Physical Sciences and Mathematics Commons^™

Bitseat: Reimagining The Financing For Airliners Using Nonfungible Tokens (Blockchain Technology), Edwin S. Ongola

Journal of Aviation Technology and Engineering

This essay describes how blockchain technology, particularly nonfungible tokens, can be used to raise funding for airliners. The essay begins with a brief overview on the costs, categories, and acquisition methods of airliners. After that, the essay introduces concepts on blockchain technology, tokens, and smart contracts. The essay then touches on how nonfungible tokens can be used to facilitate fractional ownership of airliners. From there, the essay discusses Bitseat, a conceptual nonfungible token for fractional ownership of airliners, covering its overall design, appeal, marketplace alternatives, and challenges. Finally, in the discussion, the essay summarizes the overall concept and outlines its …

Nci Research Impact And Expertise With Bibliometric Data, Senay Purzer, Wei Zakharov, Carla B. Zoltowski

Supplementary Content for Stewards of Data: A Practical Handbook for Undergraduate Researchers in Engineering and Applied Sciences

This poster explores NCI research impact and expertise with bibliometric data. This book was published by the Purdue University Press. Copyright 2026. Permission: Courtesy of Colin Roberson, Lonnie Schwartz, Vineeth Narra, and Pete E. Pascuzzi.

Quantitative Methods In Education: A Practical Introduction To Statistics, Yukiko Maeda, John Gipson, Sheila Hurt, Katie H. Dufault

Purdue University Press Books

Educational research often involves understanding complex patterns in student achievement, teacher effectiveness, and institutional performance. Quantitative Methods in Education: A Practical Introduction to Statistics is designed to equip current and future educators and researchers with a basic comprehension of the statistical tools necessary to effectively analyze and interpret educational data. In today’s data-driven world, the ability to leverage statistical techniques is essential for making informed decisions that can enhance learning outcomes and maximize learner potential. This book provides a systematic approach to exploring these trends using both descriptive and inferential statistics, providing readers with the knowledge to conduct rigorous analyses …

Research Instrumentation Center (Ric), Ryan Hilger, Purdue University Office Of Research

University Research Core Facility Boilerplate Descriptions

No abstract provided.

Statistical Analysis Of Log Transformation Effectiveness In Air Traffic Movement Forecasting During Covid-19 In South Africa, John Lehlaka Masekoameng

Journal of Aviation Technology and Engineering

This study evaluates the effectiveness of log transformation in enhancing multiple regression models used to forecast air traffic movements (ATMs) in South Africa during the COVID-19 pandemic. Using 60 monthly observations from October 2016 to September 2021, the analysis incorporates variables such as revenue, lockdown levels, COVID-19 metrics, exchange rates, gross domestic product, and population. Two models are compared: one using raw ATMs and another with log-transformed ATMs as the dependent variable.

While the untransformed model shows stronger explanatory power (R² = 0.904, adjusted R² = 0.891) compared to the log-transformed model (R² = 0.772, adjusted R² = 0.741), the …

Case Studies To Examine Farmer Perceptions And Implementations Of Buffer Capacity In Southeastern Ontario, Bryan Collins

Journal of Applied Farm Economics

Buffer capacity measures the ability of a system to maintain its original function when confronted with outside stresses and is often considered to be a key component of resilience. Farmers in southeastern Ontario are facing several threats such as seasonal drought and volatile markets, prompting necessary responses on how to effectively manage their farms. This research study used an exploratory grounded theory approach to inquire how farmers seek to enhance buffer capacity on their farm by asking (1) what short-term threats their farm faces and (2) what strategies they are employing to navigate those threats. Ten farms in the Inverary …

Does Sequence Matter? Impact Of Redesigning Sequential Calculus Course On Students’ Learning Outcomes, Chantal Levesque-Bristol Dr., Wonki Lee Dr., Emily M. Bonem, Benjamin C. Wiles, Jennifer D. Moss, Wilella D. Burgess, Weiling Li

