Because of the pervasiveness, persistence, and toxicity of per-and polyfluoroalkyl substances (PFAS), there is growing concern over PFAS contamination, exposures, and health effects. The diversity of potential PFAS is astounding, with nearly 10,000 PFAS catalogued in databases to date (and growing). The ability to detect the thousands of known PFAS, and discover previously uncatalogued PFAS, is necessary to understand the scope of PFAS contamination and to identify appropriate remediation and regulatory solutions. Current non-targeted methods for PFAS analysis require manual curation and are time-consuming, prone to error, and not comprehensive. FluoroMatch Flow 2.0 is the first software to cover all steps of data processing for PFAS discovery in liquid chromatography-high-resolution tandem mass spectrometry samples. These steps include feature detection, feature blank filtering, exact mass matching to catalogued PFAS, mass defect filtering, homologous series detection, retention time pattern analysis, class-based MS/MS screening, fragment screening, and predicted MS/MS from SMILES structures. In addition, a comprehensive confidence level criterion is implemented to help users understand annotation certainty and integrate various layers of evidence to reduce overreporting. Applying the software to aqueous film forming foam analysis, we discovered over one thousand likely PFAS including previously unreported species. Furthermore, we were able to filter out 96% of features which were likely not PFAS. FluoroMatch Flow 2 increased coverage of likely PFAS by over tenfold compared to the previous release. This software will enable researchers to better characterize PFAS in the environment and in biological systems.
A hashtag is a type of metadata tag used on social networks and can help people search for specific topics or content. To capture the interactive information between words and understand the content of microblog posts deeply, this study proposed a neural network model based on a word-level self-attention mechanism. Given a microblog post, the weight of each word was calculated through a self-attention mechanism, and then the representation of a microblog post was obtained through the weighted summation of words. Finally, a multi-layer perceptron was adopted on the representation of a microblog post to perform the classification. The effectiveness of the proposed model was verified through experiments of large-scale datasets. Results show that: (1) introducing word-level self-attention mechanism into hashtag recommendation is effective. (2) In comparison with the baseline methods used in previous studies, such as convolutional neural network or long short-term memory network, the proposed self-attentive neural networks can provide a more accurate representation of a microblog post and significantly improve the F-score of hashtag recommendation on the same dataset. This study provides references for the methods and evaluation of short-text hashtag recommendations, such as microblogs.
Three-tier high-strength prestressed combination concave dies are designed to manifacture highpower corn combine harvester engine piston heads. This design integrates the advantages of traditional casting or hot die forging and warm extrusion. The following key parameters, viz radial dimensions of each mating layer, axial bonding and radial contact interaction, are obtained by theoretical calculation. Nonlinear analysis of the contact interaction was carried out, and the die contact condition was studied at no-load and full-load. Based on the Archard wear theory, thermomechanical bonding was studied in operation of the die. Through numerical simulation of the die wear in each operation cycle, the univariate linear regression equation of the die service life was derived, and the reliability of this equation was verified. The results show that the die contact is both stable and reliable if the radial contact interaction of the inner and outer layers is d 2 = 1.9716 mm and d 3 = 1.3870 mm, respectively. With the nitriding layer thickness of 0.24 mm, the extrusion die service life in the production of piston heads corresponds to 6357 pieces.
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