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Biodegradation of plastic polymers by plastic-eating insects such as the greater wax moth (Galleria mellonella) might be promising for reducing plastic pollution, but direct in vivo evidence along with the related metabolic pathways and role of gut microbiota require further investigation. In this study, we investigated the in vivo degradation process, underlying potential metabolic pathways, and involvement of the gut microbiota in polystyrene (PS) biodegradation via enforcing injection of G. mellonella larvae (Tianjin, China) with PS microbeads (0.5 mg/larva; Mn: 540 and Mw: 550) and general-purpose PS powders (2.5 mg/larva; Mn: 95,600 and Mw: 217,000). The results indicated that the PS microplastics were depolymerized and completely digested independent of gut microbiota in G. mellonella although the metabolism could be enhanced by gut microbiota. Based on comparative metabolomic and liquid chromatography analyses, we proposed two potential metabolic pathways of PS in the intestine of G. mellonella larvae: the styrene oxide–phenylacetaldehyde and 4-methylphenol–4-hydroxybenzaldehyde–4-hydroxybenzoate pathways. These results suggest that the enzymes of G. mellonella are responsible for the efficient biodegradation of PS. Further study is needed to identify these enzymes and investigate the underlying catalytic mechanisms.

Research on the flow field inside a turbo classifier is complicated though important. According to the stochastic trajectory model of particles in gas-solid two-phase flow, and adopting the PHOENICS code, numerical simulation is carried out on the flow field, including particle trajectory, in the inner cavity of a turbo classifier, using both straight and backward crooked elbow blades. Computation results show that when the backward crooked elbow blades are used, the mixed stream that passes through the two blades produces a vortex in the positive direction which counteracts the attached vortex in the opposite direction due to the high-speed turbo rotation, making the flow steadier, thus improving both the grade efficiency and precision of the turbo classifier. This research provides positive theoretical evidences for designing sub-micron particle classifiers with high efficiency and accuracy.

In order to reduce grouting costs, a new preparation technology for wet-ground fine cement (WFC) was developed. After grinding by this method, the maximum size of cement particles in slurry is not bigger than 40 μm and the medium size (50% by weight) is less than 10 μm; thus, the slurry can be used to inject a rock body with micro-fissures. This newly developed grouting technology with the special gear-grinder has been used successfully in some hydropower projects in China such as Wan'an Project in Jiangxi province and the Three Gorges Project in Yichang city.

The ability to detect a new fault class can be a useful feature for an intelligent fault classification and diagnosis system. In this paper, we adopt two novelty detection methods, the support vector data description (SVDD) and the Parzen density estimation, to represent known fault class samples, and to detect new fault class samples. The experiments on real multi-class bearing fault data show that the SVDD can give both high identification rates for the prescribed ‘unknown’ fault samples and the known fault samples, which shows an advantage over the Parzen density estimation method in our experiments, via choosing the appropriate SVDD algorithm parameters.