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Antimicrobial peptide-mimicking antibacterial polymers represent a practical strategy to conquer the ever-growing threat of antimicrobial resistance. Herein, we report the syntheses and antibacterial performance of degradable amphiphilic cationic polyesters containing pendent quaternary ammonium motifs and hydrophobic alkyl or fluoroalkyl groups. These polyesters were conveniently prepared from poly(3-methylene-1,5-dioxepan-2-one) via highly efficient one-pot successive thiol-Michael addition reactions. The antibacterial activity of these polyesters against S. aureus and E. coli and their hemolytic activity toward red blood cells were evaluated; some of them showed moderate antibacterial activity and selectivity against Gram-positive S. aureus. The membrane disruption mechanism of these cationic polyesters was briefly explored by monitoring the bacteria killing kinetics and SEM observations. Moreover, the effects of cationic/hydrophobic ratio and the incorporation of fluoroalkyl groups on the antibacterial activity and selectivity of the polyesters were demonstrated. [ABSTRACT FROM AUTHOR]

The hydrodynamic model, based on the strict conservation of momentum and continuity equations, can accurately simulate the distribution of a flow field. However, significant computing time and storage space requirements limit real-time prediction. Machine learning is well known for its fast computing speed and powerful learning ability, but its accuracy depends on an abundance of training data, hindering its wider use in locations without sufficient measurements. Application restrictions in data-deficient areas can be addressed through transfer learning, provided that two areas share common characteristics. In this study, a machine learning method based on a deep super-resolution convolutional neural network (DSRCNN) and transfer learning is proposed, validated, and applied to model two bend flows and one realistic test case. Firstly, the hydrodynamic model was established and validated against measured data. The validated model was considered to have the ability to generate real data and was used to generate a comprehensive data set for training and validating the machine learning model. Three different methods were compared and tested, with Realizable k-ε performing better than the others in predicting the outer bank flow distribution. DSRCNN was compared to a plain SRCNN (PSRCNN), as well as Bilinear, Nearest, and Bicubic methods, and the results showed that DSRCNN had the best performance. We compared Raw, RT, and TL methods, finding that the TL method performed the best overall. Therefore, the research results showed that the developed super-resolution convolutional neural network can provide more reliable predications and serve as an ideal tool for simulating flow field distribution in bends. [ABSTRACT FROM AUTHOR]

The construction of thematic map symbols is a very complex and intellectually demanding process, but thematic symbols can be automatically generated and easily shared on the web using the syntactic structure of semantic symbols. In this paper, the symbol types, inner structure, and design pattern are discussed. A syntactic construction theory based on letter (thematic maps primitive)-word (single thematic symbol)-sentence (combined symbols or complex symbols) structure model is proposed to automate the construction of thematic map symbols. As a result of this research, symbols can be defined using cartographic primitives which are arranged according to syntactic principles. A semiotic model and word-centered construction theory can be integrated into interactive cartography as represented by the technology of Internet. Finally, the concepts and schema of this theory are discussed, and some examples are presented based a web thematic cartographic system to verify its utility.