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The Clinical Accuracy of Non-Invasive Glucose Monitoring for ex vivo Artificial Pancreas

Levy et al. | Jul 10, 2016

The Clinical Accuracy of Non-Invasive Glucose Monitoring for <i>ex vivo</i> Artificial Pancreas

Diabetes is a serious worldwide epidemic that affects a growing portion of the population. While the most common method for testing blood glucose levels involves finger pricking, it is painful and inconvenient for patients. The authors test a non-invasive method to measure glucose levels from diabetic patients, and investigate whether the method is clinically accurate and universally applicable.

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Assessing machine learning model efficacy for brain tumor MRI classification: a multi-model approach

Dhingra et al. | Mar 14, 2026

Assessing machine learning model efficacy for brain tumor MRI classification: a multi-model approach
Image credit: Dhingra and Dhingra

This manuscript explores the performance of five different machine learning models in classifying brain tumors from a dataset of MRI scans. The authors find that several of the models showed >90% accuracy. Thus, the authors suggest that machine learning models demonstrate potential for effective implementation in clinical settings, including as a diagnostic tool that can be used to complement the expertise of neuroradiologists.

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Towards an Integrated Solution for Renewable Water and Energy

Chen et al. | Jan 09, 2015

Towards an Integrated Solution for Renewable Water and Energy

An integrated plant that would generate energy from solar power and provide clean water would help solve multiple sustainability issues. The feasibility of such a plant was investigated by looking at the efficacy of several different modules of such a plant on a small scale.

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