The investment environment is the incubator for all types of domestic and foreign investments, so if their determinants are encouraging, they increase the levels of investment flows and vice versa, as there is a relationship between the nature of the investment environment and the level of investment flows, and the determinants of the investment environment are numerous and the most important of which are security and political stability, and economic and financial factors that include relative stability In the exchange rate and inflation rates, the availability of banks and their development, transparency and integrity in administrative dealings and the lack of prevalence of administrative and financial corruption, and the clarity of legal and other procedures. Therefore, the importance of the environment is highlighted. Investment and its relationship to indicators of the national economy as one of the important strategic issues as it is the incubator in which the investment and the economy in general grows and develops. The Iraqi economy suffers from structural imbalances, as a result of the decline in crude oil prices, which makes the Iraqi economy a powerless economy in the face of fluctuations in the prices of oil derivatives, as total exports for the same year amounted to 49.4 billion dollars The share of oil exports was $49.1 billion. In 2017 and 2018, the total exports amounted to $60 and 85.2 billion, respectively. The share of oil exports was $59.6 and 83.8 billion, respectively, for two years. The value of imports increased from 37.1 billion dollars in 2014 to 48.6 billion dollars in 2016 and then to 37.0 billion dollars in 2018, while the proportion of imports to GDP was 28.7% in 2016 and then The value of imports was taken by fluctuation. Any decrease in crude oil prices will make the value of imports greater than the value of exports, and therefore the trade balance will be negative, which will cause the Iraqi economy to suffer major dilemmas, as it reached negative 4.8 billion dollars in 2016 and then rose positively to 48.2 billion dollars in 2018.
In the present paper, chitosan Schiff base has been synthesized from chitosan’s reaction with the salicyldehyde. The AuNPs was manufacture by extract of onion peels as a reducing agent. The Au NPs that have been prepared were characterized through the UV-vis spectroscopy, XRD analyses and SEM microscopy. The polymer blends of the chitosan Schiff base / PVP has been prepared through using the approach of solution casting. Chitosan Schiff base / PVP Au nano-composites was prepared. Nano composites and polymer blends have been characterized by FTIR which confirm the formation of Schiff base by revealing a new band of absorption at 1651cm-1 as a result of the (C=N) imine group. SEM, DSC and TGA confirms the thermal stability of
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The health care industry is witnessing an increasing trend in the use of generic medicines because of their presumed low cost compared with innovator medicines. The aim of this study was to determine and compare the performance of the copy drug Osveral®and its innovator drug deferasirox (Exjade®).
A prospective observational study including 223 patients receiving the branded medicine Exjade®and 101 patients receiving the copy Osveral®was carried out. Data were assessed for a 1-year period and included clinical symptoms, serum ferrit
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The intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is
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A series of 4-(methylsulfonyl)aniline derivatives were synthesized in order to obtain new compounds as a potential anti-inflammatory agents with expected selectivity against COX-2 enzyme. In vivo acute anti-inflammatory activity of the final compounds 11–14 was evaluated in rat using an egg-white induced edema model of inflammation in a dose equivalent to 3 mg/Kg of diclofenac sodium. All tested compounds produced significant reduction of paw edema with respect to the effect of propylene glycol 50% v/v (control group). Moreover, the activity of compounds 11 and 14 was significantly higher than that of diclofenac sodium (at 3 mg/Kg) in the 120–300 minute time interval, while compound 12 expressed a comparable effect to that of di
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A simple, environmental friendly and selective sample preparation technique employing porous membrane protected micro-solid phase extraction (μ-SPE) loaded with molecularly imprinted polymer (MIP) for the determination of ochratoxin A (OTA) is described. After the extraction, the analyte was desorbed using ultrasonication and was analyzed using high performance liquid chromatography. Under the optimized conditions, the detection limits of OTA for coffee, grape juice and urine were 0.06 ng g−1, 0.02 and 0.02 ng mL−1, respectively while the quantification limits were 0.19 ng g−1, 0.06 and 0.08 ng mL−1, respectively. The recoveries of OTA from coffee spiked at 1, 25 and 50 ng g−1, grape juice and urine samples at 1, 25 and 50 ng mL
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A chemometric method, partial least squares regression (PLS) was applied for the simultaneous determination of piroxicam (PIR), naproxen (NAP), diclofenac sodium (DIC), and mefenamic acid (MEF) in synthetic mixtures and commercial formulations. The proposed method is based on the use of spectrophotometric data coupled with PLS multivariate calibration. The Spectra of drugs were recorded at concentrations in the linear range of 1.0 - 10 μg mL-1 for NAP and from 1.0 - 20 μg mL-1 for PIR, DIC, and MEF. 34 sets of mixtures were used for calibration and 10 sets of mixtures were used for validation in the wavelength range of 200 to 400 nm with the wavelength interval λ = 1 nm in methanol. This method has been used successfully to quant
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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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