The assessment of the environmental impact of the cement industry using the Leopold Matrix is ​​to determine the negative and positive impacts on the environment resulting from this industry, and what are the long-term and short-term effects, direct and indirect, and the amount of these effects and potential risks, and that this evaluation process is done through a number of methods, including Matrix method, including (Leopold).

The importance of the research because the cement occupies is of great importance in the world, especially in our country, Iraq, in the sector of construction and modernity, and the toxic emissions and solid waste produced by the production of this material. <

In order to reduce hydrostatic pressure in oil wells and produce oil from dead oil wells, laboratory rig was constructed, by injecting LPG through pipe containing mixture of two to one part of East Baghdad crude oil and water. The used pressure of injection was 2.0 bar, which results the hydrostatic pressure reduction around 246 to 222 mbar and flow rate of 34.5 liter/hr fluid (oil-water), at 220 cm injection depth. Effects of other operating parameters were also studied on the behavior of two phase flow and on the production of oil from dead oil wells.

The Enhanced Thematic Mapper Plus (ETM+) that loaded onboard the Landsat-7 satellite was launched on 15 April 1999. After 4 years, the image collected by this sensor was greatly impacted by the failure of the system’s Scan Line Corrector (SLC), a radiometry error.The median filter is one of the basic building blocks in many image processing situations. Digital images are often distorted by impulse noise due to errors generated by the noise sensor, errors that occur during the conversion of signals from analog-to-digital, as well as errors generated in communication channels. This error inevitably leads to a change in the intensity of some pixels, while some pixels remain unchanged. To remove impulse noise and improve the quality of the

Stone columns are widely used globally due to theirversatility and relative wide applicability to treat different soil and foundation situations but much of the research undertaken to date has focused on their use in soft soils. In countries like Iraq the use of stone columns is still limited from a practical point of view, chiefly as many other soil conditions are commonly encountered. These include collapsible soils: soils that are prone to relatively rapid volume compressions (through collapse of metastable fabrics) that occur due to the action of load and/or increases in water content. Recent work has opened up the possibility to use stone columns in these soils by the use of encasement, thereby overcoming the impact of loss of lateral

Recently, the development and application of the hydrological models based on Geographical Information System (GIS) has increased around the world. One of the most important applications of GIS is mapping the Curve Number (CN) of a catchment. In this research, three softwares, such as an ArcView GIS 9.3 with ArcInfo, Arc Hydro Tool and Geospatial Hydrologic Modeling Extension (Hec-GeoHMS) model for ArcView GIS 9.3, were used to calculate CN of (19210 ha) Salt Creek watershed (SC) which is located in Osage County, Oklahoma, USA. Multi layers were combined and examined using the Environmental Systems Research Institute (ESRI) ArcMap 2009. These layers are soil layer (Soil Survey Geographic SSURGO), 30 m x 30 m resolution of Digital Elevati

The manual classification of oranges according to their ripeness or flavor takes a long time; furthermore, the classification of ripeness or sweetness by the intensity of the fruit’s color is not uniform between fruit varieties. Sweetness and color are important factors in evaluating the fruits, the fruit’s color may affect the perception of its sweetness. This article aims to study the possibility of predicting the sweetness of orange fruits based on artificial intelligence technology by studying the relationship between the RGB values of orange fruits and the sweetness of those fruits by using the Orange data mining tool. The experiment has applied machine learning algorithms to an orange fruit image dataset and performed a co