Foraminifera are a diverse phylum of marine protists that produce elaborate shells. Because of their abundance and morphological diversity, foraminiferal fossil assemblages are used for biostratigraphy, to accurately date sedimentary rocks and to characterize past ocean environments. In this paper, authors collected fossils within the Morozaki Group in central Honshu, Japan, to assess past marine environments and species diversity.

These authors mathematically deduce a model that explains the interesting (and unintuitive) physical phenomenon that occurs when water falls.

In this study, the authors explore contrast-enhanced magnetic resonance imaging at Earth's field.

The authors look at using publicly available data and machine learning to see if they can develop a criminal activity index for counties within the state of California.

Advancement in DNA sequencing technology has greatly increased our understanding about the role of bacteria in soil. The authors of this study examine the microbial content of soil samples taken from three locations in southern New Hampshire with varying pH and plant composition.

COVID-19 has impacted the way many people go about their daily lives, but what are the main factors driving the changes in the housing market, particular house prices?

The authors looked at the dynamics of tennis serves from professional and amateur athletes.

With molecular energy being an integral element to the study of molecules and molecular interactions, computational methods to determine molecular energy are used for the preservation of time and resources. However, these computational methods have high demand for computer resources, limiting their widespread feasibility. The authors of this study employed machine learning to address this disadvantage, utilizing neural networks trained on different representations of molecules to predict molecular properties without the requirement of computationally-intensive processing. In their findings, the authors determined the Feedforward Neural Network, trained by two separate models, as capable of predicting molecular energy with limited prediction error.

The authors tested environmentally-friendly alternatives to wastewater treatment chemicals, including activated charcoal for filtration and citrus peels for preventing bacterial growth.

The authors found that SARIMAX-GARCH is more accurate than SARIMAX for load forecasting with respect to energy consumption.