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Lithium (Li) metal has been regarded as the "Holy Grail" of Li battery anodes thanks to its high theoretic specific capacity and low reduction potential, but uneven formation of Li dendrites and uncontrollable Li volume changes hinder the practical applications of Li metal anodes. A three-dimensional (3D) current collector is one of the promising strategies to address the above issues if it can be compatible with current industrialized process. Here, Au-decorated carbon nanotubes (Au@CNTs) are electrophoretically deposited on commercial Cu foil as a 3D lithiophilic skeleton to regulate Li deposition. The thickness of the as-prepared 3D skeleton can be accurately controlled by adjusting the deposition time. Benefitting from the reduced localized current density and improved Li affinity, the Au@CNTs-deposited Cu foil (Au@CNTs@Cu foil) achieves uniform Li nucleation and dendrite-free Li deposition. Compared with bare Cu foil and CNTs deposited Cu foil (CNTs@Cu foil), the Au@CNTs@Cu foil exhibits enhanced Coulombic efficiency and better cycling stability. In the full-cell configuration, the Au@CNTs@Cu foil with predeposited Li shows superior stability and rate performance. This work provides a facial strategy to directly construct a 3D skeleton on commercial Cu foils with lithiophilic building blocks for stable and practical Li metal anodes.

Germanium-based multi-metallic-oxide materials have advantages of low activation energy, tunable output voltage, and high theoretical capacity. However, they also exhibit unsatisfactory electronic conductivity, sluggish cation kinetics, and severe volume change, resulting in inferior long-cycle stability and rate performance in lithium-ion batteries (LIBs). To solve these problems, we synthesize metal-organic frameworks derived from rice-like Zn2GeO4 nanowire bundles as the anode of LIBs via a microwave-assisted hydrothermal method, minimizing the particle size and enlarging the cation’s transmission channels, as well as, enhancing the electronic conductivity of the materials. The obtained Zn2GeO4 anode exhibits superior electrochemical performance. A high initial charge capacity of 730 mAhg−1 is obtained and maintained at 661 mAhg−1 after 500 cycles at 100 mA g−1 with a small capacity degradation ratio of ~0.02% for each cycle. Moreover, Zn2GeO4 exhibits a good rate performance, delivering a high capacity of 503 mA h g−1 at 5000 mA g−1. The good electrochemical performance of the rice-like Zn2GeO4 electrode can be attributed to its unique wire-bundle structure, the buffering effect of the bimetallic reaction at different potentials, good electrical conductivity, and fast kinetic rate.

Topic models can extract consistent themes from large corpora for research purposes. In recent years, the combination of pretrained language models and neural topic models has gained attention among scholars. However, this approach has some drawbacks: in short texts, the quality of the topics obtained by the models is low and incoherent, which is caused by the reduced word frequency (insufficient word co-occurrence) in short texts compared to long texts. To address these issues, we propose a neural topic model based on SBERT and data augmentation. First, our proposed easy data augmentation (EDA) method with keyword combination helps overcome the sparsity problem in short texts. Then, the attention mechanism is used to focus on keywords related to the topic and reduce the impact of noise words. Next, the SBERT model is trained on a large and diverse dataset, which can generate high-quality semantic information vectors for short texts. Finally, we perform feature fusion on the augmented data that have been weighted by an attention mechanism with the high-quality semantic information obtained. Then, the fused features are input into a neural topic model to obtain high-quality topics. The experimental results on an English public dataset show that our model generates high-quality topics, with the average scores improving by 2.5% for topic coherence and 1.2% for topic diversity compared to the baseline model.

