低温制备≥99.5%多晶氧化铝陶瓷

本文通过引入稀土氧化物Y2O3、Tm2O3为烧结助剂低温制备了氧化铝含量大于99.5;的多晶氧化铝陶瓷.实验表明:稀土氧化物的加入能够明显降低99.5;多晶氧化铝陶瓷的烧结温度,提高致密度.Y2O3、Tm2O3混合烧结助剂与单一稀土氧化物的烧结助剂相比能够明显抑制晶粒的生长,促进晶粒的均匀发育.当Y2O3+Tm2O3的含量为0.3;质量分数时,99.5;多晶氧化铝陶瓷的相对密度可达99.2;理论密度,抗弯强度为533MPa,显微硬度为17.2GPa.陶瓷断裂主要以穿晶断裂为主.     

An unexpected all-metal aromatic tetranuclear silver cluster in human copper chaperone Atox1

Metal clusters, such as iron–sulfur clusters, play key roles in sustaining life and are intimately involved in the functions of metalloproteins. Herein we report the formation and crystal structure of a planar square tetranuclear silver cluster when silver ions were mixed with human copper chaperone Atox1. Quantum chemical studies reveal that two Ag 5s¹ electrons in the tetranuclear silver cluster fully occupy the one bonding molecular orbital, with the assumption that this Ag₄ cluster is Ag₄²⁺, leading to extensive electron delocalization over the planar square and significant stabilization. This bonding pattern of the tetranuclear silver cluster represents an aromatic all-metal structure that follows a 4n + 2 electron counting rule (n = 0). This is the first time an all-metal aromatic silver cluster was observed in a protein.

Metal clusters, such as iron–sulfur clusters, play key roles in sustaining life and are intimately involved in the functions of metalloproteins.     

基于大数据挖掘技术的智能变电站故障追踪架构研究

针对目前智能变电站缺乏有效的故障预警方法,提出了基于大数据挖掘的智能变电站故障追踪架构。设计了以大数据为基础的变电站故障信息架构,并分析了故障诊断与追踪程序。对智能变电站故障追踪的架构进行分析,设计了巡检任务管理和图像监控等硬件模块和相应的软件模块。软件模块主要包括视频监控、隔离开关、环境监测、智能巡检、远程控制等模块,并且实现了大数据平台预测模型的设计。通过实际使用结果表明,该架构能够提高智能变电站的故障诊断精准率,及时发现其中存在的故障,促进变电站的良好发展。     

一种支持多协议的低延时OFDM系统设计

未来的网络架构需要更高的灵活性来适应通信条件的变化,从而提高频谱的利用率,能够根据环境和频谱要求在多种标准下运行的认知无线电变得越来越重要。文中介绍了一种基于FPGA的支持多协议的低延时OFDM系统设计方案,该系统能够在多种不同的OFDM协议标准间进行动态快速切换。实验结果表明,与传统方法相比,使用动态可重构模块与参数化模块相结合的新型OFDM系统可以减少36.9%以上的系统配置延时,此外,该系统拥有在重配置期间缓存数据以防止传输链路中断的能力。     

Deep Multilabel Multilingual Document Learning for Cross-Lingual Document Retrieval

Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information. In this work, the cross-lingual comparison at the document level is achieved through the cross-lingual semantic space. Our method, MDL (deep multilabel multilingual document learning), leverages a six-layer fully connected network to project cross-lingual documents into a shared semantic space. The semantic distances can be calculated when the cross-lingual documents are transformed into embeddings in semantic space. The supervision signals are automatically extracted from the data and then used to construct the semantic space via a linear classifier. The ambiguity of manual labels could be avoided and the multilabel supervision signals can be acquired instead of a single label. The representation of the semantic space is enriched by multilabel supervision signals, which improves the discriminative ability of the embeddings. The MDL is easy to extend to other fields since it does not depend on specific data. Furthermore, MDL is more efficient than the models training all languages jointly, since each language is trained individually. Experiments on Wikipedia data showed that the proposed method outperforms the state-of-the-art cross-lingual document retrieval methods.     

