Acute electronic cigarette use: nicotine delivery and subjective effects in regular users

Rationale Electronic cigarettes are becoming increasingly popular among smokers worldwide. Commonly reported reasons for use include the following: to quit smoking, to avoid relapse, to reduce urge to smoke, or as a perceived lower-risk alternative to smoking. Few studies, however, have explored whether electronic cigarettes (e-cigarettes) deliver measurable levels of nicotine to the blood. Objective This study aims to explore in experienced users the effect of using an 18-mg/ml nicotine first-generation e-cigarette on blood nicotine, tobacco withdrawal symptoms, and urge to smoke. Methods Fourteen regular e-cigarette users (three females), who are abstinent from smoking and e-cigarette use for 12 h, each completed a 2.5 h testing session. Blood was sampled, and questionnaires were completed (tobacco-related withdrawal symptoms, urge to smoke, positive and negative subjective effects) at four stages: baseline, 10 puffs, 60 min of ad lib use and a 60-min rest period. Results Complete sets of blood were obtained from seven participants. Plasma nicotine concentration rose significantly from a mean of 0.74 ng/ml at baseline to 6.77 ng/ml 10 min after 10 puffs, reaching a mean maximum of 13.91 ng/ml by the end of the ad lib puffing period. Tobacco-related withdrawal symptoms and urge to smoke were significantly reduced; direct positive effects were strongly endorsed, and there was very low reporting of adverse effects. Conclusions These findings demonstrate reliable blood nicotine delivery after the acute use of this brand/model of e-cigarette in a sample of regular users. Future studies might usefully quantify nicotine delivery in relation to inhalation technique and the relationship with successful smoking cessation/harm reduction.

Fuzzy Ensembles for Embedding Adaptive Behaviours in Semi-Autonomous Avatars in 3D Virtual Worlds

Semi-autonomous avatars should be both realistic and believable. The goal is to learn from and reproduce the behaviours of the user-controlled input to enable semi-autonomous avatars to plausibly interact with their human-controlled counterparts. A powerful tool for embedding autonomous behaviour is learning by imitation. Hence, in this paper an ensemble of fuzzy inference systems cluster the user input data to identify natural groupings within the data to describe the users movement and actions in a more abstract way. Multiple clustering algorithms are investigated along with a neuro-fuzzy classifier; and an ensemble of fuzzy systems are evaluated.