The Kittanning coal seams run underneath West Virginia, southeast Ohio, and southwestern Pennsylvania. It is part of a sequence that underlies the Freeport and Pittsburgh coals. All three seams are of Pennsylvanian Age. Of the seams in the Northern Appalachian Basin, the Kittanning has the among the largest extents. For that reason, it will most likely be the greatest influencer on population patterns. Since the early 1800s, the people of the region mined and used coal to produce their energy. As such, it is the goal of this research is to determine the spatial relationship between the economic coal sources and population centers.

Anxiety related to academics, especially with regards to testing, is a rapidly growing mental health issue impacting all undergraduates at TCU. High levels of test anxiety have been shown to inhibit memory recall, reduce exam scores, and promote poor study habits. Expressive writing is a form of test anxiety intervention consisting of a type of free response developed to allow students to release their minds of anxious thoughts and emotions related to exams. Expressive writing has been shown to reduce test anxiety and improve exam performance. However, the effectiveness of expressive writing may be mitigated by a students’ level of emotional intelligence. Emotional intelligence is defined as the capacity to be aware of, control, and express one’s emotions. In order to better understand the connection between emotional intelligence and expressive writing, I conducted an exploratory mixed-methods study using quantitative survey data to inform our selection of interview participants and the initial development of my interview protocol. Initially, to gain insight into students’ responses to the expressive writing prompt, I collected and coded expressive writing samples from GEOL 10113 students during the Fall of 2020. Prior to the beginning of the semester, I asked the students to complete a Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF) to assign a numerical value to their approximate emotional intelligence levels. Next, I stratified students by quartiles into high (Q3), medium (Q2), and low (Q1) emotionally intelligent groups. Then, GEOL 10113 students were asked to participate in focus group interviews, and volunteers were subsequently grouped by their TEIQue-SF score into three focus groups relating to the high, medium, and low emotional intelligence bins. Finally, using the focus group interview data and the selected student’s responses from the initial expressive writing exercise, I found that all of the student responses showed consistent differences between the three focus groups. Highly emotionally intelligent students had more positive experiences with the expressive writing exercise while lower emotionally intelligent students tended to have neutral or negative experiences with the activity. Overall, these data suggest that the level of emotional intelligence relates to how willing students were to convey their thoughts and emotions during the activity, which helped the higher emotionally intelligent students to have positive experiences with the writing exercise. The implications of my study are that while the efficacy of the expressive writing exercise is assumed, emotional intelligence is a confounding variable. Students must engage with it in some sort of authentic manner if they are to benefit from the exercise.

Solar energy is a significant contributor to the renewable energy mix. Many urban developments are making investments to install solar systems across feasible areas. The allocation of solar systems relies on the land’s geography and the amount of solar radiation received. The purpose of this study is to apply to determine the best sites for solar installations in urban areas. Using the TCU area of Fort Worth, Texas as a case study, this study will use ESRI’s ArcMap and ArcGIS Pro to estimate the solar power potential of different residential rooftops. The results will be useful in showing what households are most suitable for solar installation based on their expected energy yield.

Landslides may be caused naturally or triggered by human activities and have enormous societal and economic impacts. Detecting and mapping landslides through the generation of landslide susceptibility maps (LSM) and understanding the factors that trigger these processes will be helpful in land use planning and risk assessments. Moreover, it will also assist landslide mitigation efforts by controlling anthropogenic-led processes that induce landslides. This study deals with the analysis to identify slow-moving landslides in Travis County, Texas. It combines geographic information systems(GIS) and remote sensing datasets and techniques to generate an LSM of the study area and identify ground displacements. Remote sensing data provide key information about the topography and land uses, combined with controlling factors for a landslide occurrence such as slope, geology/soil and geological structures, and vegetation/land uses to perform an empirical approximation to map and assess landslide susceptibility. Once the susceptible areas are identified, analysis for ground displacement is applied using a Synthetic Aperture Radar Interferometric (InSAR) technique referred to as the Small Baseline Subset (SBAS) and field-based multitemporal Real-Time Kinematic (RTK) GPS measurements.

