This Study Deals with one of the Assyrian Functions (The Position of Abarraku Minister of Finance). This Position has been Characterized by an important Historical Position in Mesopotamia in General and in the Assyrian Empire in Particular Because of its Role not limited to Administrative Duties related to financial affairs. Rather, the Duties Varied and included Also The military and political, in particular. He who holds this position governs a province, and in studying this position we have Relied on the Assyrian Cuneiform Texts that were Found by the British College of Archeology During its excavations in Nimrud in the years 1949-1962 AD, as it Included these Texts (letters, Economic, legal and Administrative Documents). These Texts are from our Meagre knowledge about the Administrative Organization of the Assyrian Empire, so we were able Through them to Know The Functional Responsibilities with Economic Tasks in the Palace and The Kingdom in Terms of Imports, Expenditures, and the Military and Administrative Entrusted to The Abarraku.
We aimed to obtain magnesium/iron (Mg/Fe)-layered double hydroxides (LDHs) nanoparticles-immobilized on waste foundry sand-a byproduct of the metal casting industry. XRD and FT-IR tests were applied to characterize the prepared sorbent. The results revealed that a new peak reflected LDHs nanoparticles. In addition, SEM-EDS mapping confirmed that the coating process was appropriate. Sorption tests for the interaction of this sorbent with an aqueous solution contaminated with Congo red dye revealed the efficacy of this material where the maximum adsorption capacity reached approximately 9127.08 mg/g. The pseudo-first-order and pseudo-second-order kinetic models helped to describe the sorption measure
Little is known about hesitancy to receive the COVID‐19 vaccines. The objectives of this study were (1) to assess the perceptions of healthcare workers (HCWs) and the general population regarding the COVID‐19 vaccines, (2) to evaluate factors influencing the acceptance of vaccination using the health belief model (HBM), and (3) to qualitatively explore the suggested intervention strategies to promote the vaccination.
This was a cross‐sectional study based on electronic survey data that was collected in Iraq during December first‐19th, 2020. The electronic surve
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
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