Trace Elements (Cd, Pb, Cu, Zn, Ni) level were examined in hair of donors from industrial areas, cities and village, and in permanent contact with a polluted workplace environment in lattakia. Hair sample were analyzed for their contents of the trace elements by inductivity coupled plasma- mass spectrometer (ICP- MS). It was found that the contents of (Cd, Pb, Cu, Zn, Ni) in the hair were significantly higher in the industrial areas and cities, while in the village had the lower concentration of elements. Correlation coefficients between the levels of the elements in hair found in this study showed that hair is a good indicator of Environmental Pollution.

Abstract:                                        

   We can notice cluster data in social, health and behavioral sciences, so this type of data have a link between its observations and we can express these clusters through the relationship between measurements on units within the same group.

    In this research, I estimate the reliability function of cluster function by using the seemingly unrelate

Knowledge of the distribution of the rock mechanical properties along the depth of the wells is an important task for many applications related to reservoir geomechanics. Such these applications are wellbore stability analysis, hydraulic fracturing, reservoir compaction and subsidence, sand production, and fault reactivation. A major challenge with determining the rock mechanical properties is that they are not directly measured at the wellbore. They can be only sampled at well location using rock testing. Furthermore, the core analysis provides discrete data measurements for specific depth as well as it is often available only for a few wells in a field of interest. This study presents a methodology to generate synthetic-geomechani

The two parameters of Exponential-Rayleigh distribution were estimated using the maximum likelihood estimation method (MLE) for progressively censoring data. To find estimated values for these two scale parameters using real data for COVID-19 which was taken from the Iraqi Ministry of Health and Environment, AL-Karkh General Hospital. Then the Chi-square test was utilized to determine if the sample (data) corresponded with the Exponential-Rayleigh distribution (ER). Employing the nonlinear membership function (s-function) to find fuzzy numbers for these parameters estimators. Then utilizing the ranking function transforms the fuzzy numbers into crisp numbers. Finally, using mean square error (MSE) to compare the outcomes of the survival

Abstract

        The research to have a clear perceptions about the knowledge value added to assess the knowledge resources of the Iraqi private banks, depending on the value added methodology of the proposed defined (Housel & Bell, 2001), which assumes that the  knowledge value added come through synergetic relationship between knowledge resource and information technology, trying to the possibility of mainstream theory and its application in the Iraqi environment and interpretation of results, and on this basis was launched search of a research problem took root synergetic nature of the relationship between knowledge (human)  resource and

Jurisprudential rules, a broad section to lower the legitimacy of the ruling on the developments of politics, which is increasing day by day, and developments of complex judicial issues, provided that the rule derived from the texts of the legitimate or verbal significance.
      And the introduction of jurisprudence rules in the provisions concerning the legitimate policy, the first to take from the provisions of the situation; because the rules of jurisprudence is a summary of the provisions reached by the scholars of the nation after study and scrutiny, which is qualified and largely to cover the need of the owners of public mandates.

In this work, measurements of activity concentration of naturally occurring radioactive materials (NORM) isotopes and their related hazard indices for several materials such as crude oil, sludge and water in Ahdeb oil fields in Waste governorate using high pure germanium coaxial detection technique. The average values for crude oil samples were174.72Bq/l, 43.46Bq/l, 355.07Bq/l, 264.21Bq/l, 122.52nGy/h, 0.7138, 1.1861, 0.601 mSv/y, 0.1503mSv/y and 1.8361 for Ra-226, Ac-228, K-40, Ra eq, D, H-external and H-internal respectively. According to the results; the ratio between ²³⁸U to ²³²Th was 4, which represents the natural ratio in the crust earth; therefore, one can be strongly suggested that the geo-stricture of the

The new novel polymers nanocomposites based modified chitosan (CS) blending with polyvinyl alcohol (PVA) and coated gold or silver nanoparticles (AuNPs), AgNPs) were synthesized from many sequence reactions as presented in (Scheme1, 2 and 3). By utilizing 1H-NMR spectroscopy, FTIR, and Field Emission Scanning electron microscope , the synthesized compounds have been identified. Molecular docking is studied, where operations are used to predict the binding status of compounds with the enzyme and to calculate the free energy (ΔG) of the compounds prepared. Also, the antibacterial activity regarding the synthesized compounds against two resistant pathogenic bacteria (G+) S. aureus and E. coli (G-) was examined in vitro compare with standard a

In present work, new tetra-dentate ligand, titled 3,5-bis ((E)-5-Bromo-2-hydroxy benzylidene amino) benzoic acid (H₃L), was prepared via an acid-catalyzed condensation process. New four metallic ligand complexes with Co(II), Ni(II), Cu(II) and Zn(II) ions, were also prepared from the refluxing of equivalent moles. Ligand's structure and its complexes; were confirmed by numerous characterization methods, including Ultraviolet-Visible, Infrared, Mass Spectrometer, ¹H and ¹³C Nuclear Magnetic Resonance spectra, atomic absorption, magnetic moments, and molar conductivity measurements. The results of the spectroscopic analyzes proved that the prepared ligand acts as tetradentate bi-ionic ligand and it was bond

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne