This paper presents an experimental study of cooling photovoltaic (PV) panels using evaporative cooling. Underground (geothermal energy) water used to extract heat from it during cooling and cleaning of PV panels. An experimental test rig was constructed and tested under hot and dusty climate conditions in Baghdad. An active cooling system was used with auxiliary an underground water tank to provide cold water as a coolant over both PV surfaces to reduce its temperature. The cellulose pad has been arranged on the back surface and sprays cooling on the front side. Two identical PV panels modules used: without cooling and evaporative water cooling. The experiments are comprised of four cases: Case (I): backside cooling, Ca

In this paper, three types of epoxy-based coatings (Polyamide, pure Polyamine, and Polyamine reinforced by glass-flake) used as a lining for potable water tanks were studied using experimental and finite element methods. Tensile, impact, and fatigue tests were conducted on uncoated and coated AISI 316 stainless steel. The test results show that the applied epoxy based coating improves the mechanical properties, increases of fatigue crack resistance, and enhance the dynamic fracture toughness. The fatigue crack propagation is influenced by the compositions of epoxy coating, and the glass-flake improves the coating resistance to fatigue crack propagation compared to other types.

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 potential application of granules of brick waste (GBW) as a low-cost sorbent for removal of Ni⁺²ions from aqueous solutions has been studied. The properties of GBW were determined through several tests such as X-Ray diffraction (XRD), Energy dispersive X-ray (EDX), Scanning electron microscopy (SEM), and BET surface area. In batch tests, the influence of several operating parameters including contact time, initial concentration, agitation speed, and the dose of GBW was investigated. The best values of these parameters that provided maximum removal efficiency of nickel (39.4%) were 1.5 hr, 50 mg/L, 250 rpm, and 1.8 g/100mL, respectively. The adsorption data obtained by batch experiments subjected to the Three i

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

BACKGROUND: Diabetes Mellitus is a complex chronic illness that has increased significantly around the world and is expected to affect 628 million in 2045. Undiagnosed type 2 diabetes may affect 24% - 62% of the people with diabetes; while the prevalence of prediabetes is estimated to be 470 million cases by 2030. AIM OF STUDY: To find the percentage of undiagnosed diabetes and prediabetes in a slice of people aged ≥ 45years, and relate it with age, gender, central obesity, hypertension, and family history of diabetes. METHODS: A cross sectional study that included 712 healthy individuals living in Baghdad who accepted to take part in this study and fulfilling the inclusion and exclusion criteria.

This paper concerns is the preparation and characterization of a bidentate ligand [4-(5,5- dimethyl-3-oxocyclohex-1-enylamino)-N-(5-methylisoxazol-3-yl) benzene sulfonamide]. The ligand was prepared from fusing of sulfamethoxazole and dimedone at (140) ºC for half hour. The complex was prepared by refluxing the ligand with a bivalent cobalt ion using ethanol as a solvent. The prepared ligand and complex were identified using Spectroscopic methods. The proposed tetrahedral geometry around the metal ions studied were concluded from these measurements. Both molar ratio and continuous variation method were studied to determine metal to ligand ratio (M:L). The M to L ratio was found to be (1:1). The adsorption of cobalt complex was carried out

Separation of Trigonelline, the major alkaloid in fenugreek seeds, is difficult because the extract of these seeds usually contains Trigonelline, choline, mucilage, and steroidal saponins, in addition to some other substances. This study amis to isolate the quaternary ammonium alkaloid (Trigonelline) and choline from fenugreek seeds (Trigonella-foenum graecum L.) which have similar physiochemical properties by modifying of the classical method. Seeds were defatted and then extracted with methanol. The presence of alkaloids was detected by using Mayer's and Dragendorff's reagents. In this work, trigonilline was isolated with traces of choline by subsequent processes of purification using analytical and preparative TLC techniques.

In this research, the water quality of the potable water network in
Al-Shuala Baghdad city were evaluated and compare them with the
Iraqi standards (IQS) for drinking water and World Health
Organization standards (WHO), then water quality index (WQI) were
calculator: pH, heavy metals (lead, cadmium and iron), chlorides,
total hardness, turbidity, dissolved oxygen, total dissolved solid and
electrical conductivity. Water samples are collected weekly during
the period from February 2015 to April 2015 from ten sites. Results
show that the chlorides, total dissolved solid and electrical
conductivity less than acceptable limit of standards, but total
hardness and heavy metals in some samples higher than acceptabl