Current multi-scale biomaterials for tissue regeneration following spinal cord injury.

Spinal cord injury (SCI) may cause loss of motor and sensory function, autonomic dysfunction, and thus disrupt the quality of life of patients, leading to severe disability and significant psychological, social, and economic burden. At present, existing therapy for SCI have limited ability to promote neural function recovery, and there is an urgent need to develop innovative regenerative approaches to repair SCI. Biomaterials have become a promising strategy to promote the regeneration and repair of damaged nerve tissue after SCI. Biomaterials can provide support for nerve tissue by filling cavities, and improve local inflammatory responses and reshape extracellular matrix structures through unique biochemical properties to create the optimal microenvironment at the SCI site, thereby promoting neurogenesis and reconnecting damaged spinal cord tissue. Considering the importance of biomaterials in repairing SCI, this article reviews the latest progress of multi-scale biomaterials in SCI treatment and tissue regeneration, and evaluates the relevant technologies for manufacturing biomaterials.

Optimal location of logistics distribution centres with swarm intelligent clustering algorithms.

A clustering algorithm is a solution for grouping a set of objects and for distribution centre location problems. But the common K-means clustering algorithm may give local optimal solutions. Swarm intelligent algorithms simulate the social behaviours of animals and avoid local optimal solutions. We employ three swarm intelligent algorithms to avoid these solutions. We propose a new algorithm for the clustering problem, the fruit-fly optimization K-means algorithm (FOA K-means). We designed a distribution centre location problem and three clustering indicators to evaluate the performance of algorithms. We compare the algorithms of K-means with the ant colony optimization algorithm (ACO K-means), particle swarm optimization algorithm (PSO K-means), and fruit-fly optimization algorithm. We find K-Means modified by the fruit-fly optimization algorithm (FOA K-means) has the best performance on convergence speed and three clustering indicators, compactness, separation, and integration. Thus, we can apply FOA K-means to improve the distribution centre location solution and the efficiency for distribution in the future.