Stander Symposium: Projects with UD Institutional Learning Goals

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  Artists and Educators: The Relationship of Research Within Practice.

  Ellana Davis, Sophia Eyerman, Gabriela Gomez, Jayonna Johnson, Kaylee Peters

  This presentation will highlight and share five individual qualitative research studies conducted by senior students pursuing BFA degrees in the Department of Art & Design. These presentations include "Encompassing the Reciprocal Relationship of Being an Artist and Art Educator"; "An Investigation into the Relationship between Christian life and Mental Health"; "Diversity in the Art Classroom and the Impact on Student Motivation and Participation"; "A Personal Exploration of Education in the Art Museum"; and Reflections on Student Teaching: What PreService Education Couldn't Have Prepared Me For".

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  Assessing how speakers with non-American accents experience and manage communication accent stigma in health care situations in the United States.

  Gift Olalusi

  The study aims to explore the challenges individuals with non-native American accents face in healthcare settings, examining different experiences based on how they manage communication stigma, and offering solutions to eliminate stigma, stereotypes, and biases. The study employed a qualitative approach, where interviews of individuals with non-native American accents in the United States were coded and discussed thematically. Results from the study seeks to improve healthcare outcomes by ensuring non-native English speakers and people with accents can access care equally and without fear or stigma. The study is an on-going research thesis, and the findings aim to raise awareness among healthcare providers about implicit biases, fostering better patient-provider communication and greater patient satisfaction and inform better healthcare policies in the United States.

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  Assessing the behavioral effects of pharmacological SERCA activation in the dizolpine-induced mouse model of psychosis

  Erin Flaherty, Hayden Ott

  Intracellular calcium (Ca2+) homeostasis plays a critical role in a variety of neural processes including neurotransmission, development, and apoptosis. The sarco-endoplasmic reticulum Ca2+ ATPase (SERCA) is a Ca2+-handling regulator that sequesters cytosolic Ca2+ into the neuron's smooth endoplasmic reticulum. Notably, disruption of mechanisms that are responsible for maintaining Ca2+ homeostasis has been implicated in the pathophysiology of neuropsychiatric disorders such as Schizophrenia. In the context of the current study, our lab sought to investigate the effects of chronic pharmacological SERCA activation via administration of CDN1163, an allosteric activator of SERCA, on the dizocilpine (MK801)-induced mouse model of psychosis. Male and female mice of the C57BL/6J strain were chronically treated with daily intraperitoneal injections of CDN1163 and their locomotor activity was assessed upon acute dizocilpine administration. The results of this study provide us with a better understanding of SERCA's role in behavior as well as its putative implication in the neurobiology of schizophrenia.

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  Assessing wildlife diversity in Dayton’s protected areas surrounded by human-modified landscapes

  Trevor Martin

  The region surrounding Dayton, Ohio, is a matrix of human development and agricultural areas, with relatively small areas of natural land in between. Montgomery County’s park system, Five Rivers MetroParks, protects and restores thousands of acres of land, much of which is used for recreation. These patches of forest and undeveloped areas provide essential habitat to local mammal wildlife, such as white-tailed deer (Odocoileus virginianus), northern raccoon (Procyon lotor), Virginia opossum (Didelphis virginiana), eastern cottontail (Sylvilagus floridanus), squirrels (eastern grey squirrel, fox squirrel, red squirrel, Sciurus sp. ), bobcats (Lynx rufus), and coyotes (Canis latrans). However, human-caused habitat fragmentation poses significant challenges for many species of wildlife. Behavioral changes in response to human activity can inevitably lead to shifts in the composition of species in a given area, thus altering the dynamics of the ecosystem. This study aims to quantify species richness and diversity in three Five Rivers MetroParks properties to analyze how the overall composition of mammalian wildlife is affected by varying degrees of human land use and presence. The results will provide insight into potential patterns of species diversity that could apply to other natural areas within the park system. Better understanding of how wildlife communities within these parks respond to human activities will help inform land management decisions that allow park officials and city planners to strike a balance between creating multi-functional landscapes and also preserving biodiversity.

