Search Results (91)

Impulse current measurement technology is widely used in various applications, including lightning protection monitoring in power systems, welding current measurement in aircraft and shipbuilding industries, as well as high-current measurement in pulsed power systems. With the advancement of industrial technology, the measurement range of impulse currents has continuously expanded, reaching levels as high as mega-amperes (MA). The calibration of the scale factor for impulse current measurement devices is determined through comparison with standard measurement devices. Developing high-accuracy impulse current measurement devices and accurately judging their characteristics are prerequisites for ensuring the precise calibration of impulse current values. This paper introduces two different types of high-impulse current measurement devices. Experimental studies were conducted on the scale factor and response characteristics of the sensors. The scale factor extension calibration method for sensors under high currents of more than 100 kA has also been introduced. Test results indicate that the developed impulse current measurement devices can serve as standard measurement devices for high impulse current measurement. Full article

Reclaiming coastal wetlands for agricultural purposes has led to intensified farming activities, which are anticipated to affect greenhouse gas (GHG) flux processes within coastal wetland ecosystems. However, how greenhouse gas exchanges respond to variations in agricultural reclamation activities across different years remains uncertain. To address this knowledge gap, this study characterized dynamic exchanges within the soil–plant–atmosphere continuum by employing continuous monitoring across four representative coastal wetland soil–vegetation systems in Jiangsu, China. The results show the carbon dioxide (CO₂) and nitrous oxide (N₂O) flux exchanges between the system and the atmosphere and soil–vegetation carbon pools, which revealed the drivers of carbon dynamics in the coastal wetland system. The four study sites, converted from coastal wetlands to agricultural lands at different times (years), generally act as CO₂ sinks and N₂O sources. Higher levels of CO₂ sequestration occur as the age of reclamation rises. In terms of time scale, crops lands were found to be CO₂ sinks during the growing period but became CO₂ sources during the crop fallow period. Although the temporal trend of the N₂O flux was generally smooth, reclaimed farmlands acted as net sources of N₂O, particularly during the crop-growing period. The RDA and PLS-PM models illustrate that soil salinity, acidity, and hydrothermal conditions were the key drivers affecting the magnitude of the GHG flux exchanges under reclamation. This study demonstrates that GHG emissions from reclaimed wetlands can be effectively regulated through science-based land management, calling for prioritized attention to post-development practices rather than blanket restrictions on coastal exploitation. Full article

Lightning-induced voltages (LIVs) computation is crucial for lightning protection of power systems and equipment, yet the effect of complex terrain on LIVs remains not fully evaluated. This study establishes a three-dimensional finite-difference time-domain model to investigate the LIVs over Gaussian-shaped mountainous terrain, considering different lightning strike locations. Simulation results show that the influence of Gaussian-shaped mountains on LIVs is directly related to the lightning strike location. Compared with the flat ground scenario, the LIVs’ amplitude can increase by approximately 56% when lightning strikes the mountain top. However, for lightning strikes to the ground adjacent to the mountain, the LIVs’ amplitude is attenuated to varying degrees due to the shielding effect of the mountain. Additionally, the influences of line configuration, as well as mountain height and width on the LIVs, are evaluated. Full article

The efficient extraction of natural gas from marine natural gas hydrate (NGH) reservoirs is challenging, due to their low permeability, high hydrate saturation, and fine-grained sediments. Hydraulic fracturing has been proven to be a promising technique for improving the permeability of these unconventional reservoirs. This study presents a comprehensive triaxial experimental investigation of the fracturing behavior and fracture initiation mechanisms of NGH-bearing sediments, using large-scale ice-saturated synthetic cubic models. The experiments systematically explore the effects of key parameters, including the injection rate, fluid viscosity, ice saturation, perforation patterns, and in situ stress, on fracture propagation and morphology. The results demonstrate that at low fluid viscosities and saturation levels, transverse and torsional fractures dominate, while longitudinal fractures are more prominent at higher viscosities. Increased injection rates enhance fracture propagation, generating more complex fracture patterns, including transverse, torsional, and secondary fractures. A detailed analysis reveals that the perforation design significantly influences the fracture direction, with 90° helical perforations inducing vertical fractures and fixed-plane perforations resulting in transverse fractures. Additionally, a plastic fracture model more accurately predicts fracture initiation pressures compared to traditional elastic models, highlighting a shift from shear to tensile failure modes as hydrate saturation increases. This research provides new insights into the fracture mechanisms of NGH-bearing sediments and offers valuable guidance for optimizing hydraulic fracturing strategies to enhance resource extraction in hydrate reservoirs. Full article

