Browse Articles

Globally, the cultivation of 77.8 million tons of grapes each year underscores their significance in both diets and agriculture. However, grapevines face mounting threats from diseases such as black rot, Esca, and leaf blight. Traditional detection methods often lag, leading to reduced yields and poor fruit quality. To address this, authors used machine learning, specifically deep learning with Convolutional Neural Networks (CNNs), to enhance disease detection.

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

Here, in an effort to identify alternatives to oil-based plastic, the authors sought to investigate the effects of plasticizers on the mechanical properties and chemical composition of gelatin bioplastic matrices. Through measurements of their tensile strength and elongation at break, along with FTIR spectroscopy, they identified 3% w/v polyethylene glycol film as having the best performance in their study..

The authors looked at survival of honey bees over the winter in regards to native and invasive plant availability. They found that native plants provided greater survivability and overall health compared to environments where there was an abundance of invasive plants.

The authors test the effects of common food industry preservatives on the heart rate of the freshwater crustacean Daphnia magna.

With the advance of technology, artificial intelligence (AI) is now applied widely in society. In the study of AI, machine learning (ML) is a subfield in which a machine learns to be better at performing certain tasks through experience. This work focuses on the convolutional neural network (CNN), a framework of ML, applied to an image classification task. Specifically, we analyzed the performance of the CNN as the type of neural activation function changes.

In this study, the authors investigate the effects of acetone on the color of copper chloride (CuCl₂) solution, which has important implications for detecting copper in the environment.

In this study the authors looked the trajectories of cosmic rays moving through a dipole field. They found that the trajectories of cosmic rays are determined by a particle's energy and interaction with Earth's B field.

A bottleneck in deleting algal blooms is that current data section is manual and is reactionary to an existing algal bloom. These authors made a custom-designed Seek and Destroy Algal Mitigation System (SDAMS) that detects harmful algal blooms at earlier time points with astonishing accuracy, and can instantaneously suppress the pre-bloom algal population.

The authors looked at how a 3D bioprinter could be used to model the blood brain barrier.