Integrated Management Systems (IMS) have gained increasing attention within the aviation industry as organisations seek to manage multiple, often fragmented, management systems more effectively. Despite this interest, empirical research and regulatory guidance on IMS implementation in aviation remain limited, with much of the existing literature focusing on conceptual models rather than practical application. Recent studies emphasize the importance of incorporating risk as a unifying element within IMS, given its central role across all management systems. This paper presents a case study evaluating the practical applicability of an IMS model developed by Meeûs, which aims to bridge the gap between theoretical integration frameworks and operational implementation. The model is derived from an analysis of the International Civil Aviation Organization’s Safety Management System components and their alignment with various management systems, resulting in a standardized integration “language.” This language consists of a shared risk component and two classification mechanisms designed to identify events and underlying causes across multiple systems. The model further introduces the IMS Cube concept, enabling reported events to be reviewed holistically rather than in isolation within individual management systems. Using empirical data, this study analyses the feasibility of implementing the proposed IMS language and integrated process in an operational aviation context. The findings provide insights into the model’s behaviour in practice and contribute empirical evidence to support the advancement of risk-based integrated management systems in aviation.
Physiological adaptation plays a critical role in skill acquisition during complex flight training processes, yet its dynamic changes across different training stages remain insufficiently understood. This study investigates physiological state changes in novice learners during helicopter flight simulation training and examines their association with skill acquisition. 14 participants without prior flight experience were recruited and completed a progressive four-stage training program, learning from basic helicopter control to independently executing the Helicopter Traffic Pattern. During the experiment, participants’ Pulse-to-Pulse Interval (PPI) signals were continuously recorded, and Pulse Rate Variability (PRV) features were extracted. Statistical analyses were conducted to examine differences in PRV features across training stages. The results showed that PRV features changed significantly across training stages. These dynamic changes in PRV reflected variations in workload and the adaptive regulation of the autonomic nervous system across different stages of skill acquisition. The findings provide physiological evidence for optimizing training program design and support the development of real-time monitoring approaches for trainees’ states.
This study explores the association between aviation-related CO2 emissions and asthma mortality in 15 European Union countries with the highest levels of air traffic between 2008 and 2021. The analysis finds a strong and statistically significant relationship: a 1% increase in aviation emissions is linked to up to a 0.125% rise in asthma-related deaths, underscoring the hidden public health burden of air transport pollution. Emissions from road transport and industrial activity also exhibit strong long-run effects; notably, a 1% rise in road transport emissions corresponds to a 0.79% increase in asthma mortality. Economic expansion, measured by GDP, is indirectly associated with higher asthma mortality, likely through increased demand for aviation and growing urban density. Urban population growth itself is also linked to heightened asthma risks in both the short and long term. These findings highlight the health risks posed by transport emissions and support the need for stronger environmental and public health policy responses. Recommended measures include enhancing emission limits for aviation, promoting sustainable aviation fuels, and integrating air quality indicators into urban and transport planning.
A key consideration in effective aviation maintenance scheduling is the satisfaction of maintenance personnel in relation to allocating tasks and work scheduling. Research that reflects the satisfaction of aviation maintenance staff is limited. Studies focusing on soft constraints in aviation maintenance are nearly non-existent. Soft constraints encompass flexible factors such as employee preferences, workload distribution, and work environment, which significantly impact employee satisfaction and job performance. Aircraft maintenance optimisation needs to consider both hard and soft constraints. Soft constraints have been extensively studied in healthcare and, as this perspective review argues, this can provide valuable insights for aviation maintenance scheduling management. Specifically, aviation maintenance requirements, such as task-based scheduling, necessitate the development of tailored tools to efficiently accommodate the sector’s particularities. This perspective review of aviation maintenance scheduling literature was focused on identifying gaps in the consideration of soft constraints. While there are some studies on incorporating soft constraints into an effective fatigue management system, there is a paucity of research in this area in aviation maintenance. Conversely, the reviewed literature reveals that hard constraints have received greater attention in modelling. This perspective review proposes the development of designated soft constraints to measure the satisfaction of aviation maintenance personnel.
Lithium-ion batteries are extensively employed in LEO satellites because of their high energy density, lightweight design, and superior cycle efficiency; however, their on-orbit performance is strongly influenced by discharge rate and post-discharge voltage relaxation during repetitive eclipse-sunlight cycling. In this study, an experimental investigation was conducted on an 8s6p Li-ion battery pack with a nominal capacity of 19.2 Ah and nominal voltage of 28.8 V. The battery was tested under three different discharge rates – C/20, C/10, and C/5 – followed by rest periods of 30 minutes, 1 hour, and 2 hours. The results indicate that increasing the C-rate significantly enhances the voltage recovery amplitude, whereas extending the rest duration provides only marginal improvement in effective capacity. The voltage relaxation behaviors is well described by a double-exponential model. The proposed model (R2 > 0.99, RMSE < 0.03) confirms the coexistence of fast and slow relaxation processes driven by Li-ion redistribution and electrode structure relaxation. These findings demonstrate that orbit-aware management of discharge rates and rest phase can improve voltage stability and effective energy utilization without increasing system mass or volume, offering practical guidance for battery management strategies in power-constrained LEO missions, including CubeSats and nanosatellites.
