Well-dispersed Cu2FeSnSe4 (CFTSe) nanoparticles were first synthesized using the hot-injection method. The structure and phase purity of as-synthesized CFTSe nanoparticles were examined by X-ray diffraction (XRD) and Raman spectroscopy. Their morphological properties were characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The average particle sizes of the nanoparticles were about 7-10 nm. The band gap of the as-synthesized CFTS nanoparticles was determined to be about 1.15 eV by ultraviolet-visible (UV-Vis) spectrophotometry. Photoelectrochemical characteristics of CFTSe nanoparticles were also studied, which indicated their potential application in solar energy water splitting.

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Copper with different concentrations doped with zinc oxide nanoparticles were prepared from a mixture of zinc acetate and copper acetate with sodium hydroxide in aqueous solution. The structure of the prepared samples was done by X-ray diffraction, atomic force microscopy (AFM) and UV-VIS absorption spectrophotometer. Debye-Scherer formula was used to calculate the size of the prepared samples. The band gap of the nanoparticle ZnO was determined by using UV-VIS optical spectroscopy.

In this work, a novel single-slope solar distillator of floating perforated absorber inserted with wicks (cotton ribbons), and a stepped distillator are designed and manufactured with the aim of developing the conventional distillator. They are examined experimentally at Baghdad, Iraq (33.3°N Latitude, 44.4°E Longitude) in order to enhance the freshwater productivity and the efficiency of the conventional distillator. Results showed that the daily productivity and efficiency of the stepped distillator are higher than that for conventional solar distillator by 30% and 36.19% respectively. The daily productivity and thermal efficiency for the distillator with the floating absorber are higher than that for the conventional distillator by 16%

High-resolution imaging of celestial bodies, especially the sun, is essential for understanding dynamic phenomena and surface details. However, the Earth's atmospheric turbulence distorts the incoming light wavefront, which poses a challenge for accurate solar imaging. Solar granulation, the formation of granules and intergranular lanes on the sun's surface, is important for studying solar activity. This paper investigates the impact of atmospheric turbulence-induced wavefront distortions on solar granule imaging and evaluates, both visually and statistically, the effectiveness of Zonal Adaptive Optics (AO) systems in correcting these distortions. Utilizing cellular automata for granulation modelling and Zonal AO correction methods,

تعد الرماية واحدة من الألعاب التي يقاس مستوى الانجاز فيها بالأرقام إذ ارتفعت الأرقام المسجلة بالنسبة لسلاح المسدس الهوائي حتى وصلت عند بعض الأبطال العالميين إلى الانجاز النهائي ، وقد لجأ المدربون إلى مختلف الطرق التدريبية, وان ضعف الثبات والتركيز هما اللذان يقودان إلى هبوط مستوى الانجاز في الرماية ، ولتحقيق التركيز والثبات المطلوبين أثناء الرمي يجب التدريب على مطاولة حمل السلاح التي تعد من أصعب تدريبات الر

Several correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability

The study employs Critical Discourse Analysis (CDA) to analyze how technological discourses are influenced by AI-generate d English texts. The research marries Fairclough’s three-dimensional discourse analysis, Van Dijk’s socio-cognitive approach, and Corpus-Assisted Discourse Studies (CADS) in the use of mixed-methods research, integrating primarily qualitative analysis with quantitative corpus-based data, to perform a thorough analysis of twenty AI-produced English texts. The findings identify the sophisticated linguistic mechanisms through which AI language employs modality, nominalization, passive voice, and interdiscursive blending to normalize and legitimize dominant contemporary ideologies. These mechanisms serve to legitimize te

This study focuses on how tax administrations in Iraq use Artificial Intelligence (AI) techniques to monitor tax evasion for individuals and companies to achieve Tax Compliance (TC). AI was measured through four dimensions: Advanced Data Analytics Techniques (ADAT), Explainable AI (EAI), Machine learning (ML), and Robotic Process Automation (RPA). At the same time, TC was measured through registration, accounting, and tax payment stages. We relied on the questionnaire form to measure the variables. A sample of employees in the General Tax Authority in Iraq was selected, and a questionnaire was distributed to 132 people. The results indicated that the dimensions of AI affect achieving TC at all stages. This study provides evidence of using A