6,664 research outputs found
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Where Do People Vape? Insights from Twitter Data.
Background: Emerging evidence suggests that exposure to secondhand and thirdhand aerosol from electronic cigarettes may have serious health risks including respiratory and cardiovascular diseases. Social media data can help identify common locations referenced in vaping-related discussions and offer clues about where individuals vape. These insights can strengthen current tobacco regulations and prioritize new policies to improve public health. This study identified commonly referenced locations in vaping-related discussions on Twitter in 2018. Methods: Vaping-related posts to Twitter were obtained from 1 January 2018 to 31 December 2018. Rule-based classifiers categorized each Twitter post into 11 location-related categories (social venues, living spaces, stores, modes of transportation, schools, workplaces, healthcare offices, eateries, correctional facilities, religious institutions, and miscellaneous) using a data dictionary of location-related keywords (n = 290,816). Results: The most prevalent category was social venues (17.9%), followed by living spaces (16.7%), stores (15.9%), modes of transportation (15.5%), schools (14.9%), and workplaces (11.9%). Other categories pertained to: healthcare offices (2.0%), eateries (1.2%), correctional facilities (0.7%), and religious institutions (0.4%). Conclusion: This study suggests that locations related to socialization venues may be priority areas for future surveillance and enforcement of smoke-free air policies. Similarly, development and enforcement of similar policies at workplaces, schools and multi-unit housing may curb exposure to secondhand and thirdhand aerosol among the public
Identification of linear time-invariant systems with Dynamic Mode Decomposition
Dynamic mode decomposition (DMD) is a popular data-driven framework to extract linear dynamics from complex high-dimensional systems. In this work, we study the system identification properties of DMD. We first show that DMD is invariant under linear transformations in the image of the data matrix. If, in addition, the data are constructed from a linear time-invariant system, then we prove that DMD can recover the original dynamics under mild conditions. If the linear dynamics are discretized with the Runge–Kutta method, then we further classify the error of the DMD approximation and detail that for one-stage Runge–Kutta methods; even the continuous dynamics can be recovered with DMD. A numerical example illustrates the theoretical findings
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Managers' Practices of Tobacco and Marijuana Smoking Policies in Hispanic-Occupied Multiunit Housing.
Purpose: This study assessed the knowledge, attitudes, and practices of managers of Hispanic-occupied multiunit housing (MUH) related to the prevalence and prevention of secondhand smoke (SHS), thirdhand tobacco smoke, and secondhand marijuana smoke (SHMS). Methods: A narrative analysis was conducted of 20 interviews with live-in apartment managers. Their opinions on policies and an educational fotonovela were also gathered. Results: The properties were located in 10 cities within the Los Angeles County, representing a wide array of local policies and practices. Only two managers were correctly informed of the existing MUH smoking policies in their cities. Participants reported ambiguity in city laws and company rules regarding smoking. Managers do not distinguish between smoking recreational marijuana and medicinal marijuana. Several respondents believed the landlords have more power to create rules. Most favored a total ban on smoking of all substances on the premises. Conclusions: Most managers report low agency in being able to pass no-smoking rules. Participants support smoking policies that include all smokable products. Managers would like new government policies, manager trainings, tenant education, and ways to enforce rules to protect apartment tenants from SHS and SHMS. Educational interventions should coincide with the timing of key manager/tenant activities. Results can be used in policy development and educational interventions
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