To address the issues of short endurance and poor flight performance of flapping wing aircraft, this study focuses on the double-crank double-rocker two-segment flapping wing mechanism and conducts structural parameter optimization research based on lift maximization. First, the complex vector method is used to derive the kinematic models of the four-bar mechanism and the two-segment flapping wing mechanism, establishing the intrinsic relationship between “geometric parameters and flapping wing motion laws”. Combined with the lift formula, a quantitative relationship between “geometric parameters, flapping velocity, and lift” is constructed. With the goal of maximizing the average lift, the genetic algorithm is applied to optimize the key geometric parameters of the mechanism. Finally, numerical simulations of the aerodynamic characteristics of the flapping wing mechanism before and after optimization are carried out to verify the optimization effect. The results show that after optimization, the swing range of the inner wing expands from approximately 57° to 90°; the folding speed of the inner and outer wings during the upstroke is significantly increased, reducing the resistance in the upstroke process; the deployment speed during the downstroke is increased, enhancing the lift in the downstroke process. Meanwhile, as the incoming flow velocity or flapping frequency increases, the growth rates of the net lift and net thrust of the optimized flapping wing are significantly higher than those before optimization. The research results of this paper provide theoretical support and technical references for the selection and parameter design of transmission mechanisms for high-performance flapping wing aircraft.
This paper presents a long short-term memory (LSTM) framework developed for predicting wind gusts 1 h in advance at Taiwan Taoyuan International Airport (RCTP) during typhoons. Hourly surface observations were collected from 12 landfalling typhoons (2010–2020) and used to compare three feature-selection strategies: Pearson correlation, recursive feature elimination with cross validation, and random-forest importance. Models were trained on 12-h multivariate histories. A leave-one-typhoon-out cross-validation scheme revealed that the LSTM model with random-forest selection achieved a mean root-mean-square error of 2.33 m/s and mean absolute percentage error of 21.12%. Although these statistics are comparable to those of a 1-h persistence baseline model on average, the proposed model considerably outperformed the persistence baseline model during rapid intensification and decay phases, reducing errors by approximately 45%. Forecast errors generally remained within the ±5 m/s operational advisory threshold. The results of this case study for RCTP suggest that feature selection can be combined with sequence-based deep learning to provide robust decision support for aviation operations during extreme weather events.
This paper presents a sectoral roadmap development framework-testing process for the Canadian Advanced Air Mobility (AAM) industry, to evaluate a methodology for expanding technological roadmaps into comprehensive sectoral frameworks. The procedural framework incorporates the regulatory perspective to the technological, infrastructural, social, and economic ones, using the S-PLAN framework to identify important strategies and practical actions. Insights were gathered from interviews with cross-disciplinary experts in the public (regulatory) and private (industry) sectors across the industry using the Delphi method to consolidate strategic topics and crucial tactical insights for the sector evolution. The research aims at proposing a methodology for broadening the scope of existing technological roadmaps, applying it in the case of Canada. This methodology is then illustrated in its application to establish the foundation for iterative advancements in the AAM sector, emphasizing a collaborative approach to address the identified challenges. The concluding strategic topics, classified by Advanced Air Mobility Maturity Levels, serve as foundations for the ongoing transformation of Canada’s aviation landscape from its current state towards a more autonomous and expansive future. While this study focuses on the AAM sector, the framework’s design offers broader applicability, providing a valuable tool for other emerging, complex, and rigidly regulated industries.
The existing literature on the aviation market has focused more on analysing high-capacity airports served by larger aircraft, often overlooking smaller airports that operate low-capacity planes on routes with low demand. This study examines Azul Conecta’s operations in Brazil using Cessna Grand Caravan aircraft, focusing on the key features of the airports served by this niche service. It applies k-means clustering to 189 airports in Brazil and examines the characteristics of the services they provide. It aims to fill the knowledge gap regarding the characteristics of airports used in this niche market. The findings indicate that airport characteristics associated with the presence of flight service, including socioeconomic variables, are typically associated with regular commercial aviation, whereas infrastructure characteristics are identified as differentiation factors in airports with more limited resources.
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.