Developing high-performance oxygen evolution reaction (OER) electrocatalysts remains a critical challenge for sustainable hydrogen production via water electrolysis. Herein, we present a self-supported atomic iridium-decorated FeOOH nanostructure on iron foam (Ir-FeOOH/IF) by a facile impregnation reduction method. The self-supported Ir-FeOOH/IF electrode integrates the high electrical conductivity and outstanding mass transfer performance of IF. The FeOOH features abundant active sites, while the Ir modification regulated the electronic structure of FeOOH. As a result, the as-prepared Ir-FeOOH/IF catalyst (with the optimized synthesis time) achieves a low overpotential of 145 and 284 mV at current densities of 0.1 and 1 A cm⁻², respectively, and exhibits excellent long-term catalytic stability for 135 h at 0.1 A cm⁻² in a 1 M KOH solution. This work provides a new strategy for the design of low-cost and highly stable OER electrocatalysts. Full article

Precise Global Navigation Satellite System (GNSS) orbit prediction is critical for real-time positioning applications. Current orbit prediction accuracy for the BeiDou Navigation Satellite System-3 (BDS-3) exhibits a notable disparity compared to GPS and Galileo, with limited advancements from traditional dynamic modeling approaches. This study introduces a novel data-driven methodology, Sample Convolution and Interaction Network with Self-Attention (SCINet-SA), to augment dynamic methods and improve BDS-3 ultra-rapid orbit prediction. SCINet-SA leverages deep learning to model the temporal characteristics of orbit differences between BDS-3 ultra-rapid and final products. By training on historical orbit difference data, SCINet-SA predicts future discrepancies, facilitating the refinement of ultra-rapid orbit estimates. The incorporation of a self-attention mechanism within SCINet-SA enables the model to effectively capture long-range temporal dependencies, thereby enhancing long-term prediction capabilities and mitigating the latency associated with final product availability. Rigorous experimental evaluation demonstrates the superior performance of SCINet-SA in enhancing BDS-3 ultra-rapid orbit prediction accuracy relative to alternative deep learning models. Specifically, SCINet-SA achieved the highest average relative improvement (IMP) in 3D Root Mean Square (RMS) error across 1 d, 7 d, and 15 d prediction horizons, yielding improvements of 21.69%, 18.66%, and 15.42%, respectively. The observed IMP range spanned from 7.78% to 38.91% for 1 d, 4.34% to 35.96% for 7 d, and 1.68% to 31.13% for 15 d predictions, underscoring the efficacy of the proposed methodology in advancing BDS-3 orbit prediction accuracy. Full article

A finite element model was established to simulate the necking and thickening process of TA18 titanium alloy thin-wall tubes to compare the effect of differential temperature and isothermal methods on the temperature and wall thickness distributions of the tubes. It is found that the temperature gradient of differential temperature tubes shifts from the force transfer area to the necking area, and these tubes exhibit a thicker thickening area and a thinner necking area compared to their isothermal counterparts. The experiments on the necking and thickening of TA18 titanium alloy tubes were conducted using both differential temperature and isothermal methods. The results show that the differential temperature method is a superior forming method compared to the isothermal process. The major thickening occurs in the thickening area of the differential temperature tube, while it occurs in the necking area of the isothermal tube. The average wall thickness of the differential temperature tubes is 25% greater in the thickening area, while it is 23% thinner in the necking area, compared to the isothermal tubes. We conducted EBSD tests on TA18 titanium alloy thin-walled tubes; the results showed that dynamic recrystallization occurred in the necking and thickening regions, with significant grain refinement. The grain size in the necking region was smaller than that in the thickening region. Full article

