Browse Articles

In this study, we examined the consistency and dependability of Google's Lighthouse tool for measuring website performance, accessibility, SEO, and best practices. Tests conducted on three websites showed that performance scores vary based on the testing mode and website complexity, while other audit categories stay constant. These results provide direction for researchers and developers by highlighting Lighthouse's benefits and drawbacks.

The authors monitored water quality over time looking for influences from construction and deforestation.

This study contributes to our understanding of how urban energy systems respond to climate variability and inform strategies for enhancing power grid resilience. The findings can help inform urban planners and infrastructure developers by identifying the factors that make regions within a power grid more vulnerable.

The authors looked at the potential of different phenolic compounds to reduce oxidative stress (i.e., act as antioxidants).

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.

The authors looked at different factors, such as age, pre-existing conditions, and geographic region, and their ability to predict what an individual's health insurance premium would be.

Improving the classifier of the difficulty level of Sudoku.

Arrhythmias vary in type and treatment, and ECGs are used to detect them, though human interpretation can be inconsistent. The researchers tested four tree-based algorithms (gradient boosting, random forest, decision tree, and extra trees) on ECG data from over 10,000 patients.

Investigating the green valley hypotheses of galaxy evolution

The authors use machine learning to analyze electroencephalogram data and identify slowing patterns that can indicate undetected disorders like epilepsy or dementia