Investigation of the potential of waxworm saliva, the secretion of Galleria mellonella, for plastic degradation.
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Wind Resistance and Automobile Shapes
Energy efficiency is becoming more important as we struggle to find better, more sustainable energy sources to power our planet; the car industry is no exception. In this article, the authors examine the effect of shape on automobile aerodynamics By finding the shape that makes cars less resistant to wind, and therefore more energy efficient, can help the automobile industry make better, more eco-friendly cars that are also cheaper to operate.
Read More...School sustainability: The implications of implementing living walls at schools for air purification
The authors compare air quality in the presence and absence of a living wall in a high school hallway in Brooklyn, NY.
Read More...Earthworms as soil quality indicators: A case study of Crissy Field and Bayview Hunters Point naval shipyard
The authors looked at soil quality of former military sites where chemical disposal was known to have occurred. Along with testing for heavy metals, the authors also looked for the presence (and number) of earthworms present in topsoil samples as a marker of soil health.
Read More...Slowing ice melting from thermal radiation using sustainable, eco-friendly eggshells
The authors looked at the ability of eggshells to slow ice melting. They found that eggshells were able to increase ice melting time when crushed showing that they were an effective thermal barrier.
Read More...Identifying the wavelength that generates the most voltage and current in a solar panel
A key barrier to adoption of solar energy technology is the low efficiency of solar cells converting solar energy into electricity. Sims and Sims tackle this problem by coding a Raspberry Pi as a multimeter to determine which wavelength of light generates the most voltage and current from a solar panel.
Read More...Utilizing meteorological data and machine learning to predict and reduce the spread of California wildfires
This study hypothesized that a machine learning model could accurately predict the severity of California wildfires and determine the most influential meteorological factors. It utilized a custom dataset with information from the World Weather Online API and a Kaggle dataset of wildfires in California from 2013-2020. The developed algorithms classified fires into seven categories with promising accuracy (around 55 percent). They found that higher temperatures, lower humidity, lower dew point, higher wind gusts, and higher wind speeds are the most significant contributors to the spread of a wildfire. This tool could vastly improve the efficiency and preparedness of firefighters as they deal with wildfires.
Read More...Heavy metal and bacterial water filtration using Moringa oleifera and coconut shell-activated carbon
One-third of the world's people do not have access to clean drinking water. Nadella and Nadella tackle this issue by testing a low-cost filtration system for removing heavy metal and bacteria from water.
Read More...Floating aquatic plants form groups faster through current
Here, the authors sought to investigate the effects of water current on the growth of colonies of duckweed, a floating plant that forms colonies in silent ponds, marshes, lakes , and streams in North America. They found that current flow mediates the formation of colonies, disrupting and recreating the colonies which provides the opportunity for reorganizations that were identified as beneficial.
Read More...Collaboration beats heterogeneity: Improving federated learning-based waste classification
Based on the success of deep learning, recent works have attempted to develop a waste classification model using deep neural networks. This work presents federated learning (FL) for a solution, as it allows participants to aid in training the model using their own data. Results showed that with less clients, having a higher participation ratio resulted in less accuracy degradation by the data heterogeneity.
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