Highlights a light CNN for efficient detection of COVID-19 from chest CT scans is proposed the accuracy is comparable with that of more complex CNN designs the efficiency is 10 times better than more complex CNNs using pre-processing no GPU acceleration is required and can be executed on middle class computers

In human interactions, hands are a powerful way of expressing information that, in some cases, can be used as a valid substitute for voice, as it happens in Sign Language. Hand gesture recognition has always been an interesting topic in the areas of computer vision and multimedia. These gestures can be represented as sets of feature vectors that change over time. Recurrent Neural Networks (RNNs) are suited to analyse this type of sets thanks to their ability to model the long term contextual information of temporal sequences. In this paper, a RNN is trained by using as features the angles formed by the finger bones of human hands. The selected features, acquired by a Leap Motion Controller (LMC) sensor, have been chosen because the majority of human gestures produce joint movements that generate truly characteristic corners. A challenging subset composed by a large number of gestures defined by the American Sign Language (ASL) is used to test the proposed solution and the effectiveness of the selected angles. Moreover, the proposed method has been compared to other state of the art works on the SHREC dataset, thus demonstrating its superiority in hand gesture recognition accuracy.• the search of a robust solution able to recognize also gestures that are similar to each other; • the achievement of the highest accuracy level compared with works of the current literature.

Consistent with insights from both trait and social cognitive theories, this study presents a theoretical model positing emotional self-efficacy beliefs in managing negative emotions at work as a key mechanism that contributes to mediate the negative relationship between emotional stabilitya trait highly associated with positive affect and mental healthand job burnout. To test this assertion, a two-wave study using a representative sample of 416 new military cadets of an Italian military academy was designed. Military cadets were involved in the study 2 months after their entrance into the academy and then again, a year later. Results from structural equation modelling supported the hypothesized model. As predicted, self-efficacy beliefs in managing negative emotions at work significantly mediated the longitudinal relation between emotional stability and job burnout, even after controlling for the effect of the other Big Five traits, education, previous experience in military contexts, gender, and age. Practical implications and directions for future research are discussed. In conclusion, our study demonstrates that self-efficacy in managing negative emotions at work represents an important mechanism linking emotional stability level to burnout symptoms. Practitioner pointsSelf-efficacy in managing negative emotions at work proved to be an important resource for workers in managing job-related stress: practitioners interested in reducing burnout symptoms in stressful working environments should take into account this variable. Self-efficacy beliefs in managing negative emotions at work are cognitive structures malleable to change. Literature on social cognitive theory offers several suggestions on how to promote individuals' positive beliefs on managing negative emotions and dysphoric affect. Hence, findings and literature reported in this study may be useful for practitioners aiming at strengthen workers' self-efficacy in managing negative emotions at work, through the development and application of coaching and training programmes.

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