Browse Articles

Investigation of the potential of waxworm saliva, the secretion of Galleria mellonella, for plastic degradation.

This study investigates the effects of the PROTAC compound A1874 on CT26 colon carcinoma cells, focusing on its ability to degrade the protein BRD4 and reduce cell viability. While A1874 had previously shown effectiveness in other colon cancer cell lines, its impact on CT26 cells was unknown.

Improving the classifier of the difficulty level of Sudoku.

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.

Here the authors investigated the use of an affordable gas sensor to detect volatile organic compound (VOC) emissions as an early indicator of tree disease, finding statistically significant differences in VOCs between diseased and non-diseased ash, beech, and maple trees. They suggest this sensor has potential for widespread early disease detection, but call for further research with larger sample sizes and diverse locations.

he rapid growth of the human population is driving food crises in Thailand and Southeast Asia, while contributing to global food insecurity and a larger carbon footprint. One potential solution is cultivating duckweed (Wolffia globosa) for consumption, as it grows quickly and can provide an alternative protein source. This research explored two methods to optimize duckweed cultivation: using phosphorus- and nitrogen-rich growing media and plant growth-promoting bacteria (PGPB).

The authors looked at potential downstream effects of raising the minimum wage. Specifically they focused on taxable wages, employment, and firm counts.

The authors use machine learning to analyze electroencephalogram data and identify slowing patterns that can indicate undetected disorders like epilepsy or dementia

The authors develop a method for detecting fake AI-generated images from real images.

Additive manufacturing (AM) is transforming the production of complex metal parts, but challenges like internal cracking can arise, particularly in critical sectors such as aerospace and automotive. Traditional methods to assess cracking susceptibility are costly and time-consuming, prompting the use of machine learning (ML) for more efficient predictions. This study developed a multi-model ML pipeline that predicts solidification cracking susceptibility (SCS) more accurately by considering secondary alloy properties alongside composition, with Random Forest models showing the best performance, highlighting a promising direction for future research into SCS quantification.