Inorganic hierarchical nanostructures induced by concentration difference and gradient

A very simple strategy for preparing hierarchical inorganic nanostructures under ambient aqueous conditions is presented. The hierarchical inorganic nanomaterials were obtained by simply adding a highly concentrated solution of one reactant to a solution of another reactant with low concentration. No surface-capping molecules or structure-directing templates were needed. The preparation of hierarchical single crystalline PbMoO(4) was used as an example in order to study the effects of varying the reaction conditions and the mechanism of the process. It was found that the large concentration difference (typically in excess of 200-fold) and the concentration gradient of the reactants both play key roles in controlling the diffusion process and the morphology of the resulting nanostructures. This kinetically controlled strategy is facile and is easily adapted to prepare a variety of inorganic materials.Chemistry, PhysicalNanoscience & NanotechnologyMaterials Science, MultidisciplinaryPhysics, AppliedSCI(E)0ARTICLE3213-220

Stress hyperglycemia is associated with disease severity in COVID-19

Introduction: Coronavirus disease 2019 (COVID-19) is a global pandemic that has affected millions of people worldwide. In this paper, we analyse the relationship between stress hyperglycaemia and disease severity in patients with COVID-19. Material and methods: A total of 252 patients with COVID-19 were included in this study. The patients were divided into the following groups: COVID-19 with stress hyperglycaemia (SHG), COVID-19 with diabetes (DM), and COVID-19 with normal blood glucose (NG). The stress hyperglycaemia rate (SHR) was calculated using the fasting blood glucose (FBG)/glycated haemoglobin (HbA1c) ratio. To further compare the disease characteristics of different SHRs, we divided the SHR into low SHR and high SHR according to the SHR median. Correlations between the severity of the disease and other factors were analysed after adjusting for sex and age. Multivariate analysis was performed using logistic regression to analyse the risk factors predicting the severity of COVID-19. Results: Compared with the NG group, the SHG group had higher disease severity (p < 0.001); the SHG group had higher HbA1c, FBG, SHR, blood urea nitrogen (BUN), interleukin 6 (IL-6), and neutrophil levels, while lymphocyte, CD3+ T cell, CD8+ T cell, CD4+ T cell, CD16+CD56 cell, and CD19+ cell counts were lower (p < 0.05). Compared with the NG group, the DM group had higher HbA1c, blood glucose, BUN, lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and neutrophils, while CD8+ T cell counts were lower (p < 0.05). Compared with the DM group, the SHG group had higher SHR and lower HbA1c, CD3+ T cell, CD4+ T cell, CD16+CD56 cell, and T cell ratio levels (p < 0.05). Compared to the low SHR group, the high SHR group had patients with more severe COVID-19 (p = 0.004). Also, the high SHR group had higher age, HbA1c, FBG, asparate aminotransferaze (AST), BUN, LDH, uric acid (UA), CRP, IL-6, and procalcitonin (PCT), while lymphocyte, CD3+ T cell, CD4+ T cell, CD8+ T cell, and CD19+ cell counts were lower (p < 0.05). Binary logistic regression analysis showed that SHR, gender, and lymphocyte count were risk factors for the severity of COVID-19. Conclusion: Stress hyperglycaemia, as indicated by a higher SHR, is independently associated with the severity of COVID-19

Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov Model

Deep reinforcement learning (DRL) algorithms have proven effective in robot navigation, especially in unknown environments, by directly mapping perception inputs into robot control commands. However, most existing methods ignore the local minimum problem in navigation and thereby cannot handle complex unknown environments. In this paper, we propose the first DRL-based navigation method modeled by a semi-Markov decision process (SMDP) with continuous action space, named Adaptive Forward Simulation Time (AFST), to overcome this problem. Specifically, we reduce the dimensions of the action space and improve the distributed proximal policy optimization (DPPO) algorithm for the specified SMDP problem by modifying its GAE to better estimate the policy gradient in SMDPs. Experiments in various unknown environments demonstrate the effectiveness of AFST

