Cell-free biologicals are a novel method of treating clinical conditions which involve chronic inflammation such as tendonitis and osteoarthritis. This study compared platelet-derived growth factor (PDGF) and vascular endothelial growth factor (VEGF) in platelet-rich plasma (PRP), activated PRP (aPRP), and platelet lysate (PL). It was hypothesized that PL would contain higher concentrations of growth factors than PRP and that different storage temperatures for PL would diminish cytokine expression. Results demonstrated PL had the highest concentrations of both cytokines, with concentrations slightly diminishing at-80C. aPRP and PRP demonstrated lower concentrations of PDGF and VEGF than PL.

In this study, high school students and teachers responded to a survey consisting of Likert-type scale, multiple-choice, and open-ended questions regarding various aspects of remote instruction. After analyzing the data collected, they found that remote learning impacted high school students academically and socially. Students took longer to complete assignments, and both students and teachers felt that students do not learn as much in remote learning compared to in-person instruction. However, most high school students demonstrated a comprehensive understanding of the topics, and an overall negative impact on students' grades was not detected.

Despite major advances in gender equality, men still far outnumber women in science, technology, engineering and math (STEM) professions. The purpose of this project was to determine whether mindset could affect a student’s future career choices and whether this effect differed based on gender. When looking within the gender groups, 86% of females who had a growth mindset were likely to consider a “male” career, whereas only 16% of females with fixed mindset would likely to consider a “male” career. Especially for girls, cultivating a growth mindset may be a great strategy to address the problem of fewer girls picking STEM careers.

The purpose of this study was to test devices installed on a gabled roof to see which reduced the actual uplift forces best. Three gabled birdhouse roofs were each modified with different mitigation devices: a rounded edge, a barrier shape, or an airfoil. The barrier edge had no significant effect on the time for the roof to blow off. The addition of airfoil devices on roofs, specifically in areas that are prone to hurricanes such as Florida, could keep roofs in place during hurricanes, thus reducing insurance bills, overall damage costs, and the loss of lives.

Acid rain has caused devastating decreases in ecosystems across the globe. To mimic the effect of acid rain on the environment, the authors analyzed the growth of gram-negative (Escherichia coli) and gram-positive (Staphylococcus epidermidis) bacteria in agar solutions with different pH levels. Results show that in a given acidic environment there was a significant decrease in bacterial growth with an increase in vinegar concentration in the agar, suggesting that bacterial growth is impacted by the pH of the environment. Therefore, increased levels of acid rain could potentially harm the ecosystem by altering bacterial growth.

In this study, the authors seek to improve a machine learning algorithm used for image classification: identifying male and female images. In addition to fine-tuning the classification model, they investigate how accuracy is affected by their changes (an important task when developing and updating algorithms). To determine accuracy, a set of images is used to train the model and then a separate set of images is used for validation. They found that the validation accuracy was close to the training accuracy. This study contributes to the expanding areas of machine learning and its applications to image identification.

In this study, the authors develop an architecture to implement in a cloud-based database used by law firms to ensure confidentiality, availability, and integrity of attorney documents while maintaining greater efficiency than traditional encryption algorithms. They assessed whether the architecture satisfies necessary criteria and tested the overall file sizes the architecture could process. The authors found that their system was able to handle larger file sizes and fit engineering criteria. This study presents a valuable new tool that can be used to ensure law firms have adequate security as they shift to using cloud-based storage systems for their files.

While tea has a complex history, recently the health benefits of this beverage have come into focus. In this study, researchers sought to compare the levels of caffeine, catechins and L-theanine between different types of tea using NMR spectroscopy. Further, the impact of brewing temperature on the release of these compounds was also assessed. Of those tested, Bao Chong tea had the highest levels of these compounds. Brewing temperatures between 45ºC and 75ºC were found to be optimal for compound release. These results can help consumers make informed choices about their tea preparation and intake.

The goal of this project was to see if the addition of wild orange essential oil to freshly squeezed orange juice would help to slow down the decay of ascorbic acid when exposed to various temperatures, allowing vital nutrients to be maintained and providing a natural alternative to the chemical additives in use in industry today. The authors hypothesized that the addition of wild orange essential oil to freshly squeezed orange juice would slow down the rate of oxidation when exposed to various temperatures, reducing ascorbic acid decay. On average, wild orange EO slowed down ascorbic acid decay in freshly squeezed orange juice by 15% at the three highest temperatures tested.

Machine learning and deep learning techniques can be used to predict the early onset of breast cancer. The main objective of this analysis was to determine whether machine learning algorithms can be used to predict the onset of breast cancer with more than 90% accuracy. Based on research with supervised machine learning algorithms, Gaussian Naïve Bayes, K Nearest Algorithm, Random Forest, and Logistic Regression were considered because they offer a wide variety of classification methods and also provide high accuracy and performance. We hypothesized that all these algorithms would provide accurate results, and Random Forest and Logistic Regression would provide better accuracy and performance than Naïve Bayes and K Nearest Neighbor.