ABSTRACT

Metal (II) complexes of Co, Ni, Cu and Zn with cefdinir C₁₄H₁₃N₅O₅S₂ derivative (L) were synthesized and identification by elemental analysis CHNS Uv-Vis, FTIR, TGA, metal analysis AA, magnetic susceptibility and conduct metric measurement. by analysis the ligand behaves as a bidentate. For the cobalt complex, Tetrahedral geometry shape was suggested, while other complexes that have nickel, copper and zinc ions were proposed as octahedral geometry shape. The experimental method was studied for prevention of corrosion carbon steel in 3.5% NaCl by using a novel Cefdinir derivations drugs. The results showed that metal complex was a strong corro

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet

In this study, the Halder-Wagner method was used for an analysisX-ray lines of Tio2 nanoparticles. Where the software was used to calculate the FWHM and integral breath (β) to calculate the area under the curve for each of the lines of diffraction. After that, the general equation of the halder- Wagner method is applied to calculate the volume (D), strain (ε), stress (σ), and energy per unit(u). Volume (β). Where the value of the crystal volume was equal to (0.16149870 nm) and the strain was equal to (1.044126), stress (181.678 N / m2), and energy per unit volume (94.8474 J m-3).The results obtained from these methods were then compared with those obtained from each of the new paradigm of the HalderWagner method, the Shearer developm

Combining ultrasonic irradiation and the Fenton process as a sono-Fenton process, the chemical oxygen demand (COD) in refinery wastewater was successfully eliminated using response surface methodology (RSM) with central composite design (CCD). The impact of two main influential operational parameters (iron dosage and reaction time) on the COD removal from wastewater generated by an Iraqi petroleum refinery facility was explored. Removal of 85.81% was attained under the optimal conditions of 21 minutes and 0.289 mM of  concentration. Additionally, the results revealed that the concentration of has the highest effect on the COD elimination, followed by reaction time. The high R² value (96.40%) validated the strong fit of the mo

Background: One of the most common problems that encountered is postburn contracture which has both functional and aesthetic impact on the patients. Various surgical methods had being proposed to treat such problem. Aim: To evaluate the effectiveness of square flap in management of postburn contracture in several part of the body. Patients and methods: From April 2019 to June 2020 a total number of 20 patients who had postburn contracture in various parts of their body were subjected to scar contracture release using square flap. The follow up period was ranging between 6 months to 12 months. Results: All of our patients had achieved complete release of their band with maximum postoperative motion together with accepted aesthetic outcome. A

Water quality planning relies on Biochemical Oxygen Demand BOD. BOD testing takes five days. The Particle Swarm Optimization (PSO) is increasingly used for water resource forecasting. This work designed a PSO technique for estimating everyday BOD at Al-Rustumiya wastewater treatment facility inlet. Al-Rustumiya wastewater treatment plant provided 702 plant-scale data sets during 2012-2022. The PSO model uses the daily data of the water quality parameters, including chemical oxygen demand (COD), chloride (Cl^-), suspended solid (SS), total dissolved solids (TDS), and pH, to determine how each variable affects the daily incoming BOD. PSO and multiple linear regression (MLR) findings are compared, and their perfor

   SMNs like Facebook, YouTube, Twitter, WhatsApp,..etc. are among the most popular sites on the Internet. These sites can provide a powerful means of sharing, organizing, finding information and knowledge. The popularity of these sites provides an opportunity to measure the use them in knowledge sharing, which needs a special scale, but unfortunately, there is no special scale for that. Thus, this study supposes to use SCT as a scale to measure the use of SMNs in electronic knowledge sharing due to it has been used to measure knowledge sharing with its traditional form. This study can help the decision-makers to use these SMNs to share the academics’ knowledge in educational institutes to the communi

Background: Pyogenic granuloma is a hyperplastic benign tumor. The most common intra-oral site is marginal gingiva. It is often occurred in the second decade of life, it has a strong tendency to recur after simple excision.

The aim of study: to evaluate the therapeutic advantages of diode laser (810-980 nm) in intraoral Pyogenic granuloma management.

Materials and method: A total of 28 patients (14 men and 14 females) were enrolled in this study and had their pyogenic granuloma surgically removed using a diode laser. All of the patients were given local anesthetic and went through the identical surgical procedure (cartridge containing 1 percent lidocaine with epinephrine 1:

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca