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Road vehicles and maglev trains have garnered significant attention, with their suspension systems being crucial for safe and stable performance. However, these systems can be compromised by faults such as sensor and actuator failures, posing risks to stability and safety. This review explores fault-tolerant controls for suspension systems, driven by the need to enhance fault tolerance in such scenarios. We examine the dynamic similarities between the semi-active/active suspension systems in road vehicles and the suspension systems in maglev trains, offering a comprehensive summary of fault-tolerant control strategies for both. Our analysis covers the histories, technical characteristics, fundamentals, modeling, mathematical derivations, and control objectives of both systems. The review categorizes fault-tolerant control methods into hardware redundancy, passive fault-tolerant control, and active fault-tolerant control. We evaluate the advantages and disadvantages of these strategies and propose future directions for the development of fault-tolerant control in suspension systems.

The motor is an important part of the flywheel energy storage system. The flywheel energy storage system realizes the absorption and release of electric energy through the motor, and the high-performance, low-loss, high-power, high-speed motors are key components to improve the energy conversion efficiency of energy storage flywheels. This paper analyzes the operating characteristics of the permanent magnet synchronous motor/generator (PMSG) used in the magnetically levitated flywheel energy storage system (FESS) and calculates the loss characteristics in the drive and power generation modes. Based on this, the electromagnetic part of the motor is optimized in detail. Aiming at this design, this paper calculates the loss characteristics of driving and power generation modes in detail, including its winding loss, core loss, rotor eddy current loss and mechanical loss. The calculation results show that the design meets the loss requirements. It can reduce the no-load loss of the permanent magnet synchronous motor at high speed and improve the energy conversion efficiency, which gives this system practical application prospects.

The levitation stability of maglev trains is determined by the interaction of vehicle-bridge dynamic characteristics. The state change of vehicle and track beam will affect the dynamic performance of maglev trains. In order to study the levitation characteristics of maglev trains, a coupled vehicle-bridge dynamic model based on an elastic beam was established to study the influence of beam stiffness and vehicle load on the dynamic performance of the maglev system. In the form of numerical simulation, the time-domain characteristics of key characteristic variables, such as levitation gap and vertical deflection of track beam, under different working conditions of stiffness and load were analyzed. The simulation shows that the levitation system can smoothly converge to the stable value under each working condition, which indicates the rationality of the field test. Based on the Shanghai Lingang medium-and-low-speed maglev test line, the maglev test was carried out, and the time-domain and frequency-domain characteristics of the above key variables were analyzed based on the measured data. The results show that the fluctuation of the levitation gap was affected by load and stiffness, and the law was consistent with the simulation results. The increase in load or the decrease in beam stiffness would lead to an increase in vertical deflection and vibration of the track beam. However, the train could still maintain good levitation performance under the above extreme conditions, which verified the reliability of the levitation system and the correctness of the simulation model. The conclusion of this paper can provide a reference for the design of the levitation system and track line of medium-and-low-speed maglev train.

When two maglev trains travel in opposite directions on two adjacent tracks, train crossing is inevitable. Especially when both trains run at full speed, the pressure wave formed by each other will have a significant impact on the structure of the vehicle. Therefore, it is important to understand the pressure distribution on the body surface during the crossing to mitigate impact of the pressure wave. In this work, numerical simulation techniques are employed to reveal the nature of pressure wave during train crossing. Firstly, the aerodynamic load calculation model and the pressure wave calculation model are established, based on the turbulence model and flow field control equation. Secondly, the governing equations are discretized together with determined corresponding boundary conditions, which leads to an effective numerical analysis method. Finally, the corresponding aerodynamic analysis is carried out for the high-speed maglev test vehicle running at speed 500 km/h on the open-air line. The simulation results reveal that the spot which sustains the most pressure fluctuation is at the widest part of the vehicle during the train crossing. This forms valuable insights on the aerodynamic nature of high-speed maglev train and provides necessary inputs to the structural design of the vehicle.

The Luang Prabang (Laos)–Loei (Thailand) metallogenic belt is located on the northwestern margin of the Indochina Block. It is one of the most important gold–copper metallogenic belts in Southeast Asia. This region underwent tectonic and magmatic evolution in the late Paleozoic-Mesozoic period within the Paleo-Tethys realm, resulting in complex metallogenic processes. Consequently, epithermal Au-Ag, porphyry-skarn Au-Cu, and hydrothermal vein-type gold deposits were formed. However, the genetic type of the vein-type gold deposits is still not fully understood. The relationship between the three types of gold deposits and the regional tectonic evolution has not been summarized up until today. We summarize the previous mineralization characteristics and exploration data of commonly known deposits and combine them with new evidence and ore deposit insights from our recent studies on the source and evolution of ore-forming fluids in the region. We confirm that the hydrothermal vein-type gold deposits in the belt are typical orogenic gold deposits. Based on previous regional tectonic-magmatic-metallogenic studies, metallogenic characteristics, and temporal and spatial distribution of three types of typical gold–copper deposits in the belt, we synthesize and establish a regional metallogenic model related to the subduction-closure of the Paleo-Tethys Ocean and subsequent continental–continental collision process, resulting in the formation of epithermal Au-Ag during the late Permian-early Triassic subduction, porphyry-skarn Au-Cu in the early–middle Triassic period during the closure of the ocean, and orogenic Au during the late Triassic collision. Since there are few reports on the geochemical characteristics of gold–copper deposits and their related magmatic rocks, the potential for gold–copper mineralization and their links to the magmatic rocks in the belt still needs further study.

Wildfire is one of the main hazards affecting large areas and causes great damage all over the world, and the rapid development of the wildland-urban interface (WUI) increases the threat of wildfires that have ecological, social, and economic consequences. As one of the most widely used methods for tracking fire, remote sensing can provide valuable information about fires, but it is not always available, and needs to be supplemented by data from other sources. Social media is an emerging but underutilized data source for emergency management, contains a wealth of disaster information, and reflects the public’s real-time witness and feedback to fires. In this paper, we propose a fusion framework of multi-source data analysis, including social media data and remote sensing data, cellphone signaling data, terrain data, and meteorological data to track WUI fires. Using semantic web technology, the framework has been implemented as a Knowledge Base Service and runs on top of WUIFire ontology. WUIFire ontology represents WUI fire–related knowledge and consists of three modules: system, monitoring, and spread, and tracks wildfires happening in WUIs. It provides a basis for tracking and analyzing a WUI fire by fusing multi-source data. To showcase the utility of our approach in a real-world scenario, we take the fire in the Yaji Mountain Scenic Area, Beijing, China, in 2019 as a case study. With object information identified from remote sensing, fire situation information extracted from Weibo, and fire perimeters constructed through fire spread simulation, a knowledge graph is constructed and an analysis using a semantic query is carried out to realize situational awareness and determine countermeasures. The experimental results demonstrate the benefits of using a semantically improved multi-source data fusion framework for tracking WUI fire.

Near-land surface air temperature (NLSAT) is an important meteorological and climatic parameter widely used in climate change, urban heat island and environmental science, in addition to being an important input parameter for various earth system simulation models. However, the spatial distribution and the limited number of ground-based meteorological stations make it difficult to obtain a large range of high-precision NLSAT values. This paper constructs neural network, long short-term memory, bi-directional long short-term memory, support vector machine, random forest, and Gaussian process regression models by combining MODIS data, DEM data, and meteorological station data to estimate the NLSAT in China’s mainland and compare them with actual NLSAT observations. The results show that there is a significant correlation between the model estimates and the actual temperature observations. Among the tested models, the random forest performed the best, followed by the support vector machine and the Gaussian process regression, then the neural network, the long short-term memory, and the bi-directional long short-term memory models. Overall, for estimates in different seasons, the best results were obtained in winter, followed by spring, autumn, and summer successively. According to different geographic areas, random forest was the best model for Northeast, Northwest, North, Southwest, and Central China, and the support vector machine was the best model for South and East China.

