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Beyond Carbon: Understanding the Role of Contrails in Aviation’s Climate Impact
No full textContrails, the condensation trails produced when jet engine exhaust encounters cold, humid air, are among aviation’s most significant non-CO₂ climate impacts. Depending on meteorological conditions, they may persist for hours and spread into cirrus clouds that trap infrared radiation otherwise destined for space. This short-lived but potent effect may account for up to 35% of aviation’s total climate impact.
This presentation will first outline the science of contrail formation and persistence, then explain why they matter for aviation’s broader climate footprint. Their effects vary greatly with weather, altitude, and season, making them difficult to model, predict, and manage.
Past research established contrails as a driver of radiative forcing, beginning with observational studies and early climate models. Current research is advancing through high-resolution modeling, satellite detection, and operational trials. Potential mitigation strategies include rerouting to avoid ice-supersaturated regions, as well as the use of sustainable aviation fuel (SAF), which may generate fewer soot particles and thus reduce contrail formation. These options hold promise but raise questions about efficiency, safety, and cost.
Looking forward, future work must expand beyond science into operational and policy integration. The European Union has begun incorporating non-CO₂ effects into climate policy, but global regulatory pathways remain limited. Progress will require interdisciplinary collaboration to refine models, quantify uncertainty, and develop operational guidelines to achieve industry net-zero goals.
Corrine Girard, Ph.D. candidate at Embry-Riddle Aeronautical University, will frame contrails as both a scientific challenge and a policy-relevant opportunity, inviting participants to think beyond carbon when addressing aviation’s climate future
Cultivating Confidence: The Intersection of Artificial Intelligence and Trust in Commercial Aviation
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Preparing Tomorrow’s Professionals: Industry-Informed AI Integration
No full textContext and Rationale
• AI reshaping aviation, aerospace, and UAS education
• Workforce readiness requires AI proficiency
• Perspectives from students, educators, employer
Hands-On Machine Learning for Online Education: Using sUAS Remote Sensing Data
Get PDFWhat if students could train AI models to analyze real-world drone data—without writing a single line of code?
1. Many students lack hands-on experience with AI and remote sensing.
2. Traditional coding barriers limit accessibility.
Solution: This project provides a no-code, hands-on framework using sUAS data and user-friendly AI platforms
Barriers and Best Practices in Diversity, Equity, and Inclusion (DEI) Initiatives: A Case Study from the United States Aerospace and Defense Industry
Get PDFThe main objective of this study was to unpack the barriers around diversity, equity, and inclusion (DEI) initiatives, and then provide best practices to improve DEI efforts in the aerospace and defense (A&D) industry. There has been a multitude of research conducted on DEI in the past few years. However, very little research has focused on DEI practices in the A&D industry. This case study focused on one large company in this industry to explore and understand various aspects of DEI initiative implementation. As organizations strive to meet industry and client objectives, as well as address employee needs, two research questions were explored in this study. First, what are the barriers in implementing diversity, equity, and inclusion initiatives/programs in the aerospace & defense industry? Second, what are the best practices to overcome the diversity, equity, and inclusion implementation barriers and then attract, retain, and develop diverse talent in the aerospace & defense industry?
