As the most popular video sharing website in the world, YouTube has the potential to reach and influence a huge audience. This study aims to gain a systematic understanding of what e-cigarette messages people are being exposed to on YouTube by assessing the quantity, portrayal and reach of e-cigarette videos. Researchers identified the top 20 search results on YouTube by relevance and view count for the following search terms: “electronic cigarettes”, “e-cigarettes”, “ecigarettes”, “ecigs”, “smoking electronic cigarettes”, “smoking e-cigarettes”, “smoking ecigarettes”, “smoking ecigs”. A sample of 196 unique videos was coded for overall portrayal and genre. Main topics covered in e-cigarette videos were recorded and video statistics and viewer demographic information were documented. Among the 196 unique videos, 94% (n = 185) were “pro” to e-cigarettes and 4% (n = 8) were neutral, while there were only 2% (n = 3) that were “anti” to e-cigarettes. The top 3 most prevalent genres of videos were advertisement, user sharing and product review. 84.3% of “pro” videos contained Web links for e-cigarette purchase. 71.4% of “pro” videos claimed that e-cigarettes were healthier than conventional cigarettes. Audience was primarily from the United States, the United Kingdom and Canada and “pro” e-cigarette videos were watched more frequently and rated much more favorably than “anti” ones. The vast majority of information on YouTube about e-cigarettes promoted their use and depicted the use of e-cigarettes as socially acceptable. It is critical to develop appropriate health campaigns to inform e-cigarette consumers of potential harms associated with e-cigarette use.

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM’s evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

In this article, an event-triggered adaptive dynamic programming (ETADP) method is proposed for the robust optimal tracking control problem of an uncertain continuous-time nonlinear system with asymmetric input constraints and external disturbances. First, an event-triggered integral reinforcement learning (ETIRL) algorithm is proposed to address the zero-sum game problem for partially unknown dynamical systems. A triggering condition with an interference attenuation level is designed to optimize the process. Second, a single-critic neural network structure is employed to approximate the optimal control law. The control and disturbance laws are updated non-periodically by the event-triggered. The asymptotic stability of the closed-loop system and the uniform ultimate boundedness (UUB) of the critic neural network weights are proven theoretically using the Lyapunov stability theorem. Finally, the feasibility of the algorithm is assessed through pneumatic muscle antagonistic joint experiments.