Search

If a subsequent commutation failure occurs in the line commutated converter-based high voltage direct current (LCC-HVDC) transmission system, it will have a seriously negative impact on the safe and stable operation of the AC-DC hybrid power grid. Aiming at the problem of commutation failure during the current deviation control stage in the fault recovery process of the LCC-HVDC system, the relationship between current deviation control parameter and commutation failure is analyzed theoretically. It is found that to avoid commutation failure at this stage, the DC voltage and AC commutation voltage at the inverter side of the LCC need to meet certain constraints. These constraints are directly affected by the current deviation control parameter. Based on the theoretical analysis results, a current deviation control parameter tuning method is proposed, which can improve the control requirements for DC voltage recovery speed and degree during the fault recovery process of the system. The method makes it easier for the system to meet the stable operation constraints between DC voltage and AC commutation voltage, thereby reducing the probability of subsequent commutation failure. Finally, the CIGRE standard test model in the PSCAD/EMTDC simulation platform is used to verify the correctness of the theoretical analysis and the effectiveness of the parameter tuning method.

A series of processable and high-temperature resistant polyimide resins terminated with 4-phenylethynyl phthalic anhydride were synthesized by PMR (for in situ polymerization of monomer reactants) using isopropyl alcohol as esterifying agent. The resin solution stored at room temperature for sixteen weeks has no solid precipitation, and the viscosity of the solution does not change significantly, which shows good storage stability at room temperature. The minimum melt viscosity of the imide oligomers is less than 300 Pa·s, which is suitable for compression moulding or autoclaved process. The cured polyimides exhibit extremely high glass transition temperatures (Tg) up to 462℃. The flexural strength and modulus of T300/PMR-PE-2 carbon fiber composite are 963 MPa and 53 GPa, respectively. The inter-laminar shear strength of the composite is 56 MPa. Moreover, the composite can maintain no lower than 66% of their room temperature mechanical properties at 300℃. After isothermal aging in air atmosphere for 500 h at 300℃, the mass loss of the composite is only 0.96%, meanwhile, the composite retention rate maintains 72% of its room temperature flexural strength and 98% of its room temperature inter-laminar shear strength, which demonstrates its excellent thermal-oxidative aging resistance.

Background: In recent years, automatic guided vehicles (AGVs) have developed rapidly and been widely applied in intelligent transportation, cargo assembly, military testing, and other fields. One of the key issues in these applications is path planning. Global path planning results based on known environmental information are used as the ideal path for AGVs combined with local path planning to achieve safe and fast arrival at the destination. The global planning method planning results as the ideal path should meet the requirements of as few turns as possible, short planning time, and continuous path curvature. Methods: We propose a global path-planning method based on an improved A * algorithm. And the robustness of the algorithm is verified by simulation experiments in typical multi obstacles and indoor scenarios. To improve the efficiency of pathfinding time, we increase the heuristic information weight of the target location and avoided the invalid cost calculation of the obstacle areas in the dynamic programming process. Then, the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method. Since the final global path needs to satisfy the AGV kinematic constraints and the curvature continuity condition, we adopt a curve smoothing scheme and select the optimal result that meets the constraints. Conclusions: Simulation results show that the improved algorithm proposed in this paper outperforms the traditional method and can help AGVs improve the efficiency of task execution by efficiently planning a path with low complexity and smoothness. Additionally, this scheme provides a new solution for global path planning of unmanned vehicles.

The NLRP3 inflammasome is a fundamental component of the innate immune system, yet its excessive activation is intricately associated with viral pathogenesis. Porcine reproductive and respiratory syndrome virus type 2 (PRRSV-2), belonging to the family Arteriviridae, triggers dysregulated cytokine release and interstitial pneumonia, which can quickly escalate to acute respiratory distress and death. However, a mechanistic understanding of PRRSV-2 progression remains unclear. Here, we screen that PRRSV-2 nsp2 activates the NLRP3 inflammasome, thereby instigating a state of hyperinflammation. Mechanistically, PRRSV-2 nsp2 interacts with the nucleotide-binding and oligomerization (NACHT) domain of NLRP3, augmenting IKKβ recruitment to driving NLRP3 translocation to the dispersed trans-Golgi network (dTGN) for oligomerization. This process facilitates ASC polymerization, culminating in the activation of the NLRP3 inflammasome. In addition, the IKKβ-dependent NLRP3 translocation to the dTGN is pivotal for pseudorabies virus (PRV) and encephalomyocarditis virus (EMCV)-induced inflammatory responses. Collectively, these results elucidate a novel mechanism of NLRP3 inflammasome activation during PRRSV-2 infection, providing valuable insights into PRRSV-2 pathogenesis. Author summary: Hyperactivation of the NLRP3 inflammasome triggers excessive proinflammatory cytokine release, skewing the host response toward hyperinflammation and severe tissue damage. PRRSV-2, a positive-stranded RNA virus that causes significant economic losses in the swine industry, triggers severe inflammation and cytopathology during infection. Gaining a deep understanding of its pathogenesis is crucial for developing effective strategies to combat the disease. Here, we show that PRRSV-2 nsp2 plays a crucial role in activating the NLRP3 inflammasome by interacting with NLRP3 and facilitating the recruitment of IKKβ, which drives the translocation of NLRP3 to the dTGN for oligomerization, ultimately leading to the activation of the NLRP3 inflammasome. This process is also crucial for the inflammatory responses in both PRV and EMCV infections. Our findings uncover a novel function of PRRSV-2 nsp2 in the activation of inflammatory responses, advancing our understanding of PRRSV-2 pathogenesis, and highlight the importance of IKKβ-dependent translocation of NLRP3 to the dTGN in virus-induced NLRP3 inflammasome activation. [ABSTRACT FROM AUTHOR]

The retrieval of global significant wave height (SWH) data is crucial for maritime navigation, aquaculture safety, and oceanographic research. Leveraging the high temporal resolution and spatial coverage of Cyclone Global Navigation Satellite System (CYGNSS) data, machine learning models have shown promise in SWH retrieval. However, existing models struggle with accuracy under high-SWH conditions and discard a significant number of such observations due to low quality, which limits their effectiveness in global SWH retrieval, particularly for monitoring tropical cyclone (TC) events. To address this, this study proposes a daily global SWH retrieval framework through the enhanced eXtreme Gradient Boosting model (XGBoost-SC), which incorporates Cumulative Distribution Function (CDF) matching to introduce prior distribution information and reduce errors for SWH values exceeding 3 m. An enhanced loss function is employed to improve accuracy and mitigate the distribution bias in low-SWH retrieval induced by CDF matching. The results were tested over one million sample points and validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) SWH product. With the help of CDF matching, XGBoost-SC outperformed all models, significantly reducing RMSE and bias while improving the retrieval capability for high SWHs. For SWH values between 3–6 m, the RMSE and bias were 0.94 m and −0.44 m, and for values above 6 m, they were 2.79 m and −2.0 m. The enhanced performance of XGBoost-SC for large SWHs was further confirmed in TC conditions over the Western North Pacific and in the Western Atlantic Ocean. This study provides a reference for large-scale SWH retrieval, particularly under TC conditions. [ABSTRACT FROM AUTHOR]