The Ouachita Trough is a basin that formed along a passive margin on the southern border of Laurentia caused by the Precambrian–Cambrian rifting of Rodinia and the opening of the Iapetus Ocean. The collision of Laurentia and Gondwana and the closing of the Iapetus Ocean thrust sediments from the Ouachita Trough onto the southern portion of the North American craton to form the Ouachita Mountains. The Ouachita Trough transitioned from a sediment-starved basin into an area of rapid sediment accumulation during the Mississippian. The Stanley Group, of interest in this study, was deposited prior to the collision of the encroaching Gondwana continent to the south. Although there have been many previous studies aiming to determine the provenance history of the Stanley Group, the results are inconclusive. In this study, nine samples from turbidite deposits of the Stanley Group were processed using both U-Pb age dating and core rim analysis. Laurentia and Gondwana have similar aged terranes that are difficult to differentiate. Using core rim analysis allowed us to date both the age of the core and rim of individual zircon grains. We were then able to correlate zircon grains of similar ages to their sources. By analyzing a large area of the Ouachita Mountains, this study shows that the Stanley Group consists of sediments sourced from both Laurentia and Gondwana terranes to the south.

Rapid industrialization and global population growth have increased the number of people living in urban areas worldwide. Developing countries, have seen tremendous increases in their industries over the past decades, which generated both positives and negative effects on their people, environment, and economy. One of the negative impacts of industrialization is industrial pollution and the increase in the number of pollutants released into the environment_ in this case, heavy metals. Heavy metal contamination is an alarming problem that many Developing countries are becoming aware of and trying to address. Heavy metal direct or indirect consumption may result in several health effects in the body, including damage and alteration of normal functioning of organs such as the brain, kidney, lungs, liver, and blood, which later result into acute or chronic diseases. This case study will look at heavy metal contamination cases in Rwanda in different drinking water sources. The focus of this case study will be on some common heavy metals released from industrial waste: Lead, Manganese, Iron, Cadmium, Zinc, and Chromium.

Teachers have experimented with the idea of virtual learning and its’ effects on student achievement. Due to the coronavirus pandemic, many schools and universities transitioned from traditional classroom-focused learning to asynchronous online learning. Asynchronous online learning is a type of instruction where online learning is not happening at the same time or place. Consequently, TCU made the abrupt transition in the Spring of 2021 to fully online asynchronous courses. To understand the magnitude of how remote learning can effect students’ academic success, my research project looks at what factors, including remote learning, can predict final grade utilizing GEOL 10113 student performance data and survey data from the spring semester of 2020 surveys. To investigate the impact of online learning, I tested several linear models to determine what confounders have a significant role in predicting students’ success in online and in remote learning. These models investigated which factors, ranging from demographic information to GPA, are significant predictors of both final grade and remote grade. I started the linear model selection process by testing a complex linear model, which had all the possible factors including interactions that can impact final grade or remote grade from the surveys. Once I knew which factors were significant from the complex model, I eliminated non-significant variables and created new models, comparing each model by their AIC values until I found the best-fit linear model for final grade and remote grade. AIC is a measurement of how well a linear model fits and the lower the AIC value the better fit the linear model has. After testing each linear model: GPA, students’ lecture section, remote grade, and exam average were significant to final grade. These models suggest that while remote grade is a significant predictor of final grade, no variable measured in this study is significant enough to impact remote grade. Differing from previous research, my results showed that there were no gaps in achievement amongst gender and underrepresented minority students. Although statistically no variable significantly impacted remote grade, there are trends amongst demographic variables and remote grade, suggesting some potential relationships that could be explored in future studies.

The Urban Heat Island (UHI) effect is characterized by the differential heating of densely populated urban areas in comparison to surrounding areas. Increased temperatures caused by buildings and other man-made infrastructure have a wide range of human and ecological impacts. One emerging methodology to combat UHI effects is the implementation of urban green spaces and trees. Trees can provide two main functions that aid in cooling; shade from the sun provided by the canopy and cooling through the process of evapotranspiration. This project aims to identify which species of tree best suits the ecoregion of Fort Worth, how much feasible green space Fort Worth can provide, and project the cooling the green spaces could provide if they are planted with trees.

The use of Interferometric Synthetic Aperture Radar (InSAR) to analyze the deformation of the Earth's surface has become an increasingly important tool for monitoring earthquakes, volcanic activity, landslides, and land subsidence. This process works by calculating the phase differences of radar signals reflected from the Earth's surface over a period of time. If the land has uplifted or subsided, the phase of the two radar signals will interfere. The image this phase difference produces is known as an interferogram, which shows the ground-surface displacement of the target area across the two time periods. This technique has been used extensively to survey Mexico City, which has been an area of concern since the beginning of the 20th century due to its dramatic rate of ground subsidence.

