Engineering Commons^™

The Wildlife Intelligence For Aviation Safety (Wild-Ai), Bao Khoa Tran

Student Research Symposium (SRS)

Wildlife strikes pose a significant safety risk and economic burden to the aviation industry. Over 291,000 strikes have been reported in the U.S. since 1990, causing an estimated $248 million in annual losses. The Wild-AI (Wildlife Intelligence for Aviation Safety) project leverages cutting-edge technologies, including large language models (LLMs), machine learning (ML), explainable AI (XAI), and Unmanned Air Vehicles (UAV) imagery, to enhance wildlife hazard management at airports. Wild-AI transforms traditional Wildlife Hazard Assessments (WHAs) by integrating advanced data analytics and UAV monitoring. By utilizing historical wildlife-strike data and ML algorithms, we will identify key predictors of damaging strikes, such …

The Ripple Effect: How The Hse Data Breach Shaped Public Opinion, Mugdha Srivastava, Abhishek Kaushik, Roisin Loughran, Fergal Mccaffery

Women+ in Early Career Research Symposium

The digital world is rapidly evolving, and healthcare systems now depend heavily on data-driven and connected technologies. While these tools bring many benefits, they also increase exposure to data breaches and cyberattacks, raising concerns about privacy, security, and trust. Although earlier research has focused on technical and policy solutions for data protection, less attention has been paid to how major cyber incidents influence public attitudes. This study addresses that gap by examining how a major cyberattack relates to people’s views on data security and trust in digital health. We surveyed individuals about their personal cybersecurity practices and their opinions on …

Beyond The Sayable: Wittgenstein, Theory Of Mind And Affective Simulation Of Llms, Haomiaomiao Wang, Lili Zhang, Tomás E. Ward

Women+ in Early Career Research Symposium

Wittgenstein’s distinction between what can be said and what can only be shown frames a limit of propositional language. Affect and aesthetic are not primarily matters of correct description but of how expressions function within shared forms of life. Large language models (LLMs), however, increasingly produce fluent language that resembles such understanding by reproducing the patterns through which people ordinarily talk about emotion and perspective.

This paper argues that apparent Theory of Mind (ToM) in LLMs is understood as competence in the publicly shared patterns of mental-state language, rather than as grounded understanding. Using abstract artworks, we show that LLMs …

Bridging Research And Practice: Your Guide To Implementing Evidence-Based Gender Equality Actions In Computing Education, Alina Berry

Women+ in Early Career Research Symposium

What if you could access proven strategies for promoting gender equality without spending months reviewing academic and grey literature? TechMate makes this possible. This free, open-access toolkit transforms scattered research into actionable guidance (actions) that anyone can use, whether you are a lecturer wanting to make your classroom more inclusive, a department head developing new policies, or a student advocate pushing for change. Actions are structured into four context-based categories and relevant subcategories. Each action comes with implementation instructions, where relevant, with ready-to-use templates, and links to the supporting evidence. While developed for computing higher education, TechMate’s research-driven approach is …

Engaging Older Adults Through Artefact-Driven Approaches For Ai Literacy Development: A Multi Stakeholder For Digital Inclusion, Paula Kelly, Mayank Parmar, Damon Berry, André Almo, Fatima Badmos, Peterson Jean

Women+ in Early Career Research Symposium

As Artificial Intelligence (AI) becomes deeply integrated into modern ICT and e-Accessibility services, older adults face a growing "digital divide" often characterised by a lack of awareness regarding how automated AI systems frequently functioning as "black boxes" infiltrate and impact their daily lives. This paper presents a multimodal workshop model designed to foster critical AI literacy among older adults, transitioning them from passive recipients to active stakeholders. Utilising an Action Research framework, the study details the co-creation phase of a three-phase national Age- Friendly AI literacy initiative in Ireland. Central to the methodology is the use of tangible exhibits as …

