The lattice Boltzmann method (LBM) is an important numerical algorithm for computational fluid dynamics. This study designs a two-layer parallel model for the Sunway TaihuLight supercomputer SW26010 ...many-core processor, which implements LBM algorithms and performs optimization. Numerical experiments with different problem sizes proved that the proposed model has better parallel performance and scalability than before. In this study, we performed numerical simulations of the flows around the two-dimensional (2D) NACA0012 airfoil, and the results of a series of flows around the different angles of attack were obtained. The results of the pressure coefficient and lift coefficient were in good agreement with those in the literature.
ZnO nanocombs with 25 μm comb teeth were synthesized by chemical vapor deposition (CVD) method. Experiments were carried out to investigate the influence of carrier gas flow rate and temperature on ...ZnO comb teeth growth. The growth mechanism was demonstrated according to the morphology of prepared nanocombs under different growth parameters. The experimental results showed that the intensity of green emission significantly increased when the ZnO nanocombs became thinner and longer. It attributed to much more hanging bonds and oxygen vacancy on the surfaces of comb teeth.
Polycomb gene silencing requires histone methyltransferase activity of Polycomb repressive complex 2 (PRC2), which methylates lysine 27 of histone H3. Information on how PRC2 works is limited by lack ...of structural data on the catalytic subunit, Enhancer of zeste (E(Z)), and the paucity of E(z) mutant alleles that alter its SET domain. Here we analyze missense alleles of Drosophila E(z), selected for molecular study because of their dominant genetic effects. Four missense alleles identify key E(Z) SET domain residues, and a fifth is located in the adjacent CXC domain. Analysis of mutant PRC2 complexes in vitro, and H3-K27 methylation in vivo, shows that each SET domain mutation disrupts PRC2 histone methyltransferase. Based on known SET domain structures, the mutations likely affect either the lysine-substrate binding pocket, the binding site for the adenosylmethionine methyl donor, or a critical tyrosine predicted to interact with the substrate lysine ϵ-amino group. In contrast, the CXC mutant retains catalytic activity, Lys-27 specificity, and trimethylation capacity. Deletion analysis also reveals a functional requirement for a conserved E(Z) domain N-terminal to CXC and SET. These results identify critical SET domain residues needed for PRC2 enzyme function, and they also emphasize functional inputs from outside the SET domain.
•A new matrix factorization strategy is designed for Nyström spectral clustering.•Incomplete Cholesky decomposition is introduced to accelerate Nyström approximation.•An efficient Nyström spectral ...clustering algorithm called ICD-NSC is proposed.•The effectiveness of ICD-NSC is demonstrated by comprehensive experiments.
Nyström method can estimate the eigenvectors of a large kernel matrix with the eigenvectors of a small sampled sub-matrix. However, we may encounter two problems when using Nyström method to speed up spectral clustering: one problem is the approximate eigenvectors generated by standard Nyström method are sub-optimal, so they may impair the performance of spectral clustering; another one is the accurate Nyström approximation needs a sufficient number of samples, which will increase the eigen-decomposition cost on the sampled sub-matrix. To solve these problems, this paper proposes an efficient Nyström spectral clustering algorithm using incomplete Cholesky decomposition, in which a new matrix factorization strategy is designed for Nyström spectral clustering to meet the orthogonal constraints, and an efficient eigensolver based on incomplete Cholesky decomposition is developed to accelerate the Nyström approximation. In this way, the obtained approximate orthogonal eigenvectors will help to improve the clustering quality, and the developed eigenvector calculation method will help to reduce the clustering complexity. The experimental results show that the proposed algorithm performs well on many challenging data sets, and it can accomplish more complex clustering tasks with limited computing resources.
In0.01Ga0.99As thin films free of anti-phase domains were grown on 7° offcut Si (001) substrates using Ge as buffer layers. The Ge layers were grown by ultrahigh vacuum chemical vapor deposition ...using ‘low/high temperature’ two-step strategy, while the In0.01Ga0.99As layers were grown by metal-organic chemical vapor deposition. The etch-pit counting, cross-section and plane-view transmission electron microscopy, room temperature photoluminescence measurements are performed to study the dependence of In0.01Ga0.99As quality on the thickness of Ge buffer. The threading dislocation density of Ge layer was found to be inversely proportional to the square root of its thickness. The threading dislocation density of In0.01Ga0.99As on 300nm thick Ge/offcut Si was about 4×108cm−2. Higher quality In0.01Ga0.99As can be obtained on thicker Ge/offcut Si virtual substrate. We found that the threading dislocations acted as non-radiative recombination centers and deteriorated the luminescence of In0.01Ga0.99As remarkably. Secondary ion mass spectrometry measurement indicated as low as 1016cm−3 Ge unintended doping in In0.01Ga0.99As.
