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The authors monitored water quality over time looking for influences from construction and deforestation.

This study strongly illustrates that optimizing 3D printing parameters would enhance a triboelectric nanogenerator's performance. The design improvements substantiate the potential of optimized, 3D-printed TENGs for developing scalable, cost-effective solutions for powering low-energy electronics, particularly in applications such as structural health monitoring.

This study found that anxiety induced by a suspenseful story increased communication between the brain’s salience, default mode, and central executive networks, with the central executive network acting as a bridge during peak tension. These findings suggest that anxiety alters large-scale brain connectivity patterns and may help inform future diagnostic tools and personalized treatments for anxiety disorders.

The authors access a panel of leaf traits across ten deciduous tree species to explore adaptive strategies between large and small trees.

The authors investigated the impact of light pollution on astrophotography.

The authors studied the ability of deep learning models to predict droughts in the midwestern United States.

The study explored converting Gracilaria seaweed waste—known for releasing toxic hydrogen sulfide when decomposed—into biochar as a sustainable solution for waste management and soil improvement.

The authors looked the ability of sound sensors to predict clogged pipes when the sound intensity data is run through a machine learning algorithm.

The authors studied the response of various construction materials to UV solar radiation and heat.

The authors looked at the impact of vitamin A (retinol) on growth of S. epidermidis (most abundant bacterium on the skin) and E. coli (found in the gut microbiome, but not on the skin).