A theoretical and protection study was conducted of the corrosion behavior of carbon steel surface with different concentrations of the derivative (Quinolin-2-one), namly (1-Amino-4,7-dimethyl-6-nitro-1H-quinolin-2-one (ADNQ2O)). Theoretically, Density Functional Theory (DFT) of B3LYP/ 6-311++G (2d, 2p) level was used to calculate the optimized geometry, physical properties and chemical inhibition parameters, with the local reactivity to predict both the reactive centers and to locate the possible sites of nucleophilic and electrophilic attacks, in vacuum, and in two solvents (DMSO and H2O), all at the equilibrium geometry. Experimentally, the inhibition efficiencies (%IE) in the saline solution (of 3.5%) NaCl were studied using potentiomet

Nine new compounds of 2-amino-5-chlorobenzothiazole derivatives were synthesized. These new compounds were formed through the reaction of 2-amino-5-chlorobenzothiazole 1 with ethyl chloroacetate and KOH, which gave an ester derivative 2, followed by refluxing compound 2 with hydrazine hydrate to afford hydrazide derivative 3. The reaction of compound 3 with CS₂ and KOH gave 1,3,4-oxadiazole-2-thiol derivative 4, and then the reaction of compound 2 with thiosemicarbazide to produce compound 5  then treated it with 4%NaOH led to ring closure to provide 1,2,4-triazole-3-thiol derivative

It is important to note that Posaconazole (POCZ) is a newly developed extended-spectrum triazole that belongs to BCS class II and has a solubility of less than 1µg/ml. In patients with a weakened immune system, POCZ has been shown to be effective as an antifungal treatment for invasive infections caused by candida and aspergillus species. The nano-micelles technique can be used to increase POCZ solubility. In order to increase their apparent solubility in water, nano-micelles are made by combining macromolecules that self-assemble into ordered structures capable of entrapping hydrophobic drug molecules in the interior domain. Dispersed colloidal systems, of which nano-micelles are a subset, are a large and diverse group. Composed of a p

Intrusion detection systems (IDS) are useful tools that help security administrators in the developing task to secure the network and alert in any possible harmful event. IDS can be classified either as misuse or anomaly, depending on the detection methodology. Where Misuse IDS can recognize the known attack based on their signatures, the main disadvantage of these systems is that they cannot detect new attacks. At the same time, the anomaly IDS depends on normal behaviour, where the main advantage of this system is its ability to discover new attacks. On the other hand, the main drawback of anomaly IDS is high false alarm rate results. Therefore, a hybrid IDS is a combination of misuse and anomaly and acts as a solution to overcome the dis

This study was conducted in the poultry field of the College of Agricultural Engineering Sciences / University of Baghdad for the period from 10/15/2021 to 11/25/2021 in order to show the effect of adding different levels of Ganoderma lucidum to broiler diets on physiological traits and indicators of fat oxidation in meat. In it, 200 unsexed (Ross 308) chicks of one-day-old breed were used, with a starting weight of (40) g. The chicks were distributed and randomly divided into four treatments, with 50 birds for each treatment. One treatment included five replicates (10 birds/repeat) and the experiment treatments were T1, T2, T3, and T4. The percentages of adding reishi mushrooms were 0, 0.5, 1, and 1.5 g/kg of feed, respectively. Th

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables