Islamic Azad University

About: Islamic Azad University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Population & Adsorption. The organization has 83635 authors who have published 113437 publications receiving 1275049 citations. The organization is also known as: Azad University.

Application of modified multiwall carbon nanotubes paste electrode for simultaneous voltammetric determination of morphine and diclofenac in biological and pharmaceutical samples

Abstract: A novel modified carbon paste electrode with vinylferrocene/multiwall carbon nanotubes was fabricated. The electrochemical response of the modified electrode toward morphine was studied by means of cyclic voltammetry (CV), chronoamperometry (CA) and electrochemical impedance spectroscopy (EIS). The structural morphology of the modified electrode was characterized by SEM technique. The prepared electrode showed an excellent electrocatalytic activity in the oxidation of morphine, leading to remarkable enhancements in the corresponding peak currents and lowering the peak potential. Using square wave voltammetry (SWV), we could measure morphine and diclofenac in one mixture independently from each other by a potential difference of about 300 mV for the first time. Square wave voltammetric peaks current of morphine and diclofenac increased linearly with their concentrations in the ranges of 0.2–250.0 μmol L −1 , and 5.0–600.0 μmol L −1 , respectively. The detection limits of 0.09 and 2.0 μmol L −1 were achieved for morphine and diclofenac, respectively. The proposed voltammetric sensor was successfully applied to the determination of morphine and diclofenac in real samples.

Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling

Abstract: The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young's modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R2). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs.

DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing

Abstract: Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and their fractional abundances. In this paper, we develop a new technique for unsupervised unmixing which is based on a deep autoencoder network (DAEN). Our newly developed DAEN consists of two parts. The first part of the network adopts stacked autoencoders (SAEs) to learn spectral signatures, so as to generate a good initialization for the unmixing process. In the second part of the network, a variational autoencoder (VAE) is employed to perform blind source separation, aimed at obtaining the endmember signatures and abundance fractions simultaneously. By taking advantage from the SAEs, the robustness of the proposed approach is remarkable as it can unmix data sets with outliers and low signal-to-noise ratio. Moreover, the multihidden layers of the VAE ensure the required constraints (nonnegativity and sum-to-one) when estimating the abundances. The effectiveness of the proposed method is evaluated using both synthetic and real hyperspectral data. When compared with other unmixing methods, the proposed approach demonstrates very competitive performance.