International Journal of Teaching and Learning in Higher Education

As colleges and universities increasingly transform their STEM courses through the adoption of more active, student-centered pedagogies, there is a growing need to understand the impact of these educational innovations on student outcomes. This study is conducted in the context of a university-wide faculty development and course redesign project, IMPACT (Instruction Matters, Purdue Academic Course Transformation). IMPACT supports faculty in implementing student-centered pedagogical practices and creating equitable and inclusive learning environments. This study focuses on a sequence of two introductory calculus courses (Calculus 1 and Calculus 2, hereafter CALC 1 and CALC 2) that were transformed as part of the …

Enhancing Education Through Virtual Reality: A Comparative Study Of Vr And Traditional Learning Environments, Shrivardhan Atluri

The Journal of Purdue Undergraduate Research

No abstract provided.

Application Of Reinforcement Learning To Precision Aerial Delivery System Control In Adverse Wind Conditions, Radman Zarbock

The Journal of Purdue Undergraduate Research

Precision aerial delivery systems (PADS) are a subset of airdropped parachute-leveraging package delivery systems that use autonomous guidance, navigation, and control (GNC) to reach targets with high degrees of accuracy. This technology emerged in the 1990s, and strides have been made since to improve the reliability of traditional physics-based controllers that guide PADS. However, these algorithms still struggle to deliver acceptable performance results when PADS are subjected to austere operating environments, such as those with unpredictable wind. Building on a foundational study in 2022 that used artificial intelligence (AI) and machine learning to improve PADS GNC performance, this study aims …

Characterization Of River Discharge And Interaction With Shallow Groundwater Aquifers Along The Wabash River Using Seismological Methods, Jessica M. Cyr, Xiaotao Yang, Marty D. Frisbee

Discovery Undergraduate Interdisciplinary Research Internship

The interaction between shallow aquifers and local rivers in the West Lafayette, Indiana area remains poorly understood. While surface water levels in the Wabash River and its tributaries are monitored at several USGS stream gauges, groundwater fluctuations are less well documented. Methods using seismic recordings, sensitive to subtle ground vibrations and subsurface structure, provide an alternative to characterize groundwater levels and river discharge. This project analyzes the temporal variations of ambient noise seismic data, examining the relationship to USGS river discharge, precipitation records, and other environmental factors. We currently focus on 1) changes in maximum amplitudes and peak frequencies within …

Fourier Analyses Of Optical Profilometry As An Inferential Measurement For Impact Coverage., Langdon Feltner, Paul Mort

15th International Conference on Shot Peening

A critical consideration in peening process design is achieving sufficient impact coverage. Conventional methods for assessing coverage rely on manual inspection, which is time-consuming and poorly suited for automated control. In this work, we investigate the use of frequency-domain analysis to quantify surface modification in peened samples using optical profilometry (OP) data. Three-dimensional surface maps of Almen strips were acquired using a high-resolution OP system and analyzed via fast Fourier transform (FFT) to compute spatial power spectral densities (PSDs). PSD maps and radially averaged profiles reveal consistent amplification of harmonic components similar to the nominal particle size, with increasing intensity …

Reduced Order Approach For Peening Stress Field Variability, Langdon Feltner, Paul Mort

15th International Conference on Shot Peening

A common goal in shot peening research is to connect operational parameters to resultant residual stress fields, providing a means to control and optimize the effectiveness of surface treatment. In practice, experimental measurements of residual stresses are often averaged values over regions that are large in comparison to an impact dimple. In fact, the stochastic nature of impact locations leads to residual stress fields that are distributed. Finite element peening simulations confirm this observation. The goal of this report is to connect operational parameters to localized fluctuations in residual stress through probabilistic reasoning (Figure 1). In particular, the development of …

