Emotions drive attention: Effects on driver’s behaviour

Music is the favourite activity while driving. However, very few studies have investigated its impact on driving performances. This study was designed to assess the effect of music’s emotional valence on driving behaviour. Happy, sad and neutral music excerpts were alternated with no-music phases while driving in a simulator. Results showed that happy music distracted drivers the most as their mean speed unexpectedly decreased and their lateral control deteriorated. Sad music influenced drivers in a different way as they drove slowly and kept their vehicle in its lane. These findings were discussed within the framework of attentional orienting and emotions.     

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

● Used a double-stage attention mechanism model to predict ozone. ● The model can autonomously select the appropriate time series for forecasting. ● The model outperforms other machine learning models and WRF-CMAQ. ● We used the model to analyze the driving factors of VOCs that cause ozone pollution. Ozone is becoming a significant air pollutant in some regions, and VOCs are essential for ozone prediction as necessary ozone precursors. In this study, we proposed a recurrent neural network based on a double-stage attention mechanism model to predict ozone, selected an appropriate time series for prediction through the input attention and temporal attention mechanisms, and analyzed the cause of ozone generation according to the contribution of feature parameters. The experimental data show that our model had an RMSE of 7.71 μg/m³ and a mean absolute error of 5.97 μg/m³ for 1-h predictions. The DA-RNN model predicted ozone closer to observations than the other models. Based on the importance of the characteristics, we found that the ozone pollution in the Jinshan Industrial Zone mainly comes from the emissions of petrochemical enterprises, and the good generalization performance of the model is proved through testing multiple stations. Our experimental results demonstrate the validity and promising application of the DA-RNN model in predicting atmospheric pollutants and investigating their causes.     

Occupational Entropy and Mind Indicators for Sustainable Energy Development

Abstract

A concept of occupational entropy is developed and related to the efficient use of mental (cognitive, emotional, and moral) resources and capacities. The corresponding “mind” indicators and pertinent response actions have proven essential for monitoring the state and projecting the behavior changes toward energy sustainability, as well as sustainable development in general.     

On-road driving test performance in veterans: Effects of age,clinical diagnosis and cognitive measures

Introduction: Veterans are at heightened risk of being in a motor-vehicle crash and many fail on-road driving evaluations, particularly as they age. This may be due in part to the high prevalence of age-associated conditions impacting cognition in this population, including neurodegenerative diseases (e.g., Alzheimer’s Disease) and acquired neurological conditions (e.g., cerebrovascular accident). However, understanding of the impact of referral diagnosis, age and cognition on Veterans’ on-road driving performance is limited. Methods: 109 Veterans were referred for a driving evaluation (mean age = 72.0, SD = 11.5) at a driving assessment clinic at the Minneapolis Veterans Affairs Healthcare System. Of the 109 Veterans enrolled, 44 were referred due to a neurodegenerative disease, 37 due to an acquired neurological condition, and 28 due to a non-neurological condition (e.g., vision loss). Veterans completed collection of health history information and administration of cognitive tests assessing visual attention, processing speed, and executive functioning, as well as a standardized, on-road driving evaluation. Results: A total of 17.9% of Veterans failed the on-road evaluation. Clinical diagnostic group was not associated with failure rate. Age was not associated with failure rates in the full sample or within diagnostic groups. After controlling for age, poorer processing speed and selective/divided attention were associated with higher failure rates in the full sample. No cognitive tests were associated with failure rates within diagnostic groups. Conclusion: Referral diagnosis and age alone are not reliable predictors of Veterans’ driving performance. Cognitive performance, specifically speed of processing and attention, may be helpful in screening Veterans’ driving safety. Practical Applications: Clinicians tasked with assessing Veterans’ driving safety should take into account cognitive performance, particularly processing speed and attention, when making decisions regarding driving safety. Age and referral diagnosis, while helpful information, are insufficient to predict outcomes on driving evaluations.