Unmanned Aerial Vehicles (UAVs) enable scalable and low-cost environmental monitoring. However, their deployment is still challenged by complex natural environmental conditions in the real world. This study employs a controlled experiment to investigate the impact of litter type, ground type, and illumination on litter detection performance in a forest park. A dataset of 375 aerial images was collected by a UAV, and a YOLO-based object detection model was trained and evaluated. ANOVA was used to test the impact of litter type, ground type, and illumination conditions on litter detection accuracy. The results show that litter type is a statistically significant factor, with significant interaction effects between litter type and ground type. Ground type and illumination alone do not exhibit significant effects on detection accuracy. Additional subgroup analysis revealed that composite environmental conditions influence detection accuracy for certain litter types. The findings indicate that detection performance is influenced by interactions between litter types and certain environmental conditions, rather than by individual factors alone. Based on the findings, three deployment-oriented strategies are proposed, including litter-type-aware monitoring, interaction-aware environmental planning, and composite condition-aware model training. These strategies can help balance automated UAV-based detection and human intervention, as well as support proper data sampling design.

This Innovative Practice full paper presents an immersive class model, "Designing Moonshots," which harnesses design thinking principles to equip students with theories, skills, and trajectories for designing moonshot projects. Through user interviews, expert feedback, and student-led "makeathons," participants reported a significant development of their abilities to need-learn and work with peers. Students also noted an increase in agency towards tackling systemic issues, the acquisition of novel problem solving strategies, and the discovery of new fields of interest. Implications of this research demonstrate that the pedagogical synthesis of the design thinking approach and moonshot solutions holds promise for a variety of skills that are often omitted in traditional education.

Algorithmic complexity vulnerabilities occur when the worst-case time/space complexity of an application is significantly higher than the respective average case for particular user-controlled inputs. When such conditions are met, an attacker can launch Denial-of-Service attacks against a vulnerable application by providing inputs that trigger the worst-case behavior. Such attacks have been known to have serious effects on production systems, take down entire websites, or lead to bypasses of Web Application Firewalls.