Browse Articles

Changing electronic use behavior in adolescents while studying: An interventional psychology experiment

Kumar et al. | Mar 02, 2024

Changing electronic use behavior in adolescents while studying: An interventional psychology experiment
Image credit: RAMSHA ASAD

Here, the authors investigated the effects of an interventional psychology on the study habits of high school students specifically related to the use of electronic distractions such as social media or texting, listening to music, or watching TV. They reported varying degrees of success between the control and intervention groups, suggesting that the methods of habit-breaking for students merits further study.

Read More...

Influence of Infill Parameters on the Tensile Mechanical Properties of 3D Printed Parts

Guan et al. | Jul 17, 2020

Influence of Infill Parameters on the Tensile Mechanical Properties of 3D Printed Parts

Manufacturers that produce products using fused filament fabrication (FFF) 3D printing technologies have control of numerous build parameters. This includes the number of solid layers on the exterior of the product, the percentage of material filling the interior volume, and the many different types of infill patterns used to fill their interior.This study investigates the hypothesis that as the density of the part increases, the mechanical properties will improve at the expense of build time and the amount of material required.

Read More...

Vitamin C in Fruits: Does Organic Make a Difference?

Mulukutla et al. | Sep 21, 2015

Vitamin C in Fruits: Does Organic Make a Difference?

Vitamin C is an essential nutrient that is involved in many important cellular processes. Humans are unable to produce Vitamin C and thus must obtain it from exogenous sources such as citrus fruits, peppers, or flowering vegetables. In this study, the authors investigate whether or not organic and non-organic fruits have comparable vitamin C levels. This type of study has important implications for consumers.

Read More...

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.

Read More...

Search Articles

Search articles by title, author name, or tags

Clear all filters

Popular Tags

Browse by school level