• Journal of Food Hygiene and Safety
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• v.34 no.4
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• pp.374-379
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• 2019

This study developed predictive growth models of Salmonella enterica Serovar Typhimurium on lettuce washed with chlorine (100~300 ppm) and ultrasound (US, 37 kHz, 380 W) treatment and stored at different temperatures ($10{\sim}25^{\circ}C$) using a polynomial equation. The primary model of specific growth rate (SGR) and lag time (LT) showed a good fit ($R^2{\geq}0.92$) with a Gompertz equation. A secondary model was obtained using a quadratic polynomial equation. The appropriateness of the secondary SGR and LT model was verified by coefficient of determination ($R^2=0.98{\sim}0.99$ for internal validation, 0.97~0.98 for external validation), mean square error (MSE=-0.0071~0.0057 for internal validation, -0.0118~0.0176 for external validation), bias factor ($B_f=0.9918{\sim}1.0066$ for internal validation, 0.9865~1.0205 for external validation), and accuracy factor ($A_f=0.9935{\sim}1.0082$ for internal validation, 0.9799~1.0137 for external validation). The newly developed models for S. Typhimurium could be incorporated into a tertiary modeling program to predict the growth of S. Typhimurium as a function of combined chlorine and US during the storage. These new models may also be useful to predict potential S. Typhimurium growth on lettuce, which is important for food safety purposes during the overall supply chain of lettuce from farm to table. Finally, the models may offer reliable and useful information of growth kinetics for the quantification microbial risk assessment of S. Typhimurium on washed lettuce.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.2
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• pp.97-103
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• 1997

DO concentration in the aquatic system is important for the water quality management perspective. Water quality model uses available reaeration coefficient (K2) estimation equations in calculating DO, however, they might include inevitable uncertainty that the model output can be less reliable. In this study, the calibrated QUAL2E model for the Passaic River in New Jersey, U.S., was used to examine the effect of K2 estimation equation on the output DO concentration of the river. The model was run with six commonly used equations separately with all the other conditions remained same. The result showed that the output DO concentration profiles varied widely with different equations, and maximum difference was 4.96 mg/L for the same location which is unacceptably large. It implies that the development of reliable equation is required for proper water quality management. The unreliable model output can lead to a wrong decision in water quality management such as unnecessarily high or too low treatment of wastewater, which will cause serious effect on the community economically and socially in either case. Generating more reliable model output with slight investment to develop a site specific K$_2$ equation can improve the decision making process significantly and is highly recommended.

• Korean Journal of Materials Research
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• v.11 no.10
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• pp.841-845
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• 2001

The steam generator tubes of power plant damaged by sliding wear due to flow-induced motion of foreign object. Amount of wear have been predicted by Achard's wear equation until now. However, there are large error and low reliability, because this equation regards wear coefficient(k) as constant. The sliding wears tests have been performed at room temperature to examine parameters of wear (wear distance, contact stress). The steam generator tube material for wear test is used Inconel 600 and foreign object material is used 304 austenite stainless steel. The sliding wear tests show that the amount of wear is not linearly proportional to the wear distance(for 374 austenite stainless steel). According to experimental result, wear coefficient is not constant k but function k(s) of wear distance. The newly modified wear predictive equation V=k(s)F have small error and high reliability.

• Korean Journal of Soil Science and Fertilizer
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• v.32 no.4
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• pp.344-356
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• 1999

A new and relatively simple equation for the soil water content-pressure head curve, ${\theta}$(h) is described in this paper. The particular form of the equation enables one to derive closed-form analytical expressions for the relative hydraulic conductivity, Kr, when substituted in the predictive conductivity models of Y. Mualem. Hopmans' equation is presented as an experimental method. The experienced method, $ET_a=K_sK_cET_o$ is introduced to estimate the actual evapotranspiration, $ET_a$(or $ET_c$). Using $ET_c$ and coil water data measured automatically in a weighing lusimeter, $K_s$ and $K_c$ values are estimated. Recently, FAO has introduced calculation procedures for the soil water(stress) coefficient, Ks in "Guidelines for computing crop water requirements".

• Korean Chemical Engineering Research
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• v.45 no.6
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• pp.619-626
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• 2007

It is of special interest in our membrane separation technology due to its low energy consumption and cost, relatively simple equipment, low investment and operation cost, et al. Full scale utilization of such processes can be widely utilized to the various fields. Using the difference of permeability of gas molecules between the filter layers, it is able to separate effectually pure gases from the mixed gases. In this paper, the membranes of PDMS, ${\gamma}-radiated$ PDMS, PTFE, PTFE-X are chosen to develop the predictive model for the separation of pure gases such as oxygen, nitrogen, hydrogen, and other gases from mixed gases. By utilizing the thermodynamic gas properties($\sigma$, $\varepsilon/k$) and experimental data of gas transport characteristics for different polymer membranes, it is able to develop the predictive model equation under the influence of temperature, pressure and polymer characteristics. Predictive model developed in this research showed good agreement with experimental data of gas permeability characteristics for develop four different polymer membranes. The proposed model can also be extended to the general equation for predicting the separation of gases based on the properties of polymeric membranes.

