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The authors looked at how different blends of synthetic and non-synthetic fabrics protected against UV radiation.

This study investigates the migration intentions of Syrian high school and university students amid ongoing conflict and economic instability. Drawing on survey data, the research examines how academic stage influences migration motivations, preferred destinations, and return intentions. The findings reveal a widespread desire to emigrate, driven by educational, economic, and security concerns, highlighting significant implications for Syria’s future workforce and post-conflict recovery.

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

Here the authors examined the relationship between education levels and the gender wage gap (GWG) in the US and Puerto Rico from 2010 to 2022, hypothesizing that higher education would correlate with a lower GWG. Their analysis of income data revealed an inverse correlation, where higher education levels were associated with reduced gender wage disparities, suggesting that policies aimed at closing the gender gap in higher education could promote socioeconomic equality.

Here, recognizing that brain aneurysms pose a severe threat, often misdiagnosed and leading to high mortality, particularly in younger individuals, the authors explored a novel computer-aided engineering approach. They used magnetic resonance angiography images and computational fluid dynamics, to improve aneurysm detection and risk assessment, aiming for more personalized treatment.

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

Skin cancer is a common and potentially deadly form of cancer. This study’s purpose was to develop an automated approach for early detection for skin cancer. We hypothesized that convolutional neural network-based models using transfer learning could accurately differentiate between benign and malignant moles using natural images of human skin.

Semantic segmentation - labelling each pixel in an image to a specific class- models require large amounts of manually labeled and collected data to train.