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Rainfall intensity and slope gradient are important factors that affect soil erosion; however, contradictory observations have been made due to different experimental conditions and materials. Colluvial deposits with loose, coarse material and steep slopes are easily erodible, but the erosion mechanism of colluvial deposition remains obscure. This work investigated the effects of heavy and storm rainfall intensity and steep slope gradients on the infiltration, runoff, and soil loss of colluvial soil. The rainfall intensity ranged from 1.00 to 2.33 mm min−1, and the slope gradient ranged from 36 to 84%. The infiltration rates declined sharply in the initial stage, whereas an opposite trend was observed for runoff rates until a steady state was reached after 5 min. Single‐ and multiple‐peak models illustrated the two types of changes for the sediment yield process. The infiltration volume and the coefficient increased with increasing rainfall intensity and decreased with increasing slope, whereas the runoff coefficient decreased with increasing rainfall intensity and increased with increasing slope. Runoff volume and sediment yield increased with increasing rainfall intensity but had a critical slope gradient of 58% and >47%. The sediment concentration increased with increasing rainfall intensity, and first increased and then decreased with increasing slope gradients at rainfall intensities of 1.00 and 1.33 mm min−1 but increased at rainfall intensities of 1.67, 2.00, and 2.33 mm min−1. The findings of this study can be used to clarify the erosion mechanisms in disturbed soils with high coarse particle content.

Mental fatigue is one of the main reasons for the decline of response inhibition. This study aimed to explore the impairing influence of mental fatigue on a driver’s response inhibition. The effects of mental fatigue on response inhibition were assessed by comparing brain activity and behavioral indices when performing a Go/NoGo task before and after a 90-min fatigue manipulation task. Participants in the driving group performed a simulated driving task, while individuals in the control group spent the same time watching movies. We found that participants in the driving group reported higher levels of mental fatigue and had a higher percentage of eye closure and larger lateral deviations from their lane positions, which indicated there was effective manipulation of mental fatigue through a prolonged simulated driving task. After manipulation of mental fatigue, we observed increased reaction time and miss rates, delayed NoGo-N2 latency and Go-P3 latency, and decreased NoGo-P3 amplitude, which indicated that mental fatigue may slow down the speed of the inhibition process, delay the evaluation of visual stimuli and reduce the availability of attentional resources. These findings revealed the underlying neurological mechanisms of how mental fatigue impaired response inhibition.

ÑThe stable relationship between personality and EEG ensures the feasibility of personality inference from brain activities. In this paper, we recognize an individualÕs personality traits by analyzing brain waves when he or she watches emotional materials. Thirty-seven participants took part in this study and watched 7 standardized film clips that characterize real-life emotional experiences and target seven discrete emotions. Features extracted from EEG signals and subjective ratings enter the SVM classifier as inputs to predict five dimensions of personality traits. Our model achieves better classification performance for Extraversion (81.08%), Agreeableness (86.11%), and Conscientiousness (80.56%) when positive emotions are elicited than negative ones, higher classification accuracies for Neuroticism (78.38-81.08%) when negative emotions, except disgust, are evoked than positive emotions, and the highest classification accuracy for Openness (83.78%) when a disgusting film clip is presented. Additionally, the introduction of features from subjective ratings increases not only classification accuracy in all five personality traits (ranging from 0.43% for Conscientiousness to 6.3% for Neuroticism) but also the discriminative power of the classification accuracies between five personality traits in each category of emotion. These results demonstrate the advantage of personality inference from EEG signals over state-of-the-art explicit behavioral indicators in terms of classification accuracy.

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