The Sonic Scanner is a multifunctional instrument designed to log wells, assess elastic characteristics, and support reservoir characterisation. Furthermore, it facilitates comprehension of rock mechanics, gas detection, and well positioning, while also furnishing data for geomechanical computations and sand management. The present work involved the application of the Sonic Scanner for both basic and advanced processing of oil-well-penetrating carbonate media. The study aimed to characterize the compressional, shear, Stoneley slowness, rock mechanical properties, and Shear anisotropy analysis of the formation. Except for intervals where significant washouts are encountered, the data quality of the Monopole, Dipole, and Stoneley modes is gen

Epithelial ovarian cancer is the leading cause of cancer deaths from gynecological malignancies. Angiogenesis is considered essential for tumor growth and the development of metastases. VEGF and IL?8 are potent angiostimulatory molecules and their expression has been demonstrated in many solid tumors, including ovarian cancer.VEGF and IL-8 concentrations were measured by ELISA test (HumanVEGF,IL-8). Bioassay ELISA/ US Biological / USA).The median VEGF and IL-8 levels were significantly higher in the sera of ovarian cancer patients than in those with benign tumors and in healthy controls.Pretreatment VEGF and IL-8 serum levels might be regarded as an additional tool in the differentiation of ovarian tumors.

This study focuses on producing wood-plastic composites using unsaturated polyester resin reinforced with Pistacia vera shell particles and wood industry waste powder. Composites with reinforcement ratios of 0%, 20%, 30%, and 40% were prepared and tested for thermal conductivity, impact strength, hardness, and compressive strength. The results revealed that thermal conductivity increases with reinforcement, while maintaining good thermal insulation, reaching a peak value of 0.633453 W/m·K. Hardness decreased with increased reinforcement, reaching a minimum nominal hardness value of 0.9479. Meanwhile, impact strength and compressive strength improved, with peak values of 14.103 k/m² and 57.3864568 MPa, respectively. The main aim is to manu

Some metal ions (Mn+2, Co+2, Ni+2, Cu+2,Zn+2 and Cd+2) complexes of quodridentats Schiff base derived from (2-hydroxy benzaldehyde and 4,4'-methylenedianiline as primary ligand and 3-picoline (3-pic) secondary ligand have been synthesized and characterized on the basis of their 1H ,13C-NMR, FT-IR, UV-Vis spectroscopy, conductivity measurements, elemental analysis, and magnetic moments, metal to ligands ratio in all complexes has been found to be (1:1:2) (M:Schiff base:3-pic), Schiff base behaves as neutral tetra dentate ligand with (N2,O2) system from the results obtained, the following general formula has suggested for the prepared complexes [M+2(2-mbd)(3-pic)2] and octahedral stereochemistry, Where M+2 = (Mn , Co , Ni , Cu , Zn and Cd), 2

Purpose: Current denture liner materials suffer from low tear strength, poor abrasion resistance, weak bond strength to denture material, and increasing risk of denture stomatitis due to adherence. The aim of this study was to evaluate the effects of the addition of cellulose nanofibers (CNFs) to commercial soft denture liner material on the adherence of and physical and mechanical properties. Materials and Methods: CNFs at concentrations of 0.0, 0.5, and 1.0 wt.% were incorporated into a soft liner material. Antifungal effects were assessed by quantifying the adherence of . The Shore A hardness, tensile strength, peel bond strength, and surface roughness tests were employed to assess the properties of the denture liner materials. Chemica

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