B. Obradovic¹, M. Radisic², G. Vunjak-Novakovic³

FDMP-Fluid Dynamics & Materials Processing, Vol.3, No.3, pp. 189-202, 2007, DOI:10.3970/fdmp.2007.003.189

Abstract Efficient transport of oxygen is one of the main requirements in tissue engineering systems in order to avoid cell death in the inner tissue regions and support uniform tissue regeneration. In this paper, we review approaches to design of tissue engineering systems with adequate oxygen delivery for cultivation of cartilage and myocardium, two distinctly different tissue types with respect to the tissue structure and oxygen requirements. Mathematical modeling was used to support experimental results and predict oxygen transport within the cultivated tissues and correlate it to the cell response and tissue properties. More >

Yuhang Jing¹, Ming Hu^2,3

CMC-Computers, Materials & Continua, Vol.38, No.1, pp. 43-59, 2013, DOI:10.3970/cmc.2013.038.043

Abstract Nanostructuring of thermoelectric materials bears promise for manipulating physical parameters to improve the energy conversion efficiency of thermoelectrics. In this paper the thermal transport in Si/Ge superlattice nanotubes is investigated by performing nonequilibrium molecular dynamics simulations aiming at realizing low thermal conductivity by surface roughening. Our calculations revealed that the thermal conductivity of Si/Ge superlattice nanotubes depends nonmonotonically on periodic length and increases as the wall thickness increases. However, the thermal conductivity is not sensitive to the inner diameters due to the strong surface scattering at thin wall thickness. In addition, introducing roughness onto the superlattice nanotubes surface can destroy… More >

Shuren Zhou^1,2,*, Wenlong Liang^1,2, Junguo Li^1,2, Jeong-Uk Kim³

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 11-24, 2018, DOI:10.32604/cmc.2018.02617

Abstract Road traffic sign recognition is an important task in intelligent transportation system. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, it presents a road traffic sign recognition algorithm based on a convolutional neural network. In natural scenes, traffic signs are disturbed by factors such as illumination, occlusion, missing and deformation, and the accuracy of recognition decreases, this paper proposes a model called Improved VGG (IVGG) inspired by VGG model. The IVGG model includes 9 layers, compared with the original VGG model, it is added max-pooling… More >

Huiyu Sun^1,*, Suzanne McIntosh¹

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 1-9, 2018, DOI:10.32604/cmc.2018.03684

Abstract The majority of big data analytics applied to transportation datasets suffer from being too domain-specific, that is, they draw conclusions for a dataset based on analytics on the same dataset. This makes models trained from one domain (e.g. taxi data) applies badly to a different domain (e.g. Uber data). To achieve accurate analyses on a new domain, substantial amounts of data must be available, which limits practical applications. To remedy this, we propose to use semi-supervised and active learning of big data to accomplish the domain adaptation task: Selectively choosing a small amount of datapoints from a new domain while… More >