Topology Optimization Methods for Flexure Hinge Type Piezoelectric Actuators

Piezoelectric actuators have the obvious advantages of simple and compact structure, high precision and long stroke. However, it is difficult to satisfy the various industrial requirements. Topology optimization method can be used to find the new configurations of the compliant mechanism, and different objective function and constraint conditions can be flexibly used to determine the compliant mechanism. In the research of piezoelectric actuators, due to the advantages of compact structure, no lubrication and large displacement magnification, compliant mechanism is extremely suitable to be introduced into the design of piezoelectric actuators. In recent years, topology optimization method is frequently used to design the compliant mechanism on piezoelectric actuator, and has become a research hotspot. In this chapter, the development of topology optimization method is introduced, the design and research on the compliant mechanism of piezoelectric actuator have been summarized, and the future research direction and challenges of topology optimization design for flexure hinge type piezoelectric actuators are prospected

Recurrent Temporal Revision Graph Networks

Temporal graphs offer more accurate modeling of many real-world scenarios than static graphs. However, neighbor aggregation, a critical building block of graph networks, for temporal graphs, is currently straightforwardly extended from that of static graphs. It can be computationally expensive when involving all historical neighbors during such aggregation. In practice, typically only a subset of the most recent neighbors are involved. However, such subsampling leads to incomplete and biased neighbor information. To address this limitation, we propose a novel framework for temporal neighbor aggregation that uses the recurrent neural network with node-wise hidden states to integrate information from all historical neighbors for each node to acquire the complete neighbor information. We demonstrate the superior theoretical expressiveness of the proposed framework as well as its state-of-the-art performance in real-world applications. Notably, it achieves a significant +9.6% improvement on averaged precision in a real-world Ecommerce dataset over existing methods on 2-layer models

The Asymmetric Flexure Hinge Structures and the Hybrid Excitation Methods for Piezoelectric Stick-Slip Actuators

Piezoelectric stick–slip actuators have become viable candidates for precise positioning and precise metering due to simple structure and long stroke. To improve the performances of the piezoelectric stick–slip actuators, our team deeply studies the actuators from both structural designs and driving methods. In terms of structural designs, the trapezoid-type, asymmetrical flexure hinges and mode conversion piezoelectric stick–slip actuators are proposed to improve the velocity and load based on the asymmetric structure; besides, a piezoelectric stick–slip actuator with a coupled asymmetrical flexure hinge mechanism is also developed to achieve the bidirectional motion. In terms of driving methods, a non-resonant mode smooth driving method (SDM) based on ultrasonic friction reduction is first proposed to restrain the backward motion during the rapid contraction stage. Then, a resonant mode SDM is further developed to improve the output performance of the piezoelectric stick–slip actuator. On this basis, the low voltage and symmetry of the SDM are also discussed. Finally, the direction-guidance hybrid method (DGHM) excitation method is presented to achieve superior performance, especially for high speed

Preparation and ferroelectric properties of (124)-oriented SrBi4Ti4O15 ferroelectric thin film on (110)-oriented LaNiO3 electrode

A (124)-oriented SrBi4Ti4O15 (SBTi) ferroelectric thin film with high volume fraction of {\alpha}SBTi(124)=97% was obtained using a metal organic decomposition process on SiO2/Si substrate coated by (110)-oriented LaNiO3 (LNO) thin film. The remanent polarization and coercive field for (124)-oriented SBTi film are 12.1 {\mu}C/cm2 and 74 kV/cm, respectively. No evident fatigue of (124)-oriented SBTi thin film can be observed after 1{\times}10e9 switching cycles. Besides, the (124)-oriented SBTi film can be uniformly polarized over large areas using a piezoelectric-mode atomic force microscope. Considering that the annealing temperature was 650{\deg}C and the thickness of each deposited layer was merely 30 nm, a long-range epitaxial relationship between SBTi(124) and LNO(110) facets was proposed. The epitaxial relationship was demonstrated based on the crystal structures of SBTi and LNO.Comment: 11 pages, 4 figures, published in Journal of Materials Science: Materials in Electronics (JMSE), 19 (2008), 1031-103

Prediction of microbe–drug associations based on a modified graph attention variational autoencoder and random forest

