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The aim of the study was to investigate a porous nanohydroxyapatite/polyamide 66 (n-HA/PA66) scaffold material that was implanted into muscle and tibiae of 16 New Zealand white rabbits to evaluate the biocompatibility and osteogenesis and osteoinductivity of the materials in vivo. The samples were harvested at 2, 4, 12 and 26 weeks respectively, and subjected to histological analysis. At 2 weeks, the experiment showed that osteogenesis was detected in porous n-HA/PA66 composite and the density of new bone formation was similar to the surrounding host bone at 12 weeks. The study indicated that three-dimensional pore structures could facilitate cell adhesion, differentiation and proliferation, and help with fibrovascular and nerve colonization. In conclusion, porous n-HA/PA66 scaffold material could be a good candidate as a bone substitute material used in clinics due to its excellent histocompatibility, osteoconductivity and osteoinductivity.

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Concrete surface crack detection based on computer vision, specifically via a convolutional neural network, has drawn increasing attention for replacing manual visual inspection of bridges and buildings. This article proposes a new framework for this task and a sampling and training method based on active learning to treat class imbalances. In particular, the new framework includes a clear definition of two categories of samples, a relevant sliding window technique, data augmentation and annotation methods. The advantages of this framework are that data integrity can be ensured and a very large amount of annotation work can be saved. Training datasets generated with the proposed sampling and training method not only are representative of the original dataset but also highlight samples that are highly complex, yet informative. Based on the proposed framework and sampling and training strategy, AlexNet is re-tuned, validated, tested and compared with an existing network. The investigation revealed outstanding performances of the proposed framework in terms of the detection accuracy, precision and F1 measure due to its nonlinear learning ability, training dataset integrity and active learning strategy.

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