While traditional energy sources such as oil, coal, and natural gas drive economic growth, they also seriously affect people’s health and the environment. Renewable energies (RE) are presently seen as an efficient choice for attaining long-term sustainability in development. They provide an adequate response to climate change and supply sufficient electricity. The current situation in Iraq results from a decades-long scarcity of reliable electricity, which has impacted various industries, including agriculture. There are diverse prospects for using renewable energy sources to address the present power crisis. The economic and environmental impacts of renewable energy systems were investigated in this study by using the solar pumping irrigation system and nanoparticle fertilizers on agri-food production with the Farm Simulation FARMSIM model. The results show that using all REs with Carefree Water Conditioner for water treatment resulted in the highest net present value and profit compared to other scenarios. The findings, however, indicate that the All RE scenario (using a solar irrigation system with a nanoparticle fertilizer system, a low-cost technology) is the most feasible and has the highest benefit–cost ratio. Due to high initial investment costs, all traditional approaches (using traditional irrigation and fertilizer systems) had a lower benefit–cost ratio than the other scenarios. According to simulation results, all four scenarios are profitable and feasible for implementation.
Twenty five samples out of sixty wound swabs taken from burn patients were identified as P. aeruginosabacteria by conventional methods. Antibiotics susceptibility tests were performed against thirteen antibiotics. P. aeruginosa samples were treated with 0.5 mg/ml of Safranin O solution then irradiated with 532nm Q-switched Nd:YAG laser at four energy densities (0.324, 0.704, 1.380, and 1.831 J/cm2) for different times of 5, 8 and 11 minutes with 5Hz repetition rate. The viability, susceptibility to antibiotic and production of pyocyanin were determined before and after irradiation. The results showed that the number of CFU/ml of P. aeruginosa decreased with increasing the dose of irradiation. Complete killing of cells was observed at 1.8
... Show MoreThe Sarmord Formation is one of the overlooked formations that require more investigations than that achieved before. There are many problems in its definition, age, stratigraphy and aerial distribution. Previously in most parts of the Sulaymaniyah, it is not mapped and its outcrops combined with Balambo Formation while in the present study it is differentiated, mapped in the field on the Qaywan anticline and its problems discussed in details with a suggestion of solutions. For laboratory studies, 31 samples are taken on the Qywan Mountain (anticline) at 23 km north of the Sulaimani city for nannofossils biozonation. This study identified 19 species of the recorded calcareous nannofossils, the studied section reveals three biozones
... Show MoreThis paper aims to improve the voltage profile using the Static Synchronous Compensator (STATCOM) in the power system in the Kurdistan Region for all weak buses. Power System Simulation studied it for Engineers (PSS\E) software version 33.0 to apply the Newton-Raphson (NR) method. All bus voltages were recorded and compared with the Kurdistan region grid index (0.95≤V ≤1.05), simulating the power system and finding the optimal size and suitable location of Static Synchronous Compensator (STATCOM)for bus voltage improvement at the weakest buses. It shows that Soran and New Koya substations are the best placement for adding STATCOM with the sizes 20 MVAR and 40 MVAR. After adding STATCOM with the sizes [20MVAR and 40MV
... Show Moreالمستودع الرقمي العراقي. مركز المعلومات الرقمية التابع لمكتبة العتبة العباسية المقدسة
Um-Al-Naaj region in Al-Hawiezah Marsh, Southern Iraq was chosen to study the environmental variations of some water characteristics during 2008, seasonally. The results showed clear seasonal changes in values of some environmental variables (temperature, depth, light penetration, turbidity, total suspended solids, pH, dissolved oxygen, reactive phosphate, reactive nitrite, and reactive nitrate), while there were no clear seasonal changes in electrical conductivity and salinity values. In addition, high nutrients concentrations and light penetration were noted. Statistical analysis showed significant positive relationship between air and water temperature; electrical conductivity and salinity. Water turbidity was significantly affecte
... Show MoreIndicators of government debt is of extreme importanse in economic activity through knowledge of the economic impact of government debt, if the phenomenon is accepted or prepared to dangerous stage by stage, and there fore it can Through these indicators to measure the degree of indebtedness in relation to the economic activity of the Government on the one hand, the governments ability to repay the other hand.
Due to this it inferred that the degree of indebtedness in Iraq specificratio has exceed 60% during the period 1990 – 2002 ntejh lack of political and economic stability of the government, which led to the governments inability to repay the ma
... Show MoreThe paper contains a theoretical analysis of the interrelation between methodology of economics used by economic researchers and the methodology of scientific research, encouraging readers to think differently about one of the most complicated & controversial subjects.
The analysis begins with the determination of propositions that were discussed and explained in the course of the argument.
1- The economic researcher, like a scientific one, goes after the procedures of physical sciences and benefits of the forms of logic.
2- Economics deals with human behavior while the natural sciences deal with the matter.
3-This similarity a
... Show MoreThe method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par
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