The reusing, recycling, and reduction of waste streams is seen as a viable sustainability strategy. One major waste stream is coffee grounds with about 11.5 million kilograms being generated per day in America of which 90% is landfilled. This waste stream can be repurposed into usable carbon-based materials to address issues of climate, pollution, or engineering applications. For my research, I am converting spent (used) coffee grounds into biochars, a type of carbon-based material, with different charring (burning) temperature to measure the removal of lead (Pb2+) from contaminated water. The charring temperature was changed in order to determine the optimal charring temperature for water treatment. This presentation will go into the maximum amount of lead the biochars can remove, how fast the biochars can remove the lead and the properties of biochars that allow for such removal. Further results, methodology, and modeling applications will be discussed in the presentation.

Nanomaterials are the new technologies reforming industrial activities. They are used to improve energy efficiency and storage, to cheaply store and process information in every internet server and personal computer, to facilitate bio-imaging and drug delivery, and in environmental remediation. These materials’ nanometric dimension, 1/100000 the width of a human hair, allows them to have novel characteristics such as strength, electrical resistivity, and conductivity, and optical absorption compared to the same materials in bigger sizes. Due to their widespread and incorporation into consumer products, it is important to understand their interactions with other elements in the environment. I used flow experiments, to understand the effects of the core and terminal groups chemistries of 3 sets of nanomaterials on their interaction with ferrihydrite, a very common and reactive mineral in the environment. The nanomaterials used in this study, namely Graphene Quantum Dot (GQD), PAMAM G4-OH, and PAMAM G3.5-COOH, have comparable sizes, 6nm, 4.5 nm, and 3.5 nm, respectively. When the experiments were conducted under acidic and circumneutral pH, the quantities of GQD and PAMAM G4-OH sorbed were equivalent and less than the quantity of PAMAM G3.5-COOH sorbed. In my presentation, I will go over the quantities and kinetics results from the interactions of the 3 sets of nanomaterials onto ferrihydrite over environmentally significant pH values (range 3-10).

Land surface temperature is a major factor used in the assessment and understanding of several processes including global climate, hydrological, geo-/biophysical, urban land use/land cover (Avdan and Jovanovska, 2016). Since the Soviet Union launched the world's first artificial satellite, Sputnik 1, in 1957 there have been about 8,900 satellites from more than 40 countries launched in space that have opened possibilities to understand the earth using remote sensing. Specifically, LANDSAT 8’s thermal infrared sensor Band 10 data has been successfully used to map land surface temperature. The specific algorithm used to derive land surface temperature from LANDSAT 8’s thermal infrared sensor Band 10 showed standard deviations of 2.4°C and 2.7°C on the first trial and second trial respectively (Avdan and Jovanovska, 2016). In my project, I used land surface temperature in addition to secondary data (geologic features, volcanoes, faults, change in cities' extents) to locate and deduce the potential geothermal plays in Rwanda. I also compared the potential geothermal plays obtained using remote sensing to those obtained using ground measurements to assess how accurate remote sensing tools are in determining geothermal plays.

Mount Rainier is a stratovolcano within the Cascade Arc situated southeast of the cities of Seattle and Tacoma. This region of Washington has undergone substantial population growth while being situated in the shadow of a large stratovolcano. Historically lahar flows, volcanoclastic debris flows, from the volcano have reached as far as Tacoma and could still pose a risk to Seattle and other smaller communities. Seismicity and annual precipitation are large contributors into predicting an eruption event as well as eruption severity. Using ArcGIS Pro and implementing various types of data including historic lahar deposit extent, population growth, seismic activity, and other contributions that can peer into a future volcanic eruption, it can be possible to assess the volcanic hazard Mount Rainier poses on nearby communities.

Anti-inflammatory drugs such as ibuprofen and triclosan are widely used and available in many pharmaceutical and personal care products (PPCP’s). The concentrations of these drugs are increasing in public surface and groundwaters and is often linked to negative impacts on aquatic life. These impacts are due to the fact that PPCP’s bypass water treatment facilities since they are not typically regulated and water treatment methods at the facilities are not designed to remove them. My research focuses on removing PPCP’s using reactive environmental sorbents like nanocrystalline ferrihydrite. Specifically, I examined the interaction of two widely used PPCP’s (Ibuprofen and Triclosan) with nanocrystalline ferrihydrite of varying particle size (<125, 125-250, >250). Results thus far show that when Ibuprofen interacts with nanocrystalline ferrihydrite at pH 4.3-4.8; 28.29% was removed when the particle size was less than 125 microns; 45.89% was removed when the particle size was 125-250 microns; and 49.92% was removed when the particle size was greater than 250. While for Triclosan 40.55%, 54.7%, 23.80% was removed by nanocrystalline ferrihydrite with size <125, 125-250, >250 respectively. My presentation will further cover surface properties of nanocrystalline ferrihydrite controlling the sorption of ibuprofen and triclosan.

