Introduction: Little research has been done on ultralight aviation accidents. A better understanding of accident factors allows for better development of preventive measures. This study analyzes ultralight accidents with respect to significant factors related to active and latent failures, as referred to in Reason's Swiss cheese model of human error.
Methods: There were 66 National Transportation Safety Board (NTSB) ultralight accident reports that were examined from 1985-2004. Contextual information about active and latent failures was identified, compiled, and cross-referenced with pilot, aircraft, and accident information for evidence of significant relationships.
Results: Pilots with less than 40 make/model-specific flying hours were significantly more likely to crash fatally (chi2 = 9.07; p < 0.005; df=1) than other pilots and/or because of losing control (chi2 = 7.24; p < 0.05; df=1) than other accident causes. In contrast, pilots with 40 or more make/model-specific flying hours were significantly more likely to crash as a result of engine failure (chi2 = 9.33; p < 0.005; df = 1). Loss of control as an active failure was usually associated with such latent failures as strong winds and insufficient mission planning, while the engine failure accidents involved inadequate maintenance.
Conclusion: In order to reduce accident prevalence, ultralight self-regulation organizations should focus on training in mission planning, aircraft familiarity, and proper maintenance procedures. Further research should concentrate on explaining the prevalence of the active and latent failures shown here and determining the effects of the FAA's new light sport aircraft category.
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Objective: Evaluating the change trajectories of triglyceride-glucose (TyG) index calculated after multiple tests in elderly heart failure (HF) patients may have clinical implications for predicting long-term adverse cardiovascular events (MACEs).
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Shanghai Jiao Tong University, Shanghai, China.
Virtual machine logs are generated in large quantities. Virtual machine logs may contain some abnormal logs that indicate security risks or system failures of the virtual machine platform. Therefore, using unsupervised anomaly detection methods to identify abnormal logs is a meaningful task.
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Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy.
Cardiovascular (CV) disease remains the leading cause of morbidity and mortality worldwide, highlighting the necessity of understanding its underlying molecular and pathophysiological pathways. Conversely, physical activity (PA) and exercise are key strategies in reducing CV event risks. Detecting latent CV conditions in apparently healthy individuals, such as athletes, presents a unique challenge.
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