This research paper is about synoptic climate specifically with in the upper air layers
using upper air layers maps analysis which are maps of thickness for the level 1000 – 500
MB, that their high average ranges between 100 – 5600 M above surface. This research paper
focuses on studying special and temporal variations of the atmosphere thickness above Iraqaccording
to this study, it is concluded that atmosphere thickness above Iraq increases
towards south with an average of 100 M as compared with north of Iraq. Regarding the
temporal variations, it is concluded that atmosphere thickness during hot months. In July, for
example, the atmosphere thickness becomes thicker than in January with an average of
(250)M

The status of the semi total stoppage and non-use and waste of economic made studying and analyzing Dutch disease of high importance because it is a major cause in aggravation of this status which happened to the Iraqi economy in almost complete way and the relative big importance that oil source has and its domination on the largest percentage in the gross domestic product and exports that Iraqi economy is relying largely in funding the national budget made the concentration of the study on this subject an important and necessary within the important economic events that Iraqi economy witnessed after 2003 till 2016 to give a clear and an overall picture of the reality of the unilateral Iraqi economy under the status of semi tota

The need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2,0,0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlation coefficien

The need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2, 0, 0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlat

A survey of blood parasites among members of two species of Iraqi babblers Timaliidae,
Turodoides caudatus salvadori (de Fillipi, 1865) and Turdoides alterostris (Hartert, 1909)
was carried out in the middle and south of Iraq. Two species of haematozoa were recovered,
Haemoproteus turdoidus sp. nov. and Plasmodium relictum Grassi &Felleti. The description
of the new taxon is provided and discussed with pertinent literature.

Abstract Diabetic nephropathy (DN) is a prevalent chronic microvascular diabetic complication. As inflammation plays a vital role in the development and progress of DN the macrophages migration inhibitory factor (MIF), a proinflammatory multifunctional cytokine approved to play a critical function in inflammatory responses in various pathologic situations like DN. This study aimed To assess serum levels of MIF in a sample of Iraqi diabetic patients with nephropathy supporting its validity as a marker for predicting nephropathy in T2DM patients. In addition, to evaluate the nephroprotective effect of angiotensin-converting enzyme (ACE) inhibitors in terms of their influence on MIF levels. This is a case-control study involving ninety

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

Background: techniques of image analysis have been used extensively to minimize interobserver variation of immunohistochemical scoring, yet; image acquisition procedures are often demanding, expensive and laborious. This study aims to assess the validity of image analysis to predict human observer’s score with a simplified image acquisition technique. Materials and methods: formalin fixed- paraffin embedded tissue sections for ameloblastomas and basal cell carcinomas were immunohistochemically stained with monoclonal antibodies to MMP-2 and MMP-9. The extent of antibody positivity was quantified using Imagej® based application on low power photomicrographs obtained with a conventional camera. Results of the software were employed