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Habibie Bacharuddin Jusuf 한국방위산업진흥회 1996 國防과 技術 Vol.- No.212
이 글은 일본 도쿄에서 1996년 5월 17일 열린 니혼 게이자이 신문 주최의 아시아의 미래에 대한 국제회의에서Habibie 박사가 발표한 주제 내용을 요약한 것이다. Habibie 박사는 인도네시아 국영 항공기업체인 IPTN 사장을 거쳐, 현 과학기술처 장관과 함께 국가 연구위원회, 기술평가 적용위원회, 전략산업위원회 의장을 역임하고 있다.
국가안보와 경제발전 달성을 위한 강력한 첨단기술정책의 적용
Habibie Bacharuddin Jusuf 한국방위산업진흥회 1996 國防과 技術 Vol.- No.211
이 글은 싱가포르 웨스틴 스템포드에서 1996년 2월 6일부터 7일까지 열린 싱가포르 SAFE-KNT 주최의 제3차 아시아.태평양 방위 회의(APD '96)에서 Habibie 박사가 발표한 주제 내용을 요약한 것이다. Habibie 박사는 인도네시아 국영 항공기업체인 IPTN 사장을 거쳐, 현 과학기술처 장관과 함께 국가 연구위원회, 기술평가 적용위원회, 전략산업위원회 의장을 역임하고 있다.
Mohammad Hossein Habibi,Amir Hossein Habibi 한국공업화학회 2014 Journal of Industrial and Engineering Chemistry Vol.20 No.1
An efficient and scalable one-pot synthetic method to prepare nanostructure composite of ZnFe2O4–FeFe2O4–ZnO (ZFZ) has been investigated. This method is based on thermal decomposition of iron(III)acetate and zinc acetate in monoethanolamine (MEA) as a capping agent. Moreover, thermogravimetricanalysis (TG-DTG) was performed to determine the temperature at which the decomposition andoxidation of the chelating agents took place. ZFZ was immobilized on glass using doctor blade methodand calcinated at different temperatures. The properties of the ZFZ nanocomposite have been examinedby different techniques, such as X-ray diffraction (XRD), field emission scanning electron microscopy(FESEM) and diffuse reflectance (DRS). FESEM shows that nanocomposite is monocrystallines and anarrow dispersion in size of 48 nm. XRD confirms that the prepared nanocomposite is composed offranklinite, ZnFe2O4 (54%), magnetite, FeFe2O4 (8%) and wurtzite, ZnO (48%). Photocatalytic activity ofZFZ immobilized on glass was carried out by choosing an azo textile dye, Reactive Red 195 (F3B) as amodel pollutant under UV irradiation with homemade photocatalytic apparatus and the resultsindicated that ZFZ exhibited good photocatalytic activity.
Mohammad Hossein Habibi,Amir Hossein Habibi 한국공업화학회 2014 Journal of Industrial and Engineering Chemistry Vol.20 No.5
This paper presents the preparation, characterization, and application of four different nanocomposites in photocatalytic degradation of the Brilliant Red M5B as a dye contaminant. Nanocomposites include ZnFe2O4, porous ZnFe2O4, ZnFe2O4–TiO2, and FeTiO3 prepared and coated on a glass slide by doctor blade method. Different techniques to characterize composites are X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM) and diffuse reflectance spectra (DRS). FESEM shows that nanocomposites are nanocrystallines and a narrow dispersion in size. XRD confirms that the prepared nanocomposites are composed of ZnFe2O4, FeTiO3 and TiO2. Degradation efficiency of composites is evaluated using Brilliant Red M5B as a model pollutant under UV irradiation with homemade photocatalytic apparatus. The results showed that the photocatalytic efficiency of ZnFe2O4–TiO2 is higher than that of other photocatalyst, which is mainly ascribed to ZnFe2O4 NPs with the spinel structure.