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 rapid emergence of electric vertical take-off and landing (eVTOL) aircraft is expected to revolutionize Urban Air Mobility (UAM) as eVTOL enabling low-emission, point-to-point aerial transportation. The viability of these aircraft is deeply tied to the concept of Single-Pilot Operations, which places intense cognitive, operational, and decision-making loads on the pilot, particularly in dense urban environments. In this context, the Human–Machine Interface (HMI) plays a critical role as it acts as pilot’s “crew member”, effectively functioning and supporting the pilot and increasing situational awareness, workload management, and safe decision-making. This study is a combined method of research design that was implemented, combining a structured online survey and interviews among aviation professionals, including pilots, engineers and human factors specialists to understand and detect their perceptions of HMI requirements for single-pilot eVTOL operations, focusing on workload management, situational awareness, automation interaction, and trust in advanced cockpit technologies. Moreover, open-source flight simulator FlightGear was used to partially depict the results. The findings revealed that maintaining situational awareness without a co-pilot is the dominant challenge, with strong preferences for “eyes-out” displays like physical controls and Head-Up Displays in high-workload scenarios. A significant connection was found between professional expertise and trust in AI copilots. Quantitative data from the survey were analyzed using descriptive and inferential statistics (e.g., t-tests, ANOVA, correlation, regression), supported by graphical representations. The findings show the crucial importance of pilot-focused HMIs for the most important determining factors namely, the problem of maintaining Situation Awareness, multi-tasking and managing cognitive load, the pilot’s central problem without the assistance or presence of the co-pilot in the aircraft. This paper presents the Adaptive, Multimodal, Context-Aware (AMCA) HMI design framework that will benefit the future design of single-pilot eVTOL aircraft cockpits. The study provides concrete design inputs for manufacturers and regulatory and training bodies regarding the challenge of certification and operation of the newly developing Urban Air Mobility solutions.
This study addresses human factors in aviation maintenance by converting routine e-log text into computable communication-resilience indicators-closure-loop ratio, read-back adherence, ambiguity density, temporal/referential completeness, error-catch latency, and cross-shift continuity-and testing whether strengthening these signals reduces defects with minimal operational burden. An integrated design-and-validation pipeline was deployed in a Maintenance, Repair and Overhaul (MRO) setting using a phased rollout (Baseline -> Assist -> Nudge), and causal effects were estimated via interrupted time-series analysis and, where applicable, stepped-wedge Generalized Linear Mixed Model (GLMM). A Natural Language Processing (NLP) stack (Term Frequency-Inverse Document Frequency (TF-IDF) + regularized logistic regression, with an optional compact transformer) extracts linguistic cues; the predicted probabilities are calibrated to support reliable dashboard thresholds. Results show immediate reductions in level and sustained improvements in slope in sign-off error rates after Assist, with larger step-downs under Nudge. Mediation analyses indicate that gains operate through improved communication KPIs rather than generic attentional effects. Model diagnostics light-strong discrimination with low calibration error; robustness checks and a cross-shift/fleet evaluation show stable transfer with minimal recalibration. Governance emphasizes de-identification, advisory-only AI with human-in-the-loop, and transparent, non-punitive use. Findings operationalize Safety-II as quantifiable communication behavior and demonstrate a scalable, low-friction pathway-advisory Assist plus light User Interface (UI) nudges-that advances Air Transport Technologies & Development while improving safety and quality in maintenance operations.
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.
Efficient and environmentally responsible pesticide application is a major challenge in precision agriculture. Excessive pesticide use in conventional farming increases costs, harms the environment, and poses health risks. Recent advancements in unmanned aerial vehicles (UAVs) or drones have enabled targeted spraying, yet optimizing multiple-drone route planning and task allocation remains complex due to dynamic field conditions and limited drone capacity. To address this gap, this study proposes a hybrid optimization approach that integrates Ant Colony Optimization (ACO), Genetic Algorithm (GA), and 3Opt to generate efficient flight routes for multiple sprayer drones based on plant health levels. In this framework, ACO assigns drones to target points, GA automatically tunes key ACO parameters, and 3Opt enhances route efficiency through local optimization. Experimental results show that GA effectively automates the tuning of four key ACO parameters and that drone capacity significantly affects route length. The integration of GA, ACO and 3Opt further reduces total route length, achieving up to 13.6% improvement in efficiency compared to traditional ACO. These findings demonstrate the potential of the proposed method to enhance route efficiency, reduce energy consumption, shorter mission completion time and offers a practical solution for improving the performance and sustainability of multiple-drone spraying operations.
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.
Lots of researchers worldwide use a big variety of forecast models to predict demand. After running the forecast model, researcher always has a question if received prediction was accurate or not. To do so, a number of methods exist to assess model accuracy. Application of accuracy assessment models itself is not complex. The most difficult part for researcher: interpretation of the result and the understanding of information to take the right decisions. Companies who do demand forecast in 95% of cases use only one accuracy assessment method for their forecast model. In case, companies do it for fast moving items and the business doesn’t have any special requirement for the result level, it could be accepted. But in case slow-moving inventory is used and the company requires a certain service level, then there is a space for potential mistakes when running one model only. This work figures out the drawbacks of the current approaches towards forecast accuracy assessment of spare parts with little transaction history and proposes approaches to choose right accuracy assessment models. Experiment on data of existing company A that does aircraft maintenance was run to study the results of various forecast accuracy assessment models.
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.
