Using machine learning to predict the risk of algae bloom
Read More...Using two-step machine learning to predict harmful algal bloom risk
Using machine learning to predict the risk of algae bloom
Read More...Predictive modeling of cardiovascular disease using exercise-based electrocardiography
The authors looked factors that could lead to earlier diagnosis of cardiovascular disease thereby improving patient outcomes. They found that advances in imaging and electrocardiography contribute to earlier detection of cardiovascular disease.
Read More...Correlating inlet gas composition to conversion efficiency in plasma-assisted landfill gas reforming
The escalating crisis of climate change, driven by the accumulation of greenhouse gases from human activities, demands urgent and innovative solutions to curb rising global temperatures. Plasma-based methane (CH4) and carbon dioxide (CO2) reforming offers a promising pathway for carbon capture and the sustainable production of hydrogen fuel and syngas components. To advance this technology, particularly in terms of energy efficiency and selectivity, it is essential to enhance the conversion efficiencies of CO2 and CH4.
Read More...Lung cancer AI-based diagnosis through multi-modal integration of clinical and imaging data
Lung cancer is highly fatal, largely due to late diagnoses, but early detection can greatly improve survival. This study developed three models to enhance early diagnosis: an MLP for clinical data, a CNN for imaging data, and a hybrid model combining both.
Read More...A comparative study of food labels in the United States and India: Adherence to Codex Alimentarius guidelines
This study investigated how well food labels from 280 different brands across multiple food and drink categories in India and the US adhered to recommended nutritional labeling standards as outlined by the Codex Alimentarius.
Read More...Comparative life cycle analysis: Solvent recycling and improved dewatering scenarios in PHB plastic production
The authors looked at alternative production processes for PHB plastic in an effort to reduce environmental impact. They found that no alternative process was able to significantly decrease the environmental impact of PHB production, but that optimizing dewatering steps during production could lead to the largest improvement on environmental impact.
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...Exploring Political Discourse Among High School Journalists with Web Scraping and AI Technology
Here the authors provided greater coverage of adolescent stances by investigating the political perspectives and trends of high school journalists, utilizing web scraping methods and artificial intelligence (ChatGPT-4o) to analyze over 153,000 articles. They found that high school publications exhibit lower levels of political polarization compared to mainstream media and that journalists' views, while tending to lean moderately liberal, showed no significant correlation with local voting patterns.
Read More...Yeast catalysis of hydrogen peroxide as an enhanced chemical treatment method for harvested rainwater
The authors looked at different treatments to clean up rainwater collected at home. They found that chlorine treatment and treatment with hydrogen peroxide catalyzed by yeast showed similar potential for cleaning up contaminated rainwater, but that further studies are needed to better assess impact on specific contaminant levels still present.
Read More...How artificial intelligence deep learning models can be used to accurately determine lung cancers
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.
Read More...