Graphene-carbon nitride can be synthesized from thiourea in a single step at a temperature of four hours at a rate of 2.3 ℃/min. Graphene-carbon nitride was characterized by Fourier-transform infrared spectroscopy (FTIR), energy dispersive X-ray analysis (EDX), scanning electron microscopy, and spectrophotometry (UV-VIS). Graphene-carbon nitride was found to consist of triazine and heptazine structures, carbon, and nitrogen. The weight percentage of carbon and the atomic percentage of carbon are 40.08%, and the weight percentage of nitrogen and the atomic percentage of nitrogen are 40.08%. Therefore, the ratio and the dimensions of the graphene-carbon nitride were characterized by scanning electron microscopy, and it was found that the radius was within the range of (2 µm-147.1 nm). In addition, it was found that it absorbed light in the visible field (VIS). The objective of the manufacture and characterization of graphene-carbon nitride for use in the manufacture of a selective electrode for an organic pollutant (currently used in the manufacture of a selective electrode for the analysis of organic dye).
Longitudinal data is becoming increasingly common, especially in the medical and economic fields, and various methods have been analyzed and developed to analyze this type of data.
In this research, the focus was on compiling and analyzing this data, as cluster analysis plays an important role in identifying and grouping co-expressed subfiles over time and employing them on the nonparametric smoothing cubic B-spline model, which is characterized by providing continuous first and second derivatives, resulting in a smoother curve with fewer abrupt changes in slope. It is also more flexible and can pick up on more complex patterns and fluctuations in the data.
The longitudinal balanced data profile was compiled into subgroup
... Show MoreThe main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared usin
... Show MoreIn this paper, estimation of system reliability of the multi-components in stress-strength model R(s,k) is considered, when the stress and strength are independent random variables and follows the Exponentiated Weibull Distribution (EWD) with known first shape parameter θ and, the second shape parameter α is unknown using different estimation methods. Comparisons among the proposed estimators through Monte Carlo simulation technique were made depend on mean squared error (MSE) criteria
The comparison of double informative priors which are assumed for the reliability function of Pareto type I distribution. To estimate the reliability function of Pareto type I distribution by using Bayes estimation, will be used two different kind of information in the Bayes estimation; two different priors have been selected for the parameter of Pareto type I distribution . Assuming distribution of three double prior’s chi- gamma squared distribution, gamma - erlang distribution, and erlang- exponential distribution as double priors. The results of the derivaties of these estimators under the squared error loss function with two different double priors. Using the simulation technique, to compare the performance for
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