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The authors looked at the development of biodegradable bioplastic and its features compared to PET packaging films. They were able to develop a biodegradable plastic with sodium alginate that dissolved in water and degrade in microbial conditions while also being transparent and flexible similar to current plastic films.

It's time-consuming to complete the calculations that are used to study nuclear reactions and energy. To uncover which computational chemistry tools are useful for this challenge, Pan, Vaiyakarnam, Li, and McMahan investigated whether the Python-based Simulations of Chemistry Framework’s Hartree-Fock (PySCF) method is an efficient and accurate way to assess alkane molecules.

Machine learning offers a powerful way to analyze large astronomical datasets and identify stars with unusual properties. In this study, students used Extreme Gradient Boosting and Isolation Forest models to investigate why some red clump stars deviate from their expected luminosities. Their results show that stellar age plays a much stronger role than metallicity in driving luminosity variation, and the models successfully detected outlier stars that may affect distance measurements in galactic studies.

This study used single-cell RNA sequencing to examine whether B cells can predict response to pembrolizumab and radiation therapy in patients with triple-negative breast cancer.

The authors looked at the ability to detect heart disease before the onset of severe clinical symptoms.

The authors used machine learning to predict the presence of anxiety and burnout in students based on facial expressions and demographic information.

The authors looked at the ability of different deep learning models to predict the presence of lung cancer from chest CT scans. They found that a pre-trained CNN model performed better than an autoencoder model.

This study explores how to predict and minimize distortion in 3D printed parts, particularly when using affordable PLA filament. The researchers developed a model using a gradient boosting regressor trained on 3D printing data, aiming to predict the necessary CAD dimensions to counteract print distortion.

Autism spectrum disorder (ASD) is hard to correctly diagnose due to the very subjective nature of diagnosing it: behavior analysis. Due to this issue, we sought to find a machine learning-based method that diagnoses ASD without behavior analysis or helps reduce misdiagnosis.

The authors look at using publicly available data and machine learning to see if they can develop a criminal activity index for counties within the state of California.