Praise be to God, Lord of the worlds, and prayers and peace be upon our master Muhammad and upon his family and companions.
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      The research dealt with an important and critical era in the history of the Seljuk State of the Romans in Asia Minor, as that country, after the assumption of Sultan Ghiath al-Din Kehsaru II in the year 634 AH / 1236 CE, was subjected to the constant Mongol threat, so the research came marked with (the Seljuk State of Rome in Asia Minor and the Mongol invasion 634 - 708 AH / 1236 - 1308 AD) The research showed the succession of the Seljuk Sultans to the rulers whose rule after the death of Sultan Alaeddin Kikbad was weak and subject to the authority of the Mongols

In this research we study a variance component model, Which is the one of the most important models widely used in the analysis of the data, this model is one type of a multilevel models, and it is considered as linear models , there are three types of linear variance component models ,Fixed effect of linear variance component model, Random effect of linear variance component model and Mixed effect of linear variance component model . In this paper we will examine the model of mixed effect of linear variance component model with one –way random effect ,and the mixed model is a mixture of fixed effect and random effect in the same model, where it contains the parameter (μ) and treatment effect (τ_i ) which  has

New complexes of M(II) with mixed ligand of 5-Chlorosalicylic acid (CSA) C7H5ClO3 as primary ligand and L- Valine (L-Val) C5H11NO2 as a secondary ligand were prepared and characterized by elemental analysis (C.H.N), UV., FT-IR, magnetic susceptibility, µeff (B.M) as well as the conductivity measurements (Λm ). In the complexes, the 5-chlorosalicylic acid is bidentate in all complexes coordinating through –OH- and –COO- groups; also L-Valine behaves as a bidentate ligand in all complexes through –NH2 and –COO- groups. These five mixed ligand complexes formulated as Na3[M(CSA)2(L-Val)]. The proposed molecular structure for all complexes is octahedral geometries. The synthesis complexes were tested in vitro for against four bacteria

New complexes of M(II) with mixed ligand of 5-Chlorosalicylic acid (CSA) C7H5ClO3 as primary ligand and L- Valine (L-Val) C5H11NO2 as a secondary ligand were prepared and characterized by elemental analysis (C.H.N), UV., FT-IR, magnetic susceptibility, μeff (B.M) as well as the conductivity measurements (Λm ). In the complexes, the 5-chlorosalicylic acid is bidentate in all complexes coordinating through –OH- and –COO- groups; also L-Valine behaves as a bidentate ligand in all complexes through –NH2 and –COO- groups. These five mixed ligand complexes formulated as Na3[M(CSA)2(L-Val)]. The proposed molecular structure for all complexes is octahedral geometries. The synthesis complexes were tested in vitro for against four bacteria

New complexes of M(II) with mixed ligand of 5-Chlorosalicylic acid (CSA) C7H5ClO3 as primary ligand and L- Valine (L-Val) C5H11NO2 as a secondary ligand were prepared and characterized by elemental analysis (C.H.N), UV., FT-IR, magnetic susceptibility, μeff (B.M) as well as the conductivity measurements (Λm ). In the complexes, the 5-chlorosalicylic acid is bidentate in all complexes coordinating through –OH- and –COO- groups; also L-Valine behaves as a bidentate ligand in all complexes through –NH2 and –COO- groups. These five mixed ligand complexes formulated as Na3[M(CSA)2(L-Val)]. The proposed molecular structure for all complexes is octahedral geometries. The synthesis complexes were tested in vitro for against four bacteria

Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 m