H. Hayashi

Publications

2 Osteogenesis by Dextran Coating on and among Fibers of a Polyvinyl Formal Sponge

Authors: M. Yoshikawa, N. Tsuji, T. Yabuuchi, Y Shimomura, H. Kakigi, H. Hayashi, H. Ohgushi

Abstract:

A scaffold is necessary for tooth regeneration because of its three-dimensional geometry. For restoration of defect, it is necessary for the scaffold to be prepared in the shape of the defect. Sponges made from polyvinyl alcohol with formalin cross-linking (PVF sponge) have been used for scaffolds for bone formation in vivo. To induce osteogenesis within the sponge, methods of growing rat bone marrow cells (rBMCs) among the fiber structures in the sponge might be considered. Storage of rBMCs among the fibers in the sponge coated with dextran (10 kDa) was tried. After seeding of rBMCs to PVF sponge immersed in dextran solution at 2 g/dl concentration, osteogenesis was recognized in subcutaneously implanted PVF sponge as a scaffold in vivo. The level of osteocalcin was 25.28±5.71 ng/scaffold and that of Ca was 129.20±19.69 µg/scaffold. These values were significantly higher than those in sponges without dextran coating (p<0.01). Osteogenesis was induced in many spaces in the inner structure of the sponge with dextran coated fibers.

Keywords: Scaffold, osteogenesis, dextran, Polyvinyl formal sponge

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1250
1 Neural Network Ensemble-based Solar Power Generation Short-Term Forecasting

Authors: A. Chaouachi, R.M. Kamel, R. Ichikawa, H. Hayashi, K. Nagasaka

Abstract:

This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensemble of bagged networks. Forecasting reliability of the proposed neural networks was carried out in terms forecasting error performance basing on statistical and graphical methods. The experimental results showed that all the proposed networks achieved an acceptable forecasting accuracy. In term of comparison the neural network ensemble gives the highest precision forecasting comparing to the conventional networks. In fact, each network of the ensemble over-fits to some extent and leads to a diversity which enhances the noise tolerance and the forecasting generalization performance comparing to the conventional networks.

Keywords: comparative study, solar power generation, neural network ensemble

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2864