A total of 28 birds were examined to investigate about the distribution of the nematode Hadjelia truncata among some members of the avian family Columbidae in Al-Diwaniya Province, Central Iraq. The percentages of the infection rate with this nematode were 27.27, 37.5, 14.28 and 0 in Columba livia, C. palumbis, Streptopelia decaocto, and S. turtur respectively. Reporting Hadjelia truncata from Streptopelia decaocto constitutes a new host record.

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

Abstract: The aim of this study was to evaluate the effect of bone density value in Hounsfield unit derived from cone beam computed tomography (CBCT), and implant dimensions in relation to implant stability parameters namely the resonance frequency analysis and the insertion torque (IT) value. It included 24 patients who received 42 dental implants (DI). The bone density of the planned implant site was preoperatively measured using cone beam computed tomography. The implant stability was measured using Osstell implant stability quotient (ISQ). The ISQ values were recorded immediately postoperatively and after 16 weeks. The IT value was categorized as 35 N/cm or > 35 N/cm. The mean (standard deviation) primary stability was 79.58 (5.27) ISQ,

Background: The main purpose of this study is to find if there is any correlation between the level of C-reactive protein (CRP) in gingival crevicular fluid with its serum level in chronic periodontitis patients and to explore the differences between them according to the probing depth. Materials and methods: Forty seven male subjects enrolled in this study. Thirty males with chronic periodontitis considered as study group whom further subdivided according to probing depth into subgroup 1 with pocket depth ≤6mm, subgroup 2 with pocket depth >6mm. The other 17 subjects considered as controls. For all subjects, clinical examination where done for periodontal parameters plaque index (PLI), gingival index (GI), bleeding on probing (BOP),

In this study, we used Bayesian method to estimate scale parameter for the normal distribution. By considering three different prior distributions such as the square root inverted gamma (SRIG) distribution and the non-informative prior distribution and the natural conjugate family of priors. The Bayesian estimation based on squared error loss function, and compared it with the classical estimation methods to estimate the scale parameter for the normal distribution, such as the maximum likelihood estimation and th

The optimization calculations are made to find the optimum properties of combined quadrupole lens consist of electrostatic and magnetic lenses to produce achromatic lens. The modified bell-shaped model is used and the calculation is made by solving the equation of motion and finding the transfer matrices in convergence and divergence planes, these matrices are used to find the properties of lens as the magnification and aberrations coefficients. To find the optimum values of chromatic and spherical aberrations coefficients, the effect of both the excitation parameter of the lens (n) and the effective length of the lens into account as effective parameters in the optimization processing

This paper aims to decide the best parameter estimation methods for the parameters of the Gumbel type-I distribution under the type-II censorship scheme. For this purpose, classical and Bayesian parameter estimation procedures are considered. The maximum likelihood estimators are used for the classical parameter estimation procedure. The asymptotic distributions of these estimators are also derived. It is not possible to obtain explicit solutions of Bayesian estimators. Therefore, Markov Chain Monte Carlo, and Lindley techniques are taken into account to estimate the unknown parameters. In Bayesian analysis, it is very important to determine an appropriate combination of a prior distribution and a loss function. Therefore, two different

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat