Objective. Standing balance forms the basis of daily activities that require the integration of multi-sensory information and coordination of multi-muscle activation. Previous studies have confirmed that the cortex is directly involved in balance control, but little is known about the neural mechanisms of cortical integration and muscle coordination in maintaining standing balance. Approach. We used a direct directed transfer function (dDTF) to analyze the changes in the cortex and muscle connections of healthy subjects (15 subjects: 13 male and 2 female) corresponding to different standing balance tasks. Main results. The results show that the topology of the EEG brain network and muscle network changes significantly as the difficulty of the balancing tasks increases. For muscle networks, the connection analysis shows that the connection of antagonistic muscle pairs plays a major role in the task. For EEG brain networks, graph theory-based analysis shows that the clustering coefficient increases significantly, and the characteristic path length decreases significantly with increasing task difficulty. We also found that cortex-to-muscle connections increased with the difficulty of the task and were significantly stronger than the muscle-to-cortex connections. Significance. These results show that changes in the difficulty of balancing tasks alter EEG brain networks and muscle networks, and an analysis based on the directed network can provide rich information for exploring the neural mechanisms of balance control.
As a common neurophysiological phenomenon, voluntary muscle fatigue is accompanied by changes in both the central nervous system and peripheral muscles. Considering the effectiveness of the muscle network and the functional corticomuscular coupling (FCMC) in analyzing motor function, muscle fatigue can be analyzed by quantitating the intermuscular coupling and corticomuscular coupling. However, existing coherence-based research on muscle fatigue are limited by the inability of the coherence algorithm to identify the coupling direction, which cannot further reveal the underlying neural mechanism of muscle fatigue. To address this problem, we applied the time-delayed maximal information coefficient (TDMIC) method to quantitate the directional informational interaction in the muscle network and FCMC during a right-hand stabilized grip task. Eight healthy subjects were recruited to the present study. For the muscle networks, the beta-band information flow increased significantly due to muscle fatigue, and the information flow between the synergist muscles were stronger than that between the synergist and antagonist muscles. The information flow in the muscle network mainly flows to flexor digitorum superficialis (FDS), flexor carpi ulnar (FCU), and brachioradialis (BR). For the FCMC, muscle fatigue caused a significant decrease in the beta- and gamma-band bidirectional information flow. Further analysis revealed that the beta-band information flow was significantly stronger in the descending direction [electroencephalogram (EEG) to surface electromyography (sEMG)] than that in the ascending direction (sEMG to EEG) during pre-fatigue tasks. After muscle fatigue, the beta-band information flow in the ascending direction was significantly stronger than that in the descending direction. The present study demonstrates the influence of muscle fatigue on information flow in muscle networks and FCMC. We proposes that beta-band intermuscular and corticomuscular informational interaction plays an adjusting role in autonomous movement completion under muscle fatigue. Directed information flow analysis can be used as an effective method to explore the neural mechanism of muscle fatigue on the macroscopic scale.
Bearings-only tracking only adopts measurements from angle sensors to realize target tracking, thus, the accuracy of the state prediction has a significant influence on the final results of filtering. There exist unpredictable approximation errors in the process of filtering due to state propagation, discretization, linearization or other adverse effects. The idea of online covariance adaption is proposed in this work, where the post covariance information is proved to be effective for the covariance adaption. With theoretical deduction, the relationship between the posterior covariance and the priori covariance is investigated; the priori covariance is modified online based on the feedback rule of covariance updating. The general framework integrates the continuous-discrete cubature Kalman filtering and the feedback rule of covariance updating. Numerical results illustrated that the proposed method has advantages over decreasing unpredictable errors and improving the computational accuracy and efficiency.
As one of the primary nodes in the flow of micro-plastics (MPs) in the environment, it is critical to examine and assess the Sewage Treatment, occurrence, and removal of MPs in waste treatment plant (WWTP). This research explored the shape, size, and composition of MPs at various stages of the WWTP process in the south of the city of Hefei, China, in dry and rainy weather conditions, as well as the removal effectiveness of MPs in a three-stage process. The collected MPs were quantitatively and qualitatively examined using an Osmosis electron microscope and micro-FTIR. The pollution risk of MPs in WWTP was assessed using the EU classification, labelling and packaging (CLP) standard and the pollution load index (PLI). The findings revealed that the average abundance of fibrous MPs was greatest in WWTP sewage and sludge, 49.3% and 39.7% in dry weather, and 50.1% and 43.2% in rainy weather, respectively. The average distribution of MPs in the 0–500 μm range was highest in WWTP wastewater and sludge, 64.9% and 60.4% in dry weather and 67.9% and 69.0% in rainy weather, respectively. Finally, the overall removal rate was 87.7% and 83.5%. At the same time, it has been demonstrated that MPs with varied compositions are strongly tied to human activities, and environmental conditions (such as rainy weather) also influence their source. In both dry and wet weather, the amount of polymers and the risk score were linked to the pollution risk of MPs in WWTP. In wet weather, the MPS pollution index was more variable. The pollution indices of MPs in row water and tail water were 2.40 and 2.46, respectively, which were heavily contaminated, and 1.0 and 1.2, which were moderately polluted. MPs in dewatered sludge had severely polluted indexes of 3.5 and 3.4, respectively. As a result, there is still MPs efflux or buildup in sludge during and after the WWTP process, which presents an ecological contamination concern.
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