Aviation safety problems lead to casualties and property damage, with unsafe behaviors of aviation maintenance personnel being a critical factor. This study firstly constructed a four-stage cognitive model (information acquisition, information processing, response selection, and action execution) to build a cognitive model about unsafe behaviors. In the first two stages, an information processing model was established to analyze personnel cognitions, while in the latter two stages, the Theory of Planned Behavior (TPB) was used to explain operational decision-making. Subsequently, an Agent-Based Modeling (ABM) framework was developed to simulate multiagent interactions in aviation maintenance environments. By synthesizing safety responsibilities across managerial hierarchies, interaction rules between operators and managers were formalized, which was rigorously described by ODD (Overview, Design concepts, Details) protocol to ensure clarity and generalizability. Finally, the ABM was visualized on NetLogo platform and validated through a case study of a maintenance operation. Then simulation analysis of different intervention strategies was conducted to quantify the efficacy in reducing non-compliant operations, providing actionable recommendations. This study innovatively integrated perspectives from social psychology and cognitive psychology to investigate the cognitive model of unsafe behaviors among aviation maintenance personnel. The findings provided a foundational reference for developing safety management strategies in aviation maintenance.

The development process of sustainable aviation fuel is observed by economic, technological, and regulatory uncertainties. Therefore, risk identification is essential for comprehending existing barriers and developing feasible strategies. Further, given the diversity in literature, specifying gaps is necessary to determine research orientations and identify priority areas for future research. These two approaches ensure a more comprehensive and target-oriented assessment of research in the field. In this mind, this paper aims to identify the main themes and primary topics, the risks discussed, and the overlooked matters related to Sustainable Aviation Fuel (SAF). A systematic literature review is employed to synthesize relevant papers. The identification process yielded 135 records from WoS and Scopus, which were eventually narrowed down to 14 studies after exclusions. Production and economic subjects are the most common topics discussed on SAF. The academics highlighted the risks regarding financial and natural resources, yet landlessness has not been sufficiently discussed. In addition, the emission-reducing efforts lack holism, and many significant questions remain unanswered. This paper presents a distinctive synthesis of the themes and risks in studies on SAF and highlights some overlooked issues. It is believed that future studies should address the unresolved questions stated to propel green aviation forward.

This paper presents an improved method for determining rheological function parameters of viscoelastic-plastic materials, demonstrated through creep under torsional deformation. The approach is based on the heredity theory (Boltzmann’s principle), using curve fitting to identify parameters (A, α, and β). The improved method from previous studies uses precise graph construction via computational tools, with curve alignment performed using a least square–like approach. An extended database of theoretical rheological function graphs and tables, developed from complex mathematical models and prior research, was employed in the analysis. Importantly, the study highlights that modern aircraft structures, where a significant portion of elements are made of advanced composite materials, are exposed during flight to complex, time-dependent loading conditions. Under these conditions, creep phenomena may develop within structural components, leading to residual deformations and gradual degradation of mechanical properties over time. Even with initially high safety margins, such effects can eventually cause the failure of critical elements after prolonged operation. Therefore, the presented method provides a scientific and practical tool for assessing and predicting the long-term viscoelastic–plastic behavior of aviation composites, ensuring structural integrity, flight safety, and an extended operational lifetime of aircraft.

Efficient passenger screening is a critical component of airport security operations, directly influencing both safety standards and the overall passenger experience. As global air traffic continues to grow, optimizing the throughput of security checkpoints while maintaining regulatory compliance has become a major operational challenge. This study investigates one often overlooked factor affecting checkpoint performance – the level of passenger preparation prior to screening. The research combines experimental and simulation-based analyses to assess how improper passenger preparation contributes to the frequency of alarms at walk-through metal detectors (WTMDs). The study focuses on a security lane operating under a free passenger flow configuration equipped with a WTMD. The results demonstrate that better passenger preparation significantly improves checkpoint throughput and overall lane capacity. This microscopic analysis, which quantifies the operational impact of passenger behavior on system performance, addresses a gap not previously covered in the literature. The findings provide practical insights for airport security managers and system designers, emphasizing the importance of targeted passenger guidance and education in enhancing checkpoint efficiency.

Reducing the mass of supersonic aerodynamic surfaces is a critical challenge in the development of high-speed rockets to further their potential range. This study presents the redesign of a supersonic fin with the primary objective of reducing its thickness from 25 mm. Two designs are investigated, with thicknesses of 10 and 12 mm, respectively, to ensure structural integrity under extreme flight conditions. A comprehensive computational approach is employed, combining static structural analysis, modal analysis, and aeroelastic analysis. Modal analysis is validated through an experimental method using a hammer impulse test for modal frequencies. The 10 mm rocket fin cannot withstand the static load simulated under the flight condition of 15-degree angle of attack, maximum operational flight speed of Mach 3.27, and air density at sea level. The 12 mm thick fin meets the requirements and demonstrates a flutter speed of Mach 11, significantly exceeding the required flutter speed of Mach 3.99. This research highlights the feasibility of substantial weight reduction in supersonic fins without compromising stability, offering a pathway for future advancements in lightweight, high-speed control surfaces.