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  Assessment of Similarity Between Two Methods of Calculating Reactive Strength Index for Different Jump Types

  Noah Clemens, Natalie Osiecki, Louisa Piotrkowski

  Reactive Strength Index (RSI) is a measurement of how much force an individual can generate when under a high downward force. It is a tool to predict explosiveness in exercise performance and the amount of stress on the lower extremity joints when performing jumps. RSI is a ratio of jump height and contact time. This study focuses on two methods of calculating the jump height needed to find RSI from force plate data. It is unclear whether these two methods produce similar results. Objective: The purpose of this study was to compare the similarity in the calculated RSI values between two different jump-height calculation methods. Methods: Nine college-aged participants were recruited to perform a series of countermovement and drop jumps. Participants completed the jumps on VALD portable force plates. The first method used the applied force and contact time to estimate takeoff velocity, from which a theoretical jump height was determined. The second method used the total flight time to estimate the jump height, under the assumption that the jumper would reach their maximum height at exactly half of their time in air. A Bland-Altman (BA) plot was used to graphically evaluate similarity in RSI values between the two calculation methods for all three jump types. Results: Between the two methods, the mean difference was 0.248. The limits of agreement (LOA) of the BA plot showed an upper limit of 0.625 and a lower limit of 0.128. There were multiple data points outside of the LOA as well. Conclusion: The presence of a large mean-difference, large LOA, and multiple data points outside of the LOA indicate that the two methods for calculating jump height from force plate data were not similar. When separated by type of jump, the countermovement jumps showed acceptable similarity between the two methods.

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  A Tour of Real and Complex Linear Spaces

  William Hach

  This presentation explores the distinctions and connections between real and complex linear spaces, with a focus on their formal structures and potential applications.

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  Automated and data-driven discovery of behavioral signatures in preclinical models of developmental disorders

  Henry Salisbury

  The primary mode of identifying developmental disorders in children involves behavioral analysis. However, behavioral datasets are mainly quantified and analyzed using manual methods that are time-consuming and cumbersome. Automated and data-driven quantification and analysis of neurobehavioral datasets is therefore a pressing need. By leveraging advanced computer vision algorithms, we aim to analyze and interpret the behaviors of mouse models of developmental disorders in specific experimental tasks. By harnessing cutting-edge computer vision algorithms such as automatic detection of body-parts and tracking using DeepLabCut, combined with data-driven identification of “behavioral signatures” using the Behavior Segmentation of Open field in DeepLabCut (B-SOiD) pipeline, we will train a system to objectively perform data analysis on data gathered from videos of mice performing diverse experimental tasks including the three-chamber social test and the Erasmus Ladder test for motor learning. Through this, we seek to find patterns between the neurobiological and behavioral data from these mouse models, to learn how developmental disorders including Down syndrome and premature brain injury impact locomotor learning and neurophysiology. Our methods provide a platform to identify potential treatment options for humans with these developmental disorders.

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  Autonomous Vision System for Hazardous Object Detection in Construction Sites

  Anurag Mallik

  Construction sites pose significant risks to workers due to the presence of various hazardous objects. These items can be sharp or blunt, or even cause electrocution. With the common denominator being that all can lead to serious accidents. During construction, all personnel on site must take proper precautions when handling hazardous objects. While larger items are easily visible, smaller tools like nails, hammers, and drill machines often go unnoticed. If these objects are left unattended in active areas, they can lead to life-threatening incidents in construction zones. In this research work, a computer vision algorithm has been proposed to identify construction tools in a construction zone as part of a larger pipeline for construction hazard detection. Specifically, this work focuses on images of construction zones which have been captured from various angles. The Segment Anything Model (SAM) is used to segment these images allowing them to analyze regions of interest. Regions selected for further processing are done by calculating bounding boxes which are based on the segmented areas. Through experimentation, an optimal size of the bounding boxes to reduce the number of boxes to processes showed bounding boxes that capture an area of 3% to 8% of the total image size are typically relevant to detect a hazardous object in a construction scenario. These resulting bounding box areas in the image are fed to a feature extractor, DINOv2 that returns the object feature matrix to feed into a fully connected three-layer neural network classifier. The classifier identifies an object feature set belonging to one of the twenty predefined hazardous objects if the score of the highest probability output node of the classifier is beyond a predefined threshold value.

