This study used hand-collected Greenhouse gas (GHG) emissions data from the Environmental Protection Agency (EPA) and aimed to understand the determinants and incentives of GHG emissions reduction. It explored how companies’ financials, Chief Executive Officer (CEO) compensation, and corporate governance affected GHG emissions. Results showed that companies reporting GHG emissions were wide-spread among the 48 industries represented by two-digit Standard Industrial Classification (SIC) codes.
In this study, the authors address the current climate concern of high CO2 levels by testing solid forms of hydroxide for CO2 reduction and designing a drone to fly it in ambient air!
In this study, the authors test ways to increase knowledge of green consumerism amongst high school students. Their knowledge was measured based on the New Ecological Paradigm Scale.
In this study, the authors engineer a cost-effective and bio-friendly water purification system using limestone, denitrifying bacteria, and sulfate-reducing bacteria. They evaluated its efficacy with samples from Eastern PA industrial sites.
In this study, the authors share their work on improving the frame rate of videos to reduce data sent to users with both 2D and 3D footage. This work helps improve the experience for both types of footage!
In this study, the authors survey middle and high school students in different states in the U.S. to evaluate stress levels, learning experiences, and activity levels during the COVID-19 pandemic.
In this study, the authors use quantitative digit ratio measurements and a survey of personality traits to evaluate the potential relationship between sex and levels of conscientiousness.
Food spoilage leads to a significant loss in agricultural produce each year. Here, the authors investigate whether certain essential oils can protect against fungus-mediated spoilage of fruits and vegetables. Their results suggest that the compounds they tested might indeed inhibit fungal growth, at various temperatures, a promising result that could reduce food wasting.
Berberine, a natural product alkaloid, has been shown to exert biological activity via in situ production of singlet oxygen when photo irradiated. Berberine utilizes singlet oxygen in its putative mechanism of action, wherein it forms an activated complex with DNA and photosensitizes triplet oxygen to singlet oxygen to specifically oxidize guanine residues, thereby halting cell replication and leading to cell death. This has potential application in photodynamic therapy, alongside other such compounds which also act as photosensitizers and produce singlet oxygen in situ. The quantification of singlet oxygen in various photosensitizers, including berberine, is essential for determining their photosensitizer efficiencies. We postulated that the singlet oxygen produced by photoirradiation of berberine would be superior in terms of singlet oxygen production to the aforementioned photosensitizers when irradiated with UV light, but inferior under visible light conditions, due to its strong absorbance of UV wavelengths.
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.