Piper nigrum L. (PN), which contains various bioactive compounds, is a plant with homologous medicine and food. Sleep deprivation (SD) profoundly impacts cognitive function and emotional health. However, the mechanisms by which PN improves cognitive function and depressive mood induced by SD remain unclear. In our study, network pharmacology and molecular docking techniques were used to predict the potential mechanisms by which PN regulates SD. In this study, 220 compounds were identified in PN, and 10 core targets were screened through network pharmacology. Animal experiments showed that PN ameliorated depressive mood and cognitive deficits in sleep-deprived mice, upregulated the serum activities of superoxide dismutase (SOD), glutathione (GSH), and catalase (CAT), and downregulated malondialdehyde (MDA) levels. The ELISA assay showed that PN significantly decreased the tumour necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-1 beta (IL-1β) levels. Histopathological staining of brain tissue demonstrated that PN mitigates SD-induced hippocampal damage, enables the hippocampus to produce more neurotransmitters, including 5-hydroxytryptamine (5-HT), gamma-aminobutyric acid (GABA), and dopamine (DA), and reduces glutamate (Glu) levels. RT-qPCR and WB analyses further indicated that PN could exert anti-SD effects by inhibiting the over-activation of the JAK1/STAT3 signalling pathway. In the PC12 cell model, PN could reduce inflammation and prevent apoptosis, exerting neuroprotective effects. In summary, PN has positive effects on alleviating depressive symptoms and cognitive dysfunction induced by SD. Full article

In the context of economic globalization, international trade facilitates cross-border production and consumption but raises concerns such as carbon transfer from corporate trade activities. This study investigates the influence of executives’ green awareness on the global green value chain (GGVC) using matched data from Chinese customs and enterprises, along with newly constructed GVC net flow indicators and econometric models. From 2007 to 2016, executives’ green awareness in Chinese A-share listed companies significantly promoted GVC development, with proactive, green-oriented executives driving greater green upgrades. Variations in executives’ backgrounds, firm types, regional education levels, and highly digitalized production environments further shaped the effectiveness of green leadership. The findings provide empirical evidence and insights into green export management practices. Full article

Underwater gliders are a new type of unmanned underwater vehicle, characterized by high energy efficiency, long endurance, and low operational costs. They hold broad application prospects in fields such as ocean exploration, resource surveying, maritime surveillance, and military defense. This paper takes underwater gliders as the research subject, analyzing the characteristics of magnetic interference signals under different operational conditions. The study found that during full operational states, the motor’s operation generates interference signals at 17 Hz; during attitude adjustment, the movement of the moving block generates significant interference magnetic fields, especially during the forward and backward motion of the block, where interference signals at 20 Hz are particularly pronounced. To meet the objective of equipping underwater gliders with magnetic field sensors for underwater target detection, this paper proposes an adaptive filtering method based on the Recursive Least Squares (RLS) algorithm. The experimental results indicate that after filtering with the RLS algorithm, the amplitude of the noise signal has been reduced by over 60%, and it can effectively eliminate the noise components at 17 Hz and 20 Hz caused by the glider’s motor. This algorithm achieves an average increase in the signal-to-noise ratio (SNR) of 12 dB, which is equivalent to an approximately 80% improvement in accuracy. It significantly enhances the stability and signal-to-noise ratio of the magnetic field signals of underwater targets. This provides a feasible solution for equipping underwater gliders with magnetic field sensors for underwater target detection, holding important practical engineering significance. Full article

Aiming at the problem of manual feature extraction and insufficient mining of feature information for partial discharge pattern recognition under different insulation faults in GIS, a deep learning model based on phase and timing features with Swin Transformer-AFPN-LSTM architecture is proposed. Firstly, a GIS insulation fault simulation experimental platform is constructed, and the PRPD phase data and TRPD timing data under different faults are obtained; secondly, the TRPD timing data are converted into MTF; then the PRPD phase data and MTF timing data are input into the Swin Transformer-AFPN-LSTM model and other deep learning models for performance comparison. The experimental results show that the Swin Transformer-AFPN-LSTM model improves the performance by 14.09–21.23% compared with the traditional CNN model and LSTM model. Moreover, using this model to extract phase features and timing features simultaneously improves the accuracy by 10.67% and 8.66%, respectively, compared with single feature extraction, and the overall accuracy reaches 98.82%, which provides a new idea for GIS insulation fault identification. Full article

Knee joint disorders pose a significant and growing challenge to global healthcare systems. Recent advancements in robotics, sensing technologies, and artificial intelligence have driven the development of robot-assisted therapies, reducing the physical burden on therapists and improving rehabilitation outcomes. This study presents a novel knee exoskeleton designed for safe and adaptive rehabilitation, specifically targeting bed-bound stroke patients to enable early intervention. The exoskeleton comprises a leg splint, thigh splint, and an actuator, incorporating a series elastic actuator (SEA) to enhance torque density and provide intrinsic compliance. A variable impedance control method was also implemented to achieve accurate position tracking of the exoskeleton, and performance tests were conducted with and without human participants. A preliminary clinical study involving two stroke patients demonstrated the exoskeleton’s potential in reducing muscle spasticity, particularly at faster movement velocities. The key contributions of this study include the design of a compact SEA with improved torque density, the development of a knee exoskeleton equipped with a cascaded position controller, and a clinical test validating its effectiveness in alleviating spasticity in stroke patients. This study represents a significant step forward in the application of SEA for robot-assisted rehabilitation, offering a promising approach to the treatment of knee joint disorders. Full article