Finite element methods and kinematically coupled schemes that decouple the fluid velocity and structure displacement have been extensively studied for incompressible fluid-structure interactions (FSIs) over the past decade. While these methods are known to be stable and easy to implement, optimal error analysis has remained challenging. Previous work has primarily relied on the classical elliptic projection technique, which is only suitable for parabolic problems and does not lead to optimal convergence of numerical solutions for the FSI problems in the standard L² norm. In this article, we propose a new stable fully discrete kinematically coupled scheme for the incompressible FSI thin-structure model and establish a new approach for the numerical analysis of FSI problems in terms of a newly introduced coupled nonstationary Ritz projection, which allows us to prove the optimal-order convergence of the proposed method in the L² norm. The methodology presented in this article is also applicable to numerous other FSI models and serves as a fundamental tool for advancing research in this field. [ABSTRACT FROM AUTHOR]

Change vector analysis (CVA) is a simple yet attractive method to detect changes with remote sensing images. Since its first introduction in 1980, CVA has received increased attention from the remote sensing community, leading to the definition of several new methodologies based on the CVAs concept while extending its applicability. In this article, we provide an extensive review of CVA-based approaches in the context of land-cover change detection (LCCD). We first reviewed the development of the CVA-based LCCD method with remote sensing images, and some classical-related methods were discussed. Then, we analyze and compare the performance of five selected methods. The analysis was carried out on seven real datasets acquired by different sensors and platforms (e.g., Landsat, Quick Bird, and airborne) and spatial resolutions (from 0.5 to 30 m/pixel), with scenes from both urban and natural landscapes. The analysis shows several Moreover, comparing the detection accuracies of different methods implies that the content of an image scene still plays an important role when disregarding the unique preferences of different methods.

BackgroundPregnancy serves as an important chapter in the life of women since more attention needs to be paid to both their physical and psychological health during this period. Adequate prenatal knowledge plays a key role in ensuring the health and safety of not only the pregnant women but also their fetuses and the entire family. With the development of information technology, web-based prenatal education has been brought into focus owing to its accessibility to comprehensive information, with high-quality information available to improve the quality of the overall gestation period, labor process, perinatal outcomes, and fetal outcomes. ObjectiveThis study aims to investigate the present situation of web-based prenatal education and to predict the future research direction of web-based prenatal education in China, thereby providing insights into improving the quality of health care of pregnant women. MethodsA national cross-sectional study was conducted on 590,912 pregnant women in 31 provincial administrations of mainland China between August 2018 and August 2019. These pregnant women were initially recruited from local hospitals across the nation during antenatal and postnatal periods via a web-based education school. Demographic information and course completion status (including the categories and the number of courses they completed) of all the participants were collected. ResultsA total of 590,912 pregnant women participated in the web-based prenatal education in 2018. Among them, 188,508 (31.90%) participants were excluded because they did not complete any course, while 17,807 (3.01%) actively participated in web-based prenatal education and completed more than 100 courses. There were 5 categories of web-based courses; almost half of the pregnant women attended the courses on first and second trimesters (293,262/590,912, 49.63% and 298,168/590,912, 50.46%, respectively). We found that pregnant women were more concerned about the gestational diet, fetal-related knowledge, and other precautions before the labor. ConclusionsIn the era of digitalization where information is rapidly disseminated, web-based prenatal education could become a more convenient, productive, and effective pathway for pregnant women since it could help them obtain adequate and optimal pregnancy-related information and gain more intellectual awareness about their pregnancy or preparation for pregnancy.

Characterized with remarkable tensile ductility, toughness and multi-cracking behavior under tension, Engineered Cementitious Composites (ECC) with Chinese local ingredients was developed in this paper, aiming to reduce the cost and to match a tensile strain capacity of 4%. Compression, tension and bending tests were carried out to determine the mechanical performance of this composite material. In addition, a numerical study to characterize the tensile and flexural behaviors of ECC specimens was conducted based on Lattice Discrete Particle Model (LDPM), which was recently developed to simulate quasi-brittle materials through a three dimensional assemblage of spherical particles. The effect of fiber reinforcement was investigated using the so-called LDPM-F which accounts for dispersed fibers at the meso-level. Plain mortar specimens without fibers were tested to calibrate the relevant LDPM parameters, while the LDPM-F parameters related to the fiber-matrix interaction were identified by matching three-point bending test of ECC specimens. Results show that the validated ECC numerical model realistically predicts the tension response of dogbone specimens. In addition, typical multi-cracking phenomenon and strain-hardening behavior of ECC are successfully captured. Finally, the effect of fiber length on ECC tensile response was further studied, and the results suggest that the best tensile performance of ECC is obtained for 18-mm-long polyvinyl alcohol (PVA) fiber reinforcement.

