In this study, the authors study features of exoplanet 189733 b. This exoplanet, or planets that orbit stars other than the Sun, is found in the HD star system. Using a DSLR camera, they constructed a high caliber exoplanet transit detection tracker to study the orbital periods, radial velocity, and photometry of 189733 b. They then compared results from their system to data collected by other high precision studies. What they found was that their system produced results supporting previously published studies. These results are exciting results from the solar system demonstrating the importance of validating radial velocity and photometry data using high-precision studies.

In this study, the authors investigate whether the addition of microbes from native soil enhanced the seed germination and growth of mung beans, pumpkins, and pea flower plants.

In this experiment, the authors modify the heat equation to account for imperfect insulation during heat transfer and compare it to experimental data to determine which is more accurate.

Music can affect the behavior of humans and other animals. In this study, the authors studied five types of music with different tempos and demonstrated how each one affected dogs' heart rates.

In this study, the authors test different infill patterns to determine which would be the strongest and most durable for 3D printing applications, which have become an integral part of many facets of life.

In this study, the biodiversity of marine animals was studied at different locations along a mangrove, which is a salt-tolerant shrub with elaborate root structures that is found on tropical coastlines.

In this study, the authors developed a model named DNA Sequence Embedding Network (DNA-SEnet) to classify DNA-asthma associations using their genomic patterns.

Every year, around 40% of undergraduate students in the United States discontinue their studies, resulting in a loss of valuable education for students and a loss of money for colleges. Even so, colleges across the nation struggle to discover the underlying causes of these high dropout rates. In this paper, the authors discuss the use of machine learning to find correlations between the built environment factors and the retention rates of colleges. They hypothesized that one way for colleges to improve their retention rates could be to improve the physical characteristics of their campus to be more pleasing. The authors used image classification techniques to look at images of colleges and correlate certain features like colors, cars, and people to higher or lower retention rates. With three possible options of high, medium, and low retention rates, the probability that their models reached the right conclusion if they simply chose randomly was 33%. After finding that this 33%, or 0.33 mark, always fell outside of the 99% confidence intervals built around their models’ accuracies, the authors concluded that their machine learning techniques can be used to find correlations between certain environmental factors and retention rates.

The authors investigate the ability of machine learning models to developing new drug-like molecules by learning desired chemical properties versus simply generating molecules that similar to those in the training set.

Oligodendrocytes are specialized brain cells that can change to cells that produce myelin and protect nerves. This study investigates the capacity for different extracellular matrix cues to induce this effect in culture.