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Physical activity when performed regularly has beneficial effects on all systems of the body, including pulmonary functions. This study, conducted at Springdales School in Dhaula Kuan, New Delhi, aimed to determine the effect of sports and singing on the vital capacity (the maximum amount of air a person can expel from the lungs after a maximum inhalation), an important measure of pulmonary health.

This study uses neuroimaging to investigate cognitive set-shifting, a type of executive function that involves shifting from one task to another. This study tested whether cortical gray-white matter contrast in subregions of the prefrontal cortex (PFC) was associated with set-shifting abilities in adults.

The authors looked at the feasibility to predict wind speeds that will have less reliance on using historical data.

In this study, the authors investigate whether microplastics affect terrestrial plant growth and soil quality.

In this study, the authors determine optimal pH levels for maximizing isopropanol degradation in water. This has important applications for cleaning up polluted wastewater in the environment.

The authors looked at genes and pathways that are enriched in glioblastoma multiforme.

The authors found that SARIMAX-GARCH is more accurate than SARIMAX for load forecasting with respect to energy consumption.

The authors set out to develop an electrochemical device that would have efficient and sustained carbon dioxide capture.

Microbial fuel cells (MFCs) are bio-electrochemical systems that utilize bacteria and are promising forms of alternative energy. Similar to chemical fuel cells, MFCs employ both an anode (accepts electrons) and a cathode (donates electrons), but in these devices the live bacteria donate the electrons necessary for current. In this study, the authors assess the functionality of a photosynthetic MFC that utilizes a purple non-sulfur bacterium. The MFC prototype they constructed was found to function over a range of environmental conditions, suggesting its potential use in industrial models.

This study examined behavioral and psychological predictors of burnout among high school and university students in Pakistan using survey data and machine-learning models. Shorter sleep and greater mental fatigue—especially fatigue—were associated with higher burnout risk, while a Random Forest model successfully identified students at risk of burnout.