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Based on real data, a new parameterized model of the Main Drift Chamberresponse is proposed. In this model, we tune the ratio of good hits and theresidual distribution separately. The difference between simulation and data intrack reconstruction efficiency reduces from 1% to 0.5%, and the momentumresolution agreement improves significantly.

For improving the emergency capacities during road transportation accidents of dangerous goods, this paper divides the accident emergency procedure into four subsystems: accident information processing, personnel rescue and evacuation, vehicle evacuation, and rescue materials delivery. On this basis, the causal mechanism and the loop diagrams of the emergency process are established by using the System Dynamics Method and Vensim software. First, six factors including transportation distance, the degree of mechanical modernization, delay time of personnel control, delay time of traffic information release, command level coefficient, and delay time of department arrival are selected as parameters to test the model's sensitivity analysis. Then, the influences of the abovementioned factors on the observed value, such as the demand gap of on-site materials, number of people in safe area, number of vehicles in safe area, amount of disposal information, and the dynamic evolution behavior of the system, are analyzed. The results show that the transportation distance is shortened by half, and the time to fill the demand gap is reduced by 39%. The level of mechanical modernization is doubled, the peak inventory of materials in transit will increase by 9.2%, and the time to reach the peak will be shortened by 6.8%. If the delay time of personnel control is shortened by 480s, the number of people to be evacuated in accident area will be reduced by 56. The delay time of traffic information release is shortened by 480s, the number of vehicles to be evacuated is reduced by 74, and the time when the vehicle stops entering accident area is 1646s in advance. The command level coefficient increases by 9.5%, and the speed of action execution increases by 9.6%., Author(s): Guo-sheng Zhang [1]; Xiao-yan Shen (corresponding author) [2]; Jun Hua [3]; Jun-wei Zhao [3]; Hao-xue Liu [2] 1. Introduction Dangerous goods are an important support for the normal operation [...]

In this study, we investigated the effect of astragaloside IV on skeletal muscle energy metabolism disorder caused by statins and explored the possible mechanisms. High-fat diet-fed apolipoprotein E knockout (ApoE-/- ) mice performed aerobic exercise and were administered simvastatin, simvastatin + trimetazidine, or simvastatin + astragaloside IV by gavage. At the end of treatment, exercise performance was assessed by the hanging grid test, forelimb grip test, and running tolerance test. Moreover, plasma lipid and creatine kinase concentrations were measured. After sacrifice, the gastrocnemius muscle was used to assess muscle morphology, and energy metabolism was evaluated by determining the concentration of lactic acid and the storage capacity of adenosine triphosphate and glycogen. Mitochondrial function was assessed by measuring mitochondrial complex III and citrate synthase activity and membrane potential. In addition, oxidative stress was assessed by determining the level of hydrogen peroxide. Finally, using western blotting and reverse transcription polymerase chain reaction, we explored the mechanism of astragaloside IV in alleviating simvastatin-induced muscle injury. Our results demonstrated that astragaloside IV reversed simvastatin-induced muscle injury without affecting the lipid-lowering effect of simvastatin. Moreover, astragaloside IV promoted the phosphorylation of AMPK and activated PGC-1α, which upregulated the expression of NRF1 to enhance energy metabolism and inhibit skeletal muscle cell apoptosis. [ABSTRACT FROM AUTHOR]

Brain 'Computer Interface (BCI) is an emerging technology for communication between human brain and external devices. The traditional feature extraction method based on time-frequency characteristics cannot reflect the nonlinear characteristics of EEG signals. In order to further improve the accuracy of classification, the pretreatment method of wave threshold denossing is firstly used to improve the signal-to-noise ratio of EEG signals. Then, the feature extraction of the three kinds of imaginary motion EEG signal is carried out by the parameter-sample entropy of nonlinear dynamics, and the nonlinear features of EEG signal are preserved. Among them, the research of Motor-Imagery (MI) EEG has always been the focus of BCI that is a highspeed development field. This paper studies three classifiers including support vector machine, LVQ neural network and BP neural network. The experimental results show that BP neural network has higher recognition rate for classification and recognition of EEG signal. [ABSTRACT FROM AUTHOR]