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AI Survivor: Outwit, Outlast, Outteach
Get PDFStep into the world of Survivor, where professors are challenged to navigate the ever-changing landscape of higher education using AI as their ultimate survival tool. This interactive session will showcase cutting-edge AI tools to help educators boost efficiency, streamline workflows, and conquer their academic practices. Tools to assist with grading , organization and planning, transcription, and content creation will take center stage. The goal? To empower professors to navigate challenges, reduce their workload, and develop content
Empowering Faculty to Use GenAI through Asynchronous Professional Development
No full textPresenters will share research from a cross-disciplinary asynchronous Getting Started with AI course, which helped faculty learn about GenAI ethics, develop a course-specific policy, and design low- and high-stakes GenAI activities. This course offers one possibility for flexible professional development programming to mitigate initial anxieties around AI integration while providing all faculty an opportunity to learn about the roles GenAI can play to support learning while guiding them to apply these principles to their disciplinary and course contexts. Exemplary faculty work was showcased as part of a GenAI open access resource
Leveraging Multiple AI Tools During Business Research Consultations at a Small Liberal Arts College
No full textThis poster will share strategies from a business librarian at a small liberal arts college who integrated multiple AI tools into research consultations while reinforcing core concepts from business research and information literacy. While classroom instruction comes with time constraints, it is possible in a single business research consultation to discuss one AI tool for brainstorming and outlining, another for literature reviews, and still another for market research. These strategies will not only be technical but also social-emotional in nature, as attendees will learn some soft skills to help promote a healthy and balanced perspective of AI tools among students
An Exploratory Journey to Develop a Chatbot for Discovering and Using Open Educational Resources
No full textThis poster session will explore the interdisciplinary journey of creating a chatbot to enhance the search and discovery of open textbooks for the university library. The interactive tool will enable users to find and access open textbooks through natural language conversation, supporting teaching, learning, and research. We will share how our team of librarians and a Computer Science faculty member collaborated to bring this idea to life, including initial brainstorming, creative funding, and the ongoing development process. The poster will also provide an update on the chatbot\u27s progress, lessons learned, and plans for the next twelve months of the project
The Scoop, Vol. 12 Issue 1, April 2025
Get PDFLatest news and updates from the Health Sciences Library in our monthly newsletter for April 2025. Please see page 2 for a text-only version of this issue!https://stars.library.ucf.edu/scoop-vol12/1000/thumbnail.jp
Applying Universal Design for Learning Strategies to Increase Student Engagement with your Course Syllabus
No full textApply Universal Design for Learning to syllabi design
Positivity and Invariance: From Radial Basis Functions to Graph Signal Spaces
No full textThis dissertation investigates the positivity problem and zero localization of special functions or integral transforms, with particular emphasis on their implications in analysis, approximation theory, and signal processing. The first half centers on the characterization of positive definite radial functions via Hankel transforms involving Bessel functions. Motivated by classical results such as Bochner\u27s theorem and Schoenberg’s work, we examine analytic conditions under which these transforms remain nonnegative, thereby ensuring the positive definiteness of radial basis functions (RBFs). We then explore the transition beyond positivity by focusing on the reality of zeros in Hankel (or Fourier) transforms. This leads to two complementary approaches: one based on the Laguerre–P{\\u27o}lya class, involving structural criteria and positivity of the Wronskian, and the other grounded in moment-based techniques using Hankel determinants and orthogonal polynomials to detect non-real zeros. Applications include sharp uniform bounds for regular Coulomb wave functions and and insights into zero distribution problems.
