Assessment of Body Composition, Endurance and Nutrient Intakes among Females Team Players in Sports Club

Aim: The aim of the study was to assess the body composition, endurance level and usual nutrient intakes in female players representing a Sports Club in Sharjah, United Arab Emirates. Materials and Methods: Twenty-six adult female players aged between 15-24 years were selected from three different teams (basketball=12, tennis=4, volleyball=10) using convenience sampling technique. All participants were assessed for body composition through bioelectrical impedance method, endurance level using step test and nutrient intakes using 24-hour recall method. Significant differences (P < 0.05) were determined among the three teams in relation to body composition, endurance levels and nutrient intakes. Results: Body composition of players in three sports was significantly different in terms of body mass index, body fat mass, and percentage body fat and fitness scores. Tennis players had significantly higher body fat mass (28.5 ± 8.2 kg) and percent body fat (41 ± 7%) in contrast to that in basketball players (body fat mass: 19.2 ± 10.5 kg; percent body fat: 30.6 + 7.9%) and tennis players (body fat mass: 13 ± 4.2 kg; percent body fat: 26.5 ± 6.5%), respectively. On the other hand, volleyball players had significantly higher fitness score (72.2 ± 3.5) as compared to basketball players (71 ± 6.7), and tennis players (63 ± 8.2). On an average, volleyball players scored “very good” endurance level in contrast to “good” scores in basketball and tennis team players. However, this difference was not statistically significant. The average intakes of all nutrients including energy, protein, vitamins and minerals were below the recommended intakes among players of all sports teams.   Conclusions: Body composition and endurance level differ with the type of sports. Volleyball team players had the lowest BMI, body fat mass as well as percent body fat and highest fitness score and endurance level. However, the overall nutrient intakes of the female players representing the three teams were less than the recommended allowances for highly active women and did not differ with the type of sports played

The Spatial and Diurnal Distribution of Lower Atmospheric Dust as Revealed by the Emirates Mars Infrared Spectrometer

&amp;lt;p&amp;gt;The Emirates Mars Mission (EMM) is on its way to achieving 1 Martian year of Scientific Observations by the end of April 2023 to explore the dynamics of the Martian atmosphere on a global scale. The Emirates Mars Infrared Spectrometer (EMIRS) instrument onboard EMM, is an interferometric thermal infrared spectrometer designed to characterize the geographic, seasonal, and diurnal variability of key characteristics of Mars such as atmospheric dust, which will be the focus of this talk, and other constituents such as water ice optical depth, water vapor abundance, surface temperature, and atmospheric temperature profiles on sub-seasonal timescales. &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;EMIRS observations provide full local solar time coverage at multiple emission angles providing data on these constituents over the entire Martian disk. Here, we present initial results of the spatial, seasonal and diurnal variation of dust on a global scale with particular attention to the diurnal variations of dust and the evolution of dust storms. Preliminary results show more diurnal variations during dust storm seasons. In addition, results on the biggest storm of the year will be presented which occurred after solar longitude of 300.&amp;amp;#160; These new observations will continue to enhance our understanding of the dust cycle on Mars and how dust influences the current climate and atmospheric dynamics on Mars by relating the effect of dust to other EMIRS constituents mentioned above.&amp;lt;/p&amp;gt;</jats:p

Exploring PM2.5 and PM10 ML forecasting models: a comparative study in the UAE

Abstract Particulate Matters PM $$_{2.5}$$ and PM $$_{10}$$ present a major health and environmental concern in urban regions. This research compares machine learning and time series models, such as Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Facebook Prophet, for predictions of these matters. Their performances have been evaluated over 1-2 hours, 1 day and 1 week forecasting periods using five years real-life data from six ground stations in Abu Dhabi, UAE. Performance metrics including Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Percent Bias (PBIAS) were applied. Linear SVR was generally the best performing model for PM $$_{2.5}$$ predictions at all stations with averages of 18.7% and 28.2% MAPE for 1 and 2-hour periods, respectively. However, CNN performed best in forecasting PM $$_{10}$$ for 1-hour horizon, with an average MAPE of 12.6%. For the 2-hour forecast, SVR outperformed other models, with 18.3% MAPE. Facebook Prophet consistently outperformed others for both PM $$_{2.5}$$ and PM $$_{10}$$ with 21.8% and 13.4% MAPE for 1-day and 21.3% and 13.8% MAPE for 1-week, respectively. These best performing models yielded similar RMSE, MAE, and PBIAS values for both PM $$_{2.5}$$ and PM $$_{10}$$