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MXenes are an emerging class of 2D materials with unique properties including metallic conductivity, mechanical flexibility, and surface tunability, which ensure their utility for diverse applications. However, the synthesis of MXenes with high crystallinity and atomic stoichiometry in a low‐cost process is still challenging because of the difficulty in controlling the oxygen substitute in the precursors and final products of MXenes, which limits their academic understanding and practical applications. Here, a novel cost‐effective method is reported to synthesize a highly crystalline and stoichiometric Ti3C2Tx MXene with minimum substitutional oxygen impurities by controlling the amount of excess carbon and time of high‐energy milling in carbothermal reduction of recycled TiO2 source. The highest used content (2 wt%) of excess‐carbon yields TiC with the highest carbon content and minimal oxygen substitutes, which leads to the Ti3AlC2 MAX phase with improved crystallinity and atomic stoichiometry, and finally Ti3C2Tx MXene with the highest electrical conductivity (11738 S cm−1) and superior electromagnetic shielding effectiveness. Additionally, the effects of carbon content and substitutional oxygen on the physical properties of TiC and Ti3AlC2 are elucidated by density‐functional‐theory calculations. This inexpensive TiO2‐based method of synthesizing high‐quality Ti3C2Tx MXene can facilitate large‐scale production and thus accelerate global research on MXenes. [ABSTRACT FROM AUTHOR]

Numerous collision conditions can occur during ship operations, resulting in various consequences that require specific consideration for optimizing crashworthiness design. Existing studies have investigated crashworthiness design in ship structures; however, they often focus on single working conditions and do not comprehensively consider the diverse scenarios encountered during ship operations. To overcome this drawback, this paper proposes a novel method that addresses multi-working conditions and combines orthogonal testing with a backpropagation neural network (BPNN) to establish an efficient surrogate model for collision optimization. The accuracy of the BPNN was improved by introducing the genetic algorithm and Adam algorithm. The technique for order preference by similarity to ideal solution (TOPSIS) is introduced to formulate a multi-working condition optimization function. The crashworthiness of the ship structure is optimized using the sparrow search algorithm (SSA) while considering the constraint of lightweight design. The results demonstrate a substantial reduction in the objective functions for the optimized collision conditions. Moreover, the BPNN predicted values are in good agreement with the finite element simulation results, affirming the effectiveness of the proposed method in improving the crashworthiness of the ship structure and providing valuable guidance for engineering design. [ABSTRACT FROM AUTHOR]

Cloud storage service enables users to migrate their data and applications to the cloud, which saves the local data maintenance and brings great convenience to the users. But in cloud storage, the storage servers may not be fully trustworthy. How to verify the integrity of cloud data with lower overhead for users has become an increasingly concerned problem. Many remote data integrity protection methods have been proposed, but these methods authenticated cloud files one by one when verifying multiple files. Therefore, the computation and communication overhead are still high. Aiming at this problem, a hierarchical remote data possession checking (hierarchical-remote data possession checking (H-RDPC)) method is proposed, which can provide efficient and secure remote data integrity protection and can support dynamic data operations. This paper gives the algorithm descriptions, security, and false negative rate analysis of H-RDPC. The security analysis and experimental performance evaluation results show that the proposed H-RDPC is efficient and reliable in verifying massive cloud files, and it has 32–81% improvement in performance compared with RDPC.

Background: Studies have shown that long noncoding RNAs and N6-methyladenosine play important roles in gastric cancer. The purpose of this study was to determine the correlation and prognostic value of m6A-related lncRNAs and immune infiltration in gastric cancer. Methods: We downloaded the clinically related information and RNA-Seq transcriptome data of gastric cancer patients from the TCGA database. Univariate Cox regression analysis and Pearson analysis were used to screen out m6A-related lncRNAs. Consensus cluster analysis was used to divide the sample into two clusters, and LASSO analysis and Cox regression analysis were used to construct a risk scoring model. Results: A total of 25 lncRNA expression profiles were screened, and gastric cancer patients were divided into different subtypes. Cluster 2 had a better prognosis, but its stromal score, ESTIMATE score and immune score were low. Cluster 1 was rich in resting memory CD4 T cells, regulatory T cells, monocytes, and resting mast cells, and Cluster 2 was rich in activated memory CD4 T cells and follicular helper T cells. Thirteen lncRNAs were selected to construct a risk model, and the prognosis of gastric cancer patients in the high-risk group was poor. The expression of PD-L1 in tumours is significantly higher than that in normal tissues. Univariate and multivariate Cox regression analysis results showed that the overall survival rate was significantly related to stage and the risk score, which can be used as an independent prognostic factor. The results of the heatmap and scatter plot showed that clusters (P = 0.0045) and grade (G1–2, G3, P = 0.0037) were significantly related to prognosis. The relationship between the risk score and immune cell infiltration showed that memory B cells, resting dendritic cells, M0 macrophages, and M2 macrophages were positively correlated with the risk score, while resting mast cells, monocytes, activated NK cells, and follicular helper T cells were negatively correlated with the risk score. Conclusion: The results of this study indicate that m6A-related lncRNAs may play an important role in the prognosis of gastric cancer patients and the tumour immune microenvironment and may provide help for the treatment of gastric cancer patients. [ABSTRACT FROM AUTHOR]