21497 research outputs found
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Parker Northrup, Participant
No full textProfessor Parker W. Northrup III, Colonel, USAF (Retired), serves as the Chair of the Flight Department and Assistant Professor of Aeronautical Science at Embry‑Riddle Aeronautical University\u27s Prescott Campus. In this role, he oversees operations, maintenance and support functions for over 850 flight students, facilitating more than 50,000 flight hours annually using a fleet of Textron-Cessna 172S and Diamond DA42 aircraft. Beyond administrative duties, Professor Northrup actively engages in teaching, both in the cockpit and online courses focusing on leadership and the role of airpower in U.S. national security strategy.
Before joining Embry‑Riddle in 2017, Professor Northrup amassed over 24 years of service in the United States Air Force, accumulating more than 3,500 flight hours, primarily in the B-52 Stratofortress. His distinguished military career includes commanding roles such as leading the Air Force Element-Defense Threat Reduction Agency, the 5th Operations Group and the 11th Bomb Squadron. In recognition of his exceptional performance, he received multiple Department of Defense commendations and was awarded the 1996 Mackay Trophy for the most meritorious USAF flight of the year.
Professor Northrup\u27s academic credentials are extensive, holding a Master of Science in National Security Strategy from the National Defense University, a Master of Arts in Industrial/Organizational Psychology from Louisiana Tech University and two master\u27s degrees from Air University. He also possesses FAA certifications as a Commercial Pilot and Certified Flight Instructor. Beyond his duties at Embry‑Riddle, he contributes to the community as a member of the Prescott Airport Advisory Committee.https://commons.erau.edu/avcysecworkshop-bios-2025/1039/thumbnail.jp
Brian North, Participant
No full textBrian A. North is a seasoned Cyber Survivability Architect with over two decades of experience in the aerospace and defense industry, specializing in mission systems, cyber engineering and avionics architecture. Throughout his career, he has held pivotal roles at leading organizations such as Lockheed Martin, Bell Helicopter, BAE Systems and Sikorsky Aircraft, where he has driven innovative architecture development and system integration strategies. With a strong focus on aerospace cybersecurity, Brian has led the design of secure cyber architectures, conducted Cyber Kill Chain analyses and authored System Security Plans, notably for programs including the V-280 FLRAA and F-35. He earned a Bachelor of Science in Information Technology from the University of Phoenix and holds a CISSP from ISC2.https://commons.erau.edu/avcysecworkshop-bios-2025/1038/thumbnail.jp
1967 5BFTS First Reunion Dinner. October 14.
No full textThe First Reunion Dinner for RAF pilots who had trained at 5BFTS during WW2 was held on October 14, 1967, at the Royal Aero Club, London. This image is annotated “Mostly we listened (ii)”.https://commons.erau.edu/bfts-1967-dinner-images/1020/thumbnail.jp
1967 5BFTS First Reunion Dinner. October 14. Tony Linfield
No full textThe First Reunion Dinner for RAF pilots who had trained at 5BFTS during WW2 was held on October 14, 1967, at the Royal Aero Club, London. This image is annotated “Tony Linfield recalls”. Tony Linfield who was on Course 18 was the secretary.https://commons.erau.edu/bfts-1967-dinner-images/1004/thumbnail.jp
Leveraging Machine Learning and Causal Inference for Loan Default Prediction
No full textThis research explores a systematic application of machine learning techniques combined with causal inference to predict loan defaults in peer-to-peer lending. Accurately forecasting loan defaults is crucial for mitigating financial risk and optimizing lending strategies. This analysis is based on multiple datasets of loan applications spanning over a decade, containing detailed financial and credit information about borrowers. Beginning with extensive Exploratory Data Analysis (EDA) coupled with scaling strategies, the research identifies key trends in loan performance across a large number of factors, such as interest rates or borrower creditworthiness, and one objective is to determine from the many available predictors which are the most essential. The research introduces two different Recursive Feature Elimination (RFE) variations based on: (1): occlusion sensitivity and (2): double machine learning (DML), each providing a unique view of feature engineering. These RFE methods were then coupled with machine learning models such as Logistic Regression, Random Forest, Naive Bayes, XGBoost, and CatBoost final classification. For performance validation, the modeling pipelines (feature selection + prediction) were evaluated for accuracy, precision, recall, and F1-score to determine their effectiveness in predicting loan defaults.
Among the models tested, Naive Bayes overall demonstrated the best performance (F1-score), significantly outperforming the next best model, XGBoost. However, Naive Bayes exhibited lower precision, suggesting that while it captured more defaulted loans, it also produced a higher number of false positives. Neural network models developed using TensorFlow and Keras to explore non-linear relationships in the data lacked in performance compared to the traditional models. Between the two RFE methods tested, the results showed that models that selected their features using DML were more consistent with each other, making the same selection choices more often and indicating this method is less sensitive to model choice. Occlusion sensitivity had more varied results due to its greedy selection algorithm, but often showed better performance than DML for the model used. This study demonstrates the potential of machine learning in financial risk assessment and highlights the need for intelligent selection of features. Future work will extend this analysis by refining feature engineering, using more sophisticated non-linear models, and performing in-depth causal inference to uncover specific useful features
Sustainable Practices for Aircraft Decommissioning and Recycling in a Circular Aviation Economy
No full textThe aviation industry requires a series of actions that will transform its current status, aiming for sustainable operations. Aviation’s end-of-life stream is a pivotal lever for circularity, yet current dismantling and recycling practices leave significant value unrealized. Circular Economy could be considered as a transformational approach to the aviation industry and address its environmental and economic challenges, meeting sustainability principles. This study conducts a PRISMA-guided qualitative systematic review across academic and industry sources to synthesize regulations, technologies, and economics of aircraft decommissioning. It aims to quantify material recovery potential and environmental gains at the aircraft level and assess technology readiness and cost drivers for metals, polymers, and composites. Findings indicate that optimized decommissioning enables high-value part reuse and substantial material recovery (notably aluminum), with associated lifecycle greenhouse-gas avoidance at the aircraft scale. However, high costs, weak regulations, and limited recycling technologies hinder adoption. Results show that optimized dismantling and certified part-reuse pathways can recover up to 85–90% of total aircraft mass, with potential CO2-emission avoidance of 25–35 t per narrow-body aircraft compared with landfill disposal. Metal recycling technologies (TRL 8–9) already achieve high yields, whereas polymer and composite recycling remain limited (TRL 5–6) by purity and certification barriers. A comparative assessment of EU, US, and Asia–Pacific regulations identifies enforcement and infrastructure gaps hindering implementation. The study introduces an integrated CE roadmap for aviation comprising (i) standard saligned design-for-disassembly and digital traceability, (ii) accredited MRO-to-reuse networks, and (iii) performance-based policy incentives
Turning Language Into Insight: How AI and Forensic Linguistic Analysis Are Transforming Investigations
No full textLanguage is the fabric of human thought and communication, subtle, layered, and deeply revealing. When scrutinized, it can offer far more than simple testimony; it can hold the keys to motive, identity, intent, contradiction, and context. Traditionally, law enforcement has focused on fingerprints, DNA, surveillance, and physical evidence to solve crimes. Yet one of the most untapped wells of intelligence lies between the lines of written statements from suspects, witnesses, and victims. Now, with the advent of advanced artificial intelligence (AI) and the sophisticated science of forensic linguistics, investigators have powerful new tools to mine language for insights that were once only accessible to expert analysts