A Technical And Statistical Analysis Of Unleaded Aviation Fuel (Ul94) Adoption In A High- Volume Collegiate Aviation Environment, Nicholas D. Wilson, Ryan Guthridge, Brandon Wild, Jeremy Roesler, Daniel Kasowski, Nick Geinert, Aaron Terbest, Aaron Fettig, Robert Kraus

Journal of Aviation Technology and Engineering

The University of North Dakota (UND) adopted unleaded aviation fuel (UL94) for approximately a four-month period in the summer and early fall of 2023. The UL94 fuel was used in all reciprocating engine fleets based at the university’s primary training airport, Grand Forks International Airport in North Dakota. During the operational implementation of UL94, the UND flew 46,600 flight hours, consuming 386,778 gallons of fuel across all fleets powered by Lycoming engines. After approximately two months of using UL94, operational reports and maintenance inspections began to indicate potential for exhaust valve seat recession (EVSR), although early indications were limited in …

A Deep Learning Framework With Explainable Ai For Atmospheric Blocking Detection And Interpretation, Devansh Khandelwal

Discovery Undergraduate Interdisciplinary Research Internship

Atmospheric blocking is a large-scale quasi-stationary phenomenon in mid-latitude circulation, characterized by persistent high-pressure systems that disrupt the typical west-to-east flow of the jet stream. These systems can cause extreme weather events—such as heatwaves, cold spells, or droughts—that persist for days or even weeks. This study proposes a deep learning framework to predict and interpret the occurrence of atmospheric blocking by integrating geophysical precursors such as geopotential height (Z500), stream function (SF200), and potential vorticity. These features, which are dynamically linked to blocking onset and persistence, serve as inputs to a Convolutional Neural Network model trained on the CESM Large …

Crop Yield Prediction At Multiple Spatial Scales With Statistical Machine Learning, Vaibhav Charan, Pratishtha Poudel

Discovery Undergraduate Interdisciplinary Research Internship

Understanding and accurately predicting crop yield is becoming increasingly important today in the face of global food security challenges, and thus, the availability of standardized data and scalable models is the need of the hour. To support this, researchers have developed CY-Bench (Crop Yield Benchmark), a comprehensive dataset that helps forecast maize and wheat yields on a global scale. This research project primarily involved working with the CY-Bench dataset aiming to improve crop yield prediction through machine learning. Initially, papers explaining the CY-Bench dataset and other papers for agriculture modeling were studied and analyzed in detail. The research then progressed …

Discovering And Designing Novel Perovskite Photovoltaic Materials Via Machine Learning, Junyeong Ahn

Discovery Undergraduate Interdisciplinary Research Internship

Perovskite semiconductors are promising materials for high-efficiency photovoltaics due to their outstanding optoelectronic properties, emerging as a sustainable energy source through solar cell applications. Perovskites with the ABX₃ composition (A, B = metal or organic cations with varying oxidation states; X = chalcogen or halogen anions) have gained interest for their excellent phase stability and compositional tunability. However, combinatorial possibilities arising from the many choices of A, B, and X site species, and their respective mixing fractions, a large number of possible ABX₃ perovskites remain undiscovered. In this work, we used machine learning (ML) methods to design new stable and …

Mapping Responsible Workflows For Geospatial Data Science: Developing The I-Guide Data Ethics Toolkit, Peter T. Darch, Kyra M. Abrams, Ivan Y M Kong

I-GUIDE Forum

AI workflows in geospatial data science offer significant societal benefits but raise ethical, transparency, and reproducibility challenges. Current ethical frameworks and tools are often hard to integrate into daily research practice. This paper introduces the I-GUIDE Data Ethics Toolkit (DET), a lightweight suite designed for users of the NSF-funded Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE). Based on a longitudinal mixed-methods study, including surveys, interviews, and observations, we identified five design priorities: usability, anticipatory planning, distributed responsibility, comprehensive coverage, and policy compliance. We integrated existing AI and data research lifecycles into an eight-stage I-GUIDE Research Lifecycle, serving …

