Air traffic control (ATC) performance is important to ensure flight safety and the sustainability of aviation growth. To better evaluate the performance of ATC, this paper introduces the HFACS-BN model (HFACS: Human factors analysis and classification system; BN: Bayesian network), which can be combined with the subjective information of relevant experts and the objective data of accident reports to obtain more accurate evaluation results. The human factors of ATC in this paper are derived from screening and analysis of 142 civil and general aviation accidents/incidents related to ATC human factors worldwide from 1980 to 2019, among which the most important 25 HFs are selected to construct the evaluation model. The authors designed and implemented a questionnaire survey based on the HFACS framework and collected valid data from 26 frontline air traffic controllers (ATCO) and experts related to ATC in 2019. Combining the responses with objective data, the noisy MAX model is used to calculate the conditional probability table. The results showed that, among the four levels of human factors, unsafe acts had the greatest influence on ATC Performance (79.4%), while preconditions for safe acts contributed the least (40.3%). The sensitivity analysis indicates the order of major human factors influencing the performance of ATC. Finally, this study contributes to the literature in terms of methodological development and expert empirical analysis, providing data support for human error management intervention of ATC in aviation safety.

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Background Bicuspid aortic valve (BAV) is the most prevalent congenital valvular heart defect, and around 50% of severe isolated calcific aortic valve disease (CAVD) cases are associated with BAV. Although previous studies have demonstrated the cellular heterogeneity of aortic valves, the cellular composition of specific BAV at the single-cell level remains unclear. Methods Four BAV specimens from aortic valve stenosis patients were collected to conduct single-cell RNA sequencing (scRNA-seq). In vitro experiments were performed to further validate some phenotypes. Results The heterogeneity of stromal cells and immune cells were revealed based on comprehensive analysis. We identified twelve subclusters of VICs, four subclusters of ECs, six subclusters of lymphocytes, six subclusters of monocytic cells and one cluster of mast cells. Based on the detailed cell atlas, we constructed a cellular interaction network. Several novel cell types were identified, and we provided evidence for established mechanisms on valvular calcification. Furthermore, when exploring the monocytic lineage, a special population, macrophage derived stromal cells (MDSC), was revealed to be originated from MRC1+ (CD206) macrophages (Macrophage-to-Mesenchymal transition, MMT). FOXC1 and PI3K-AKT pathway were identified as potential regulators of MMT through scRNA analysis and in vitro experiments. Conclusions With an unbiased scRNA-seq approach, we identified a full spectrum of cell populations and a cellular interaction network in stenotic BAVs, which may provide insights for further research on CAVD. Notably, the exploration on mechanism of MMT might provide potential therapeutic targets for bicuspid CAVD.