Mohammad Hossein Habibi,Bahareh Karimi,Mahmoud Zendehdel,Mehdi Habibi 한국공업화학회 2014 Journal of Industrial and Engineering Chemistry Vol.20 No.4
Mixed copper-zinc oxide nanostructures (average size 43 nm) were effectively fabricated via coprecipitation route. Field-emission scanning electron microscope (FESEM), powder X-ray diffraction (XRD), fourier-transform infrared spectroscopy (FT-IR) and UV–vis diffuse reflectance spectrum (DRS) were used to characterize the properties of the oxides. At the optimized condition, copper-zinc oxide nanostructures were used for fabrication of working electrodes by doctor blade technique on the fluorine-doped tin oxide (FTO) in dye sensitized solar cells. Their photovoltaic behavior were compared with standard using D35 dye and an electrolyte containing [Co(bpy)3](PF6)2, [Co(pby)3](PF6)3, LiClO4, and 4-tert-butylpyridine (TBP). The ranges of short-circuit current (Jsc) from 0.13 to 0.30 (mA/cm2), open-circuit voltage (Voc) from 0.20 to 0.51 V, and fill factor from 0.34 to 0.29 were obtained for the DSSCs made using the working electrodes. A titania blocking layer on the copper-zinc oxide surface improve both the open-circuit voltage (Voc), short-circuit current (Jsc) and the power-conversion efficiency is consequently enhanced by a factor of approximately five.
Habibi-Yangjeh, Aziz,Esmailian, Mahdi Korean Chemical Society 2007 Bulletin of the Korean Chemical Society Vol.28 No.9
Artificial neural networks (ANNs), for a first time, were successfully developed for the prediction partial molar heat capacity of aqueous solutions at infinite dilution for various polar aromatic compounds over wide range of temperatures (303.55-623.20 K) and pressures (0.1-30.2 MPa). Two three-layered feed forward ANNs with back-propagation of error were generated using three (the heat capacity in T = 303.55 K and P = 0.1 MPa, temperature and pressure) and six parameters (four theoretical descriptors, temperature and pressure) as inputs and its output is partial molar heat capacity at infinite dilution. It was found that properly selected and trained neural networks could fairly represent dependence of the heat capacity on the molecular descriptors, temperature and pressure. Mean percentage deviations (MPD) for prediction set by the models are 4.755 and 4.642, respectively.
Habibi-Yangjeh, Aziz Korean Chemical Society 2007 Bulletin of the Korean Chemical Society Vol.28 No.9
Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (ETN) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q+), total charge in molecule (qt), molecular volume of solvent (Vm), dipole moment (μ) and polarizability term (πI) are input descriptors and its output is ETN. It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the ETN values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the ETN of solvents shows non-linear correlations with the molecular descriptors.
Evaluation of Seismic performance of RC setback frames
Habibi, Alireza,Vahed, Meisam,Asadi, Keyvan Techno-Press 2018 Structural Engineering and Mechanics, An Int'l Jou Vol.66 No.5
When the irregularities occurred in buildings, affect their seismic performance. This paper has focused on one of the types of irregularities at the height that named setback in elevation. For this purpose, several multistorey Reinforced Concrete Moment Resisting Frames (RCMRFs) with different types of setbacks were designed according to new edition of Iranian seismic code. The nonlinear time history analysis was performed to predict the seismic performance of frames subjected to seven input ground motions. The assessment of the seismic performance was done considering both global and local criteria. Results showed that the current edition of Iranian seismic code needs to be modified in order to improve the seismic behaviour of reinforced concrete moment resisting setback buildings. It was also shown that the maximum damages happen at the elements located in the vicinity of the setbacks. Therefore, it is necessary to strengthen these elements by appropriate modification of Iranian seismic code.
Habibi-Yangjeh, Aziz,Pourbasheer, Eslam,Danandeh-Jenagharad, Mohammad Korean Chemical Society 2008 Bulletin of the Korean Chemical Society Vol.29 No.4
Principal component-genetic algorithm-multiparameter linear regression (PC-GA-MLR) and principal component-genetic algorithm-artificial neural network (PC-GA-ANN) models were applied for prediction of melting point for 323 drug-like compounds. A large number of theoretical descriptors were calculated for each compound. The first 234 principal components (PC’s) were found to explain more than 99.9% of variances in the original data matrix. From the pool of these PC’s, the genetic algorithm was employed for selection of the best set of extracted PC’s for PC-MLR and PC-ANN models. The models were generated using fifteen PC’s as variables. For evaluation of the predictive power of the models, melting points of 64 compounds in the prediction set were calculated. Root-mean square errors (RMSE) for PC-GA-MLR and PC-GA-ANN models are 48.18 and $12.77{^{\circ}C}$, respectively. Comparison of the results obtained by the models reveals superiority of the PC-GA-ANN relative to the PC-GA-MLR and the recently proposed models (RMSE = $40.7{^{\circ}C}$). The improvements are due to the fact that the melting point of the compounds demonstrates non-linear correlations with the principal components.