No abstract

Polyvinyl alcohol (PVA) hydrogel is considered the most promising candidate for artificial cartilage because of its good lubricity and low permeability. However, the efficacy of single-network PVA hydrogels under variable loads has not yet been determined. In this study, a one-step physical-cross-linking method was used to compose PVA/PEG double-network composite hydrogels. We changed the polyethylene glycol (PEG) content of the composite gel and tested the frictional-wear performance under different loads and speeds. With the mass fraction of PEG at 30 wt.%, the hardness of the PVA/PEG composite gel increased to 167.7% that of pure PVA. Under dry-friction conditions, the average coefficient of friction was approximately 0.14 and the wear rate on the surface of the hydrogel was insignificant. The cross-linking between PVA and PEG greatly enhanced the stability of the composite hydrogel polymer network. The structure of the composite gel was analyzed by a variety of standard methods. The hydrogel has excellent biocompatibility and self-healing ability and is a self-lubricating, self-healing candidate material for articular-cartilage repair.

Background: CD8+ T cells, a critical component of the tumor immune microenvironment, have become a key target of cancer immunotherapy. Considering the deficiency of robust biomarkers for head and neck squamous cell carcinoma (HNSCC), this study aimed at establishing a molecular signature associated with CD8+T cells infiltration.Methods: Single-cell RNA sequencing data retrieved from the Gene Expression Omnibus (GEO) database was analyzed to obtain the different cell types. Next, the cell proportions were investigated through deconvolution of RNA sequencing in the Cancer Genome Atlas (TCGA) database, and then the immune-related genes (IRGs) were identified by weighted gene co-expression network analysis (WGCNA). LASSO-Cox analysis was employed to establish a gene signature, followed by validation using a GEO dataset. Finally, the molecular and immunological properties, and drug responses between two subgroups were explored by applying “CIBERSORT”, “ESTIMATE”, and single sample gene set enrichment analysis (ssGSEA) methods.Results: A total of 215 differentially expressed IRGs were identified, of which 45 were associated with the overall survival of HNSCC. A risk model was then established based on eight genes, including DEFB1, AICDA, TYK2, CCR7, SCARB1, ULBP2, STC2, and LGR5. The low-risk group presented higher infiltration of memory activated CD4+ T cells, CD8+ T cells, and plasma cells, as well as a higher immune score, suggesting that they could benefit more from immunotherapy. On the other hand, the high-risk group showed higher abundance of activated mast cells and M2 macrophages, as well as a lower immune score.Conclusion: It was evident that the 8-gene signature could accurately predict HNSCC prognosis and thus it may serve as an index for clinical treatment.

Aiming at solving the problem of dual resource constrained flexible job shop scheduling problem (DRCFJSP) with differences in operating time between operators, an artificial intelligence (AI)-based DRCFJSP optimization model is developed in this paper. This model introduces the differences between the loading and unloading operation time of workers before and after the process. Subsequently, the quantum genetic algorithm (QGA) is used as the carrier; the process is coded through quantum coding; and the niche technology is used to initialize the population, adaptive rotation angle, and quantum mutation strategy to improve the efficiency of the QGA and avoid premature convergence. Lastly, through the Kacem standard calculation example and the reliability analysis of the factory workshop processing process example, performance evaluation is conducted to show that the improved QGA has good convergence and does not fall into premature ability, the improved QGA can solve the problem of reasonable deployment of machines and personnel in the workshop, and the proposed method is more effective for the DRCFJSP than some existing methods. The findings can provide a good theoretical basis for actual production and application.

Dimensional variation has significant effect on the quality of product. Recently, Monte Carlo (MC) simulation is widely used in dimensional variation analysis, with high accuracy and adaptability, but there is the problem of low computational efficiency. Aiming to address this problem, an improvement of MC simulation is proposed through a two-phases solution. In the first phase, surrogate model is used to approximate the locating constraint equations for a 3D part, which reduces the nonlinear coupling between dimensional deviations and avoids large-scale solution of nonlinear equations in dimensional variation analysis on the condition of ensuring the accuracy. In the second phase, random samples used in MC simulation are replaced by low discrepancy sequences, which enable the samples to have better homogeneity and representativeness and reduce the number of samples required in the dimensional variation analysis. Finally, two examples are used to demonstrate the effectiveness of the method, and the results show that the two-phases solution has advantages both in the accuracy and efficiency.