Abstract
Global Warming has been a problem at the heart of Earth’s environmental issues for nearly 5 decades, with the potential to affect a significant portion of the global population. Changes in Earth’s climate due to the rise in global temperatures will have an enormous impact on communities around the world, along with a drastic displacement of humans and an extreme loss in natural biodiversity. Current methods of combating this issue have proven to be ineffective, requiring a more comprehensive and innovative approach. This project aims to propose a potential solution to mitigate the effects of global warming and limit temperatures to sustainable levels through the use of stratospheric aerosols. Through a process of data collection, experimentation, and modeling, we were able to correlate the presence of aerosols in the stratosphere to a consequent drop in temperatures and utilize regression prediction to forecast a 16% drop in global temperatures after examining the effects of volcanic ash in the stratosphere. We were also able to compare monthly aerosol concentration levels to declines in the temperature growth, finding a benchmark to stabilize global temperatures. By implementing the changes to Earth’s atmosphere, we can reflect heat from the Sun and create a cooling effect for the planet.
References
Abstract
The Long Island Sound is a tidal estuary that lies between Long Island and Connecticut in the United States [5]. It houses 9 million people and provides around 200,000 jobs which has an estimated value of $17-37 billion dollars a year [3, 2]. The purpose of this experiment is to figure out if the distance from Route 25A, a major road on Long Island, to the coast of the Long Island Sound has an effect on the coastal water nitrates, nitrites, ammonia or pH. It is hypothesized that the distance from Route 25A will have an effect on the nitrates, nitrites, ammonia or pH of the coastal water. The testing took place at Cedarmere Park, North Hempstead Beach Park, Bay Walk Park, and Sunset Park. At each location, a water bottle was filled with water from the tides and were brought home, where the pH, ammonia, nitrate and nitrite levels were tested for with an API saltwater testing kit and logged into a notebook. It was found that distance from Route 25A has no effect on pH or ammonia, but it does have an effect on the nitrate levels. This means that car engine emissions may have an impact on the local ecosystem, and may lead to eutrophication occurring, which can kill off marine organisms. Future research may want to increase the amount of locations tested and possibly use automatic sensors to increase accuracy and ease of testing.
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Abstract
Phytoplankton is a critical producer in, but copious blooms can harm ecosystems through eutrophication. In northern urban estuaries, seasonal fluctuations in water properties encourage phytoplankton growth, leading to seasonal blooms. The current experiment analyzed phytoplankton growth in the urban estuaries bordering the NY-NJ coasts and the San Francisco region. Remotely-sensed [Chl-a], FLH, and SST data was gathered, spanning the years 2002 to 2020, whereas remotely-sensed salinity data was available from 2015 to 2020. Data composite medians were the primary form of data analysis. Spring blooms, autumn blooms, and seasonal SST and salinity trends were expected. The study found that median [Chl-a] confirmed known blooms and confirmed hurricanes’ impacts on blooms, while FLH data raised questions regarding low measurements during months of known peak phytoplankton activity. Remote sensing limitations may have impacted data. The data indicated an overall decline in phytoplankton, but the relationships between FLH and [Chl-a] were weaker than expected (r2 = .184 for NY; r2 = .254 for SF). The greatest significant changes (p<.05) in Chl-a, FLH, and SST were only found in NY. Monthly SST values increased across the seasons in NY (0.04-2% per year) and SF (0.08-0.6% per year). High phytoplankton biomass was consistently found near coasts, highlighting the need for continued monitoring of NY bays and rivers and the SF Bay area.
References
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Abstract
The spin-lattice relaxation time in a rotating frame(T1rho) is a tool that is currently under investigation. T1rho can map macromolecular properties within a tissue, giving experts new diagnostic opportunities. Current mapping techniques are error-prone due to signal evolution during image data acquisition. The neural network (NN) overcomes these errors, improves robustness, and accomplishes greater accuracy.
AI with NN algorithms was applied in the acceleration of T1rho, running on the testing and training data to find the optimal parameter for T1rho; it was evaluated and measured in terms of Normalized Absolute Deviation (NAD).
The parameters differ for each trial; the NN ran multiple times to ensure accuracy. As the number of iterations increases, accuracy increases. Minimizing the number of iterations is pivotal because a large number of iterations can lead to overfitting, and increased run time. The parameters of the testing data between 10-12 iterations were similar and closely aligned with that of the training data. Hence, having ten iterations satisfies the demands to find the best parameter for accuracy ensuring minimal computational cost and exposure time of MRI radiation.
The data has shown that the parameter at the tenth iteration accelerates T1rho mapping without sacrificing accuracy. This project demonstrates the application of the NN on the brain, which may be possible in other parts of the body.
References