Our goal is to create a user friendly dashboard with data related to the current COVID-19 pandemic. This includes an interactive map, charts, and numbers presented to the user in a simplified manner. The data spans every county in the United States. Beyond just being a COVID-19 Tracker, our tool will be available as an API that can be used with any other state and county specific data.

Identifying new and cutting-edge investment strategies is a crucial step in establishing any large business within its relative industry. Fort Capital, whose primary investment focus is on industrial-grade buildings, is taking an innovative and insightful approach to geographic understanding. Fort Capital aims to identify trade routes used by major market players, such as Amazon and Walmart, to find the areas where industrial warehouses and large-scale distribution centers are in highest demand. To locate such trade routes, identifying the main travelers on these routes is essential, and Truck Detective aims to do exactly that. Using machine learning and artificial intelligence models such as a deep neural network, Truck Detective enables Fort Capital to detect, with high accuracy, the location of big rig trucks, and can additionally help identify where they came from or where they are heading. This, in turn, illuminates geographically important areas with promising investment opportunities for Fort Capital.

Vehicle Re-identification, which aims to retrieve matching vehicles across different cameras, is a challenging problem in Intelligent Transport System due to different factors such as illumination conditions, occlusions, and video resolution. Numerous studies are proposing the use of Deep Neural Networks, a recent advance in Artificial Intelligence, thanks to their exceptional feature embedding extraction. However, Deep Neural Networks perform poorly on cross-domain settings. Furthermore, vehicle re-identification training data is relatively limited because public videos are only accessible to the authority only. Our study tackles the above challenges by utilizing several state-of-the-art techniques on domain learning to expand the model's generalization capability. Our research shows that we can outperform other state-of-the-art models by large margins on popular vehicle re-identification benchmarks.

The purpose of this project is to create a closed loop system that will enable a continuous drying cycle of mined limestone through a rotating cylindrical dryer. Our client, Lhoist North America, has tasked us with designing this system, and our biggest issue has been putting together the system on a limited budget. We have determine that the most efficient method of designing the system is to used scrapped equipment that Lhoist has available and reconfiguring it for our design, rather than buying a new system. Another challenge we have faced is the method of transporting the mined limestone due to its sand-like qualities. We believe that the most effective method of designing the system will be by altering scrapped material from Lhoist’s scrapyard to complete a closed loop system of the limestone for the rotary dryer.

Dryer Testing
The parameters which were used to test the dryer was that the incline was set at 5 degrees, and the dryer rpm was at 5 and 10. Further, we used four rows of 90-degree lifters followed by four rows of radial lifters. We tested using a small grain limestone sample to be a middle of the road test. Originally, we started testing with one scoop (one quart) inside the cylinder, started the motor and turned to the 10 rpm, and added one quart every ten seconds until 4 total scoops were through the cylinder. The time this took was consistently right around the 90 second mark. However, when the volume was turned up, the findings were more interesting. When we started with a full five gallons inside of the cylinder, turned the motor up until 10 rpm, and added another five gallons at the 30 second mark, the time that it took for all of the material to exit the cylinder was right around the 90 second mark, the same time as when only a gallon of material went through the dryer.

We accumulate several cloud services on Amazon Web Services (AWS) into developing a serverless system in the cloud that replaces the current technical support request, which occurs via calls, in a classroom setting. The instructor can notify the so-called IT person with a press on the programmable Internet of Things (IoT) button. We plan to deploy the system at our university as a way for class instructors to request help without interrupting the lecture. The system is low-cost thanks to AWS's pay-as-you-go policy and easy to install.

The most viable path to alleviate the Global Climate Change is the substitution of fossil fuel power plants for the generation of electricity with renewable energy units. The substitution requires the development of very large (utility-level) energy storage capacity, with the inherent thermodynamic irreversibility of the storage-recovery process. Currently the world also experiences a significant growth in the numbers of electric vehicles, which use very large batteries. A fleet of electric vehicles is equivalent to a relatively efficient storage capacity that may be used to supplement the energy storage system of the electricity grid. Calculations based on the demand-supply data of a large electricity grid show that, even though a fleet of electric vehicles cannot provide all the needed capacity for a large electricity grid, the superior round-trip storage efficiency of batteries significantly reduces the energy dissipation associated with the storage and recovery processes. A very small amount of battery storage significantly reduces the dissipated energy in the electricity grid. Also, improvements in the round-trip efficiencies of batteries are three times more effective than improvements in hydrogen storage systems.

