Rail impedance directly affects the transmission performance of track circuit . Considering the condition of earth stratification, for the difficult to calculate the rail impedance due to the semi-infinite integration interval and the oscillation of the integrand by using the Carson formula, The truncation method is proposed to divide the impedance formula is divided into definite integral and tail integral. The integral is approximated by the spline function, and the tail integral is calculated by using the exponential integral and Euler formula. Based on it, the rail impedance calculation formula of track circuit is obtained. The electromagnetic field model of track circuit with earth stratification is simulated by finite element method, and the correctness of the method is verified. Based on the formula, the influence of current frequency, soil depth and conductivity on rail impedance is studied. The relative error between the calculated results of rail impedance and the simulation results of finite element is within 5%. It can be seen that the formula has high accuracy and correctly reflects the law of rail impedance variation with current frequency, soil depth and resistivity. It provides a reliable reference for the theoretical calculation of rail impedance of track circuit.

Mn3O4 /graphene nanosheets (GNS) composites serve as very excellent electrode materials for supercapacitors. They can fully combine the advantages of two materials such as graphene and metal oxide. Meanwhile, they can improve not only the specific energy and specific power of the materials, but also the cyclic stability of the materials. The results of the cyclic voltammetry and constant current charge discharge test on the composite electrode material have shown that the Mn3O4 /GNS powder sample has good capacitive performance. When the scanning rate is 5~50mV, the specific capacity retention rate of the composite electrode is 80.3% and 88% respectively. Mn3O4 nanoparticles, with the highest ratio of network coated GNS, exhibit a specific capacitance value of 957.6 F g−1 at a current density of 2 A g−1 in 1 M Na2SO4 solution. Besides, its network structure demonstrates high specific capacity and multiplying performance.

This paper proposes a solution for the problem of mobile robot navigation and trajectory interpolation in dynamic environments with large scenes. The solution combines a semantic laser SLAM system that utilizes deep learning and a trajectory interpolation algorithm. The paper first introduces some open-source laser SLAM algorithms and then elaborates in detail on the general framework of the SLAM system used in this paper. Secondly, the concept of voxel is introduced into the occupation probability map to enhance the ability of local voxel maps to represent dynamic objects. Then, in this paper, we propose a PointNet++ point cloud semantic segmentation network combined with deep learning algorithms to extract deep features of dynamic point clouds in large scenes and output semantic information of points on static objects. A descriptor of the global environment is generated based on its semantic information. Closed-loop completion of global map optimization is performed to reduce cumulative error. Finally, T-trajectory interpolation is utilized to ensure the motion performance of the robot and improve the smooth stability of the robot trajectory. The experimental results indicate that the combination of the semantic laser SLAM system with deep learning and the trajectory interpolation algorithm proposed in this paper yields better graph building and loop closure effects in large scenes at SIASUN large scene campus. The use of T-trajectory interpolation ensures vibration-free and stable transitions between target points.

Water splitting technology is an efficient approach to generate hydrogen (H2) energy, which can well address the problems of environmental deterioration and energy shortage, as well as establishment of a clean and sustainable hydrogen economy powered by renewable energy sources due to the green reaction of H2 with O2. While the efficiency of H2 production by water splitting technology is intimately related with the reactions on electrode. Nowadays, the efficient electrocatalysts in water splitting reactions are the precious metal-based materials, i.e., Pt/C, RuO2 and IrO2. Ni (Co, Fe)-based layered double hydroxides (LDH) 2D materials are the typical non-precious metal-based materials in water splitting with advantages of low cost, excellent electrocatalytic performance and simple preparation methods, which exhibits a great potential for the substitution for precious metal-based materials. This review summarizes the recent progress of Ni (Co, Fe)-based LDH 2D materials for water splitting, which mainly focuses on discussing and analyzing the different strategies to modify LDH materials towards high electrocatalytic performance. We also discuss the recent achievements including their electronic structure, electrocatalytic performance, catalytic center, preparation process and catalytic mechanism. Furthermore, the characterization progress in revealing electronic structure and catalytic mechanism of LDH is highlighted in this review. Finally, we put forward some future perspectives related to design and explore advanced LDH catalysts in water splitting.

The dynamics of hydrological lateral nutrient fluxes contribute to our understanding of ecological functions related to energy, materials, and organism flows across various spatiotemporal scales. To explore the connectivity between multiple spatial flow processes, we conducted a one-year field measurement to assess lateral hydrologic carbon (C) and nitrogen (N) fluxes over the continental shelf in the Yangtze estuary. We observed a significant correlation between the differences in remote sensing-based estimates of gross primary production (GPP) (∆GPPMODIS) and the differences in eddy covariance (EC) tower-based GPP (∆GPPEC) at both high-elevation and low-elevation sites. Over the course of a year, our predicted daily maximum tidal elevation (TE) closely matched the observed values in the creek, which facilitated the development of theoretical models to simulate biogeochemical cycling processes and aquatic ecosystem functions. Our findings indicate that the studied saltmarsh acts as a net exporter of dissolved total C (DTC) while serving as a net sink for dissolved total N (DTN). Furthermore, there is a significant correlation in the total dissolved stoichiometry of the C/N ratio between imports and exports. These findings highlight the importance of integrating ecological stoichiometric principles to gain a deeper understanding of the complex relationships between physical, chemical, and biological processes, particularly within the context of the meta-ecosystem framework. Additionally, when considering reciprocal hydrological lateral C and N flows, single ecosystem can function both as sources and sinks within the meta-ecosystem framework.