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  Bachelor of Fine Arts Fine Art Senior Thesis Presentations

  Gretel Helm, Jacqueline Patton, Sarah Ryan, Kyleigh Streeter

  Senior Bachelor of Fine Arts student presentations

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  Benchmarking Sustainable Procurement for Higher Education: Procurement for the Common Good - Spring 2025 Update

  Angelo Catapano

  I will be presenting my current research regarding the vendors of the University of Dayton. The goal of my work is to understand these vendors from a sustainable perspective, both for the environment and social considerations, and see how their goals align with those of UD. Topics of discussion will include data collection methods, interpretation of such data, and a fundamental understanding of UD goals for vendors.

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  Benefits of an Intervention to Enhance Self-Efficacy for Diabetes Care in Homeless Shelter Residents

  Maggie Hofner, Samantha Jordan, Ella Lamb, Emma Petraglia, Mia Venanzi

  Despite a great need for healthcare, unhoused individuals encounter significant barriers to utilizing public healthcare. Given the inequities in access to healthcare, accompanied by disabilities and health risks associated with homelessness, self-efficacy for self-care is particularly critical. Because of barriers associated with the system (e.g. lack of healthcare insurance), homelessness (e.g., lack of transportation), and the healthcare system (e.g., stigmatizing attitudes) encountered by unhoused people in attempts to access public healthcare, it is necessary to provide (and evaluate) self-care interventions on site (e.g., in homeless shelters). The prevention/management of diabetes is one of many problems among unhoused individuals. Driven by self-efficacy theory and the health psychology knowledge based on diabetes risks, this study examines the benefits of an intervention designed to enhance self-efficacy for the prevention/management of diabetes among residents at the St. Vincent de Paul Gateway Shelter for Men (Dayton, Ohio). The intervention is implemented by supervised undergraduate students within a long-standing participatory community action research project for homeless shelters. The primary purpose of the research is to examine the hypothesis that the intervention will lead to improvements in self-efficacy for prevention/management of diabetes. It is also hypothesized that the intervention will be (a) approximately equal in effectiveness for residents with disabilities versus those without disabilities and (b) approximately equal in effectiveness for residents with diabetes versus those with pre-diabetes. The Self-Efficacy for Diabetes Management Scale, which includes Likert-like items and prompts to collect qualitative data, is used to assess pre- to post-intervention changes in self-efficacy. The study uses a multifactorial design with two between-subject factors (i.e., disability versus non-disability status and diabetes versus pre-diabetes status) and one within-subjects factor (pre- versus post-intervention). A secondary purpose is to examine the psychometric properties of the new measure (Self-Efficacy for Diabetes Management Scale).

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  Benefits of Collaborative Learning in High School Math Classes

  Matthew Carpenter

  Group work, collaboration, and cooperation in high school math classes promote student learning. In the studies shared, findings include strong benefits of group work in high school math classes and how teachers can include it in their teaching. Benefits include peer-to-peer learning, self-evaluation skills, and increased conversation in class.

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  Benzalkonium chloride enhances Listeria monocytogenes biofilm formation under various conditions

  Evelyn Joynt

  Listeria monocytogenes is a bacterial foodborne pathogen that can cause severe invasive infections with high mortality rates. To protect vulnerable populations from L. monocytogenes infections, there is a stringent cleaning, disinfecting, and surveillance process in place in the food facilities. However, outbreaks as well as recalls of potentially contaminated food products continue to occur regularly. L. monocytogenes can persist in the environment because of its ability to survive various harsh conditions, including low temperatures, and to form biofilms on food-contact surfaces. To eliminate L. monocytogenes, benzalkonium chloride (BC) is one of the major disinfectants used in the food industry. However, multiple studies have shown mechanisms of L. monocytogenes developing resistance to BC, potentiating the future need for higher concentrations of BC. In this study, we investigated the effects of higher BC concentrations on L. monocytogenes biofilm formation. Using the standard crystal violet staining method, we observed that at concentrations (1% or 5% [wt/vol]) that inhibit planktonic growth, biofilm formation was significantly enhanced, compared to no BC controls, regardless of oxygen availability, surface materials, and temperatures. These results suggest that higher concentrations of BC will not be an effective strategy to remove L. monocytogenes biomass from surfaces and could potentially create additional adhesion sites for other biofilm-forming bacteria in the same environment.

