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What Can You See in the Dark? The Effects of Contrast, Light, and Age on Contrast Sensitivity in Low Light

Virostek et al. | Apr 25, 2014

What Can You See in the Dark? The Effects of Contrast, Light, and Age on Contrast Sensitivity in Low Light

Many of us take our vision for granted, but rarely do we measure how well we can see. In this study, the authors investigate the ability of people of different ages to read progressively fainter letters in dark light. They find that the ability to see in dim light drops drastically after age 30. The ability to read fainter letters worsens after age 30 as well. These findings should help inform lighting decisions everywhere from restaurants to road signs.

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Polluted water tested from the Potomac River affects invasive species plant growth

Chao et al. | Sep 20, 2023

Polluted water tested from the Potomac River affects invasive species plant growth
Image credit: Alex Korolkoff

Here recognizing the potential for pollution to impact the ecosystems of local waterways, the authors investigated the growth of tiger lilies, which are invasive to the Potomac River, in relation to the level of pollution. The authors report that increasing levels of pollution led to increased growth of the invasive species based on their study.

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Maximizing anaerobic biogas production using temperature variance

Verma et al. | Aug 03, 2023

Maximizing anaerobic biogas production using temperature variance

We conducted this research as our start-up's research that addresses the problem of biogas production in cow-dense regions like India. We hypothesized that the thermophilic temperature (45-60oC) would increase biogas production. The production process is much faster and more abundant at temperatures around 55-60oC.

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How Ya Doin'? with COVID-19

Kung et al. | Dec 02, 2021

How Ya Doin'? with COVID-19

In this study, the authors survey students and adults about how the COVID-19 pandemic has impacted their sleep patterns, eating habits, mood, physical activity, and screen time.

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Similarity Graph-Based Semi-supervised Methods for Multiclass Data Classification

Balaji et al. | Sep 11, 2021

Similarity Graph-Based Semi-supervised Methods for Multiclass Data Classification

The purpose of the study was to determine whether graph-based machine learning techniques, which have increased prevalence in the last few years, can accurately classify data into one of many clusters, while requiring less labeled training data and parameter tuning as opposed to traditional machine learning algorithms. The results determined that the accuracy of graph-based and traditional classification algorithms depends directly upon the number of features of each dataset, the number of classes in each dataset, and the amount of labeled training data used.

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