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Periprosthetic joint infection (PJI) is one of the most pivotal issues accounting for clinical failure of titanium (Ti)-based implants. Therefore, it is of great significance to develop an antibacterial coating with high biocompatibility on the Ti implant surface. In this article, a novel zinc-doped strontium phosphate coating, denoted as Sr-Zn-PCC, was deposited on Ti substrates by phosphate chemical conversion (PCC) method. The texture characterizations indicated that continuous coatings containing SrHPO4, Sr3(PO4)2 and SrZn2(PO4)2 phases were formed on the Ti surface. It is found that the doping of Zn could tune the surface microstructure, roughness, wettability and corrosion resistance of the coatings. The Sr-Zn-PCC coating showed the same bacterial adhesion property at short time (2 h), but tended to hinder biofilm formation on coatings after incubation for up to 24 h. Up to 80% of Staphylococcus aureus (S. aureus) were killed within 24 h by contact with the Sr-Zn-PCC surface. Moreover, cytocompatibility assay indicated that MC-3T3 cells had good adhesion, spreading properties on the coatings, suggesting that it is a promising alloy with both excellent antibacterial ability and high biocompatibility for the applications on orthopedic implants.

In order to predict the distribution of gas flow, we need to get the temperature of each point in blast furnace throat in advance. In this paper, firstly, two intelligent modeling methods are used to establish a multiple-input multiple-output prediction model, one is extreme learning machine (ELM) algorithm and the other is online sequential extreme learning machine (OS-ELM) algorithm. And the model is a single-step prediction model of temperature in blast furnace, single-step prediction means the prediction of temperature in the next moment. We use autocorrelation analysis to determine input vector and output vector of the model. The result of autocorrelation analysis indicates that the method of temperature sequence prediction has a higher prediction accuracy and better prediction stability than the method of single point prediction. Next, based on real industrial data, we make a comparison between the multiple - input multiple-output model and least squares support vector machines (LS-SVM) model used in common. The experiment results show that OS-ELM model has a better forecast effect than the ELM model and LS-SVM model.

An underpinning software is devoted to THz spectrum analyzing and 3-D imaging. The paper describes the software's outline, structure, functions and some of considerations. Users in LAN (local area network) can access it and implement some basic and advanced works such as files operation, echoes cutting, spectrum calculation, baseline cancelling, peak fitting, qualitative and quantitative measuring of solid-state samples.