Nodes localization in a wireless sensor network (WSN) aims for calculating the coordinates of unknown nodes with the assist of known nodes. The performance of a WSN can be greatly affected by the localization accuracy. In this paper, a node localization scheme is proposed based on a recent bioinspired algorithm called Salp Swarm Algorithm (SSA). The proposed algorithm is compared to well-known optimization algorithms, namely, particle swarm optimization (PSO), Butterfly optimization algorithm (BOA), firefly algorithm (FA), and grey wolf optimizer (GWO) under different WSN deployments. The simulation results show that the proposed localization algorithm is better than the other algorithms in terms of mean localization error, computing time, and the number of localized nodes.
Objectives
To examine whether tobacco initiation via e-cigarettes increases the likelihood of subsequent tobacco use among a large representative sample of US adolescents.
Methods
This study is a retrospective longitudinal analysis from a representative sample of US middle and high school students (n = 39,718) who completed the 2014 and 2015 National Youth Tobacco Survey. The adjusted odds ratios of lifetime and current use of tobacco use were estimated by logistic regression analysis while controlling for important socioecological factors associated with tobacco use.
Results
E-cigarette initiators were more likely to report current use of cigarettes (AOR 2.7; 1.9–4.0, p < 0.001), cigars (AOR 1.7; 1.2–2.4, p < 0.01), or smokeless tobacco (AOR 3.1; 2.2–5.4, p < 0.001), and lifetime use of the same products as well. Also, lifetime and current use of e-cigarettes significantly increased the likelihood of cigarettes, cigars, and smokeless tobacco use.
Conclusions
Initiation of tobacco via e-cigarette, lifetime, and current use of e-cigarettes are associated with higher odds of lifetime and current use of cigarettes, cigars, and smokeless tobacco. Collectively this suggests e-cigarettes may lead to an increased use of tobacco among adolescents.
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