Lidar Mirror Detection Model For Autonomous Robotics, Sofiia Sarana

Women+ in Early Career Research Symposium

LiDAR sensors are widely used in autonomous robots and vehicles for mapping, navigation, and obstacle avoidance. Because mapping is a foundational capability in nearly all robotic systems, it must be both accurate and reliable. However, LiDAR frequently fails in environments containing mirrors or highly reflective surfaces, where distorted returns corrupt maps and generate false obstacles. Current solutions typically rely on additional sensors and computationally expensive sensor-fusion pipelines, which is costly and difficult to scale for compact robots with limited onboard resources. This paper presents a machine-learning–based LiDAR mirror detection model that identifies reflective surfaces directly from LiDAR data, reducing the …

When Irl Is Not An Option: Social Media Bans And The Exclusion Of Autistic Youth, Melanie Gruben, Ashley Sheil, Anne Ryan Duffy, Jacob Camillleri, Michelle O'Keeffe, Hazel Murray

Women+ in Early Career Research Symposium

After the roll-out of the social media ban in Australia in 2025, policy makers are considering psychosocial benefits and logistical implications. However, the needs of autistic people, who may rely more on the Internet for social connection earlier in life, should be taken into account. This white paper proposes a responsibility to support alternative means of social connection for autistic young people where bans are considered, as IRL (in real life) socialising is not always accessible.

A Human-Centred View Of Maritime Autonomous Surface Ships Operations Using The Shell Model, Natasya Habibah, Paul Hegarty, Breda Kenny

Women+ in Early Career Research Symposium

The concept of Maritime Autonomous Surface Ships (MASS) has existed for decades and is advancing rapidly as a result of developments in artificial intelligence. Despite these technological advancements, human involvement remains essential to ensure safety and reliability. This study applies the Software, Hardware, Environment, and Liveware (SHELL) model to develop a human-centric framework for examining the integration of human elements with intelligent and automated technologies in MASS. The framework considers software, with particular attention to regulatory aspects; hardware, including general system and sub-system architecture; environmental factors, including both natural and artificial, as well as dynamic and static elements; and liveware, …

Comparative Time Series Forecasting Of Pedestrian Footfall In Dublin City Centre, Mercy Koech

Women+ in Early Career Research Symposium

Research on urban populations has turned out to be a significant field of study for a long duration.Forecasting pedestrian footfall is important for urban management,retail planning,crime analysis and evaluating the influence of public gatherings.With the rise of smart cities and the proliferation of sensor technologies there now exists a rich supply of data that offers unprecedented insight into the behaviour of pedestrain traffic.This study undertakes a comprehensive analysis of timeseries forecasting methods to predict pedestrian footfall trends in Dublin City Centre a key urban area with diverse spatial characteristics and high foot traffic variability.Using data collected via PYRO-BOX Counters sensors …

Selfhealth: Continuous Semantic Intelligence For Privacy-Aware Lifelogging, Allie Tran

Women+ in Early Career Research Symposium

Lifelogging — the continuous digital recording of everyday experience through wearable cameras and sensors — has long promised benefits for memory augmentation, health monitoring, and lifestyle analysis. However, current systems follow a capture–store–retrieve model that delays analysis until after data collection, limiting their practical usefulness and creating significant privacy risks through prolonged retention of sensitive raw data. This paper argues that lifelogging should instead analyse data at the moment of capture, enabling timely, context-sensitive support and safer handling of personal information through privacy-by-design. We demonstrate the feasibility of this approach through SelfHealth, an early prototype system built on a custom …

The Role Of Optical And Synthetic Aperture Radar Satellites In Military Intelligence, Khalifa Alnaqbi

Student Research Symposium (SRS)

The objective of this paper is to demonstrate that while optical and Synthetic Aperture Radar satellites are the two most powerful remote sensing tools in military intelligence, neither is sufficient on its own. This research aims to articulate their distinct operational roles and argue that their synergistic integration, together with other intelligence assets such as unmanned aerial vehicles, signals intelligence, and human intelligence, is essential for achieving comprehensive situational awareness in modern warfare. To establish this, the paper will first analyze the role of optical satellites in target identification and Synthetic Aperture Radar satellites in persistent detection and monitoring, detailing …

