A series of new Schiff bases and 1, 3-Oxazepine derivatives have been synthesised from condensation compound (1,1 -bis (4-aminophenyl) -4-phenyl cyclohexane [C1] with different aromatic aldehydes in the presence of catalytic glacial acetic acid to produce the Schiff bases [2-4]. These Schiff bases were reacted with maleic anhydride and phthalic anhydride in dry benzene to give seven-membered heterocyclic ring derivatives [5-10].  The structure formula of these compounds were confirmed by using FT-IR, (¹H and ¹³C) NMR spectroscopy. The synthesized compounds were screened for their anti‐bacterial activity using ampicillin as a standard drug.

The research involves using phenol – formaldehyde (Novolak) resin as matrix for making composite material, while glass fiber type (E) was used as reinforcing materials. The specimen of the composite material is reinforced with (60%) ratio of glass fiber.

      The impregnation method is used in test sample preparation, using molding by pressure presses.

      All samples were exposure to (Co⁶⁰) gamma rays of an average energy (2.5)Mev. The total doses were (208, 312 and 728) KGy.

      The mechanical tests (bending, bending strength, shear force, impact strength and surface indentation) were performed on un irradiated and irrad

     The current issues in spam email detection systems are directly related to spam email classification's low accuracy and feature selection's high dimensionality. However, in machine learning (ML), feature selection (FS) as a global optimization strategy reduces data redundancy and produces a collection of precise and acceptable outcomes. A black hole algorithm-based FS algorithm is suggested in this paper for reducing the dimensionality of features and improving the accuracy of spam email classification. Each star's features are represented in binary form, with the features being transformed to binary using a sigmoid function. The proposed Binary Black Hole Algorithm (BBH) searches the feature space for the best feature subsets,

Nonlinear time series analysis is one of the most complex problems ; especially the nonlinear autoregressive with exogenous variable (NARX) .Then ; the problem of model identification and the correct orders determination considered the most important problem in the analysis of time series . In this paper , we proposed splines  estimation method for model identification , then we used three criterions for the correct orders determination. Where ; proposed method used to estimate the additive splines for model identification , And the rank determination depends on the additive property  to avoid the problem of curse dimensionally . The proposed method is one of the nonparametric methods , and the simulation results give a

The paper presents a highly accurate power flow solution, reducing the possibility of ending at local minima, by using Real-Coded Genetic Algorithm (RCGA) with system reduction and restoration. The proposed method (RCGA) is modified to reduce the total computing time by reducing the system in size to that of the generator buses, which, for any realistic system, will be smaller in number, and the load buses are eliminated. Then solving the power flow problem for the generator buses only by real-coded GA to calculate the voltage phase angles, whereas the voltage magnitudes are specified resulted in reduced computation time for the solution. Then the system is restored by calculating the voltages of the load buses in terms