Browse Articles

Comparing Measurements of Sun-Earth Distance: Shadow Method and Two Pinhole Method Variations

Rajakumar et al. | Feb 21, 2022

Comparing Measurements of Sun-Earth Distance: Shadow Method and Two Pinhole Method Variations

This study compares three methods regarding their accuracy in calculating the distance between the Earth and the Sun. The hypothesis presented was that the shadow method would have the greatest mean accuracy, followed by the tube pinhole method, and finally the plate pinhole method. The results validate the hypothesis; however, further investigation would be helpful in determining effective mitigation of each method’s limitations and the effectiveness of each method in determining the distance of other light-emitting objects distant from the Earth.

Read More...

Variations in Heat Absorption and Release of Earth Surfaces During Fall in Laramie, Wyoming

Ramesh et al. | Sep 08, 2020

Variations in Heat Absorption and Release of Earth Surfaces During Fall in Laramie, Wyoming

Here the authors investigate the contributions of man-made surfaces in Laramie, Wyoming to the Urban Heat Island (UHI) effect. Heat absorption and release by five surfaces were measured in the autumn of 2018. By recording temperatures of man-made and natural surfaces at early morning, mid-afternoon, and evening using an infrared thermometer, the authors determined that man-made surfaces retained more heat in fall than natural surfaces.

Read More...

Using data science along with machine learning to determine the ARIMA model’s ability to adjust to irregularities in the dataset

Choudhary et al. | Jul 26, 2021

Using data science along with machine learning to determine the ARIMA model’s ability to adjust to irregularities in the dataset

Auto-Regressive Integrated Moving Average (ARIMA) models are known for their influence and application on time series data. This statistical analysis model uses time series data to depict future trends or values: a key contributor to crime mapping algorithms. However, the models may not function to their true potential when analyzing data with many different patterns. In order to determine the potential of ARIMA models, our research will test the model on irregularities in the data. Our team hypothesizes that the ARIMA model will be able to adapt to the different irregularities in the data that do not correspond to a certain trend or pattern. Using crime theft data and an ARIMA model, we determined the results of the ARIMA model’s forecast and how the accuracy differed on different days with irregularities in crime.

Read More...

The journey to Proxima Centauri b

Ramaswamy et al. | Apr 01, 2024

The journey to Proxima Centauri b
Image credit: The authors

Someday, rockets from Earth may be launched towards worlds beyond our solar system. But will these rockets be able to reach their destination within a human lifetime? Ramaswamy and Giovinazzi simulate rocket launches to an Earth-like exoplanet to uncover whether it's physically possible to complete the journey within a lifetime.

Read More...

Utilizing meteorological data and machine learning to predict and reduce the spread of California wildfires

Bilwar et al. | Jan 15, 2024

Utilizing meteorological data and machine learning to predict and reduce the spread of California wildfires
Image credit: Pixabay

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...

Temperature and Precipitation Responses to a Stratospheric Aerosol Geoengineering Experiment Using the Community Climate System Model 4

Anderson et al. | Aug 19, 2014

Temperature and Precipitation Responses to a Stratospheric Aerosol Geoengineering Experiment Using the Community Climate System Model 4

We are changing our environment with steadily increasing carbon dioxide emissions, but we might be able to help. The authors here use a computer program called Community Climate System Model 4 to predict the effects of spraying small particles into the atmosphere to reflect away some of the sun's rays. The software predicts that this could reduce the amount of energy the Earth's atmosphere absorbs and may limit but will not completely counteract our carbon dioxide production.

Read More...

Search Articles

Search articles by title, author name, or tags

Clear all filters

Popular Tags

Browse by school level