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.

Dithen: A Computation-as-a-Service Cloud Platform For Large-Scale Multimedia Processing

We present Dithen, a novel computation-as-a-service (CaaS) cloud platform specifically tailored to the parallel ex-ecution of large-scale multimedia tasks. Dithen handles the upload/download of both multimedia data and executable items, the assignment of compute units to multimedia workloads, and the reactive control of the available compute units to minimize the cloud infrastructure cost under deadline-abiding execution. Dithen combines three key properties: (i) the reactive assignment of individual multimedia tasks to available computing units according to availability and predetermined time-to-completion constraints; (ii) optimal resource estimation based on Kalman-filter estimates; (iii) the use of additive increase multiplicative decrease (AIMD) algorithms (famous for being the resource management in the transport control protocol) for the control of the number of units servicing workloads. The deployment of Dithen over Amazon EC2 spot instances is shown to be capable of processing more than 80,000 video transcoding, face detection and image processing tasks (equivalent to the processing of more than 116 GB of compressed data) for less than $1 in billing cost from EC2. Moreover, the proposed AIMD-based control mechanism, in conjunction with the Kalman estimates, is shown to provide for more than 27% reduction in EC2 spot instance cost against methods based on reactive resource estimation. Finally, Dithen is shown to offer a 38% to 500% reduction of the billing cost against the current state-of-the-art in CaaS platforms on Amazon EC2 (Amazon Lambda and Amazon Autoscale). A baseline version of Dithen is currently available at dithen.com.