Evaluating The Evaluation Matrices: Integrating Spatial Assessment In Geospatial Ai Model Training And Evaluation, Fangzheng Lyu

I-GUIDE Forum

This paper examines the limitations of current evaluation metrics in GeoAI. Through two case studies on deep learning models—a building detection classification problem and a remote sensing image fusion regression problem—this paper demonstrates how traditional statistical evaluation matrices alone can be misleading in geospatial problems. The findings indicate that traditional metrics (e.g., RMSE, MAE) used in current GeoAI models can have difficulty capturing the spatial dimensions inherent to geospatial problems. This paper suggests that the model evaluation process in GeoAI should move beyond traditional evaluation matrices by integrating spatial thinking throughout the modeling pipeline—not only incorporating spatial accuracy in model …

Expanding Access To Cybergis-Compute Through Support For Heterogeneous Workflows, Alexander C. Michels, Ian Zhang, Anand Padmanabhan, John Speaks, Rebecca Vandewalle, Shaowen Wang

I-GUIDE Forum

CyberGIS-Compute is a geospatial middleware tool designed to lower technical barriers to High-Performance Computing (HPC) resources. It provides end-users with a Graphical User Interface (GUI) for submitting models to HPC and allows model developers to contribute their workflows by adding a manifest to their repositories. However, the simplification of the user interface and streamlining of model contribution have unintentionally limited the scope of models that could be run on CyberGIS-Compute. In this paper, we discuss recent developments to the CyberGIS-Compute project that are aimed at supporting a wider variety of workflows including performance enhancements, supporting additional configuration options for jobs, …

Machine Learning-Based Variance Analysis Of Brightness Temperature In Simulated Satellite Footprints, Chhaya R. Kulkarni, Nikki Prive, Vandana P. Janeja

I-GUIDE Forum

This study investigates the variance in brightness temperature (BT) within simulated satellite footprints for Observing System Simulation Experiments (OSSE), focusing specifically on Channels 5 and 11 of the Advanced Microwave Sounding Unit (AMSU-A). High-resolution atmospheric simulations from the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) dataset were utilized to generate brightness temperature data using the Python interface for the Community Radiative Transfer Model (PyCRTM). A computational design map incorporating Random Forest and Association Rule Mining was employed to identify and validate key atmospheric variables influencing BT variance. This ensemble approach facilitated a deeper understanding of atmospheric …

Selected Artificial Intelligence Provisions In U.S. Fiscal Year 2025 National Defense Authorization Act, Bert Chapman

Libraries Faculty and Staff Presentations

The 2025 Fiscal Year National Defense Authorization Act contains multiple provisions relating to artificial intelligence (AI). These congressionally mandated provisions direct various sections of the Department of Defense (DOD) and individual U.S. armed service branches to execute congressional intent for AI policymaking. Examples of such intent include identifying and planning DOD's AI workforce, demonstrating AI biotechnology applications for national security, improving the human usability of AI systems, and establishing an AI security center. This presentation will note that reports on these initiatives must be prepared for relevant congressional oversight committees, and, in many cases, are in many cases, publicly released …

Recent U.S. Government Policy Literature On Critical And Strategic Minerals, Bert Chapman

Libraries Faculty and Staff Scholarship and Research

Critical and strategic minerals have become increasingly important in U.S. government civilian and military policymaking in recent years. This is demonstrated by the heavy use of such minerals in many critical civilian and military infrastructures. This work will discuss how this subject has been addressed in laws, presidential documents, and works by government agencies along with congressional oversight committees and support agencies. It will stress how the United States is heavily dependent on strategic minerals from adversarial foreign countries such as China and will examine U.S. efforts to increase its ability to produce such materials in the United States by …

The Spice (Sustainable, Physics-Inspired Culinary Education) Lab – A Digestible Few-Nexus Educational Platform, Carla Ramsdell

National Collaborative for Research on Food, Energy, and Water Education (NC-FEW)

The SPICE (Sustainable, Physics-Inspired Culinary Education) Lab is a unique educational platform that enables participants from a wide disciplinary background to engage and become literate in the unique connections between food, energy and water. This small but mighty space allows for outreach education in many formats, including formal college courses, university-centered hands-on activities and public outreach.

