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Breaking the Ice: A Scientific Take on the Ice Melting Abilities of Household Salts

Sehgal et al. | Dec 04, 2017

Breaking the Ice: A Scientific Take on the Ice Melting Abilities of Household Salts

The use of salt to melt ice is a common and important practice to keep roadways safe during winter months. However, various subtypes of salt differ in their chemical and physical properties, as well as their environmental impact. In this study, the authors measure the effectiveness of different salts at disrupting ice structures and identify calcium chloride as the most effective.

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The Impact of Age on Post-Concussive Symptoms: A Comparative Study of Symptoms Related and Not Related to the Default Mode Network

Wurscher et al. | Mar 05, 2017

The Impact of Age on Post-Concussive Symptoms: A Comparative Study of Symptoms Related and Not Related to the Default Mode Network

The Default Mode Network (DMN) is a network of connected brain regions that are active when the brain is not focused on external tasks. Minor brain injuries, such as concussions, can affect this network and manifest symptoms. In this study, the authors examined correlations between DMN age and post-concussion symptoms in previously concussed individuals and healthy controls.

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Functional Network Connectivity: Possible Biomarker for Autism Spectrum Disorders (ASD)

Wang et al. | Feb 23, 2015

Functional Network Connectivity: Possible Biomarker for Autism Spectrum Disorders (ASD)

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder and is difficult to diagnose in young children. Here magnetoencephalography was used to compare the brain activity in patients with ASD to patients in a control group. The results show that patients with ASD have a high level of activity in different areas of the brain than those in the control group.

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An explainable model for content moderation

Cao et al. | Aug 16, 2023

An explainable model for content moderation

The authors looked at the ability of machine learning algorithms to interpret language given their increasing use in moderating content on social media. Using an explainable model they were able to achieve 81% accuracy in detecting fake vs. real news based on language of posts alone.

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