The second half turns to shift-invariant spaces on graphs and their applications in signal recovery. Extending classical shift-invariant theory to graph settings, we introduce graph shift-invariant spaces (GSIS) defined via commutative shift operators and investigate their analytic and spectral structure. These spaces are shown to admit reproducing kernel representations and band-limited property. The final chapter develops a graph-theoretic analogue of Barron spaces, motivated by the approximation theory of shallow neural networks. We establish function space characterizations, universal approximation and learnability results for graph convolutional neural networks (GCNNs), demonstrating that functions in this graph Barron space can be efficiently approximated, independent of the input dimension
Inhibition of Activin A Signaling Slows the Progression of Barrett\u27s Esophagus Induced Esophageal Adenocarcinoma
No full textIn 2020, approximately 1.03 billion cases of Gastro-esophageal Reflux Disease (GERD) were reported worldwide, representing a significant global health burden. Chronic GERD is a potent initiator of Barrett’s Esophagus (BE), a precancerous condition in which the squamous epithelium is converted into columnar-like epithelium in the distal esophagus in response to reflux injury. Barrett’s Esophagus is the only known precursor to Esophageal Adenocarcinoma (EAC), a disease with a five-year survival rate of 21%. First line treatments focus on acid suppression and endoscopic mucosal resection but did not result in a decrease of EAC incidence highlighting the importance of new therapeutics. Previous studies in our laboratory have shown an increase in endogenous secretion of Activin A (ActA), a cytokine that promotes inflammation and wound healing in the distal esophagus, in experimental GERD. Furthermore, upregulation of the ActA gene, Inhibin A (INHBA), is reported throughout the progression of BE and EAC afflicted tissues, leading us to hypothesize inhibition of ActA in the progression from BE to EAC could be promising novel therapeutic strategy. We established pre-clinical three-dimensional spheroid cultures of human esophageal cell lines representing the progression from normal to late-stage BE to assess the functional consequences of ActA inhibition utilizing Garetosmab (REGN2477), an ActA neutralizing antibody. Additionally, we utilized a transgenic mouse model of BE challenged with a high fat diet and simulated GERD to assess the functional consequences of a bivalent ActA and myostatin neutralizing antibody, V08-035 (Vaxxinity Inc.). We observed a reduction in BE cell growth mediated in part by cellular senescence and apoptosis which resulted in decrease spheroid size and diameter. Further, we observed alterations in BE mouse weight and murine serum secretome mirroring in vitro findings using human cell lines. Our data suggests targeting ActA signaling with neutralizing therapeutics could prevent the progression of BE
Aluminum Alloys for Laser Powder Bed Fusion Additive Manufacturing and Process Optimization by Machine Learning
No full textLaser powder bed fusion (LPBF) additive manufacturing is a promising manufacturing technology enabling enhanced design freedom through layer-by-layer production. However, traditional, wrought aluminum (Al) alloys suffer from solidification cracking during LPBF processing and the initial printer parameter optimization process required for every novel or untested material is time and resource intensive. There exists a need to formulate Al alloys for LPBF and a method to reduce the resources consumed during initial printer parameter optimization studies. Al-9.5Ce-xMo (x = 0.2, 0.6, 1.0 wt. %) alloys have been designed specifically for LPBF utilizing the CALPHAD approach. Additionally, a neural network model was developed and capable of predicting the window of print parameters likely to yield high density printed parts. The Al-9.5Ce-xMo alloys were processed by LPBF and found to be crack free. Each alloy underwent microstructural characterization and tensile testing both in the as-built state and after long thermal exposure at various times and temperatures to determine the thermal stability of the alloys. Comparisons were made to the Al-10Ce base alloy. In the as-built state, additions of Mo as low as 0.2 wt.% increased strength and ductility, with ductility increasing from 10.8 ± 0.1 % in Al-10Ce to 16.8 ± 0.2 %. With 1.0 wt. % Mo addition, yield strength increased by 44 MPa over the Al-10Ce alloy to 266.2 ± 1.6 MPa and ductility was further enhanced to 16.9 ± 0.8 %. Full retention of tensile properties was found for all Mo-containing alloys after exposure to 200 °C for 120 hours. The neural network model, guided by material thermophysical properties and print parameters, successfully predicted the printability windows of Fe-, Ni-, and Al-based alloys, is capable of base alloy differentiation, and making predictions for novel alloys
Integration of System Dynamics and Agent-Based Simulation to Emulate Cyberattacks in an IoT-Based Smart Grid
No full textThe electric power grid is a cyber-physical system (CPS) that plays a fundamental role in modern society. With the integration of renewable energy sources and advanced communication technologies, Smart Grids (SGs) can enhance both the profitability and reliability of the electric power system. The communication network that interconnects numerous remotely distributed generators, devices, and controllers plays a vital role in grid control, and current trends favor the widespread adoption of Internet of Things (IoT) devices. However, this network is inherently vulnerable to cyberattacks. This dissertation presents a hybrid methodology to model and analyze the dynamic behavior of an electrical Smart Grid (SG) by integrating System Dynamics and Agent-Based Modeling. As a primary contribution, it incorporates a cyberattack module that simulates the impact of malware targeting IoT devices, exploiting vulnerabilities within the electric system. This approach provides a robust platform for identifying cyber-physical vulnerabilities, evaluating mitigation strategies, and contributing to developing future resilience and energy cybersecurity policies. Finally, the study presents its conclusions and recommendations for future research on modeling emerging cyber threats in distributed power systems