The research involved conducting interviews with 16 participants who held different titles/roles and levels within the chosen A&D organization. Data analysis was carried out using member validation and check, data triangulation, and a grounded theory approach. As a data analysis tool, NVivo software was used, which provided a user-friendly interface for coding, categorization, and data exploration. The qualitative research approach resulted in 11 themes. The first theme concerned employees perceiving opposition inside the organization toward the implementation of DEI, as there is reluctance to adopt any changes. The second theme signified that DEI initiatives are present; however, their implementation is deficient. The third theme indicated many employees inside the corporation are unaware of DEI policies and believe the company must recognize its lack of DEI efforts and programs. The fourth theme involved microaggressions and stereotyping, compounded by unconscious bias, which fosters a hostile climate within the company, hence posing a hurdle to effective DEI implementation. The fifth theme signified the capacity to effectively attract, retain, and develop diverse talent inside the organization, serving as a DEI barrier. The sixth theme indicated employees perceive the business as struggling to uphold the status quo, reminiscent of a military setting and a historically White male-dominated industry, characterized by the “good ole boys club.” The seventh theme pertained to regions inside the country that do not possess a significant A&D industry, inherently excluding most talent and limiting exposure. The eighth theme pertained to the association between the social environment, influenced by political or geographical factors, including climate, and employee mobility, resources, and career success. The ninth theme illustrated that minority employees, when well trained, encouraged, and communicated with, exhibit a heightened degree of involvement and discretionary effort, hence increasing their likelihood of achieving success inside the organization. The 10th theme involved the representation of individuals from diverse backgrounds in senior leadership positions and recruitment materials, as well as inside the workplace, signaling a company’s commitment to diversity. The 11th theme entailed establishing a supportive environment through mentorship programs for diverse people, exposing them to unique career paths and opportunities.
The results of this study provide a pragmatic strategy for leaders of companies in the A&D industry to use to improve their efforts in promoting DEI. This study enhances the existing body of knowledge and offers valuable insights through qualitative research on how A&D businesses can achieve optimal DEI results
The Scent of Butter and Ghosts
Get PDFMaeve Holliday hated rainy Mondays, until a stranger blew in and changed it all
Examination of Solar Canopies on ERAU Campus Parking Lots
No full textIn 2023, President Biden set a U.S. goal of achieving carbon free power sector by 2035 and net zero emissions by 2050. This ambitious goal will require new energy generation strategies, including solar photovoltaic (PV) farms. Most solar farms have been built on rural, undeveloped land. While cheap, the use of this land destroys ecosystems, reduces farmland, and creates losses due to long transmission distances. By moving solar generation to urban areas these problems are mitigated but introduces a new problem of where to put them. Solar canopies aim to fix that issue by using already existing parking lots. This solution has already been tested and implemented around the world. Due to our car centric infrastructure Embry-Riddle Aeronautical University campus has an excess of parking lots with 41.37 acres available for solar canopies. An array of that size could generate 38.6 GWh of electricity per year. Between 2020 and 2021 Embry-Riddle Aeronautical University used 47.5 GWh. At Daytona’s current energy price of 8.72¢/kWh, solar canopies would save the school $3.36M each year. The installation cost of such an array would be recouped within 8-10 years. Solar canopies also offer multiple benefits to the cars under them from reduced temperatures, protection from the elements and easy installation of electric vehicle chargers. Installation of solar canopies on the Embry-Riddle Daytona Campus would reduce the school’s carbon massively footprint, save the school money, provide better parking conditions, and allow for easy and cheap installation of electric vehicle chargers, without negatively impacting the ecosystem
Implementing Homomorphic Encryption in Federated Learning Architectures: Challenges and Way Forward
Get PDFFederated Learning (FL) is a machine learning approach that enables distributed or edge devices to collaboratively train a model without sharing their local data, preserving privacy. FL has been widely applied in various fields, particularly in autonomous and connected vehicles, due to its privacy-preserving nature. This is achieved by aggregating local gradient updates from individual devices (clients) without sharing the raw sensor data with the server. However, despite its privacy guarantees, recent research has identified a critical vulnerability in FL: protecting model updates during communication. To address this, Homomorphic Encryption (HE) has been proposed as a solution to secure model updates during communication and aggregation. While HE enhances the security of FL, it is computationally intensive and introduces significant overhead, making it impractical for many real-world applications, including connected and autonomous vehicles. This study seeks to explore the feasibility of integrating HE within FL systems in practical settings. Our experiments focus on identifying the challenges of integrating HE with FL, specifically evaluating the trade-offs in terms of computation time, communication cost, and accuracy across different encryption parameter configurations. By testing HE across a range of machine learning models, including large and complex deep learning architectures, we aim to quantify the encryption overhead and assess its impact on model performance. The goal is to determine the practical feasibility and usability of HE in real-world applications, even in scenarios involving computationally intensive models