This  study was concerned with the adsorption of Toluene ,0·

nitro Toluene and 2,4.dinitro Toluene on The surface of kaolin clay.

The  adsorption isotherms  were of types( S3,S4 ) according  to Giles classification and were in a good correlation with Freundlich equation  in the range of cuncenlraliuns studied. The re5ult indicated that adsorption capacity increase with the increase of equilibrium concentration. The effect of the nwnber of substituted nitro groups in the adsorption extent of toluene was found to decrease in the following order:·

Toluene   > 2,4-dinitro toluene  > 0-nitro toluene

Adsorption on  kaolin surface were examined as afunction

Thin films Tin sulfide SnS pure and doped with different ratios of Cu (X=0, 0.01, 0.03 and 0.05) were prepared using thermal evaporation with a vacuum of 4*10^-6mbar on two types of substrates n-type Si and glass with (500) nm thickness for solar cell application. X-ray diffraction and AFM analysis were carried out to explain the influence of Cu ratio dopant on structural and morphological properties respectively. SnS phase appeared forming orthorhombic structure with preferred orientation (111), increase the crystallinity degree and surface roughness with increase Cu ratio. UV/Visible measurement revealed the decrease in energy gap from 1.9eV for pure SnS to 1.5 for SnS: Cu (0.05) making these samples suitable f

Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

A few examinations have endeavored to assess a definitive shear quality of a fiber fortified polymer (FRP)- strengthened solid shallow shafts. Be that as it may, need data announced for examining the solid profound pillars strengthened with FRP bars. The majority of these investigations don't think about the blend of the rigidity of both FRP support and cement. This examination builds up a basic swagger adequacy factor model to evaluate the referenced issue. Two sorts of disappointment modes; concrete part and pulverizing disappointment modes were examined. Protection from corner to corner part is chiefly given by the longitudinal FRP support, steel shear fortification, and cement rigidity. The proposed model has been confirmed util

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog