Annual Research Symposium

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  China vs Democracy: The Strategic Use of Malign Cyber Influence Campaigns

  Alexander Deak

  How and why do China’s methods for malign influence vary between different democracies?

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  Does the Use of Audits Decrease the Infection Rate in a Medical Care Setting?

  Kylie Borchert and Kristel K. Bakker

  Nosocomial infections are a significant health concern in medical settings. Reports of low compliance rates with hand hygiene standards, guidelines of which are outlined and mandated by the CDC and WHO, are frequent. Factors contributing to nonadherence include lack of knowledge and an unclear understanding of correct techniques (4). Evidence shows that improved hand hygiene can reduce infection rates (1), especially when healthcare providers are included in interventions that aim to improve compliance (4).

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  Ethanol Concentration in Gasoline

  Hannah Feser, Wyatt Olson, and Jeffrey Elbert

  For this research project, we will be measuring the concentration of ethanol present in different types of gasoline from various gas stations. The purpose of this project is to analyze and determine whether there is a substantial variance in the concentration, especially if it is possibly detrimental towards gas mileage and the product you intend to purchase is not what you are receiving. Our results have potential to lead to further research and discovery dependent upon if there is a significant variance found, this could mean a fault in the production or delivery lines of the gasolines.

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  If You Have Nothing to Hide, You Still Have Something to Fear: How Libraries Can Support Alternative Information Channels

  Abbie Steuhm

  Libraries in the U.S. have seen massive increases in book challenges in recent years. Since books are one of the library’s vital resources, censoring books can have a great impact on libraries and patrons. To counteract the effects of censorship, libraries need to adopt and support alternative information channels so information can still be accessed through other channels even if one is shut down.

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  Investigating the energetics and feeding ecology of a range of Azhdarchid Pterosaurs

  Kierra Miller and T. Alexander Deccechi

  Here we aim to gain insight into the ecology of Azhdarchid pterosaurs. By exploring their genetic requirements and feeding capabilities, we gain a deeper understanding of what their day-to-day life may have looked like.

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  Is Al-Driven threat detection an effective substitute for current threat detection architectures?

  Connor J. Ford

  This research evaluates the use of Artificial Intelligence (Al] in the development of cyber defense systems.

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  Natural Bone Human Education Skeletons: Investigating Restoration and Ancestry

  Emelye Josko

  •Three real human skeletons owned by DSU were restored to a fully functional state for classroom usage, using anatomical guides and museum preservation techniques. •These skeletons have inspired the development of a new curriculum that promotes interactive learning, allowing students to engage with the skeletal remains in a hands-on environment. •Non-invasive DNA extraction methods are being employed to gather additional information about the heritage of the skeletons, enhancing the understanding of their origins and significance.

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  Natural Language Processing Applications in Medical Data

  Sureh San and Mark Spanier

  Natural Language Processing (NLP) is the utilization of Artificial Intelligence / Machine Learning in understanding human language. NLP is increasingly being applied in the realm of healthcare as it can make information processing highly efficient through data summarization. In this modern day of technology, data is increasing at an unprecedented pace, and AI can assist in organizing unstructured information. In this use case, Named Entity Recognition, which is the classification of words, can recognize key terms in medical information. This study provides in depth research on the foundations of Natural Language Processing for healthcare use cases. Furthermore, comparison of AI models were put in place to apply information gained in order to see which models are most effective in Named Entity Recognition for the purpose of healthcare term. Specifically, open-source models like BERT and SpaCy were fine-tuned to process medical texts. In some cases, synthesized data was created to generate more results in a controlled environment.

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  Quantifying Dynamic Response in Web Application Honeypots

  Luke Constantino

  Honeypots are intentionally vulnerable computers designed to attract and analyze malicious activity. While an effective cybersecurity tool, research quantifying the effectiveness of various honeypot web services that respond dynamically is limited.

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  Research Poster

  Ivan Casamalhuapa

  For my project | sought out to make a traffic light simulator and Opticom as well as a Mobile Infared Transmitter(MIRT). | explored how to wire an Arduino and write instructions to GPIO pins.

