Most of the Weibull models studied in the literature were appropriate for modelling a continuous random variable which assumes the variable takes on real values over the interval [0,∞]. One of the new studies in statistics is when the variables take on discrete values. The idea was first introduced by Nakagawa and Osaki, as they introduced discrete Weibull distribution with two shape parameters q and β where      0 < q < 1 and b > 0. Weibull models for modelling discrete random variables assume only non-negative integer values. Such models are useful for modelling for example; the number of cycles to failure when components are subjected to cyclical loading. Discrete Weibull models can be obta

A new Schiff base o-hydroxybenzylidene-1-phenyl-2,3-dimethyl-4-amino-3-pyrazolin-5-on (HL) ,have been prepared and characterization.(HL) has been used as a chelating ligand to prepare a number of metal complexes VO(II) ,Cr(III) ,Mn(II),Fe(II),Hg(II) and UO2(II) .and mixed ligands complexes have been prepared between o-hydroxybenzylidene-1-phenyl-2,3-dimethyl-4-amino-3-pyrazolin-5-on and 8- hydroxy quinoline with VO(II),Zn(II),Cd(II), Hg(II) and UO2(II) the prepared complexes were isolated and characterized by (FT-IR)and (UV-Vis) spectroscopy. Elemental analysis (C.H.N) Chloride contents, Flame atomic absorption technique. in addition to magnetic susceptibility and conductivity measurement. Molar ratio measurement in solution gave comparabl

New metal complexes of the ligand 4-[5-(2-hydoxy-phenyl)-[1,3,4- oxadiazol -2-ylimino methyl]-1,5-dimethyl-2-phenyl-1,2-dihydro-pyrazol-3-one (L) with the metal ions Co(II), Ni(II), Cu(II) and Zn(II) were prepared in alcoholic medium. The Schiff base was synthesized through condensate of [4-antipyrincarboxaldehyde] with[2-amino-5-(2-hydroxy-phenyl-1,3,4- oxadiazol] in alcoholic medium . Two tetradentate Schiff base ligand were used for complexation upon two metal ions of Co2+, Ni2+, Cu2+ and Zn2+ as dineucler formula M2L2.4H2O. The metal complexes were characterized by FTIR Spectroscopy, electronic Spectroscopy, elemental analysis, magnetic susceptidbility measurements, and also the ligand was characterized by 1H-NMR spectra, and m

New ligand of N-(pyrimidin-2-yl carbamothioyl)acetamide was synthesized and its complexes with (VO(II), Mn (II), Cu (II), Zn (II), Cd (II) and Hg (II) are formed with confirmation of their structures on the bases of spectroscopic analyses. Antimicrobial activity of new complexes are studied against Gram positive S. aureus and Gram negative E. coli, Proteus, Pseudomonas. The octahedral geometrical structures are proved depending on the outcomes from the preceding procedures

New ligand of N-(pyrimidin-2-yl carbamothioyl)acetamide was synthesized and its complexes with (VO(II), Mn (II), Cu (II), Zn (II), Cd (II) and Hg (II) are formed with confirmation of their structures on the bases of spectroscopic analyses. Antimicrobial activity of new complexes are studied against Gram positive S. aureus and Gram negative E. coli, Proteus, Pseudomonas. The octahedral geometrical structures are proved depending on the outcomes from the preceding procedures. Keywords: pyrimidin-2-amine, acetyl isothiocyanate, complexes, Antimicrobial activity

New Schiff base ligand (E)-6-(2-(4-(dimethylamino)benzylideneamino)-2-(4-hydroxyphenyl)acetamido)-3,3- dimethyl-7-oxo-4-thia-1- azabicyclo[3.2.0]heptane-2-carboxylic acid = (HL) was synthesized via condensation of Amoxicillin and 4(dimethylamino)benzaldehyde in methanol. Figure -1 Polydentate mixed ligand complexes were obtained from 1:1:2 molar ratio reactions with metal ions and HL, 2NA on reaction with MCl2 .nH2O salt yields complexes corresponding to the formulas [M(L)(NA)2Cl],where M=Fe(II),Co(II),Ni(II),Cu(II),and Zn(II), A=nicotinamide .

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Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a