• Applied Chemistry for Engineering
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• v.32 no.5
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• pp.581-588
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• 2021

In this study, soil slurry bioreactors were used to treat soils containing 16 polycyclic aromatic hydrocarbons (PAHs) for 35 days. Five different soil samples were taken from manufactured gas plant (MGP) and coal tar disposal sites. Soil properties, such as carbon content and particle distribution, were measured. These properties were significantly correlated with percent biodegradation and degradation rate. The cumulative amount of PAH degraded (P), degradation rate (K_m), and lag phase (𝜆) constants of PAHs in different MGP soils for 16 PAHs were successfully obtained from nonlinear regression analysis using the Gompertz equation, but only those of naphthalene, anthracene, acenaphthene, fluoranthene, chrysene, benzo[k]fluoranthene, benzo(a)pyrene, and benzo(g,h,i)perylene are presented in this study. A comparison between total non-carcinogenic and carcinogenic PAHs indicated higher maximum amounts of PAH degraded in the former than that in the latter owing to lower partition coefficients and higher water solubilities (S). The degradation rates of total non-carcinogenic compounds for all soils were more than four times higher than those of total carcinogenic compounds. Carcinogenic PAHs have the highest partitioning coefficients (K_oc), resulting in lower bioavailability as the molecular weight (MW) increases. Good linear relationships of K_m, 𝜆, and P with the octanol-water partitioning coefficient (K_ow), MW, and S were used to estimate PAH remaining, lag time, and biodegradation rate for other PAHs.

• The Plant Pathology Journal
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• v.26 no.1
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• pp.17-24
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• 2010

A logistic model for describing combined effects of both temperature and wetness period on appressorium formation was developed using laboratory data on percent appressorium formation of Colletotrichum acutatum. In addition, the possible use of the logistic model for forecasting infection risks was also evaluated as compared with a first-order linear model. A simplified equilibrium model for enzymatic reactions was applied to obtain a temperature function for asymptote parameter (A) of logistic model. For the position (B) and the rate (k) parameters, a reciprocal model was used to calculate the respective temperature functions. The nonlinear logistic model described successfully the response of appressorium formation to the combined effects of temperature and wetness period. Especially the temperature function for asymptote parameter A reflected the response of upper limit of appressorium formation to temperature, which showed the typical temperature response of enzymatic reactions in the cells. By having both temperature and wetness period as independent variables, the nonlinear logistic model can be used to determine the length of wetness periods required for certain levels of appressorium formation under different temperature conditions. The infection model derived from the nonlinear logistic model can be used to calculate infection risks using hourly temperature and wetness period data monitored by automated weather stations in the fields. Compared with the nonlinear infection model, the linear infection model always predicted a shorter wetness period for appressorium formation, and resulted in significantly under- and over-estimation of response at low and high temperatures, respectively.

• Proceedings of the KSRS Conference
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• 2001.03a
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• pp.104-110
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• 2001

Ocean heat fluxes over a wide region are generally estimated by an aerodynamic bulk fromula. Though a remote sensing technique can be expected to estimated global heat flux, it is difficult to obtain air temperature and specific humidity at sea surface by a remote sensor. In this study present a new method with which to determine near-sea surface air temperature from in situ data. Also, These methods compared with other methods. A new method used a linear regression equation between sea surface temperature and air temperature of the buoys data. In this study new method is validated using observed monthly mean data at the Japan Meteorological Agency(JMA), National Data Buoy Center(NDBC) and Tropical Ocean-Global Atmosphere(TOGA)-Tropical Atmosphere Ocean(TAO) buoys. The result that bias and rmse are 0.28, 1.5$0^{\circ}C$ respectively. The correlation coefficient is 0.98. Also, to retrieve near-sea surface specific humidity(Q) from good nonlinear regression relationship between vapor pressure(Ea) of buoy data and air temperature, after obtained the third-order polynomial function, compared with that of estimated from SSM/I empirical equation by Schussel et al(1995). The result that bias and rmse are -1.42 and 1.75(g/kg).

• Computers and Concrete
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• v.31 no.4
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• pp.349-358
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• 2023

This paper describes an in-depth analysis on flexural strength of a girder-deck system experiencing a strand debonding damage with various strengthening systems, based on finite element software ABAQUS. A detailed finite element analysis (FEA) model was developed and verified against the relevant experimental data performed by other researchers. The proposed analytical model showed a good agreement with experimental data. Based on the verified FE model, over a hundred girder-deck systems were investigated with the consideration of following variables: 1) debonding level, 2) span-to-depth ratio (L/d), 3) strengthening type, 4) strengthening material thickness. Based on the data above, a new detailed analytical model was developed and proposed for estimating residual flexural strength of the strand-debonding damaged girder-deck system with strengthening systems. It was demonstrated that both finite element model and analysis model could be used to predict flexural behaviors for debonding damaged prestressed girder-deck systems. Since the strands are debonding from surrounding concrete over a certain zone over the length of the beam, the increase of strain in strands can be linked with a ratio ψ, which is L_p/c. The analytical model was proposed and developed regarding the ratio ψ. By conducting procedure of calculating ψ, the ψ value varies from 9.3 to 70.1. Multiple nonlinear regression analysis was performed in Software IBM SPSS Statistics 27.0.1 to derive equation of ψ. ψ equation was curved to be an exponential function, and the independent variable (X) is a linear function in terms of three variables of debonding level (λ), span length (L), and amount of strengthening material (A_s). The coefficient of determinate (R²) for curve fitting in nonlinear regression analysis is 0.8768. The developed analytical model was compared to the ultimate capacities computed by FEA model.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.5
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• pp.623-628
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• 2007

