Development and mechanical characterization of solvent-cast polymeric films as potential drug delivery systems to mucosal surfaces

Solvent-cast films from three polymers, carboxymethylcellulose (CMC), sodium alginate (SA), and xanthan gum, were prepared by drying the polymeric gels in air. Three methods, (a) passive hydration, (b) vortex hydration with heating, and (c) cold hydration, were investigated to determine the most effective means of preparing gels for each of the three polymers. Different drying conditions [relative humidity - RH (6-52%) and temperature (3-45 degrees C)] were investigated to determine the effect of drying rate on the films prepared by drying the polymeric gels. The tensile properties of the CMC films were determined by stretching dumbbell-shaped films to breaking point, using a Texture Analyser. Glycerol was used as a plasticizer, and its effects on the drying rate, physical appearance, and tensile properties of the resulting films were investigated. Vortex hydration with heating was the method of choice for preparing gels of SA and CMC, and cold hydration for xanthan gels. Drying rates increased with low glycerol content, high temperature, and low relative humidity. The residual water content of the films increased with increasing glycerol content and high relative humidity and decreased at higher temperatures. Generally, temperature affected the drying rate to a greater extent than relative humidity. Glycerol significantly affected the toughness (increased) and rigidity (decreased) of CMC films. CMC films prepared at 45 degrees C and 6% RH produced suitable films at the fastest rate while films containing equal quantities of glycerol and CMC possessed an ideal balance between flexibility and rigidity.

A novel neural/symbolic hybrid approach to heuristically optimized fuel allocation and automated revision of heuristics in nuclear engineering

FUELCON is an expert system for optimized refueling design in nuclear engineering. This task is crucial for keeping down operating costs at a plant without compromising safety. FUELCON proposes sets of alternative configurations of allocation of fuel assemblies that are each positioned in the planar grid of a horizontal section of a reactor core. Results are simulated, and an expert user can also use FUELCON to revise rulesets and improve on his or her heuristics. The successful completion of FUELCON led this research team into undertaking a panoply of sequel projects, of which we provide a meta-architectural comparative formal discussion. In this paper, we demonstrate a novel adaptive technique that learns the optimal allocation heuristic for the various cores. The algorithm is a hybrid of a fine-grained neural network and symbolic computation components. This hybrid architecture is sensitive enough to learn the particular characteristics of the ‘in-core fuel management problem’ at hand, and is powerful enough to use this information fully to automatically revise heuristics, thus improving upon those provided by a human expert.