Surface modeling utilizing Bezier technique is one of the more important tool in computer aided geometric design (CAD). The aim of this work is to design and implement multi-patches Bezier free-form surface. The technique has an effective contribution in technology domains and in ships, aircrafts, and cars industry, moreover for its wide utilization in making the molds. This work is includes the synthesis of these patches in a method that is allow the participation of these control point for the merge of the patches, and the confluence of patches at similar degree sides due to degree variation per patch.  The model has been implemented to represent the surface. The interior data of the desired surfaces designed by M

The free zone or the free economy cities are cities with classification and functional specificity, although the history of  the concept of these areas has been It dates back to distant eras, but the intellectual and philosophical construction with the support of intellectual approaches, the most important of which is globalization contributed to its rapid spread globally and taking a variety of forms and models. With the diversity of its formulas and objectives countries have competed in adopting the establishment of these areas, meanwhile The influence of related trends affected the contemporary formation of these sites. Therefore ,the research was directed focus on the importance of adopting a set of common indicators (collection

In this research the effect of cooling rate and mold type on mechanical properties of the eutectic
and hypoeutectic (Al-Si) alloys has been studied. The alloys used in this research work were (Al- 12.6%Si
alloy) and (Al- 7%Si alloy).The two alloys have been melted and poured in two types of molds with
different cooling rates. One of them was a sand mold and the other was metal mold. Mechanical tests
(hardness, tensile test and impact test) were carried out on the specimens. Also the metallographic
examination was performed.
It has been found that the values of hardness for the alloys(Al-12.6%Si and Al-7%Si) which poured in
metal mold is greater than the values of hardness for the same alloy when it poured in a heated

The last decade has seen a variety of modifications of glass-ionomer cements (GICs), such as inclusion of bioactive glass particles and dispensing systems. Hence, the aim was to systematically evaluate effect of mixing modes and presence of reactive glass additives on the physical properties of several GICs.

   Asphaltenes are a solubility class described as a component of crude oil with undesired characteristics. In this study, Sharqy Baghdad heavy oil upgrading was achieved utilizing the solvent deasphalting approach as asphaltenes are insoluble in paraffinic solvents; they may be removed from heavy crude oil by adding N-Hexane as a solvent to create deasphalted oil (DAO)of higher quality. This method is known as Solvent De-asphalting (SDA). Different effects have been assessed for the SDA process, such as solvent to oil ratio (4-16/1 ml/g), the extraction temperature (23 ºC) room temperature and (68 ºC) reflux temperature at (0.5 h mixing time with 400 rpm mixing speed). The best solvent deasphalting results were obtained at room temp

 

In the present work a dynamic analysis technique have been developed to investigate and characterize the quantity of elastic module degradation of cracked cantilever plates due to presence of a defect such as surface of internal crack under free vibration. A new generalized technique represents the first step in developing a health monitoring system, the effects of such defects on the modal frequencies has been the main key quantifying the elasticity modulii due to presence any type of un-visible defect. In this paper the finite element method has been used to determine the free vibration characteristics for cracked cantilever plate (internal flaws), this present work achieved by different position of crack. Stiffness re

General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k

This study investigates the treatment of used lubricating oils from AL-Mussaib Gas Power Station Company-Iraq, which was treated with different extractive solvents (heptane and 2-propanol). The performance activity of these solvents in the extraction process was examined and evaluated experimentally. Operating parameters were solvent to oil ratios of (1:2, 1:4, 1:6, and 1:8), mixing time (20, 35, 50, and 65 min), temperatures (30, 40, 50, and 60 ºC), and mixing speed (500 rpm). These parameters were studied and analyzed. The quality is determined by the measuring and assessment of important characteristics specially viscosity, viscosity index, specific gravity, pour point, flash point, and ash content. The results confirm that the solve

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi