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For a multi-component hydrocarbon mixture under supercritical conditions, especially for fuels injected into compression ignition engines, the mechanism for the transition of the dominant mixing mode from evaporation to diffusion is not well established. In this paper, evaporation processes of a six-component hydrocarbon fuel (13.16 mol% toluene, 13.81 mol% n-decane, 22.30 mol% n-dodecane, 24.60 mol% n-tetradecane, 14.66 mol% n-hexadecane and 11.47 mol% n-octadecane) droplet in nitrogen environments were studied using molecular dynamics (MD) simulations, in comparison with those of three-component and single-component fuel droplets. The ambient pressure ranged from 2 MPa to 16 MPa and the ambient temperature ranged from 750 K to 1350 K. Results indicated that the transition characteristics of the mixed fuel were not the linearly weighted average of the physical properties of individual components in the mixture based on their mole fractions. The reason why there is a limitation on the maximum transition temperature when diffusion dominates the fuel-ambient gas mixing process under high pressures has been discussed. The average resultant force on a fuel atom of an individual component increases with increasing pressure or decreasing temperature at the supercritical temperature, and diffusion will gradually dominate the mixing process of the fuel. The clustering behavior of fuels under supercritical conditions has also been discussed.

Convolutional Neural Network (CNN) has achieved great success in decoding EEG signals, decoders based on these architectures make separate feature extraction and classification into an integrated stage, however, a large number of trainable parameters introduced by the model hinder the improvement of EEG decoding performance and challenge the interpretability of decoding process used CNNs. In this paper, we propose an end-to-end shallow and lightweight CNN framework, which allows EEG-Motor Raw dataset as inputs, to boost decoding accuracy by the Channel-Mixing-ConvNet. The first block of network is designed in the way of implicitly stacking temporal–spatial convolution layers for learning temporal and spatial EEG features after EEG channels were mixed, compared to previously independently building a single temporal and spatial convolutional layer, this method combines the feature extraction capabilities of the two layers. The Mixed Channel Process block introducing a depthwise convolution layer is applied for a series of processing such as to decouple and supplement the internal and external mapping relationships existing in the mixed multi-dimensional EEG feature maps. Finally, the classification block is constructed to finish EEG decoding tasks. The lightweight architecture of Channel-Mixing-ConvNet leaves space for the model to exploit its potential performance by stacking other layers. In our experiments, the proposed Channel-Mixing-ConvNet and variants based on different hyper-parameters were evaluated on public EEG-motor datasets BCI-IV 2a and HGD respectively, Channel-Mixing-ConvNet outperformed state-of-the art (SOA) algorithms for EEG decoding. Additionally, via post-hoc interpretation techniques, the results show the learned features are consistent with the neurophysiological principle of the EEG motor imagery, meanwhile, the model also captures the remarkable features associated with channels.

For a multi-component hydrocarbon mixture under supercritical conditions, the mechanism and criterion for the transition of the dominant mixing mode from evaporation to diffusion are not well established. In this paper, phase transition processes of three-component hydrocarbon fuel (5.3 wt% isooctane, 25.8 wt% n-dodecane and 68.9 wt% n-hexadecane) droplets in sub- and supercritical nitrogen environments were studied using molecular dynamics, in comparison with those of single-component n-hexadecane droplets. The initial diameters of the droplets were 25.5 nm (three-component) and 26.5 nm (single-component), respectively. Based on the quantitative Voronoi tessellation, a new criterion, which was a combination of two dimensionless critical values of Hc = 0.85 and Wc = 0.35, was proposed to determine the transition of the dominant mixing mode from evaporation to diffusion during fuel-ambient gas mixing. Results indicated that when the ambient pressure ranged from 2 MPa to 10 MPa and the ambient temperature ranged from 750 K to 1200 K, the density difference between the vapor phase and the liquid phase decreased gradually with increasing ambient pressure or decreasing ambient temperature. And the dominant mixing mode gradually transitioned from evaporation to diffusion. Increasing the ambient pressure did not necessarily promote the occurrence of phase transition, while increasing ambient temperature accelerated the phase transition monotonically. Light fuel components increased the minimum pressure of the diffusive mixing zone. A major finding was that under a certain ambient pressure, the supercritical transition temperature had not only a minimum but also a maximum.

This work characterized the metabolism disorders of acute liver failure (ALF) induced by carbon tetrachloride (CCl(4)) in a mouse model with different dosage of intoxication (100, 500 and 1000 mg/kg). Metabolic profiles of mice plasma were detected by gas chromatography/mass spectrometry (GC/MS) after chemical derivatization. Here an effective information-extracting approach was implemented on the basis of partial least square regression analysis (PLS-RA). PLS modeling was achieved with two kinds of Y-vectors for the acquired metabonomics data and eight metabolites with different changing behaviors were selected. ALF of mice induced by CCl(4) was characterized by the elevation of glutamate, citrate, serine and threonine, as well as the decrease of alpha-glycerophosphate, docosahexaenoic acid, palmitic acid and oleic acid in plasma. The difference in the concentrations of serine, threonine, palmitic acid and oleic acid remained insignificant between the control and 100mg/kg groups, while significant distinction appeared when comparing the control and two higher dosed groups. The underlying regulation of CCl(4)-perturbed metabolic pathways was discussed according to the selected metabolites. The present study demonstrated a great potential of PLS-RA in exploiting a comprehensive metabolic effects of CCl(4) intoxication and its efficient capability to reveal the hepatotoxic mechanism of ALF induced by reactive oxygen species (ROS).

Quantitative analysis and chromatographic fingerprinting for the quality evaluation of a Chinese herb Scutellaria baicalensis Georgi using capillary electrophoresis (CE) technique was developed. The separation was performed with a 50.0 cm (42.0 cm to the detector window) × 75 μm i.d. fused-silica capillary, and the CE fingerprint condition was optimized using the combination of central composite design and multivariate analysis. The optimized buffer system containing 15 mM borate, 40 mM phosphate, 15 mM SDS, 15% (v/v) acetonitrile and 7.5% (v/v) 2-propanol was employed for the method development, and the baseline separation was achieved within 15 min. The determination of the major active components (Baicalin, Baicalein and Wogonin) was carried out using the optimized CE condition. Good linear relationships were provided over the investigated concentration ranges (the values of R2: 0.9997 for Baicalin, 0.9992 for Baicalein, and 0.9983 for Wogonin, respectively). The average recoveries of these target components ranged between 96.1–105.6%, 98.6–105.2%, and 96.3–105.0%, respectively. CE fingerprints combined with the quantitative analysis can be used for the quality evaluation of S. baicalensis.