In the aviation sector, the variability in the appreciation of safety risk perception factors and responses to risk behaviours has not been sufficiently studied for engineers and technicians. Through a questionnaire survey, this study investigated differences amongst professionals and trainees across eleven risk perception factors and five indicative risk behaviour scenarios. The findings indicated significant differences between the two groups in four factors and three scenarios as well as within groups. Moreover, age, years of work and study and educational level were other factors accounting for such differences within each group of professionals and trainees. The results showing these significant differences are aligned with relevant research about pilots and indicate that the appreciation of risk perception factors by aviation engineers and the development of their risk behaviours deserves more attention. Our findings cannot be generalised due to the small sample and its distribution across the demographic variables. However, the results of this study suggest the need tailoring risk communication and training to address the different degrees to which influences of risk perception factors are comprehended, and risk behaviours emerge in aviation engineering trainees and professionals. Further research could focus on the development of a respective uniform framework and tool for the specific workforce group and could administer surveys to more extensive and more representative samples by including open-ended questions and broader social, organisational and systemic factors.
Background: Manual muscle mass assessment based on Computed Tomography (CT) scans is recognized as a good marker for malnutrition, sarcopenia, and adverse outcomes. However, manual muscle mass analysis is cumbersome and time consuming. An accurate fully automated method is needed. In this study, we evaluate if manual psoas annotation can be substituted by a fully automatic deep learning-based method.Methods: This study included a cohort of 583 patients with severe aortic valve stenosis planned to undergo Transcatheter Aortic Valve Replacement (TAVR). Psoas muscle area was annotated manually on the CT scan at the height of lumbar vertebra 3 (L3). The deep learning-based method mimics this approach by first determining the L3 level and subsequently segmenting the psoas at that level. The fully automatic approach was evaluated as well as segmentation and slice selection, using average bias 95% limits of agreement, Intraclass Correlation Coefficient (ICC) and within-subject Coefficient of Variation (CV). To evaluate performance of the slice selection visual inspection was performed. To evaluate segmentation Dice index was computed between the manual and automatic segmentations (0 = no overlap, 1 = perfect overlap).Results: Included patients had a mean age of 81 ± 6 and 45% was female. The fully automatic method showed a bias and limits of agreement of -0.69 [-6.60 to 5.23] cm2, an ICC of 0.78 [95% CI: 0.74-0.82] and a within-subject CV of 11.2% [95% CI: 10.2-12.2]. For slice selection, 84% of the selections were on the same vertebra between methods, bias and limits of agreement was 3.4 [-24.5 to 31.4] mm. The Dice index for segmentation was 0.93 ± 0.04, bias and limits of agreement was -0.55 [1.71-2.80] cm2.Conclusion: Fully automatic assessment of psoas muscle area demonstrates accurate performance at the L3 level in CT images. It is a reliable tool that offers great opportunities for analysis in large scale studies and in clinical applications.
The NDT methods currently used in aviation MRO are predominantly labour-intensive and time-consuming processes performed by human operators throughout the lifespan of an aircraft. These techniques are time-consuming, require perpetual training and are highly dependent on the operator's skills. Thus, there is a growing need for more efficient, automated, and accurate NDT tools that will be able to provide faster and less labour-intensive assessments. This study presents a novel, non-contact, automated NDT scanning system under development, which aims to reduce the inspection time significantly. The proposed technique uses a non-contact, Lamb wave-based approach. A further essential step during the process is to use an automated positioning system. Thickness mapping and defect detection in metal and composite structures have been performed. A local thickness map in the order of 1 mm has been obtained through a fast-scanning process with comparable resolution to conventional inspection techniques. Overall, it is currently concluded that the proposed NDT scanner is a promising tool that potentially can reduce the inspection time while also having the potential to automate the damage assessment resulting in more efficient MRO inspection processes.
