91341 research outputs found
Sort by
Postcard from Unknown to [Milton Wright], from None (Wright Brothers First Power Driven Plane)
Get PDFAn undated postcard featuring the Wright Brothers\u27 first flight and portraits of Orville and Wilbur. Collected by Milton Wright.https://corescholar.libraries.wright.edu/special_ms711_postcards/1108/thumbnail.jp
Postcard from Unknown to [Milton Wright], from Paris, France (Rue de la Barre, Montmartre)
Get PDFAn undated postcard from Paris, France featuring the Rue de la Barre, Montmartre. Collected by Milton Wright.https://corescholar.libraries.wright.edu/special_ms711_postcards/1084/thumbnail.jp
Postcard from Ewing , to Baby Brother Wright from Denver, Colorado (Tunnel No. 30)
Get PDFAn undated Denver, Colorado postcard featuring tunnel number 30 of the Moffat Line. Sent from Ewing to Baby Brother Wrighthttps://corescholar.libraries.wright.edu/special_ms711_postcards/1051/thumbnail.jp
Postcard from Unknown to [Milton Wright], from Monte-Carlo, Monaco (General View of Monte Carlo)
Get PDFAn undated postcard from Monte-Carlo, Monaco featuring a general view of the city. Collected by Milton Wright.https://corescholar.libraries.wright.edu/special_ms711_postcards/1103/thumbnail.jp
Postcard from Albert to Milton Wright from Oshkosh, Wisconsin (Entrance to North Park)
Get PDFAn undated Oshkosh, Wisconsin postcard featuring the entrance to North Park. Sent from Albert to Milton Wright.https://corescholar.libraries.wright.edu/special_ms711_postcards/1034/thumbnail.jp
Postcard from Orville Wright to Milton Wright from ( A Little Surprise for You )
Get PDFAn undated postcard that says A Little Surprise for You , picturing a man in a top-hat tripping and falling. Sent from Orville Wright to his nephew, Milton Wright.https://corescholar.libraries.wright.edu/special_ms711_postcards/1021/thumbnail.jp
Creating Scenarios for the Evaluation of Situational Awareness
Get PDFSituational Awareness (SA) is critical for safe task performance in aviation, yettraining and evaluating situational awareness remain challenging, often relying ontheoretical and subjective methods. Most current situational awarenessassessments are either intrusive or lack objectivity. Typically, situationalawareness training is lecture-based and focused on single pilot resourcemanagement. To change the training paradigm and address the lack of objectivity,we propose a structured framework for scenario-based situational awarenesstraining and evaluation. Using a toolbox of scenario elements—such as systemfailures or environmental factors—generates scenarios with specific difficultylevels aligned with cognitive engineering assessment dimensions. These scenariosare then tested in simulators to assess SA. Our toolbox is informed by accidentdata and detailed case studies and is designed to be modular for future expansionbased on safety trends or individual needs. Ultimately, we aim to embed thisframework into a broader training concept that incorporates composite situationalawareness measurement, real-time feedback, and both Safety I and Safety IIprinciples to enhance situational awareness development and assessment
Crossing the AI Threshold: Advancing the AVIAN-S Machine Learning Model for Safety Report Analysis
Get PDFVoluntary safety reporting programs (VSRPs) can be essential in gaining insights into aviation safety and operations. However, analyzing safety reports can be time-consuming and tedious for subject matter experts (SMEs). AVIAN-S is a novel machine learning model that automatically labels aviation safety reporting data. This model utilizes an aviation-specific training set with over 70,000 samples of manually labeled aviation VSRP data. The model labels safety reports with a set of factors from a human factors taxonomy. Previous iterations of themodel utilized report rationales as the model input (Hinson, et al., 2023a). While utilizing rationales as input is helpful in the analysis of safety reports, it still includes the process of SMEs identifying rationales within the safety report. Including full report narratives as the model input can further expedite the report labeling process by eliminating the need to pull out specific rationales from thesafety report narrative. Building upon results presented at ISAP in 2023, the AVIAN-S model has been further developed to incorporate full report narrativesas the model input. This paper discusses results from the revised AVIAN-S model that uses full report narrative inputs and presents comparisons between the AVIAN-S model, a publicly available AI model, and SMEs
Exploring Students’ Perceptions of Learning Transport Category Aircraft Systems With Virtual Reality
Get PDFA course was developed in a collegiate aviation program to teach transport categoryaircraft systems using virtual reality (VR) as a supplemental teaching tool. To betterexplore the experiences and viability of using VR in this course, students from theinaugural class were interviewed at the conclusion of the course. Feedback from thestudents were used to improve the course, as well as to understand the benefits,drawbacks, and limitations of using VR to teach aircraft systems. Students found that VRenhanced their learning experience and reinforced the concepts learned in class; however,they also noted that overcoming software limitations is an area for improvement
Themes Related to Work Induced Fatigue and Burnout Among Certified Flight Instructors
Get PDFIn the flight training environment, Certified Flight Instructors (CFI) experienceinconsistent schedules, long hours at the airport, and limited organizationalsupport in their roles. These factors can contribute to fatigue and burnout whichcan also impact Flight Students. This study explores such experiences of CFIs andflight students through semi structured focus groups across three collegiate flightschools. Analysis of these focus group transcripts revealed five themes:organizational issues, financial pressures, fatigue, burnout, and trainingeffectiveness. Our findings identified how systemic stressors, like rigidscheduling, financial stress, and lack of career infrastructure, degrade the CFIs’wellbeing which leads to compromised training quality and flight safety. Flightstudents also observed signs of the CFIs’ fatigue and lapses in instruction. Tomitigate such detrimental spiraling effects, we suggest interventions includingscheduling flexibility, revising pay scale, compensation structures, and careerdevelopment pathways, to better support CFI wellbeing and enhance the overallquality and safety of flight training