• 전기학회논문지P
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• 제53권2호
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• pp.65-69
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• 2004

This paper calculates and gives the analysis of electron swarm transport coefficients as described electric conductive characteristics of pure Ar, pure $SiH_4$, Ar-$SiH_4$ mixture gases($SiH_4$-0.5%, 2.5%, 5%) over the range of E/N = 0.01~300[Td], P = 0.1, 1, 5.0 [Torr] by Monte Carlo the backward prolongation method of the Boltzmann equation using computer simulation without using expensive equipment. The results have been obtained by using the electron collision cross sections by TOF, PT, SST sampling, compared with the experimental data determined by the other author. It also proved the reliability of the electron collision cross sections and shows the practical values of computer simulation. Electron swann parameters in argon were drastically changed by adding a small amount of mono-silane. The electron drift velocity in these mixtures showed unusual behaviour against E/N. It had negative slope in the medium range of E/N, yet the slope was not smooth but contained a small hump. The longitudinal diffusion coefficient also showed a corresponding feature in its dependence on E/N. A two-tenn approximation of the Boltzmann equation analysis and Monte Carlo simulation have been used to study electron transport coefficients.

• Bulletin of the Korean Chemical Society
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• 제17권2호
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• pp.162-167
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• 1996

Two redox processes of methyl viologen (+2/+, +/0) in acetonitrile were investigated by using channel electrode voltammetric and in situ electrochemical ESR methods. Two separated unequal plateau currents of the first (+2/+) and second (+/0) redox processes of the viologen were observed in the channel electrode voltammograms and showed a cube-root depedndence on the electrolyte flow rate, respectively. The simple Levich analysis resulted in two different diffusion coefficients of $D_{+2}=2.2{\times}10^{-5}\;cm^2/s$ and $D_+=3.0{\times}10^{-5}cm^2/s$ from the limiting currents. In situ electrochemical ESR studies were performed for the monocation radicals generated at the potentials of the two plateau currents in the electrolyte flow range $1.3{\times}10^{-1}{\geq}v_f{\geq}2.7{\times}10^{-3}\;cm^3/s$. Backward implicitfinite difference method was employed to simulate the electrochemical kinetic problem of two sequential electron transfers ($MV^{+2}+e{\leftrightarrows}MV^+,\;MV^{+}+e{\leftrightarrows}MV^0$) coupled with reversible comproportionation ($MV^{2+}+MV^0{{\leftrightarrows}^{k_f}_{k_b}}2MV^+$). $k_f$ was found to be greater than ($10^6M^{-1}s^{-1}.

• Korean Journal of Radiology
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• 제23권8호
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• pp.811-820
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• 2022

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825-0.910) in the training cohort and 0.890 (0.844-0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion: The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.