Pre-eclampsia is the most common medical complication of pregnancy associated with increased maternal and infant mortality and morbidity. Its exact etiology is not known, although several evidences indicate that various elements might play an important role in pre-eclampsia. This study was carried out to analyze and to compare the concentration of calcium, in mild pre-eclampsia and in normal pregnant women , and to determine the effect of oral supplementation with calcium on mild pre-eclampsia  , and whether this effect is related to the change in the level of serum calcium. Forty- five women  in the third trimester of pregnancy were selected to participate in this study and divided  into: fifteen apparently healthy, normo

The present paper deals with experimental investigation of the performance of air cooled split air conditioner, with evaporative water mist pre cooling to increase the cooling capacity and reduce the consumption power under hot and dry climate. This investigation considers how the performance can be enhanced by using water mist to pre-cool ambient air entering the condensers by adiabatic cooling process which depends on the ambient air wet bulb temperature; as well the condensing temperature and condensing pressure will be decreased accordingly. So the cooling capacity would be increased and consumption power would be decreased, consequently the energy ratio, EER would be improved. The performance of air cooled air conditioner with water

Anaerobic digestion (AD) is the most common process for dealing with primary and secondary wastewater sludge. In the present work, four pre-treatment methods (ultrasonic, chemical, thermal, and thermo-chemical) are investigated in Al-Rustumya Wastewater Treatment plant in order to find their effect on biogas production and volatile solid removal efficiency during anaerobic digestion.
Two frequencies of ultrasonic wave were used 30 KHz and 50 KHz during the pre-treatment. Sodium hydroxide was added in different amounts to give three pH values of 9, 10 and 11 in chemical pre-treating processes. The sludge was heated at 60oC and 80oC through thermal pre-treatment experiment. Also, the sludge was treated thermo-chemically at 80 oC and pH

In the present work, pattern recognition is carried out by the contrast and relative variance of clouds. The K-mean clustering process is then applied to classify the cloud type; also, texture analysis being adopted to extract the textural features and using them in cloud classification process. The test image used in the classification process is the Meteosat-7 image for the D3 region.The K-mean method is adopted as an unsupervised classification. This method depends on the initial chosen seeds of cluster. Since, the initial seeds are chosen randomly, the user supply a set of means, or cluster centers in the n-dimensional space.The K-mean cluster has been applied on two bands (IR2 band) and (water vapour band).The textural analysis is used

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

The availability of different processing levels for satellite images makes it important to measure their suitability for classification tasks. This study investigates the impact of the Landsat data processing level on the accuracy of land cover classification using a support vector machine (SVM) classifier. The classification accuracy values of Landsat 8 (LS8) and Landsat 9 (LS9) data at different processing levels vary notably. For LS9, Collection 2 Level 2 (C2L2) achieved the highest accuracy of (86.55%) with the polynomial kernel of the SVM classifier, surpassing the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) at (85.31%) and Collection 2 Level 1 (C2L1) at (84.93%). The LS8 data exhibits similar behavior. Conv

This study has dealt with, the issue of classification of rural road network , in addition to prepare a suggested for the classification for this network in Iraq , this classification account , the specifications and characteristics of rural roads, population, and the range taking of settlements , then this classification was applied on the rural road network in the Najaf province there are four categories of classification ,the first is major arterial rural roads divided into two major arterial and minor arterial roads , while the second category collected roads which was divided into minor arterial roads and main collected roads. The third category was represented by Local Roads , it has been divided into paved roads and unpaved, the f