In this study, thin films of pure titanium dioxide (TiO 2 ) and titanium dioxide dual mixed with zinc oxide (ZnO) and magnesium oxide (MgO) with varying concentrations of (ZnO: MgO) x ranging from 0 to 30 wt% undoped and doped gold nanoparticles (AuNPs) were prepared on glass using the chemical spray pyrolysis (CSP) technique. The morphological, structural, and sensing properties of the prepared thin films were examined. Atomic Force Microscopy (AFM) analysis revealed that these films exhibit a consistent structure before and after doping. Initially, the roughness of these films was observed to increase upon the introduction of impurities (ZnO: MgO) x . This trend reversed at x = 0.20, where a decrease in roughness occurred. Interestin

Background: The most widely used material for fabrication of denture base is poly methyl methacrylate, despite its popularity, the main problems associated with it as a denture base material are poor strength particularly under fatigue failure inside the patient mouth, impact failure outside the patient mouth, which are the main causes for fracture of denture, several studies was done to increase mechanical properties of denture base. The present study was conducted to evaluate and compare the effect of addition single walled carbon nanotubes in different concentrations to polymethyl methacrylate on some mechanical properties (surface hardness, surface roughness, impact strength and transverse strength). Materials and methods: Forty eight

In this research, the results of the Integral breadth method were used to analyze the X-ray lines to determine the crystallite size and lattice strain of the zirconium oxide nanoparticles and the value of the crystal size was equal to (8.2nm) and the lattice strain (0.001955), and then the results were compared with three other methods, which are the Scherer and Scherer dynamical diffraction theory and two formulas of the Scherer and Wilson method.the results were as followsScherer crystallite size(7.4nm)and lattice strain(0.011968),Schererdynamic method crystallite size(7.5 nm),Scherrer and Wilson methodcrystallite size( 8.5nm) and lattice strain( 0.001919).And using another formula for Schearer and Wilson methodwe obtain the size of the c

Weibull Distribution is one of most important distribution and it is mainly used in reliability and in distribution of life time. The study handled two parameter and three-parameter Weibull Distribution in addition to five –parameter Bi-Weibull distribution. The latter being very new and was not mentioned before in many of the previous references. This distribution depends on both the two parameter and the three –parameter Weibull distributions by using the scale parameter (α) and the shape parameter (b) in the first and adding the location parameter (g)to the second and then joining them together to produce a distribution with five parameters.

Weibull Distribution is one of most important distribution and it is mainly used in reliability and in distribution of life time. The study handled two parameter and three-parameter Weibull Distribution in addition to five –parameter Bi-Weibull distribution. The latter being very new and was not mentioned before in many of the previous references. This distribution depends on both the two parameter and the three –parameter Weibull distributions by using the scale parameter (α) and the shape parameter (b) in the first and adding the location parameter (g)to the second and then joining them together to produce a distribution with five parameters.

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

In this paper a new idea was introduced which is finding a new distribution from other distributions using mixing parameters; w_i  where  0 < w_i < 1 ­and . Therefore we can get many mixture distributions with a number of parameters. In this paper I introduced the idea of a mixture Weibull distribution which is produced from mixing two Weibull distributions; the first with two parameters, the scale parameter , and the shape parameter,  and the second also has the scale parameter , and the shape parameter,  in addition to the location parameter, . These two distributions were mixed using a new parameter which is the mixing parameter w which represents the proportion

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