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AI in AI: Adjudicating Academic Integrity Cases Alleging Use of Artificial Intelligence
No full textThis session provides an overview of AI\u27s impact on academic integrity at Valencia College. Beginning with introductions, we will explore the AI landscape through a combination of lecture and large group activities. The session will then cover Valencia College\u27s approach to academic integrity, including a detailed review of the student conduct process for academic dishonesty cases. Participants will engage in an Egregiousness Spectrum activity to assess varying levels of misconduct, followed by a reflection session. The event will conclude with a collaborative discussion on strategies for maintaining academic integrity in an AI-driven world
Streamlining Admissions Packet Review: Leveraging Gradescope for Efficient Evaluation
No full textPacket review in higher education admissions is often time-consuming and prone to bias, as different evaluators may review different application packets or grade the same criteria inconsistently. This can result in unfair evaluations due to the subjective nature of scoring. Gradescope offers a solution by streamlining the process and assigning specific criteria to individual evaluators, ensuring consistency and reducing bias. This paper examines how Gradescope enhances the efficiency and fairness of the packet review process, offering improvements over traditional review methods
AI-Driven Innovation: Revolutionizing Global Business Education and Collaboration
No full textThis presentation unites four experts to examine artificial intelligence\u27s transformative impact on international education. The session explores four key areas: AI\u27s role in database education for data-driven decision-making; AI integration in Collaborative Online International Learning (COIL), highlighting a cybersecurity class and cross-cultural project with Lucerne University; AI language models with a societal focus; and AI startup ecosystems in Europe, focusing on global business planning. The discussion aims to help educators prepare students for an AI-driven global landscape through adapted curricula and teaching methodologies
Integrating Practical AI into Business Courses
No full textIn a prior semester, approximately 100 business students were given the opportunity to utilize AI tools to complete an assessment. Two-thirds of the students who chose not to use AI cited either unfamiliarity with the technology or ethical concerns. Students who lack experience with AI may face challenges during job interviews, particularly when asked, “How would you utilize AI to improve efficiencies and processes in the position you are applying for?” To address this gap, business courses have begun to incorporate assignments that emphasize the role of AI in business careers and AI’s practical applications in the workplace
Engaging Literature Learners: Exploring Themes in Fiction with Generative Artificial Intelligence (GenAI)
No full textStudents often struggle with traditional reading approaches, leading to superficial understanding and disengagement. This session demonstrates how generative AI tools, like ChatGPT, can inspire student engagement with critical societal themes such as equity, prejudice, and class division. Using The Outsiders by S.E. Hinton as a foundational text, we explore strategies to connect literature with interdisciplinary materials and current events. The result is a dynamic classroom where students actively engage with texts, promoting understanding and academic growth. Attendees will leave with practical tools that leverage AI and encourage creativity, critical thinking, and classroom discussions
Effects of Cerium Concentration and Scattering Centers on the Performance of GAGG Transparent Ceramic Scintillators
No full textCerium-doped Gadolinium Aluminum Gallium Garnet (Ce:GAGG) is a rugged scintillator material renowned for its high light output, fast luminescence decay, good stopping power, and energy resolution, making it an ideal candidate for gamma radiation detection. Its optically isotropic crystalline structure enables the fabrication of transparent optical ceramics, effectively addressing certain limitations associated with single-crystal growth. However, the production of highly transparent ceramics often involves costly processing methods with low overall yield. Reactive sintering offers a more economical alternative, but as with other processing techniques, the elimination of scattering centers—such as porosity and secondary phases—remains a significant challenge. These defects hinder the production of optically superior materials and impede wider adoption. Despite efforts to address these issues, the precise impact of microstructural defects on scintillation performance remains insufficiently understood. This thesis investigates the relationship between ceramic microstructure, optical quality, and scintillation performance in Ce:GAGG ceramics synthesized via reactive sintering. A series of GAGG samples with cerium doping between 0.1 and 10 at% was prepared using solid- state powder processing under varied conditions to achieve reduced levels of optical scattering. Radioluminescence characterizations indicate that while increased cerium concentration reduces luminescence intensity, it accelerates decay kinetics. Using absorption spectra measured by spectrophotometry alongside GEANT4 Monte-Carlo simulations, the reduction in optical transport efficiency due to cerium\u27s self-absorption is estimated to be less than 10%. Scattering centers within the ceramics were successfully minimized by compositional balancing of cationic site ratios, resulting in materials with Fresnel limited optical transmission (80% transmission). Although bulk optical scattering is identified as the dominant factor affecting performance, near-opaque materials experience optical transport efficiency loss of no more than 46%. The findings of this thesis highlight the potential for substantial cost savings in the fabrication of scintillator ceramics for radiography with trade-offs such as optical quality and transport performance
Numerical Solutions for Deterministic and Stochastic Partial Differential Equations and Effective Integral Evaluation
Full text linkThis thesis explores advancements in numerical methods, focusing on their applications to some classes of PDEs, Stochastic PDEs (SPDEs), and integral evaluations. The first part of this proposal provides algorithms for computing the mean curvature flow, including cases with topological changes. We propose energy-penalized and multilevel minimization algorithms. Benchmark problems with random initial conditions reveal that small changes in interaction length led to different solution patterns under topological changes. Our methods demonstrate robustness and efficiency, even with poor initial guesses, making them suitable for a wide range of scenarios.
