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With global warming and escalating environmental pressures, low-carbon development has become a consensus. Against this backdrop, this study proposes an innovative method for calculating the carbon emissions from electric traction of high-speed trains. Utilizing reverse power flow tracing technology, the method precisely determines the supply structure of high-speed railway traction stations, where the carbon emission factors of electricity vary at different times and locations due to changes in power grid dispatching and supply mix. This approach facilitates the construction of a finely-tuned dynamic model of carbon emission factors at the provincial, municipal, and traction station levels to calculate the carbon emissions from high-speed railway electric traction. Based on this model, the study conducts time-segmented calculations of carbon emissions, comprehensively capturing the dynamics of carbon emissions during the electric traction process of high-speed railways. A lateral comparison with traditional modes of travel, such as cars and airplanes, reveals that high-speed railways have a significant advantage in terms of per capita carbon emissions, furthering the scientific basis for promoting high-speed railways as a green mode of travel. In fact, empirical analysis results show that this method can accurately quantify the carbon emissions during the electric traction phase of high-speed railway operations, providing decision support for train operators to optimize their operational strategies, and offering passengers reference information for making more environmentally-friendly travel choices.

This study proposed a wearable inertial motion capture (Mocap) method for Parkinson’s lower limb motion reconstruction and analysis. To eliminate the error accumulation caused by sensor noise, the error state Kalman filter is used for attitude estimation. The inertial Mocap method proposed in this study is compared with the OptiTrack system to verify the motion tracking effect. The accuracy of different phase-detection methods is compared, and the highest recognition accuracy is 99.11% using the long short-term memory (LSTM) method. In addition, this study collected subjects in healthy control (HC), Parkinson’s disease (PD), and freezing of gait (FOG) groups for comparative analysis. The experimental results revealed differences in motion characteristics and kinematics between PD and healthy subjects. Thus, the quantitative analysis results can be used as an important reference for the clinical application of PD. [ABSTRACT FROM AUTHOR]

As a typical application of remote sensing technology, change detection can find the ground information changes by acquiring the images of the same region at different times. The change detection using the synthetic aperture radar (SAR) with the advantages of all day and all-weather usually monitors the significant surface change, such as flood disasters and earthquake deformation. However, when it comes to detecting subtle changes such as vehicle traces, the traditional methods ignoring the phase coherence between image pairs cannot intensify these faint changes in the difference image. The SAR coherent change detection (CCD) based on repeat-pass repeat-geometry complex images utilizing both the intensity and phase fraction could exhibit the subtle vehicle trace in the difference image. However, the complicated background and decorrelation factors significantly affect the quality of difference images, further causing great trouble for automatic trace detection. This article proposes the spatial feature enhanced Unet and adaptive data augmentation to realize vehicle trace detection. More specifically, the pseudocolor image is first synthesized based on a two-stage coherence estimation method. Then, considering the long-continuity and parallel distribution of vehicle trace samples, the enhanced Unet is constructed by fusing spatial convolutional neural network and spatial attention mechanism. After that, the adaptation data augmentation strategy is presented by introducing manual registration errors and multiple estimation windows. Finally, the experimental results on the Sandia CCD data and our measured data demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The Rogowski coil transducer's integrator will amplify the transmission error, which may lead to mal-operation of the relay. In this paper, a novel method of line relay based on the differential output of Rogowski coil is proposed. Besides, an improved fast distance relay based on the differential output of Rogowski coil is presented. Based on the equal transfer process of transmission lines(ETPTL), the voltage signal should be processed by the virtual Rogowski coil to solve the inconsistent transfer problem between voltage and current. Furthermore, this paper also presents the reconstruction method of voltage at the fault point and the least-square algorithm to solve the R-L differential equation, so as to improve the algorithm accuracy. Furthermore, the equal transfer process error of the virtual Rogowski coil is analyzed in the paper, and the design principle of low pass filter is also proposed. Simulation results and experiment results demonstrate that the improved fast distance relay scheme has high operation speed, which is signed prior to other distance relay schemes. The proposed method also provides a valuable reference for other types of transmission line relay based on Rogowski coil transducer. [ABSTRACT FROM AUTHOR]

Lignocellulose is an abundant xylose-containing biomass found in agricultural wastes, and has arisen as a suitable alternative to fossil fuels for the production of bioethanol. Although Saccharomyces cerevisiae has been thoroughly used for the production of bioethanol, its potential to utilize lignocellulose remains poorly understood. In this work, xylose-metabolic genes of Pichia stipitis and Candida tropicalis, under the control of different promoters, were introduced into S. cerevisiae. RNA-seq analysis was use to examine the response of S. cerevisiae metabolism to the introduction of xylose-metabolic genes. The use of the PGK1 promoter to drive xylitol dehydrogenase (XDH) expression, instead of the TEF1 promoter, improved xylose utilization in “XR-pXDH” strain by overexpressing xylose reductase (XR) and XDH form C. tropicalis, enhancing the production of xylitol (13.66 $\pm$ 0.54 g/L after 6 days fermentation). Overexpression of xylulokinase and XR/XDH from P. stipitis remarkably decreased xylitol accumulation (1.13 $\pm$ 0.06 g/L and 0.89 $\pm$ 0.04 g/L xylitol, respectively) and increased ethanol production (196.14 $\%$ and 148.50 $\%$ increases during the xylose utilization stage, respectively), in comparison with the results of XR-pXDH. This result may be produced due to the enhanced xylose transport, Embden-Meyerhof and pentose phosphate pathways, as well as alleviated oxidative stress. The low xylose consumption rate in these recombinant as well as alleviated strains comparing with P. stipitis and C. tropicalis may be explained by the insufficient supplementation of NADPH and NAD $^+$. The results obtained in this work provide new insights on the potential utilization of xylose using bioengineered S. cerevisiae strains. [ABSTRACT FROM AUTHOR]