Federal Aviation Administration

Plain Language Summary During bimodal periods of tropical cyclone (TC) activity over the Bay of Bengal (BOB) in May and October‐November (Oct‐Nov), the Asian subtropical westerly jet is usually located at lower latitudes over the southern Tibetan Plateau, where South Branch Trough (SBT) is active. We quantified the extent of BOB TC‐SBT interaction based on the negative potential vorticity advection by TC‐associated irrotational wind. The mean location of the TC center and maximum interaction point are around (20°N, 88°E) and (33°N, 89°E), respectively. Rossby wave trains (RWT) associated with BOB TCs may extend eastward to 150°W, dispersing at a faster zonal group speed in May compared to Oct‐Nov. In May, the downstream response in South China featured by amplified upper‐level divergent outflow, intensified mid‐level warm and cold air convergence, and enhanced low‐level southwesterly water vapor transport, which was conducive to precipitation. In Oct‐Nov, the RWT shifted northward and the anticyclone anomaly related to the subtropical high dominated South China, leading to a northward shift in water vapor transport, unfavorable for precipitation in South China. The results are significant for the short‐ to medium‐range weather forecasts in downstream regions of BOB TCs.

Loss of tail rotor effectiveness (LTE), also known as unanticipated yaw, has been recognized to be a major contributing factor in several helicopter accidents due to the loss of directional control. Flight tests have identified specific wind azimuth regions that may alter the angle and the speed of the airflow through the tail rotor leading to LTE. Through the helicopter flight data monitoring (HFDM) program, pilots receive constant flight evaluation reports to support LTE risk mitigation. Nevertheless, the existing LTE safety metric presents several pitfalls that hinder the reliability of the detection of violations of boundaries for flight safety. To identify the appropriate methods to properly simulate and flag LTE events and develop a more comprehensive and reliable LTE safety metric to be used within the HFDM environment, a physics-based investigation of the different LTE flight characteristics is performed. The results from this investigation are expected to contribute towards an improvement in detection of LTE events, leading to a reduction in LTE-related accidents.

Metal additive manufacturing (AM) is a transformative set of technologies that are increasingly being used for demanding structural applications. However, persistent challenges regarding reliability and properties of the printed parts seriously impact qualification and certification (Q&C). Computational approaches can mitigate these challenges, but availability of benchmark measurement data for model validation is a key requirement. Q&C will be discussed in the context of the Computational Materials for Qualification and Certification (CM4QC) steering group, a tightly focused collaboration of aviation-focused companies, research and regulatory government agencies, and universities that is working to develop a roadmap for increasing the use of computational approaches in the aviation Q&C process. Benchmark measurement data will be discussed in the context of the Additive Manufacturing Benchmark Test Series (AM Bench), a broad collaboration of 10 NIST divisions and about 20 external organizations, including several that are collaborators on CM4QC, that provide rigorous measurement data for validating AM simulations for a wide range of AM technologies and material systems. Technical standards also play an important role for Q&C and the confluence between CM4QC, AM Bench, and standards organizations will be discussed.

The implementation of new performance-based navigation procedures at an East Coast airport in 2016 required the airport authority to step up its engagement with airport community residents. This case study leverages natural language processing to explain changes in the sentiments of airport community residents from 2015 to 2021. Natural language processing algorithms made it possible to create a community engagement grid that highlights issues identified in digital prints and social media and allows decision-makers to prioritize them based on awareness and urgency.

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p-FDR < 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in higher-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD.

The Organizational Life Cycle (OLC) and the Organizational Culture (OC) are two important aspects that determine the ability of an organization to remain operational in a dynamic and competitive market over time. Despite the significance of this topic to the literature on organizations, no review has yet provided a holistic mapping of the relationship between them. To bridge this theoretical gap, this article studies the panorama of the scientific production involving OLC and OC. To achieve this goal, we provide a systematic literature review of studies over a period of 60 years. The results point to a greater amount of work dealing with the underpinnings of Institutional Theory, structural changes, and precepts, strategies, and values established by the organizational culture. In addition, in the last 5 years, the investigations focusing mainly on institutional and strategic factors for the organization can be seen in light of culture and organizational climate. It was possible to identify that there is a tendency for discussions linked to strategy and innovation. Overall, this article provides an essential understanding of the OLC and OC relationship, shedding light on the current state of research and future opportunities in this area.

Air traffic flow management is supported by a highly distributed work system in which airline dispatchers and Federal Aviation Administration (FAA) traffic managers must coordinate. To support asynchronous coordination between a dispatcher and a traffic manager, the FAA has developed software that allows the flight operators to submit multiple, prioritized alternative flight plans. This set of alternative flight plans, submitted along with a filed route, is referred to as a Trajectory Option Set (TOS). And some airlines have now developed initial versions of software capable of generating and submitting such TOSs. This paper reports on cognitive walkthroughs with 5 dispatchers and 3 traffic managers on 5 scenarios designed to evaluate the operational concept, procedures and supporting FAA and airline software. The findings provide guidance for application of the concept of collaborative constraint propagation to support distributed work, as well as 42 recommendations for enhancing associated procedures and supporting software designs.

The remarkable level of safety of aviation operations is due, in part, to Federal Aviation Administration requirements and guidance that govern civilian aviation activities in the United States National Airspace System. At times, these requirements and guidelines need reevaluation and updating—for example, due to a known change in the pilot workforce or with flight deck technology. This paper provides examples of when this may be necessary, and explains the role that human factors research plays in this process. One objective of this paper is to make the case that it is necessary to periodically review and update requirements and guidance, even if the equipment or technology haven’t changed; often the human operators have changed or additional information is now available. Additionally, the process described here may be applicable to international aviation authorities, other federal agencies, and the private sector, as a best practice and critical safety initiative.

Safety culture has ranked as a top human factors challenge for aviation maintenance, but there is a lack of actionable guidance for properly assessing and improving safety culture. As part of its efforts to promote a positive safety culture in aviation maintenance, the researchers developed and validated a new safety culture toolkit. The toolkit consists of a customizable survey, scoring guidance, and a roadmap for safety culture improvement. The data from n = 987 participants across five participating organizations supported the Job Demands-Resources (JD-R) model. Safety culture was a significant predictor of both Individual and Organizational Outcomes, with Individual Outcomes partially mediating the impact of safety culture on Organizational Outcomes. These results provided initial evidence of the content and predictive validity.

Background Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.