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

Here the authors explored the development of biodegradable bioplastic films derived from mango peels as a sustainable solution to plastic pollution and greenhouse gas emissions from fruit waste. They optimized the film's mechanical properties and water resistance through adjusting processing conditions and incorporating plasticizers and a hydrophobic coating, ultimately demonstrating its potential as a bacteriostatic and biodegradable alternative to conventional plastic food wrap.

Investigation of resonant vibration using cello model

The authors looked at student behaviors around disposal of face masks. The goal of the study was to bring awareness to improper mask disposal and how the resulting litter contributes to overall environmental pollution.

The authors use machine learning and electroencephalogram data to propose a method for improving epilepsy diagnosis.

The authors propose and test a method that would allow for the generation of a magnetic field on Mars sufficient to support future colonization.

People with Type One diabetes often rely on Continuous Blood Glucose Monitors (CGMs) to track their blood glucose and manage their condition. Researchers are now working to help people with Type One diabetes more easily monitor their health by developing models that will future blood glucose levels based on CGM readings. Jalla and Ghanta tackle this issue by exploring the use of AI models to forecast blood glucose levels with CGM data.

This study explores how different economic sectors, geographic regions, and greenhouse gas types might affect future global mean surface temperature (GMST) anomalies differently from historical patterns. Using the Finite Amplitude Impulse Response (FaIR) model and four Shared Socioeconomic Pathways (SSPs) — SSP126, SSP245, SSP370, and SSP585 — the research reveals that future contributions to GMST anomalies.

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 looked at the effect of monetary vs. food incentives on math test performance. They found that financial incentives did increase student performance, but not necessarily food incentives.