Browse Articles

This study used machine learning models to examine which factors most influenced U.S. household energy consumption in 2020 using data from 18,496 households.

Paper found that heat waves in India are linked to lower household income, agricultural income, and consumption, with agriculture being affected the most. It also suggests farm workers may adapt to extreme heat over time by increasing labor inputs despite rising temperatures.

This study contributes to our understanding of how urban energy systems respond to climate variability and inform strategies for enhancing power grid resilience. The findings can help inform urban planners and infrastructure developers by identifying the factors that make regions within a power grid more vulnerable.

Iridescent materials reflect different colored depending on viewing angle. This specific effect can be achieved by biomimetic photonic materials. This project models the quantitative relationship between these material’s coloring and its nanostructure to facilitate personalized design of art materials.

The authors looked at which measurements from the NFL combine were the most predictive of success for defensemen in the NFL.

The authors looked at the correlation between C reactive protein levels and neurological deficits in patients who had suffered an ischemic stroke.

India’s Digital Public Infrastructure (DPI)—including the Unified Payments Interface (UPI) and Aadhaar—has been globally recognized for advancing financial inclusion and efficient governance. This study analyzes data from 2016–17 to 2023–24 the impact of these services on India's GDP.

The authors looked at unionization petitions from Starbucks workers between August 2021 and July 2024 to determine what factors influence votes for or against unionization.

The authors looked at the ability of different machine learning algorithms to predict the level of financial corruption in different countries.

The authors combine fine needle aspiration biopsy and machine learning algorithms to develop a breast cancer detection method suitable for resource-constrained regions that lack access to mammograms.