Multi-frequency and multi-GNSS PPP phase bias estimation and ambiguity resolution

Multi-frequency and multi-GNSS measurements from modernized satellites are properly integrated for PPP with ambiguity resolution to achieve the state-of-the-art fast and accurate positioning, which provides an important contribution to GNSS precise positioning and applications. The multi-frequency and multi-GNSS PPP phase bias estimation and ambiguity resolution, which is accomplished by a unified model based on the uncombined PPP, are thoroughly evaluated with special focus on Galileo and BDS

Multi-frequency and multi-GNSS PPP phase bias estimation and ambiguity resolution

Multi-frequency and multi-GNSS measurements from modernized satellites are properly integrated for PPP with ambiguity resolution to achieve the state-of-the-art fast and accurate positioning, which provides an important contribution to GNSS precise positioning and applications. The multi-frequency and multi-GNSS PPP phase bias estimation and ambiguity resolution, which is accomplished by a unified model based on the uncombined PPP, are thoroughly evaluated with special focus on Galileo and BDS

Metal deficient AlB2_{2}-type (Ti0.2_{0.2}Zr0.2_{0.2}Hf0.2_{0.2}Nb0.2_{0.2}Ta0.2_{0.2})1−δ_{1-\delta}B2_{2} high-entropy diborides with high hardness

We report the synthesis and characterization of metal deficient (Ti$_{0.2}$Zr$_{0.2}$Hf$_{0.2}$Nb$_{0.2}$Ta$_{0.2}$)$_{1-\delta}$B$_{2}$ high-entropy diborides (HEBs). A single homogeneous AlB$_{2}$-type phase is successfully obtained over the $\delta$ range of 0.03 $\leq$ $\delta$ $\leq$ 0.18. With increasing $\delta$, the unit-cell volume exhibits a nonmonotonic variation with a maximum at $\delta$ = 0.07. These metal-deficient HEBs possess high Vickers hardness of 16.6-18.9 GPa at a load of 9.8 N and their phase stability is attributed to the increased mixing entropy. Our results not only present the first series of metal-deficient AlB$_{2}$-type HEBs, but also suggest the existence of similar multicomponent diborides.Comment: 7 pages, 4 figure

A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention

Deep neural networks (DNNs) have been widely employed in recommender systems including incorporating attention mechanism for performance improvement. However, most of existing attention-based models only apply item-level attention on user side, restricting the further enhancement of recommendation performance. In this paper, we propose a knowledge-enhanced recommendation model ACAM, which incorporates item attributes distilled from knowledge graphs (KGs) as side information, and is built with a co-attention mechanism on attribute-level to achieve performance gains. Specifically, each user and item in ACAM are represented by a set of attribute embeddings at first. Then, user representations and item representations are augmented simultaneously through capturing the correlations between different attributes by a co-attention module. Our extensive experiments over two realistic datasets show that the user representations and item representations augmented by attribute-level co-attention gain ACAM's superiority over the state-of-the-art deep models

Telomere maintenance-related genes are important for survival prediction and subtype identification in bladder cancer

Background: Bladder cancer ranks among the top three in the urology field for both morbidity and mortality. Telomere maintenance-related genes are closely related to the development and progression of bladder cancer, and approximately 60%–80% of mutated telomere maintenance genes can usually be found in patients with bladder cancer.Methods: Telomere maintenance-related gene expression profiles were obtained through limma R packages. Of the 359 differential genes screened, 17 prognostically relevant ones were obtained by univariate independent prognostic analysis, and then analysed by LASSO regression. The best result was selected to output the model formula, and 11 model-related genes were obtained. The TCGA cohort was used as the internal group and the GEO dataset as the external group, to externally validate the model. Then, the HPA database was used to query the immunohistochemistry of the 11 model genes. Integrating model scoring with clinical information, we drew a nomogram. Concomitantly, we conducted an in-depth analysis of the immune profile and drug sensitivity of the bladder cancer. Referring to the matrix heatmap, delta area plot, consistency cumulative distribution function plot, and tracking plot, we further divided the sample into two subtypes and delved into both.Results: Using bioinformatics, we obtained a prognostic model of telomere maintenance-related genes. Through verification with the internal and the external groups, we believe that the model can steadily predict the survival of patients with bladder cancer. Through the HPA database, we found that three genes, namely ABCC9, AHNAK, and DIP2C, had low expression in patients with tumours, and eight other genes—PLOD1, SLC3A2, RUNX2, RAD9A, CHMP4C, DARS2, CLIC3, and POU5F1—were highly expressed in patients with tumours. The model had accurate predictive power for populations with different clinicopathological features. Through the nomogram, we could easily assess the survival rate of patients. Clinicians can formulate targeted diagnosis and treatment plans for patients based on the prediction results of patient survival, immunoassays, and drug susceptibility analysis. Different subtypes help to further subdivide patients for better treatment purposes.Conclusion: According to the results obtained by the nomogram in this study, combined with the results of patient immune-analysis and drug susceptibility analysis, clinicians can formulate diagnosis and personalized treatment plans for patients. Different subtypes can be used to further subdivide the patient for a more precise treatment plan

Quantitatively analyzing the failure processes of rechargeable Li metal batteries.

Practical use of lithium (Li) metal for high–energy density lithium metal batteries has been prevented by the continuous formation of Li dendrites, electrochemically isolated Li metal, and the irreversible formation of solid electrolyte interphases (SEIs). Differentiating and quantifying these inactive Li species are key to understand the failure mode. Here, using operando nuclear magnetic resonance (NMR) spectroscopy together with ex situ titration gas chromatography (TGC) and mass spectrometry titration (MST) techniques, we established a solid foundation for quantifying the evolution of dead Li metal and SEI separately. The existence of LiH is identified, which causes deviation in the quantification results of dead Li metal obtained by these three techniques. The formation of inactive Li under various operating conditions has been studied quantitatively, which revealed a general “two-stage” failure process for the Li metal. The combined techniques presented here establish a benchmark to unravel the complex failure mechanism of Li metal