Civil Aviation Flight University of China

Abstract Variations in the thermal forcing of the Qinghai–Tibet Plateau (QTP) constitute an important precursor for downstream climate prediction. Especially for its downstream population, the Sichuan Chongqing region with dense economy and frequent extreme high temperatures in summer has a significant impact. Based on meteorological observations from the QTP and the Sichuan–Chongqing Region (SCR) during 1981–2023, this study investigates the spatiotemporal characteristics of the winter Land–Air Temperature Difference (ΔT) over the QTP and its predictive relevance for summer high temperatures in the SCR. The results show that winter ΔT over the QTP has exhibited a significant upward trend over the past 43 years, with an interdecadal shift occurring in the early 21st century. The spatial enhancement is mainly concentrated in the eastern and southern parts of the plateau. Singular value decomposition (SVD) analysis reveals a dominant coupled mode between pre-winter ΔT over the QTP and summer high temperatures in the SCR, explaining 91.74% of the covariance and passing the 99% confidence level in Monte Carlo tests. The corresponding time coefficients are significantly positively correlated, indicating a stable cross-seasonal statistical linkage between winter thermal anomalies over the plateau and downstream summer heat. Composite circulation analysis further supports this relationship. On this basis, a statistical prediction framework using only pre-winter ΔT over the QTP as a predictor is constructed. Hindcast experiments for summer high temperatures during 2021–2023 demonstrate that the method effectively reproduces the primary spatial patterns of summer heat in the SCR, with high spatial similarity between predicted and observed fields. These results suggest that pre-winter ΔT over the QTP can serve as an important precursor signal for summer high temperatures in the SCR and has potential value for cross-seasonal heat prediction.

The North Atlantic Oscillation (NAO) modulates mid-latitude circulation and cloudiness and thereby alters the surface radiation balance across the North Atlantic–European sector. Focusing on northern winter (DJF) 1979–2023 over 45°–55°N, 10°W–15°E, the paper combines ERA5 monthly single-level reanalysis fields with the NOAA monthly NAO index. The paper applies (i) composite differences between positive and negative NAO phases (NAO⁺–NAO⁻) with Welch’s t-test, (ii) grid-point regression on standardized NAO, and (iii) point-cloud diagnostics linking low cloud changes to radiation terms. Results show that NAO⁺ winters feature reduced low-cloud cover, enhanced net shortwave, weakened net longwave, and overall positive net-radiation anomalies; the shortwave contribution dominates the net-radiation response. These results underscore the influence of the NAO on the surface energy balance, with implications for understanding regional climate variability and improving predictive models. These findings highlight a robust wintertime NAO–cloud–radiation coupling over mid-latitude land that shapes the surface energy balance and offers observational constraints for regional climate assessment and model development.

This study addresses a critical gap in understanding how teacher innovation drives the utilization of digital resources in education, mediated by teachers' self-efficacy and collaboration, and moderated by professional development. Using data from 68,054 teachers across 81 countries in the PISA 2022 teacher questionnaire, we retained an analytic sample of 64,852 teachers after applying inclusion criteria and listwise deletion of missing values. We employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze these relationships. Results reveal that teacher innovation significantly predicts digital resource usage, with self-efficacy and collaboration serving as key mediators. Professional development amplifies these relationships. This study offers novel insights into the mechanisms linking teacher attributes to digital resource adoption, providing actionable strategies for policymakers to enhance educational digitalization. This study moves beyond the conventional individual-centered perspective of the technology acceptance model, offering a comprehensive and systematic strategy to support the digital transformation of education.

Accurate prediction of air traffic noise is critical for advancing environmentally sustainable operations in high density terminal areas. Conventional noise prediction models often exhibit significant limitations due to discrepancies between actual and nominal flight trajectories. To overcome this challenge, this study introduces a probabilistic framework that integrates real air-traffic-flow data to generate realistic flight trajectory distributions. The proposed methodology extracts key operational features—including trajectory distribution probabilities, and essential trajectory operation features—within a machine learning architecture. Furthermore, we develop a dedicated air traffic noise prediction model for clustered flight paths that explicitly incorporates traffic flow patterns, enabling high-fidelity simulation of noise propagation under actual air traffic operation. The framework is validated using a QAR (Quick Access Recorder) dataset from the terminal area of Changsha Huanghua International Airport. Experimental results demonstrate the model’s high predictive accuracy for both air traffic noise distribution and its influence, coupled with computational efficiency and practical applicability. The findings indicate that the proposed approach successfully addresses the challenge of predicting air traffic noise from divergent, real-world flight trajectories, offering a robust method for supporting noise-abatement strategies and sustainable aviation-planning initiatives.

Titanium aluminide intermetallics have gained considerable attention as high-temperature structural materials for aerospace applications, but are susceptible to "titanium fire" under extreme service conditions. The role of Al elements on the combustion behavior of titanium aluminide intermetallics remains not fully understood. Herein, the influence of Al content on the ignition critical condition and burning rate of Ti1-xAlx alloys was investigated by using promoted ignition combustion (PIC) tests under oxygen-enriched atmosphere. Results indicated that the critical oxygen pressure of Ti1-xAlx alloys increases from 0.11 MPa to 0.23 MPa, and the ignition temperature under oxygen pressure of 0.41 MPa increases from 1059.5 ± 4.8 K to 1120.4 ± 2.5 K as Al content increases from 20 at% to 70 at%. However, the combustion rate increases from 11.85 ± 0.13 mm·s-1 to 14.05 ± 0.09 mm·s-1 as Al content increases from 20 at% to 70 at%. Moreover, the activation energy for ignition increases from 105.44 kJ·mol-1 to 153.04 kJ·mol-1 as Al content increases from 20 at% to 70 at%. According to the microstructure analysis after combustion, the influence of Al content on the ignition activation energy and burning rate is attributed to multiple factors involving bonding energy, melting temperature, and heat release.

Accurate prediction of network-wide delay is crucial for air traffic management and passenger service. However, the inherent complexity of large-scale air traffic networks, with their dense interconnectivity and multi-dimensional operational dynamics such as flight connectivity, makes this task highly challenging. While Graph Neural Networks (GNNs) offer a promising framework, prevailing models are constrained by a “node → edge → node” representation paradigm, which fails to preserve the high-fidelity, edge-centric operational data that encodes delay propagation paths. To overcome this limitation, we propose a novel edge-based GNN. Our approach begins with a flight-connectivity-informed delay characterization, introducing delay width and delay strength as core metrics. The model implements an “edge → node” message-passing mechanism that explicitly encodes inbound and outbound flights, enabling direct learning of delay diffusion dynamics along air routes. Extensive experiments on real-world datasets demonstrate that our method outperforms state-of-the-art benchmarks, achieving the lowest RMSE, MAE, and MSE. A layered performance analysis reveals a key strength: the model delivers superior accuracy at major hub airports—which are critical to network performance—while maintaining robust precision at small-to-medium-sized airports. This balanced capability underscores the model’s practical utility and its enhanced capacity to capture the essential spatial–temporal dependencies governing delay propagation across diverse airport tiers.