Browse Articles

A Study on the Coagulating Properties of the M. oleifera Seed

Lakshmanan et al. | Feb 14, 2020

A Study on the Coagulating Properties of the <em>M. oleifera</em> Seed

In this study, the authors investigate whether Moringa Oleifera seeds can serve as material to aid in purifying water. M. oleifera seeds have coagulating properties and the authors hypothesized that including it in a water filtration system would reduce particles, specifically bacteria, in water. Their results show that this system removed the largest percent of bacteria. When used in combination with cilantro, it was actually more efficient than the other techniques! These findings have important implications for creating better and more economical water purification systems.

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Using satellite surface temperature data to monitor urban heat island

Meister et al. | Feb 13, 2026

Using satellite surface temperature data to monitor urban heat island
Image credit: Meister, Horvath, and Brown de Colstoun

This manuscript investigates the urban heat island (UHI) effect by utilizing two satellite datasets: Landsat (high spatial resolution, lower temporal resolution) and MODIS (lower spatial resolution, high temporal resolution). The authors hypothesized that Landsat would provide better spatial detail, while MODIS would better capture temporal variations. Their analysis in the Washington D.C.–Baltimore region supports these hypotheses, demonstrating that Landsat offers finer spatial details, whereas MODIS provides more consistent seasonal patterns and better detects heatwave frequencies.

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AeroPurify: Autonomous air filtration UAV using real-time 3-D Monte Carlo gradient search

Kadakia et al. | Sep 01, 2025

AeroPurify: Autonomous air filtration UAV using real-time 3-D Monte Carlo gradient search
Image credit: Ian Usher

Here the authors present an autonomous drone air filtration system that uses a novel algorithm, the gradient ascent ML particle filter (GA/MLPF), to efficiently locate and mitigate outdoor air pollution. They demonstrate that their GA/MLPF algorithm is significantly more efficient than the conventional gradient ascent algorithm, reducing both the time and number of waypoints needed to find the source of pollution.

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