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  SURVEY OF PRAIRIE LAKES FOR MICROPLASTICS IN EASTERN SOUTH DAKOTA

  Vanessa Ocansey, Basbo Ayelazono, and Kristel K. Bakker

  Microplastics are plastics that have been worn down into small fragments that invade aquatic environments and have cultivated themselves into most corners of the world. They are defined as being less than 5 mm in size and categorized as either primary (manufactured small) or secondary (broken down from larger plastics) (Calcutt, Jo, et al., 2018). Microplastics have been found in ocean surface water and deep-sea benthic zones as well as freshwater systems. They can be harmful to organisms, ecosystems, and human health, though the full extent of their impact is not yet known.

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  Using LLMs to Synthesize Product Desirability Datasets

  John Hastings, Sherri Weitl-Harms, Joseph Doty, Zachary J. Myers, and Warren Thompson

  This research investigates the use of large language models (LLMs) to generate synthetic datasets for the evaluation of product desirability using the Product Desirability Toolkit (PDT). The study aims to identify if LLMs can effectively create cost-efficient and scalable datasets to enhance sentiment analysis and user experience design.

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  Assessing Security Flaws in Modern Precision Farming Systems

  Joseph Boyd and Robert Richardson

  The increasing interconnectivity of the digital age brings new vulnerabilities for cyber criminals and nation state threat actors alike. Every day, threat actors make millions of at-tacks against a variety of systems. Though not all these attacks prove successful, they all share the same goal of manipulating or extracting data from their targets. The dawn of digitalization in the agricultural industry finds itself under the same threats. Because agriculture sits within the category of critical infrastructure, digitizing this industry should be accompanied with special concern for implementing good security practices. At a basic level, good cyber security practice includes ensuring that data remains confidential to users and processes, ensuring that data remains unmodified by unauthorized methods, and ensuring the accessibility of the data. However, no system can be made completely secure. Some form of exploitation will always exist by which proprietary data of the customer, or the manufacturer, can be leaked or abused. Thus, this paper does not ask if vulnerabilities exist on on-board precision farming equipment, but rather it asks what level of risk these exploits possess. In other words, if a vulnerability becomes exploited, what access or data does the attacker gain and does its value equal the amount of effort required to obtain it? The likely answer exposes vulnerabilities that damage the finances or reputations of individual producers and manufactures, which may provide an equal level of danger to the industry if these attacks can be scaled up against multiple targets.

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  Assessing Security Vulnerabilities in Wireless IoT Devices

  John Brumels

  This research project aims to systematically assess and analyze security vulnerabilities in accessibility technology, specifically devices related to the American Disabilities Act (ADA), such as wheelchair lifts, ADA buttons, and light switches. The project focuses on the potential risks associated with radio frequency (RF) replay attacks, a well-documented threat in IoT security. By examining the vulnerabilities and consequences of RF replay attacks in these critical areas, the research seeks to enhance the security and safety of individuals with disabilities and the broader public. The project also explores potential countermeasures and ethical considerations for responsible vulnerability disclosure, contributing to the fields of accessibility technology, IoT security, and ethical cybersecurity practices.

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  Do online test proctoring services abide by standard data protections?

  Tristan Stapert and Andrew Kramer

  In the aftermath of the COVID-19 pandemic, schools adopted new software to allow for online learning. Online exam proctoring has seen rapid growth in both K-12 and higher education. The security of these suites is critical due to their extensive access. Online proctoring suites have the capabilities to assess and configure student devices, access the microphone and cam-era, and view student information in the scope of the exam. This case study investigates the security of data sent over the network using dynamic software analysis and network monitoring while using the Respondus Lockdown Browser.