► In0.01Ga0.99As layers were grown on offcut Si substrate using Ge as buffer layer. ► Dislocation density of Ge-on-Si was inverse proportional to thickness square root. ► GaAs islands were observed in the 100nm region on In0.01Ga0.99As/Ge interface. ► Unintended Ge-doping as low as 1016cm−3 was observed in In0.01Ga0.99As layer.
Face clustering has important applications in image retrieval and criminal investigation. Face images can be seen as the nodes of a graph and the possibility of links between the nodes will help us ...find clusters. Graph Convolutional Networks (GCNs) are powerful tools to infer the possibility of linkage between a given node and its neighbors. However, existing face clustering methods use shallow GCNs and have limited learning capabilities. We propose a deep face clustering method using Residual Graph Convolutional Network (RGCN), which contains more hidden layers. For each node, k-Nearest Neighbor (kNN) algorithm is used to construct its sub-graphs. Then we apply the idea of ResNet into GCNs and construct RGCN to learn the possibility of linkage between two nodes. Compared with other popular face clustering approaches, our method is more efficient and has better clustering results in the experiments. In addition, the proposed RGCN clustering approach is able to detect the quantity of clusters automatically and can be extended to large datasets.
Arsenic is a potent human carcinogen to which there is significant worldwide exposure through natural contamination of food and drinking water sources. Because arsenic is detoxified via methylation ...using a methyltransferase (MTase) and
S-adenosylmethionine (SAM) as the methyl donor, we hypothesized that a mechanism of carcinogenesis of arsenic could involve alterations of
MTase
SAM
-dependent
DNA methylation of a tumor suppressor gene. We found that exposure of human lung adenocarcinoma A549 cells to sodium arsenite (0.08 – 2 μM) or sodium arsenate (30–300 μM), but not dimethylarsenic acid (2–2000 μM), produced significant dose-responsive hypermethylation within a 341-base pair fragment of the promoter of
p53. This was determined by quantitative
PCR
Hpa
II
restriction site analysis to analyze methylation status of two CCGG sites. In experiments with arsenite, DNA sequencing using bisulfite to visualize 5-methylcytosine (5-MeC) over the entire promoter region confirmed data obtained by restriction analysis. Limited data using
SssI methylase also suggested that over-methylation of CpG sequences may exist over the entire genome in response to arsenite exposure. We propose that alteration of DNA methylation by arsenic offers a plausible, unified hypothesis for the carcinogenic mechanism of action of arsenic, and we present a model for arsenic carcinogenesis that utilizes perturbations of DNA methylation as the basis for the carcinogenic effects of arsenic.
•A novel global and local structure preserving nonnegative subspace clustering model is designed.•Nonnegative subspace clustering can directly obtain the cluster indicators and allocate cluster ...members.•Efficient multiplicative updating rules are developed by nonnegative Lagrangian relaxation to optimize the model.•The data similarities and cluster indicators are learned simultaneously and can promote each other through iterative optimization.•The effectiveness of the model is demonstrated by theoretical analysis and comprehensive experiments.
Most subspace clustering methods construct the similarity matrix based on self-expressive property and apply the spectral relaxation on the similarity matrix to get the final clusters. Despite the advantages of this framework, it has two limitations that are easily ignored. Firstly, the original self-expressive model only considers the global structure of data, and the ubiquitous local structure among data is not paid enough attention. Secondly, spectral relaxation is naturally suitable for 2-way clustering tasks, but when dealing with multi-way clustering tasks, the assignment of cluster members becomes indirect and requires additional steps. To overcome these problems, this paper proposes a global and local structure preserving nonnegative subspace clustering method, which learns data similarities and cluster indicators in a mutually enhanced way within a unified framework. Besides, the model is extended to kernel space to strengthen its capability of dealing with nonlinear data structures. For optimizing the objective function of the method, multiplicative updating rules based on nonnegative Lagrangian relaxation are developed, and the convergence is guaranteed in theory. Abundant experiments have shown that the proposed model is better than many advanced clustering methods in most cases.
The development of cathode catalysts with a porous structure is essential to design Li–O 2 batteries with a high rate performance and good cycle stability. Herein, spinel-type porous cobalt–manganese ...oxide (Co–Mn–O) nanocubes derived from metal organic frameworks were employed as an electrocatalyst in a Li–O 2 battery. The battery with the porous Co–Mn–O nanocubes electrode showed a low overpotential and enhanced capacity. The synergistic effects of large specific surface area, porous structure, and the high electrocatalytic activity of the porous Co–Mn–O nanocubes electrode endowed the Li–O 2 battery with a good rate performance and excellent cycle stability up to 100 cycles.
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