Bond-Order Time Series Analysis for Detecting Reaction Events in Ab Initio Molecular Dynamics Simulations.

Ab initio molecular dynamics is able to predict novel reaction mechanisms by directly observing the individual reaction events that occur in simulation trajectories. In this article, we describe an approach for detecting reaction events from simulation trajectories using a physically motivated model based on time series analysis of ab initio bond orders. We found that applying a threshold to the bond order was insufficient for accurate detection, whereas peak finding on the first time derivative resulted in significantly improved accuracy. The model is trained on a reference set of reaction events representing the ideal result given unlimited computing resources. Our study includes two model systems: a heptanylium carbocation that undergoes hydride shifts and an unsaturated iron carbonyl cluster that features CO ligand migration and bridging behavior. The results indicate a high level of promise for this analysis approach to be used in mechanistic analysis of reactive AIMD simulations more generally

Smoking cessation and related factors in middle-aged and older Chinese adults: Evidence from a longitudinal study

Objectives There are more than 300 million smokers in China. This study aimed to evaluate the rate of smoking cessation, smoking relapse and related factors in middle-aged and older smokers in China. Methods We performed a secondary analysis of data from China Health and Retirement Longitudinal Study (CHARLS) that recruited a nationally representative sample of adults aged 45 and older. Participants were 3708 smokers in 2011 who completed two waves of follow-up interviews in 2013 and 2015. Self-reported quit and relapse rates at follow-ups were estimated. Multiple logistic regressions were conducted to identify factors associated with smoking cessation and relapse. Results The overall quit rate was 8.5% (95% CI 7.7% - 9.5%) at the 2-year follow-up in 2013, and 16.6% (95% CI 15.5% - 17.9%) at the 4-year follow up. Smoking cessation in 2013 was associated with not living in the northeast region (p = 0.003), fewer cigarettes smoked daily (p <0.001), and longer time to the first cigarette in the morning (p<0.001). Smoking cessation in 2015 was associated with older age (p = 0.049), smoking initiation at age ≥20 years (p<0.001), longer time to the first cigarette in the morning (p<0.001), and self-perceived poor health (p<0.001). Of the 317 participants who stopped smoking in 2013, 13.3% (95% CI 9.9% - 17.5%) relapsed by 2015. Smoking relapse was associated with younger age (p = 0.025), shorter time to the first cigarette in the morning (p = 0.003), and self-perception of not poor health (p = 0.018). Conclusion The overall quit rate was 8.5% at the 2-year follow up, and 16.6% at the 4-year follow up in the middle-aged and older smokers, but 13% of quitters returned to smoking in two years. Successful smoking cessation was associated with older age, lower nicotine dependence, and self-perceived poor health

ASGR: An Artificial Spider-Web-Based Geographic Routing in Heterogeneous Vehicular Networks

Recently, vehicular ad hoc networks (VANETs) have been attracting significant attention for their potential for guaranteeing road safety and improving traffic comfort. Due to high mobility and frequent link disconnections, it becomes quite challenging to establish a reliable route for delivering packets in VANETs. To deal with these challenges, an artificial spider geographic routing in urban VAENTs (ASGR) is proposed in this paper. First, from the point of bionic view, we construct the spider web based on the network topology to initially select the feasible paths to the destination using artificial spiders. Next, the connection-quality model and transmission-latency model are established to generate the routing selection metric to choose the best route from all the feasible paths. At last, a selective forwarding scheme is presented to effectively forward the packets in the selected route, by taking into account the nodal movement and signal propagation characteristics. Finally, we implement our protocol on NS2 with different complexity maps and simulation parameters. Numerical results demonstrate that, compared with the existing schemes, when the packets generate speed, the number of vehicles and number of connections are varying, our proposed ASGR still performs best in terms of packet delivery ratio and average transmission delay with an up to 15% and 94% improvement, respectively