Showing papers by "Xin Liu published in 2022"

Effect of laser shock peening on fatigue properties of U75VG rail flash-butt welding joints

Abstract: Rail-welded joints have an important impact on the safety of high-speed railways. Laser shock peening (LSP) is performed on these joints to improve their bending fatigue performance. This study examines the effect of LSP treatment on residual stress, surface microstructure, and surface morphology of the rail base of U75VG flash-butt welding joints. First, the characteristics of the LSP-treated and untreated specimens were evaluated, then the fatigue test was carried out. The results indicated that the LSP treatment generated compressive residual stress with an average value of −436.2 MPa on the surface, and the depth of the corresponding stress layer exceeded 1.5 mm. Both the surface dislocation density and hardness were increased. The fatigue S-N curves indicated that LSP treatment increased the fatigue life of the specimens, particularly under low bending stress conditions. The fatigue limit increased by 6.7% from 264.0 MPa. The fatigue fracture revealed that high-density dislocations enhanced the resistance to the fatigue crack growth, resulting in the crack propagation zone near the LSP-treated surface having a rough morphology with steps. The mass-produced pits on the LSP-treated surface and the induced compressive residual stress could be significant at the crack initiation phase.

An Evolutive Frequent Pattern Tree-based Incremental Knowledge Discovery Algorithm

Abstract: To understand current situation in specific scenarios, valuable knowledge should be mined from both historical data and emerging new data. However, most existing algorithms take the historical data and the emerging data as a whole and periodically repeat to analyze all of them, which results in heavy computation overhead. It is also challenging to accurately discover new knowledge in time, because the emerging data are usually small compared to the historical data. To address these challenges, we propose a novel knowledge discovery algorithm based on double evolving frequent pattern trees that can trace the dynamically evolving data by an incremental sliding window. One tree is used to record frequent patterns from the historical data, and the other one records incremental frequent items. The structures of the double frequent pattern trees and their relationships are updated periodically according to the emerging data and a sliding window. New frequent patterns are mined from the incremental data and new knowledge can be obtained from pattern changes. Evaluations show that this algorithm can discover new knowledge from evolving data with good performance and high accuracy.

Strategies for the preparation of high-performance inorganic mixed-halide perovskite solar cells

Abstract: Inorganic halide perovskites have attracted significant attention in the field of photovoltaics (PV) in recent years due to their superior intrinsic thermal stability and excellent theoretical power conversion efficiency (PCE). CsPbI3 with a bandgap of ∼1.7 eV is considered to be the most potential candidate for PV application. However, bulk CsPbI3 films exhibit poor phase stability. The substitution of some iodide ions with bromide/chloride in CsPbI3 results in the formation of mixed-halide CsPbX3 perovskites, which exhibit a good balance between phase stability and efficiency. The halogen-tunable mixed-halide inorganic perovskites have a bandgap matching the sunlight region and show great potential for application in multi-junction tandem and semitransparent solar cells. Herein, the progress of mixed-halide CsPbX3 PSCs is systematically reviewed, including CsPbIxBryCl3−x−y- and CsPbIBr2-based IPSCs. In the case of CsPbIBr2 IPSCs, we introduce the low-temperature deposition of CsPbIBr2 films, doping methods for the preparation of high-quality CsPbIBr2 films and strategies for improving the performance of solar cells. Furthermore, the mechanism of crystallization/interface engineering for the preparation of high-quality CsPbIBr2 films and efficient solar cells devices is emphasized. Finally, the development direction of further improving the PV performance and commercialization of mixed-halide IPSCs are summarized and prospected.

Transcriptomic signatures responding to PKM2 activator TEPP-46 in the hyperglycemic human renal proximal epithelial tubular cells

Abstract: Pyruvate kinase M2 (PKM2), as the terminal and last rate-limiting enzyme of the glycolytic pathway, is an ideal enzyme for regulating metabolic phenotype. PKM2 tetramer activation has shown a protective role against diabetic kidney disease (DKD). However, the molecular mechanisms involved in diabetic tubular have not been investigated so far. In this study, we performed transcriptome gene expression profiling in human renal proximal tubular epithelial cell line (HK-2 cells) treated with 25 mM high D-glucose (HG) for 7 days before the addition of 10 μM TEPP-46, an activator of PKM2 tetramerization, for a further 1 day in the presence of HG. Afterwards, we analyzed the differentially expressed (DE) genes and investigated gene relationships based on weighted gene co-expression network analysis. The results showed that 2,902 DE genes were identified (adjusted P-value ≤ 0.05), where 2,509 DE genes (86.46%) were co-expressed in the key module. Four extremely downregulated DE genes (HSPA8, HSPA2, HSPA1B, and ARRB1) and three extremely upregulated DE genes (GADD45A, IGFBP3, and SIAH1) enriched in the downregulated endocytosis (hsa04144) and upregulated p53 signaling pathway (hsa04115), respectively, were validated by qRT-PCR experiments. The qRT-PCR results showed that the relative expression levels of HSPA8 [adjusted P-value = 4.45 × 10-34 and log2(FC) = -1.12], HSPA2 [adjusted P-value = 6.09 × 10-14 and log2(FC) = -1.27], HSPA1B [adjusted P-value = 1.14 × 10-11 and log2(FC) = -1.02], and ARRB1 [adjusted P-value = 2.60 × 10-5 and log2(FC) = -1.13] were significantly different (P-value < 0.05) from the case group to the control group. Furthermore, the interactions and predicted microRNAs of the key genes (HSPA8, HSPA2, HSPA1B, and ARRB1) were visualized in networks. This study identified the key candidate transcriptomic biomarkers and biological pathways in hyperglycemic HK-2 cells responding to the PKM2 activator TEPP-46 that can highlight a possibility of PKM2 tetramerization reshaping the interplay among endocytic trafficking through the versatile networks of Hsp70s and rewiring the crosstalk between EGFR signal transduction circuits and metabolic stress to promote resilience, which will be valuable for further research on PKM2 in DKD.

Weibo Spammer Detection Based On Social Network Digital Twin

Abstract: Users are increasingly willing to share their comments on the Internet. The popularity of Weibo has spawned spammers. Comments from spammers affect normal Internet public opinion. The traditional spammer detection methods are mainly based on the static characteristics of users and accuracies are not ideal. In this paper, we apply the parallel system framework to build a social network digital twin. The nodes of the digital twin are mapped to the nodes of the graph attention network and the relationships between nodes in the digital twin are mapped to the neighbor nodes in the graph attention network. The feature vectors of nodes are updated by the stacked graph attention layer. We take the output of the attention layer as the input of the full connection layer. The softmax classifier is used to get the classification results. In this paper, we wrote a crawler to collect the individual information and follow the relationship of 2,000 users, screened out 15 user characteristics, and manually annotated them. The experimental results show that the model we proposed has higher accuracy than the naive Bayes model and decision tree.