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ABSTRACT Longitudinal studies are often subject to missing data. The recent guidance from regulatory agencies, such as the ICH E9(R1) addendum addresses the importance of defining a treatment effect estimand with the consideration of intercurrent events. Jump-to-reference (J2R) is one classical control-based scenario for the treatment effect evaluation, where the participants in the treatment group after intercurrent events are assumed to have the same disease progress as those with identical covariates in the control group. We establish new estimators to assess the average treatment effect based on a proposed potential outcomes framework under J2R. Various identification formulas are constructed, motivating estimators that rely on different parts of the observed data distribution. Moreover, we obtain a novel estimator inspired by the efficient influence function, with multiple robustness in the sense that it achieves n1/2-consistency if any pairs of multiple nuisance functions are correctly specified, or if the nuisance functions converge at a rate not slower than n−1/4 when using flexible modeling approaches. The finite-sample performance of the proposed estimators is validated in simulation studies and an antidepressant clinical trial. 

Here we report a novel and simple method for triggering internal short circuit (ISC) in Li-ion cells on demand and measuring critical ISC parameters in situ. The method works by inserting a pair of metal pads into a Li-ion cell and connecting the pads outside the cell through a switch and a current sensor. Different types of ISC that are common in field failures can be simulated by this method. ISC current and ISC resistance can be measured. The measurement further enables determination of local ISC heat generation rate. By attaching a micro temperature sensor between the metal pads, the ISC temperature can be also measured. The method was applied to both lab-made single-layer cells and commercially available multiple-layer cells in this work. The results generally verified previous modeling predictions about the effects of ISC types and ISC resistance on behaviors of Li-ion cells. It suggested that the proposed method can be used to validate models and enhance understanding of highly localized and transient behaviors of ISC. It was also demonstrated that the method has minimal effects on Li-ion cell performance and has good repeatability in triggering ISC. 

A scaling law to predict the conformability of flexible sheets on spherical surfaces is derived and used to enhance the wrap. 

Different news articles about the same topic often offer a variety of perspectives: an article written about gun violence might emphasize gun control, while another might promote 2nd Amendment rights, and yet a third might focus on mental health issues. In communication research, these different perspectives are known as “frames”, which, when used in news media will influence the opinion of their readers in multiple ways. In this paper, we present a method for effectively detecting frames in news headlines. Our training and performance evaluation is based on a new dataset of news headlines related to the issue of gun violence in the United States. This Gun Violence Frame Corpus (GVFC) was curated and annotated by journalism and communication experts. Our proposed approach sets a new state-of-the-art performance for multiclass news frame detection, significantly outperforming a recent baseline by 35.9% absolute difference in accuracy. We apply our frame detection approach in a large scale study of 88k news headlines about the coverage of gun violence in the U.S. between 2016 and 2018.