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This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.

Alzheimer's disease (AD) involves the reduction of cholinergic activity due to a decrease in neuronal levels of nAChR α7. In this work, Sanyal and Cuellar-Ortiz explore the role of the nAChR α7 in learning and memory retention, using Drosophila melanogaster as a model organism. The performance of mutant flies (PΔEY6) was analyzed in locomotive and olfactory-memory retention tests in comparison to wild type (WT) flies and an Alzheimer's disease model Arc-42 (Aβ-42). Their results suggest that the lack of the D. melanogaster-nAChR causes learning, memory, and locomotion impairments, similar to those observed in Alzheimer's models Arc-42.

This study investigates how the hyperparameters epochs and batch size affect the classification accuracy of a convolutional neural network (CNN) trained on pulsar candidate data. Our results reveal that accuracy improves with increasing number of epochs and smaller batch sizes, suggesting that with optimized hyperparameters, high accuracy may be achievable with minimal training. These findings offer insights that could help create more efficient machine learning classification models for pulsar signal detection, with the potential of accelerating pulsar discovery and advancing astrophysical research.

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

In this study the authors looked at the ability to navigate planes in space between randomly placed planets. They used machine and reinforcement learning to run simulations and found that they were able to identify optimal paths for travel. In the future these techniques may allow for safer travel in unknown spaces.

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.

The authors looked at different ways to build computational resources that would analyze shooting form for basketball players.

The authors developed and tested machine learning methods to diagnose tuberculosis from pulmonary X-ray scans.

The authors use machine learning on MRI images of brain tissue to predict tumor onset as an avenue for early detection of brain cancer.

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.