The authors looked at the correlation between C reactive protein levels and neurological deficits in patients who had suffered an ischemic stroke.
Read More...C reactive protein and risk of neurological deficits and disability in patients with acute ischemic stroke
The authors looked at the correlation between C reactive protein levels and neurological deficits in patients who had suffered an ischemic stroke.
Read More...India’s digital public infrastructure: Analyzing UPI and Aadhaar in GDP growth and cost optimization
India’s Digital Public Infrastructure (DPI)—including the Unified Payments Interface (UPI) and Aadhaar—has been globally recognized for advancing financial inclusion and efficient governance. This study analyzes data from 2016–17 to 2023–24 the impact of these services on India's GDP.
Read More...Predicting voting and union support in certification elections: Evidence from Starbucks workers, 2021-2024
The authors looked at unionization petitions from Starbucks workers between August 2021 and July 2024 to determine what factors influence votes for or against unionization.
Read More...Predicting and explaining illicit financial flows in developing countries: A machine learning approach
The authors looked at the ability of different machine learning algorithms to predict the level of financial corruption in different countries.
Read More...Applying machine learning to breast cancer diagnosis: A high school student’s exploration using R
The authors combine fine needle aspiration biopsy and machine learning algorithms to develop a breast cancer detection method suitable for resource-constrained regions that lack access to mammograms.
Read More...Environmental contributors of asthma via explainable AI: Green spaces, climate, traffic & air quality
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.
Read More...Deep dive into predicting insurance premiums using machine learning
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.
Read More...Identifying anxiety and burnout from students facial expressions and demographics using machine learning
The authors used machine learning to predict the presence of anxiety and burnout in students based on facial expressions and demographic information.
Read More...Associations between fentanyl usage and social media use among U.S. teens
Here the authors aimed to understand factors influencing adolescent fentanyl exposure, hypothesizing a positive association between social media usage, socioeconomic factors, and fentanyl abuse among U.S. teens. Their analysis of the Monitoring the Future dataset revealed that a history of suspension and use of marijuana or alcohol were linked to higher fentanyl use, and while not statistically significant, a notable positive correlation between social media use and fentanyl frequency was observed.
Read More...The impact of environmental noise on the cognitive functions and mental workload of high school students
Authors examine the impact of environmental noise on cognitive processes in teenagers, focusing on five different noise conditions: two types of noise (aircraft and construction) at two different decibel levels (30 dBA and 60 dBA) and a quiet condition.
Read More...