Bacterial cellulose (BC), a network of pure cellulose nanofibers with fine crystallinity, high mechanical strength and wet capability, and good biocompatibility, is a good material candidate for wound dressing. Hyaluronan (HA) has obvious curative properties, promoting the healing of wound skin tissue and reducing scar formation. This study explored an ''orifice plate'' culture method to obtain BC samples of different sizes but consistent thicknesses. Novel BC-HA nanocomposites with a 3-D network structure were obtained through a solution impregnation method. The total surface area and the pore volume of the BC-HA composite films gradually decreased with the increase of HA content. The elongation of BC-HA composite films at the break point gradually increased as the HA content increased while the tensile strength of the BC-HA composite films decreased during the same process. The BC-HA composite films had a better water uptake capability than pure BC, and water vapor transmission rate (WVTR) measurements showed that the BC-HA composite films can satisfy breathing requirements of injured skin. The BC-HA composite films facilitated the growth of primary human fibroblast cells, showing their low toxicity, and the BC-HA composite films with 0.1% HA lead to higher levels of cell viability than the pure BC. In vivo experiments indicated that the BC-HA with 0.1% HA had the shortest wound healing time while BC-HA with 0.05% HA yielded best tissue repair results. The BC-HA composite films are expected to be useful as novel wound dressing materials for clinical skin repair.

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Predicting the degradation process of bearings before they reach the failure threshold is extremely important in industry. This paper proposed a novel method based on the support vector machine (SVM) and the Markov model to achieve this goal. Firstly, the features are extracted by time and time-frequency domain methods. However, the extracted original features are still with high dimensional and include superfluous information, and the nonlinear multifeatures fusion technique LTSA is used to merge the features and reduces the dimension. Then, based on the extracted features, the SVM model is used to predict the bearings degradation process, and the CAO method is used to determine the embedding dimension of the SVM model. After the bearing degradation process is predicted by SVM model, the Markov model is used to improve the prediction accuracy. The proposed method was validated by two bearing run-to-failure experiments, and the results proved the effectiveness of the methodology.

Laser speckle photometry (LSP) is an innovative, non-destructive monitoring technique based on the detection and analysis of thermally or mechanically activated speckle dynamics in a non-stationary optical field. With the development of speckle theories, it has been found that speckle patterns carry information about surface characteristics. Therefore, LSP offers a great potential for the characterization of material properties and monitoring of manufacturing processes. In contrast to the speckle interferometry method, LSP is very simple and robust. The sample is illuminated by only one laser beam to generate a speckle pattern on the surface. The signals obtained are directly recorded by a CCD or CMOS camera. By appropriate optical solutions for the beam path, typically, resolutions of less than 10 μm are reached if larger areas are illuminated. LSP is definitely a contactless, quick and quality relevant material characterization and defect detection method, allowing process monitoring in many industrial fields. Examples from online biotechnological monitoring and laser based manufacturing demonstrate further potentials of the method for process monitoring and controlling.