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Here, recognizing the difficulty associated with tracking the progression of dementia, the authors used machine learning models to predict between the presence of cognitive normalcy, mild cognitive impairment, and Alzheimer's Disease, based on blood DNA methylation levels, sex, and age. With four machine learning models and two dataset dimensionality reduction methods they achieved an accuracy of 53.33%.

Several studies have applied different machine learning (ML) techniques to the area of forecasting solar photovoltaic power production. Most of these studies use weather data as inputs to predict power production; however, there are numerous practical issues with the procurement of this data. This study proposes models that do not use weather data as inputs, but rather use past power production data as a more practical substitute to weather-based models. Our proposed models demonstrate a better, cheaper, and more reliable alternatives to current weather models.

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.

In this study, the impact of thermal effects on the orbit of an asteroid is investigated. This included determining if the asteroid's orbit would push into a region devoid of asteroids due to the gravitational pull of Jupiter.

Using a convolution neural network, these authors show machine learning can clinically diagnose breast cancer with high accuracy.

In this work, the authors investigate the accuracy with which two different population growth models can predict population growth over time. They apply the Malthusian law or Logistic law to US population from 1951 until 2019. To assess how closely the growth model fits actual population data, a least-squared curve fit was applied and revealed that the Logistic law of population growth resulted in smaller sum of squared residuals. These findings are important for ensuring optimal population growth models are implemented to data as population forecasting affects a country's economic and social structure.

In this article, the authors identify the characteristics that make a book a best-seller. Knowing what, besides content, predicts the success of a book can help publishers maximize the success of their print products.

The authors looked at using genetic algorithms to look at the Indian labor market and what features might best explain any variation seen. They found that features such as economic growth and household consumption, among others, best explained variation.

Here, seeking to identify a method to locally produce and capture renewable energy in Hawai'i and other island communities, the authors built and tested a small-scale model wave energy converter. They tested various configurations of a floated magnet surrounded by a wire coal, where the motion of the magnet due to a wave results in induction current in the coil. While they identified methods to increase the voltage and current generated, they also found that corrosion results in significant deterioration.

In this study, a deep learning model is used to classify post-traumatic stress disorder patients through novel markers to assist in finding candidate biomarkers for the disorder.