This research presents a statistical study of radiation generated from communication towers in the Nineveh Plain region Baghdeda. The intensity of radiation energy was measured at 10 meters away from the communication tower in different locations, using a (1PC XH-901 Dosimeter/ Personal Dose Alarm / Radiation Detector, dosage rate: 0.01 μSv/h to 150μSv/h) to measure the amount of radiation at various times. Energy densities were measured and compared with standard limits provided by other authorities, such as the International Committee for Radiation Protection. Results were analyzed using SPSS version 26 to implement the data. The results show that the means of the radiation levels measured at all the zones do not statistically differ

Abstract                                                         

The issue of the protection of the environment is a shared responsibility between several destinations and sectors, and constitutes a main subject in which they can achieve sustainable development. In the sectors of government programs can be set up towards the establishment of the government sector to the green environment, so to be the implementati

The launch of the EU’s Eastern Partnership in 2009 intended to signal a new, elevated level of EU engagement with its Eastern neighborhood. Yet there remain several long-simmering and potentially destabilizing conflicts in the region, with which EU engagement thus far has been sporadic at best. The Union’s use of its Common Security and Defense Policy (CSDP) in the region and to help solve these disputes has been particularly ad hoc and inconsistent, wracked by inter-institutional incoherence and undermined by Member States’ inability to agree on a broad strategic vision for engagement with the area.

The three CSDP missions deployed to the region thus far have all suffered from this incoherence to various extents. In particu

The research took the spatial autoregressive model: SAR and spatial error model: SEM  in an attempt to provide practical evidence that proves the importance of spatial analysis, with a particular focus on the importance of using regression models spatial and that includes all of the spatial dependence, which we can test its presence or not by using Moran test. While ignoring this dependency may lead to the loss of important information about the phenomenon under research is reflected in the end on the strength of the statistical estimation power, as these models are the link between the usual regression models with time-series models. The spatial analysis had been applied to Iraq Household Socio-Economic Survey: IHS

         The antidiabetic thiozolidinediones (TZDs) a class of peroxisome proliferators-activated receptor (PPAR) ligands has recently been the focus of much interest for their possible role in regulation of inflammatory response. The present study was designed to evaluate the anti-inflammatory activity of pioglitazone in experimental models of inflammation in rats. The present study was conducted to evaluate the anti inflammatory effect of TZDs (pioglitazone 3mg/Kg) on acute, sub acute and chronic model of inflammation by using egg-albumin and formalin–induced paw edema in 72 rats, relative to reference drugs Dexamethasone 5mg/Kg and Piroxicam 5mg/Kg. In each inflammation model, 24 rats wer

It takes a lot of time to classify the banana slices by sweetness level using traditional methods. By assessing the quality of fruits more focus is placed on its sweetness as well as the color since they affect the taste. The reason for sorting banana slices by their sweetness is to estimate the ripeness of bananas using the sweetness and color values of the slices. This classifying system assists in establishing the degree of ripeness of bananas needed for processing and consumption. The purpose of this article is to compare the efficiency of the SVM-linear, SVM-polynomial, and LDA classification of the sweetness of banana slices by their LRV level. The result of the experiment showed that the highest accuracy of 96.66% was achieved by the

The Artificial Neural Network methodology is a very important & new subjects that build's the models for Analyzing, Data Evaluation, Forecasting & Controlling without depending on an old model or classic statistic method that describe the behavior of statistic phenomenon, the methodology works by simulating the data to reach a robust optimum model that represent the statistic phenomenon & we can use the model in any time & states, we used the Box-Jenkins (ARMAX) approach for comparing, in this paper depends on the received power to build a robust model for forecasting, analyzing & controlling in the sod power, the received power come from

This work aims to analyze a three-dimensional discrete-time biological system, a prey-predator model with a constant harvesting amount. The stage structure lies in the predator species. This analysis is done by finding all possible equilibria and investigating their stability. In order to get an optimal harvesting strategy, we suppose that harvesting is to be a non-constant rate. Finally, numerical simulations are given to confirm the outcome of mathematical analysis.

Simulation experiments are a means of solving in many fields, and it is the process of designing a model of the real system in order to follow it and identify its behavior through certain models and formulas written according to a repeating software style with a number of iterations. The aim of this study is to build a model  that deals with the behavior suffering from the state of (heteroskedasticity) by studying the models (APGARCH & NAGARCH) using (Gaussian) and (Non-Gaussian) distributions for different sample sizes (500,1000,1500,2000) through the stage of time series analysis (identification , estimation, diagnostic checking and prediction). The data was generated using the estimations of the parameters resulting f

<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c