Of 1,994 group B streptococcal isolates collected, 26 (1.3%) of the isolates were resistant to levofloxacin, and cross-resistance to other fluoroquinolones was observed. The emergence and prevalence of high-level fluoroquinolone resistance in genetically unrelated isolates were linked to the presence of gyrA, parC, and parE triple mutations in each isolate.

Pavement macrotexture is one of the major factors affecting pavement functions, and it is meaningful to reconstruct the pavement macrotexture rapidly and accurately for pavement life cycle performance and quality evaluation. To reconstruct pavement macrotexture from monocular image, a novel method was developed based on a deep convolutional neural network (CNN). First, the red-greenblue (RGB) images and depth maps (RGB-D) of pavement texture were acquired by smartphone and laser texture scanner, respectively, from various asphalt mixture slab specimens fabricated in the laboratory, and the pavement texture RGB-D dataset was established from scratch. Then, an encoder-decoder CNN architecture was proposed based on residual network-101, and different training strategies were discussed for model optimization. Finally, the precision of the CNN and the three-dimensional characteristics of the reconstructed macrotexture were analyzed. The results show that the established RGB-D dataset can be used for training directly, and the established CNN architecture is plausible and effective. The mean texture depth and f 8mac of the reconstructed macrotexture both correlate with the benchmarks significantly, and the correlation coefficients are 0.88 and 0.96, respectively. It could be concluded that the proposed CNN can reconstruct the macrotexture from monocular RGB images precisely, and the reconstructed macrotexture could be further used for pavement macrotexture evaluation.

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The kneading process and formulations of feedstock obviously affect the quality of MIM products. In the present work, the rheological behaviour of the composite MIM feedstock, metal matrix (Cu) with few additions of ceramic powders (Al2O3), was measured by a selfdesigned/ manufactured simple capillary rheometer. Experimental results show that the distribution between powders and binder is more uniformly when blending time increased. Though high powder loading will increase the feedstock viscosity, the fluidity reveals relatively stable through the load curves of extrusion. Besides, the temperature-dependence of viscosity of the feedstock approximately follows an Arrehnius equation. Basing on Taguchi’s method, the kneading optimization conditions and the rheological model of the feedstock were established, respectively.

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