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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.

Droughts kill over 45,000 people yearly and affect the livelihoods of 55 million others worldwide, with climate change likely to worsen these effects. However, unlike other natural disasters (hurricanes, etc.), there is no early detection system that can predict droughts far enough in advance to be useful. Bora, Caulkins, and Joycutty tackle this issue by creating a drought prediction model.

The purpose of our study was to examine the correlation of glycosylated hemoglobin (HbA1c), blood pressure (BP) readings, and lipid levels with retinopathy. Our main hypothesis was that poor glycemic control, as evident by high HbA1c levels, high blood pressure, and abnormal lipid levels, causes an increased risk of retinopathy. We identified the top two features that were most important to the model as age and HbA1c. This indicates that older patients with poor glycemic control are more likely to show presence of retinopathy.

The authors test the effectiveness of machine learning to predict onset of cardiovascular disease.

The authors developed a quantum inspired model for stock market fluctuations.

Here the authors sought to use three machine learning models to predict poverty levels in Cambodia based on available household data. They found teat multilayer perceptron outperformed the other models, with an accuracy of 87 %. They suggest that data-driven approaches such as these could be used more effectively target and alleviate poverty.

In this study, the authors test whether providing a larger dataset of glass transition temperatures (T_g) to train the machine-learning platform Polymer Genome would improve its accuracy. Polymer Genome is a machine learning based data-driven informatics platform for polymer property prediction and T_g is one property needed to design new polymers in silico. They found that training the model with their larger, curated dataset improved the algorithm's T_g, providing valuable improvements to this useful platform.

The authors compare current machine learning algorithms with a new Explainable AI algorithm that produces a human-comprehensible decision tree alongside predictions.

Predicting antibody structures and antibody-antigen complexes using AlphaFold

The global issue of water quality has led to the use of machine learning models, like ANN and SVM, to predict water potability. However, these models can be complex and resource-intensive. This research aimed to find a simpler, more efficient model for water quality prediction.