Rehmannia glutinosa is an important medicinal herb; but its long-term cultivation often leads to continuous cropping problems. The underlying cause can be attributed to the accumulation of and alterations in root exudates; which interact with soil-borne pathogens; particularly Fusarium oxysporum; triggering disease outbreaks that severely affect its yield and quality. It is therefore crucial to elucidate the mechanisms by which root exudates induce F. oxysporum CCS043 outbreaks. In this study; the genome of F. oxysporum CCS043 from R. glutinosa’s rhizosphere microbiota was sequenced and assembled de novo; resulting in a 47.67 Mb genome comprising 16,423 protein-coding genes. Evolutionary analysis suggests that different F. oxysporum strains may adapt to the host rhizosphere microecosystem by acquiring varying numbers of specific genes while maintaining a constant number of core genes.The allelopathic effects of ferulic acid; verbascoside; and catalpol on F. oxysporum CCS043 were examined at the physiological and transcriptomic levels. The application of ferulic acid was observed to primarily facilitate the proliferation and growth of F. oxysporum CCS043; whereas verbascoside notably enhanced the biosynthesis of infection-related enzymes such as pectinase and cellulase. Catalpol demonstrated a moderate level of allelopathic effects in comparison to the other two. Furthermore; 10 effectors were identified by combining the genomic data. Meanwhile; it was found that among the effector-protein-coding genes; ChiC; VRDA; csn; and chitinase exhibited upregulated expression across all treatments. The expression patterns of these key genes were validated using qRT-PCR. Transient overexpression of the two effector-encoding genes in detached R. glutinosa leaves provided further confirmation that ChiC (GME8876_g) and csn (GME9251_g) are key effector proteins responsible for the induction of hypersensitive reactions in R. glutinosa leaf cells. This study provides a preliminary indication that the use of allelochemicals by F. oxysporum CCS043 can promote its own growth and proliferation and enhance infection activity. This finding offers a solid theoretical basis and data support for elucidating the fundamental causes of fungal disease outbreaks in continuous cropping of R. glutinosa and for formulating effective mitigation strategies. Full article

Broadcast ephemeris data are essential for the precision and reliability of the BeiDou Navigation Satellite System (BDS) but are highly susceptible to anomalies caused by various interference factors, such as ionospheric and tropospheric effects, solar radiation pressure, and satellite clock biases. Traditional threshold-based methods and manual review processes are often insufficient for detecting these complex anomalies, especially considering the distinct characteristics of different satellite types. To address these limitations, this study proposes an automated anomaly detection method using the IF-TEA-LSTM model. By transforming broadcast ephemeris data into multivariate time series and integrating anomaly score sequences, the model enhances detection robustness through data integrity assessments and stationarity tests. Evaluation results show that the IF-TEA-LSTM model reduces the RMSE by up to 20.80% for orbital parameters and improves clock deviation prediction accuracy for MEO satellites by 68.37% in short-term forecasts, outperforming baseline models. This method significantly enhances anomaly detection accuracy across GEO, IGSO, and MEO satellite orbits, demonstrating its superiority in long-term data processing and its capacity to improve the reliability of satellite operations within the BDS. Full article

Sprite halos are transient luminous phenomena in the lower ionosphere triggered by tropospheric lightning. The effect of removed charge distributions on sprite halos has not been sufficiently discussed. A three-dimensional (3D) quasi-electrostatic (QES) field model was developed in this paper, including the ionospheric nonlinear effect and optical emissions. Simulation results show that, for a total charge of 150 C removed within 1 ms with different spatial distributions, higher altitudes of charge removal lead to stronger electric fields and increase sprite halos’ emission intensities. The non-axisymmetric horizontal distribution of charge affects mesospheric electric fields, and the corresponding scales and intensities of emissions vary with observation orientations. Considering the tilted dipole charge structure due to wind shear, the generated electric field and the corresponding position of sprite halos shift accordingly with the tropospheric removed charge, providing an explanation for the horizontal displacement between sprite halos and the parent lightning. Full article