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Changes in driving behavior caused by reduced visibility in fog can lead to crashes. To improve driving safety in fog weather, a fog warning system based on connected vehicle (CV) technology is proposed. From the perspective of human factors, this study evaluates the driving safety based on drivers' speed change under different fog levels (i.e., no fog, light fog, and heavy fog) and different technical levels (i.e., normal, with a dynamic message sign (DMS), and with a human-machine interface (HMI)). The driving behavior data were collected by a driving simulation experiment. The experimental road was divided into three zones: clear zone, transition zone, and fog zone. To quantify the change of vehicle speed comprehensively, the speed and acceleration were selected. Meanwhile, the vehicle speed safety entropy and acceleration safety entropy were proposed based on sample entropy theory. Furthermore, the changes of each index in different zones were analyzed. The results show that the use of fog warning system can improve speed stability and driving safety in fog zones and can make the driver decelerate in advance with a smaller deceleration before entering the fog zones. The higher the technical level is, the earlier the driver decelerates. Under the condition of light fog, the fog warning system with HMI has a better effect in terms of improving speed stability, while under the condition of heavy fog, there is little difference between the two technical levels. In general, this study proposed a novel safety evaluation index and a general evaluation method of the fog warning system., Author(s): Xuewei Li [1]; Yuchen Jia [1]; Yufei Chen [2]; Guanyang Xing [1]; Xiaohua Zhao (corresponding author) [1]; Jian Rong [1] 1. Introduction With the decrease of visibility on foggy [...]

BACKGROUND: Aortic stiffness, assessed as carotid-femoral pulse wave velocity, provides a measure of vascular age and risk for adverse cardiovascular disease outcomes, but it is difficult to measure. The shape of arterial pressure waveforms conveys information regarding aortic stiffness; however, the best methods to extract and interpret waveform features remain controversial. METHODS: We trained a convolutional neural network with fixed-scale (time and amplitude) brachial, radial, and carotid tonometry waveforms as input and negative inverse carotid-femoral pulse wave velocity as label. Models were trained with data from 2 community-based Icelandic samples (N=10 452 participants with 31 126 waveforms) and validated in the community-based Framingham Heart Study (N=7208 participants, 21 624 waveforms). Linear regression rescaled predicted negative inverse carotid-femoral pulse wave velocity to equivalent artificial intelligence vascular age (AI-VA). RESULTS: The AI-VascularAge model predicted negative inverse carotid-femoral pulse wave velocity with R2=0.64 in a randomly reserved Icelandic test group (n=5061, 16%) and R2=0.60 in the Framingham Heart Study. In the Framingham Heart Study (up to 18 years of follow-up; 479 cardiovascular disease, 200 coronary heart disease, and 213 heart failure events), brachial AI-VA was associated with incident cardiovascular disease adjusted for age and sex (model 1; hazard ratio, 1.79 [95% CI, 1.50-2.40] per SD; P<0.0001) or adjusted for age, sex, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, prevalent diabetes, hypertension treatment, and current smoking (model 2; hazard ratio, 1.50 [95% CI, 1.24-1.82] per SD; P<0.0001). Similar hazard ratios were demonstrated for incident coronary heart disease and heart failure events and for AI-VA values estimated from carotid or radial waveforms. CONCLUSIONS: Our results demonstrate that convolutional neural network--derived AI-VA is a powerful indicator of vascular health and cardiovascular disease risk in a broad community-based sample. [ABSTRACT FROM AUTHOR]