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An efficient approach to automated geometry diagram parsing

Date et al. | Oct 02, 2022

An efficient approach to automated geometry diagram parsing

Here, beginning from an initial interest in the possibility to use a computer to automatically solve a geometry diagram parser, the authors developed their own Fast Geometry Diagram Parser (FastGDP) that uses clustering and corner information. They compared their own methods to a more widely available, method, GeoSolver, finding their own to be an order of magnitude faster in most cases that they considered.

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Comparing the Dietary Preference of Caenorhabditis elegans for Bacterial Probiotics vs. Escherichia coli.

Lulla et al. | Dec 18, 2020

Comparing the Dietary Preference of <i>Caenorhabditis elegans</i> for Bacterial Probiotics vs. <i>Escherichia coli</i>.

In this experiment, the authors used C. elegans as a simple model organism to observe the impact of probiotics on the human digestive system. The results of the experiments showed that the C. elegans were, on average, most present in Chobani cultures over other tested yogurts. While not statistically significant, these results still demonstrated that C. elegans might prefer Chobani cultures over other probiotic yogurts, which may also indicate greater gut benefits from Chobani over the other yogurt brands tested.

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Time-Efficient and Low-Cost Neural Network to detect plant disease on leaves and reduce food loss and waste

Singh et al. | Apr 24, 2023

Time-Efficient and Low-Cost Neural Network to detect plant disease on leaves and reduce food loss and waste

About 25% of the food grown never reaches consumers due to spoilage, and 11.5 billion pounds of produce from gardens are wasted every year. Current solutions involve farmers manually looking for and treating diseased crops. These methods of tending crops are neither time-efficient nor feasible. I used a convolutional neural network to identify signs of plant disease on leaves for garden owners and farmers.

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