The dryer is a steel cylinder, approximately 36 inches in diameter and five feet in length. The cylinder also spins at a rate up to 10 rpm. The inside surface contains 48 lifters. These lifters have two variants and are made of mild steel. They are designed to move limestone through the cylinder while the cylinder spins.

The drying of limestone is usually done industrially in a rotary drum dryer. The purpose of this project is to generate a model that will predict limestone particle motion as it passes through the dryer.  By creating an accurate model of the particle movement during the drying cycle, the operator will be able increase the dryer’s efficiency.  Using basic physics and through experimental testing, our team was able to produce a model that will provide detail of particle motion inside the dryer.

The Mississippi River Delta is the 7th largest river delta on Earth that consists of the Mississippi River and the Gulf of Mexico. Additionally, it contains 40% of the wetlands in the contiguous United States and over two million hectares (4,942,108 acres), an area equivalent to the size of two football fields, of agricultural lands. Due to fertilizer runoff from agricultural lands, the river delta has been experienced excess levels of nitrogen and phosphorus. The excess levels of these nutrients have contributed to water pollution in the delta and the hypoxia zone in the Gulf of Mexico. This research will focus on mapping the levels of nitrogen and phosphorous across the river delta to determine where the highest levels are

To understand wildlife ecology, one common strategy is a technique known as telemetry. This technique involves attaching radio-transmitters to animals. For volant species, such as bats, transmitters are attached to their backs with an adhesive. However, one issue is that it is easy for the bats to remove the transmitter. The loss of transmitters early in surveys is not only costly, but limits the amount of data that can be collected. Thus, there is a real need to extend the length of time a transmitter remains on a bat. To address this, we conducted a two-part behavioral observation study in the bat flight facility at TCU. In part 1 from April to September 2019, we tested 1) two currently available transmitter brands and 2) three different prototype designs to determine if the overall shape and size of the transmitter impacted the length of time they remained attached. We found that regardless of transmitter brand or design, the antennas incurred a significant amount of damage, suggesting the bats used the antennas to grab and pull them off. Thus, for part 2 from August to September 2020, we conducted a series of trials to establish the effectiveness of three coatings at preventing bats from damaging the antennas. We found that transmitters did not remain attached significantly longer with cayenne pepper, nail-biting deterrent, or Tabasco sauce on the antenna, suggesting that either 1) bats were not deterred by the three coatings we selected, or 2) bats may not be chewing the antennas or putting them in their mouths to provide leverage to remove them. Overall, our study revealed that bats can and will remove transmitters by grooming them off using the antennas and recommend that future research focuses on integrating the antenna into the body of the transmitter as a preventative measure.

Agricultural soils require the presence of three basic plant nutrients: Nitrogen, phosphorus, and Potassium (NPK). Each nutrient has its role in plant growth and their deficiencies have adverse effects. Therefore, detailed analysis was required to analyze whether soils from a lake were sufficient in terms of these nutrients and could be suggested for use as an agricultural soil amendment on a particular plot of land. Experimental study by way of specific chemical analysis methods (LOI, spectroscopy, etc.) looked at water content, pH, EC, SOC (Soil Organic Carbon), phosphorus and nitrogen content in the lake sediments; these values were used to determine what contents/nutrients in the lake sediments would need to be optimized for crops of interest. Results from the concluding experimental procedures would allow the property owner to effectively make use of their resources.

The Atlantic Forest, which extends over 17 Brazilian States, is one of the richest biomes in the world. Historically it has been a hot spot for biodiversity including endangered and endemic species. Despite this, only 15% of the forest’s original range remains. This loss is disproportionally distributed as 4 of the 17 states accounts for 90% of the loss. The source of this deforestation can be attributed primarily to logging developments and eucalyptus plantations. Through using public geodatabases, satellite imagery, and ArcGIS Pro we will show this forest loss and fragmentation in a visually accessible way.

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.

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 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.

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.