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Depression detection in social media text: leveraging machine learning for effective screening

Shin et al. | Mar 25, 2025

Depression detection in social media text: leveraging machine learning for effective screening

Depression affects millions globally, yet identifying symptoms remains challenging. This study explored detecting depression-related patterns in social media texts using natural language processing and machine learning algorithms, including decision trees and random forests. Our findings suggest that analyzing online text activity can serve as a viable method for screening mental disorders, potentially improving diagnosis accuracy by incorporating both physical and psychological indicators.

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The optimization of high-protein duckweed cultivation in eutrophicated water with mutualistic bacteria

Akkarajeerawat et al. | Mar 18, 2025

The optimization of high-protein duckweed cultivation in eutrophicated water with mutualistic bacteria

he rapid growth of the human population is driving food crises in Thailand and Southeast Asia, while contributing to global food insecurity and a larger carbon footprint. One potential solution is cultivating duckweed (Wolffia globosa) for consumption, as it grows quickly and can provide an alternative protein source. This research explored two methods to optimize duckweed cultivation: using phosphorus- and nitrogen-rich growing media and plant growth-promoting bacteria (PGPB).

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