Porous CoNiOOH nanosheets derived from the well-mixed MOFs as stable and highly efficient electrocatalysts for water oxidation

The development of efficient and stable electrocatalysts is of great significance for improving water splitting. Among them, transition metal oxyhydroxides show excellent performance in oxygen evolution reactions (OER), but there are certain difficulties in direct preparation. Recently, Metal–organic frameworks (MOFs) as precatalysts or precursors have shown promising catalytic performance in OER and can be decomposed under alkaline conditions. Therefore, using a mild and controllable way to convert MOFs into oxyhydroxides and retaining the original structural advantages is crucial for improving the catalytic activity. Herein, a rapid electrochemical strategy is used to activate well-mixed MOFs to prepare Co/Ni oxyhydroxide nanosheets for efficient OER catalysts, and the structural transformation in this process was investigated in detail by using scanning electron microscope, X-ray diffraction, Raman, X-ray photoelectron spectroscopy and electrochemical methods. It is discovered that electrochemical activation can promote ligand substitution of well-mixed MOFs to form porous oxyhydroxide nanosheets and tune the electronic structure of the metal (Co and Ni), which can lead to more active site exposure and accelerate charge transfer. In addition, the change of structure also improves hydrophilicity, as well as benefiting from the strong synergistic effect between multiple species, the optimal a-MCoNi–MOF/NF has excellent OER performance and long-term stability. More obviously, the porous CoNiOOH nanosheets are formed in situ during electrochemical activation process through structural transformation and acts as the active centers. This work provides new insights for mild synthesis of MOFs derivatives and also provides ideas for the preparation of highly efficient catalysts.     

Tuning of emission by effect of local symmetry in BaLaLiWO6:Dy3+/Eu3+ for WLEDs

The luminescence center energy transfer, crystal field strength, and covalency are limited by the crystal structure of the host and subsequently affect the luminescence efficiency, color, and intensity. Here, we report an excellent red phosphor BaLaLiWO₆:0.40Eu³⁺ and the dependence between symmetry and luminous performance. A model for changing symmetry is drawn by analyzing the Coulomb potential and structure for the application of a double-perovskite phosphor BLLWO: Dy³⁺, Eu³⁺ in white light LEDs. The addition of Dy³⁺/Eu³⁺ makes the W-O bond formed by the B-site and oxygen ion longer and the Li-O bond shorter, and the difference between the eight octahedral around the A-site is reduced, increasing the symmetry of the A-site. Local symmetry was successfully modulated by changing the Eu³⁺ concentration to control the Y/B ratio of Dy³⁺ and the R/O ratio of Eu³⁺ and smoothly achieved (0.382, 0.373) warm white light color coordinate. The phosphor has excellent thermal stability and still has 92.3% intensity at 475 K. The above results show that the wavelength composition of the luminescence is tunable by changing the symmetry of the environment in which the doped ions are located. It applies to single hosts for the regulation of white light emission.     

Structural health monitoring system based on multi-agent coordination and fusion for large structure

In practical applications of structural health monitoring technology, a large number of distributed sensors are usually adopted to monitor the big dimension structures and different kinds of damage. The monitored structures are usually divided into different sub-structures and monitored by different sensor sets. Under this situation, how to manage the distributed sensor set and fuse different methods to obtain a fast and accurate evaluation result is an important problem to be addressed deeply. In the paper, a multi-agent fusion and coordination system is presented to deal with the damage identification for the strain distribution and joint failure in the large structure. Firstly, the monitoring system is adopted to distributedly monitor two kinds of damages, and it self-judges whether the static load happens in the monitored sub-region, and focuses on the static load on the sub-region boundary to obtain the sensor network information with blackboard model. Then, the improved contract net protocol is used to dynamically distribute the damage evaluation module for monitoring two kinds of damage uninterruptedly. Lastly, a reliable assessment for the whole structure is given by combing various heterogeneous classifiers strengths with voting-based fusion. The proposed multi-agent system is illustrated through a large aerospace aluminum plate structure experiment. The result shows that the method can significantly improve the monitoring performance for the large-scale structure.     

