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BackgroundThis study describes the patterns and socioeconomic influences of tobacco use among adults in tobacco-cultivating regions of rural southwest China.MethodsA cross-sectional survey was conducted in 8681 adults aged ≥18 years in rural areas of Yunnan Province, China from 2010 to 2011. A standardized questionnaire was administered to obtain data about participants’ demographic characteristics, individual socioeconomic status, ethnicity, self-reported smoking habits, and exposure to secondhand smoke (SHS). The socioeconomic predictors of current smoking, nicotine addiction, and SHS exposure were analyzed using multivariate logistic regression.ResultsThe prevalence rates of tobacco use were much higher in men compared with women (current smoking 68.5% vs. 1.3%; and nicotine dependence 85.2% vs. 72.7%). However, the rate of SHS exposure was higher in women compared with men (76.6% vs. 70.5%). Tobacco farmers had higher prevalence rates of current smoking, nicotine dependence, and SHS exposure compared with participants not engaged in tobacco farming (P<0.01). Most tobacco users (84.5%) reported initiating smoking during adolescence. A total of 81.1% of smokers smoked in public places, and 77.6% smoked in workplaces. Individuals belonging to an ethnic minority had a lower probability of SHS exposure and nicotine dependence. Individual educational level was found to be inversely associated with the prevalence of current smoking, exposure to SHS, and nicotine dependence. Higher annual household income was associated with a greater risk of nicotine dependence.ConclusionsThis study suggests that tobacco control efforts in rural southwest China must be tailored to address tobacco-cultivating status and socioeconomic factors.

ObjectivesTo examine contextual and individual demographical predictors of smoking and exposure to second-hand smoke (SHS) in a tobacco-cultivating rural area of southwest China.MethodsA cross-sectional survey of 4070 consenting individuals aged 18 years or more was conducted in 2010. Information on demographical characteristics, tobacco smoking status and SHS exposure were obtained by a standard questionnaire. Multilevel logistic regression was used to model the variation in prevalence of smoking and SHS exposure.ResultsIn the study population, the prevalence rates of smoking and exposure to SHS were 63.5% and 74.7% for men, and 0.6% and 71.2% for women, respectively. Men were more likely to use tobacco than women: OR 8.27, 95% CI (4.83 to 10.97). Age was inversely associated with the probability of tobacco use (OR 0.98, 95% CI 0.97 to 0.99), and exposure to SHS (OR 0.97, 95% CI 0.96 to 0.99). Individual educational level was inversely associated with smoking, but showed no association with exposure to SHS. Adults who did not grow tobacco were less likely to consume tobacco (OR 0.75, 95% CI 0.57 to 0.99) and to be exposed to SHS (OR 0.76, 95% CI 0.58 to 0.99). Living in a high-income community was associated with a low rate of current smoking (OR 0.66, 95% CI 0.57 to 0.77) and SHS exposure (OR 0.58, 95% CI 0.52 to 0.65).ConclusionsFuture interventions to reduce smoking and exposure to SHS in China should focus more on tobacco farmers, less-educated individuals and on poor rural communities.

Extensive studies have shown that many animals’ capability of forming spatial representations for self-localization, path planning, and navigation relies on the functionalities of place and head-direction (HD) cells in the hippocampus. Although there are numerous hippocampal modeling approaches, only a few span the wide functionalities ranging from processing raw sensory signals to planning and action generation. This paper presents a vision-based navigation system that involves generating place and HD cells through learning from visual images, building topological maps based on learned cell representations and performing navigation using hierarchical reinforcement learning. First, place and HD cells are trained from sequences of visual stimuli in an unsupervised learning fashion. A modified Slow Feature Analysis (SFA) algorithm is proposed to learn different cell types in an intentional way by restricting their learning to separate phases of the spatial exploration. Then, to extract the encoded metric information from these unsupervised learning representations, a self-organized learning algorithm is adopted to learn over the emerged cell activities and to generate topological maps that reveal the topology of the environment and information about a robot’s head direction, respectively. This enables the robot to perform self-localization and orientation detection based on the generated maps. Finally, goal-directed navigation is performed using reinforcement learning in continuous state spaces which are represented by the population activities of place cells. In particular, considering that the topological map provides a natural hierarchical representation of the environment, hierarchical reinforcement learning (HRL) is used to exploit this hierarchy to accelerate learning. The HRL works on different spatial scales, where a high-level policy learns to select subgoals and a low-level policy learns over primitive actions to specialize on the selected subgoals. Experimental results demonstrate that our system is able to navigate a robot to the desired position effectively, and the HRL shows a much better learning performance than the standard RL in solving our navigation tasks.

The digital curling game is a two-player zero-sum extensive game in a continuous action space. There are some challenging problems that are still not solved well, such as the uncertainty of strategy, the large game tree searching, and the use of large amounts of supervised data, etc. In this work, we combine NFSP and KR-UCT for digital curling games, where NFSP uses two adversary learning networks and can automatically produce supervised data, and KR-UCT can be used for large game tree searching in continuous action space. We propose two reward mechanisms to make reinforcement learning converge quickly. Experimental results validate the proposed method, and show the strategy model can reach the Nash equilibrium.