AI-enabled prediction of video game player performance using the data from heterogeneous sensors.

The emerging progress of video gaming and eSports lacks the tools for ensuring high-quality analytics and training in professional and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors. For this reason, we collected the physiological, environmental, and the smart chair data from professional and amateur players. The player performance is assessed from the game logs in a multiplayer game for each moment of time using a recurrent neural network. We have investigated an attention mechanism improves the generalization of the network and provides a straightforward feature importance as well. The best model achieves Area Under the Receiver Operating Characteristic Curve (ROC AUC) score 0.73 in predicting whether a player will perform better or worse in the next 240 seconds based on in-game metrics. The prediction of the performance of a particular player is realized although their data are not utilized in the training set. The proposed solution has a number of promising applications for professional eSports teams and amateur players, such as a learning tool or performance monitoring system.

Cs2CO3-Promoted reaction of tertiary bromopropargylic alcohols and phenols in DMF: a novel approach to α-phenoxyketones.

The reaction of bromopropargylic alcohols with phenols in the presence of Cs2CO3/DMF affords α-phenoxy-α'-hydroxyketones (1:1 adducts) and α,α-diphenoxyketones (1:2 adducts) in up to 92% and 24% yields, respectively. Both products are formed via ring opening of the same intermediates, 1,3-dioxolan-2-ones, generated in situ from bromopropargylic alcohols and Cs2CO3.

Analysis of Video Game Players' Emotions and Team Performance: An Esports Tournament Case Study.

Video gaming and eSports is a quickly developing industry already involving billions of players worldwide. Gaming and eSports tournaments require strong mental abilities to avoid severe stress and other negative consequences upon completing the game. In this article, we report on the impact of emotions on a team performance. For this reason, we collect audio recordings and game logs from the players in real conditions at an eSports tournament. This data is further used in trained machine learning models for analysis of players' emotional conditions from the voice during the game. We considered recognition of several types of emotions as well as the background sounds. To do this, we trained 92.7% accuracy classifier of six most common classes of emotions and sounds in eSports audio and applied it to eSports data. As a result, we demonstrate that there is an opportunity to measure the eSports team's performance from the players' emotional conditions obtained from the voice communication. We found that there is a strong correlation among the performance of the team, communication between the players, and emotional sentiment of communication. The teams achieve much better results when they had much more internal conversations during the game.

Closing the gap between training needs and training provision in addiction medicine.

Substance use disorders pose a significant global social and economic burden. Although effective interventions exist, treatment coverage remains limited. The lack of an adequately trained workforce is one of the prominent reasons. Recent initiatives have been taken worldwide to improve training, but further efforts are required to build curricula that are internationally applicable. We believe that the training needs of professionals in the area have not yet been explored in sufficient detail. We propose that a peer-led survey to assess those needs, using a standardised structured tool, would help to overcome this deficiency. The findings from such a survey could be used to develop a core set of competencies which is sufficiently flexible in its implementation to address the specific needs of the wide range of professionals working in addiction medicine worldwide.