The changing climate as well as the cycling of nutrients and contaminants throughout our planet is heavily influenced by interactions involving plant biomass. For example, interactions of plant biomass with soil biota (specifically fungi)regulates climate and pollution by controlling 1) the quantity of CO2 released from the respiration of organic matter and 2) the movement of pollutants on land and in water. This study focused on 1) investigating fungal colonization of coffee grounds, as a model for understanding the fungi-plant biomass interactions in soils, and 2) studying how fungal colonization changes in the physical and chemical properties of coffee grounds after molding them for 0,3,4,5 and 7 months. The objectives of the next phase of this research will be to examine how the fungi-induced changes in physical and chemical properties of coffee grounds impact 1)carbon sequestering potential (i.e. ease of respiration to CO2) of the coffee grounds and 2) the capacity of the coffee grounds to bind Gentian violet dye (as a model for organic/cationic pollutant).

Synthetic nanomaterials continue to revolutionize how we do things industrially, medically and domestically. As we continue to utilize these materials, the inevitability of them entering the environment and the need to understand the associated consequences rises to the forefront. My research focuses on understanding the chemo-dynamics of interactions between polyamidoamine (PAMAM)-based nanomaterials (most commonly in the biomedical field through drug and gene delivery) and reactive minerals in the environment. Specifically, this presentation will cover the size-dependent binding (and debinding) dynamics of carboxyl-terminated PAMAMs (G-COOH) onto (and from) ferrihydrite (FFH), a form of naturally-occurring iron oxide mineral. Early results suggest that at pH 5, the smaller G1.5-COOH PAMAM binds to (and debinds from) FFH in higher quantities but at much slower rates that the larger G3.5-COOH PAMAM. The higher quantities of G1.5-COOH PAMAM being bound to (or debound) from FFH is attributable to its smaller size - facilitating access to internal micropore space in FFH that are inaccessible by the larger G3.5-COOH PAMAM. Difference in the accessibility of internal FFH micropore space by the different sized PAMAMs would also explain observed trends in their rates of binding and debinding. In future research, I will be targeting the confirmation of early results and the expansion of my study to include G-COOH PAMAMs larger than G3.5-COOH.

Nitrate contamination of groundwater has been a growing problem in Texas and California from increased food demands, requiring growing agricultural inputs of synthetic fertilizer and manure. Pyrolysis of pistachio agro-waste is a promising method for reducing waste products and engineering biochar with the capacity to support zerovalent iron impregnation (ZVI). This study examined the efficiency of pistachio biochar for nitrate (NO₃-N) removal in water with and without ZVI. Pistachio biochar was functionalized through varied temperature pyrolysis (400-600℃) over three heating durations (0 min, 5 min, 10 min). Biochar samples from both 400°C and 600℃ pyrolysis were tested with and without ZVI impregnation over a 5 day period in a 20 ppm solution of NO₃-N. The biochar-nitrate solutions were recorded in intervals (1 hr, 3 hr, 7 hr, 24 hr, 68 hr, 96 hr, 120 hr) and Ultraviolet-Visible Spectroscopy was utilized to measure NO₃-N absorbance of samples at 400nm. The experimental data show that pistachio biochar with and without ZVI decreased nitrate levels from water; presenting a potential low-cost and sustainable option for repurposing agro-waste for water remediation.

Rivers are an essential part of any urban or rural landscape, providing drinking water, transportation, and recreational opportunities for local residents. However, with the continuous growth and development of urban areas like Fort Worth and Dallas, flooding poses a significant risk to human life and property. This increased development creates a need for careful monitoring and forecasting of river conditions and flood probabilities. This study explores the associated historical river data for USGS Gauges on the Trinity River in Tarrant and Dallas Counties. This data, along with topographic information and land use surveys, are used to project the possible impacts of flooding scenarios. These possible impacts include damage to property, critical infrastructure, and threats to human life. This data can then be interpreted spatially to effectively inform the public and public officials of risks and monetary costs associated with future flooding events.

