Subject-specific finite element modelling of the human hand complex : muscle-driven simulations and experimental validation

This paper aims to develop and validate a subject-specific framework for modelling the human hand. This was achieved by combining medical image-based finite element modelling, individualized muscle force and kinematic measurements. Firstly, a subject-specific human hand finite element (FE) model was developed. The geometries of the phalanges, carpal bones, wrist bones, ligaments, tendons, subcutaneous tissue and skin were all included. The material properties were derived from in-vivo and in-vitro experiment results available in the literature. The boundary and loading conditions were defined based on the kinematic data and muscle forces of a specific subject captured from the in-vivo grasping tests. The predicted contact pressure and contact area were in good agreement with the in-vivo test results of the same subject, with the relative errors for the contact pressures all being below 20%. Finally, sensitivity analysis was performed to investigate the effects of important modelling parameters on the predictions. The results showed that contact pressure and area were sensitive to the material properties and muscle forces. This FE human hand model can be used to make a detailed and quantitative evaluation into biomechanical and neurophysiological aspects of human hand contact during daily perception and manipulation. The findings can be applied to the design of the bionic hands or neuro-prosthetics in the future

A survey on composition and microbiota of fresh and fermented yak milk at different Tibetan altitudes

Yak milk is a type of milk that people are less familiar with due to its remote geographical location, the particular geographical environment and climatic conditions in Tibet, which may have significant effects on composition, microbiota and fermentation outcome. To investigate the chemical composition and microbiota of fresh and fermented yak milk, and to isolate and characterize the predominant microorganisms in the fermented milk, yak milk (24 fresh and 30 fermented milk samples) was collected from four areas of different altitudes in Tibet, and their microbiological profile and chemical composition were investigated. Yak milk had a higher fat, crude protein, lactose and dry matter content than cow milk. The fermented yak milk showed a great diversity in fat and dry matter levels due to the different ways of processing in different localities, and lower pH and higher lactic acid content compared with commercial cow milk yogurt. Fermented yak milk had a better sanitary quality than fresh yak milk. Three species of lactobacilli (Lactobacillus fermentum, Lactobacillus helveticus and Lactobacillus curvatus) and five species of yeast (Saccharomyces cerevisiae, Candida kefyr, Candida lambica, Candida famat and Candida holmii) were identified phenotypically and encountered as predominant fermentation microbiota. The predominant lactic species in fermented milk was L. fermentu

Evidence for Two Gaps and Breakdown of the Uemura Plot in Ba0.6_{0.6}K0.4_{0.4}Fe2_2As2_2 Single Crystals

We report a detailed investigation on the lower critical field $H_{c1}$ of the superconducting Ba$_{0.6}$K$_{0.4}$Fe$_2$As$_2$ (FeAs-122) single crystals. A pronounced kink is observed on the $H_{c1}(T)$ curve, which is attributed to the existence of two superconducting gaps. By fitting the data $H_{c1}(T)$ to the two-gap BCS model in full temperature region, a small gap of $\Delta_a(0)=2.0\pm 0.3$ meV and a large gap of $\Delta_b(0)=8.9\pm 0.4$ meV are obtained. The in-plane penetration depth $\lambda_{ab}(0)$ is estimated to be 105 nm corresponding to a rather large superfluid density, which points to the breakdown of the Uemura plot in FeAs-122 superconductors.Comment: 5 pages, 4 figure

Mixed Information Flow for Cross-domain Sequential Recommendations

Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains. Previous studies have investigated the flow of behavioral information by exploring the connection between items from different domains. The flow of knowledge (i.e., the connection between knowledge from different domains) has so far been neglected. In this paper, we propose a mixed information flow network for cross-domain sequential recommendation to consider both the flow of behavioral information and the flow of knowledge by incorporating a behavior transfer unit and a knowledge transfer unit. The proposed mixed information flow network is able to decide when cross-domain information should be used and, if so, which cross-domain information should be used to enrich the sequence representation according to users' current preferences. Extensive experiments conducted on four e-commerce datasets demonstrate that mixed information flow network is able to further improve recommendation performance in different domains by modeling mixed information flow.Comment: 26 pages, 6 figures, TKDD journal, 7 co-author