Minimally invasive surgery will be gradually applied to the surgical treatment of bone tumors. One of the difficulties in the minimally invasive treatment of bone tumors is the lack of injectable materials that can be used to treat tumor-induced bone defects. Therefore, it is imperative to develop an injectable bone filler that can not only be injected into the defect site by minimally invasive methods to provide strong support and repair bone tissue but also inactivate residual tumor cells around the defect. To achieve this aim, in our study, for the first time, we doped Fe3O4/graphene oxide (GO) nanocomposites into α-tricalcium phosphate (α-TCP)/calcium sulfate (CS) biphasic bone cement to prepare an injectable magnetic bone cement (α-TCP/CS/Fe3O4/GO, αCFG), which can be applied in bone tumor minimally invasive surgery and fit ideally even if the area is irregular. The magnetothermal performance of the αCFG bone cement could be well adjusted by altering the content of Fe3O4/GO nanocomposites and the magnetic field parameters, but a 10 wt % Fe3O4/GO content formed the most stable bone cement with excellent magnetothermal performance. The αCFG bone cement not only promotes bone regeneration but also exhibits enhanced tumor treatment effects. Such multifunctional bone cement could provide a promising clinical strategy for the minimally invasive treatment of tumor-induced bone destruction.

Continuous action recognition in video is more complicated compared with traditional isolated action recognition. Besides the high variability of postures and appearances of each action, the complex temporal dynamics of continuous action makes this problem challenging. In this study, the authors propose a hierarchical framework combining convolutional neural network (CNN) and hidden Markov model (HMM), which recognises and segments continuous actions simultaneously. The authors utilise the CNN's powerful capacity of learning high level features directly from raw data, and use it to extract effective and robust action features. The HMM is used to model the statistical dependences over adjacent sub‐actions and infer the action sequences. In order to combine the advantages of these two models, the hybrid architecture of CNN‐HMM is built. The Gaussian mixture model is replaced by CNN to model the emission distribution of HMM. The CNN‐HMM model is trained using embedded Viterbi algorithm, and the data used to train CNN are labelled by forced alignment. The authors test their method on two public action dataset Weizmann and KTH. Experimental results show that the authors’ method achieves improved recognition and segmentation accuracy compared with several other methods. The superior property of features learnt by CNN is also illustrated.

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SummaryIdentifiability of parameters in structural system identification (SSI) is of primary importance in any SSI method. It depends on the number and the location of the measurements, which is linked with sensor configuration. In this paper, under the framework of SSI by observability method (OM), the number of necessary measurements to identify all parameters of structural system was clarified first. Then, an example was solved step by step to show the lacking constraints among unknowns in SSI by OM. In a frame example, it was found that no measurement set having as many measurements as the number of unknowns was able to identify all parameters. To further understand this phenomenon, the observability of a simply supported beam was analyzed in an exhaustive way using 252 possible measurement sets. Three quarters of these sets were not able to identify all the parameters. In order to solve this issue, for the very first time, SSI by constrained observability method (COM), which appends the nonlinear constraints to SSI by OM, was proposed. With SSI by COM applied, the observability of the structural parameters with respect to the 252 sets was greatly improved. Finally, the efficacy of SSI by COM was verified by a 13-story frame building.

This is the peer reviewed version of the following article: [Lei, J., Lozano-Galant, J. A., Nogal, M., Xu, D., and Turmo, J. (2017) Analysis of measurement and simulation errors in structural system identification by observability techniques. Struct. Control Health Monit., 24: . doi: 10.1002/stc.1923.], which has been published in final form at http://onlinelibrary.wiley.com/wol1/doi/10.1002/stc.1923/full. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.During the process of structural system identification, errors are unavoidable. This paper analyzes the effects of measurement and simulation errors in structural system identification based on observability techniques. To illustrate the symbolic approach of this method a simply supported beam is analyzed step-by-step. This analysis provides, for the very first time in the literature, the parametric equations of the estimated parameters. The effects of several factors, such as errors in a particular measurement or in the whole measurement set, load location, measurement location or sign of the errors, on the accuracy of the identification results are also investigated. It is found that error in a particular measurement increases the errors of individual estimations, and this effect can be significantly mitigated by introducing random errors in the whole measurement set. The propagation of simulation errors when using observability techniques is illustrated by two structures with different measurement sets and loading cases. A fluctuation of the observed parameters around the real values is proved to be a characteristic of this method. Also, it is suggested that a sufficient combination of different load cases should be utilized to avoid the inaccurate estimation at the location of low curvature zones.Peer ReviewedPostprint (author's final draft