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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 COVID-19 pandemic demonstrated the depth and significance of healthcare inequality in the United States. Xiao, Xiao, and Gong examine healthcare disparities in the Richmond (Virginia) metropolitan area by analyzing whether people from disadvantaged populations must travel for longer to reach healthcare facilities.

In this report, Bhardwaj and Sharma tested whether placing specific plants indoors can reduce levels of indoor air pollution that can lead to lung-related illnesses. Using machine learning, they show that plants improved overall indoor air quality and reduced levels of particulate matter. They suggest that plant-based interventions coupled with sensors may be a useful long-term solution to reducing and maintaining indoor air pollution.

In this study, the authors studied the ability for bacteria to develop antibiotic resistance over successive generations and modeled the trajectory to predict how antibiotic resistance is developed.

The authors demonstrate computationally that two types of nanoplastics can bind to estrogen receptor beta and potentially disrupt endocrine systems.

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

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.

The authors propose and test a method that would allow for the generation of a magnetic field on Mars sufficient to support future colonization.

We know relatively little about how vegan diets and non-vegan diets compare when it comes to the gut microbiome. Gollamudi and Gollamudi tackle this challenge by investigating how changes in a participant's diet affected the diversity of their intestinal microbiome.

In this study, a deep learning model is used to classify post-traumatic stress disorder patients through novel markers to assist in finding candidate biomarkers for the disorder.