Metallic states in Pb10_{10}(PO4_4)6_6O induced by the Cu/O-insertions and carrier doping

The reports of room-temperature superconductivity in Cu-doped Pb-apatite draw intense interests and debates. Herein, based on the density functional theory, we show that the Cu-insertion in Pb-apatite is thermodynamically stable and further carrier doping can convert the system CuPb$_{10}$(PO$_4$)$_6$O into metal. The electric conduction is mainly along the one-dimensional (1D) Cu-O chains in the c-axis (out-of-plane). The calculated conductance along the c-axis is larger than the in-plane ones by 2 magnitude orders, indicating the 1D conduction behavior. Moreover, the 1D Cu-O chain is an anti-ferromagnetic Mott insulator at zero-doping point due to the super-exchange. Further electron/hole-doping will erase the anti-ferromagnetism. Therefore, the Cu-inserted system CuPb$_{10}$(PO$_4$)$_6$O show transport and magnetic features similar to the cuprate superconductors. On the other hand, the O-insertion can also induce the metallic states, in which the conductance along the out-of-plane direction is higher than the in-plane direction by 6-folds. Our results display the metallization of Pb$_{10}$(PO$_4$)$_6$O via Cu/O-insertions, and suggesting the electric conductions along the c-axis might dominate the transport behaviors.Comment: 17 pages, 9 figure

Interactive Contrastive Learning for Self-supervised Entity Alignment

Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without seed alignments. The current SOTA self-supervised EA method draws inspiration from contrastive learning, originally designed in computer vision based on instance discrimination and contrastive loss, and suffers from two shortcomings. Firstly, it puts unidirectional emphasis on pushing sampled negative entities far away rather than pulling positively aligned pairs close, as is done in the well-established supervised EA. Secondly, KGs contain rich side information (e.g., entity description), and how to effectively leverage those information has not been adequately investigated in self-supervised EA. In this paper, we propose an interactive contrastive learning model for self-supervised EA. The model encodes not only structures and semantics of entities (including entity name, entity description, and entity neighborhood), but also conducts cross-KG contrastive learning by building pseudo-aligned entity pairs. Experimental results show that our approach outperforms previous best self-supervised results by a large margin (over 9% average improvement) and performs on par with previous SOTA supervised counterparts, demonstrating the effectiveness of the interactive contrastive learning for self-supervised EA.Comment: Accepted by CIKM 202

Annual report 1984-1985

BACKGROUND: HOTAIR, a newly discovered long intergenic noncoding RNA (lincRNA), has been reported to be aberrantly expressed in many types of cancers. This meta-analysis summarizes its potential role as a biomarker in malignancy. METHODS: A quantitative meta-analysis was performed through a systematic search in Pubmed, Medline and Web of Science for eligible papers on the prognostic impact of HOTAIR in cancer from inception to Feb. 28, 2014. Pooled hazard ratios (HRs) with 95% confidence interval (95% CI) were calculated to summarize the effect. RESULTS: Nineteen studies were included in the study, with a total of 2033 patients. A significant association was observed between high HOTAIR expression and poor overall survival (OS) in patients with cancer (pooled HR 2.22, 95% CI: 1.68-2.93). Place of residence (Asian or Western countries), type of cancer (digestive or non-digestive disease), sample size (more or less than 100), and paper quality (score more or less than 85%) did not alter the significant predictive value of HOTAIR in OS from various kinds of cancer but preoperative status did. By combining HRs from Cox multivariate analyses, we found that HOTAIR expression was an independent prognostic factor for cancer patients (pooled HR 2.26, 95% CI: 1.62-3.15). Subgroup analysis showed that HOTAIR abundance was an independent prognostic factor for cancer metastasis (HR 3.90, 95% CI: 2.25-6.74). For esophageal carcinoma, high HOTAIR expression was significantly associated with TNM stage (III/IV vs. I/II: OR 6.90, 95% CI: 2.81-16.9) without heterogeneity. In gastric cancer, HOTAIR expression was found to be significantly associated with lymph node metastases (present vs. absent: OR 4.47, 95% CI: 1.88-10.63) and vessel invasion (positive vs. negative: OR 2.88, 95% CI: 1.38-6.04) without obvious heterogeneity. CONCLUSIONS: HOTAIR abundance may serve as a novel predictive factor for poor prognosis in different types of cancers in both Asian and Western countries

Enhancement of Magnetoimpedance in Fe

With increasing Cu content x in Fe 74:5Àx Cu x Nb 3 Si 13:5 B 9 as cast ribbons, an enhancement of magnetoimpedance can be observed. The magnetoimpedance ðZðHÞ À Zð0ÞÞ=Zð0Þ for Fe 72 Cu 2:5 Nb 3 Si 13:5 B 9 as cast ribbon reaches À35% under a dc field H ¼ 7162 A/m at a frequency f ¼ 1 MHz. The longitudinal magnetoimpedance effect in Fe 74:5Àx Cu x Nb 3 Si 13:5 B 9 as cast ribbons is connected with the variation of transverse permeability. The grain size of -Fe(Si) in Fe 72 Cu 2:5 Nb 3 Si 13:5 B 9 as cast ribbon was estimated as about 7-10 nm. This revealed that soft magnetic nanocrystalline materials with giant magnetoimpedance effect can be obtained directly from the melt -spinning technique without additional annealing processes

An Ultrahigh Sensitivity Acetone Sensor Enhanced by Light Illumination

Au:SmFe0.9Zn0.1O3 is synthesized by a sol-gel method and annealed at 750 °C. Through XRD, SEM and XPS analysis methods, the microstructure of the material has been observed. The average particle size is about 50 nm. The sensor shows a high sensitivity toward acetone vapor. As the relative humidity increases, the resistance and sensitivity of the sensor decline. To obtain a low optimum operating temperature, light illumination with different wavelengths has been introduced. The sensitivity toward acetone is improved at lower operating temperature when the sensor is irradiated by light. The smaller the wavelengths, the better the sensitivity of the sensor. Compared with other gases, the sensor shows excellent selectivity to acetone vapor, with better sensitivity, selectivity and stability when under light illumination