A control volume finite element method for three-dimensional three-phase flows

A novel control volume finite element method with adaptive anisotropic unstructured meshes is presented for three-dimensional three-phase flows with interfacial tension. The numerical framework consists of a mixed control volume and finite element formulation with a new P₁DG-P₂ elements (linear discontinuous velocity between elements and quadratic continuous pressure between elements). A “volume of fluid” type method is used for the interface capturing, which is based on compressive control volume advection and second-order finite element methods. A force-balanced continuum surface force model is employed for the interfacial tension on unstructured meshes. The interfacial tension coefficient decomposition method is also used to deal with interfacial tension pairings between different phases. Numerical examples of benchmark tests and the dynamics of three-dimensional three-phase rising bubble, and droplet impact are presented. The results are compared with the analytical solutions and previously published experimental data, demonstrating the capability of the present method.     

Existence of multiple and sign-changing solutions for a second-order nonlinear functional difference equation with periodic coefficients

In the present paper, we apply the method of invariant sets of descending flow to establish a series of criteria to ensure that a second-order nonlinear functional difference equation with periodic boundary conditions possesses at least one trivial solution and three nontrivial solutions. These nontrivial solutions consist of sign-changing solutions, positive solutions and negative solutions. Moreover, as an application of our theoretical results, an example is elaborated. Our results generalize and improve some existing ones.     

Physical,Thermal and Biological Properties of Yellow Dyes with Two Azodiphenylether Groups of Anthracene

Two yellow bis-azo dyes containing anthracene and two azodiphenylether groups (BPA and BTA) were prepared, and an extensive investigation of their physical, thermal and biological properties was carried out. The chemical structure was confirmed by the FTIR spectra, while from the UV–Vis spectra, the quantum efficiency of the laser fluorescence at the 476.5 nm was determined to be 0.33 (BPA) and 0.50 (BTA). The possible transitions between the energy levels of the electrons of the chemical elements were established, identifying the energies and the electronic configurations of the levels of transition. Both crystals are anisotropic, the optical phenomenon of double refraction of polarized light (birefringence) taking place. Images of maximum illumination and extinction were recorded when the crystals of the bis-azo compounds rotated by 90° each, which confirms their birefringence. A morphologic study of the thin films deposited onto glass surfaces was performed, proving the good adhesion of both dyes. By thermal analysis and calorimetry, the melting temperatures were determined (~224–225 °C for both of them), as well as their decomposition pathways and thermal effects (enthalpy variations during undergoing processes); thus, good thermal stability was exhibited. The interaction of the two compounds with collagen in the suede was studied, as well as their antioxidant activity, advocating for good chemical stability and potential to be safely used as coloring agents in the food industry.     

Universality of deep convolutional neural networks

Deep learning has been widely applied and brought breakthroughs in speech recognition, computer vision, and many other domains. Deep neural network architectures and computational issues have been well studied in machine learning. But there lacks a theoretical foundation for understanding the approximation or generalization ability of deep learning methods generated by the network architectures such as deep convolutional neural networks. Here we show that a deep convolutional neural network (CNN) is universal, meaning that it can be used to approximate any continuous function to an arbitrary accuracy when the depth of the neural network is large enough. This answers an open question in learning theory. Our quantitative estimate, given tightly in terms of the number of free parameters to be computed, verifies the efficiency of deep CNNs in dealing with large dimensional data. Our study also demonstrates the role of convolutions in deep CNNs.     

Information percolation and cutoff for the random‐cluster model

We consider the random‐cluster model (RCM) on with parameters p∈(0,1) and q ≥ 1. This is a generalization of the standard bond percolation (with edges open independently with probability p) which is biased by a factor q raised to the number of connected components. We study the well‐known Fortuin‐Kasteleyn (FK)‐dynamics on this model where the update at an edge depends on the global geometry of the system unlike the Glauber heat‐bath dynamics for spin systems, and prove that for all small enough p (depending on the dimension) and any q>1, the FK‐dynamics exhibits the cutoff phenomenon at with a window size , where λ_∞ is the large n limit of the spectral gap of the process. Our proof extends the information percolation framework of Lubetzky and Sly to the RCM and also relies on the arguments of Blanca and Sinclair who proved a sharp mixing time bound for the planar version. A key aspect of our proof is the analysis of the effect of a sequence of dependent (across time) Bernoulli percolations extracted from the graphical construction of the dynamics, on how information propagates.     

Analysis of a micro–macro acceleration method with minimum relative entropy moment matching

We analyse convergence of a micro–macro acceleration method for the simulation of stochastic differential equations with time-scale separation. The method alternates short bursts of path simulations with the extrapolation of macroscopic state variables forward in time. After extrapolation, a new microscopic state is constructed, consistent with the extrapolated macroscopic state, that minimises the perturbation caused by the extrapolation in a relative entropy sense. We study local errors and numerical stability of the method to prove its convergence to the full microscopic dynamics when the extrapolation time step tends to zero and the number of macroscopic state variables tends to infinity.     

密度泛函理论研究MN@H_2O (M=Ga,Ge, In,Sn, Sb; N=M,Al)团簇的特性

采用密度泛函理论的B3LYP, B3P86, B1B95, P3PW91和PBE1PBE方法结合SDD, LANL2DZ和CEP-121G基组计算了d~(10)组态二聚物MN(M=Ga, Ge, In, Sn和Sb; N=M和Al)的几何结构.采用B3P86/SDD进一步研究了MN@H_2O团簇的几何结构及吸附能.结果表明,水分子结合在二聚物M_2上时,对二聚物影响较大,对水分子自身影响较小.将M_2中Ga, Ge, In, Sn或Sb替换一个原子为Al时,水分子在GeAl和SnAl上的吸附能变化较大,而在GaAl, InAl和SbAl上吸附能变化较小.另外, H_2O吸附在Ga, Ge, In, Sn和Sb上时,与吸附在Al上时,吸附能的变化不大.