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The 5G networks are broadly characterized by three unique features: ubiquitous connectivity, extremely low latency, and extraordinary high-speed data transfer. The challenge of 5G is to assure the network performance and different quality of service (QoS) requirements of different services, such as machine type communication (MTC), enhanced mobile broad band (eMBB), and ultra-reliable low latency communications (URLLC) over 5G networks. Unlike the previous ''one size fits all'' system, the softwarization, slicing and network capability exposure of 5G provide dynamic programming capabilities for QoS assurance. With the increasing complexity and dynamics of the network behaviors, it is non-trivial for a programmer to develop traditional software codes to schedule the network resources based on expert knowledge, especially when there is no quantitative relationship among the network events and the QoS anomalies. Machine learning is a computer technology that gives computer systems the ability to learn with data and improve performance and accuracy of decision making on a specific task, without being explicitly programmed. The areas of machine learning and communication technology are converging. Supervised learning based QoS assurance architecture for 5G networks was proposed in this paper. The supervised machine learning mechanisms can intelligently learn the network environment and react to dynamic situations. They can learn from the fore passed QoS related information and anomalies, and further reconstruct the relationship between the fore passed data and the current QoS related anomalies automatically and accurately. They, then, can trigger automatic mitigation or provide suggestions. The supervised machine learning mechanisms can also predict future QoS related anomalies with high confidence. In this paper, a case study for QoS anomaly root cause tracking based on decision tree was given to validate the proposed framework architecture.

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Zinc is a prospective metal for biodegradable cardiovascular stent applications, but the excessively released Zn2+ during degradation remains a huge challenge in biocompatibility. Considerable efforts have been made to develop a high-efficient surface modification method, while maintaining adhesion strength, mechanical support, and vascular compatibility. Biomimetic polydopamine (PDA) can adhere to Zn tightly, subsequently achieving robust chemical bonds with poly­(lactic-co-glycolic acid) (PLGA) coating. However, the deposition of PDA on Zn depends on the controlled conditions such as a sensitive pH and a long period of time. Herein, we introduce vacuum ultraviolet-ozone (VUV/O3) assist-deposition technology to accelerate the polymerization of PDA on pure Zn, which shortens the process to 40 min at a moderate pH of 8.5 and improves the deposition rate by 1–2 orders of magnitude under sufficient active oxygen species (ROS). Additionally, PLGA/PDA coating enhances the corrosion resistance, and their effective protection maintains the mechanical properties after long-term corrosion. Moreover, the controlled Zn2+ release contributes to the superior in vitro biocompatibility, which inhibits the hemolysis rate and smooth muscle cell (SMC) proliferation. The enhanced endothelial cell (EC) proliferation is promising to promote the re-endothelialization, avoiding in-stent restenosis and neointimal hyperplasia. Such modified Zn might be a viable candidate for the treatment of cardiovascular diseases.