Cancer-Associated Mutations of the Adenosine A2A Receptor Have Diverse Influences on Ligand Binding and Receptor Functions

The adenosine A_2A receptor (A_2AAR) is a class A G-protein-coupled receptor (GPCR). It is an immune checkpoint in the tumor micro-environment and has become an emerging target for cancer treatment. In this study, we aimed to explore the effects of cancer-patient-derived A_2AAR mutations on ligand binding and receptor functions. The wild-type A_2AAR and 15 mutants identified by Genomic Data Commons (GDC) in human cancers were expressed in HEK293T cells. Firstly, we found that the binding affinity for agonist NECA was decreased in six mutants but increased for the V275A mutant. Mutations A165V and A265V decreased the binding affinity for antagonist ZM241385. Secondly, we found that the potency of NECA (EC₅₀) in an impedance-based cell-morphology assay was mostly correlated with the binding affinity for the different mutants. Moreover, S132L and H278N were found to shift the A_2AAR towards the inactive state. Importantly, we found that ZM241385 could not inhibit the activation of V275A and P285L stimulated by NECA. Taken together, the cancer-associated mutations of A_2AAR modulated ligand binding and receptor functions. This study provides fundamental insights into the structure–activity relationship of the A_2AAR and provides insights for A_2AAR-related personalized treatment in cancer.     

Numerical Study on an RBF-FD Tangent Plane Based Method for Convection–Diffusion Equations on Anisotropic Evolving Surfaces

In this paper, we present a fully Lagrangian method based on the radial basis function (RBF) finite difference (FD) method for solving convection–diffusion partial differential equations (PDEs) on evolving surfaces. Surface differential operators are discretized by the tangent plane approach using Gaussian RBFs augmented with two-dimensional (2D) polynomials. The main advantage of our method is the simplicity of calculating differentiation weights. Additionally, we couple the method with anisotropic RBFs (ARBFs) to obtain more accurate numerical solutions for the anisotropic growth of surfaces. In the ARBF interpolation, the Euclidean distance is replaced with a suitable metric that matches the anisotropic surface geometry. Therefore, it will lead to a good result on the aspects of stability and accuracy of the RBF-FD method for this type of problem. The performance of this method is shown for various convection–diffusion equations on evolving surfaces, which include the anisotropic growth of surfaces and growth coupled with the solutions of PDEs.     

概率语言多属性群决策方法及其在新型智慧城市市民获得感评价中的应用

针对公众参与的语言信息多属性群决策问题,研究了考虑参与者满意度的概率语言多属性群决策方法。首先,根据参与者的语言评价信息确定并规范化概率语言决策矩阵。然后,对大群体进行共识分析,由最大化参与者群体的满意度构建线性规划模型,确定参与者群组的权重;构造正、负理想方案的评价向量,构建多目标规划模型,用拉格朗日乘子法求解属性权重;定义各方案的加权贴近度,并以此对方案进行排序和优选。最后,通过新型智慧城市市民获得感评价案例验证了模型的可行性和有效性。     

基于小波域隐马尔科夫树模型的图像分割

改进了传统的基于小波域隐马尔科夫树模型的图像分割方法.由于传统方法均为直接选择小波子带系数作为训练特征,不能直接得到像素级分割结果;同时传统方法在后融合方面对所有尺度均采用同一种上下文背景,而忽略不同尺度上初分割类标志图的特点.因此,本文在粗分割阶段首先处理了训练时参数设置的问题,并选取了更能表征纹理的特征,能直接得到像素级分割结果;在多尺度融合阶段,充分利用不同尺度上类标志图的特性,不仅考虑粗尺度信息对融合结果的影响也考虑了细尺度信息对结果的影响.实验表明本文算法的视觉效果好干与本文进行比较的Choi提出的HMTseg以及孙强提出的WD-HMTseg遥感图像分割算法.     

激光聚焦非球面透镜设计

本文论述激光聚焦光学系统的设计特点，介绍用等光程原理设计激光聚焦非球面透镜的方法及实例。