ℋ︁‐LU factorization in preconditioners for augmented Lagrangian and grad‐div stabilized saddle point systems

The (mixed finite element) discretization of the linearized Navier–Stokes equations leads to a linear system of equations of saddle point type. The iterative solution of this linear system requires the construction of suitable preconditioners, especially in the case of high Reynolds numbers. In the past, a stabilizing approach has been suggested which does not change the exact solution but influences the accuracy of the discrete solution as well as the effectiveness of iterative solvers. This stabilization technique can be performed on the continuous side before the discretization, where it is known as ‘grad‐div’ (GD) stabilization, as well as on the discrete side where it is known as an ‘augmented Lagrangian’ (AL) technique (and does not change the discrete solution). In this paper, we study the applicability of ??‐LU factorizations to solve the arising subproblems in the different variants of stabilized saddle point systems. We consider both the saddle point systems that arise from the stabilization in the continuous as well as on the discrete setting. Recently, a modified AL preconditioner has been proposed for the system resulting from the discrete stabilization. We provide a straightforward generalization of this approach to the GD stabilization. We conclude the paper with numerical tests for a variety of problems to illustrate the behavior of the considered preconditioners as well as the suitability of ??‐LU factorization in the preconditioners. Copyright © 2010 John Wiley & Sons, Ltd.     

Global Structure-Acute Toxicity Relationships for Mice Using Structural Parameter Ratios: New Approach to Molecular Design and Screening

Abstract

A method for a preliminary survey of the relationship between molecular structure and performance was described using 1506 random data of structure-acute toxicity for mice (intravenously dosed). The structural patterns of the weakest toxic structures (111) were extracted from the data and the patterns discriminated for 64.2% of the other structures (1395). As for the 826 structures of strongest toxicity, 78.3% were discriminated by these structural patterns. These results were obtained by using structural parameter ratios to describe the structural patterns and the exhaustive elimination process to select the best parameter ratio from many candidates. The results were summarized in the form of a chart which can be used for practical screening for the weakest toxic structures.     

Thermochemistry of Surfactant-Templating of USY Zeolite

With the aim of understanding the thermochemistry of the introduction of mesoporosity in zeolites by using surfactants, high temperature oxide melt solution calorimetry was used to determine the change in the enthalpy of formation of USY zeolite before and after the introduction of mesoporosity. Our results confirm that this process only slightly destabilizes the zeolite by the additional surface area. However, this can be overcome by the stabilizing effect of the interactions between the surfactant and the zeolite framework.     

Glutamine: fructose-6-phosphate amidotransferase (GFAT): homology modelling and designing of new inhibitors using pharmacophore and docking based hierarchical virtual screening protocol

Glutamine: fructose-6-phosphate amidotransferase (GFAT), also termed GFPT1 and GFAT1, catalyzes the first committed step of the hexosamine biosynthesis pathway in mammals and consequently plays an important role in type 2 diabetes. In the present study, a combination of pharmacophore modelling, homology modelling, and molecular docking analysis was performed to design new glutamine competitive inhibitors of human GFAT, and to investigate important interaction details of inhibitor molecules. A pharmacophore model of GFAT inhibitors was developed, subsequently validated, and utilized for the screening by the PHASE database to identify new molecules. Afterwards, homology modelling was performed to construct the glutamine-binding site of the GFAT protein. The modelled active site was utilized to dock the studied molecules to investigate important receptor-ligand interactions and to scrutinize database-screened molecules on the basis of essential interactions. This systematic in silico protocol helped us to identify new molecules that would be explored for the treatment of type 2 diabetes and its complications.     

Método de elementos de borde jerárquico basado en el árbol de Barnes-Hut aplicado a flujo reptante exterior

In this work, a hierarchical variant of a boundary element method and its use in Stokes flow around three-dimensional rigid bodies in steady regime is presented. The proposal is based on the descending hierarchical low-order and self-adaptive algorithm of Barnes-Hut, and it is used in conjunction with an indirect boundary integral formulation of second class, whose source term is a function of the undisturbed velocity. The solution field is the double layer surface density, which is modified in order to complete the eigenvalue spectrum of the integral operator. In this way, the rigid modes are eliminated and both a non-zero force and a non-null torque on the body could be calculated. The elements are low order flat triangles, and an iterative solution by generalized minimal residual (GMRES) is used. Numerical examples include cases with analytical solutions, bodies with edges and vertices, or with intricate shapes. The main advantage of the presented technique is the possibility of considering a greater number of degrees of freedom regarding traditional collocation methods, due to the decreased demand of main memory and the reduction in the computation times.     

Protein Secondary Structure Prediction with Partially Recurrent Neural Networks

Abstract

Partially recurrent neural networks with different topologies are applied for secondary structure prediction of proteins. The state of some activations in the network is available after a pattern presentation via feedback connections as additional input during the processing of the next pattern in a sequence. A reference data set containing 91 proteins in the training set and 15 non-homologous proteins in the test set is used for training and testing a network with a modified, hierarchical Elman architecture. The network predicts the secondary structures α-helix, β-sheet, and “coil” for each amino acid. The percentage of correctly classified amino acids is 67.83% on the training set and 63.98% on the test set. The best performance of a three-layer feedforward network is 62.7% on the same test set. A cascaded network, where the outputs of the recurrent network are processed by a second net with 13 × 3 inputs, four hidden and three output units has a predictive performance of 64.49%. The best corresponding feedforward net has a performance of 64.3%.     

Engineering Hierarchical Nanostructures by Elastocapillary Self‐Assembly

Surfaces coated with nanoscale filaments such as silicon nanowires and carbon nanotubes are potentially compelling for high‐performance battery and capacitor electrodes, photovoltaics, electrical interconnects, substrates for engineered cell growth, dry adhesives, and other smart materials. However, many of these applications require a wet environment or involve wet processing during their synthesis. The capillary forces introduced by these wet environments can lead to undesirable aggregation of nanoscale filaments, but control of capillary forces can enable manipulation of the filaments into discrete aggregates and novel hierarchical structures. Recent studies suggest that the elastocapillary self‐assembly of nanofilaments can be a versatile and scalable means to build complex and robust surface architectures. To enable a wider understanding and use of elastocapillary self‐assembly as a fabrication technology, we give an overview of the underlying fundamentals and classify typical implementations and surface designs for nanowires, nanotubes, and nanopillars made from a wide variety of materials. Finally, we discuss exemplary applications and future opportunities to realize new engineered surfaces by the elastocapillary self‐assembly of nanofilaments.     

黄渤海近岸海域酚类内分泌干扰物分布特征及其来源解析

在黄渤海近岸海域采集了34个水体样品,利用HPLC-MS/MS分析了双酚A、辛基酚、壬基酚、2,4-二氯酚、对叔丁基苯酚和对特辛基苯酚等6种酚类内分泌干扰物的含量,并探讨了其分布特征及来源.结果表明,中国北部近岸海域6种酚类内分泌干扰物的含量范围在5.25～1351.20ng/mL之间.结合因子分析和层次聚类分析结果,说明渤海、黄海近岸海域中酚类化合物主要以辛基酚、壬基酚、2,4-二氯酚为主,局部海域伴有双酚A的高残留;从整个海域范围看,黄渤海近岸海域水体中酚类化合物污染状况具有区域特征,整体呈现出南高北低的特点,且酚类物质分布具有明显的地区特性,一定程度上具有聚集性;来源解析结果表明黄渤海近岸海域中酚类内分泌干扰物主要来源为生活污水和工业废水.