Osha’S Knocking! A Descriptive Cross-Sectional Analysis Of Inspections At Private Universities In Florida., Acuna Gonzalez Santiago

Student Research Symposium (SRS)

Section 8 of the Occupational Safety and Health Act (OSH Act) empowers OSHA to conduct inspections and enforce standards prescribed in Title 29 of the Code of Federal Regulations; however, limited literature and lack of descriptive statistics exist regarding oversight and inspection patterns across Higher Education Institutions (HEI) in Florida. Assessing these patterns is paramount for aiding higher education stakeholders in evaluating their hazard identification and control techniques. This descriptive cross-sectional study examined OSHA's inspection database — January 1, 2015 to October 20, 2025 — indexed by NAICS code 611310, analyzing all 32 four-year non-profit HEI with at least 1,000 …

Enhancing Wildlife Strike Risk Assessment Through Large Language Model–Driven Data Analysis, Bill Deng Pan

Student Research Symposium (SRS)

Wildlife strikes remain a persistent safety and economic concern across global aviation operations, highlighting the need for advanced analytical methods to improve risk assessment and mitigation. Traditional statistical approaches to wildlife-strike data, while effective for structured variables such as altitude, phase of flight, or aircraft type, often overlook valuable insights embedded in the unstructured narrative components of strike reports. This study proposes the application of Large Language Models (LLMs) for the automated extraction, classification, and interpretation of information within the Federal Aviation Administration (FAA) Wildlife Strike Database. Using natural language processing (NLP) techniques, LLMs will be utilized to identify key …

Modeling And Analysis Of Opportunistic Traffic Offloading Between Wireless Networks, Shensheng Tang

Day of Scholarship

This paper examines traffic offloading between two wireless networks operating in the same service region, where a beneficiary network can temporarily redirect calls to a benefactor network during periods of congestion. To analyze this interaction, we develop a finite level‑dependent quasi‑birth‑and‑death (LD‑QBD) model that captures the joint dynamics of admission control, opportunistic offloading, and preemption. The LD‑QBD structure enables exact computation of the steady‑state distribution using efficient sparse linear solvers. From these steady‑state probabilities, we derive key performance metrics, including blocking and drop probabilities, mean occupancy, throughput, mean system time, admission gain, and successful throughput gain. Numerical results show that …

Design And Implementation Of A Microblaze‑Based Embedded System For Programmable Led Control On An Artix‑7 Fpga, Shensheng Tang

Day of Scholarship

This paper presents the design and implementation of a MicroBlaze‑based embedded system developed using the AMD/Xilinx VIVADO Design Suite to control programmable LED patterns on an Artix‑7 FPGA development board via push‑button inputs. The MicroBlaze, a 32/64‑bit RISC Harvard‑architecture soft‑core processor, provides a flexible and configurable platform that enables integration of customized peripheral, memory, and interface components within a single FPGA. The proposed system employs two GPIO controllers—one interfacing with sixteen LEDs and the other with five push buttons—connected to the MicroBlaze processor through an AXI interconnect to support efficient on‑chip communication. LED functionality is managed through a software application …

Fpga Implementation Of A Sequence Detector Using A Finite State Machine, Shensheng Tang, Nick Zander

Day of Scholarship

This project presents the design and hardware implementation of a Finite State Machine (FSM) capable of detecting the binary sequences “1001” and “1111” with full support for overlapping occurrences. The FSM was designed to operate efficiently in real time and was implemented on an Artix‑7 FPGA development board using the AMD/Xilinx Vivado Design Suite. The system processes a serial input stream and asserts a detection signal whenever either target sequence appears, even when sequences share bits across consecutive detections. The FSM architecture, state transition logic, and verification strategy were developed and validated through simulation before being deployed to hardware. Experimental …

Identify Top Journals, Ibis Anette Moreno-Lozano Phd.

