Plant diseases can cause up to 50% crop yield loss for the popular tomato plant. A mobile device-based method to identify diseases from photos of symptomatic leaves via computer vision can be more effective due to its convenience and accessibility. To enable a practical mobile solution, a “shallow” convolutional neural networks (CNNs) with few layers, and thus low computational requirement but with high accuracy similar to the deep CNNs is needed. In this work, we explored if such a model was possible.

This study focused on finding more sustainable growing methods that reduce chemical fertilizer or water usage and can be used at the household level for garden plants. Metrics for healthy plant growth were height at first bloom, growing time, and survival rate. The Deep Water Culture (DWC) treatment for garden cress plants significantly increased the height at first bloom compared to the control group. For rates of surviving plants, the treatments had little effect on garden cress, but the Eggshell Grounds, Wick System, and DWC system groups outperformed the control group for marigolds.

Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.

In this study, the authors tested different approaches for removing arsenic from rice. Due to higher arsenic levels in water, some areas grow rice with higher levels as well. This is a health hazard and so developing methods to remove arsenic from the rice will be helpful to many. Using a rapid arsenic kit, the authors found that activated charcoal was the most effective at removing arsenic from rice.

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

Large corporations often are known for their financial power, but what social and environmental power and conscious do they have? The more responsible corporations are in relation to environmental, social, and governance criteria the better they do fiscally.

In this study, the authors created a survey to assess misconceptions and knowledge deficits regarding cardiovascular diseases exist among US adults and minors.

Playing video games may improve mental performance by encouraging practicing logical reasoning skills. Students who played video games in between two tests tended to perform better on the second test than those that did not play video games.

In this study the authors looked at sustainable ways to clean up oil spills that harm marine life. Using water spangle leaves and milk week the authors looked at the ability to recovery oil from both fresh and salt water and the ability to reuse the organic material to clean up spills. Their results show promise to help find a sustainable, eco-friendly way to clean up oil spills and protect marine life and habitats.

This study assesses the capacity for milkweed species, an important host plant for Monarch butterflies, to grow in desert environments with different water levels.