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.

Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility

Abstract: The main objective of the present study was to produce a novel ensemble data mining technique that involves an adaptive neuro-fuzzy inference system (ANFIS) optimized by Shuffled Frog Leaping Algorithm (SFLA) and Particle Swarm Optimization (PSO) for spatial modeling of landslide susceptibility Step-wise Assessment Ratio Analysis (SWARA) was utilized for the evaluation of the relation between landslides and landslide-related factors providing ANFIS with the necessary weighting values The developed methods were applied in Langao County, Shaanxi Province, China Eighteen factors were selected based on the experience gained from studying landslide phenomena, the local geo-environmental conditions as well as the availability of data, namely; elevation, slope aspect, slope angle, profile curvature, plan curvature, sediment transport index, stream power index, topographic wetness index, land use, normalized difference vegetation index, rainfall, lithology, distance to faults, fault density, distance to roads, road density, distance to rivers and river density A total of 288 landslides were identified after analyzing previous technical surveys, airborne imagery and conducting field surveys Also, 288 non-landslide areas were identified with the usage of Google Earth imagery and the analysis of a digital elevation model The two datasets were merged and later divided into two subsets, training and testing, based on a random selection scheme The produced landslide susceptibility maps were evaluated by the receiving operating characteristic and the area under the success and predictive rate curves (AUC) The results showed that AUC based on the training and testing dataset was similar and equal to 089 However, the processing time during the training and implementation phase was considerable different SWARA-ANFIS-PSO appeared six times faster in respect to the processing time achieved by SWARA-ANFIS-SFLA The proposed novel approach, which combines expert knowledge, neuro-fuzzy inference systems and evolutionary algorithms, can be applied for land use planning and spatial modeling of landslide susceptibility

Sensing behavior of Al and Si doped BC3 graphenes to formaldehyde

Abstract: Electronic sensitivity of pristine, Al- and Si-doped BC3 nanosheets to formaldehyde (H2CO) molecule was investigated by utilizing density functional theory. It was found that H2CO is weakly adsorbed on the sheet, releasing energy of 11.35 kcal/mol, and electronic properties of the sheet are not significantly changed. Although both Al and Si dopings make the sheet more reactive and sensitive to H2CO, Si doping seems to be a better strategy to manufacture H2CO chemical sensors due to the shorter recovery time and higher sensitivity of the Si-doped sheet. Our calculations show that the HOMO/LUMO gap of the Si-doped sheet is significantly decreased from 2.20 to 1.58 eV upon H2CO adsorption, which may increase the electrical conductance of the sheet. Therefore, the doped sheet might convert the presence of H2CO molecules to electrical signal. Moreover, the shorter recovery time of the Si-doped sheet is due to middle adsorption energy of −26.49 kcal/mol in comparison with −40.50 kcal/mol for the Al-doped sheet.