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.
