The research dealt with the effect of Kut Barrage on the geomorphological processes and the natural environment system in the course of the Tigris between the cities of Al-Ahrar and Kut in central Iraq. It was clear from the research the contribution of Kut Barrage in changing the surface runoff system between the front and back of the barrage, as well as changing the type of processes and the prevailing geomorphic forms, as the sedimentation activates the front of the barrage and erosion at its back, which affected the change in the morphology of the river, sediment retention at the front of the barrage, the burial of the bottom and reducing the validity of the stream. This also affects the efficiency of the barrage’s work and coastal er

This research was based on two pillars. The first is a comparison between of research done and the result of a particular  variable of research  indebendently in the united state, and second, knowledge and determine the effect of managers  practices on the contributions of personnel. The manager and staff are considered to be the most important elements in the organization and all of them  have a duty towards the others which governed by the relationship rules of procedure of the ministry researched, on the one  hand  and the interests of the organization and hence employees , whether executives  or employees within management formations that the availability of an acceptable level of good pract

<p><span>A Botnet is one of many attacks that can execute malicious tasks and develop continuously. Therefore, current research introduces a comparison framework, called BotDetectorFW, with classification and complexity improvements for the detection of Botnet attack using CICIDS2017 dataset. It is a free online dataset consist of several attacks with high-dimensions features. The process of feature selection is a significant step to obtain the least features by eliminating irrelated features and consequently reduces the detection time. This process implemented inside BotDetectorFW using two steps; data clustering and five distance measure formulas (cosine, dice, driver &amp; kroeber, overlap, and pearson correlation

Abstract

This study aims to identify the repercussions of the Corona pandemic (Covid 19) and its impact on the educational and psychological functions of the Omani family from the point of view of a number of fathers and mothers. Drive for a group of fathers and mothers, some of whom work in the government sector and others are mothers enrolled in graduate studies programs at the university, their ages range between (30-50 years) totally (28) mothers and fathers: 22 mothers and 6 fathers. The results showed that the repercussions of the transformation of e-learning, home quarantine, social distancing, and the challenges associated with them were among the most frequent responses that posed a real challenge to the

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

It is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works represent valid and reliable results, they are expensive and time consuming. In contrast, continuous and regular determination of the rheological fluid properties can perform its essential functions during good construction. The aim of this study is to develop empirical models to estimate the drilling mud rheological properties of water-based fluids with less need for lab measurements. This study provides two predictive techniques, multiple regression analysis and artificial neural networks, to determine the rheological

Five heavy metals, namely Cd, Cu, Fe, Mn, and Pb in the surface water and through the water column were studied at 10 selected stations in the Razzazah lake and Karbala drainage canal for the period between November 1990 to October 1991*. pH and total hardness were also measured. Lead was found to be the highest in concentration as overall average values, followed by an manganese, iron, copper then cadmium at the surface as well as along the water column. All the studied metals were below or close to the maximum allowed limits of Iraqi standards for inland water. The spatial and seasonal variations were discussed.