The mangrove ecosystem is one of the most productive ecosystems, and it has unique ecological functions and great social and economic value. Accurate mangrove mapping is very important for mangrove dynamic monitoring and management. Many studies have yielded good results in mangrove mapping, and the optical remote sensing images are used as the main data source for mangrove index construction and mangrove mapping. However, less information is available for optical images affected by clouds and fog. This study constructed an Optical and SAR images Combined Mangrove Index (OSCMI) based on the idea of multi-feature fusion. An OSCMI-based classification scheme for rapid and accurate mangrove mapping was proposed, and the effect of tidal inundation is considered. We have carried out extraction experiments in four different types of typical mangrove areas in China using Sentinel-1 SAR and Sentinel-2 optical images. Several groups of comparative experiments were conducted to verify the superiority of OSCMI and the necessity of VV polarization mode. The results show that OSCMI performs well in the four typical mangrove areas of different types and has great application potential in mangrove large-scale mapping. And the introduction of VV polarization mode information is helpful to improve the accuracy of mangrove extraction.

Abstract Background Hypomagnesemia, seizures, and mental retardation (HSMR) syndrome is a rare genetic disease. Presently, only 24 cases have been reported and the clinical features of the disease are yet to be fully described, thereby making diagnosis challenging. Methods Trio‐whole‐exome sequencing was used for the patient and her parents, and the structure of the variant protein was analyzed by molecular dynamics. Finally, the characteristics of HSMR were summarized by reviewing the previous literature. Results The main disease manifestations in the patient were seizures, liver function damage, hypomagnesemia, atrial septal defect, and sinus arrhythmia. A novel mutation in CNNM2 (c.566A>G/p.Tyr189Cys) was identified by genetic detection. The parents were wild type, and the mutation was rated as pathogenic by American College of Medical Genetics and Genomics guidelines. Ab initio modeling and molecular dynamics simulation show that the mutation destroys the surrounding hydrogen bonds, which may reduce the local stability of the protein structure. In the previous literature, only 24 children with HSMR have been reported, mainly manifested as hypomagnesemia, mental retardation, seizures, and language and motor impairment. Conclusion We have reported the second case of HSMR in the Chinese population, which further expands the phenotypic spectrum of congenital heart disease and the variation spectrum of CNNM2.

Background: The aim of this study was to estimate the attributable risk for all-cause mortality in hypertensive adults living in Beijing, China. Methods: We conducted a prospective cohort study on the basis of the disease risk prediction model, which included 3006 hypertensive patients aged 50 and over who participated in the annual health examination from thirty-eight community health centers were randomly selected from all 53 community health centers in Dongcheng district of Beijing in China. This cohort study was conducted from January 1, 2013 to June 31, 2018 in these community health centers. Data included age, gender, education level, BMI, smoking and drinking status, renal function, diabetes mellitus (DM), coronary heart disease, levels of blood pressure, use of medications, and blood lipid levels. Results: the follow-up time was 4.90 ± 0.51 years. There were significant survival differences by gender, renal function (eGFR > 90 vs. 60–90 vs. 12 years), coronary heart disease (CHD) (CHD vs. NO CHD). In the multivariate Cox proportional hazard analysis, the prognostic factors of all-cause mortality in hypertensive patients were male [HR 1.662, 95% CI 1.110–2.489, p = 0.014], educational level