In this study, the authors conduct a survey to evaluate the impact of household socioeconomic status on effectiveness of distance learning for students.

The authors develop a new method for training machine learning algorithms to differentiate between hate speech and cultural speech in online platforms.

Alzheimer’s disease (AD) is a type of dementia that affects more than 5.5 million Americans, and there are no approved treatments that can delay the advancement of the disease. In this work, Xu and Mitchell test the effects of various herbal extracts (bugleweed, hops, sassafras, and white camphor) on Aβ_1-40 peptide levels in human neuroblastoma cells. Their results suggest that bugleweed may have the potential to reduce Aβ_1-40 levels through its anti-inflammatory properties.

Several studies have applied different machine learning (ML) techniques to the area of forecasting solar photovoltaic power production. Most of these studies use weather data as inputs to predict power production; however, there are numerous practical issues with the procurement of this data. This study proposes models that do not use weather data as inputs, but rather use past power production data as a more practical substitute to weather-based models. Our proposed models demonstrate a better, cheaper, and more reliable alternatives to current weather models.

Alcohol is known to cause various developmental diseases including Fetal Alcohol Syndrome. Here the authors investigate the effect of ethanol on the development of zebrafish beta cells, the part of the pancreas associated with Type 1 Diabetes. They find that exposure to ethanol does adversely affect beta-cell development, suggesting that alcohol ingestion during pregnancy may be linked to diabetes in newborns.

This study utilizes machine learning models to predict missing and unclear signs from the Indus script, a writing system from an ancient civilization in the Indian subcontinent.

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.

Using a convolution neural network, these authors show machine learning can clinically diagnose breast cancer with high accuracy.

Naturally occurring neuroactive alkaloids are often studied for their potential to treat Neurological diseases. This team of students study Rivastigmine, a potent cholinesterase inhibitor that is a synthetic analog of physostigmine, which comes from the Calabar bean plant Physostigma venenosum. By comparing the effects of optimized synthetic analogs to the naturally occurring alkaloid, they determine the most favorable analog for inhibition of acetylcholinesterase (AChE), the enzyme that breaks down the neurotransmitter acetylcholine (ACh) to terminate neuronal transmission and signaling between synapses.

In the battle against Alzheimer's disease, early detection is critical to mitigating symptoms in patients. Here, the authors use a collection of MRI scans, layering with deep learning computer modeling, to investigate early stages of AD which can be hard to catch by human eye. Their model is successful, able to outperform previous models, and detected regions of interest in the brain for further consideration.