Day Family Research Lab Workshop Series

No abstract provided.

Ai Applications In Assessing Risk For Periodontal Disease: A Systematic Review, Grant O. Korte, Claudia M. Tellez Freitas

Annual Research Symposium

Objectives:

The goal of this systematic review is to examine the impact of using artificial intelligence (AI) to predict a patient’s risk level for periodontal disease by analyzing proven systemic health diseases linked to periodontitis. This is being done by primarily focusing on prevention and early diagnosis using machine learning programs that have been proven effective in other fields of periodontitis research.

Methods:

To conduct this study, a comprehensive literature review of journals published after 2020 was performed through four databases: PubMed, Scopus, Web of Science and Dentistry and Oral Science Source. The search was completed in adherence …

Polar Reconnaissance & Imagining Survey (Polaris), Max Brooks, Zion Carpenter, Isabel La Coss

Student Research Symposium (SRS)

In preparation for the permanent human presence on the lunar south pole as part of the ARTEMIS Accords, planned human excursions will conduct science on regions of interest to locate resources for sustained habitation. The objective of the Polar Reconnaissance & Imaging Survey simulation is to take a predetermined region sized image of a planned astronaut excursion site to identify terrain features and threats. This shall be a satellite mission control (MCC) simulation with four Earth-based satellite console operators, and one moon-based EVA encompassing.

Injuries And Accidents On Airport Ramps, Kavya Banarjee

Student Research Symposium (SRS)

Media coverage of aircraft incidents/accidents often triggers public scrutiny and heightened anxiety. But behind that are injuries and fatalities caused on the airport ramps; incidents like these are rarely covered by the media but pose just as much of a safety concern as an aircraft incident. This paper aims to inform aviation industry professionals and airline executives about the causes of incidents and accidents on the airport ramp; it also covers the importance of finding and implementing solutions for safety. The methodology for this paper focuses on research and analysis of publicly available information. Relevant data and perspectives were gathered …

Emulating Camera Parameters For A Digital Twin Lunar Terrain Simulation, Samuil Nikolov

Student Research Symposium (SRS)

Digital twin simulations play an integral role in the design, validation and implementation of a plethora of systems in any industry - including aerospace. EagleCam 2 presents a great technical challenge, where we need to evaluate how our systems will do data acquisition best - image capturing in particular. In order to assist with the design and validation of our systems, a digital twin that emulates the Lunar environment as we expect it to be during the lifecycle of the mission is a crucial component. Such digital twin system allows us to simulate all the parameters that are considered variable …

Crew Resource Management Analyzed In Guyana Military Accident, Kaylee Lall

Student Research Symposium (SRS)

"In December 2023, a Guyana Defence Force Bell 412EP helicopter crashed in mountainous jungle terrain near the Venezuela border, killing five of the seven personnel on board. Preliminary investigation findings indicate the probable cause of the accident was pilot error, rather than mechanical failure. Due to deteriorating weather conditions, the crew deviated from the standard route and elected to fly under visual flight rules (VFR) along the Mazaruni River. The combination of limited visibility, rising mountainous terrain, and potential spatial disorientation likely contributed to this controlled flight into terrain.

This case study applies the SHELL model to analyze the interactions …

Contrasting Coastal Dune Environments In Chile, Aurora Christianson

Student Research Symposium (SRS)

Emerging coastalization and urbanization threats to the prehistoric Concón Dunes and Humedal de Mantagua coastal area of Chile is being investigated by researchers via uncrewed aircraft systems (UAS). Four UAS were utilized: Anzu Raptor T, DJI Mavic 3E, DJI Mavic 3M, and DJI Air 3 to collect various images of the coastal dunes. Multispectral and RGB cameras gather images by photogrammetry to create orthomosaics and monitor vegetation indexes in Pix4Dmapper. Thermal cameras provided images in rainbow, white hot infrared, and black hot infrared schemes to monitor wildlife and vegetation. The normalized difference vegetation index (NDVI) was calculated to visualize overall …

