Attention has been theorized as a system comprising three networks that can be estimated reliably by the attention network test ; the three networks are defined as alerting, orienting, and conflict control. The present study aims to identify the attention networks that are crucial for elite shooting and archery athletes and to examine whether mindfulness training can improve elite athletes' attention networks. We compared the performances in ANT between 62 elite athletes from the Chinese national team of shooting and archery (...) and 49 athletes from a provincial team in China. The results indicate three well-functioned attention networks in both groups, but elite athletes in the national team responded faster overall than athletes in the provincial team. The 62 elite athletes in the national team then received mindfulness training with varied periods ranging from 5 to 8 weeks, after which the ANT was re-administered. After mindfulness training, the elite athletes improved in orienting and conflict control networks compared with their pre-training performances. These results suggest that elite shooting and archery athletes in the national team are more efficient in all three attention networks, which means that they are able to reach the alerting state faster, make better use of environmental information, and suppress interference from distractors more efficiently. Moreover, the orienting and conflict control networks of the elite shooting and archery athletes can be improved by mindfulness training. We conclude that mindfulness practice should be considered as a useful addition to daily training for shooting and archery athletes. (shrink)

Predicting stock movements is a valuable research field that can help investors earn more profits. As with time-series data, the stock market is time-dependent and the value of historical information may decrease over time. Accurate prediction can be achieved by mining valuable information with words on social platforms and further integrating it with actual stock market conditions. However, many methods still cannot effectively dig deep into hidden information, integrate text and stock prices, and ignore the temporal dependence. Therefore, to solve (...) the above problems, we propose a transformer-based attention network framework that uses historical text and stock prices to capture the temporal dependence of financial data. Among them, the transformer model and attention mechanism are used for feature extraction of financial data, which has fewer applications in the financial field, and effective analysis of key information to achieve an accurate prediction. A large number of experiments have proved the effectiveness of our proposed method. The actual simulation experiment verifies that our model has practical application value. (shrink)