In this study, three models are used to test the hypothesis that data-centric artificial intelligence (AI) will improve the performance of machine learning.

In this study, the authors investigate just how effectively paper towels can absorb different types of liquid and whether changing the properties of the towel (such as folding it) affects absorbance. Using variables of either different liquid types or the folded state of the paper towels, they used thorough approaches to make some important and very useful conclusions about optimal ways to use paper towels. This has important implications as we as a society continue to use more and more paper towels.

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.

Studying other galaxies can help us understand the origins of the universe. Here, the authors study a type of galaxies known as Green Peas gaining insights that could help inform our understanding of Lyman alpha emitters, one of the first types of galaxies that existed in the early universe.

Intelligent vehicles utilize a combination of video-enabled object detection and radar data to traverse safely through surrounding environments. However, since the most momentary missteps in these systems can cause devastating collisions, the margin of error in the software for these systems is small. In this paper, we hypothesized that a novel object detection system that improves detection accuracy and speed of detection during adverse weather conditions would outperform industry alternatives in an average comparison.

While tea has a complex history, recently the health benefits of this beverage have come into focus. In this study, researchers sought to compare the levels of caffeine, catechins and L-theanine between different types of tea using NMR spectroscopy. Further, the impact of brewing temperature on the release of these compounds was also assessed. Of those tested, Bao Chong tea had the highest levels of these compounds. Brewing temperatures between 45ºC and 75ºC were found to be optimal for compound release. These results can help consumers make informed choices about their tea preparation and intake.

Minerals such as iron are essential for life, but too much of a good thing can be poisonous. Here the authors investigate the effect of iron concentrations on the growth of an aquatic plant and find that supplementing small amounts of iron can help, but adding too much can be bad for the plant. These results should help inform decisions on allowable iron concentrations in the environment, aquatic farming, and even home aquariums.

The authors trained a machine learning model to detect kidney stones based on characteristics of urine. This method would allow for detection of kidney stones prior to the onset of noticeable symptoms by the patient.

This study compares different methods for cooking vegetables to determine which retain iron and ascorbic acid, or vitamin C, levels the most.

There are believed to be ~20,000 nebulae in the Milky Way Galaxy. However, humans have only cataloged ~1,800 of them even though we have gathered 1.3 million nebula images. Classification of nebulae is important as it helps scientists understand the chemical composition of a nebula which in turn helps them understand the material of the original star. Our research on nebulae classification aims to make the process of classifying new nebulae faster and more accurate using a hybrid of deep learning and machine learning techniques.