The energy aimed at examining the mode of energy drinks consumption among athletes in
Baghdad and assessing their drinks were spread greatly among the athletes and students. This
study impression toward such drinks. The study sample comprised of 102 mal athletes aged
between 19-27 years and selected randomly .The obtained results showed that football was
most practiced among the test samples at 40.54% based on twice daily .The athletes
consumed one can each day at 41.18% .As the data on energy drinks was supplied from
friends .The prefared period for drinking was before or during exercise .The athletes thought
that there products can provide energy ,vitamins ,tell ale materials ,does not affect
appetite.The most f

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par

This work evaluates the influence of combining twisted fins in a triple-tube heat exchanger utilised for latent heat thermal energy storage (LHTES) in three-dimensional numerical simulation and comparing the outcome with the cases of the straight fins and no fins. The phase change material (PCM) is in the annulus between the inner and the outer tube, these tubes include a cold fluid that flows in the counter current path, to solidify the PCM and release the heat storage energy. The performance of the unit was assessed based on the liquid fraction and temperature profiles as well as solidification and the energy storage rate. This study aims to find suitable and efficient fins number and the optimum values of the Re and the inlet tem

A simple , sensitive and accurate spectrophotometric method for the trace determination of bismuth (III) has been developed .This method is based on the reaction of bismuth (III) with arsenazo(III) in acid solution (pH=1.9) to form a blue water soluble complex which exhibits maximum absorption at 612nm .Beer's law is obeyed over the concentration range of 2-85 ?g bismuth (III) in a final volume of 20 mL( i.e. 0.1 – 4.25?g.mL-1) with a correlation coefficient of (0.9981) and molar absorptivity 1.9×104 L.mol-1.cm-1 . The limit of detection (LOD) and the limit of quantification (LOQ) are 0.0633 and 0.0847 ?g.mL-1 , respectively . Under optimum conditions,the stoichiometry of the reaction between bismuth (III) and arsenazo(III) r

Natural polymers are often non-toxic, biodegradable, biocompatible, and safe. A novel ligand was synthesized as a natural polymer using chitosan and oleander plant extract [(2R,3S,4R,5S)-5-(acetoxyamino)-4-hydroxy-3,6-dimethoxytetrahydro-2H-pyran-2-yl) methyl (16R)-3-(((2S,4S,5R)-4-methoxy-2,5-dimethyltetrahydro-2H-pyran-2-yl)oxy-10,13,16-trimethyl-17-(5-oxo-2,5-dihydrofuran-3-yl) hexadecahydro-14H-cyclopenta [a] phenanthren-14-yl) phthalate] (Chitosan-Ph-Oleander). This ligand and its complexes with several metals (Cr+3, Mn+2, Fe+3, Ni+2, Cu+2, Zn+2) were characterized using FTIR, UV-visible and 1H-NMR spectroscopy, as well as by molar conductivity, magnetic moment, and TGA analysis. The biological activity for the prepared polymer