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  Between Authority and Service: The LeBar Sisters

  Andrew McNeely

  American evangelicals have long engendered strict codes of gender hierarchy in churches and their related institutions. Twentieth-century evangelical education, however, discloses unique ironies women educators embodied in their reliance on these designated gender roles. Placed in liminal positions between authority and service, women educators transgressed boundaries between the “maleness” of given authority and the subservient nature proper to womanhood. This research explores this phenomenon by examining the educational careers of Mary and Lois LeBar, both of whom taught at Wheaton College from 1945 to 1975. Instead of rejecting gender hierarchy, they used their distinct gender roles as women tasked to serve others by appropriating progressive educational pedagogy that proved amenable to their agency as evangelical women educators. For the LeBars, this was as theologically imperative as it was pedagogically effective.

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  Beyond Barriers: A Study of Cultural Identity and Belonging Among Underrepresented Engineering Students

  Sama Ahmed

  Representation in engineering is essential for promoting diversity, inclusion, and student success. This study focuses on the cultural identity and sense of belonging among underrepresented engineering students, particularly those enrolled in the Multi-Ethnic Engineering Program (MEP) at the University of Dayton (UD). Data for this study emerges from semi-structured interviews of 10 students enrolled in MEP. It investigates the barriers these students faced, including limited resources, gender disparities, and negative stereotypes, with the goal of illuminating the challenges that affect their academic and professional goals. Building on existing research about identity and belonging in STEM fields, this study aims to pinpoint support systems that can enhance student retention and success. By delving into the experiences of these multi-ethnic engineering students, the research offers valuable insights into initiatives that foster inclusivity, such as mentorship programs and necessary institutional changes. Understanding the lived experiences of these students is crucial for advancing efforts to create a more equitable and supportive environment in engineering education.

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  Bio-Inspired Rotating Empennage (BIRE) - Desktop Model

  Ryan Rotsching

  A concept fighter aircraft is being investigated by the Air Force Research Labs that eliminates the vertical tail and uses a bio inspired rotating empennage (BIRE). The motion of the empennage is intended to mimic the agile flight displayed by birds of prey. To assist in communicating the mechanical concept, a desk-top demonstration model was created. Each part in the model is constructed primarily of additively manufactured (AM) components, allowing each component to be custom designed and swiftly manufactured to maximize functionality and accuracy. These components were based on the existing structure of the baseline F-16 and modeled in SolidWorks. The project involved research into different AM techniques and most appropriate process for each component. Two different variants are being constructed: 1) a simple internal structure demonstrating the functions of the BIRE, and 2) a topographically optimized solution. The results of these models allow visualization of the functionality and viability of the BIRE concept.

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  Bird Family Recognition

  Soham Chousalkar, Kasturi Avinash Jamale, Jayanth Merakanapalli

  In this research, we present a novel deep learning-based approach for bird detection and classification. Using YouTube videos as a data source, we train a model capable of accurately identifying bird species in diverse environments. Our dataset consists of 20 bird species, each categorized into two subclasses: parent and chick. Leveraging YOLO models, our system effectively detects and classifies birds under varying environmental conditions. The proposed method demonstrates high classification accuracy, contributing to advancements in automated bird identification. This work has significant applications in ecological monitoring and conservation efforts, aiding researchers in tracking and studying avian populations.

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  Breaking the Cycle Through Transformative Pedagogy: Exploring Reflection, Critical Thinking, and Structural Barriers to Social Deviance

  Destiny Rivera, Asja Steele

  Over one million people are incarcerated in the United States, with 62% of those released rearrested within three years. In Ohio alone, nearly one in three individuals returns to state prison within three years of release. Recent studies suggest that educational and vocational programming in correctional institutions can decrease recidivism rates while increasing post-release employment opportunities and wages. Education represents a significant opportunity for meaningful second chances. However, there is limited pedagogical research examining thestructural challenges incarcerated individuals face that either hinder or facilitate reentry into their communities. This qualitative study explores how transformative pedagogy in correctional settings fosters critical awareness of systemic barriers to rehabilitation. Using a phenomenological approach, we analyze reflective essays written by incarcerated students enrolled in educational programs across various correctional institutions in Ohio. By centering student voices, the study highlights the role of transformative learning in empoweringincarcerated individuals to recognize and challenge the social conditions influencing their behavior. Preliminary findings will be discussed, and the study contributes to broader discussions on prison education as a tool for disrupting cycles of deviance and fostering long-term socialchange.