With the rapid development and gradual perfection of GNSS in recent years, improving the real-time service performance of GNSS has become a research hotspot. In GNSS single-point positioning, broadcast ephemeris is used to provide a space–time reference. However, the orbit parameters of broadcast ephemeris have meter-level errors, and no mathematical model can simulate the variation of this, which restricts the real-time positioning accuracy of GNSS. Based on this research background, this paper uses a BP (Back Propagation) neural network and a PSO (Particle Swarm Optimization)–BP neural network to model the variation in the orbit error of GPS and BDS broadcast ephemeris to improve the accuracy of broadcast ephemeris. The experimental results showed that the two neural network models in GPS can model the broadcast ephemeris orbit errors, and the results of the two models were roughly the same. The one-day and three-day improvement rates of RMS(3D) were 30–50%, but the PSO–BP neural network model was better able to model the trend of errors and effectively improve the broadcast ephemeris orbit accuracy. In BDS, both of the neural network models were able to model the broadcast ephemeris orbit errors; however, the PSO–BP neural network model results were better than those of the BP neural network. In the GEO satellite outcome of the PSO–BP neural network, the STD and RMS of the orbit error in three directions were reduced by 20–70%, with a 20–30% improvement over the BP neural network results. The IGSO satellite results showed that the PSO–BP neural network model output accuracy of the along- and radial-track directions experienced a 70–80% improvement in one and three days. The one- and three-day RMS(3D) of the MEO satellites showed that the PSO–BP neural network has a greater ability to resist gross errors than that of the BP neural network for modeling the changing trend of the broadcast ephemeris orbit errors. These results demonstrate that using neural networks to model the orbit error of broadcast ephemeris is of great significance to improving the orbit accuracy of broadcast ephemeris.

Our previous studies demonstrated that tentacle extract (TE) from the jellyfish, Cyanea capillata, could cause a dose-dependent increase of systolic blood pressure, which seemed to be the result of direct constriction of vascular smooth muscle (VSM). The aim of this study is to investigate whether TE could induce vasoconstriction in vitro and to explore its potential mechanism. Using isolated aorta rings, a direct contractile response of TE was verified, which showed that TE could induce concentration-dependent contractile responses in both endothelium-intact and -denuded aortas. Interestingly, the amplitude of contraction in the endothelium-denuded aorta was much stronger than that in the endothelium-intact one, implying that TE might also bring a weak functional relaxation in addition to vasoconstriction. Further drug intervention experiments indicated that the functional vasodilation might be mediated by nitric oxide, and that TE-induced vasoconstriction could be attributed to calcium influx via voltage-operated calcium channels (VOCCs) from the extracellular space, as well as sarcoplasmic reticulum (SR) Ca2+ release via the inositol 1,4,5-trisphosphate receptor (IP3R), leading to an increase in [Ca2+]c, instead of activation of the PLC/DAG/PKC pathway or the sympathetic nerve system.

To investigate the hematologic changes and the activities of jellyfish venoms other than hemolytic and cardiovascular toxicities, the acute toxicity of tentacle-only extract (TOE) from the jellyfish Cyanea capillata was observed in mice, and hematological indexes were examined in rats. The median lethal dose (LD50) of TOE was 4.25 mg/kg, and the acute toxicity involved both heart- and nervous system-related symptoms. Arterial blood gas indexes, including pH, PCO2, HCO3−, HCO3std, TCO2, BEecf and BE (B), decreased significantly. PO2 showed a slight increase, while SO2c (%) had no change at any time. Na+ and Ca2+ decreased, but K+ increased. Biochemical indexes, including LDH, CK, CK-MB, ALT, AST and sCr, significantly increased. Other biochemical indexes, including BUN and hemodiastase, remained normal. Lactic acid significantly increased, while glucose, Hct% and THbc showed slight temporary increases and then returned to normal. These results on the acute toxicity and hematological changes should improve our understanding of the in vivo pathophysiological effects of TOE from C. capillata and indicate that it may also have neurotoxicity, liver toxicity and muscular toxicity in addition to hemolytic and cardiovascular toxicities, but no kidney or pancreatic toxicity.

Catalytic asymmetric [2 + 4] cycloadditions of 3-vinylindoles with ortho-quinone methides and their precursors were carried out in the presence of chiral phosphoric acid to afford a series of indole-containing chroman derivatives with structural diversity in overall high yields (up to 98%), good diastereoselectivities (up to 93:7 dr) and moderate to excellent enantioselectivities (up to 98% ee). This approach not only enriches the chemistry of catalytic asymmetric cycloadditions involving 3-vinylindoles but is also useful for synthesizing chiral chroman derivatives.