Analysis of Deforestation in Nilgiri Biosphere Reserve
This research will focus on Nilgiri Biosphere Reserve, a mountainous region located in the Western Ghats of southern India that encompasses several major national parks. Recent developments have caused mass deforestation in the region for lumber and area for plantations. In addition, more roads are being developed connecting urban centers to Nilgiri, which is only worsening the deforestation issue. In this research, Landsat satellite images will be used to track change over time with regards to deforestation and the development of road networks to see how that impacts wildlife. Geospatial data geoprocessing tools will be used to categorize change in land use over time (the change in some land areas from forest/untouched reserve to agricultural or road). False and true color composites in addition to Normalized Difference Vegetation Index (NDVI) assessments will be undertaken to track the deforestation and differentiate between land types, since vegetation will be in a bright red, soil will be brown, and urban areas will be cyan blue to determine how much live green vegetation there is in the reserve as well.

Assessing Land Use Impact on Urban Heat Island Formation in Fort Worth

This research project will focus on assessing the impacts of human activity on the environment in Fort Worth as urbanization has increasingly taken hold over the years. Specifically, the project focuses on analyzing the change in land use in the city over a span of roughly three decades and its contributions to urban heat island formation. Landsat band data products will be used to estimate variations in land surface temperature (LST). LST calculations will highlight the factors contributing to urban heat island formation in Fort Worth.

Plant biomass represents an important component within the biogeochemical cycling of nutrients and contaminants. Transformation of this plant biomass in the environment to organic residuals is dictated primarily by interactions with micro-organisms specifically fungi. My research investigates the effects of fungal colonization of spent coffee grounds as a model for plant biomass to organic matter transformation and how this transformation impacts environmental stability and its ability to bind to contaminants. This presentation will cover; 1) physical and chemical changes in the spent coffee grounds after molding for 0,3,4,5 and 7 months, 2) how these physical changes impact the environmental degradability, and 3) how these physical and chemical changes impact the capacity to bind Gentian violet dye (as a model for organic cations).

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 wound healing and also determine the necessary parameters to achieve the stability of the ADI method. The data, which we are using, are pictures of the wounds, and the task is finding the initial conditions, that is exact boundary data from photos. We believe that Deep Learning is an excellent method to deal with this segmentation problem.

Background: A relationship exists between perception of one’s health based on weight, and how they manage their health. Normal-weight college students who physically appear healthy may unknowingly develop a chronic disease because they view good physical appearance as verification of good health.
Objective: The purpose of this study was twofold: 1) compare the perceived health status of TCU students to their actual health status; and 2) assess the health status of TCU students of normal BMI.
Design: This study was a cross-sectional, descriptive design.
Methods: Twenty-five normal-weight college students between ages 18-24 of any sex and race were recruited to complete a health perception assessment survey 24 hours prior to their lab visit. Participant’s anthropometric measurements (height, weight, body fat percentage, waist/hip circumference, and waist-hip ratio), blood pressure, fasting blood glucose (via finger prick) and a 10mL blood sample were collected. Blood was analyzed for hemoglobin A1c and a lipid panel. Self-reported survey results were compared with results obtained during the study visit to identify any discrepancies between actual and perceived health status and evaluate the overall health status of participants.
Results: Average BMI and waist-hip ratio of participants were 22.39±1.94 and 0.76±0.04, respectively. The most commonly elevated measured values were fasting blood glucose (29% prevalence), and body fat percentage, blood pressure, and LDL cholesterol (21% prevalence each). Fifty-two percent of participants presented with at least one measured value outside normal limits and 29% presented with two or more values outside normal limits. However, 92% described themselves as “very healthy”, “healthy”, or “somewhat healthy”.
Conclusion: Despite the appearance and perception of health, a significant proportion of TCU students ages 18-24 may risk developing a chronic disease. Our results suggest that regardless of self-perceived health status, TCU students should receive regular check-ups to identify and manage physiological markers of health.