The depreciation of the Iraqi dinar against the US dollar, reaching low levels and causing disruptions in the local markets, has had detrimental effects on individuals and companies, particularly those with limited income and the poor. The local currency approached around 1600 dinars per dollar, after the official exchange rate had stabilized at around 1450 dinars per US dollar. This depreciation in the value of the Iraqi dinar can be attributed to financial speculation among currency traders, which directly affected exchange rates and illicit dollar smuggling operations. Bank transfers are also important alongside financial transactions, especially in light of current economic developments in the 21st century. To prevent currency smuggling, financial corruption, and money laundering, the Central Bank of Iraq suspended four Iraqi banks, which used to receive half of the central bank's daily sales, based on a recommendation from the US Department of Treasury. Additionally, there has been increased scrutiny on the participating banks in the currency auction, with approximately 35 banks involved. These measures have significantly impacted dollar sales and led to a shortage in the market, resulting in the depreciation of the Iraqi dinar.The significance of this research lies in the impact of bank transfers in the context of fluctuations in exchange rates in the local financial markets in Iraq. The Iraqi economy faces various challenges, including structural imbalances in economic sectors and increased reliance on foreign currency to cover high import demands. The research hypothesis asserts that bank transfers are used as a means to regulate banking operations and achieve monetary stability, which the Central Bank of Iraq seeks amidst exchange rate fluctuations. The aim of the research is to highlight the importance of bank transfers as a study of exchange rate fluctuations in Iraq and to elucidate the role of bank transfers in implementing the new electronic platform program.
Industrial development has recently increased, including that of plastic industries. Since plastic has a very long analytical life, it will cause environmental pollution, so studies have resorted to reusing recycled waste plastic (sustainable plastic) to produce environmentally friendly concrete (green concrete). In this research, producing environmentally friendly load-bearing concrete masonry units (blocks) was considered where five concrete mixtures were compressed at the blocks producing machine. The cement content reduced from 400 kg/m3 (B-400) to 300 kg/m3 (B-300) then to 200 kg/m3 (B-200). While (B-380) was produced using 380 kg/m3 cement and 20 kg/m3 nano-sil
... Show MoreIn this study, several ionanofluids (INFs) were prepared in order to study their efficiency as a cooling medium at 25 °C. The two-step technique is used to prepare ionanofluid (INF) by dispersing multi-walled carbon nanotubes (MWCNTs) in two concentrations 0.5 and 1 wt% in ionic liquid (IL). Two types of ionic liquids (ILs) were used: hydrophilic represented by 1-ethyl-3-methylimidazolium tetrafluoroborate [EMIM][BF4] and hydrophobic represented by 1-hexyl-3-methylimidazolium hexafluorophosphate [HMIM][PF6]. The thermophysical properties of the prepared INFs including thermal conductivity (TC), density and viscosity were measured experimental
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
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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