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  Exploring the Antimicrobial Potential of Honey from Alfalfa (Medicago sativa) Against Both Human and Plant Pathogens

  Denyce Bravo and Andrew Santhoff

  Alfalfa (Medicago sativa) is the third most valuable crop in the United States. It produces a large amount of nectar from which honeybees and other types of bees produce a high yield of honey. It is estimated that around 416 to 1,933 pounds of nectar per acre is produced by alfalfa (Kropacova, 1963). However, even with the great antimicrobial features that honey offers, there is still limited research on the antimicrobial properties of specific types of honey, such as alfalfa honey. Topical honey application clears wounds swiftly, aiding rapid healing, even in infections resistant to typical antibiotics like methicillin-resistant Staphylococcus aureus (MRSA). Manuka honey from tea tree in Australia and New Zealand effectively combats human pathogens including Escherichia coli, Klebsiella aerogenes, Salmonella typhimurium, and Staphylococcus aureus, as well as MRSA (Mandal et al., 2011). Alfalfa honey, among others from Saudi Arabia, exhibits high antioxidant potential and significant antimicrobial activity, ranking second only to Acacia honey (Ismail et al., 2021). Honey comprises primarily of different sugars and proteins (0.5%), with variations based on bee species and flora. Hydrogen peroxide (H2O2) is the main antimicrobial agent in honey, confirmed by spectrophoto-metric assays. Other non-peroxide antimicrobial factors include low water content, low pH, phenolic compounds, and bee defensin-1 (Def-1).

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  Flying through the air with the greatest of ease? Evaluation of glide capability in basal maniraptoran theropods

  Norah Zoller, Kierra Miller, Michael B. Habib, Hans C. Larsson, and T. Alexander Deccechi

  Pterosaurs, a lineage of Mesozoic flying archosaurs, include the largest flying animals ever known. Quetzalcoatlus nortropi a Late Cretaceous representative, had a wingspan of over 10 m and likely weighed more than 200 kg. It presents a combination of features (large head, massive wingspan, shoulder height equivalent to an extant giraYe) that has led to ecological interpretations of it as a major predator in the North American Maastrichtian Biome, perhaps second to only Tyrannosaurus rex. Here we examine the probability of that from an energetics perspective. Despite the great wingspan of Quetzalcoatlus, the body length (gleno- acetabular distance) is relatively small (~500 mm) and a volume of ~1.5 times that of an average sized human male. When factoring in lung volume this restricted the gut capacity and thus prey size. We estimate, for a 200- 250 kg adult, a maximum prey size of 5-7 kg. We then examined if this would be enough to sustain an adult Quetzalcoatlus and based on extant mammalian and avian field metabolic rates (FMR). We suggest that a daily food requirement would be around 3.5-5 kg per day using FMR. This suggests that such large creatures would be feeding on either very small prey items or could scavenge leftovers well after the larger theropods had secured their fill. In addition, take oY and flapping flight would be so extremely costly at such a large size, as demonstrated by the fact that in extant birds this value is often 20 times basal metabolic rate or greater. Given these factors (expected glide speeds on the order of 20 m/s or more, the costs of landing and launching are high, the maximum gut capacity low) we suggest that the primary ecological role was a terrestrial walking small prey specialist and/or scavenger. In the largest adults, flight would likely be minimized to extreme cases like escape or long-distance migration, with the daily locomotion primarily done terrestrially, though juveniles were likely more aerial. Given the lack of mid-sized carnivores in the environment Quetzalcoatlus likely used its size to intimidate smaller rivals such as Saurornitholestes, while feeding on similar sized prey. It was not in competition with sub adult or mature Tyrannosaurs, nor preying on all but the smallest members of the dinosaurian fauna. Thus, we suggest it played the role of a lower trophic level consumer and not an apex predator.

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  Micromouse - Maze Solving Robot

  Aiden Schramm, David Medin, and Andrew Kramer

  A Micromouse is a path-finding robot designed to traverse and map a maze to determine the shortest path possible. These robots are typically entered in annual Micromouse competitions to compete against other teams for achieving the best possible time in solving the maze in the most efficient manner.

