Five levels of Zn-EDTA fertilizer and foliar application of boron were used to study the local rice response through studying of some vegetative and reproductive growth characters, by conducting two field experiments at Kanipanka Agricultural Research Station during the summer season of 2004 by using RCBD with three replications. Significant differences were found in studied characters, there were increase in the number of days from seeding to 50% flowering (94.330-96.233) days, from 50% flowering to physiological maturity (37.50-38.28) days, plant height (82.50-91.423) cm and LAI (5.441-7.525). Reproductive characters such as number of grains panicle-1 (74.11-85.88), number of panicles m-2 (321.00-426.083), biological yield (8166.166-11082

Hydrogeological investigation and water budget calculation of Koi Sanjaq basin is carried out. This investigation includes the determination of the aquifer types extending through the study area and flow direction as well as aquifer hydraulic properties values. Three main unconfined aquifer types were distinguished , they are (Pilaspi), , (Bihassan-Muqdadiya and Fatha –Injana) Formations , where the flow map of the unconfined aquifers shows that the flow direction is from northern and northeastern parts towards the south and southeastern parts i.e. .Lesser Zab River. Analysis of pumping test data of 9 selected wells from unconfined aquifers show that T values range from 1.51m2/day to 64.4 m2/day revealing the great variations in the aq

 Active worms have posed a major security threat to the Internet, and many research efforts have focused on them. This paper is interested in internet worm that spreads via TCP, which accounts for the majority of internet traffic. It presents an approach that use a hybrid solution between two detection algorithms: behavior base detection and signature base detection to have the features of each of them. The aim of this study is to have a good solution of detecting worm and stealthy worm with the feature of the speed. This proposal was designed in distributed collaborative scheme based on the small-world network model to effectively improve the system performance.

Abstract 

          The research the impact of the application of some of the production system tools in the specified time, which can be adapted in the service sectors (banking sector) over the improvement and increase the quality of banking services, and highlights the research problem in the low quality of banking services provided to customers because of the reliance on traditional banking systems in the provision of services Because of the lack keep pace with global developments in the banking industry, and the goal of research is to clarify the applicability of the production system in the time specified in the service sector and th

Mixed-effects conditional logistic regression is evidently more effective in the study of qualitative differences in longitudinal pollution data as well as their implications on heterogeneous subgroups. This study seeks that conditional logistic regression is a robust evaluation method for environmental studies, thru the analysis of environment pollution as a function of oil production and environmental factors. Consequently, it has been established theoretically that the primary objective of model selection in this research is to identify the candidate model that is optimal for the conditional design. The candidate model should achieve generalizability, goodness-of-fit, parsimony and establish equilibrium between bias and variab

In this research ,we will study the phenomenon of dust storms for all types
(Suspended dust , rising dust , dust storm) , and its relationship with some climate
variables (Temperature , rainfall ,wind speed , Relative humidity ) through
regression models to three different locations ( Kirkuk , Rutba , Diwaniya ) almost
covering Iraq area for the period (1981 – 2012) . Time series has been addressing the
phenomenon of storms and climate variables for the time period under study to
attain the best models for long range forcast to the dust storms.

The objective of this study was to develop neural network algorithm, (Multilayer Perceptron), based correlations for the prediction overall volumetric mass-transfer coefficient (k_La), in slurry bubble column for gas-liquid-solid systems. The Multilayer Perceptron is a novel technique based on the feature generation approach using back propagation neural network. Measurements of overall volumetric mass transfer coefficient were made with the air - Water, air - Glycerin and air - Alcohol systems as the liquid phase in bubble column of 0.15 m diameter. For operation with gas velocity in the range 0-20 cm/sec, the overall volumetric mass transfer coefficient was found to decrease w

The present study develops an artificial neural network (ANN) to model an analysis and a simulation of the correlation between the average corrosion rate carbon steel and the effective parameter Reynolds number (Re), water concentration (Wc) % temperature (T o) with constant of PH 7 . The water, produced fom oil in Kirkuk oil field in Iraq from well no. k184-Depth2200ft., has been used as a corrosive media and specimen area (400 mm2) for the materials that were used as low carbon steel pipe. The pipes are supplied by Doura Refinery . The used flow system is all made of Q.V.F glass, and the circulation of the two –phase (liquid – liquid ) is affected using a Q.V.F pump .The input parameters of the model consists of Reynolds number , w

In this paper, the memorization capability of a multilayer interpolative neural network is exploited to estimate a mobile position based on three angles of arrival. The neural network is trained with ideal angles-position patterns distributed uniformly throughout the region. This approach is compared with two other analytical methods, the average-position method which relies on finding the average position of the vertices of the uncertainty triangular region and the optimal position method which relies on finding the nearest ideal angles-position pattern to the measured angles. Simulation results based on estimations of the mobile position of particles moving along a nonlinear path show that the interpolative neural network approach outperf