Urinary tract infection (UTI) is a considerable problem aecting the health of people each year. It is caused by various Gram-positive (G+ve) and Gram-negative (G-ve) pathogens. It is an important illness in the world aecting all age groups across their life span. Objectives: To identify the most common aerobic bacteria that cause UTIs and their antibiotic susceptibility and antimicrobial activity of plant extracts of the males' patients. Materials and methods: The study involved 35 midstream urine samples from the male students (University of Baghdad, Baghdad, Iraq) with suspicious symptoms of UTI, during the period from January-March 2018. Each urine sample was cultured rst on Mannitol Salt Agar and MacConkey agar plates to dierentiate

Three isolates of  P. aeruginosa were isolated from burnt patients. The ability of these isolates for adhesion and formation of slime layer were tested, the result showed that all isolates were able to adherence on the smooth surface. The sensitivity of  P. aeruginosa isolates for antibiotics were tested , all isolates were sensitive  to Gentamycin, Piperacillin and Amikacin Ciprofloxacin, and  resist to Tetracyclin, Amoxicillin, Cephalexine , Ceftriaxone. Ciprofloxacin and Amikacin were found effective against P. aeruginosa isolates with MIC values of 3.8 μg/ ml for  Ciprofloxacin  and 0.244 μg/ ml for Amikacin The antibacterial effect of Different concentrations of Aloe

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

One of the recent significant but challenging research studies in computational biology and bioinformatics is to unveil protein complexes from protein-protein interaction networks (PPINs). However, the development of a reliable algorithm to detect more complexes with high quality is still ongoing in many studies. The main contribution of this paper is to improve the effectiveness of the well-known modularity density ( ) model when used as a single objective optimization function in the framework of the canonical evolutionary algorithm (EA). To this end, the design of the EA is modified with a gene ontology-based mutation operator, where the aim is to make a positive collaboration between the modularity density model and the proposed

Environmental pollution is experiencing an alarming surge within the global ecosystem, warranting urgent attention. Among the significant challenges that demand immediate resolution, effective treatment of industrial pollutants stands out prominently, which for decades has been the focus of most researchers for sustainable industrial development aiming to remove those pollutants and recover some of them. The liquid membrane (LM) method, specifically electromembrane extraction (EME), offers promise. EME deploys an electric field, reducing extraction time and energy use while staying eco-friendly. However, there's a crucial knowledge gap. Despite strides in understanding and applying EME, optimizing it for diverse industrial pollutant

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de

The main risks arising from the WTO Agreement are the inequality and lack of competitiveness of most pharmaceutical goods, as well as the fact that Iraq is a net importer of medicines that are at the core of consumer needs, The subject matter of the Convention on the Protection of Intellectual Property Rights and its implications for the pharmaceutical industry, in particular, coinciding with the situation of financial and administrative corruption, all of which has resulted in drug fraud in the Iraqi market and its impact on public health. The control of medical technology, the persistence of the technological gap and its effects on high price levels, and the fact that domestic drug producers are obliged to obtain production licenses from

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