The optimal path search is important in navigation learning with an electrical wheelchair. This paper studies six basic approaches for path planning in a static environment: Breath-First Search, Depth-First Search, A*, Moore-Dijkstra, Neural approach and genetic algorithms.Firstly, the environment is modeled in a 2D grid and the genetic approach has fixed chromosome length. Then the genetic approach is evaluated within a grid free environment and with variable chromosome lengths. The latter approach offers good solution in precision and in computation time, under certain conditions. Tests are led and the results are discussed through experiments.
The success of stents to restore blood flow in atherosclerotic peripheral arteries is low relative to coronary arteries. It has been shown that joint flexion induces a mechanical environment that makes stent placement in these arteries highly incompatible, and damage and destruction of stents has been recorded. However, the effect of this environment on the stresses in the arteries is unknown. It is hypothesised that the stresses induced in arteries as a result of this mechanical environment could be sufficient to explain the relatively low success rates. To investigate this hypothesis, a finite element model of the stent-artery interaction was developed. Following stent expansion, bending was simulated by applying a displacement boundary condition to the artery. It is found that high stresses occur at the proximal/distal ends of the stent. As high stress and vascular injury are hypothesised to cause restenosis, the results presented here suggest that the mechanical environment of peripheral arteries could be the predominant cause of high restenosis rates.
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