Investing in renewable energies, including biomass, is an important topic in Iraq. Research indicates that there is great potential for renewable energy in Iraq, including biomass, but achieving this great potential requires clear strategies and significant investments. This research sought to determine the amount of biomass energy that can be produced by the residues of eight Iraqi crops: wheat, barley, oats, corn, rice (straw), rice (husk), cotton, and sugar beets. could produce. Calorific value and accessible residue amount were considered to determine the residue's potential for energy. Estimates for 2021 showed that 1,308,516 tons of agricultural residue would be available overall for the eight crops. The two crops with the highest residue percentages were wheat at 52.31% and rice (straw), at 19.98%. The total calorific value of the residue was also obtained at 20,744,442 GJ. Wheat and rice (straw) also gave the highest calorific value at 52.79% and 18.81%, respectively. Therefore, according to the results obtained in this study, a portion of the country's energy consumption can be saved in this way given the abundance of agricultural resources in Iraq and the appropriate climate. Implementing residue-to-energy projects will help Iraq harness these resources and contribute to sustainable energy development.
In 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
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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