Browse Articles

Investigating the green valley hypotheses of galaxy evolution

Droughts have a wide range of effects, from ecosystems failing and crops dying, to increased illness and decreased water quality. Drought prediction is important because it can help communities, businesses, and governments plan and prepare for these detrimental effects. This study predicts drought conditions by using predictable weather patterns in machine learning models.

There are two types of competing TV screens on the market, organic light emitting diode (OLED) and liquid crystal display (LCD). The better capability to exhibit black results in higher contrast images. Here, authors compared the ability of the two types of screens to show black in an environment eliminating external light.

This article investigates the study methodologies, learning strategies, and motives of spelling bee participants. The authors identify several important educational implications of this work.

The authors test the effectiveness of machine learning to predict onset of cardiovascular disease.

The authors analyzed the heat transfer of different containers in the microwave aiming to identify the most optimal material of container to reduce heating time.

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.

The authors explore how diversity in data sets contributes to bias in artificial intelligence.

In this study, the impact of shutting down schools on the emotional aspects of high school students was analyzed using survey responses.

Diabetes is a growing health concern in the developing world. This study aimed to develop a questionnaire that uses factors including age, blood pressure, BMI, and family history to predict whether Filipino participants are at risk for diabetes.