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The majority of chitosan-based bone tissue engineering (BTE) scaffolds have the problem of poor mechanical properties. However, modifying chitosan with conventional silane coupling agents to improve the mechanical properties of scaffolds will introduce additional complications, including cytotoxicity and poor biocompatibility. In this study, two types of organic–inorganic composite scaffolds (F-A-T0/T3/T5 and F-B-T5-P0/P0.5/P1.5/P2.5) were prepared using chitosan nanofibers (CSNF) prepared by the beating-homogenization method, combined with the sol–gel method, and further introduced polyvinyl alcohol (PVA). The F-A-T3 and F-B-T5-P1.5 exhibited interconnected pore and surface nanofibers structures, high porosity (>70%), outstanding swelling properties, and a controllable degradation rate. The Young’s modulus of TEOS: 5.0% (w/w), PVA: 1.5% (w/w) chitosan fiber scaffold is 8.53 ± 0.43 MPa in dry conditions, and 237.78 ± 8.86 kPa in wet conditions, which is four times that of F-A-T5 and twice that of F-B-T5-P0. Additionally, cell (MC3T3-E1) experiments confirmed that the two composite scaffolds had great cytocompatibility and were predicted to be used in the future in the field of BTE scaffolds.

Many studies based on single-point measurement data have demonstrated the impressive ability of wavelet coherency analysis to catch the coherent structures in the wall-bounded flows; however, the question of how the events found by the wavelet coherency analysis relate to the features of the coherent structures remains open. Time series of velocity fields in x−y plane of the steady open channel flow was obtained from a time-resolved particle imaging velocimetry system. The local wavelet spectrum found shows that one of the main energetic scales in open channel flows is 3h motions. The wavelet coherent coefficients of u and v series from the same measurement points successfully detected the occurrence and the scale of these 3h motions, and the phase angle indicates their inside velocity structure is organized by the Q2 and Q4 events. The wavelet coherency analysis between different measurement points further reveals the incline feature of the 3h scale motions. All the features of this 3h motion found by the wavelet coherency analysis coincide well with the flow field that is created by the passing of hairpin packets.

The controlled outflows from a reservoir are highly dependent on the decisions made by the reservoir operators who mainly rely on available hydrologic information, such as past outflows, reservoir water level and forecasted inflows. In this study, Random Forests (RF) algorithm is used to build reservoir outflow simulation model to evaluate the value of hydrologic information. The Three Gorges Reservoir (TGR) in China is selected as a case study. As input variables of the model, the classic hydrologic information is divided into past, current and future information. Several different simulation models are established based on the combinations of these three groups of information. The influences and value of hydrologic information on reservoir outflow decision-making are evaluated from two different perspectives, the one is the simulation result of different models and the other is the importance ranking of the input variables in RF algorithm. Simulation results demonstrate that the proposed model is able to reasonably simulate outflow decisions of TGR. It is shown that past outflow is the most important information and the forecasted inflows are more important in the flood season than in the non-flood season for reservoir operation decision-making.