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The water we use must be treated and cleaned before we release it back into the environment. Here, the authors investigate two new techniques for purifying dissolved impurities from waste water. Their findings may give rise to more cheaper and more efficient water treatment and help keep the planet greener.

In this study the authors sought to understand the influence of implicit social pressure on prosocial behavior (voluntary acts that benefit someone else) in adolescents age 12–19. They considered how participants divided money between themselves and four teammate they did a school project with. Despite being shown examples of how to divide money (social pressure), their results showed that both boys and girls were unaffected by prosocial, neutral, and antisocial pressures.

The authors develop a machine learning method to reduce misclassification of objects in safety-critical applications such as medical diagnosis.

We systematically evaluated the effects of raw material composition, heat treatment, and mechanical properties on 13-8PH stainless steel alloy. The results of the neural network models were in agreement with experimental results and aided in the evaluation of the effects of aging temperature on double shear strength. The data suggests that this model can be used to determine the appropriate 13-8PH alloy aging temperature needed to achieve the desired mechanical properties, eliminating the need for many costly trials and errors through re-heat treatments.

Neural networks are used throughout modern society to solve many problems commonly thought of as impossible for computers. Fountain and Rasmus designed a convolutional neural network and ran it with varying levels of training to see if consistent, accurate, and precise changes or patterns could be observed. They found that training introduced and strengthened patterns in the weights and visualizations, the patterns observed may not be consistent between all neural networks.

Recent advances in generative AI have made it increasingly hard to distinguish real images from AI-generated ones. Traditional detection models using CNNs or U-net architectures lack precision because they overlook key spatial and frequency domain details. This study introduced a hybrid model combining Convolutional Neural Networks (CNN) with Fast Fourier Transform (FFT) to better capture subtle edge and texture patterns.

Domestic waste classification using convolutional neural network

Robot-assisted minimally invasive surgery (RMIS) benefits from increased precision and faster recovery, with force feedback from the surgical tool being critical for control. Researchers tested the use of neural networks for detecting the vanishing point of the tool, a key element for force feedback.

The authors test various machine learning models to improve the accuracy and efficiency of pneumonia diagnosis from X-ray images.

The authors test different machine learning algorithms to remove background noise from audio to help people with hearing loss differentiate between important sounds and distracting noise.