This study was carried out to investigate the protective role of taurine (2-aminoethanesulphonicacid) against morphine-induced neurotoxicity in C6 cells. It was found that taurine significantly increased the viability of C6 cells treated by morphine, showing the neuroprotective role against morphine-induced neurotoxicity. However, such neuroprotective effect of taurine could not be blocked by bicuculline, an antagonist of gamma-amino butyrate (GABA) receptor. To determine the oxidative damage induced by morphine, the superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) were measured in C6 cells. The decreased activities of SOD, CAT, and GPx in C6 cells were observed after morphine treatment for 48 h. However, taurine administration effectively ameliorated morphine-induced oxidative insult. To estimate anti-apoptosis effect of taurine, flow cytometry analysis as well as detection for caspase-3 and Bcl-2 expressions was performed after morphine exposure for 48 h. It was found that Bcl-2 expression was down regulated by morphine, whereas taurine could reverse morphine-induced decrease in Bcl-2 expression. Taurine showed no effect on caspase-3 expression. Collectively, the results show that taurine possesses the capability to ameliorate morphine-induced oxidative insult and apoptosis in C6 cells, probably due to its antioxidant activity rather than activation of GABA receptors.

When applying the foreground removal methods to uncover the faint cosmological signal from the epoch of reionization (EoR), the foreground spectra are assumed to be smooth. However, this assumption can be seriously violated in practice since the unresolved or mis-subtracted foreground sources, which are further complicated by the frequency-dependent beam effects of interferometers, will generate significant fluctuations along the frequency dimension. To address this issue, we propose a novel deep-learning-based method that uses a nine-layer convolutional denoising autoencoder (CDAE) to separate the EoR signal. After being trained on the SKA images simulated with realistic beam effects, the CDAE achieves excellent performance as the mean correlation coefficient (ρ) between the reconstructed and input EoR signals reaches 0.929 ± 0.045. In comparison, the two representative traditional methods, namely the polynomial fitting method and the continuous wavelet transform method, both have difficulties in modelling and removing the foreground emission complicated with the beam effects, yielding onlyρ poly = 0.296 ± 0.121 andρ cwt = 0.198 ± 0.160, respectively. We conclude that, by hierarchically learning sophisticated features through multiple convolutional layers, the CDAE is a powerful tool that can be used to overcome the complicated beam effects and accurately separate the EoR signal. Our results also exhibit the great potential of deep-learning-based methods in future EoR experiments.

In this work, the isothermal gas–liquid equilibrium (GLE) data were measured for the system of polyethylene glycol 400 (PEG 400) + N,N-dimethylformamide (DMF) + SO2 + N2 at 308.15 K and 123 kPa with SO2 partial pressures in the range of (16.8 to 115) Pa. The Henry’s law constant (H′) and standard Gibbs free energy change (ΔG) were calculated from these GLE data. Furthermore, the densities and viscosities of binary mixtures of DMF + PEG 400 were also measured over the whole concentration range at T = (298.15 to 313.15) K. From the experimental data, including density and viscosity values, the excess molar volumes (V m E), and viscosity deviations (Δη), the calculated results are fitted to a Redlich–Kister equation to obtain the coefficients and estimate the standard deviations between the experimental and the calculated quantities.

The analysis of electromagnetic interference (EMI) noise of power electronic circuits involves both the transient characteristics of power semiconductor devices and the wideband stray parameters of passive equipment. Modular multilevel converters (MMCs) used in high-voltage direct current (HVdc) transmission systems contain thousands of submodules (SMs), which makes it considerably challenging to perform device-level simulation on the traditional commercial software. This article presents an accurate and fast method for wideband modeling and simulation of MMC-HVdc system for the assessment of conducted EMI during the design stage. Physical characteristics of the semiconductor devices, parasitic parameters of the insulated-gate bipolar transistor (IGBT) packages, and stray capacitances of the SMs are all taken into consideration, and massively parallel transient simulation of the wideband MMC model is carried out on the graphics processor (GPU). The accuracy and efficiency of the GPU-based parallel algorithm are validated by the comparison with the experimental measurement of an 11-level full-bridge MMC prototype. Furthermore, the stray capacitance network of the valve tower in HVdc project is extracted, and a matrix partition method based on the shielding plate configuration is utilized to conduct the computation in a fully parallelized manner. The developed GPU program is used to run the large-scale case of a 201-level two-terminal MMC-HVdc system, and the primarily affected frequency range by various factors is analyzed. Execution time test is conducted for different level topology, and it is demonstrated that the GPU can achieve a remarkable speedup over multicore CPUs, especially when the system scale is more substantial.

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The SiC MOSFET in the medium-voltage directcurrent (MVdc) transportation electrification system features faster switching performance, while simultaneously binging more significant electromagnetic interference (EMI) issues within the rolling stocks, substations, and radiated disturbance into space along the catenaries and tracks. Due to the necessity to involve both the transient characteristics of power semiconductor devices and the stray parameters of all the equipment in the analysis of EMI, it is considerably challenging to perform wideband device-level simulation on traditional commercial software for such a complex system with numerous trains and stations. A computationally efficient method for wideband modeling and simulation of the MVdc highspeed railway system for the assessment of conducted EMI during the project design stage is proposed in this article. Physical characteristics of the semiconductor devices, parasitic parameters of the MOSFET package, and converter topology are all taken into consideration to provide not only accurate system-level performance of the system but also an insight into high-frequency characteristics under different operation conditions. The calculation burden is alleviated by a hierarchical circuit partitioning architecture based on the frequency-dependent time-domain transmission line model and the Norton equivalent parameter extraction of each MOSFET module to split the whole system into several smaller subcircuits in terms of matrix size, and a fully parallel implementation of the MVdc system is carried out on the graphics processor. The developed program is used to study the case of Jing-Zhang high-speed railway system topology, which is compatible to be modified to the MVdc project. Simulation results show that it is essential to estimate the EMI level comprehensively considering the alternative of speed and dc voltage.

The validation of electromagnetic compatibility for the microgrid of a more electric aircraft (MEA) is an essential test item before delivery for a trial flight, and it has always been urgently expected to be involved during the design stage. This article presents a universal method for wideband modeling and simulation of the MEA microgrid system in the time domain, regardless of the fact that motors are driven by which kind of converter, e.g., modular multilevel converter (MMC), 3-L neutral-point clamped (NPC), or 2-L pulsewidth modulation (PWM) converters. The insulated gate bipolar transistor and diodes are modeled with the physics-based dynamic model to emulate not only precise system-level performance of the system, but also to get an insight into the high-frequency oscillation between junction capacitance of the semiconductor modules and the parasitic parameters and high-frequency branch of other components, such as the permanent magnet synchronous motor (PMSM), transformer, and generator. To alleviate the attendant computational challenge, which could be extremely time-consuming (if no nonconvergence problem is encountered) when solved on traditional simulation platform, circuit partition based on transmission line decoupling, Norton equivalent parameter extraction, and TLM-link decoupling of submodules from the MMC bridge arms are utilized. The simulation program is executed on GPU to achieve massively parallel and accelerated solution. The accuracy and efficiency of the GPU-based parallel algorithm are validated by the comparison with the experimentally verified model in ANSYS Simplorer.

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