AI and Ted Bundy: Exploring Artificial Intelligence usage in criminal profiling

Poster presentation at the Undergraduate Research Day 2024.We have seen a meteoric rise in Artificial Intelligence (AI) creation and usage; yet little attention has been paid to using AI for societal benefits. To leverage AI in new ways, this study focused on integrating AI in criminal profiling. We assessed the accuracy of AI-generated profiles by modifying well-documented criminal cases with a known, and convicted, perpetrator to compare to published perpetrator data created by (human) experts. Using profiling inputs from the crimes of infamous serial killer Theodore ‘Ted’ Bundy, we prompted ChatGPT 3.5 to create profiles for the first eight attacks Bundy committed and compared the accuracy of the outputs to the known information. Initial results show AI creates vague but detailed, and fairly accurate, profiles compared to known information from cases and can find patterns between crimes. But, these profiles were created with specific prompts and the prompt type impacted accuracy. Our findings suggest that using AI in profiling warrants further research and consideration in ongoing investigations, potentially saving time and lives. Caution is advised given the limitations regarding specificity of details and we do not yet know if human-generated profiles are more accurate as we only compared the AI profiles to known information. Future research should compare AI-generated profiles with human-generated profiles and explore paid versions of AI that might reveal further capabilities which might be useful in law enforcement, where costs of using AI may be nominal, especially in relation to the savings of lives and in manpower hours

Past and Present: Immigration and Museum Exhibitions in the Anthracite Coal Region

Northeastern Pennsylvania was home to the anthracite coal industry for about two centuries. The area was originally settled by various waves of immigrants, first from Western, then Southern and Eastern Europe. The new immigrant miners faced many forms of prejudice and were exploited in a system of unchecked capitalism. They were racialized and placed at the bottom of the job hierarchy. Some capitalists did not consider them human and, therefore, not deserving of safe working conditions, decent housing, and equal pay. At the turn of the twenty-first century, a new wave of Hispanic immigrants from the Caribbean, Mexico, and South and Central America entered the region to work mainly in low-paying fulfillment center jobs. Their arrival is being met with various forms of xenophobia, much like the immigrant miners faced over a century ago. The online exhibition “We Are Anthracite,” hosted by the Anthracite Heritage Museum, addresses the call from the American Alliance of Museums (AAM) for museums to be civically engaged, build social capital and connecting new populations to place. The exhibition bridges the experiences between the past coal mining communities and new Hispanic immigrants. The state operated museum hosting this exhibition lends validity to the new immigrants’ place in this region, creating a narrative that their experiences are similar to the region’s inhabitants’ ancestors. By connecting common experiences, past and present, we are creating a form of bridging social capital that connects these different populations. While the northeastern Pennsylvania immigrant story is not well-known, it is rich and complex, like many Rust Belt communities undergoing similar major demographic shifts

Effects of Plastic Bag Regulations on the Chesapeake Bay

Microplastic pollution in waterways poses a critical environmental challenge worldwide by having long lasting effects on wildlife, human health, and ecological balance. To address this issue, many local governments have implemented plastic bag bans as a mitigation measure. This research investigates the effectiveness of such policies in reducing microplastic levels within the Chesapeake Bay Watershed by drawing data from the USGS Water and Chesapeake Bay Data Hub. After merging plastic ban status from each county’s website with water quality data, we examined changes in particulate inorganic carbon and total organic nitrogen levels across various locations surrounding the Chesapeake Bay. Previous research reveals that higher levels of microplastics in water typically increases the amount of organic nitrogen present in the water and decreases the amount of inorganic carbon found in the water. Since our current data contains water quality up until 2021, Harford county is the only county with an effective plastic ban. Because of this, we do not yet have conclusive evidence regarding the effectiveness of plastic bans on microplastic pollution levels. Despite inconclusive findings, this study underscores the importance of addressing microplastic pollution for environmental sustainability and underscores the need for further investigation to inform policy-making and conservation efforts

Fine Tuning Sol-gel Synthesis and Further Applications

Sol-gel synthesis consists of the hydrolysis and polycondensation of alkoxide precursors to result in a glass-like material. Within these sol-gels, properties of the bulk liquids can be regulated by pore size of the sol-gels. Previous research has suggested that the mechanical stresses are predominant during the drying process of the sol-gel synthesis because the constrained sol-gels must shrink and debond from the substrate. Although the sol-gels may not fracture during this stage, increased stress may cause cracking as they are subject to higher temperatures during the firing process. This experiment involves modifying the procedure to assist in separating the gel from the substrate and reducing the effect of higher temperatures during the firing process. Major results to date have centered around optimization of the sol-gel synthesis process and production of gels with good optical quality. Through calculated edits to the procedure such as troubleshooting the amount of stir time, temperature, and adding new reagents such as RainX in the polyethylene tubes, yield has increased by 84% from our first to most recent batches prior to firing. After successful synthesis, the sol-gels can be used to study the behavior of monomers and confined liquids through optical spectroscopy. Future projects include immersion in a monomer with rhodamine 6G dye to create fluorescent nanoparticles controlled by the pores of the gel

Cool Diffusion Flames and Their Applications on Earth

Cool diffusion flames are a type of diffusion flame that burns at a much lower temperature (~700 K) than a traditional hot flame. Discovered in 2012 on the ISS, these flames have been a growing research topic for the last decade. These flames are observed at UMD through a dual hot plate setup in which a pool of a liquid hydrocarbon is heated. An intensified camera and color camera were used to view the movement of the flames and take pictures of the flames, respectively. Thermocouples attached to the apparatus output temperatures of both plates as well as the flame temperature. An anemometer was used to measure flow in the vent above the apparatus, and a formaldehyde sensor was used to track formaldehyde yield of the flames. The fuel that created the most stable flame, n-heptane, was found to work best at a lower heater temperature of 105-110 degrees Celsius and an upper heater temperature of 400-420 degrees Celsius. The peak temperatures of these flames rested between 420-440 degrees Celsius (693-713 K). It is possible that cool flames could be applicable to increasing efficiency in gasoline engines by using them to ignite the hot flame. Results from these experiments could help to improve the accuracy of models that can simulate these technologies

