The reported experiments showed that a high-precision articulated measurement arm could be used as a motion-tracking device for surgical instruments in augmented-reality surgical navigation.

The mechanical performance of biological tissues is underpinned by a complex and finely balanced structure. Central to this is collagen, the most abundant protein in our bodies, which plays a dominant role in the functioning of tissues, and also in disease. Based on the collagen meshwork of articular cartilage, we have developed a bottom-up spring-node model of collagen and examined the effect of fibril connectivity, implemented by crosslinking, on mechanical behaviour. Although changing individual crosslink stiffness within an order of magnitude had no significant effect on modelling predictions, the density of crosslinks in a meshwork had a substantial impact on its behaviour. Highly crosslinked meshworks maintained a 'normal' configuration under loading, with stronger resistance to deformation and improved recovery relative to sparsely crosslinked meshwork. Stress on individual fibrils, however, was higher in highly crosslinked meshworks. Meshworks with low numbers of crosslinks reconfigured to disease-like states upon deformation and recovery. The importance of collagen interconnectivity may provide insight into the role of ultrastructure and its mechanics in the initiation, and early stages, of diseases such as osteoarthritis.

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Accurate placenta super micro-vessels segmentation is the key to diagnose placental diseases. However, the current automatic segmentation algorithm has issues of information redundancy and low information utilization, which reduces the segmentation accuracy. To solve this problem, we propose a model based on ResNeXt with convolutional block attention module (CBAM) and UNet (RC-UNet) for placental super micro-vessels segmentation. In the RC-UNet model, we choose the UNet as the backbone network for initial feature extraction. At the same time, we select ResNeXt-CBAM as the attention module for feature refinement and weighting. Specifically, we stack the blocks of the same topology following the splittransform-merge strategy to reduce the redundancy of hyperparameter. Moreover, we conduct CBAM processing on each group of the detailed features to get informative features and suppress unnecessary features, which improve the information utilization. The experiments on the self-collected data show that the proposed algorithm has better segmentation results for anatomical structures (umbilical cord blood (UC), stem villus (ST), maternal blood (MA)) than other selected algorithms.

Horadam sequence is a general recurrence of second order in the complex plane, depending on four complex parameters (two initial values and two recurrence coefficients). These sequences have been investigated over more than 60 years, but new properties and applications are still being discovered. Small parameter variations may dramatically impact the sequence orbits, generating numerous patterns: periodic, convergent, divergent, or dense within one dimensional curves. Here we explore Horadam sequences whose orbit is dense within a 2D region of the complex plane, while the complex argument is uniformly distributed in an interval. This enables the design of a pseudo-random number generator (PRNG) for the uniform distribution, for which we test periodicity, correlation, Monte Carlo estimation of π, and the NIST battery of tests. We then calculate the probability distribution of the radii of the sequence terms of Horadam sequences. Finally, we propose extensions of these results for generalized Horadam sequences of third order.