Air traffic management is moving towards higher levels of human–automation teaming and decision support. Brain–computer interfaces (BCIs) can infer controllers’ cognitive workload from electroencephalography (EEG), enabling workload-aware automation. However, EEG-based decoding is highly sensitive to compound distribution shifts across subjects and operational scenarios, which degrades cross-domain representation learning and continuous monitoring. We propose a Dual-Shift Contrastive Alignment Learning Network (DSCAL-Net) for EEG-based cognitive workload decoding in air-traffic control. DSCAL-Net first employs a heterogeneous spatio-temporal encoding module that tightly integrates multi-band local time–frequency dynamics with connectivity-aware global networks, so that local features are shaped by global context and global connectivity is refined by local evidence. A factorized projection module then constructs subject-invariant and scenario-invariant subspaces within a shared latent space, while dual-shift contrastive alignment jointly regularizes subject-invariant, scenario-invariant and dual-domain representations, mitigating compound shifts while preserving class discriminability. Using multi-level workload EEG recorded in high-fidelity aerodrome-control simulations, DSCAL-Net is evaluated against representative baselines under cross-scene, cross-subject and dual-domain protocols, achieving average accuracies of 93.22%, 73.25% and 84.96%, respectively, with consistently improved accuracy and prediction stability. Simulated online monitoring further shows that the workload index derived from DSCAL-Net can smoothly track multi-stage workload evolution, demonstrating its transferability and potential as a computational foundation and proof-of-concept for future neuroadaptive support in air-traffic control.
This study investigates the mechanical performance and blast resistance of high-performance aramid, carbon, and ultra-high molecular weight polyethylene (UHMWPE) fiber fabrics, responding to the need for lightweight and flexible materials in anti-explosion containers for aviation and critical infrastructure. The experimental methodology integrated quasi-static and dynamic tensile tests to characterize the strain-rate effect, followed by near-field air blast tests on both single-material and hybrid multi-ply fabric specimens to analyze their dynamic response, failure modes, and overpressure attenuation. Key findings revealed that carbon fabric exhibited high stiffness but was strain-rate insensitive and susceptible to brittle perforation failure, whereas aramid and UHMWPE fabrics demonstrated strain-rate sensitivity, with UHMWPE showing superior ductility and energy absorption. The hybrid multi-ply configuration (A-C-U sequence) achieved the least amount of failure, effectively utilizing the wave impedance of aramid fabric for initial shock reflection, high stiffness of carbon fabric for stress homogenization, and plasticity of UHMWPE fabric for energy dissipation. Additionally, all fabrics attenuated peak overpressure by over 80%, with enhancement observed for increased thickness. The study concludes that the strategic layering of different fabrics creates a synergistic effect, mitigating the weaknesses of individual fabrics and establishing an effective design paradigm for advanced blast-resistant structures, further enhancing the protective performance.
The characteristics and causes of the long-term trends of diurnal variation of sea surface temperature (DSST) in the South China Sea (SCS) are investigated in this study based on the global hourly sea surface temperature data generated by the mixed layer model (MLSST) from the National Marine Environmental Forecasting Center (NMEFC) of China. Validation of the MLSST dataset demonstrates excellent agreement with in-situ buoy observations in the SCS with a correlation coefficient of 0.951, confirming its reliability in the SCS. Based on this dataset, the long-term trend of DSST in the SCS exhibits significant seasonal variations with the strongest magnitude in spring and the weakest in winter. Specifically, a significant decreasing trend of -0.0014 degrees C yr-1 during 1982-2009 transitioned to a pronounced increasing trend of 0.0057 degrees C yr-1 from 2010-2019. Both climatic factors and local atmospheric variables jointly modulate the DSST in the SCS. On the long-term timescale, the Pacific Decadal Oscillation (PDO) served as the dominant factor driving DSST changes in most areas of the SCS. After 2010, the PDO shifted to a persistent positive phase, providing a crucial climatic background for the basin-wide DSST increase. While the El Ni & ntilde;o-Southern Oscillation (ENSO) showed enhanced correlation with DSST post-2010, the Indian Ocean Dipole (IOD) had negligible influence overall. In addition, the SCS summer monsoon played an important regulatory role in shaping the long-term trend of summer DSST by altering air-sea heat exchange processes. Among local atmospheric variables, sea surface wind speed was significantly negatively correlated with DSST, and net heat flux was significantly positively correlated with DSST, with their effects showing regional differentiation. The regulatory role of wind speed dominated in the western SCS, whereas the net heat flux exerted a more prominent impact in parts of the eastern SCS. This work clarifies the spatiotemporal patterns and multi-driver framework governing DSST variability in the SCS, providing a basis for understanding regional ocean-atmosphere interactions.