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  Natural Language Processing: Understanding Slang and Colloquial Speech

  Beau Miller

  Within the field of Natural Language Processing, slang and colloquial language provide a unique challenge for the training and use of language models. This speech holds an odd, transient space with public consciousness, as each instance has a variable time of relevancy and popularity, amongst other factors such as their fluid definition, usage amongst certain groups, and relative lack of information and training data concerning new or evolving terms. These characteristics make it difficult for models to learn these phrases in a timely manner. This project focuses on how a NLP model could learn new additions to colloquial language in a timely and efficient manner, as well as keep up with a rapidly mutating lexicon.

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  Resonant Landscapes

  Tate Carson and Cater Gordon

  Immersive ambisonics field recordings of 13 South Dakota State Parks. Used for: interactive smartphone-based sound art exhibit and immersive soundscape compositions. Goal: by studying a park's soundscape through immersive recording, we can gain a deeper understanding and appreciation for its unique sonic characteristics.

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  Surveying for Ophidiomyces ophidiicola, the causal agent of Snake Fungal Disease in South Dakota.

  Adam Peak, Brandon Daniels, and Andrew E. Sathoff

  For the past decade there has been an emerging disease plaguing wild snakes across the Eastern United States and Europe. In 2006, researchers started investigating the decline of Timber rattlesnake populations in New Hampshire. They discovered a fungal infection killing off the young to mid-juvenal snakes, thus know as Snake Fungal Disease or Ophidiomycosis. In 2011, San Deigo State University, identified the pathogen that causes infection, the fungus Ophidiomyces ophiodiicola. O. ophiodiicola has now affected 30 different snakes from six families within at least 20 different states since it’s discovery. This study pertains to determining the prevalence of Snake Fungal Disease within South Dakota.

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  Terror from the Skies?: Investigating the energetics and feeding ecology of one of the largest pterosaurs: Quetzalcoatlus northropi

  Kierra Miller and T. Alexander Deccechi

  Our goal is to assess the gut capacity and energetics requirements for Q. northropi in order to understand the role that it played when living.

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  The Impact of Family Business Ownership and Involvement on Entrepreneurial Self-Identification

  Ephrata Yared Feyissa and Robert Girtz

  In this study I analyze the impact that family business ownership has on the tendency of individuals to identify as being entrepreneurial. Drawing data from the 1979 cohort of the National Longitudinal Survey of Youth, I use two-sample t-tests and logistic regression models to explore the relationship between personal entrepreneurial aspirations and having come from an environment involving a family business. As part of this analysis, I control for several demographic factors, such as cognition, gender, and ethnicity. The outcomes from this research highlight the importance of family business as a key factor in fostering entrepreneurial mindsets, suggesting that the experiences and cultural context provided by family-owned enterprises are instrumental in encouraging future generations of entrepreneurship.

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  Do AI-Generated Political Advertisements Create Positive or Negative Responses from Voters?

  Grace Schofield

  Artificial intelligence (AI) is advancing at breakneck speeds, and it’s more important than ever that voters understand AI’s implications on the United States’ political landscape. Political campaigns can synthesize political advertisements using genera-tive AI, fabricating images and videos alike. The public often has difficulty distinguishing these advertisements from their genuine, non-AI counterparts—and deepfake technology improves exponentially, causing the public to struggle even more in identifying AI-generated advertisements.

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  Feature Extraction and Analysis of Binaries for Classification

  Micah Flack

  The research project, Feature Extraction and, Analysis of Binaries for Classification, provides an in-depth examination of the features shared by unlabeled binary samples, for classification into the categories of benign or malicious software using several different methods. Because of the time it takes to manually analyze or reverse engineer binaries to determine their function, the ability to gather features and then instantly classify samples without explicitly programming the solution is incredibly valuable. It is possible to use an online service; however, this is not always viable depending on the sensitivity of the binary. With Python3 and the Pefile library, we can gather the necessary features to begin choosing different classifier models from the Scikit-learn library for machine learning. This all addresses the issue of local automated classification, and we present several different classifier models, datasets and methods that allow for the classification of unknown binaries with a high degree of accuracy for predicting malware and benignware.