To address the threat of invading drones along railway lines, this paper proposes a multi-UAV cooperative capture strategy based on the Grey Wolf Optimizer (GWO) algorithm and dynamic capture points. Firstly, a motion model in three-dimensional space is established according to the movement characteristics of invading drones along railway lines. Secondly, three-dimensional capture points are dynamically generated based on the movement direction of invading drones, and a negotiation allocation mechanism is designed to achieve optimal matching between capture points and UAVs. Then, an objective function combining path consumption and encirclement effect is constructed, and the GWO algorithm is used to optimize the UAV heading angle increment in real-time. Finally, the effectiveness of the algorithm is verified through three-dimensional simulations. The simulations show that this strategy can achieve efficient capture in three-dimensional environments. Compared with strategies without GWO optimization, the average capture time is reduced by 55.5%, and the capture success rate is improved by 4.8%. Furthermore, in comparison with other mainstream optimization algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE), our approach yields superior performance in both the average number of capture steps (55.7 steps) and success rate (100%), providing an efficient and reliable technical solution for railway airspace security protection.

With the growth of the aviation transportation industry, aircraft engines, as the core components of flight safety, are facing increasingly severe challenges in health status assessment and fault warning technology. To achieve accurate evaluation and fault warning of engine status, this study proposes a new method using improved multi-channel network and hybrid network models. The new method can achieve life prediction and evaluation of engine health status in different time-varying scenarios by improving the multi-channel network. Meanwhile, the method achieves early warning of operational faults by using a hybrid network model for real-time analysis of aircraft engine operation data. The results demonstrated that the new method had root mean square errors of only 12.35 and 12.84 on different datasets, significantly better than other models. The score of the new model has also significantly decreased, with accuracy rates of 91.5% and 93.4% on different datasets, far exceeding other models. Moreover, although the new model had a large number of parameters, it had short training time, low latency, small memory usage, and excellent system performance. The new method can significantly improve the health status assessment and fault warning of engines, which has good guiding significance for achieving stable operation of aircraft engines.

The Automatic Terminal Information Service (ATIS) is an important part of the organization of air traffic, which automatically provides pilots with important information about the status of the airport, runways in use, meteorological and other important data. In the absence of ATIS, air traffic controllers (ATC) must repeatedly relay this information, increasing communication demands and potentially contributing to ATC workload and operational inefficiencies. This study investigates the impact of ATIS implementation on sector capacity and controller workload. The research utilizes a year-long observational dataset from a regional airport operating without ATIS, where controller–pilot communication durations and frequencies were recorded under live operational conditions. Communication parameters, including the number of transmissions, mean message duration, and controller availability factor, were extracted and used to model sector capacity according to the Instruction of the Aeronautics Command (ICA) 100-30 methodology. The introduction of ATIS was then simulated by adjusting these parameters to reflect the automated transmission of routine information. Results show that ATIS significantly reduces the number and average duration of controller–pilot communications, leading to an increase in controller availability. Consequently, sector capacity rose by 36.03% for fixed-wing aircraft and 37.56% for rotorcraft. Statistical testing confirmed that these improvements were not attributable to random variation. The findings suggest that implementing ATIS can support more efficient communication, contribute to reducing controller workload, and may lead to a noticeable increase in sector capacity.

Accurate Terminal Aerodrome Forecasts (TAFs) are essential for aviation safety and operational efficiency worldwide. This study develops an AI framework for automated TAF generation, including data preprocessing, model development, and evaluation. Using GFS and ECMWF datasets from 2020–2023 and real TAF forecasts from Brno International Airport the study explores the effectiveness of ML approaches for wind speed and visibility prediction. Principal Component Analysis (PCA) efficiently reduced dimensionality for wind speed predictors but proved less effective for visibility, highlighting its complex nature. Feature importance analysis identified initial observations and seasonal patterns as dominant predictors, underscoring the influence of data quality. Regression models for wind speed met ICAO standards. While Gradient Boosting (GB) classification outperformed human forecasts in raw accuracy, it suffered from poor probability calibration due to dataset imbalance. A critical evaluation of accuracy metrics – such as log-loss and F1-score – revealed their advantages and limitations, particularly in handling imbalanced datasets and probabilistic forecasting. Beyond its empirical findings, the study provides a theoretical foundation for integrating machine learning (ML) into TAF generation, discussing methodological considerations and the interaction between model performance and forecast interpretability. Future research is recommended to focus on the local models, explore advanced models, and expand the framework to diverse climatic conditions.

The aviation industry is experiencing significant growth due to the growing global demand for air travel. The International Civil Aviation Organization predicts that air passenger volumes will quadruple by 2040, putting pressure on airport infrastructure and airspace capacity. This growth is causing environmental challenges, particularly concerning emissions from aircraft operations and airport activities. These emissions contribute to local air pollution and global climate change. Airports are complex operational hubs, requiring sophisticated planning and efficient operations management to mitigate emissions and maximize throughput. This thesis investigates how airport complexity and air traffic management strategies influence inefficiencies in fuel use, time, cost, and environmental impact. Traffic scenarios were generated and analysed using MATLAB code, calculating emissions and fuel consumption across all phases of the landing and take-off (LTO) cycle. The results show significant differences in operational efficiency and environmental impact, offering insights into the effectiveness of modern traffic control methods.