Growing metropolitan areas bring rapid urbanization and air pollution problems. As diseases and mortality rates increase because of the air pollution problem, it becomes a necessity to estimate the air pollution density and inform the public to protect the health. Air pollution problem displays contextual characteristics such as meteorological conditions, industrial and technological developments, traffic problem etc. that change from country to country and also from city to city. In this study, we determined PM 10 as the target pollutant and designed a new deep learning based air quality forecasting model, namely DFS (Deep Flexible Sequential). Our study uses real world hourly data from Istanbul, Turkey between 2014 and 2018 to forecast the air pollution 4, 12, and 24 hours before. DFS model is a hybrid & flexible deep model including Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). The proposed model also is capable of generalization with standard and flexible Dropout layers. Through flexible Dropout layer, the model also obtains flexibility to adapt changing window sizes in sequential modelling. Moreover, this model can be applied to other air pollution time series data problems with small modifications on parameters by taking into account the nature of the data set.
Aerobic endurance describes the ability of the body's cardio-respiratory system to perform physical activity for an extended period of time and resist fatigue. Standard tests to determine aerobic endurance involves measuring the maximum volume of oxygen (VO 2 max) an athlete uses up while exercising at maximal capacity. Given that the tests of direct measurement of VO 2 max needs expensive equipment, a great deal of time, and trained staff with expertise, many researchers have attempted to find indirect and simpler ways of predicting VO 2 max based on prediction equations. The aim of this study is to establish new prediction equations for estimating the VO 2 max from gender, age, height, weight, body mass index (BMİ), maximal heart rate (HRmax) and test time (TT) for college-aged students in Turkey. Particularly, 18 students from the College of Physical Education and Sports at Gazi University volunteered for this study. Gender has been used as a common predictor variable in all prediction models. By using different combinations of the rest of predictor variables together with the common predictor variable, twelve VO 2 max prediction equations have been established with the help of Multiple Linear Regression (MLR). The performance of the prediction equations have been evaluated using two well-known metrics, namely standard error of estimate (SEE) and multiple correlation coefficient (R). The results reveal that the regression equation, VO 2 max =-(12.
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