분 관계를 고려한 연구개발과 경제성장의 상호관계 연구 (Long-run Relationship between R&D Expenditures and Economic Growth)

  • 한웅용;전용일
    • 국제지역연구
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    • 제20권1호
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    • pp.147-165
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    • 2016
  • 본 연구는 OECD 21개 국가의 패널 자료(1981~2011)를 활용하여 제2세대내생적성장이론의 실증적 타당성을 검정하였다. 모든 변수들이 비정상성을 지니므로 변수들 간의 공적분 검정을 수행한 결과 연구개발수준은 총요소생산성에 유의한 영향을 미친다는 준 내생적성장이론이 강력히 지지되는 반면, 완전한 내생적 성장이론에 따른 연구개발집약도와 총요소생산성 성장 간의 유의한 관계는 일부 모형에 국한하여 적합한 것으로 분석되었다. 총요소생산성성장 함수 추정 결과 국내 및 국외 연구개발수준의 증가는 총요소생산성에 모두 유의한 영향을 미치는 것으로 나타났으나, 연구개발집약도의 경우는 일부 모형에서만 국내 및 국외 연구개발집약도가 총요소생산성에 유의한 영향을 미치는 것으로 나타나 준 내생적 성장이론이 강하게 지지되었다. 다만 국가 간의 기술격차는 총요소생산성 격차를 줄이는 효과가 있는 것으로 분석되어 완전한 내생적 성장이론의 기술격차가설은 타당한 것으로 나타났다. 이상의 분석결과를 바탕으로 준 내생적 성장이론적 관점에서 정책적 함의를 살펴보면 OECD 국가들의 경제는 총연구개발투자에 대한 생산성 체감의 가정이 타당하며, 따라서 지속적인 경제성장을 유지하기 위해서는 연구개발투자의 지속적인 증가가 수반되어야 한다는 시사점을 얻을 수 있다.

Probabilistic Modeling of Fish Growth in Smart Aquaculture Systems

  • Jongwon Kim;Eunbi Park;Sungyoon Cho;Kiwon Kwon;Young Myoung Ko
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제17권8호
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    • pp.2259-2277
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    • 2023
  • We propose a probabilistic fish growth model for smart aquaculture systems equipped with IoT sensors that monitor the ecological environment. As IoT sensors permeate into smart aquaculture systems, environmental data such as oxygen level and temperature are collected frequently and automatically. However, there still exists data on fish weight, tank allocation, and other factors that are collected less frequently and manually by human workers due to technological limitations. Unlike sensor data, human-collected data are hard to obtain and are prone to poor quality due to missing data and reading errors. In a situation where different types of data are mixed, it becomes challenging to develop an effective fish growth model. This study explores the unique characteristics of such a combined environmental and weight dataset. To address these characteristics, we develop a preprocessing method and a probabilistic fish growth model using mixed data sampling (MIDAS) and overlapping mixtures of Gaussian processes (OMGP). We modify the OMGP to be applicable to prediction by setting a proper prior distribution that utilizes the characteristic that the ratio of fish groups does not significantly change as they grow. We conduct a numerical study using the eel dataset collected from a real smart aquaculture system, which reveals the promising performance of our model.

Deep learning for the classification of cervical maturation degree and pubertal growth spurts: A pilot study

  • Mohammad-Rahimi, Hossein;Motamadian, Saeed Reza;Nadimi, Mohadeseh;Hassanzadeh-Samani, Sahel;Minabi, Mohammad A. S.;Mahmoudinia, Erfan;Lee, Victor Y.;Rohban, Mohammad Hossein
    • 대한치과교정학회지
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    • 제52권2호
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    • pp.112-122
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    • 2022
  • Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model's performance using weighted kappa and Cohen's kappa statistical analyses. Results: The model's validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model's validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

A Response Surface Model Based on Absorbance Data for the Growth Rates of Salmonella enterica Serovar Typhimurium as a Function of Temperature, NaCl, and pH

  • Park, Shin-Young;Seo, Kyo-Young;Ha, Sang-Do
    • Journal of Microbiology and Biotechnology
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    • 제17권4호
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    • pp.644-649
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    • 2007
  • Response surface model was developed for predicting the growth rates of Salmonella enterica sv. Typhimurium in tryptic soy broth (TSB) medium as a function of combined effects of temperature, pH, and NaCl. The TSB containing six different concentrations of NaCl (0, 2, 4, 6, 8, and 10%) was adjusted to an initial of six different pH levels (pH 4, 5, 6, 7, 8, 9, and 10) and incubated at 10 or $20^{\circ}C$. In all experimental variables, the primary growth curves were well $(r^2=0.900\;to\;0.996)$ fitted to a Gompertz equation to obtain growth rates. The secondary response surface model for natural logarithm transformations of growth rates as a function of combined effects of temperature, pH, and NaCl was obtained by SAS's general linear analysis. The predicted growth rates of the S. Typhimurium were generally decreased by basic (9, 10) or acidic (5, 6) pH levels or increase of NaCl concentrations (0-8%). Response surface model was identified as an appropriate secondary model for growth rates on the basis of coefficient determination $(r^2=0.960)$, mean square error (MSE=0.022), bias factor $(B_f=1.023)$, and accuracy factor $(A_f=1.164)$. Therefore, the developed secondary model proved reliable predictions of the combined effect of temperature, NaCl, and pH on growth rates for S. Typhimurium in TSB medium.

성장곡선 예측 모형의 특성치 보정에 따른 매개변수의 재추정 (Re-estimation of Model Parameters in Growth Curves When Adjusting Market Potential and Time of Maximum Sales)

  • 박주석;고영현;전치혁;이재환;홍승표;문형돈
    • 산업공학
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    • 제16권1호
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    • pp.103-110
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    • 2003
  • Growth curves are widely used in forecasting the market demand. When there are only a few data points available, the estimated model parameters have a low confidence. In this case, if some expert opinions are available, it would be better for predicting future demand to adjust the model parameters using these information. This paper proposes the methodology for re-estimation of model parameters in growth curves when adjusting market potential and/or time of maximum sales. We also provide the detailed procedures for five growth curves including Bass, Logistic, Gompertz, Weibull and Cumulative Lognormal models. Applications to real data are also included.