Bone tissue engineering has been continuously developing since the concept of “tissue engineering” has been proposed. Biomaterials, as the basic material for the fabrication of scaffolds, play a vital role in bone tissue engineering.

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This article reports the mechanical properties and in vitro evaluation of a collagen scaffold fabricated using an indirect 3D printing technique. Collagen scaffolds, featuring predefined internal channels and capillary networks, were manufactured using phase change printing. It was observed that the collagen scaffolds featured internal channels and a hierarchical structure that varied over length scales of 10-400 microm. In vitro evaluation using hMSCs demonstrated that the resultant collagen based scaffolds have the ability to support hMSC cell attachment and proliferation; cells can migrate and survive deep within the structure of the scaffold. The cell numbers increased 2.4 times over 28 days in culture for the lysine treated scaffolds. The cells were spread along the collagen fibers to form a 3D structure and extracellular matrix was detected on the surface of the scaffolds after 4 weeks in culture. The crosslinking treatment enhanced the biostability and dynamic properties of the collagen scaffolds significantly.

This paper presents a technique to determine the occupancy and indoor environment quality (IEQ) in buildings by enhancing physical measurements from a distributed sensor network with a statistical estimation method. The research is motivated by the increasing demand for improving energy efficiency while maintaining healthy and comfortable environment in buildings. Features representing the occupancy level and the relative changes are extracted from a suite of sensors: passive infra-red (PIR) sensors, Carbon Dioxide (C02) concentration sensors, and relative humidity (RH) sensors, which are networked and installed in a laboratory. An Autoregressive Hidden Markov Model (ARHMM) has been developed to model the occupancy pattern, based on the measurements, given its ability to establish correlations among the observed variables. The result is compared with that obtained from the classical Hidden Markov Model (HMM) and Support Vector Machines (SVM), which indicates that the ARHMM estimation method performed better than the other two methods, with an average estimation accuracy of 80.78%.