IntroductionThe identification of microbe–drug associations can greatly facilitate drug research and development. Traditional methods for screening microbe-drug associations are time-consuming, manpower-intensive, and costly to conduct, so computational methods are a good alternative. However, most of them ignore the combination of abundant sequence, structural information, and microbe-drug network topology.MethodsIn this study, we developed a computational framework based on a modified graph attention variational autoencoder (MGAVAEMDA) to infer potential microbedrug associations by combining biological information with the variational autoencoder. In MGAVAEMDA, we first used multiple databases, which include microbial sequences, drug structures, and microbe-drug association databases, to establish two comprehensive feature matrices of microbes and drugs after multiple similarity computations, fusion, smoothing, and thresholding. Then, we employed a combination of variational autoencoder and graph attention to extract low-dimensional feature representations of microbes and drugs. Finally, the lowdimensional feature representation and graphical adjacency matrix were input into the random forest classifier to obtain the microbe–drug association score to identify the potential microbe-drug association. Moreover, in order to correct the model complexity and redundant calculation to improve efficiency, we introduced a modified graph convolutional neural network embedded into the variational autoencoder for computing low dimensional features.ResultsThe experiment results demonstrate that the prediction performance of MGAVAEMDA is better than the five state-of-the-art methods. For the major measurements (AUC =0.9357, AUPR =0.9378), the relative improvements of MGAVAEMDA compared to the suboptimal methods are 1.76 and 1.47%, respectively.DiscussionWe conducted case studies on two drugs and found that more than 85% of the predicted associations have been reported in PubMed. The comprehensive experimental results validated the reliability of our models in accurately inferring potential microbe-drug associations

High-Performance Phototransistors by Alumina Encapsulation of a 2D Semiconductor with Self-Aligned Contacts

2D semiconductors are promising candidates for next generation electronics and optoelectronics. However, their exposure to air and/or resists during device fabrication can cause considerable degradation of material quality, hindering their study and exploitation. Here, field effect transistors (FETs) are designed and fabricated by encapsulation of the 2D semiconductor indium selenide (InSe) with alumina (Al2O3) and by self-aligned electrical contacts. The Al2O3-film is grown directly on InSe immediately after its exfoliation to provide a protecting capping layer during and after device fabrication. The InSe-FETs exhibit a high electron mobility of up to ?103 cm2 V?1 s?1 at room temperature for a 4-nm-thick InSe layer, a low contact resistance (down to 0.18 k?) and a high, fast, and broad-band photoresponsivity. The photoresponsivity depends on the InSe-layer thickness and photon wavelength, reaching a value of up to 108 A W?1 in the visible spectral range, at least one order of magnitude larger than previously reported for similar photodetectors. The proposed fabrication is scalable and suitable for high-precision pattern definition. It could be extended to other 2D materials and multilayer structures where alumina could also provide effective screening of the electric field induced by polar molecules and/or charged impurities present near the surface of the 2D layer

Genetic Dissection of Root Angle of Brassica napus in Response to Low Phosphorus

Plant root angle determines the vertical and horizontal distribution of roots in the soil layer, which further influences the acquisition of phosphorus (P) in topsoil. Large genetic variability for the lateral root angle (root angle) was observed in a linkage mapping population (BnaTNDH population) and an association panel of Brassica napus whether at a low P (LP) or at an optimal P (OP). At LP, the average root angle of both populations became smaller. Nine quantitative trait loci (QTLs) at LP and three QTLs at OP for the root angle and five QTLs for the relative root angle (RRA) were identified by the linkage mapping analysis in the BnaTNDH population. Genome-wide association studies (GWASs) revealed 11 single-nucleotide polymorphisms (SNPs) significantly associated with the root angle at LP (LPRA). The interval of a QTL for LPRA on A06 (qLPRA-A06c) overlapped with the confidence region of the leading SNP (Bn-A06-p14439400) significantly associated with LPRA. In addition, a QTL cluster on chromosome C01 associated with the root angle and the primary root length (PRL) in the “pouch and wick” high-throughput phenotyping (HTP) system, the root P concentration in the agar system, and the seed yield in the field was identified in the BnaTNDH population at LP. A total of 87 genes on A06 and 192 genes on C01 were identified within the confidence interval, and 14 genes related to auxin asymmetric redistribution and root developmental process were predicted to be candidate genes. The identification and functional analyses of these genes affecting LPRA are of benefit to the cultivar selection with optimal root system architecture (RSA) under P deficiency in Brassica napus