In light of crises, the need for efficient and flexible public administrations to make quick and decisive decisions, also institutions capable of directing the internal elements and components of them and adapting them to the requirements of rapid change due to crises and disasters, which led to scarce resources becoming scarcer and economic, political and social problems becomes more prominent. For the majority of developing countries, including Iraq, the increasing need for the importance of moving towards enhancing the efficiency of the performance of public institutions while trying to predict their future, can only be achieved. Through solid mechanisms and principles of governance that enhance the ability of institutions and make them

Highway embankments stability during its service period represents an important factor for the safety of highway users and vehicles. Consequently, the cost of construction of these embankments should be adequate to maintain the safety and durability during this period through proper estimation of the loading on asphalt pavement, slope stability, horizontal and vertical deformation, etc. Slope stability of the embankment mainly depends on the shear strength of the soil layers materials; this shear strength is affected by the water table level through the contribution of the capillary water. Negative pore water pressure above the water table level evolves matric suction in the unsaturated zone above water table; this matric suction increases

In this work, satellite images classification for Al Chabaish marshes and the area surrounding district in (Dhi Qar) province for years 1990,2000 and 2015 using two software programming (MATLAB 7.11 and ERDAS imagine 2014) is presented. Proposed supervised classification method (Modified Vector Quantization) using MATLAB software and supervised classification method (Maximum likelihood Classifier) using ERDAS imagine have been used, in order to get most accurate results and compare these methods. The changes that taken place in year 2000 comparing with 1990 and in year 2015 comparing with 2000 are calculated. The results from classification indicated that water and vegetation are decreased, while barren land, alluvial soil and shallow water

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

The study consisted in the development and use of a practical method to detect and
monitor, analyze and produce maps of changes in land use and land cover in the district of
Mahmudiya in Baghdad during the period 1990-2007 using the applications of remote sensing
techniques and with the assisstant of geographic information systems (GIS),as a valuable
contribution to land degradation studies.
This study is based maiuly on the processing on two subsets of landsat5 TM images picked up
in August 1990 and 2007 respectively in order to facilitate comparision and were thengeometrically and radiometrcally calibrated ,to used for digital classification purposes using
maximum liklihoods classification or six spectral bands of