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  Breaking the Silence: Addressing Harassment and Assault at the University of Dayton

  Taylor Gallagher, Katerina Metheney, Alexandria Moore, Lianna Shakoor

  In this class project for SOC 324: Communities & Crime, we worked to identify a crime problem or a plan to promote safety that is pertinent to our own community. We applied relevant social science theories in order to understand the problem of harassment and assault at the University of Dayton. Based on these theoretical explanations, we offer strategies to address the crime problem and to enhance community safety, as well as an action plan to implement our recommended solutions.

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  Bridging the Gap Between High School and College Sense of Belonging

  Julie Huber

  Students' sense of belonging in high school and students' sense of belonging in college have both been studied separately, but not much information has been recorded about the connection between the two. This study seeks to understand this relationship and look at other factors such as personality, athletics, and family life, and their role in this relationship. I conducted a short anonymous survey and concluded the study with follow up interviews to gain a more personal insight into what students' thoughts are on the connection between belonging in college and high school. The sample consisted of 82 undergraduate students currently attending the University of Dayton. Overall, I find a moderate positive association between belonging in high school and college that is influenced by multiple personal and social factors. These findings highlight the importance of considering high school experiences in shaping students’ perception of belonging and community in college.

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  Bridging the Gap in Industrial Energy Modeling through Lean Energy Analysis

  Sean Kapp, Gavin Mchale

  HVAC systems account for a significant portion of the energy consumption within the industrial sector. Specifically within chemical manufacturing facilities, fume hoods contribute heavily to HVAC energy use by continuously exhausting conditioned air. While advanced energy modeling techniques exist, small and medium-sized manufacturers (SMMs) often lack the resources and data required to implement complex machine learning-based solutions. An inability to collect useful information on energy patterns throughout the year can be a large obstacle for these facilities in deciding which changes will have the largest benefit for the company. Depending on the complexity of the energy model, predictions can be made based on a variety of factors. Changes in outdoor temperature plays a primary role in the variation of monthly energy usage. This study presents Lean Energy Analysis (LEA) as a practical and effective approach for assessing weather-dependent energy consumption in manufacturing facilities. LEA utilizes energy billing data to model energy-weather dependence through a piecewise linear changepoint analysis, enabling manufacturers to identify inefficiencies and predict energy savings from efficiency measures. A comparative analysis between LEA and the Random Forest (RF) machine learning model was conducted to validate the accuracy and utility of LEA for energy modeling. The results demonstrate that while RF models can provide strong predictive accuracy, they lack transparency and requires many features to be robust. In contrast, LEA effectively identifies independent energy usage, changepoint temperatures, and weather-dependent slopes as distinct, physically meaningful quantities, offering actionable insights for energy optimization without the need for extensive sensor networks. A case study is conducted at a chemical manufacturing facility where excessive fume hood usage was identified as a major contributor to HVAC energy waste. By applying LEA, the research team quantified the energy savings potential of lowering fume hood doors when not in use. Implementing this measure resulted in an annual reduction of 191,259 kWh in electricity and 729 MMBtu in natural gas, leading to cost savings of $18,851 and a carbon footprint reduction of 129 metric tons. This study highlights the advantages of LEA for SMMs seeking to optimize energy efficiency without the cost and complexity of high-tech energy modeling solutions. By leveraging historical data, LEA provides a low-cost, data-driven framework for energy assessment and sustainability improvements in industrial facilities.

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  Bridging Theory and Practice: Advising Skills in the Career Services Functional Area

  Danny Bean

  Higher education and student affairs professionals must cultivate strong advising skills if they wish to maximize their marketability and become the best advocates for students that they can be. In this brief presentation, I will connect student development and organizational leadership theory to my internship with the University of Dayton Career Services office, specifically focusing on the bridge between academic study, vocation, and future career path.

