Polyblend fibrous scaffolds in mass ratios of 100/0, 90/10, 80/20, and 70/30 from poly(l-lactide) (PLLA) and poly(⑀-caprolactone) (PCL) for cartilage tissue engineering were prepared in three steps: gelation, solvent exchanging, and freeze-drying. Effects of the blend ratio, the exchange medium, and the operating temperature on the morphology of scaffolds were investigated by SEM. PLLA/PCL scaffolds presented an ultrafine fibrous network with the addition of a "small block" structure. Smooth and regular fibrous networks were formed when ethanol was used as the exchange medium. Properties of the scaffolds, such as thermal and mechanical properties, were also studied. The results suggested that the compressive modulus declined as PCL amount increased. The incorporation of PCL effectively contributed to reduce the rigidity of PLLA. Bovine chondrocytes were seeded onto PLLA/PCL scaffold. Cells attached onto the fibrous network and their morphology was satisfactory. This polyblend fibrous scaffold will be a potential scaffold for cartilage tissue engineering.

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State estimation suffers some new challenging problems when wireless sensor networks (WSNs) are used in mobile target tracking systems. An important problem among them is low observability, which makes it necessary to further study performance of tracking estimators from an observable degree point of view. This paper studies observable degree analysis (ODA) to formulate the estimator performance for a kind of wireless tracking filter. For a kind of time-varying Kalman estimator, an improved ODA method is proposed by using weighted least square, Cauchy Schwarz inequality and normalization processing. The main contributions are that the relation is firstly analytically established between the observable degree and the estimation performance, and finally the observable degree is normalized in [0,1]. Meanwhile, observable degrees are both given for local state components and the global state. Thereby, we have the conclusion that higher observable degree corresponds to better estimation performance of the Kalman filter. Accordingly, it is potentially hopeful to achieve an effective function to study estimation performance of tracking estimators by directly using the observable degree. Simulation is provided to verify the results on real-time and generality, completeness and consistency/matching of the proposed observable degree analysis.