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This study explored whether planaria, known for their regenerative abilities, can retain learned memories after regeneration and how stressors like alcohol affect memory.

The authors looked at public knowledge regarding suggested daily limits for sugar intake and then looked at how sugar levels vary in the same drink obtained from different sources and across different days.

The authors used Monte Carlo simulations to assess the impacts of various factors on neonatal seizure risk.

The authors developed and tested machine learning methods to diagnose tuberculosis from pulmonary X-ray scans.

The global issue of water quality has led to the use of machine learning models, like ANN and SVM, to predict water potability. However, these models can be complex and resource-intensive. This research aimed to find a simpler, more efficient model for water quality prediction.

Utilization of neural network to analyze Free Word Association to predict accurately age, gender, first language, and current country.

Brain-Computer Interface (BCI) allows users, especially those with paralysis, to control devices through brain activity. This study explored using a custom transformer model to decode neural signals into handwritten text for individuals with limited motor skills, comparing its performance to a traditional RNN-based BCI.

The authors compare rates of renewable energy adoption between states that historically vote for democrats versus republicans in presidential elections.

As atmospheric carbon dioxide (CO₂) levels rise, ocean acidification poses a growing threat to marine ecosystems. To better understand these changes, this study investigates how varying CO₂ levels influence the growth of brine shrimp. The findings offer important insights into the resilience of aquatic life and the broader implications of environmental change.

Arsenic contamination in rice, caused by the use of arsenic-laden groundwater for irrigation, is a growing global concern, affecting over 150 million people. To address this, researchers hypothesized that genetically modifying rice plants with arsenic-resistant genes could reduce arsenic uptake and allow the plants to detoxify arsenic, making them safer to consume.