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Authors emphasize the challenges of manual tumor segmentation and the potential of deep learning models to enhance accuracy by automatically analyzing MRI scans.

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

Based on the success of deep learning, recent works have attempted to develop a waste classification model using deep neural networks. This work presents federated learning (FL) for a solution, as it allows participants to aid in training the model using their own data. Results showed that with less clients, having a higher participation ratio resulted in less accuracy degradation by the data heterogeneity.

In this study, the authors use bioinformatic approaches to characterize the mirror neurons, which are active when performing and seeing certain actions. They also investigated whether mirror neuron impairment was connected to neural degenerative diseases and psychiatric disorders.

The COVID-19 pandemic has caused disruption in social interactions. In this study, the authors tested if walking a dog will change human interactions and found that walking with a dog increased social interaction.

Here the authors investigate the effects of plastic pollutants on terrestrial life. Specifically they look at the growth of Brassica rapa and determine that phosphate levels have the most negative impact on growth.

This study investigated how fashion brand personalities are similar to people’s personalities and whether people may prefer a particular clothing brand based on their own personal traits. All together, Stevenson and Scott found that the Big Five Personality Factors are generally not related to participants’ preferred brand personalities. Generally, brands should consider different factors besides the Big Five Personality Factors for identifying potential customers.

In this study the author undertakes a careful characterization of a special type of chemical reaction, called an oscillating Belousov-Zhabotinsky (or B-Z) reaction, which has a number of existing applications in biomedical engineering as well as the potential to be useful in future developments in other fields of science and engineering. Specifically, she uses experimental measurements in combination with computational analysis to investigate whether the reaction is cohesive – that is, whether the oscillations between chemical states will remain consistent or change over time as the reaction progresses. Her results indicate that the reaction is not cohesive, providing an important foundation for the development of future technologies using B-Z reactions.

Microbial fuel cells (MFCs) are bio-electrochemical systems that utilize bacteria and are promising forms of alternative energy. Similar to chemical fuel cells, MFCs employ both an anode (accepts electrons) and a cathode (donates electrons), but in these devices the live bacteria donate the electrons necessary for current. In this study, the authors assess the functionality of a photosynthetic MFC that utilizes a purple non-sulfur bacterium. The MFC prototype they constructed was found to function over a range of environmental conditions, suggesting its potential use in industrial models.

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.