scholarly journals Google shopping queries for vaping products, JUUL and IQOS during the E-cigarette, or Vaping, product use Associated Lung Injury (EVALI) outbreak

2021 ◽  
pp. tobaccocontrol-2021-056481 ◽  
Author(s):  
Eric C Leas ◽  
Natalie H Moy ◽  
Alicia L Nobles ◽  
John Ayers ◽  
Shu-Hong Zhu ◽  
...  
Keyword(s):  
Lung Injury ◽  
Online Shopping ◽  
Prediction Interval ◽  
Moving Average ◽  
Disease Outbreaks ◽  
Safe Alternative ◽  
Product Use ◽  
Philip Morris ◽  
Prior Approval ◽  
Moving Average Model

ObjectivesTo assess whether the late 2019 US outbreak of pulmonary disease linked to vaping (‘E-cigarette, or Vaping, product use Associated Lung Injury’ (EVALI)) impacted online shopping queries for vaping products and the Philip Morris ‘IQO’ brand of heated tobacco.MethodsWe tracked online shopping queries for vape(s), JUUL and IQOS by analysing rates of Google queries indicative of shopping (eg, buy IQOS) after news of the outbreak was first reported (the week of 29 July 2019) until hospitalisations ceased (the week of 16 February 2020). We compared observed rates of shopping during the outbreak to counterfactual expected rates that were predicted using an autoregressive iterative moving average model fit to queries from 1 January 2014 to the week of 21 July 2019.ResultsDuring the outbreak, vape shopping queries were 34% (95% CI 30% to 38%) lower than expected and JUUL shopping queries were 39% (95% CI 34% to 45%) lower than expected, translating into about 7.2 and 1.0 million fewer searches. IQOS shopping queries were 58% (95% prediction interval (PI): 34–87) higher than expected, translating into 35 000 more searches. Moreover, IQOS shopping queries reached a historic high the week they were discussed as a potentially safe alternative to vaping (the week of 29 September 2019), when they were 382% (95% PI: 219–881) above expected rates for the week.ConclusionsThese results suggest that unplanned events, such as the EVALI outbreak, can provoke changes in the epidemiology of product usage. Tobacco companies should be prohibited from using events such as disease outbreaks to position their products as less harmful without prior approval.

scholarly journals Nicotine Toxicity Secondary to Aftermarket Modifications to a Vaping Device

2021 ◽  
Author(s):  
Garrett S Bendel ◽  
Hugh M Hiller ◽  
Aaron Ralston
Keyword(s):  
Emergency Department ◽  
Cigarette Smoking ◽  
Lung Injury ◽  
Potential Risk ◽  
Electronic Cigarettes ◽  
Active Duty ◽  
Safe Alternative ◽  
Relative Ease ◽  
Product Use

ABSTRACT Electronic cigarettes continue to rise in popularity as a reportedly safe alternative to standard cigarette smoking. Their use has become common in our society and specifically in our young active duty population. This cigarette smoking alternative has come under recent scrutiny with the discovery of e-cigarette or vaping product use-associated lung injury. However, there is another potential risk associated with vaping: the relative ease at which vaping devices can be modified has allowed a growing community of users to invent novel ways of delivering higher concentrations of nicotine. Here, we describe two cases of active duty patients who presented to an emergency department with clinical nicotine toxicity after using a heavily modified e-cigarette.

Philip Morris International used the e-cigarette, or vaping, product use associated lung injury (EVALI) outbreak to market IQOS heated tobacco

2021 ◽  
pp. tobaccocontrol-2021-056661
Author(s):  
John W Ayers ◽  
Eric C Leas ◽  
Mark Dredze ◽  
Theodore L Caputi ◽  
Shu-Hong Zhu ◽  
...  
Keyword(s):  
Lung Injury ◽  
Product Use ◽  
Philip Morris

scholarly journals Highway Speed Prediction Using Gated Recurrent Unit Neural Networks

2021 ◽  
Vol 11 (7) ◽  
pp. 3059
Author(s):  
Myeong-Hun Jeong ◽  
Tae-Young Lee ◽  
Seung-Bae Jeon ◽  
Minkyo Youm
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Moving Average ◽  
Computational Cost ◽  
Nonlinear Problems ◽  
Transportation Systems ◽  
Mobility Data ◽  
Challenges And Opportunities ◽  
Moving Average Model ◽  
Gated Recurrent Unit

Movement analytics and mobility insights play a crucial role in urban planning and transportation management. The plethora of mobility data sources, such as GPS trajectories, poses new challenges and opportunities for understanding and predicting movement patterns. In this study, we predict highway speed using a gated recurrent unit (GRU) neural network. Based on statistical models, previous approaches suffer from the inherited features of traffic data, such as nonlinear problems. The proposed method predicts highway speed based on the GRU method after training on digital tachograph data (DTG). The DTG data were recorded in one month, giving approximately 300 million records. These data included the velocity and locations of vehicles on the highway. Experimental results demonstrate that the GRU-based deep learning approach outperformed the state-of-the-art alternatives, the autoregressive integrated moving average model, and the long short-term neural network (LSTM) model, in terms of prediction accuracy. Further, the computational cost of the GRU model was lower than that of the LSTM. The proposed method can be applied to traffic prediction and intelligent transportation systems.

scholarly journals Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1403
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Solid Earth ◽  
Spectrum Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Autoregressive Moving Average Model ◽  
Seasonal Characteristics ◽  
Geocenter Motion ◽  
Moving Average Model

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.

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