Further improvements of Steiner tree approximations

The Steiner tree problem requires to find a shortest tree connecting a given set of terminal points in a metric space. We suggest a better and fast heuristic for the Steiner problem in graphs and in rectilinear plane. This heuristic finds a Steiner tree at most 1.757 and 1.267 times longer than the optimal solution in graphs and rectilinear plane, respectively

Perceived Benefits of a Designated Smoking Area Policy on a College Campus: Views of Smokers and Non-smokers

Designated smoking areas are meant to: (1) limit secondhand smoke exposure to non-smokers, and (2) reduce cigarettes consumption by smokers. One year after the implementation of a designated smoking area protocol on a college campus, students were intercepted and asked to complete a short Likert survey designed to assess its perceived benefits. Analysis of the data showed that both smokers and non-smokers consider a reduction in the number of cigarettes consumed by smokers to be an unlikely outcome, which is consistent with research conducted in a variety of setting showing that designated smoking areas typically do not lead to less smoking by smokers. However, whereas the non-smokers agreed that the policy resulted in lowering exposure to second-hand smoke, smokers were unwilling to endorse a statement indicating that this occurred. This suggests that it may be unrealistic to assume that appeals to empathy (i.e. pointing out the negative impact of second hand smoke) when promoting the benefits of a designated smoking area will result in an automatic buy-in

Perceived Benefits of a Designated Smoking Area Policy on a College Campus: Views of Smokers and Non-smokers

Designated smoking areas are meant to: (1) limit secondhand smoke exposure to non-smokers, and (2) reduce cigarettes consumption by smokers.  One year after the implementation of a designated smoking area protocol on a college campus, students were intercepted and asked to complete a short Likert survey designed to assess its perceived benefits.  Analysis of the data showed that both smokers and non-smokers consider a reduction in the number of cigarettes consumed by smokers to be an unlikely outcome, which is consistent with research conducted in a variety of setting showing that designated smoking areas typically do not lead to less smoking by smokers.  However, whereas the non-smokers agreed that the policy resulted in lowering exposure to second-hand smoke, smokers were unwilling to endorse a statement indicating that this occurred. This suggests that it may be unrealistic to assume that appeals to empathy (i.e. pointing out the negative impact of second hand smoke) when promoting the benefits of a designated smoking area  will  result in an automatic buy-in

Predicting Opioid Epidemic by Using Twitter Data

Opioid crisis was declared as a public health emergency in 2017 by the President of USA. According to the Centers for Disease Control and Prevention, more than 91 Americans die every day from an opioid overdose. Nearly $4B is provided to address the opioid epidemic in the 2018 spending bill and help fulfill the President’s Opioid Initiative. How to monitor and predict the opioid epidemic accurately and in real time? The traditional methods mainly use the hospital data and usually have a lag of several years. Even though they are accurate, the long lag period prevents us from monitoring and predicting the epidemic in real time. We observe that people discuss things related to the epidemic a lot in social media platforms. These user behavior data collected from social media platforms can potentially help us monitor and predict the epidemic in real time. In this paper, we study how to use Twitter to monitor the epidemic. We collect the historic tweets containing the set of keywords related to the epidemic. We count the frequency of the tweets posted at each month and each state. We compare the frequency values with the real-world death rates at each month and each state. We identify high correlation between tweet frequency values and real-world death rates. The statistical significance demonstrates that the Twitter data can be used for predicting the death rate and epidemic in future