Equilibrium adsorption isotherm for the removal of trifluralin from aqueous solutions using ? –alumina clay has been studied. The result shows that the isotherms were S3 according Giels classification. The effects of various experimental parameters such as contact time, adsorbent dosage, effect of pH and temperature of trifluralin on the adsorption capacities have been investigated. The adsorption isotherms were obtained by obeying freundlich adsorption isotherm with (R2 = 0.91249-0.8149). The thermodynamic parameters have been calculated by using the adsorption process at five different temperature, the values of ?H, ?G and ?S were (_1.0625) kj. mol-1, (7.628 - 7.831) kj.mol-1 and (_2.7966 - _2.9162) kg.

The main purpose of this work is to introduce the concept of higher N-derivation and study this concept into 2-torsion free prime ring we proved that:Let R be a prime ring of char. 2, U be a Jordan ideal of R and be a higher N-derivation of R, then , for all u U , r R , n N .

For the graph ⁠, the behavior associated with to the majority of the graphical properties of this graph is covered in this article. The reflection of the capabilities of on the Ly constructions is one of the key ideas addressed throughout this paper. For instance, by this technique we can comprehend the mechanism via which groups of relatively tiny structure are exist within Ly.

Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In

In this paper, we define some generalizations of topological group namely -topological group, -topological group and -topological group with illustrative examples. Also, we define grill topological group with respect to a grill. Later, we deliberate the quotient on generalizations of topological group in particular -topological group. Moreover, we model a robotic system which relays on the quotient of -topological group.

      In this paper, we introduce the concepts of Large-lifting and Large-supplemented modules as a generalization of lifting and supplemented modules.  We also give some results and properties of this new kind of modules.