Considering that driving stress is a major contributor to traffic accidents, detecting drivers’ stress levels in time is helpful for ensuring driving safety. This paper attempts to investigate the ability of ultra-short-term (30-s, 1-min, 2-min, and 3-min) HRV analysis for driver stress detection under real driving circumstances. Specifically, the t-test was used to investigate whether there were significant differences in HRV features under different stress levels. Ultra-short-term HRV features were compared with the corresponding short-term (5-min) features during low-stress and high-stress phases by the Spearman rank correlation and Bland–Altman plots analysis. Furthermore, four different machine-learning classifiers, including a support vector machine (SVM), random forests (RFs), K-nearest neighbor (KNN), and Adaboost, were evaluated for stress detection. The results show that the HRV features extracted from ultra-short-term epochs were able to detect binary drivers’ stress levels accurately. In particular, although the capability of HRV features in detecting driver stress also varied between different ultra-short-term epochs, MeanNN, SDNN, NN20, and MeanHR were selected as valid surrogates of short-term features for driver stress detection across the different epochs. For drivers’ stress levels classification, the best performance was achieved with the SVM classifier, with an accuracy of 85.3% using 3-min HRV features. This study makes a contribution to building a robust and effective stress detection system using ultra-short-term HRV features under actual driving environments.

Tobacco use presents substantial challenges for the environment, social and economic development along global supply chains. Here we assess the global sustainability impacts of tobacco use and control across global supply chains in 164 countries and regions. We find that tobacco use in the top ten downstream countries drove over 50% of global upstream impacts. High-income countries tended to have larger per capita impacts than low-income countries, displacing 42% of health burden and 34% of greenhouse gas emissions to upstream low-income countries. We also observe declines in environmental stressors, disease burden, and employment during 2010-2020, driven by impact intensity, production structure, and tobacco prevalence. Reducing the prevalence of tobacco use as appropriate by 2027 could generate substantial benefits to the environment, health, and economy, albeit with a reduction in employment relative to 2020 levels. Our analyses contribute to a just and sustainable transition towards a healthy and tobacco-free world.