A group of amino derivatives [4-aminobenzenesulfonamide,4-amino-N¹ methylbenzenesulfonamide, or N¹-(4-aminophenylsulfonyl)acetamide] bound to carboxyl group of mefenamic acid a well known nonsteroidal anti-inflammatory drugs (NSAIDs) were designed and synthesized for evaluation as a potential anti-inflammatory agent.  In vivo acute anti-inflammatory activity of the final compounds (9, 10 and 11) was evaluated in rat using egg-white induced edema model of inflammation in a dose equivalent to (7.5mg/Kg) of mefenamic acid. All tested compounds produced a significant reduction in paw edema with respect to the effect of propylene glycol 50% v/v (control group). Moreover, the 4-amino-N-methylbenzenesulfonamide derivative (c

  In this work two moles of 2-amino benzothiazole were allowed to react with one mole of pyromellitic dianhydride to produce N,N‾-Bis-(benzathiazol-2-yl) pyromellitamic diacid [I] which was submitted to esterification via the reaction with dimethyl sulphate in sodium carbonate in acetone as a solvent to synthesize N,N‾-bis-(benzothiazol-2-yl) pyromellitam diacetate [II] .This ester  was used to produce  novel  compounds through  two paths :- Path one:- Reaction of ester [II] with hydrazine in ethanol as a solvent to form the corresebonding  N,N‾-bis (benzothiazole-2-yl) –pyromellitamic acid hydrazide [III] which react with acetyl acetone in ethanol or with phthalic anhydride in dioxa

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

 In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through the deviations of the transformed random process to find an efficient estimate of Hurst exponent using eigenvalue regression of the covariance matrix. The results of simulations experiments shown that the performance of the proposed estimator was efficient in bias but the variance get increase as signal change from short to long memory the MASE increase relatively. The estimation process was made by calculating the eigenvalues for the variance-covariance matrix of Meyer’s continuous wavelet details coefficients.

      A new approach presented in this study to determine the optimal edge detection threshold value. This approach is base on extracting small homogenous blocks from unequal mean targets. Then, from these blocks we generate small image with known edges (edges represent the lines between the contacted blocks). So, these simulated edges can be assumed as true edges .The true simulated edges, compared with the detected edges in the small generated image is done by using different thresholding values. The comparison based on computing mean square errors between the simulated edge image and the produced edge image from edge detector methods. The mean square error computed for the total edge image (Er), for edge regio

 In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through the deviations of the transformed random process to find an efficient estimate of Hurst exponent using eigenvalue regression of the covariance matrix. The results of simulations experiments shown that the performance of the proposed estimator was efficient in bias but the variance get increase as signal change from short to long memory the MASE increase relatively. The estimation process was made by calculating the eigenvalues for the variance-covariance matrix of Meyer’s continuous wavelet details coefficients.

Huge number of medical images are generated and needs for more storage capacity and bandwidth for transferring over the networks. Hybrid DWT-DCT compression algorithm is applied to compress the medical images by exploiting the features of both techniques. Discrete Wavelet Transform (DWT) coding is applied to image YCbCr color model which decompose image bands into four subbands (LL, HL, LH and HH). The LL subband is transformed into low and high frequency components using Discrete Cosine Transform (DCT) to be quantize by scalar quantization that was applied on all image bands, the quantization parameters where reduced by half for the luminance band while it is the same for the chrominance bands to preserve the image quality, the zig

   For extraction chloro anion complexes of Cd2+ and Hg2+ used many organic agents as extractant according to liquid ion exchange method such as α-Naphthyl amine (α-NA), 4-Amino benzoic acid (4-ABA), 2-[(4-Carboxy methyl phenyl) azo]-4,5-diphenyl imidazole (4CMePADPI) and Cryptand (C222). This study includes definition hydrochloric acid concentration in aqueous phase and shaking with organic phase necessary for extraction as well as shaking time, organic solvent effect, interferences and alkaline salt effect. Thermodynamic showed the ion exchange reaction was exothermic for α-NA, C222 and endothermic for 4-ABA, 4-CMePADPI for extraction CdCl4=, but for extraction HgCl4= was exothermic with 4-ABA, 4CMePADPI and C222 but