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The problem of impact-entrainment relationship is one of the central issues in understanding saltation, a primary aeolian transport mode. By using particle dynamic analyser measurement technology the movement of saltating particles at the very near-surface level (1 mm above the bed) was detected. The impacting and entrained particles in the same impact-entrainment process were identified and the speeds, angle with respect to the horizontal, and energy of the impacting and entrained sand cloud were analysed. It was revealed that both the speed and angle of impacting and entrained particles vary widely. The probability distribution of the speed of impacting and entrained particles in the saltating cloud is best described by a Weibull distribution function. The mean impact speed is generally greater than the mean lift-off speed except for the 0Ð1-0Ð2 mm sand whose entrainment is significantly influenced by air drag. Both the impact and lift-off angles range from 0°to 180°. The mean lift-off angles range from 39°to 94°while the mean impact angles range from 40°to 78°, much greater than those previously reported. The greater mean lift-off and especially the mean impact angles are attributed to mid-air collisions at the very low height, which are difficult to detect by conventional high-speed photography and are generally ignored in the existing theoretical simulation models. The proportion of backward-impacting particles also evidences the mid-air collisions. The impact energy is generally greater than the entrainment energy except for the 0Ð1-0Ð2 mm sand. There exists a reasonably good correlation of the mean speed, angle and energy between the impacting and entrained cloud in the impact-entrainment process. The results presented in this paper deserve to be considered in modelling saltation.

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Brain-computer interfaces (BCIs) are used to translate brain activity signals into control signals for external devices. Currently, it is difficult for BCI systems to provide the multiple independent control signals necessary for the multi-degree continuous control of a wheelchair. In this paper, we address this challenge by introducing a hybrid BCI that uses the motor imagery-based mu rhythm and the P300 potential to control a brain-actuated simulated or real wheelchair. The objective of the hybrid BCI is to provide a greater number of commands with increased accuracy to the BCI user. Our paradigm allows the user to control the direction (left or right turn) of the simulated or real wheelchair using left- or right-hand imagery. Furthermore, a hybrid manner can be used to control speed. To decelerate, the user imagines foot movement while ignoring the flashing buttons on the graphical user interface (GUI). If the user wishes to accelerate, then he/she pays attention to a specific flashing button without performing any motor imagery. Two experiments were conducted to assess the BCI control; both a simulated wheelchair in a virtual environment and a real wheelchair were tested. Subjects steered both the simulated and real wheelchairs effectively by controlling the direction and speed with our hybrid BCI system. Data analysis validated the use of our hybrid BCI system to control the direction and speed of a wheelchair.

Despite the apparent importance of mental fatigue detection, a reliable application is hindered due to the incomprehensive understanding of the neural mechanisms of mental fatigue. In this paper, we investigated the topological alterations of functional brain networks in the theta band (4 - 7 Hz) of electroencephalography (EEG) data from 40 male subjects undergoing two distinct fatigue-inducing tasks: a low-intensity one-hour simulated driving and a high-demanding half-hour sustained attention task [psychomotor vigilance task (PVT)]. Behaviorally, subjects demonstrated a robust mental fatigue effect, as reflected by significantly declined performances in cognitive tasks prior and post these two tasks. Furthermore, characteristic path length presented a positive correlation with task duration, which led to a significant increase between the first and the last five minutes of both tasks, indicating a fatigue-related disruption in information processing efficiency. However, significantly increased clustering coefficient was revealed only in the driving task, suggesting distinct network reorganizations between the two fatigue-inducing tasks. Moreover, high accuracy (92% for driving; 97% for PVT) was achieved for fatigue classification with apparently different discriminative functional connectivity features. These findings augment our understanding of the complex nature of fatigue-related neural mechanisms and demonstrate the feasibility of using functional connectivity as neural biomarkers for applicable fatigue monitoring.