The world's population growth and the increasing demand for new infrastructure facilities and buildings , present us with the vision of a higher resources consumption, specially in the form of more durable concrete such as High Performance Concrete (HPC) . Moreover , the growth of the world pollution by plastic waste has been tremendous. The aim of this research is to investigate the change in mechanical properties of HPC with added waste plastics in concrete. For this purpose 2.5%, 5% and 7.5% in volume of natural fine aggregate in the HPC mixes were replaced by an equal volume of Polyethylene Terephthalate (PET) waste , got by shredded PET bottles. The mechanical propert

Seeds of five cultivars of oats (Avena sativa) were introduced from Italy in 2009. Seeds were propagated on the farm of the Dept. of Field Crops Sci. / Coll. of Agric. / Univ. of Baghdad in the season 2009 – 2010. The cultivars Anatolia, Alguda, Hamel, Pimula and Genzania were planted under 3 irrigation intervals; 3, 4 and 5 weeks to give water depth of 480, 400 and 320 mm, respectively . The depth of water was 80 mm each irrigation. A factorial experiment with RCBD of 4 replicates was conducted in 2 consecutive seasons in 2010 – 2011 and 2011 – 2012. The cultivar Alguda gave highest grain yield (8.07 t/ ha) under 480 mm, and 7.02 t / ha average of 3 water depths. This cultivar was characterized by high growth rate (13.2 g/m2/ d) that

The sensors based on Nickel oxide doped chromic oxide (NiO: Cr2O3) nanoparticals were fabricated using thick-film screen printing of sol-gel grown powders. The structural, morphological investigations were carried out using XRD, AFM, and FESEM. Furthermore, the gas responsivity were evaluated towards the NH3 and NO2 gas. The NiO0.10: Cr2O3 nanoparticles exhibited excellent response of 95 % at 100oC and better selectivity towards NH3 with low response and recovery time as compared to pure Cr2O3 and can stand as reliable sensor element for NH3 sensor related applications.

  This research aims to test the relation and effect of the process of organizational change as an independent variable (change in human resources, technological change, change in tasks, change in organizational structure) in organizational performance as a variable of success (financial performance, operational performance, customer satisfaction, growth). And learning) in the Office of the province of Baghdad, as well as determine the existence of differences of statistical significance between the variables of research, and then try to come out with a set of recommendations to contribute to the strengthening of organizational performance, and carried out this research on the eye of the vertical number (75) individuals, The

The aim of this research is to test the relationship of influence and correlation between strategic performance and its five dimensions (financial dimension, after internal processes, after internal customer satisfaction, after learning and growth, environmental and social dimension), by adopting international indicators in agricultural projects To determine the extent of the differences between the research variable and its dimensions, and then try to come out with a number of recommendations that contribute to the evaluation of agricultural projects and their performance by diagnosing and treating deviations, and based on the importance of the research topic in agricultural institutions. Institutions of the Environment and Soci

In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve