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https://www.selleckchem.com/products/roc-325.html Optimizing athlete's performance is one of the most important and challenging aspects of coaching. Physiological and positional data, often acquired using wearable devices, have been useful to identify patterns, thus leading to a better understanding of the game and, consequently, providing the opportunity to improve the athletic performance. Even though there is a panoply of research in pattern recognition, there is a gap when it comes to non-controlled environments, as during sports training and competition. This research paper combines the use of physiological and positional data as sequential features of different artificial intelligence approaches for action recognition in a real match context, adopting futsal as its case study. The traditional artificial neural networks (ANN) is compared with a deep learning method, Long Short-Term Memory Network, and also with the Dynamic Bayesian Mixture Model, which is an ensemble classification method. The methods were used to process all data sequences, which allowed to determine, based on the balance between precision and recall, that Dynamic Bayesian Mixture Model presents a superior performance, with an F1 score of 80.54% against the 33.31% achieved by the Long Short-Term Memory Network and 14.74% achieved by ANN.Low vegetable intake is associated with higher incidence of noncommunicable diseases. Data on global vegetable intake excluding legumes and potatoes is currently lacking. A systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted to assess vegetable consumption and supply in adult populations and to compare these data to the existing recommendations (≥240 g/day according to World Health Organization). For vegetable intake data online, websites of government institutions and health authorities, European Food Safety Authority (EFSA) Comprehensive European Food Consumption Database, STEPwise a

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