Browse Articles

The authors use machine learning and electroencephalogram data to propose a method for improving epilepsy diagnosis.

Consumption of non-alcoholic beverages, like non-alcoholic beer, is growing in popularity in the United States. These beverages raise important societal questions, such as whether minors should be allowed to purchase or consume non-alcoholic beer. An and An investigate this issue by surveying adults to see if they support minors purchasing and consuming non-alcoholic beer.

The authors looked at how a student's own background influence their attitude towards integration of diverse cultures and ethnicities. While overall students viewed other groups positively, the authors found that groups still indicated they felt judged by their peers.

Here the authors investigated the relationship between fitness-related social media and the high usage of performance-enhancing drugs (PEDs) specifically by men in the US age 18-35. In a survey with 149 participants they identified that young men that use fitness-related social media are more likely to use PEDs. Their results suggest the necessity to consider potential risk behaviors which may be related to social media consumption.

The authors use pictures of candy wrappers and neural networks to improve nutritional accuracy of diet-tracking apps.

In this manuscript the authors looked at current vaccine strategies against different strains of influenza. Looking at several factors they found that influenza strain as well as vaccinated age group, among other factors, impact vaccine effectiveness.

In this article the authors look at sugar consumption and the relationship to productivity in the work/labor force.

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.

Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules.

Here, seeking to develop more efficient solar cells, the authors investigated photo-electrochemical (PEC) solar cells, specifically molybdenum diselenide (MoSe₂) based on its high resistance to corrosion. They found that the percentage efficiency of these PEC solar cells was proportional to light intensity^–0.9 and that performance was positively influenced by increasing the electrolyte volume. They suggest that studies such as these can lead to new insight into reaction-based solar cells.