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The poor corrosion resistance of magnesium limits its clinical applications. Accordingly, in the present study, carbon ions were incorporated into a AZ31b magnesium alloy surface via carbon plasma immersion ion-implantation to improve its corrosion resistance and biocompatibility. The surface morphology and properties of the modified alloy were evaluated by field-emission scanning electron microscopy, water contact angle measurement, Raman scattering, X-ray photoelectron spectroscopy, and energy dispersive X-ray spectroscopy. Furthermore, compositional depth profiles were obtained by glow discharge optical emission spectroscopy, revealing a Gaussian-like distribution of carbon concentration. Electrochemical and hydrogen-evolution analysis demonstrated the successfully improved corrosion resistance of the AZ31b Mg alloy, while its biocompatibility was demonstrated by MTT and cell-adherence assays. [ABSTRACT FROM AUTHOR]

Deep learning models have been widely applied in the field of long-term forecasting has achieved significant success, with the incorporation of inductive bias such as periodicity to model multi-granularity representations of time series being a commonly employed design approach in forecasting methods. However, existing methods still face challenges related to information redundancy during the extraction of inductive bias and the learning process for multi-granularity features. The presence of redundant information can impede the acquisition of a comprehensive temporal representation by the model, thereby adversely impacting its predictive performance. To address the aforementioned issues, we propose a De-Redundant Multi-Period Hybrid Modeling Network (DPHM-Net) that effectively eliminates redundant information from the series inductive bias extraction mechanism and the multi-granularity series features in the time series representation learning. In DPHM-Net, we propose an efficient time series representation learning process based on a period inductive bias and introduce the concept of de-redundancy among multiple time series into the representation learning process for single time series. Additionally, we design a specialized gated unit to dynamically balance the elimination weights between series features and redundant semantic information. The advanced performance and high efficiency of our method in long-term forecasting tasks against previous state-of-the-art are demonstrated through extensive experiments on real-world datasets. [ABSTRACT FROM AUTHOR]