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Vineyard vigilance: Harnessing deep learning for grapevine disease detection

Mandal et al. | Aug 21, 2024

Vineyard vigilance: Harnessing deep learning for grapevine disease detection

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.

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Analysis of electrodialysis as a method of producing potable water

Shen et al. | May 03, 2024

Analysis of electrodialysis as a method of producing potable water

Here, seeking a way to convert the vast quantity of seawater to drinking water, the authors investigated the purification of seawater to drinking water through electrodialysis. Using total dissolved solids (TDS) as their measure, they found that electrodialysis was able to produce deionized water with TDS values under the acceptable range for consumable water.

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Enhancing marine debris identification with convolutional neural networks

Wahlig et al. | Apr 03, 2024

Enhancing marine debris identification with convolutional neural networks
Image credit: The authors

Plastic pollution in the ocean is a major global concern. Remotely Operated Vehicles (ROVs) have promise for removing debris from the ocean, but more research is needed to achieve full effectiveness of the ROV technology. Wahlig and Gonzales tackle this issue by developing a deep learning model to distinguish trash from the environment in ROV images.

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Analyzing the effects of multiple adhesives on elastic collisions and energy loss in a Newton’s Cradle

Isham et al. | Feb 02, 2024

Analyzing the effects of multiple adhesives on elastic collisions and energy loss in a Newton’s Cradle

The energy conservation in a system of objects in collision depends on the elasticity of the objects and environmental factors such as air resistance. One system that relies heavily on elasticity is the Newton’s Cradle. We aimed to determine the extent to which these adhesives serve to mitigate or worsen the chaotic movements and elastic collisions.

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Applying centrality analysis on a protein interaction network to predict colorectal cancer driver genes

Saha et al. | Nov 18, 2023

Applying centrality analysis on a protein interaction network to predict colorectal cancer driver genes

In this article the authors created an interaction map of proteins involved in colorectal cancer to look for driver vs. non-driver genes. That is they wanted to see if they could determine what genes are more likely to drive the development and progression in colorectal cancer and which are present in altered states but not necessarily driving disease progression.

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Locating sources of a high energy cosmic ray extensive air shower using HiSPARC data

Aziz et al. | Oct 24, 2023

Locating sources of a high energy cosmic ray extensive air shower using HiSPARC data

Using the data provided by the University of Twente High School Project on Astrophysics Research with Cosmics (HiSPARC), an analysis of locations for possible high-energy cosmic ray air showers was conducted. An example includes an analysis conducted of the high-energy rain shower recorded in January 2014 and the use of Stellarium™ to discern its location.

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Fractal dimensions of crumpled paper

Zhou et al. | Aug 10, 2023

Fractal dimensions of crumpled paper
Image credit: Richard Dykes

Here, beginning from an interest in fractals, infinitely complex shapes. The authors investigated the fractal object that results from crumpling a sheet of paper. They determined its fractal dimension using continuous Chi-squared analysis, thereby testing and validating their model against the more conventional least squares analysis.

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