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  1. Abstract Background

    Retaining women and racially minoritized individuals in engineering programs has been a subject of widespread discussion and investigation. While the sense of belonging and its link to retention have been studied based on student characteristics, there is an absence of studies investigating the importance of students' social identities to their sense of belonging in engineering.


    This study examines differences in race/ethnic identity centrality, gender identity centrality, and sense of belonging in engineering by subgroups of undergraduate engineering students at Hispanic‐Serving Institutions (HSIs). Subsequently, it examines the extent to which these identity centralities predict a sense of belonging in engineering for each subgroup.


    Survey data was collected from 903 Latinx and 452 White undergraduate engineering students from seven HSIs across the continental United States. Multivariate analysis of variance and sequential multivariate linear regression were used to evaluate the research questions.


    Latinx students had higher identity centralities but a similar sense of belonging in the engineering community as White students. Latinos and Latinas had an equivalent sense of belonging in engineering, whereas White women were higher than White men. In the full models, race/ethnic identity centrality significantly, and positively predicted a sense of belonging in engineering for Latinos and White women. Gender identity centrality was not a significant predictor of a sense of belonging in engineering for either Latinx or White students.


    Race/ethnic and gender identity centrality are differentially important to the sense of belonging in engineering for students at Hispanic‐Serving Institutions based on their group membership at the intersection of race and gender.

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  2. An important task in the machine reading of biochemical events expressed in biomedical texts is correctly reading the polarity, i.e., attributing whether the biochemical event is a promotion or an inhibition. Here we present a novel dataset for studying polarity attribution accuracy. We use this dataset to train and evaluate several deep learning models for polarity identification, and compare these to a linguistically-informed model. The best performing deep learning architecture achieves 0.968 average F1 performance in a five-fold cross-validation study, a considerable improvement over the linguistically informed model average F1 of 0.862. 
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  3. null (Ed.)
  4. Abstract

    This paper describes the use of aluminum and zinc as anodic materials for a battery employing nickel (II) oxide (NiO) as cathode. Comparison of both materials resulted in the development of a compact, cost effective, and easy to use primary NiO/Al battery employing an alkaline electrolyte. The system features electrodes composed of powder forms of the active materials on modified paper substrates that are contained in a simple multilayer design utilizing thin laminated plastic materials to provide structure and flexibility to the battery as well as a paper separator. Various concentrations of potassium hydroxide (KOH) electrolyte were examined and maximum performance was observed at 6 M KOH. A maximum current density and power density of 1.94 mA/cm2and 1 mW/cm2, respectively was achieved. This user‐friendly device was able to produce a maximum capacity of 2.33 mAh/g when 2 mA/g was applied. This work demonstrates the viability of a paper‐based battery featuring powder electrodes as a possible power source for microelectronic devices.

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