Automation surprises in aviation continue to be a significant safety concern and the community’s search for effective strategies to mitigate them are ongoing. The literature has offered two fundamentally divergent directions, based on different ideas about the nature of cognition and collaboration with automation. In this paper, we report the results of a field study that empirically compared and contrasted two models of automation surprises: a normative individual-cognition model and a sensemaking model based on distributed cognition. Our data prove a good fit for the sense-making model. This finding is relevant for aviation safety, since our understanding of the cognitive processes that govern human interaction with automation drive what we need to do to reduce the frequency of automation-induced events.
As every new generation of civil aircraft creates more on-wing data and fleets gradually become more connected with the ground, an increased number of opportunities can be identified for more effective Maintenance, Repair and Overhaul (MRO) operations. Data are becoming a valuable asset for aircraft operators. Sensors measure and record thousands of parameters in increased sampling rates. However, data do not serve any purpose per se. It is the analysis that unleashes their value. Data analytics methods can be simple, making use of visualizations, or more complex, with the use of sophisticated statistics and Artificial Intelligence algorithms. Every problem needs to be approached with the most suitable and less complex method. In MRO operations, two major categories of on-wing data analytics problems can be identified. The first one requires the identification of patterns, which enable the classification and optimization of different maintenance and overhaul processes. The second category of problems requires the identification of rare events, such as the unexpected failure of parts. This cluster of problems relies on the detection of meaningful outliers in large data sets. Different Machine Learning methods can be suggested here, such as Isolation Forest and Logistic Regression. In general, the use of data analytics for maintenance or failure prediction is a scientific field with a great potentiality. Due to its complex nature, the opportunities for aviation Data Analytics in MRO operations are numerous. As MRO services focus increasingly in long term contracts, maintenance organizations with the right forecasting methods will have an advantage. Data accessibility and data quality are two key-factors. At the same time, numerous technical developments related to data transfer and data processing can be promising for the future.
While tourism and air transport are recovering from the impacts of the Covid pandemic, it seems timely to draw a synthetic view of future stakes combining the following topics: the greenhouse gas emissions scenarios for tourism, regarding which recent work has improved their understanding; the climatic impact of aviation, almost 60% of which is due to non-CO 2 emissions; alternative fuels (biofuels, E-fuels, hydrogen) and engine designs (fuel cells...) which are complex and controversial issues, and whose potentials should be assessed regarding their timing, environmental impacts, and their ability to meet long distance travel requirements. This paper analyses the extent to which the new options regarding fuels and engines can help decarbonize tourism and air transport. The answer is that they can partly contribute but do not render obsolete previous work on substitutions between types of tourism (short versus long distance...), between transport modes (ground transport versus air), length of stay, etc. Following this step, the paper deals with the position of aviation players and the type of arguments they use. We conclude on the necessity to make strategic choices among the options to avoid wasting investments.
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