Carbon-fiber-reinforced polymer (CFRP) is widely acknowledged as a leading advanced material structure, offering superior properties compared to traditional materials, and has found diverse applications in several industrial sectors, such as that of automobiles, aircrafts, and power plants. However, the production of CFRP composites is prone to fabrication problems, leading to structural defects arising from cycling and aging processes. Identifying these defects at an early stage is crucial to prevent service issues that could result in catastrophic failures. Hence, routine inspection and maintenance are crucial to prevent system collapse. To achieve this objective, conventional nondestructive testing (NDT) methods are utilized to i

A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twenty four samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

Terrestrial isopods play an important role in the biodegradation of many wastes which gives agreat importance in the nutrient cycles and ecosystem services , therefore this paper aims to use species Orthodillo chiltoni (Vandel, 1973) as a model organism in bioremediation of urban environment with municipal wastes, agricultural fields, parks and markets wastes. Samples of these wastes included potato peels, orange fruit leaves, thyme plant and sawdust,cardboard residues , the experiments were carried out in laboratory condition similar to the living environment of this species in terms of temperature, humidity and intensity lighting. The applied results have shown that

Elemental capture spectroscopy (ECS) is an important tool in the petroleum industry for determining the composition and properties of rock formations in a reservoir. Knowledge of the types and abundance of different minerals in the reservoir is crucial for accurate petrophysical interpretation, reservoir engineering practices, and stratigraphic correlation. ECS measures the elemental content of the rock, which directly impacts several physical properties that are essential for reservoir characterization, such as porosity, fluid saturation, permeability, and matrix density. The ability to accurately determine these properties leads to better reservoir mapping, improved production, and more effective resource management. Accurately determi

The study aims to evaluate the removal of sulfur content from Iraqi light naphtha produced in Al-Dora refinery by adsorption desulfurization DS technique using modified activated carbon MAC loaded with nickel Ni and copper Cu as single binary metals. The experiments were carried in a batch unit with various operating parameters;  MAC dosage, agitation speed, and a contact time of 300 min at constant initial sulfur concentration 155 ppm and temperature. The results showed higher DS% by AC/Ni-Cu (66.45)% at 500 rpm and 1 g dosage than DS (29.03)% by activated carbon AC, increasing MAC dosage, agitation speed, and contact time led to increasing DS% values.  The adsorption capacity of MAC results was recorded (16, 15, and 20) mg sulfu

Industrial wastewater containing nickel, lead, and copper can be produced by many industries. The reverse osmosis (RO) membrane technologies are very efficient for the treatment of industrial wastewater containing nickel, lead, and copper ions to reduce water consumption and preserving the environment. Synthetic industrial wastewater samples containing Ni(II), Pb(II), and Cu(II) ions at various concentrations (50 to 200 ppm), pressures (1 to 4 bar), temperatures (10 to 40 ^oC), pH (2 to 5.5), and flow rates (10 to 40 L/hr), were prepared and subjected to treatment by RO system in the laboratory. The results showed that high removal efficiency of the heavy metals could be achieved by RO process (98.5%, 97.5% and 96% for Ni(II),

This study is aimed to use the aerobic packed bed in biotreatment of the wastewater which is discharge from AL-KARAMA teaching hospital in Baghdad. The performance of packed-bed treatment method was examined for elimination of the organic compounds from wastewater under aerobic conditions. In this research different parameters were studied. They were: inoculums concentration, circulation rate of wastewater through the bed, packing type and the temperature. Results showed that the system efficiently removed about 82% of the chemical oxygen demand (COD) and 80% of the Biological oxygen demand (BOD). Percent reduction in turbidity was about 92% and reduction in nitrate concentration was about 87%. It was found that best performance of the pack

In this paper, we derived an estimator of reliability function for Laplace distribution with two parameters using Bayes method with square error loss function, Jeffery’s formula and conditional probability random variable of observation. The main objective of this study is to find the efficiency of the derived Bayesian estimator compared to the maximum likelihood of this function and moment method using simulation technique by Monte Carlo method under different Laplace distribution parameters and sample sizes. The consequences have shown that Bayes estimator has been more efficient than the maximum likelihood estimator and moment estimator in all samples sizes

In this paper, we made comparison among different parametric ,nonparametric and semiparametric estimators for partial linear regression model users parametric represented by ols and nonparametric methods represented by cubic smoothing spline estimator and Nadaraya-Watson estimator, we study three nonparametric regression models and samples sizes  n=40,60,100,variances used σ²=0.5,1,1.5 the results  for the first model show that N.W estimator for partial linear regression model(PLM) is the best followed the cubic smoothing spline estimator for (PLM),and the results of the second and the third model show that the best estimator is C.S.S.followed by N.W estimator for (PLM) ,the