The second part of this proposal introduces a new approach for time and spatial discretization of semi-linear SPDEs with multiplicative noise, under minimal assumptions on the drift and diffusion terms. Using a Milstein-based time discretization and an interpolation-based finite element method for space, we establish strong convergence of nearly order 1 and various stability results. New Hölder continuity estimates, and energy methods underpin the analysis, ensuring nearly optimal convergence in time and space. Numerical experiments validate the theoretical findings and demonstrate the effectiveness of the proposed scheme.
The final part of this proposal presents adjusted algorithms for the Multilevel Dimension Iteration (MDI) method. High-dimensional integration often suffers from the curse of dimensionality. The recent MDI method has demonstrated significant success in overcoming this issue for smooth, well-behaved functions with simple structures. However, its performance declines when applied to functions with non-smooth features, singularities, or complex structures. This limitation restricts its use in real-world applications. To address these challenges, we propose modifications to the MDI method. These adjustments aim to enhance its ability to handle complex functions and broaden its applicability to practical problems
Multilevel Network Governance for United Nations Sustainable Development Goals: The Role of European Subnational Entities in Urban Resilience
No full textThis dissertation investigates the role of subnational actors in European Union (EU) multilevel governance networks focused on reducing urban poverty and achieving the United Nations Sustainable Development Goals (SDGs). Focusing on the SDG #1 (No Poverty), SDG #11 (Sustainable Cities and Communities) and SDG #17 (Partnerships for the Goals) it examines the participation and impact of subnational actors in two case studies: Naples, Italy and Krakow, Poland. Using Social Network Analysis (SNA), the research investigates the configuration and performance of the networks created within the framework of the URBinclusion projects, which are part of the URBACT and Urban Innovative Actions (UIA) programmes. The results show that subnational entities are central to the design and delivery of urban poverty policies, particularly through collaborative governance with other levels of government and other stakeholders. The study adds to the increasing literature on multilevel governance by revealing the relationships between subnational entities, urban resilience and sustainable development, and the necessity of partnership in addressing global challenges
Towards an Intelligent Speculative Software-Defined Networking
No full textSoftware-Defined Networking (SDN) separates the control and data planes, enabling better programmability of the control plane to predict, route, and schedule traffic at the data plane. Reactive SDN dynamically installs flow rules when a new flow arrives, making it adaptable to application dynamics. This design is well-suited for emerging low-latency applications like online gaming and AR/VR, which require millisecond-level response times for an acceptable quality of experience. However, Reactive SDN faces limitations, as each new flow triggers a miss, sending a Packet-in message from the switch to the SDN controller, increasing latency. To reduce delay, it\u27s crucial to predict flow arrivals and install the necessary rules in advance. Reinforcement Learning (RL), where agents make decisions based on rewards, can help by learning to predict flow arrivals and installing flow rules ahead of time. We propose a Speculative SDN design to address the limitations of Reactive SDN for low-latency applications. This approach uses RL to predict and install unseen flows, reducing delays and improving overall performance. By speculatively installing flow rules, we enhance SDN\u27s ability to meet the fast-response demands of emerging applications
Statistical Analysis of Interstitial Impacts on Shock-Driven Fuel Droplet Atomization
No full textUnderstanding the deformation and aerobreakup of liquid droplets is critical for designing fuel injectors for pressure gain combustion systems. Previous literature has observed the morphology and timescales of individual liquid droplets exposed to the convective flow fields behind moving shocks propagating between Mach 1 and Mach 10. However, the liquid droplets produced by an injection system are not isolated and have many close neighbor droplets of similar diameter. Understanding interactions between these droplets is necessary to reconcile existing theory with droplet behavior observed in liquid jets and droplet clouds. The present study uses a simple injector to create vertically oriented droplet arrays of RP-2 and Jet A-1. The droplets had a mean diameter of 273 microns with a standard deviation of 52 microns. The spacing between droplets in these arrays was random and ranged from 1 to 4 droplet diameters. The displacement and breakup time of each droplet in the arrays were tabulated and compared against a nondimensional separation L/Do. The tests were performed in the shock Tube for HyperSonic Research at UCF’s propulsion and energy research laboratory. Testing was performed using normal shocks propagating at Mach 5.0, 6.6, and 7.5. It was found that vertical droplet spacing had little to no effect on droplet acceleration or breakup time, suggesting that horizontally oriented droplets may be responsible for any differences observed between individual droplet breakup and grouped droplet breakup