Performance evaluation of structural concrete using controlled quality coarse and fine recycled concrete aggregate

This paper presents the fresh, mechanical, and durability performance, of a structural concrete mix classified as C-1, by the Canadian Standards Association (CSA) made with controlled quality Recycled Concrete Aggregate (RCA). Five mixes with water-to-cementing material (w/cm) ratio of 0.40 were produced with various RCA contents and tested against two 0% RCA control mixes made with General Use (GU) cement, and General Use Limestone cement (GUL). The RCA contents in the mixes were 10%, 20%, and 30% by coarse aggregate volume replacement, as well as 10% and 20% fine and coarse (granular) aggregate volume replacement. All evaluated mixes met the specifications from the CSA for fresh, mechanical, and durability properties. The coarse RCA mixes performed better than the granular RCA mixes in terms of flexural and splitting tensile strengths, linear drying shrinkage, water sorptivity, and rapid chloride-ion permeability, where the test results were significantly affected by the ultra fines present in the granular RCA.     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

Stabilization of 6H-Hexagonal SrMnO3 Polymorph by Al2O3 insertion

We demonstrate the structural evolution of polymorphic phases in Al₂O₃-inserted SrMnO₃ ceramics synthesized by solid state reaction. While the 4H-hexagonal phase is predominant in pure SrMnO₃ ceramics, a small amount of 6H-hexagonal polymorph is identified in addition to the primary 4H-hexagonal SrMnO₃ and the secondary hexagonal SrAl₂O₄ phases in the as-sintered ceramics, evidenced by x-ray diffraction and subsequent Rietveld refinement analyses. The existence of the 6H-hexagonal SrMnO₃ phase is corroborated using Raman spectroscopy. The chemical compositions and electronic structures of the Al₂O₃-inserted SrMnO₃ compounds are also examined using energy dispersive spectroscopy and x-ray photoelectron spectroscopy, respectively. The first-principles calculations reveal that there is no clear difference between the total energies of 4H- and 6H-hexagonal polymorphs regardless of the presence/absence of Sr and oxygen vacancies. Possible origins are discussed with the estimation of actual strain based on the refined lattice parameter of 6H SrMnO₃.     

一种基于小字典不对等语料的跨语言词嵌入方法

双语词嵌入通常采用从源语言空间到目标语言空间映射,通过源语言映射嵌入到目标语言空间的最小距离线性变换实现跨语言词嵌入。然而大型的平行语料难以获得,词嵌入的准确率难以提高。针对语料数量不对等、双语语料稀缺情况下的跨语言词嵌入问题,该文提出一种基于小字典不对等语料的跨语言词嵌入方法,首先对单语词向量进行归一化,对小字典词对正交最优线性变换求得梯度下降初始值,然后通过对大型源语言(英语)语料进行聚类,借助小字典找到与每一聚类簇相对应的源语言词,取聚类得到的每一簇词向量均值和源语言与目标语言对应的词向量均值,建立新的双语词向量对应关系,将新建立的双语词向量扩展到小字典中,使得小字典得以泛化和扩展。最后,利用泛化扩展后的字典对跨语言词嵌入映射模型进行梯度下降求得最优值。在英语—意大利语、德语和芬兰语上进行了实验验证,实验结果证明该文方法可以在跨语言词嵌入中减少梯度下降迭代次数,减少训练时间,同时在跨语言词嵌入上表现出较好的正确率。     

基于模糊谱聚类的不确定蛋白质相互作用网络功能模块挖掘

针对谱聚类融合模糊C-means（FCM）聚类的蛋白质相互作用（PPI）网络功能模块挖掘方法准确率不高、执行效率较低和易受假阳性影响的问题，提出一种基于模糊谱聚类的不确定PPI网络功能模块挖掘（FSC-FM）方法。首先，构建一个不确定PPI网络模型，使用边聚集系数给每一条蛋白质交互作用赋予一个存在概率测度，克服假阳性对实验结果的影响；第二，利用基于边聚集系数流行距离（FEC）策略改进谱聚类中的相似度计算，解决谱聚类算法对尺度参数敏感的问题，进而利用谱聚类算法对不确定PPI网络数据进行预处理，降低数据的维数，提高聚类的准确率；第三，设计基于密度的概率中心选取策略（DPCS）解决模糊C-means算法对初始聚类中心和聚类数目敏感的问题，并对预处理后的PPI数据进行FCM聚类，提高聚类的执行效率以及灵敏度；最后，采用改进的边期望稠密度（EED）对挖掘出的蛋白质功能模块进行过滤。在酵母菌DIP数据集上运行各个算法可知，FSC-FM与基于不确定图模型的检测蛋白质复合物（DCU）算法相比，F-measure值提高了27.92%，执行效率提高了27.92%；与在动态蛋白质相互作用网络中识别复合物的方法（CDUN）、演化算法（EA）、医学基因或蛋白质预测算法（MGPPA）相比也有更高的F-measure值和执行效率。实验结果表明，在不确定PPI网络中，FSC-FM适合用于功能模块的挖掘。