Synthesis of structure-defined branched hyaluronan tetrasaccharide glycoclusters

Hyaluronan is a glycosaminoglycan with a large number of biological activity. Hyaluronan of different molecular weight often shows different biological activity, sometimes even completely opposite, but the mechanism is not clear. Herein, the hyaluronan tetrasaccharide glycoclusters using hyaluronan tetrasaccharide obtained by enzymolysis of natural hyaluronan were firstly synthesized in high yield. The structurally determined and diverse glycoclusters were of wide molecular weight range and might be used for mimicking the biological activity of natural hyaluronan and facilitating the mechanism study.     

Modeling spherical explosions with aluminized energetic materials

This paper deals with the numerical solution and validation of a reactive flow model dedicated to the study of spherical explosions with an aluminized energetic material. Situations related to air blast as well as underwater explosions are examined. Such situations involve multiscale phenomena associated with the detonation reaction zone, the aluminium reaction zone, the shock propagation distance and the bubble oscillation period. A detonation tracking method is developed in order to avoid the detonation structure computation. An ALE formulation is combined to the detonation tracking method in order to solve the material interface between detonation products and the environment as well as shock propagation. The model and the algorithm are then validated over a wide range of spherical explosions involving several types of explosives, both in air and liquid water environment. Large-scale experiments have been done in order to determine the blast wave effects with explosive compositions of variable aluminium content. In all situations the agreement between computed and experimental results is very good.     

Localized method of fundamental solutions for large-scale modeling of two-dimensional elasticity problems

The traditional method of fundamental solutions (MFS) based on the “global” boundary discretization leads to dense and non-symmetric coefficient matrices that, although smaller in sizes, require huge computational cost to compute the system of equations using direct solvers. In this study, a localized version of the MFS (LMFS) is proposed for the large-scale modeling of two-dimensional (2D) elasticity problems. In the LMFS, the whole analyzed domain can be divided into small subdomains with a simple geometry. To each of the subdomain, the traditional MFS formulation is applied and the unknown coefficients on the local geometric boundary can be calculated by the moving least square method. The new method yields a sparse and banded matrix system which makes the method very attractive for large-scale simulations. Numerical examples with up to 200,000 unknowns are solved successfully using the developed LMFS code.     

Preparation of a Water-Soluble ZnXCd1-XS Quantum Dot Photocatalyst at Room Temperature Assisted by 3-Mercaptopropionic Acid

An effective path to synthesize Zn_xCd_1-xS quantum dots (ZCS QDs) in aqueous phase at room temperature has remained relatively unexplored. Here, we developed a room-temperature, aqueous-phase approach to ZCS QDs, using 3-mercaptopropionic acid (MPA) to adjust the pH of the reaction precursor solution to regulate the competition between sulfur source and hydroxyl group, and realize the large-scale preparation of water-soluble ZCS QDs photocatalyst at room temperature. Without recombination with other materials, and only by regulating the ratio of pH, excess sulfur sources and Zn/Cd, the photocatalytic degradation of rhodamine B (RhB) can reach 98% within 1 min, showing high photocatalytic activity. ZCS QDs show high stability and recoverability, and are expected to be able to deal with organic pollutants on a large scale. This study provides a new idea for the preparation of other QDs at room temperature.     

A two-stage successive overrelaxation algorithm for solving the symmetric linear complementarity problem

We propose a two-stage successive overrelaxation method (TSOR) algorithm for solving the symmetric linear complementarity problem. After the first SOR preprocessing stage this algorithm concentrates on updating a certain prescribed subset of variables which is determined by exploiting the complementarity property. We demonstrate that this algorithm successfully solves problems with up to ten thousand variables.This material is based on research supported by National Science Foundation Grants DCR-8420963 and DCR-8521228 and Air Force Office of Scientific Research Grants AFSOR-86-0172 and AFSOR-86-0255 while the author was at the Computer Sciences Department at the University of Wisconsin-Madison, USA.     

Matrix augmentation and structure preservation in linearly constrained control problems

Matrix augmentation is used for the inversion of bases associated with large linearly constrained control problems. It is shown how an efficient data structure can be maintained by keeping all state variables in the basis, and then nullifying some of them explicitly by using additional constraints. The proposed methodology, together with a basis updating scheme based on augmentation, forms the skeleton for an in-core algorithm using either the revised simplex method or the generalized reduced gradient method.     

Large-scale linearly constrained optimization

An algorithm for solving large-scale nonlinear programs with linear constraints is presented. The method combines efficient sparse-matrix techniques as in the revised simplex method with stable quasi-Newton methods for handling the nonlinearities. A general-purpose production code (MINOS) is described, along with computational experience on a wide variety of problems.This research was supported by the U.S. Office of Naval Research (Contract N00014-75-C-0267), the National Science Foundation (Grants MCS71-03341 A04, DCR75-04544), the U.S. Energy Research and Development Administration (Contract E(04-3)-326 PA #18), the Victoria University of Wellington, New Zealand, and the Department of Scientific and Industrial Research Wellington, New Zealand.     

Robust Decentralized Stabilization of Uncertain Large-Scale Discrete-Time Systems with Delays

This paper describes the synthesis of robust decentralized controllers for uncertain large-scale discrete-time systems with time delays in the subsystem interconnections. Based on the Lyapunov method, a sufficient condition for robust stability is derived in terms of a linear matrix inequality (LMI). The solutions of the LMIs can be obtained easily using efficient convex optimization techniques. A numerical example is given to illustrate the proposed method.