This project will consist of designing an AI application. The application will use a deep learning algorithm able to take attendance of the class as students are joining the classroom. I will further expand the patent to recognize the individual students and measure their temperature. Furthermore, the system will classify different emotions during the lecture and give helpful feedback to the professors. This tool will assist with time management, as professors spend several minutes to take attendance, and it will act as an extra tool for the prevention of spreading COVID-19 and any new virus. The patent will further provide useful feedback for the improvement of lectures through emotion detection. An external camera will be used hand in hand with the Open-CV package in python that will allow us to detect the students and identify them. The students' temperature will be measured by an infrared forehead thermometer and welcome them in the class. The algorithm will be using cascade classifiers, and transfer learning. Data for the training process of the algorithm will be collected from volunteering TCU student subjects.

The process of successful skin healing from a wound involves different combinations of interactions. Moreover, by clearly understanding this process, we can provide and determine the appropriate amount of medicine to give to patients with varying types of wounds. Thus, this can improve the healing process of patients. In this research, we use the ADI method to solve a partial differential equation that models the wound healing process. Moreover, we try to explore the relationship between parameters in the model for different patients. Wound images are used as our dataset experiment. To segment the image's wound, we implement U-Net, a deep learning-based model, as our model for this segmentation problem. We believe the combination of ADI and Deep Learning helps us understand the process of wound healing.

Markov chains are stochastic models characterized by the probability of future states depending solely on one's current state. Google's page ranking system, financial phenomena such as stock market crashes, and algorithms to predict a company's projected sales are a glimpse into the array of applications for Markov models. In this research, we analyzed the board game "The Settlers of Catan" using transition matrices. Transition matrices are composed of the current states which represent each row i and the proceeding states across the columns j with the entry (i,j) containing the probability the current state i will transition to the state j. Using these transition matrices, we delved into addressing the question of which starting positions are optimal. Furthermore, we worked on determining optimality in conjunction with a player's gameplay strategy. After building a simulation of the game in python, we tested the results of our theoretical research against the mock run throughs to observe how well our model prevailed under the limitations of time (number of turns before winner is reached).

In the field of Riemannian geometry, the condition on the Riemannian metric so that a manifold has positive scalar curvature (PSC) is important for a number of reasons. Many famous researchers have contributed gradually to this area of geometry, and in this project, we study more about PSC metrics on such manifolds. Specifically, we refine and provide some details to the proof of Gromov and Lawson that the connected sum of 2 n-dimensional manifolds will admit a PSC metric, provided each of the manifolds has a metric with the same condition. We then derive some useful formulas related to the Riemann curvature tensor, the Ricci tensor, and the scalar curvature in many different scenarios. We compute the quantities for a manifold equipped with an orthonormal frame and its dual coframe, namely the connection one-form and the curvature two-form. Then, we observe the change in the structure functions, defined as a function that determines the Lie derivative of the orthonormal frame, under a nearly conformal change of the said frame. The aim of these calculations is that, by expressing the scalar curvature of a manifold M entirely in terms of the structure functions, we can determine a condition on the conformal factor so that when dividing the tangent bundle of M into two sub-bundles, then the scalar curvature restricted to one sub-bundle will “dominate” that of the other one, so that if we know the scalar curvature of the former sub-bundle is positive, we c

Impact of the COVID-19 pandemic on diet and health-related behaviors of adults

Background:
Study objectives were to describe how diet and health habits changed and identify factors impacting diet and health behaviors during the pandemic.

Methods:
An electronic, anonymous survey was developed and distributed via local social media and through a community food-bank following IRB approval. Data were coded into and analyzed for frequencies and correlations using SPSS.

Results:
Participants (n=80) were 97% (n=77) female and 41.37+/-11.7 years. Participants receiving food assistance primarily accessed community food/mobile pantries (22%, n=17). Participants (54%, n=43) agreed that, “I was healthier before the pandemic”, while 15% (n=12) disagreed. Participants (52%, n=42) reported 13.2+/-6.8 pounds unwanted, pandemic weight gain, while 22.5% (n=18) reported 14.1+/-13.9 pounds desired, weight loss. Among participants earning <$50,000/year, 89.5% (n=17) reported inability to afford healthy food, while 2.6% (n=1) earning >$150,000/year reported inability to afford healthy food. Inability to afford healthy food correlated with BMI (ρ=.40, p<.01). Income negatively correlated with pandemic weight gain (ρ=-0.31, p<0.05) and ability to afford healthy foods (ρ=-.73, p<0.01). Participants reported increased pandemic snacking (61.25%, n=49) and alcohol consumption (37.5%, n=30). Higher pandemic stress levels correlated with increased pandemic alcohol and snack consumption, (ρ=.30, p<.01) and (ρ=.44, p<.01), respectively.