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  Bugs Under the Sun: the Influence of Solar Infrastructure on Insect Communities

  Claire Qua

  As renewable energy is growing in its popularity for both industrial and residential use it is my goal to help fill the gaps in knowledge regarding the influence of solar infrastructure on insect communities. Insects are not only crucial pollinators for the flowers we love, but also the agriculture we depend on. Additionally, a particular emphasis was placed during this study on Orthopterans (specifically grasshoppers) as they are prolific in nature at these sites as well as in their extensive predatory behavior. Sweep net samples were collected at Topaz Solar Farm in San Margarita California within the two studied microhabitats (array aisles, and intentionally planted reference areas). Each sample was sorted morphologically to order and subsequently counted to measure abundance and order richness. Statistical analysis using including T-Tests, GLMM, and NMDS were conducted when appropriate to the common orders found. There was a statistical significance indicating a greater overall insect abundance in the reference area affirming my hypothesis. Of the studied orders no additional differences were seen aside from block location of Dipterans and microhabitat preference in Hemipterans. In regard to Orthopteran size, stage of development and pink morph prevalence results are forthcoming as data collection is underway. I anticipate a difference in tibia and overall length as well as mass indicating larger individuals within the reference area. The results exemplify the differences between microhabitats demonstrating the effectiveness of reference areas in mitigating possible land degradation that may occur when developing a solar farm. Solar prairies are a beneficial collaboration of renewable energy and ecological stewardship and are a significant step in the right direction.

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  Building a Dataset for American Sign Language Recognition

  Soham Chousalkar

  Sign language is a crucial means of communication for the Deaf and Hard of Hearing (DHH) community. However, the language barrier between ASL users and non-signers is a relevant factor. The purpose of this research is to create a real-time American Sign Language (ASL) to English translation system using computer vision and deep learning techniques. The ultimate objective is to bridge the gap by enabling seamless communication with the assistance of an AI-based translation model.The system adopts a deep learning-based approach, leveraging Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to handle ASL hand gestures and facial expressions from video input collected using a normal camera. Pre-trained models, i.e., MediaPipe Hands and OpenPose, are integrated to attain effective feature extraction. The Transformer-based model is integrated to interpret visual input into its corresponding English text with effective translation accuracy and contextual understanding.The database includes publicly available ASL gesture datasets and specially recorded sequences to improve recognition across different hand shapes, orientations, and illuminations. The system is trained to recognize isolated signs, sentence structures, and nuances such as finger spelling and co-articulated gestures. Natural Language Processing (NLP) techniques also refine the generated text for syntactic correctness.It aims to provide an intuitive application that delivers real-time feedback as text and speech output. The potential contributions include enhanced accessibility for ASL users, contributing to educational resources for learning sign language, and assisting the advancement of gesture-based AI modeling. Future includes extending the system to support different sign languages and implementing it on mobile and augmented reality platforms for usability by general users.This research falls into the agenda of innovation and inclusiveness, and it suggests an applicable AI-powered solution to make everyday communication for the DHH community better.

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  Building Bridges to Math Success: Exploring Children’s Flexible Attention to Numerical and Spatial Magnitudes

  Deja Richardson

  The study will analyze how children in early childhood engage with number books and how they influence mathematical abilities such as flexible attention to magnitudes (FAM) and executive functioning. It is hypothesized that the number of books consisting of mathematical language will lead to an increase in math ability. We will also explore the correlations between children’s engagement with the books and their outcomes. It is suggested that number books will engage children using mathematical language and real-world settings. This study will be a pretest-intervention-posttest design, where 40 participants between the ages of three and five years old recruited from daycare centers and preschools in the Dayton area will complete 6 one-on-one sessions with an experimenter in their school. The pretest will consist of the following assessments: Woodcock-Johnson IV Tests of Early Cognitive and Academic Development (WJ ECAD) Number Sense, WJ ECAD Picture Vocabulary, Give-N, and Minnesota Executive Function Scale (MEFS). The intervention will involve participants, who will be randomly assigned to one of two conditions, completing 4 reading sessions with an experimenter. The mixed condition involves number books with questions about size and number language, while the control condition will have questions about special colors. Each condition has two book settings, a farm, and a restaurant. The post-test will include the FAM Task and WJ ECAD Number Sense assessments. The study will analyze results from the book trials and assessments used in the pre and post-tests using ANCOVA analyses that control for pre-test and demographic covariates.