Background: Urinary tract infections (UTIs) and their complications such as Bladder cancer (Bl. C.) are a health growing problem worldwide. Objective: To shed light on this subject, present study was done to investigate relationship between recurrent urinary tract infection (RUTI) due to Escherichia coli (E. coli) and Bl. C.Type of study: Cross-sectional study. Methods: This study included 130 patients with RUTI, 50 patients with Bl. C. and 50 control of both sexes (aged 7-85 years) attending Al-Zahra Teaching Hospital in Al-Kut/Wassit governorate and Al-Harery Teaching Hospital of specialized surgeries/Baghdad. The patients were divided into two groups: the first group (n=130) included those who were suffering from recurrent UTI without

Samples of the green algae were collected from water of Shatt al-Arab in Garmat Ali in Basra. After purification, the green algae identified on Enteromorpha sp. The samples were dried and milled, then sulfated polysaccharides were extracted with hot water at 90°C precipitated with absolute ethanol, dialysed and lyophilized. The chemical composition was total sugars 56.4%, protein 1.3% and sulfur 19.7%. Antioxidation activity of sulfated polysaccharides was studied by four method and included estimation of ability of scavenging hydroxylated radicals, the results showed an increased in ability with increasing concentrations. Ability of scavenging and was 59.86% at the concentration of 2.5 mg/ ml, but BHT was 81.36%. Ability of scavenging

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

CNC machine is used to machine complex or simple shapes at higher speed with maximum accuracy and minimum error. In this paper a previously designed CNC control system is used to machine ellipses and polylines. The sample needs to be machined is drawn by using one of the drawing software like AUTOCAD® or 3D MAX and is saved in a well-known file format (DXF) then that file is fed to the CNC machine controller by the CNC operator then that part will be machined by the CNC machine. The CNC controller using developed algorithms that reads the DXF file feeds to the machine, extracts the shapes from the file and generates commands to move the CNC machine axes so that these shapes can be machined.

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

The microbend sensor is designed to experience a light loss when force is applied to the sensor. The periodic microbends cause propagating light to couple into higher order modes, the existing higher order modes become unguided modes. Three models of deform cells are fabricated at (3, 5, 8) mm pitchand tested by using MMF and laser source at 850 nm. The maximum output power of (8, 5, 3)mm model is (3, 2.7, 2.55)nW respectively at applied force 5N and the minimum value is (1.9, 1.65, 1.5)nW respectively at 60N.The strain is calculated at different microbend cells ,and the best sensitivity of this sensor for cell 8mm is equal to 0.6nW/N.