Stander Symposium: College of Arts and Sciences

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  A Numerical Study of an Influenza Epidemic Diffusion Model using MATLAB

  Erin Millhouse, Payton Reaver

  The aim of this study is to replicate the results from a publication “Numerical Study of an Influenza Epidemic Model with Diffusion”, 2010. The strategy used to approximate the population was the SEIR (Susceptibility, Exposure, Infected, Recovered) model. This specific model is comprised of four partial differential equations, each with a linear diffusion term and non-linear reaction term. Using Operator Splitting, the authors were able to simulate the solutions of the model. The overarching goal is to replicate the numerical results of the paper.

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  Applying the Social Work Framework to Advocate for Change on Ohio Senate Bill 1

  Fiona Ackroyd, Morgan DiRocco, Madelyn Hales, Abiageal Newell

  In this project prepared for SWK 310, we share our reflections and insights gained from our experience participating in the Social Work Advocacy Day in Columbus, Ohio in March 2025. During our trip we attended advocacy training and planning sessions and met with state legislators and/or their staff. This presentation details our experiences at Advocacy Day, outlines an area of advocacy, and describes how we advocated for change through the lens of the Social Work framework.

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  Artificial Intelligence Applications in Real Estate

  Amira Yousif

  The real estate industry relies heavily on accurately predicting the price of a house based on numerous factors such as size, location, amenities, and season. In this study, we explore the use of machine learning techniques for predicting house prices by considering both visual cues and estate attributes. We collected a dataset (REPD-3000) of 3000 houses across 74 cities in the USA and annotated 14 estate attributes and five visual images for each house's exterior, interior-living room, kitchen, bedroom, and bathroom. We extracted features from the input images using convolutional neural network (CNN) and fed them along with the estate attributes into a multi-kernel deep learning regression model to predict the house price. Our model outperformed baseline models in extensive experiments, achieving the best result with a mean absolute error (MAE) of 16.60. We compared our model with a multi-kernel support vector regression and analyzed the impact of incorporating individual feature sets. In future, we plan to address class imbalance by having the same number of houses in each class and explore feature engineering for improving the model's performance.

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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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  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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  Attitudes Toward Service Dogs In-Training

  Lindsey Person

  The partnership between service dogs and people with disabilities provides important social, psychological, and physical benefits. The early training of service dogs involves learning the basic commands, such as sit and stay, and exposing the dogs to many different people and situations. Some universities partner with service dog training organizations to perform the early training. Such training involves bringing the service dogs in-training into classrooms. Having dogs that are not fully trained in classrooms could be disruptive for reasons such as allergies, religious beliefs, fear, and distraction. Having dogs in a classroom can also improve the mood of and reduce the stress of students and instructors. This study will use a diverse and inclusive sample to investigate the instructor’s, students’, and dog handler’s attitudes toward having the service dog in-training in the classroom. Perceived stressors for the service dogs in-training will be measured. Best practices for mitigating any issues that are found will be created.

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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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  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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  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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  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 the Red Line: Building Equitable Communities

  Maya Dalal, Peter Grant, Henry Hipp, Allison Vandergriff

  Historical redlining has created marginalized communities in the 21st century, including inside the classroom walls. Students from diverse populations bring their different funds of knowledge into the classroom, but historical redlining has created barriers to accessible resources and opportunities. Beyond the Red Line: Building Equitable Communities challenges students to create a forward-looking vision for equitable communities. Students will choose their preferred medium for their project. Aspects of students' designs will include innovative solutions to housing, economic mobility, and access to resources. It advocates for at-risk communities and prioritizes reimagining urban spaces where all students can access appropriate resources, opportunities, and a sense of belonging. Students will collaborate to create a neighborhood or community that promotes social justice and advocacy for their peers in their community.

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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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  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.

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  Channel Assignment Problem

  Tom Jacob

  This is a project for MTH 466, Graph Theory and Combinatorics. A graph is a mathematical object that consists of two sets, a set of vertices and a set of edges. An edge joins two vertices and depicts a relationship between those vertices. By considering the vertices on a connected graph G of order n to be transmitters and the colors of the vertices to be the channels assigned to the transmitters, we can construct a model that represents the Channel Assignment Problem. This problem deals with the task of efficiently allocating channels to transmitters. Concepts such as radio k-coloring and radio labeling were inspired by the Channel Assignment Problem. By stipulating a minimum permitted distance rule, labeling vertices based on the assignment of their colors, and considering two vertices to be adjacent if they are sufficiently close to each other, it is possible to organize a network of channels that does not overlap each other. The Channel Assignment Problem has origins in assigning channels to FM radio stations through prevention of interference by keeping separation between stations based on signal power, height of their antennas, and frequency.

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  Characterization of a Drosophila CRC 3-hit model using genetic approaches and impact of inhibitors on tumor growth

  Sydney Anderson

  Colorectal cancer (CRC) is the second leading cause of cancer deaths in the United States, resulting in the deaths of over 50,000 people every year (American Cancer Society, 2023). The similarities shared between mammal and Drosophila melanogaster anatomy within the intestinal tract make Drosophila a great model for studying colorectal cancer. This study will investigate tumor characteristics of a Drosophila CRC model generated by modulating three genes within the key Hippo pathway to create a 3-hit model: p53, RasV12, and APC. The gene combination in the 3-hit model closely emulates how CRC presents in humans and is therefore important to study. This study will (a) characterize the tumors in the guts of Drosophila for invasion, metastasis, and other phenotypes such as blockage of the intestinal tract. This study will also (b) investigate the impact of different pathway inhibitors as single or combination therapies on tumor size and metastasis. The results of this study will expand the discipline’s knowledge of CRC tumor characteristics, and metastasis to investigate the effects of new single or combination therapies on CRC.

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  Characterizing Limitations and Biases of Diffuse Reflectance-Based Technologies for Disease Detection and Prognosis to Facilitate More Equitable and Inclusive Healthcare Outcomes

  Rana Dey

  Diffuse reflectance-based technologies have shown potential to significantly advance disease detection and prognosis in dermatology and other clinical applications. However, these technologies also have the potential to exhibit biases, particularly against individuals with darker skin tones, which can lead to disparities in effectiveness of diagnosis and treatment if the sources of the bias are not properly identified and corrected. For instance, patients with darker skin who are diagnosed with melanoma typically receive the diagnosis at a later stage than their white counterparts. Similarly, current hypoxia assessment methods, including pulse oximetry, have demonstrated reduced accuracy in measured blood oxygenation values for individuals with darker skin, contributing to potential misdiagnosis and inadequate treatment. This study aims to investigate and address these types of disparities through computational modeling of light-tissue interaction, as well as the design of tissue-mimicking materials for experimental analysis. This research will inform the development of more inclusive diagnostic technologies, ultimately improving accuracy and facilitating equitable healthcare outcomes.