Exploring emotional patterns in social media through NLP models to unravel mental health insights.

This study aimed to develop an advanced ensemble approach for automated classification of mental health disorders in social media posts. The research question was: can an ensemble of fine-tuned transformer models (XLNet, RoBERTa, and ELECTRA) with Bayesian hyperparameter optimization improve the accuracy of mental health disorder classification in social media text. Three transformer models (XLNet, RoBERTa, and ELECTRA) were fine-tuned on a dataset of social media posts labelled with 15 distinct mental health disorders.

Design enhancement in hydroxide ion conductivity of viologen-bakelite organic frameworks for a flexible rechargeable zinc-air battery.

Quasi-solid-state rechargeable zinc-air batteries (ZABs) are suitable for the generation of portable clean energy due to their high energy and power density, safety, and cost-effectiveness. Compared to the typical alkaline aqueous electrolyte in a ZAB, polymer or gel-based electrolytes can suppress the dissolution of zinc, preventing the precipitation of undesirable irreversible zinc compounds. Their low electronic conductivity minimizes zinc dendrite formation.