The survey was carried out From January to April of 2018 on macrofungi samples collected from different places in Halabja province located in north eastern parts of Iraq-Kurdistan region. This region is rich in forest trees and pasture lands with diversity of shrubs and herbs and is expected to support the growth of several macro fungal species. However, this part of Kurdistan in Iraq is still unexplored from macrofungal point of view. In this paper three species from Pezizaceae and Pyronemataceae families that belonging to (Pezizales, Ascomycota), were reported from Iraqi Kurdistan. These macrofungal species are recorded for the first time from Iraq. Also the species were identified and showing their locations distributed on a map prepared

Image recognition is one of the most important applications of information processing, in this paper; a comparison between 3-level techniques based image recognition has been achieved, using discrete wavelet (DWT) and stationary wavelet transforms (SWT), stationary-stationary-stationary (sss), stationary-stationary-wavelet (ssw), stationary-wavelet-stationary (sws), stationary-wavelet-wavelet (sww), wavelet-stationary- stationary (wss), wavelet-stationary-wavelet (wsw), wavelet-wavelet-stationary (wws) and wavelet-wavelet-wavelet (www). A comparison between these techniques has been implemented. according to the peak signal to noise ratio (PSNR), root mean square error (RMSE), compression ratio (CR) and the coding noise e (n) of each third

This paper aims at presenting a comparison between objective and subjective tests . This paper attemptsto shed light on these two aspects of tests and make do a compression by using suitable techniques for objective and subjective tests .

     The paper compares between the two techniques used by the objective and subjective tests respectively, the time and efforts required by each type, the extent to which each type can be reliable, and the skills each type is suitable to measure.

     The paper shows that objective tests, on the contrary of the subjective ones, encourages guess> Objective tests are used to test specific areas of langua

In this paper, some commonly used hierarchical cluster techniques have been compared. A comparison was made between the agglomerative hierarchical clustering technique and the k-means technique, which includes the k-mean technique, the variant K-means technique, and the bisecting K-means, although the hierarchical cluster technique is considered to be one of the best clustering methods. It has a limited usage due to the time complexity. The results, which are calculated based on the analysis of the characteristics of the cluster algorithms and the nature of the data, showed that the bisecting K-means technique is the best compared to the rest of the other methods used.

Background: Anti-oxidants are used as supplements to counteract the over production of free radicals in periodontal disease.Co-Q10 functions as an intercellular antioxidant by acting as a primary scavenger of free radicals (FRs) and reactive oxygen species (ROS), this study aimed to evaluate the effect of intra pocket application of perio Q gel (coenzyme Q10) alone and as adjunct to scaling and root planing on the periodontal clinical parameters in the treatment of patients with chronic periodontitis and compare the better improvement on the clinical periodontal parameters among different treatment modalities at 3 and 6 weeks. Materials and methods: A total of 323 sites with pocket depth (5-8) mm in patients with chronic periodontitis were

Linear regression is one of the most important statistical tools through which it is possible to know the relationship between the response variable and one variable (or more) of the independent variable(s), which is often used in various fields of science. Heteroscedastic is one of the linear regression problems, the effect of which leads to inaccurate conclusions. The problem of heteroscedastic may be accompanied by the presence of extreme outliers in the independent variables (High leverage points) (HLPs), the presence of (HLPs) in the data set result unrealistic estimates and misleading inferences. In this paper, we review some of the robust

 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the others