The accurate peak response estimation of a seismically excited structure with frictional damping system(FDS) is very difficult since the structure with FDS shows nonlinear behavior dependent on the structural period, loading characteristics, and relative magnitude between the frictional force and the excitation load. Previous studies have estimated that by replacing a nonlinear system with an equivalent linear one or by employing the response spectrum obtained based on nonlinear time history and statistical analysis. In the case that an earthquake load is defined with probabilistic characteristics, the corresponding response of the structure with FDS has probabilistic distribution. In this study, nonlinear time history analyses were performed for the structure with FDS subjected to artificial earthquake loads generated using Kanai-Tajimi filter. An equation for the probability density function (PDF) of the displacement response is proposed by adapting the PDF of the normal distribution. Finally, coefficients of the proposed PDF are obtained by regression analysis of the statistical distribution of the time history responses. Finally the correlation between PDFs and statistical response distribution is presented.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1194-1202
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• 2021

Objective: To investigate the age-dependent changes in regional cerebral blood flow (CBF) in healthy adults by fitting mathematical models to imaging data. Materials and Methods: In this prospective study, 90 healthy adults underwent pseudo-continuous arterial spin labeling imaging of the brain. Regional CBF values were extracted from the arterial spin labeling images of each subject. Multivariable regression with the Akaike information criterion, link test, and F test (Ramsey's regression equation specification error test) was performed for 7 models in every brain region to determine the best mathematical model for fitting the relationship between CBF and age. Results: Of all 87 brain regions, 68 brain regions were best fitted by cubic models, 9 brain regions were best fitted by quadratic models, and 10 brain regions were best fitted by linear models. In most brain regions (global gray matter and the other 65 brain regions), CBF decreased nonlinearly with aging, and the rate of CBF reduction decreased with aging, gradually approaching 0 after approximately 60. CBF in some regions of the frontal, parietal, and occipital lobes increased nonlinearly with aging before age 30, approximately, and decreased nonlinearly with aging for the rest of life. Conclusion: In adults, the age-related perfusion patterns in most brain regions were best fitted by the cubic models, and age-dependent CBF changes were nonlinear.

• Bulletin of the Korean Chemical Society
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• v.29 no.8
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• pp.1549-1553
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• 2008

A molecular imprinted polymer (MIP) using (+)catechin ((+)C) as a template and acrylamide (AM) as a functional monomer was prepared. Acetonitrile was used as the porogen with ethylene glycol dimethacrylate (EGDMA) as the crosslinker and 2,2'-azobis(isobutyronitrile) (AIBN) as the initiator. The adsorption isotherms in the MIP were measured and the parameters of the equilibrium isotherms were estimated by linear and nonlinear regression analyses. The linear equation for original concentration and adsorpted concentrations was then expressed, and the adsorption equilibrium data were correlated into Langmuir, Freundlich, quadratic, and Langmuir Extension isotherm models. The mixture compounds of (+)C and epicatechin (EC) show competitive adsorption on specific binding sites of the (+)catechin-MIP. The adsorption concentrations of (+)C, epicatechin (EC), epicatechin gallate (ECG), and epigallocatechin gallate (EGCG), on the (+)catechin-molecular imprinted polymer were compared. Through the analysis, the (+)catechin-molecular imprinted polymer showed higher adsorption ability than blank polymer which was synthesized molecular imprinted polymer without (+)catechin. Furthermore, the competitive Langmuir isotherms were applied to the mixture compounds of (+)C and EC.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2856-2858
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• 1999

In this paper, the FPNN(Fuzzy Polynomial Neural Networks) algorithm with multi-layer fuzzy inference structure is proposed for the model identification of a complex nonlinear system. The FPNN structure is generated from the mutual combination of PNN (Polynomial Neural Network) structure and fuzzy inference method. The PNN extended from the GMDH(Group Method of Data Handling) uses several types of polynomials such as linear, quadratic and modifled quadratic besides the biquadratic polynomial used in the GMDH. In the fuzzy inference method, simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used Each node of the FPNN is defined as a fuzzy rule and its structure is a kind of fuzzy-neural networks. Gas furnace data used to evaluate the performance of our proposed model.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.43-54
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• 2018

The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.

• Fire Science and Engineering
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• v.18 no.1
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• pp.1-6
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• 2004

In the reutilization process using limonene, the organic solvent to reduce volume of EPS, the AIT was measured with the variation of concentration and volume of mixture, in order to present the fund-mental data on the fire hazard assessment of limonene - EPS mixture at storage and handling. And ignition zone was compared with non-ignition zone. The equation related to AIT, activation energy and ignition delay time, used by the most scientific basis for predicting AIT values, was suggested using linear regression analysis as ln t = 0.704/T-5.819. And the equation related to concentration of mixture and AIT was also suggested to predict ignition hazard of combustible mixture using nonlinear regression analysis as $T_m/=248.32+69.27X+172.60X^2$. It enabled to predict ignition temperature according to variation of ignition delay time and concentration of mixture by the suggested equations.