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The authors looked at ways to provide better forecasting on website traffic. They found that deep learning models performed better than statistical models.

Researching gun violence and mass shootings in the U.S. is difficult due to the lack of consistent data collection. Some studies have linked mass shootings to personal financial stress, but little formal research exists on the impact of broader economic conditions. This study hypothesized an inverse relationship between mass shootings and economic performance, using the S&P 500 and unemployment rate as indicators.

The authors computationally screened potential ALDH1 inhibitors, for use as potential cancer therapeutics.

The authors used machine learning to predict the presence of anxiety and burnout in students based on facial expressions and demographic information.

The authors looked at the impact of different harvest and feeding treatments on Heterandria formosa over three generations as a model for changes in marine ecosystems.

The authors looked at the impacts of short-chain fatty acids on muscle fiber formation as well as insulin sensitivity using a model of mouse myoblasts.

Here the authors aimed to find the best preservative combinations to stop bacterial growth in food, using data modeling and biochemical experiments. They discovered that single preservatives are often not enough, with varying effectiveness against different bacteria, and suggest future research into combining preservatives for better results.

Using machine learning to predict the risk of algae bloom

The authors looked factors that could lead to earlier diagnosis of cardiovascular disease thereby improving patient outcomes. They found that advances in imaging and electrocardiography contribute to earlier detection of cardiovascular disease.

The escalating crisis of climate change, driven by the accumulation of greenhouse gases from human activities, demands urgent and innovative solutions to curb rising global temperatures. Plasma-based methane (CH₄) and carbon dioxide (CO₂) reforming offers a promising pathway for carbon capture and the sustainable production of hydrogen fuel and syngas components. To advance this technology, particularly in terms of energy efficiency and selectivity, it is essential to enhance the conversion efficiencies of CO₂ and CH₄.