Background: Energy drinks are non alcoholic beverages which contain stimulant drugs chiefly caffeine and marketed as mental and physical stimulators. Consumption of energy drinks is popular practice among college students as they are exposed to academic stress. Caffeine which is the main constituent of energy drinks could become an addictive substance or cause intoxication. Objectives: This study aims to assess the prevalence of energy drinks consumption among medical students of alkindy college of Medicine.Type of the study: A cross sectional study.Methods: It was performed at alkindy medical college on March 2016. A total number of 600 students were contacted to participate in this study. A self administered questionnaire was used to collect the data. Spss version 18.0 was used for statistical analysis.Results: Out of 600 students, 501 (83.5%) participated in the study. The majority were females 304 (60.7%) and only 197 (39.3%) were males with a mean age of (20.43 ± 1.74). 120 (24%) of participants had consumed energy drinks at least once. Higher proportion of male students 77 (64%) consumed energy drinks compared to females 43 (36%). Regarding inspiration of first use of energy drinks, the highest percentage 9.8% was due to friends. Majority of consumers 85 (17.2%) used energy drinks irregularly. The main cause of energy drinks consumption was focusing for studying 7.2% (n=36). Conclusions: Energy drinks consumption is a common practice among medical students. Friends had a strong influence on usage of energy drinks. Students consumed energy drinks mainly for focusing for studying. Further studies are recommended to evaluate factors involved in consumption of these drinks among medical students and their understanding of the risks involved as well as possible interventions to promote safe consumption
The purposes of this study are to measure masculine's identification among secondary school students, and to examine the significant differences among secondary school students in respect of students age (16-18) year. The researcher has prepared a questionnaire comprised (25) item to measure masculine identification among students: this scale was applied on (100) student who were selected as a sample from four different school at al-rusafa side in Baghdad. The scale then modified to (23) item to be more consistent. The results revealed that there were no significant differences among students based on the variable of age, and the mean of masculine identification showed a high level than the hypothesized mean.
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
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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
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