Novel Hybrid Flow Diverter for Intracranial Aneurysm

An intracranial aneurysm is a weakened area in the wall of a cerebral artery which causes abnormal localized ballooning of the blood vessel. As an aneurysm grows, it puts pressure on adjacent structures and may eventually rupture, leading to severe complications or even sudden death. The standard treatments for intracranial aneurysms include traditional craniotomy and endovascular coiling. The purpose of these treatments is to stop the blood flow to an aneurysm to reduce the risk of rupture. In recent years, another new device, "flow diverter", has gained popularity. It is placed in the parent artery to divert the blood flow away from the weakened area, isolating aneurysms from normal circulation. Although flow diverter stents have great potential, there remains clinical issues to be resolved. This paper proposes a unique hybrid flow diverter, the first of its kind in the world, for treatment of the intracranial aneurysm. The hybrid flow diverter is designed to have variable mesh densities, with the denser side facing an aneurysm to block the blood flow and the lighter side facing the artery to prevent stenosis. It is deployed in the main cerebral artery next to an aneurysm to divert the blood flow away from the weakened aneurysm. Simulation results showed that the hybrid flow diverter reduced the blood flow into an aneurysm by a whopping 75-95%. The residence time of the blood flow inside an aneurysm was 12.47 times longer with the hybrid flow diverter, which may trigger thrombogenic reaction to fill an aneurysm and thus reduce the risk of rupture.

Niching Multimodal Landscapes Faster Yet Effectively: VMO and HillVallEA Benefit Together

Variable Mesh Optimization with Niching (VMO-N) is a framework for multimodal problems (those with multiple optima at several search subspaces). Its only two instances are restricted though. Being a potent multimodal optimizer, the Hill-Valley Evolutionary Algorithm (HillVallEA) uses large populations that prolong its execution. This study strives to revise VMO-N, to contrast it with related approaches, to instantiate it effectively, to get HillVallEA faster, and to indicate methods (previous or new) for practical use. We hypothesize that extra pre-niching search in HillVallEA may reduce the overall population, and that if such a diminution is substantial, it runs more rapidly but effective. After refining VMO-N, we bring out a new case of it, dubbed Hill-Valley-Clustering-based VMO (HVcMO), which also extends HillVallEA. Results show it as the first competitive variant of VMO-N, also on top of the VMO-based niching strategies. Regarding the number of optima found, HVcMO performs statistically similar to the last HillVallEA version. However, it comes with a pivotal benefit for HillVallEA: a severe reduction of the population, which leads to an estimated drastic speed-up when the volume of the search space is in a certain range.

Solving optimal reactive power problem by improved variable mesh optimization algorithm

<p>In this work Improved Variable Mesh Optimization Algorithm (IVM) has been applied to solve the optimal reactive power problem. Projected Improved VMO algorithm has been modeled by hybridization of Variable mesh optimization algorithm with Clearing-Based Niche Formation Technique, Differential Evolution (DE) algorithm. Mesh formation and exploration has been enhanced by the hybridization. Amongst of niche development process, clearing is a renowned method in which general denominator is the formation of steady subpopulations (niches) at all local optima (peaks) in the exploration space. In Differential Evolution (DE) population is formed by common sampling within the stipulated smallest amount and maximum bounds. Subsequently DE travel into the iteration process where the progressions like, mutation, crossover, and selection, are followed. Proposed Improved Variable Mesh Optimization Algorithm (IVM) has been tested in standard IEEE 14,300 bus test system and simulation<br />results show the projected algorithm reduced the real power loss extensively.</p>