Microbursts can cause severe low-level wind shear, posing a threat to aviation safety. However, due to their small scale, they are difficult to capture by conventional equipment such as surface Automated Weather Observing Systems (AWOS) and Doppler weather radar. On 25 June 2022, a microburst event occurred at Urumqi Diwopu International Airport. Based on multi-source and high-resolution observation data, including AWOS data, microwave radiometer data and Doppler wind lidar data, an analysis was conducted on the characteristics of this microburst event. The results indicate that before the occurrence of this microburst, the atmosphere was in an unstable state, where the lower atmosphere exhibited a significant dry adiabatic lapse rate, and the temperature and humidity profiles displayed a typical “inverted-V” thermodynamic structure. Approximately 10 min prior to the occurrence of the microburst, the Convective Available Potential Energy (CAPE), Downdraft Convective Available Potential Energy (DCAPE), Microburst Wind Potential Index (MWPI), and WINDEX increased rapidly and reached their peak values, with CAPE and DCAPE reaching 4967 J·kg−1 and 1004 J·kg−1, respectively, MWPI reaching 3.0, and WINDEX reaching 26.2 m·s−1, before subsequently decreasing rapidly. During the transition from rapid accumulation to release of the unstable energy, precipitation particles from aloft passed through the dry and warm low-level region, undergoing evaporative cooling that enhanced negative buoyancy. This effect, combined with precipitation loading, facilitated the accelerated descent and ground impact of the dry-cold air intruding from the mid-to-lower troposphere. The Doppler wind lidar data showed that the downdraft first formed at an altitude of approximately 1.5 km and rapidly extended toward the ground, reaching a maximum descent speed of 4.78 m·s−1. After impinging on the ground, the downdraft produced a pronounced divergent outflow, with the maximum surface wind speed reaching 25.5 m·s−1, which exceeded the operational downburst gust threshold of 17.9 m·s−1. This evolution of the microburst was consistent with the observed ground pressure surge, rapid temperature drop, and downward momentum transport aloft. Integrated observations from microwave radiometers and Doppler wind lidar can effectively reveal the thermodynamic stratification and dynamic characteristics of microburst, offering valuable reference for airport microburst monitoring and nowcasting early warning.
This work provides a large-scale trajectory optimization approach based on air traffic complexity to balance the overall airspace situation under the trajectory-based operation mode.It uses real operation data simulation to verify its effectiveness and optimization effect.Firstly,an air traffic complexity calculation model is constructed based on the potential interaction relationship between flights.Secondly,a multi-objective large-scale trajectory optimization model that meets the operational requirements of air traffic control is constructed based on the air traffic complexity calculation model,and a high-quality genetic solution algorithm is proposed.Finally,using the national flight operation data from June 2019,a simulation simulation of air traffic complexity-based trajectory optimization is performed,and a comparison between conflict-free trajectory optimization and air traffic complexity-based trajectory optimization is conducted.Conflict trajectory optimization is compared and analyzed.The simulation results show that the proposed method can resolve 93.74%of potential conflicts.Compared with conflict-free trajectory optimization,its optimization scheme exhibits less air traffic complexity fluctuations when facing environmental perturbations such as waypoint waiting and area bans.By adjusting 21.11%of the flights,it can reduce the average complexity of each time period by 24.98%on average,and the overall average complexity of the whole day is reduced from 120.52 to 72.82.
