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Forecasting air quality index: A statistical machine learning and deep learning approach

Pasula et al. | Feb 17, 2025

Forecasting air quality index: A statistical machine learning and deep learning approach
Image credit: Amir Hosseini

Here the authors investigated air quality forecasting in India, comparing traditional time series models like SARIMA with deep learning models like LSTM. The research found that SARIMA models, which capture seasonal variations, outperform LSTM models in predicting Air Quality Index (AQI) levels across multiple Indian cities, supporting the hypothesis that simpler models can be more effective for this specific task.

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Nanotexturing as a method to reduce dust accumulation on solar panels

Choi et al. | Jan 30, 2025

Nanotexturing as a method to reduce dust accumulation on solar panels

Dust accumulation on solar panels can reduce electricity output by 20–50%, posing a major challenge for solar energy collection. Instead of altering panel design, we explored a simpler approach by modifying surface energy through nanotexturing, predicting that hydrophobic surfaces would repel both water and dust. This study found that treating glass and silicone surfaces with potassium hydroxide (KOH) for 13 and 10 minutes, respectively, created optimal nanotextures (445 nm for glass, 205 nm for silicone), significantly reducing dirt accumulation and improving solar energy capture.

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The impact of attending a more selective college on future income

Ho et al. | Oct 16, 2024

The impact of attending a more selective college on future income

Debates around legacy preferences, recruited athletes, and affirmative action in U.S. college admissions often focus on the belief that graduating from a more selective institution leads to higher future earnings. The study hypothesized a positive correlation between college selectivity and future income due to enhanced resources and opportunities.

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