The work performed in this space fills a unique need in the FEW-Nexus education opportunities because it is adaptable to many learners and learning opportunities and its focus on kitchen science makes these concepts approachable which can later be expanded to understand other critical …

System Change, Not Climate Change: Collaborative Development Of A Few Nexus Experiential Learning Course For A Justice-Centered Comparative Study Abroad Program, Sonya Ahamed

National Collaborative for Research on Food, Energy, and Water Education (NC-FEW)

The International Honors Program (IHP) on Climate Change: The Politics of Land, Water and Energy Justice has been offered by the School for International Training (SIT) since 2013. This thematic multidisciplinary program currently travels to Morocco, Nepal, and Ecuador for one month each, introducing students to local food, water, and energy challenges in the context of a changing climate. In updating the program following its relaunch in 2023, renewed attention was given to the food, energy, and water nexus from a comparative perspective across and within these countries. This presentation will focus on the development of experiential learning cycles for …

The Few-Nexus: Using Soil To Grow Meaning And Relevance In Undergraduate General Education Earth Science Courses, Katherine Mccarville

National Collaborative for Research on Food, Energy, and Water Education (NC-FEW)

Soils are central to human survival, through their critical roles in regulating water supplies and providing the medium for most agricultural productivity. Even as fewer and fewer students know where their water and food come from, and where their wastes go, soils tend to be under-emphasized in Earth science general education courses. Combining a place-based focus on soils with the FEW-Nexus model provides an integrating context for concepts and information that students sometimes perceive as disconnected and irrelevant to them. This approach can transform the experiences and learning of undergraduate students in the Earth science general education curriculum.

Perceived And Recorded Temperature In Aircraft Cabins During 143 Flights, Matthew Nicholls, Peter Vink

Journal of Aviation Technology and Engineering

This essay reports on the perceived and recorded temperature in aircraft cabins during 143 flights. In each flight, passengers reported their experience. They recorded temperature and humidity after boarding and one hour later. The reports show that fewer than half of the passengers perceived the temperature as “good.” Most passengers (39%) perceived it as hot, and some (17%) perceived it as cold. Comparing the temperatures with guidelines shows that 18% of the recorded temperatures in the cabin were empirically too hot and 2% empirically too cold. The differences between passengers in what they perceive as a good temperature are large, …

Application Of Physics-Informed Neural Networks On Crop Yield Prediction At Multiple Scales, Aditya P. Prabhu, Pratishtha Poudel, James V. Krogmeier

Discovery Undergraduate Interdisciplinary Research Internship

Accurately predicting crop yields is a critical challenge in sustainable agriculture, food security, and farm management. Traditional process-based models rely on agronomic domain knowledge, crop physiology and statistical approaches, while purely data-driven approaches leverage machine learning or deep learning models using meteorological and spatial data. Unfortunately, these black-box models(Data-drive approaches) often lack interpretability and fail to incorporate well-established physical principles. This project explores a hybrid approach by implementing Physics Informed Neural Networks, mainly, physics-based recurrent neural networks (PI-RNNs) for time-series yield prediction. PINNs allow for the integration of scientific knowledge directly into the model by embedding physical laws as constraints …

The Mathematical Laws Of Morphology And Biomechanics Through Ontogeny, Wijesooriya Kisal Wijesooriya

The Journal of Purdue Undergraduate Research

No abstract provided.

Compact Control System For Superconducting Qubits, Santiago Lopez

The Journal of Purdue Undergraduate Research

No abstract provided.

Validating Atmospheric Freezing Level Heights Using Purdue University Weather Balloon Data, Danielle Harr

The Journal of Purdue Undergraduate Research

No abstract provided.