Layered silicate nanocomposites based on various high-functionality epoxy resins. Part II: The influence of an organoclay on the rheological behavior of epoxy prepolymers

The effect of an organically surface modified layered silicate on the viscosity of various epoxy resins of different structures and different functionalities was investigated. Steady and dynamic shear viscosities of the epoxy resins containing 0-10 wt% of the organoclay were determined using parallel plate rheology. Viscosity results were compared with those achieved through addition of a commonly used micron-sized CaCO3 filler. It was found that changes in viscosities due to the different fillers were of the same order, since the layered silicate was only dispersed on a micron-sized scale in the monomer (prior to reaction), as indicated by X-ray diffraction measurements. Flow activation energies at a low frequency were determined and did not show any significant changes due to the addition of organoclay or CaCO3. Comparison between dynamic and steady shear experiments showed good agreement for low layered silicate concentrations below 7.5 wt%, i.e. the Cox-Merz rule can be applied. Deviations from the Cox-Merz rule appeared at and above 10 wt%, although such deviations were only slightly above experimental error. Most resin organoclay blends were well predicted by the Power Law model, only concentrations of 10 wt% and above requiring the Herschel-Buckley (yield stress) model to achieve better fits. Wide-angle X-ray measurements have shown that the epoxy resin swells the layered silicate with an increase in the interlayer distance of approximately 15 Angstrom, and that the rheology behavior is due to the lateral, micron-size of these swollen tactoids.

Genetic algorithm optimization of adaptive multi-scale GLCM features

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

"Only a GP?'': is the solution to the general practice crisis intellectual?

General practice is suffering a crisis of status, as shown by financial, power and intellectual markers. This is serious as a strong general-practice workforce is important to deliver cost-effective, high-quality healthcare. We argue that strengthening the intellectual aspects of general practice (particularly critical thinking) is essential. Most strategies to achieve this centre on research, with many initiatives in Australia and overseas to enhance research by general practitioners; there is still insufficient clinical research in general practice. Other ways to improve critical thinking include promoting use of evidence-based medicine, provided it is not implemented only via cook-book guidelines. Other innovations are desperately needed.