Growing Plants On The Moon: How Mizuna Roots And Organic Matter Spark Microbial Metabolism, Stefani Capasso Villanueva, Nupur Kulkarni

Student Research Symposium (SRS)

Establishing functional plant–microbe systems in extraterrestrial substrates is critical for sustainable lunar agriculture. We tested whether manure amendment and Mizuna mustard (Brassica rapa var. japonica) rhizosphere activity enhance the metabolic potential of a lunar regolith simulant (LHS-1). Plants were grown in four treatments: Unplanted manure-amended regolith (LRM), planted manure-amended regolith (LRMP), planted unamended regolith (LRP), and soil controls. Plant height and community-level physiological profiles (CLPP; Biolog EcoPlates) were measured to assess growth and metabolic diversity. Mizuna grew best in soil, with modest improvements in LRMP compared to LRP, though regolith treatment differences were not statistically significant. CLPP revealed the highest …

Examining Self-Direction In Learning Programming Among First-Year Students, Abigail Blalock

Student Research Symposium (SRS)

In this poster, we present empirical insights into first-year students’ self-direction in learning programming. In this study, we use a 25-item Personal Responsibility Orientation to Self-Direction in Learning Scale (PRO-SDLS) to assess the self-direction of first-year students in learning programming. The PRO-SDLS is comprised of 4 constructs: self-efficacy, initiative, motivation, and control. Additionally, this study examines the results of the PRO-SDLS across different demographic groups (i.e., first-generation, students with employment, financial aid, etc.). A pre- and post-survey was administered during the Fall 2024 semester in an introductory programming course for first-year engineering students. Data was collected from seven course sections, …

Finding Research Datasets And Evaluating Data Quality, Ibis Anette Moreno-Lozano Phd.

Day Family Research Lab Workshop Series

No abstract provided.

Building And Sustaining Chosen Family Amongst Low-Income First-Generation Engineers, Justin C. Major

First-Generation Symposium

Support is integral for low-income first-generation student success in and out of engineering, particularly because of the role support plays in building belonging. Students receive support from many individuals including their traditional and chosen families. While traditional family are those family “assigned at birth,” chosen family are those family you pick and choose, inclusive of teachers, friends, and others. Our research group’s (ASPIRE) extensive work on the topic has documented how chosen family come to be and why they are important for low-income first-generation success. Amongst many components, we have found that chosen family are more trustable given their authenticity …

Data Management Plans For Grant Proposals, Rubab Shahzad

Day Family Research Lab Workshop Series

Fundamentals of research data management and how to create effective Data Management Plans (DMPs) and Data Management Sharing Plans (DMSP)

Proceedings Of The Eleventh Annual Indiana Stem Education Conference: Stem Journeys, William S. Walker Iii, S. Selcen Guzey

Indiana STEM Education Conference

The Proceedings of the Eleventh Annual Indiana STEM Education Conference are edited by the Center for Advancing the Teaching and Learning of STEM (CATALYST, https://www.education.purdue.edu/catalyst/) at Purdue University. The theme for the 2026 conference is STEM Journeys, celebrating the multiple pathways and experiences that advance student learning in STEM education.

Renewable Energy Innovation: A Mathematics-Driven Integrated Stem Approach For K-12 Classrooms, Humphrey Chinenye Ochulor, Dayat Hidayat

Indiana STEM Education Conference

This research brief presents Renewable Energy Innovation (REI) as a mathematics-driven Integrated STEM (iSTEM) approach for engaging upper elementary and middle school students to design, analyze, test, and refine solar cars and wind turbines through mathematical modeling (MM) and problem solving (PS). Research suggests that renewable energy activities support students’ abilities to design systems, quantify variables, construct multiple representations, and refine mathematical models to clarify relationships between energy output and design features. Positioning mathematics as the driver in REI iSTEM engineering design contexts aligns with the Common Core State Standards for Mathematics and foregrounds mathematics as a primary organizing discipline …