Conclusion:
Changes in health perceptions and weight were common. BMI and income impacted ability to afford healthy foods. Increased stress levels were significantly associated with increased alcohol intake and snacking, although weight changes were not associated with alcohol or snacking. This research provides information about pandemic dietary and health behavior changes and how impacts differ based upon income level.

Determination of Caffeine Use and its Effects on University Students
Jason Balters, Senior-Coordinated Program in Dietetics; Jordan Pitts, Senior-Coordinated Program in Dietetics; Anne VanBeber, PhD, RD, LD, FAND, CCMS; Kelly Fisher, DCN, RD, LD, CSP; Lyn Dart, RD, LD, PhD - Department of Nutritional Sciences
Background
Many studies have observed caffeine intake of university students. Investigators have found that the majority of consumers who over consume caffeine were not aware of the FDA recommended limit of 400 mg caffeine/day. Purposes of this research were to determine the level of caffeine consumption among students attending Texas Christian University (TCU) and the perceived effects that users experienced related to sleep, stress, and withdrawal symptoms. A desired outcome of this research was to clarify confounding values of average caffeine consumption in the university population that varied from 124 mg/day to 228 mg/day in the existing literature.

Methods
A survey questionnaire was created to assess the quantity, modality and situational scenarios of caffeine consumption, as well as caffeine’s perceived effect on sleep, energy and stress. Caffeine withdrawal effects felt by students were also ascertained in the survey. The survey was administered via Survey Monkey® and distributed via the TCU email system to approximately 1000 students. Basic descriptive and frequency statistics were calculated using the Survey Monkey® software.

Results
The 217 respondents were 76.5% female with 94% between the ages of 18-25 years old and 94.0% current undergraduate students. Of the total respondents, 70.1% consumed 1-2 caffeinated beverages per day, 66.5% used caffeine to help study, and 67.5% used caffeine to increase energy. Negative perceptions of caffeine’s impact on sleep were reported by 30%; while 18.5% reported a negative impact on stress, and 73.7% reported withdrawal symptoms.

Conclusions
The consumption of 95-190 mg caffeine/day by the majority (54.9%) of study participants was consistent with reported scientific literature. While only 1.8% of those surveyed consumed more than the FDA recommendation of 400 mg caffeine/day, reasons for this are unknown. Additionally, of the 70% who stated they experienced withdrawal symptoms, it is uncertain if these symptoms were indeed caused from the caffeine withdrawal or from another unrelated cause. It is advised that the research survey be revised to include questions that more acutely seek information sought in the stated purpose of the study. Further research regarding caffeine consumption by consumers is also warranted to better assist registered dietitian nutritionists and other health professionals when assessing diet and lifestyle habits of patients.

Background:
Studies in the general population assessing knowledge/attitudes regarding a plant-based diet found that the majority of participants were reluctant to follow the diet. Specific concerns included perceived lack of satiety, low protein content, and undesirable taste. However, there is lack of research in the college-age population. The purpose of this study was to determine university students' general knowledge/attitudes of a whole-foods, plant-based diet.

Study Design:
After providing informed consent, participants (N=209) completed a 28-question online research survey via Survey Monkey®. Participant demographics, health status and eating habits were gathered at the beginning of the survey. The remaining questions assessed participant knowledge and attitudes pertaining to a whole-foods, plant-based diet. Analysis using Pearson correlation coefficients was performed to assess associations between knowledge and attitude towards a whole-foods, plant-based diet in concordance with participants’ health status and eating habits (SPSS, p ≤ 0.05).

Results:
Males were more likely to disagree that following a plant-based diet is beneficial to their health (p ≤ 0.01). Male students were also more likely to believe that a whole-foods, plant-based diet would never satisfy their hunger (p ≤ 0.01), decrease their energy levels (p ≤ 0.05); males were also less likely to understand the meaning of a whole-foods, plant-based diet (p ≤ 0.01). Similarly, students who had never taken a nutrition course were more likely to disagree that a whole-foods, plant-based diet is beneficial to their health (p ≤ 0.01).

Conclusions:
Respondents who had negative attitudes toward a whole-foods, plant-based diet tended to lack knowledge/understanding of the diet. Providing nutrition education about the whole-foods, plant-based diet by registered dietitian nutritionists could result in improved health outcomes among this population.