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Control and its Impact on Controlling FOD

Foreign Object Debris (FOD) pose significant risks in aviation maintenance environments, leading to potential safety hazards, operational inefficiencies, and financial losses. Our project aims to evaluate the effectiveness of implementing a structured tool control program in reducing Foreign Object Debris (FOD) occurrences. Standardized paperwork of broken, worn, and missing reports identifies trends of regularly failing tools posing potential FOD risks. With the incorporation of engineering controls such as weight scales, single-point entry systems, ID card scanners, logging systems, motion-light-equipped toolboxes, and magnetized trays, operators can increase accountability, minimize human error, and maintain compliance with safety regulations. This project uses observational studies, compliance tracking, and incident report analyses to assess the impact of these measures. The proposed structured tool control systems are anticipated to significantly reduce the occurrence of misplaced tools, streamline efficiency, and improve overall safety compliance. By controlling access to maintenance areas, providing real-time accountability of tools, and using automated alert systems, aviation operators can proactively prevent FOD-related incidents. This study highlights the necessity of an upgraded tool control system in aviation maintenance, emphasizing the benefits of integrating technology-driven solutions along with simple ones, such as magnetizing tools. Implementing these measures will offer a culture of safety and operational excellence, highlighting the importance of structured tool management in aviation environments

Lockheed Martin Ethics in Engineering Competition

The Lockheed Martin Ethics in Engineering Competition held in Bethesda, Maryland connects engineering students nationwide to develop professional communication skills and foster discussion of ethical values. By working together to solve technical and ethical challenges, future engineers develop important skills for the workplace that supplement their university education. The 2025 Ethics in Engineering Competition led competitors to research AI decision making processes in wildland fire management, and the Implications of leaving decisions involving human lives to AI. Teams were required to present a solution that Integrated AI responsibly into fire management, while navigating workplace challenges. ERAU – Prescott students networked with Lockheed Martin mentors and other students nationwide while developing valuable workplace skills: Navigating conflict, voicing ethical values, and communicating technically and professionally

Trust in Human-AI Symbiosis

As Artificial Intelligence increasingly supports critical decision-making in different real-world contexts, understanding trust between humans and Al becomes crucial. The goal of this study is to explore how various levels of Al transparency, control, and error influence human trust in Al-based decision support in safety-critical systems. By evaluating participant responses to Al recommendations across different operational modes, this research aims to identify optimal conditions that foster trust and enhance collaboration between humans and Al in high-risk environments. The findings will inform the development of more effective and reliable Al systems

Identifying Individual Factors Which Impact Student Learning in a Desktop Flight Training Simulator

With expected global growth in air transport demand and forecasted shortage of pilots, there is a need for faster and cheaper flight training. A range of protocols was used to assess factors such as student’s emotional state, adaptive performance, and situational awareness as possible predictors of flight performance based on the influence of these factors on student flight performance conducted using a Personal Computer-based Aviation Training Devices (PCATD). Factor analysis and regression models were used to identify how these factors related to student performance during a visual flight conducted using a desktop simulator and our findings demonstrated some influence of adaptive performance, emotional state, and situation awareness upon student flight scores

Determining critical vehicle connectivity in connected autonomous vehicles using information theory

The idea of connected autonomous vehicles, which can share information among themselves, offers the potential to enhance traffic efficiency. However, putting this technology into practice comes with challenges. Real-world challenges such as data throughput limitations can make it hard for vehicles to share information smoothly. Consequently, it becomes crucial to identify critical vehicle connectivity, which specifies the minimum number of connected vehicles required to maintain stable traffic flow. This paper proposes an information-theoretic metric that uses information flow among connected vehicles to identify critical vehicle connectivity. The model-free nature of information-theoretic tools eliminates the need for closed-form expressions of the model, which are necessary for stability analysis methods to identify critical connectivity. We demonstrate our proposed approach using a recent connected vehicles model. To the best of our knowledge, this paper presents the first application of information theory for analyzing critical vehicle connectivity in the context of connected autonomous vehicles