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The purpose of the study was to determine whether graph-based machine learning techniques, which have increased prevalence in the last few years, can accurately classify data into one of many clusters, while requiring less labeled training data and parameter tuning as opposed to traditional machine learning algorithms. The results determined that the accuracy of graph-based and traditional classification algorithms depends directly upon the number of features of each dataset, the number of classes in each dataset, and the amount of labeled training data used.

Gamma radiation can be produced by both natural and man-made sources and abnormally high exposure levels could lead to an increase in cell damage. In this study, gamma radiation was measured at different locations and any correlation with various geographic factors, such as distance from a city center, elevation and proximity to the nearest nuclear reactor, was determined.

Here the authors investigated the effectiveness of algal pretreatment in reducing nitrate toxicity and extending the survival of fish in contaminated river water. Their findings demonstrate that treating nitrate-polluted water with algae extended fish survival by up to 50%, suggesting that algae could serve as a valuable tool for mitigating the destructive impacts of agricultural and industrial runoff on aquatic ecosystems.

In this study, the authors created a survey to assess misconceptions and knowledge deficits regarding cardiovascular diseases exist among US adults and minors.

In this study, the authors tested different approaches for removing arsenic from rice. Due to higher arsenic levels in water, some areas grow rice with higher levels as well. This is a health hazard and so developing methods to remove arsenic from the rice will be helpful to many. Using a rapid arsenic kit, the authors found that activated charcoal was the most effective at removing arsenic from rice.