Design of binary polymeric systems containing ɩ-carrageenan and hydroxypropylcellulose for use in cataract surgery.

This study describes the design and characterisation of the rheological and mechanical properties of binary polymeric systems composed of 2-Hydroxypropylcellulose and ɩ-carrageenan, designed as ophthalmic viscoelastic devices (OVDs). Platforms were characterised using dilute solution, flow and oscillatory rheometry and texture profile analysis. Rheological synergy between the two polymers was observed both in the dilute and gel states. All platforms exhibited pseudoplastic flow. Increasing polymer concentrations significantly decreased the loss tangent and rate index yet increased the storage and loss moduli, consistency, gel hardness, compressibility and adhesiveness, the latter being related to the in-vivo retention properties of the platforms. Binary polymeric platforms exhibited unique physicochemical properties, properties that could not be engineered using mono-polymeric platforms. Using characterisation methods that provide information relevant to their clinical performance, low-cost binary platforms (3% hydroxypropylcellulose and either 1% or 2% ɩ-carrageenan) were identified that exhibited rheological, textural and viscoelastic properties advantageous for use as OVDs.

Green Composites and Their Properties: A Brief Introduction

Green composites are important class of biocomposites widely explored due to their enhanced properties. The biodegradable polymeric material is reinforced with natural fibers to form a composite that is eco-friendly and environment sustainable. The green composites have potential to attract the traditional petroleum-based composites which are toxic and nonbiodegradable. The green composites eliminate the traditional materials such as steel and wood with biodegradable polymer composites. The degradable and environment-friendly green composites were prepared by various fabrication techniques. The various properties of different fiber composite were studied as reinforcement for fully biodegradable and environmental-friendly green composites.

The use of binary polymeric networks in stabilising polyethylene oxide solid dispersions

The objective of this study was to determine if a high Tg polymer (Eudragit® S100) could be used to stabilize amorphous domains of polyethylene oxide (PEO) and hence improve the stability of binary polymer systems containing celecoxib (CX). We propose a novel method of stabilizing the amorphous PEO solid dispersion through inclusion of a miscible, high Tg polymer, namely, that can form strong inter-polymer interactions. The effects of inter-polymer interactions and miscibility between PEO and Eudragit S100 are considered. Polymer blends were first manufactured via hot-melt extrusion at different PEO/S100 ratios (70/30, 50/50, and 30/70 wt/wt). Differential scanning calorimetry and dynamic mechanical thermal analysis data suggested a good miscibility between PEO and S100 polymer blends, particularly at the 50/50 ratio. To further evaluate the system, CX/PEO/S100 ternary mixtures were extruded. Immediately after hot-melt extrusion, a single Tg that increased with increasing S100 content (anti-plasticization) was observed in all ternary systems. The absence of powder X-ray diffractometry crystalline Bragg’s peaks also suggested amorphization of CX. Upon storage (40°C/75% relative humidity), the formulation containing PEO/S100 at a ratio of 50:50 was shown to be most stable. Fourier transform infrared studies confirmed the presence of hydrogen bonding between Eudragit S100 and PEO suggesting this was the principle reason for stabilization of the amorphous CX/PEO solid dispersion system.

Directed Selection using Reinforcement Learning for the Examination Timetabling Problem

Traditional heuristic approaches to the Examination Timetabling Problem normally utilize a stochastic method during Optimization for the selection of the next examination to be considered for timetabling within the neighbourhood search process. This paper presents a technique whereby the stochastic method has been augmented with information from a weighted list gathered during the initial adaptive construction phase, with the purpose of intelligently directing examination selection. In addition, a Reinforcement Learning technique has been adapted to identify the most effective portions of the weighted list in terms of facilitating the greatest potential for overall solution improvement. The technique is tested against the 2007 International Timetabling Competition datasets with solutions generated within a time frame specified by the competition organizers. The results generated are better than those of the competition winner in seven of the twelve examinations, while being competitive for the remaining five examinations. This paper also shows experimentally how using reinforcement learning has improved upon our previous technique.