It is of great importance to build an accurate model for the multi-step forecasting of household power consumption. In recent years, more and more researchers have focused on adopting hybrid models to execute forecasting due to the irregularity and nonlinearity of power data. However, existing forecasting models usually make predictions directly on original data. This paper introduces secondary decomposition algorithm used to decompose primal data. We use singular spectrum analysis (SSA) to decompose the original series into several subseries, utilize variational mode decomposition (VMD) optimized by whale optimization algorithm to decompose the subseries with the highest frequency into several intrinsic mode functions . Then all subseries obtained from SSA and VMD are fed into long short-term memory model to get predictions. In order to confirm the validity of proposed model, this paper performs several experimental analyses. The results of experiments show that the proposed model effectively improves the accuracy of forecasting.
The application of deep learning technology in healthcare has been widely studied, but there has been limited research on the management of surgical tools. Therefore, we propose a study focusing on the recognition and classification of surgical tools to reduce the risk caused by their loss. Firstly, we design a surgical tool collection system and construct a commonly used surgical tool dataset (STD). Secondly, we investigate two embedding strategies using an attention mechanism in the benchmark network to select suitable tool recognition methods. The first strategy is to embed attention in the base module of the extraction network, known as the embedding strategy. The second strategy is to embed attention in the last step before prediction, called the additional strategy. Our experimental results show that the performance of the additional strategy is superior to that of the embedded strategy in the surgical tool detection task. Using the additional strategy with the improved Yolov5s network based on SimAM attention resulted in an average accuracy of 0.972 while keeping GFLOPs unchanged. The model proposed in this paper outperformed advanced models and showed performance improvement on STD.
Interleukin‐31 (IL‐31), belonging to the IL‐6 cytokine family, is involved in skin inflammation and pruritus, as well as some tumors’ progression. Here, we reported the expression and purification of recombinant human IL‐31 (rhIL‐31) using a prokaryotic system. This recombinant protein was expressed in the form of inclusion bodies, refolded and purified by size‐exclusion chromatography. Circular dichroism analysis revealed that the secondary structure of rhIL‐31 was mainly composed of alpha‐helix, which is in consistence with the 3D model structure built by AlphaFold server. In vitro studies showed that rhIL‐31 exhibited a good binding ability to the recombinant hIL‐31 receptor alpha fused with human Fc fragment (rhIL‐31RA‐hFc) with EC50 value of 16.36 µg/mL in ELISA assay. Meanwhile, flow cytometry demonstrated that rhIL‐31 was able to bind to hIL‐31RA or hOSMRβ expressed on the cell surface, independently. Furthermore, rhIL‐31 could induce the phosphorylation of STAT3 in A549 cells. In conclusion, the prepared rhIL‐31 in this study possesses the binding ability to its receptors, and can activate the signal pathway of JAK/STAT. Thus, it can be applied in further studies, including investigation of hIL‐31‐related diseases, structural analysis, and development of therapeutic drugs, and monoclonal antibodies targeting hIL‐31.
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