Putian University (Chinese: 莆田学院; pinyin: Pútián Xué Yuán) is a public university located in Putian, Fujian, China..
Tungsten(W)-based materials are promising plasma-facing materials (PFMs) for fusion reactors but face challenges of high ductile-brittle transition temperature (DBTT) and low recrystallization temperature. In this study, Doped W powders with 0.25 Q_gg :265.443/334.406 kJ/mol) to inhibit grain growth. HIPed alloys had the best performance: DBTT (300–350 °C) was lower than SPSed alloys (500-600 °C) and pure W, with higher tensile properties. Strengthening mechanism of Zr(Y)O2 particles in tungsten alloys and the biphase grain were discussed. This work provides a route for high-performance W-based PFMs, supporting their future fusion reactor application.
Background: Ferroptosis has emerged as a pivotal mechanism in epilepsy development. Kelch-like ECH-associated protein 1 (KEAP1), a critical regulator of the nuclear factor erythroid 2-related factor 2 (NRF2) pathway, plays a pivotal role in oxidative stress and is implicated in seizure recurrence. This study aimed to elucidate the mechanistic role of KEAP1 in kainic acid (KA)-induced ferroptosis and neuroinflammation. Methods: HT22 hippocampal neuronal cells were stimulated with KA to establish the in vitro excitotoxicity model. Cell viability and apoptosis were detected using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and flow cytometry, respectively. Inflammatory responses were evaluated by detecting interleukin 6 (IL-6) and tumor necrosis factor alpha (TNF-alpha) secretion levels. Ferroptosis was evaluated by measuring Fe2+, glutathione (GSH), and reactive oxygen species (ROS) levels. The parkin RBR E3 ubiquitin protein ligase (PRKN)-KEAP1 and histone deacetylase 6 (HDAC6)-PRKN interactions were assessed by co-immunoprecipitation (Co-IP) experiments. Results: KEAP1 (p = 0.0008) and HDAC6 (p = 0.0004) protein levels were upregulated in the KA-induced HT22 cells, while PRKN expression was downregulated (p = 0.0034). Knockdown of KEAP1 alleviated KA-induced neuroinflammation by reducing the secretion of IL-6 (p = 0.0021) and TNF-alpha (p = 0.0003). Furthermore, it suppressed ferroptosis, as demonstrated by decreased levels of Fe2+ (p = 0.0022) and ROS (p < 0.01), and an increased GSH content (p = 0.0057). In addition, KEAP1 silencing attenuated apoptosis (p = 0.0016) in KA-induced cells. Mechanistically, PRKN mediated KEAP1 ubiquitination and degradation (p = 0.0054); HDAC6 induced PRKN deacetylation (p < 0.001), and HDAC6 modulated KEAP1 expression via PRKN. In KA-treated HT22 cells, KEAP1 reconstitution counteracted PRKN overexpression-mediated anti-inflammatory and ferroptosis-inhibitory effects (p < 0.05), while PRKN knockdown reversed the protective effects conferred by HDAC6 silencing (p < 0.05). Additionally, PRKN regulated the NRF2/solute carrier family 7 member 11 (SLC7A11)/glutathione peroxidase 4 (GPX4) pathway through KEAP1 (p < 0.05). Conclusion: Our study uncovers a novel HDAC6-PRKN-KEAP1 cascade that critically modulates KA-induced ferroptosis and neuroinflammation in HT22 neuronal cells.
Urban rail transit systems encounter challenges in short-term passenger flow prediction due to complex spatiotemporal data and dynamic travel patterns. Traditional models often struggle to capture these complexities. This study introduces a hybrid prediction framework that employs a two-stage feature selection strategy and multiscale decomposition to enhance forecasting accuracy. Key innovations include (1) a two-stage feature selection combining correlation analysis and recursive feature elimination, enabling the identification of the most influential temporal, spatial, and external features; (2) complete ensemble empirical mode decomposition with adaptive noise for isolating multiscale patterns; (3) parallel modeling with modern temporal convolutional networks and extended long short-term memory; and (4) a cross-attention mechanism for spatiotemporal feature fusion. Tested on 22 stations of Fuzhou Rail Transit Line 2 (8 million automatic fare collection records), the proposed model significantly outperforms classical and deep learning baselines. An ablation analysis further validates the contribution of each module to the overall performance. By bridging advanced AI methodologies with practical transit management needs, this work advances scalable, data-driven decision-making for sustainable urban mobility systems.
As an innovative and interactive emerging marketing model, blind- box live streaming provides businesses with new sales channels. However, there have been few studies of the drivers of impulse buying in blind-box live streaming scenarios. Thus, we extended stimulus-organism-response theory to explore factors correlated with impulsive buying in the context of blind-box live streaming, analyzing data from 263 live streaming users through structural equation modeling. We found that live streamers'characteristics, the live streaming servicescape, and the attributes of the blind box each had a positive relationship with consumers'impulsive buying, while positive affect played a significant mediating role. These findings suggest that industry practitioners should focus on cultivating the unique charm of live streamers, creating an attractive live streaming environment, and using the attributes of blind boxes to create positive consumer emotions and promote impulsive consumption behavior.
Understanding the complex drivers of water yield is essential for ensuring basin water resource security, yet existing linear approaches often overlook the critical nonlinear effects arising from factor interactions. Previous studies combining the InVEST model with attribution methods have typically treated climate and land use as independent factors, failing to quantify their interactive effects beyond additive assumptions. This study addresses this gap by introducing a coupled framework that explicitly isolates and quantifies nonlinear climate-land interactions through scenario-based residual decomposition and spatial interaction detection. Focusing on the Minjiang River Basin, this study first applies a locally calibrated InVEST model to analyze the spatiotemporal patterns of water yield from 2000 to 2023. Through scenario analysis and the Geographical Detector method, we decoupled the contributions of climatic factors, land use, and their interactions. The results show significant spatiotemporal heterogeneity in water yield, averaging 1053.59 mm, with a spatial pattern aligned closely with precipitation. Climatic factors dominated the changes (average contribution 93.43%), while the direct contribution of land use was minimal (-1.56%). Importantly, a significant nonlinear interaction effect was identified (average 8.13%), with the interplay between precipitation and forest land proportion showing the strongest explanatory power for spatial differentiation (q-statistic up to 96.4%). These findings highlight the necessity of an integrated climate-land regulatory strategy that enhances climate resilience and optimizes key land uses to promote sustainable water management, providing a methodological framework for analyzing complex hydrological drivers.
