Property-microstructure correlation in in situ formed Al2O3, TiB2 and Al3Ti mixture-reinforced aluminium composites

The in situ formed Al₂O₃, TiB₂ and Al₃Ti mixture-reinforced aluminium composites were successfully fabricated by the reaction sintering of the TiO₂-B-Al system in a vacuum. With increasing boron content in the TiO₂-B-Al system, the amount of generated TiB₂ in the composites increased and Al₃Ti content decreased. At the same time the distribution uniformity of the in situ formed Al₂O₃ and TiB₂ particulates was obviously improved, and the size of the Al₃Ti particles was reduced. The in situ Al₂O₃ and TiB₂ particulates had sizes from 0.096–1.88 m. The interface between the in situ formed particulates and the aluminium matrix was clean, and no consistent crystallographic orientation relationship was found. The strength and elastic modulus of the composites was significantly improved by lowering the Al₃Ti content. When the boron content in the TiO₂-B-Al system rose, the morphology of the tensile fracture surface of the composites was changed from large fractured Al₃Ti blocks and fine dimples, to fine dimples and pulled-out particulates. The strengthening and fracture of the composites have been modelled.     

Comparison of approaches based on optimization and algebraic iteration for binary tomography

Binary tomography represents a special category of tomographic problems, in which only two values are possible for the sought image pixels. The binary nature of the problems can potentially lead to a significant reduction in the number of view angles required for a satisfactory reconstruction, thusly enabling many interesting applications. However, the limited view angles result in a severely underdetermined system of equations, which is challenging to solve. Various approaches have been proposed to address such a challenge, and two categories of approaches include those based on optimization and those based on algebraic iteration. However, the relative strengths, limitations, and applicable ranges of these approaches have not been clearly defined in the past. Therefore, it is the main objective of this work to conduct a systematic comparison of approaches from each category. This comparison suggested that the approaches based on algebraic iteration offered both superior reconstruction fidelity and computation efficiency at low (two or three) view angles, and these advantages diminished at high view angles. Meanwhile, this work also investigated the application of regularization techniques, the selection of optimal regularization parameter, and the use of a local search technique for binary problems. We expect the results and conclusions reported in this work to provide valuable guidance for the design and development of algorithms for binary tomography problems.     

Direct raycasting of unstructured cell-centered data by discontinuity Roe-average computation

In the field of computational fluid dynamics (CFD), the upwind finite volume method (FVM) is widely applied to solve 3D flows with discontinuity phenomena (e.g., shock waves). It produces unstructured data at the center of each cell (cell-centered data) with the flow discontinuity constraint on the inner-face between face-neighboring cells. For visualization, existing approaches with interpolation usually pre-extrapolate cell-centered data into cell-vertexed data (data values given at cell vertices) and only handle cell-vertexed data during actual rendering, which unconsciously depress the rendering accuracy and violate the discontinuity constraint. In this paper, we propose a novel method to visualize cell-centered data directly avoiding extrapolation and keep the discontinuity in the rendering data on the framework of multi-pass raycasting. During resampling, the field is reconstructed using the original cell-centered data value and the cell-gradient estimated by Green–Gauss theorem. To keep the discontinuity, we reconstruct the field at an inner-face resampled point using both the face-adjacencies and get two discontinuous field values. Then the field is obtained by computing Roe-average of the two. The analysis and experiments demonstrate that our approach gains a high-accuracy reconstruction and leads to a high-quality image.     

Application-Driven Compression for Visualizing Large-Scale Time-Varying Data

The authors present an application-driven approach to compressing large-scale time-varying volume data. Their approach identifies a reference feature to partition the data into space-time blocks, which are compressed with various precisions depending on their association to the feature. Runtime decompression is performed with bit-wise texture packing and deferred filtering. This method achieves high compression rates and interactive rendering while preserving fine details surrounding regions of interest. Such an application-driven approach could help computational scientists cope with the large-data problem.     

Optimal Error Estimates of the Legendre Tau Method for Second-Order Differential Equations

In this paper, we prove that the Legendre tau method has the optimal rate of convergence in L ²-norm, H ¹-norm and H ²-norm for one-dimensional second-order steady differential equations with three kinds of boundary conditions and in C([0,T];L ²(I))-norm for the corresponding evolution equation with the Dirichlet boundary condition. For the generalized Burgers equation, we develop a Legendre tau-Chebyshev collocation method, which can also be optimally convergent in C([0,T];L ²(I))-norm. Finally, we give some numerical examples.     

Dynamically modulated intensity interrogation scheme using waveguide coupled surface plasmon resonance sensors

An electro-optically modulated intensity interrogation method based on tunable waveguide coupled surface plasmon resonance sensors has been proposed. It has been theoretically and experimentally demonstrated that the proposed scheme can enable sensitive measurement of measurand variations. By modulating the refractive index in the waveguide layer, this interrogation method yields modulated signal whose amplitude is related to measurand's refractive index. This amplitude modulated signal offers a higher signal to noise ratio and eliminates additive noise in the sensor system. A preliminary investigation using saline buffers with different NaCl concentrations shows a resolution of 2.3 × 10^?6 refractive index unit by our approach. Resolution can be controlled by the amplitude of the applied modulation voltage and can be further enhanced by optimizing the device structure or improving the electro-optical (E-O) coefficient of the E-O material. This approach is simple, stable, and promising for low-cost or multi-channel SPR biosensor applications.     

Ordered mesoporous Pd/SnO2 synthesized by a nanocasting route for high hydrogen sensing performance

Ordered mesoporous SnO₂ and mesoporous Pd/SnO₂ have been successfully synthesized via nanocasting method using the hexagonal mesoporous SBA-15 as template. Two different procedures, impregnation technique and direct synthesis, were utilized for the doping of Pd in the mesoporous SnO₂. The results of small angle X-ray diffraction (SAXD), nitrogen adsorption–desorption and transmission electron microscopy (TEM) demonstrate that the SnO₂ and Pd/SnO₂ display ordered mesoporous structures and high surface areas. Wide angle X-ray diffraction (WAXD) and X-ray photoelectron spectroscopy (XPS) reveal tetragonal structure of SnO₂ and the existence of Pd element. The sensing properties of mesoporous SnO₂ and mesoporous Pd/SnO₂ for H₂ were detected. The sensor utilizing mesoporous Pd/SnO₂ via direct synthesis method exhibits excellent response and recovery behavior and much higher sensitivity to H₂, compared to those using mesoporous SnO₂ and mesoporous Pd/SnO₂ via impregnation technique. It is believed that its high gas sensing performance is derived from the large surface area, high activity and well dispersion of Pd additive, as well as high porosity, which lead to highly effective surface interaction between the target gas molecules and the surface active sites.     

Dual graph regularized NMF model for social event detection from Flickr data

In this work, we aim to discover real-world events from Flickr data by devising a three-stage event detection framework. In the first stage, a multimodal fusion (MF) model is designed to deal with the heterogeneous feature modalities possessed by the user-shared data, which is advantageous in computation complexity. In the second stage, a dual graph regularized non-negative matrix factorization (DGNMF) model is proposed to learn compact feature representations. DGNMF incorporates Laplacian regularization terms for the data graph and base graph into the objective, keeping the geometry structures underlying the data samples and dictionary bases simultaneously. In the third stage, hybrid clustering algorithms are applied seamlessly to discover event clusters. Extensive experiments conducted on the real-world dataset reveal the MF-DGNMF-based approaches outperform the baselines.