Microwave-assisted hydrogel synthesis: a new method for crosslinking polymers in aqueous solutions

It has been found that hydrogels may be formed by microwave irradiation of aqueous solutions containing appropriate combinations of polymers. This new method of hydrogel synthesis yields sterile hydrogels without the use of monomers, eliminating the need for the removal of unreacted species from the final product. Results for two particularly successful combinations, poly(vinyl alcohol) with either poly(acrylic acid) or poly(methylvinylether-alt-maleic anhydride), are presented. Irradiation using temperatures of 100–150 °C was found to yield hydrogels with large equilibrium swelling degrees of 500–1000 g g−1. Material leached from both types of hydrogel shows little cytotoxicity towards HT29 cells.

An efficient and rapid microwave-assisted synthesis of 1-acetyl-1H-indol-3-YL acetates

An efficient and rapid synthesis of 1-acetyl-1H-indol-3-yl acetate 1 and its derivatives 7 via the microwave-assisted cyclisation and decarboxylation of 2-[(carboxymethyl)amino]benzoic acids 5 is described. The latter were left to react with acetic anhydride using triethylamine as the base and were subjected to microwave irradiation for 1 minute, at 80 °C with initial power of 300 W. The target 1-acetyl-1H-indol-3-yl acetate 1 and derivatives 7 were isolated in 34-71% yield. In particular, synthesis of 1-acetyl-6-(trifluoromethyl)-1H-indol-3-yl acetate 7f and 1-acetyl-3-methyl-1H-indol-3-yl acetate 7h is reported for the first time.

Palladium(II) and platinum(II) complexes with tridentate iminophosphine ligands; synthesis and structural studies

The previously synthesised Schiff-base ligands 2-(2-Ph2PC6H4N = CH) - R' - C6H3OH (R' = 3-OCH3, HL1; 5-OCH3, HL2; 5-Br, HL3; 5-Cl, HL4) were prepared by a faster, more efficient route involving a microwave assisted co-condensation of 2-(diphenylphosphino) aniline with the appropriate substituted salicylaldehyde. HL1-4 react directly with (MCl2)-Cl-II (M = Pd, Pt) or (PtI2)-I-II(cod) affording neutral square-planar complexes of general formula [(MCl)-Cl-II(eta(3)-L1-4)] (M = Pd, Pt, 1 - 8) and [(PtI)-I-II(eta(3)-L1-4)] (M = Pd, Pt, 9 - 12). Reaction of complexes 1 - 4 with the triarylphosphines PR3 (R = Ph, p-tolyl) gave the novel ionic complexes [Pd-II(PR3)(eta(3)- L1-4)] ClO4 (13 - 20). Substituted platinum complexes of the type [Pt-II(PR3)(eta(3)- L1-4)] ClO4 (R = P(CH2CH2CN)(3) 21 - 24) and [Pt-II( P(p-tolyl)(3))(eta(3)-L-3,L-4)] ClO4 ( 25 and 26) were synthesised from the appropriate [(PtCl)-Cl-II(eta(3)-L1-4)] complex (5 - 8) and PR3. The complexes are characterised by microanalytical and spectroscopic techniques. The crystal structures of 3, 6, 10, 15, 20 and 26 were determined and revealed the metal to be in a square-planar four-coordinate environment containing a planar tridentate ligand with an O, N, P donor set together with one further atom which is trans to the central nitrogen atom.

PSO assisted NURB Neural Network Identification

A system identification algorithm is introduced for Hammerstein systems that are modelled using a non-uniform rational B-spline (NURB) neural network. The proposed algorithm consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples are utilized to demonstrate the efficacy of the proposed approach.

Particle swarm optimisation assisted classification using elastic net prefiltering

A novel two-stage construction algorithm for linear-in-the-parameters classifier is proposed, aiming at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage to construct a sparse linear-in-the-parameters classifier. For the first stage learning of generating the prefiltered signal, a two-level algorithm is introduced to maximise the model's generalisation capability, in which an elastic net model identification algorithm using singular value decomposition is employed at the lower level while the two regularisation parameters are selected by maximising the Bayesian evidence using a particle swarm optimization algorithm. Analysis is provided to demonstrate how “Occam's razor” is embodied in this approach. The second stage of sparse classifier construction is based on an orthogonal forward regression with the D-optimality algorithm. Extensive experimental results demonstrate that the proposed approach is effective and yields competitive results for noisy data sets.

Dimensionality reduction assisted tensor clustering

This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In this formulation, an extra tensor mode is formed by a collection of tensors of the same dimensions and then used to assist a Tucker decomposition in order to achieve data dimensionality reduction. We design two types of clustering models for the tensors: PCA Tensor Clustering model and Non-negative Tensor Clustering model, by utilizing different regularizations. The tensor clustering can thus be solved by the optimization method based on the alternative coordinate scheme. Interestingly, our experiments show that the proposed models yield comparable or even better performance compared to most recent clustering algorithms based on matrix factorization.