Recently new concepts such as free data or Volunteered Geographic Information (VGI) emerged on Web 2.0 technologies. OpenStreetMap (OSM) is one of the most representative projects of this trend. Geospatial data from different source often has variable accuracy levels due to different data collection methods; therefore the most concerning problem with (OSM) is its unknown quality. This study aims to develop a specific tool which can analyze and assess the possibility matching of OSM road features with reference dataset using Matlab programming language. This tool applied on two different study areas in Iraq (Baghdad and Karbala), in order to verify if the OSM data has the same quality in both study areas. This program, in general, consists

One of the most important and common problems in petroleum engineering; reservoir, and production engineering is coning; either water or gas coning. Almost 75% of the drilled wells worldwide contains this problem, and in Iraq water coning problem is much wider than the gas coning problem thus in this paper we try to clarify most of the reasons causing water coning and some of applicable solutions to avoid it using the simulation program (CMG Builder) to build a single well model considering an Iraqi well in north of Iraq black oil field with a bottom water drive, Coning was decreased by 57% by dividing into sub-layers (8) layers rather than (4) layers, also it was decreased (Coning) by 45% when perforation numbers and positions was chang

Autorías: Mustafa Abdulamir Hussain, Ahmed Sebeaatea Almujamay, Riyadh khaleel khammas. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 5, 2022. Artículo de Revista en Dialnet.

Multilevel models are among the most important models widely used in the application and analysis of data that are characterized by the fact that observations take a hierarchical form, In our research we examined the multilevel logistic regression model (intercept random and slope random model) , here the importance of the research highlights that the usual regression models calculate the total variance of the model and its inability to read variance and variations between levels ,however in the case of multi-level regression models, the calculation of  the total variance is inaccurate and therefore these models calculate the variations for each level of the model, Where the research aims to estimate the parameters of this m

Background Many antidiabetic medications with distinct modes of action are available, The sodium glucose cotransporter-2 (SGLT-2) inhibitors are among the most recent oral antihyperglycemic medications. The American Diabetes Association recommends metformin, a biguanide medication, as the first option for oral control of type 2 diabetes because it has demonstrated promise in this regard. Aim of the study To evaluate and compare the effects of metformin and SGLT-2 inhibitors on general urine parameters in T2DM patients from Iraq. Method a prospective cohort study where 101 adult patients of both sexes aged under 70 years and newly diagnosed with T2DM, patients were divided into two groups, Metformin group (n=52), where metformin was p

Abstract

      In this study, we compare between the autoregressive approximations (Yule-Walker equations, Least Squares , Least Squares ( forward- backword ) and Burg’s (Geometric and Harmonic ) methods, to determine the optimal approximation to the time series generated from the first - order moving Average non-invertible process, and fractionally - integrated noise process, with several values for d (d=0.15,0.25,0.35,0.45) for different sample sizes (small,median,large)for two processes . We depend on figure of merit function which proposed by author Shibata in 1980, to determine the theoretical optimal order according to min

This research a study model of linear regression problem of autocorrelation of random error is spread when a normal distribution as used in linear regression analysis for relationship between variables and through this relationship can predict the value of a variable with the values of other variables, and was comparing methods (method of least squares, method of the average un-weighted, Thiel method and Laplace method) using the mean square error (MSE) boxes and simulation and the study included fore sizes of samples (15, 30, 60, 100). The results showed that the least-squares method is best, applying the fore methods of buckwheat production data and the cultivated area of the provinces of Iraq for years (2010), (2011), (2012),

Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face ima