Brain-Computer Interfaces (BCIs) translate neuronal information into commands to control external software or hardware, which can improve the quality of life for both healthy and disabled individuals. Here, a multi-modal BCI which combines motor imagery (MI) and steady-state visual evoked potential (SSVEP) is proposed to achieve stable control of a quadcopter in three-dimensional physical space. The complete information common spatial pattern (CICSP) method is used to extract two MI features to control the quadcopter to fly left-forward and right-forward, and canonical correlation analysis (CCA) is employed to perform the SSVEP classification for rise and fall. Eye blinking is designed to switch these two modes while hovering. Real-time feedback is provided to subjects by a global camera. Two flight tasks were conducted in physical space in order to certify the reliability of the BCI system. Subjects were asked to control the quadcopter to fly forward along the zig-zag pattern to pass through a gate in the relatively simple task. For the other complex task, the quadcopter was controlled to pass through two gates successively according to an S-shaped route. The performance of the BCI system is quantified using suitable metrics and subjects are able to acquire 86.5% accuracy for the complicated flight task. It is demonstrated that the multi-modal BCI has the ability to increase the accuracy rate, reduce the task burden, and improve the performance of the BCI system in the real world.

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Hedging is an important measure for investors to resist extreme risks and improve their profits. This paper develops a FIGARCH–EVT–copula–VaR model to derive hedge ratio when hedging crude oil spot and futures markets, overcoming the limitations of static models and simple dynamic models in existing literature. The empirical results indicate that the FIGARCH–EVT–copula–VaR model is superior to the other three commonly used models based on four criteria: mean of returns, variance of returns, ratio of mean to variance of returns, and hedging effectiveness. Comparatively, the new model has superior performance to other three models during the sample period and can be used by investors to obtain excellent hedging effect.

For exploring the relationship between the mental or cognitive state and metric of vigilance test for unmanned aerial vehicle (UAV), a vigilance state evaluation method and sphere of application based on behavior signals is established. A classical vigilance test avoiding to crash is set. During the experiments, the subjective ratings as well as behavior signals (Response Time, Lapse) are recording. The dynamic changing of behavior signals is analyzed using statistical analysis. The results demonstrate that compared with continuous PVT test, the subject's mental workload in rest PVT test decreases dramatically. Compared other metrics, the speed of response time can reflect the dynamic changing of subject's mental state. The metric of Q-50 has a strong robustness for outlier of subject. Considering that the metrics have strong correlation with operator's cognitive state, they can effectively analyze the different workload.

Mining functional modules with biological significance has attracted lots of attention recently. However, protein-protein interaction (PPI) network and other biological data generally bear uncertainties attributed to noise, incompleteness and inaccuracy in practice. In this paper, we focus on received PPI data with uncertainties to explore interesting protein complexes. Moreover, some novel conceptions extended from known graph conceptions are used to develop a depth-first algorithm to mine protein complexes in a simple uncertain graph. Our experiments take protein complexes from MIPS database as standard of accessing experimental results. Experiment results indicate that our algorithm has good performance in terms of coverage and precision. Experimental results are also assessed on Gene Ontology (GO) annotation, and the evaluation demonstrates proteins of our most acquired protein complexes show a high similarity. Finally, several experiments are taken to test the scalability of our algorithm. The result is also observed.