• Health Policy and Management
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• v.33 no.4
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• pp.450-456
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• 2023

Background: This study aims to develop a "Predictive Model for the Possibility of Collection Delinquent Health Insurance Contributions" for the National Health Insurance Service to enhance administrative efficiency in protecting and collecting contributions from livelihood-type defaulters. Additionally, it aims to establish customized collection management strategies based on individuals' ability to pay health insurance contributions. Methods: Firstly, to develop the "Predictive Model for the Possibility of Collection Delinquent Health Insurance Contributions," a series of processes including (1) analysis of defaulter characteristics, (2) model estimation and performance evaluation, and (3) model derivation will be conducted. Secondly, using the predictions from the model, individuals will be categorized into four types based on their payment ability and livelihood status, and collection strategies will be provided for each type. Results: Firstly, the regression equation of the prediction model is as follows: phat = exp (0.4729 + 0.0392 × gender + 0.00894 × age + 0.000563 × total income - 0.2849 × low-income type enrollee - 0.2271 × delinquency frequency + 0.9714 × delinquency action + 0.0851 × reduction) / [1 + exp (0.4729 + 0.0392 × gender + 0.00894 × age + 0.000563 × total income - 0.2849 × low-income type enrollee - 0.2271 × delinquency frequency + 0.9714 × delinquency action + 0.0851 × reduction)]. The prediction performance is an accuracy of 86.0%, sensitivity of 87.0%, and specificity of 84.8%. Secondly, individuals were categorized into four types based on livelihood status and payment ability. Particularly, the "support needed group," which comprises those with low payment ability and low-income type enrollee, suggests enhancing contribution relief and support policies. On the other hand, the "high-risk group," which comprises those without livelihood type and low payment ability, suggests implementing stricter default handling to improve collection rates. Conclusion: Upon examining the regression equation of the prediction model, it is evident that individuals with lower income levels and a history of past defaults have a lower probability of payment. This implies that defaults occur among those without the ability to bear the burden of health insurance contributions, leading to long-term defaults. Social insurance operates on the principles of mandatory participation and burden based on the ability to pay. Therefore, it is necessary to develop policies that consider individuals' ability to pay, such as transitioning livelihood-type defaulters to medical assistance or reducing insurance contribution burdens.

• Journal of Ocean Engineering and Technology
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• v.15 no.3
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• pp.43-48
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• 2001

In this paper, the real-time prediction of high temperature creep strength and creep for nickel-based superalloy Udimet 720 (high-temperature and high-pressure gas turbine engine materials) was performed on round-bar type specimens under pure load at the temperatures of 538, 649 and 704$^{\circ}C$. The predictive equation of ISM creep has better reliability than that of LMP and LMP-ISM, and its reliability is getting better for long time creep prediction ($10^3~10^5$h).

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.168-172
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• 1994

This paper shows that the GPC with exponential weighting(GPCEW) can be applied to Electric furnace system which has large time delay. Stability of GPCEW can be guarantee from monotonically non-increasing property of Riccati difference equation. We show that the performance of GPCEW versus GPC and auto-tuning PID control is better than that of GPC or atito-tuning PID.

• Imaging Science in Dentistry
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• v.46 no.1
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• pp.1-7
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• 2016

Purpose: This study assessed the accuracy of age estimates produced by a regression equation derived from lower third molar development in a Thai population. Materials and Methods: The first part of this study relied on measurements taken from panoramic radiographs of 614 Thai patients aged from 9 to 20. The stage of lower left and right third molar development was observed in each radiograph and a modified Gat score was assigned. Linear regression on this data produced the following equation: Y=9.309+1.673 mG+0.303S (Y=age; mG=modified Gat score; S=sex). In the second part of this study, the predictive accuracy of this equation was evaluated using data from a second set of panoramic radiographs (539 Thai subjects, 9 to 24 years old). Each subject's age was estimated using the above equation and compared against age calculated from a provided date of birth. Estimated and known age data were analyzed using the Pearson correlation coefficient and descriptive statistics. Results: Ages estimated from lower left and lower right third molar development stage were significantly correlated with the known ages (r=0.818, 0.808, respectively, $P{\leq}0.01$). 50% of age estimates in the second part of the study fell within a range of error of ${\pm}1year$, while 75% fell within a range of error of ${\pm}2years$. The study found that the equation tends to estimate age accurately when individuals are 9 to 20 years of age. Conclusion: The equation can be used for age estimation for Thai populations when the individuals are 9 to 20 years of age.

• Journal of Food Hygiene and Safety
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• v.26 no.1
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• pp.25-30
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• 2011

In this study, predictive mathematical models were developed to predict the kinetics of Listeria monocytogenes growth in the mixed fresh-cut vegetables, which is the most popular ready-to-eat food in the world, as a function of temperature (4, 10, 20 and $30^{\circ}C$). At the specified storage temperatures, the primary growth curve fit well ($r^2$=0.916~0.981) with a Gompertz and Baranyi equation to determine the specific growth rate (SGR). The Polynomial model for natural logarithm transformation of the SGR as a function of temperature was obtained by nonlinear regression (Prism, version 4.0, GraphPad Software). As the storage temperature decreased from $30^{\circ}C$ to $4^{\circ}C$, the SGR decreased, respectively. Polynomial model was identified as appropriate secondary model for SGR on the basis of most statistical indices such as mean square error (MSE=0.002718 by Gompertz, 0.055186 by Baranyi), bias factor (Bf=1.050084 by Gompertz, 1.931472 by Baranyi) and accuracy factor (Af=1.160767 by Gompertz, 2.137181 by Baranyi). Results indicate L. monocytogenes growth was affected by temperature mainly, and equation was developed by Gompertz model (-0.1606+$0.0574^*Temp$+$0.0009^*Temp^*Temp$) was more effective than equation was developed by Baranyi model (0.3502-$0.0496^*Temp$+$0.0022^*Temp^*Temp$) for specific growth rate prediction of L.monocytogenes in the mixed fresh-cut vegetables.