Sixteen new complexes with the general formula [M(L)2(H2O)2] were prepared resulting from the reaction of the two new Schiff base ligands, which are: - L1= (E)-5-((2-hydroxybenzylidene)amino)-2-phenyl-2,4-dihydro-3H-pyrazol-3-one) L2 = (E)-5-((2-hydroxy-3-methoxybenzylidene)amino)-2-phenylpyrazolidin-3-one) With divalent metal ions (manganese, cobalt, nickel, copper, zinc, cadmium, mercury) and (tetravalent platinum). Ligands was derived from the reaction of the amine (5-amino-2-phenyl-2,4-dihydro-3H-pyrazol-3-one) with Salicylaldehyde and ortho-vanillin, which is linked to the metal ions via the nitrogen atoms are the isomethene group and the oxygen is the hydroxide group of the pyrazoline ring. The two prepared ligands are distinguished by their ability to dissolve in organic solvents such as alcohols, ether, acetone, chloroform, carbon tetrachloride and ethanol, and their inability to dissolve in water. The prepared compounds were characterized using infrared spectroscopy and spectroscopy nuclear magnetic resonance, ultraviolet spectroscopy, micro-analysis of elements (C.H.N), atomic absorption analysis, electrical conductivity of solutions of complexes at a concentration of 1×10-3 , the magnetic susceptibility of the complexes was measured, and the effect of the solvent on the prepared compounds was studied, and from the results of the measurements, the octahedral geometric structure of the prepared complexes was proposed.. The inhibitory ability of the prepared compounds was tested against two types of selected bacteria, Staphylococcus aureus and Escherichia coli, and a type of fungus, Candida. The results showed that the ability of the metal complexes to inhibit is higher than the ligand.
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The undetected error probability is an important measure to assess the communication reliability provided by any error coding scheme. Two error coding schemes namely, Joint crosstalk avoidance and Triple Error Correction (JTEC) and JTEC with Simultaneous Quadruple Error Detection (JTEC-SQED), provide both crosstalk reduction and multi-bit error correction/detection features. The available undetected error probability model yields an upper bound value which does not give accurate estimation on the reliability provided. This paper presents an improved mathematical model to estimate the undetected error probability of these two joint coding schemes. According to the decoding algorithm the errors are classified into patterns and their decoding
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In this study, the response and behavior of machine foundations resting on dry and saturated sand was investigated experimentally. In order to investigate the response of soil and footing to steady state dynamic loading, a physical model was manufactured. The manufactured physical model could be used to simulate steady state harmonic load at different operating frequencies. Total of (84) physical models were performed. The parameters that were taken into considerations include loading frequency, size of footing and different soil conditions. The footing parameters were related to the size of the rectangular footing and depth of embedment. Two sizes of rectangular steel model footing were used (100 200 12.5 mm) and (200 400 5.0 mm).
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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
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Electronic Health Record (EHR) systems are used as an efficient and effective method of exchanging patients’ health information with doctors and other key stakeholders in the health sector to obtain improved patient treatment decisions and diagnoses. As a result, questions regarding the security of sensitive user data are highlighted. To encourage people to move their sensitive health records to cloud networks, a secure authentication and access control mechanism that protects users’ data should be established. Furthermore, authentication and access control schemes are essential in the protection of health data, as numerous responsibilities exist to ensure security and privacy in a network. So, the main goal of our s
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This study investigates the feasibility of a mobile robot navigating and discovering its location in unknown environments, followed by the creation of maps of these navigated environments for future use. First, a real mobile robot named TurtleBot3 Burger was used to achieve the simultaneous localization and mapping (SLAM) technique for a complex environment with 12 obstacles of different sizes based on the Rviz library, which is built on the robot operating system (ROS) booted in Linux. It is possible to control the robot and perform this process remotely by using an Amazon Elastic Compute Cloud (Amazon EC2) instance service. Then, the map to the Amazon Simple Storage Service (Amazon S3) cloud was uploaded. This provides a database
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