New hydrazone derivatives of Fenoprofen were synthesized and evaluated for their anti-inflammatory activity by means of egg white induced paw edema method. All the synthesized target compounds were characterized by FT-IR spectroscopy, 1HNMR analysis and by measure of their physical properties. The synthesis of the target compounds(H1-H4) was accomplished by multistep reaction procedures. The synthesized target compounds were show activity in reducing paw edema thickness and their anti-inflammatory effect was comparable to that of the standard (Fenoprofen) except for compound H3 which show anti-inflammatory activity higher than Fenoprofen.

In this study, a system of nonthermal plasma that was operated under atmospheric pressure and was powered by argon gas was employed. The particular plasma properties are affected by changes in the Ar gas flow ranges from 0.5 to 2.5 l/min, product by stream of the plasma jet that is utilized. By using the aforementioned method generated from AC and DC. After placing Ar gas as the cathode, which represents the negative pole, flows toward the anode, which is represented by a tiny metal plate of Zn measuring 6 × 1 cm2 in size, with a submerged part of 4 cm2 long, with both types of current employed having a high voltage of 13.5 kV and the frequency of AC was 30 kHz, we measured these variable parameters. It has been shown that when argon f

                          In this research work, synthesis, antimicrobial and antioxidant bioactivity of a chain of compounds having unsaturated ketones bond and isoxazoline moiety have been described. New chalcone derivatives containing isoxazoline moiety have been synthesized. Generally, Chalcones are unsaturated ketones bearing (-CO-CH=CH-) as reactive ketoethylenic group that give the bright yellow colored compounds due to this chromophore group.  Firstly, chalcones (IIa-d) have been prepared by cyclocondensation (Claisen-Schmidt condensation) of triphenyl aminobenzaldehyde with different substituted acetophenone in ethyl alcohol to produce a series of chalcones compounds with bright yellow colored as a

Tin Oxide (SnO2) films have been deposited by spray pyrolysis technique at different substrate temperatures. The effects of substrate temperature on the structural, optical and electrical properties of SnO2 films have been investigated. The XRD result shows a polycrystalline structure for SnO2 films at substrate temperature of 673K. The thickness of the deposited film was of the order of 200 nm measured by Toulansky method. The energy gap increases from 2.58eV to 3.59 eV when substrate temperature increases from 473K to 673K .Electrical conductivity is 4.8*10-7(.cm)-1 for sample deposited at 473K while it increases to 8.7*10-3 when the film is deposited at 673K

Carbon fibre reinforced polymers are widely used to strengthen steel structural elements. These structural elements are normally subjected to static, dynamic and fatigue loadings during their life-time. A number of studies have focused on the characteristics of CFRP sheets bonded to steel members under static, dynamic and fatigue loadings. However, there is a gap in understanding the bonding behaviour between CFRP laminates and steel members under impact loading. This paper shows the effect of different load rates from quasi-static to 300 × 103 mm/min on this bond. Two types of CFRP laminate, CFK 150/2000 and CFK 200/2000, were used to strengthen steel joints using Araldite 420 epoxy. The results show a significant bond strength enhancemen

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to