Decreased Host-Cell ATP Levels Affects Bacteriophage Replication in Knockout E. coli Strains

Bacteriophages are viruses that use host cell metabolic resources for replication. Altering Escherichia coli's ATP production pathway can inhibit bacteriophage replication, offering a new approach to bacteriophage therapy.The atp genes encode ATP synthase subunits crucial for ATP generation in E. coli. Knockouts ΔatpA, B, D, E, and H, alongside the parent strain, were studied. Focus narrowed to ΔatpA and B due to significant deviations from the parent strain. It is hypothesized that these knockout strains reduce growth in E. coli and bacteriophage due to decreased ATP production, vital for metabolism and phage replication. Comparative growth assays of E. coli parent and ATP knockout strains were conducted in LB-rich media and M9 minimal media. T4 bacteriophage replication was measured through lysis curves, plaque assays, and two time-point phage titer experiments, chosen for consistent replication. Characterization of T4 bacteriophage replication revealed ΔatpA's crucial role, showing difficulties in growth and lysing. ΔatpA required 10-4 dilutions in phage titer experiments due to low PFU/mL, contrasting with 10-7 dilutions for other strains. ATP assay data showed significantly lower ATP concentration (319nM) in ΔatpB compared to the parent strain, also implying its crucial role in ATP synthesis.Future research will focus on characterizing phage replication in ATP synthase knockout strains using E. coli ATP synthase inhibitors to deepen understanding of phage-host interactions. Controlled bacteriophage manipulation can be studied further to have a better understanding of the application of bacteriophage therapy and to potentially improve its clinical efficacy

Attention! Data Helps Diagnoses: A machine learning approach to predicting ADHD

Attention deficit hyperactivity disorder (ADHD) is often dismissed as a “childhood condition”, since easy-to-identify features (e.g., hyperactivity) are more prevalent in children. Yet, for almost half of diagnosed individuals, the effects of ADHD persist through adulthood, impacting important areas such as jobs/academic performance and relationships. These implications make early diagnoses and effective treatments salient issues for medical professionals. However, as ADHD affects brain development, symptoms often greatly vary person to person. Further, research suggests that the high comorbidity of ADHD with other disorders compounds this issue, explaining why many diagnoses do not come until adulthood. One solution to more accurate diagnoses is machine learning, a class of models that have become increasingly prevalent in research. However, few researchers have developed models to predict ADHD diagnoses. In this study, we performed a secondary data analysis from a study on 103 anonymous participants (51 diagnosed with ADHD, 52 clinical controls). We employed a K-nearest neighbors algorithm to identify key features of ADHD (e.g., prevalence of comorbid disorders) that can accurately predict one’s diagnosis. The results of our analysis suggest: 1.) Objective metrics like this may improve ADHD diagnoses, since current methods are subjective and vary by physician, 2.) Some comorbidities are more predictive than others, and 3.) Research should continue in this area to include more predictive features. Implications for practitioners and researchers are discussed

Downcycling Opportunities For Reverse Osmosis Membranes

Our project aims to tackle the issue of reverse-osmosis (RO) membrane waste. RO membranes are integral to water purification as they possess selective permeability, which can allow clean water to pass while capturing specific contaminants. However, their typical disposal pathway contributes to the increasing global amount of solid waste. This project aligns with existing literature on waste reduction and resource sustainability in the water treatment sector. Previous studies have explored various methods for repurposing waste materials. To address these issues, we propose downcycling RO membrane components into microfiltration (MF) filters, extending their useful life beyond the typical outcome of landfill disposal and/or incineration. Both of these disposal methods have negative implications for the environment over time, including increased emissions and loss of resource potential. Key results from our project include the potential to significantly reduce RO membrane waste and find greater usage for specific membrane components, including membrane sheets. By repurposing membrane components and implementing MF filters, a more circular and sustainable economy can be obtained. The implications of our results extend beyond waste reduction and resource management as they underscore the importance of innovative approaches in addressing environmental challenges and advancing toward a more sustainable future

Groundwater Contamination and Property Values: A Hedonic Price Analysis

About 15% of the United States population (~43 million people) rely on private wells for their source of drinking water. This water is not regulated by the Environmental Protection Agency, and as a result, the water in these wells can contain harmful contaminants (e.g., arsenic, nitrates, and nitrites) that go undetected by homeowners unless otherwise tested. Using a dataset of housing transactions (n=3,908) in the Orlando, Florida Metropolitan Statistical Area, I examine the impact of testing well water on the property value at time of sale. In Florida, not all homes with wells are tested before sale. I address the possibility of selection bias by using a subsample of homes from this dataset (n=1,566) that had all tested their well water before being sold. Using a hedonic pricing model, I test the impact of a well water test finding a contaminant above the detectable limit on sales price, controlling for housing characteristics, geospatial characteristics, and the date of sale. My results indicate a 10% decrease in property value when a well test revealed a contaminant to be above the detectable limit, relative to properties with well tests that did not reveal any contaminant above the detectable limit. The most robust, significant effects are found when homes were tested within a 3-year window prior to transaction. This has implications for the public health and financial stability of homeowners using private well water