Similarity solutions of a Becker-Döring system with time-dependent monomer input

We formulate the Becker-Döring equations for cluster growth in the presence of a time-dependent source of monomer input. In the case of size-independent aggregation and ragmentation rate coefficients we find similarity solutions which are approached in the large time limit. The form of the solutions depends on the rate of monomer input and whether fragmentation is present in the model; four distinct types of solution are found.

High glucose mediates prooxidant and antioxidant enzyme activities in coronary endothelial cells

Objective: Excess levels of free radicals such as nitric oxide (NO) and superoxide anion (O2-)are associated with the pathogenesis of endothelial cell dysfunction in diabetes mellitus. This study was designed to investigate the underlying causes of oxidative stress in coronary microvascular endothelial cells (CMEC) exposed to hyperglycaemia. Methods: CMEC were cultured under normal (5.5 mmol/L) or high glucose (22 mmol/L)concentrations for 7 days. The activity and expression (protein level) of eNOS, iNOS, NAD(P)H oxidase and antioxidant enzymes, namely, superoxide dismutase (SOD), catalase and glutahione peroxidase (GPx) were investigated by specific activity assays and Western analyses,respectively while the effects of hyperglycaemia on nitrite and O2 - generation were investigated by Griess reaction and cytochrome C reduction assay, respectively. Results: Hyperglycaemia did not alter eNOS or iNOS protein expressions and overall nitrite generation, an index of NO production. However, it significantly reduced the levels of intracellular antioxidant glutathione by 50% (p<0.05) and increased the protein expressions and/or activities of p22-phox, a membrane-bound component of pro-oxidant NAD(P)H oxidase and antioxidant enzymes (p<0.05). Free radical-scavengers, namely, Tiron and MPG (0.1-1 mol/L) reduced hyperglycaemia-induced antioxidant enzyme activity and increased glutathione and nitrite generation to the levels observed in CMEC cultured in normoglycaemic medium (p<0.01). The differences in enzyme activity and expressions were independent of the increased osmolarity generated by high glucose levels as investigated by using equimolar concentrations of mannitol in parallel experiments. Conclusions: These results suggest that hyperglycaemia-induced oxidative stress may arise in CMEC as a result of enhanced prooxidant enzyme activity and diminished generation of 3 antioxidant glutathione. By increasing the antioxidant enzyme capacity CMEC may protect themselves against free radical-induced cell damage in diabetic conditions. The definitive version is available at http://www.blackwell-synergy.com

An Indirect Genetic Algorithm for Set Covering Problems

This paper presents a new type of genetic algorithm for the set covering problem. It differs from previous evolutionary approaches first because it is an indirect algorithm, i.e. the actual solutions are found by an external decoder function. The genetic algorithm itself provides this decoder with permutations of the solution variables and other parameters. Second, it will be shown that results can be further improved by adding another indirect optimisation layer. The decoder will not directly seek out low cost solutions but instead aims for good exploitable solutions. These are then post optimised by another hill-climbing algorithm. Although seemingly more complicated, we will show that this three-stage approach has advantages in terms of solution quality, speed and adaptability to new types of problems over more direct approaches. Extensive computational results are presented and compared to the latest evolutionary and other heuristic approaches to the same data instances.

'An Indirect Genetic Algorithm for Set Covering Problems'

This paper presents a new type of genetic algorithm for the set covering problem. It differs from previous evolutionary approaches first because it is an indirect algorithm, i.e. the actual solutions are found by an external decoder function. The genetic algorithm itself provides this decoder with permutations of the solution variables and other parameters. Second, it will be shown that results can be further improved by adding another indirect optimisation layer. The decoder will not directly seek out low cost solutions but instead aims for good exploitable solutions. These are then post optimised by another hill-climbing algorithm. Although seemingly more complicated, we will show that this three-stage approach has advantages in terms of solution quality, speed and adaptability to new types of problems over more direct approaches. Extensive computational results are presented and compared to the latest evolutionary and other heuristic approaches to the same data instances.