Objectives: Two derivatives of cephalexin were synthesized by reaction with isatin-glycine Schiff base and bromoisatin-glycine Schiff base separately. Methods: Cephalexin was linked through the amine group to isatin glycine and bromoisatin glycine Schiff bases by amide bond formation. Results: These derivatives were characterized by FT-IR, H-NMR, elemental CHN analysis and then tested for their antimicrobial activity compared to cephalexin against gram-positive, gram-negative bacteria and Candida albicans fungi. Conclusion: The two compounds showed better activity against Staphylococcus aureus, compound 3b is more active against Escherichia coli, and compound 3a is more active against Klebsiella pneumonia.

This research included the preparation of 2-mercaptobenzoxazole (N1) by the reaction of ortho-aminophenol with carbon disulfide in an alcoholic potassium hydroxide solution. The 2-mercapto benzoxazole (N1) was then treated with hydrazine to obtain the 2-hydrazino benzoxazole (N2). A number of hydrazones (N3-N5) were prepared through the reaction of N2 with different benzaldehydes. The compound (N6) was also prepared whereby the ring closing of hydrazone (N3) using chloroacetylchloride, while the compound (N7) was prepared by treating 2-hydrazino benzoxazole with acetylacetone. When the compound (N1) was treated with formaldehyde, it afforded the compound (N8). Also, the N9 was obtained from the reaction of N1 with chloroacetic acid in th

The convergence speed is the most important feature of Back-Propagation (BP) algorithm. A lot of improvements were proposed to this algorithm since its presentation, in order to speed up the convergence phase. In this paper, a new modified BP algorithm called Speeding up Back-Propagation Learning (SUBPL) algorithm is proposed and compared to the standard BP. Different data sets were implemented and experimented to verify the improvement in SUBPL.

Metoclopramide HCl (MTB) is a potent antiemetic drug used for the treatment of nausea and vomiting. Many trials were made to prepare a satisfactory MTB orodispersible tablet using direct compression method.Various super disintegrants were used in this study which are croscarmellose sodium (CCS), sodium starch glycolate (SSG) and crospovidone (CP). The latter was the best in terms of showing the fastest disintegration time in the mouth.Among the different diluents utilized, it was found that a combination of microcrystalline cellulose PH101 (MCC 101), mannitol, dicalcium phosphate dihydrate (DPD) and Glycine was the best in preparing MTB orodispersible tablet with fastest disintegration time in the mouth.The physical parameters of the pre

A variety of new phenolic Schiff bases derivatives have been synthesized starting from Terephthaladehyde compound, all proposed structures were supported by FTIR, 1H-NMR, 13C-NMR, Elemental analysis, some derivatives evaluated by Thermal analysis (TGA).

A series of new coumarin and N-amino-2-quinolone derivatives have been synthesized. The reaction of coumarin (1) with excess of  Hydrazine hydrate 98% yielded 1-amino-2-quinolone (2), Compound (2) was reacted with different Sulfonyl chloride to yield Sulfonamides [ N-(2-oxoquinolin-1(2H)-yl) methane sulfonamide (3), N-(2-oxoquinolin-1(2H)-yl) Benzene sulfonamide (4) and 4-methyl-N-(2-oxoquinolin-1(2H)-yl) benzene sulfonamide (5) ], while  reaction of  2-(4-methyl-2-oxo-2H-chromen-7-yloxy) acetic acid (8) with different amines yielded compounds [  2-(4-methyl-2-oxo-2H-chromen-7-yloxy)-N-(2-oxoquinolin-1(2H)-yl) acetamide (9) and N-(5-methyl-1,3,4-thiadiazol-2-yl)-2-(4-methyl-2-oxo-2H-chromen-7-yloxy)acetamide (10) ] th

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number