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- 저자 : Ali Reza Khodabakhshi (검색결과 2 건)
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Farhad Heidary,Ali Reza Khodabakhshi,Ali Nemati Kharat 한국화학공학회 2016 Korean Journal of Chemical Engineering Vol.33 No.4
A new type of cation-exchange nanocomposite membrane was prepared via in-situ formation of FeOOH nanoparticles in a blend containing sulfonated poly (2,6-dimethyl-1,4-phenylene oxide) and sulfonated polyvinylchloride by a simple one-step chemical method. Prepared nanocomposite membranes were characterized using Fourier transform infrared spectroscopy, scanning electron microscopy and X-ray diffraction. The SEM images showed uniform dispersion of FeOOH nanoparticles throughout the polymeric matrices. The effect of additive loading on physicochemical and electrochemical properties of prepared cation-exchange nanocomposite membranes was studied. Various characterizations showed that the incorporation of different amounts of FeOOH nanoparticles into the basic membrane structure had a significant influence on the membrane performance and could improve the electrochemical properties.
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Faradmal, Javad,Soltanian, Ali Reza,Roshanaei, Ghodratollah,Khodabakhshi, Reza,Kasaeian, Amir Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.14
Background: Breast cancer is the most common cancers in female populations. The exact cause is not known, but is most likely to be a combination of genetic and environmental factors. Log-logistic model (LLM) is applied as a statistical method for predicting survival and it influencing factors. In recent decades, artificial neural network (ANN) models have been increasingly applied to predict survival data. The present research was conducted to compare log-logistic regression and artificial neural network models in prediction of breast cancer (BC) survival. Materials and Methods: A historical cohort study was established with 104 patients suffering from BC from 1997 to 2005. To compare the ANN and LLM in our setting, we used the estimated areas under the receiver-operating characteristic (ROC) curve (AUC) and integrated AUC (iAUC). The data were analyzed using R statistical software. Results: The AUC for the first, second and third years after diagnosis are 0.918, 0.780 and 0.800 in ANN, and 0.834, 0.733 and 0.616 in LLM, respectively. The mean AUC for ANN was statistically higher than that of the LLM (0.845 vs. 0.744). Hence, this study showed a significant difference between the performance in terms of prediction by ANN and LLM. Conclusions: This study demonstrated that the ability of prediction with ANN was higher than with the LLM model. Thus, the use of ANN method for prediction of survival in field of breast cancer is suggested.
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