Non-linear micromechanics of soft tissues

Microstructure-based constitutive models have been adopted in recent studies of non-linear mechanical properties of biological soft tissues. These models provide more accurate predictions of the overall mechanical responses of tissues than phenomenological approaches. Based on standard approximations in non-linear mechanics, we classified the microstructural models into three categories: (1) uniform-field models with solid-like matrix, (2) uniform-field models with fluid-like matrix, and (3) second-order estimate models. The first two categories assume affine deformation field where the deformation of microstructure is the same as that of the tissue, regardless of material heterogeneities; i.e., they represent the upper bounds of the exact effective strain energy and stress of soft tissues. In addition, the first type is not purely structurally motivated and hence cannot accurately predict the microscopic mechanical behaviors of soft tissues. The third category considers realistic geometrical features, material properties of microstructure and interactions among them and allows for flexible deformation in each constituent. The uniform-field model with fluid-like matrix and the second-order estimate model are microstructure-based, and can be applied to different tissues based on micro-structural features

How Embeddedness Affects the Evolution of Collaboration: The Role of Knowledge Stock and Social Interactions

Science and technology are becoming increasingly collaborative. This paper aims to explore the factors and mechanisms that impact the dynamic changes of collaborative innovation networks. We consider both collaborative interactions of organizations and their knowledge element exchanges to reveal how social and knowledge network embeddedness affects the collaboration dynamics. Knowledge elements are extracted to present the core concepts of scientific and technical information, overcoming the limitations of using predefined categorizations such as IPC when representing the content. Based on multiple collaboration and knowledge networks, we then conduct a longitudinal analysis and apply a stochastic actor-oriented model (SAOM) to model network dynamics over different periods. The influence of network features and structures, individual node characteristics, and various dimensions of proximity on collaboration dynamics is tested and analyzed.Comment: 2 pages, 1 figure. Conference presentatio

Graph Pre-training for AMR Parsing and Generation

Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. Recently, pre-trained language models (PLMs) have advanced tasks of AMR parsing and AMR-to-text generation, respectively. However, PLMs are typically pre-trained on textual data, thus are sub-optimal for modeling structural knowledge. To this end, we investigate graph self-supervised training to improve the structure awareness of PLMs over AMR graphs. In particular, we introduce two graph auto-encoding strategies for graph-to-graph pre-training and four tasks to integrate text and graph information during pre-training. We further design a unified framework to bridge the gap between pre-training and fine-tuning tasks. Experiments on both AMR parsing and AMR-to-text generation show the superiority of our model. To our knowledge, we are the first to consider pre-training on semantic graphs.Comment: ACL2022 camera-ready final versio

Driver’s Shy Away Effect in Urban Extra-Long Underwater Tunnel

For urban extra-long underwater tunnels, the obstacle space formed by the tunnel walls on both sides has an impact on the driver\u27s driving. The aim of this study is to investigate the shy away characteristics of drivers in urban extra-long underwater tunnels. Using trajectory offset and speed data obtained from real vehicle tests, the driving behaviour at different lanes of an urban extra-long underwater tunnel was investigated, and a theory of shy away effects and indicators of sidewall shy away deviation for quantitative analysis were proposed. The results show that the left-hand lane has the largest offset and driving speed from the sidewall compared to the other two lanes. In the centre lane there is a large fluctuation in the amount of deflection per 50 seconds of driving, increasing the risk of two-lane collisions. When the lateral clearances are increased from 0.5 m to 2.19 m on the left and 1.29 m on the right, the safety needs of drivers can be better met. The results of this study have implications for improving traffic safety in urban extra-long underwater tunnels and for the improvement of tunnel traffic safety facilities

Strong Anomalous Optical Dispersion of Graphene: Complex Refractive Index Measured by Picometrology

We apply spinning-disc picometrology to measure the complex refractive index of graphene on thermal oxide on silicon. The refractive index varies from n = 2.4-1.0i at 532 nm to n = 3.0-1.4i at 633 nm at room temperature. The dispersion is five times stronger than bulk graphite (2.67-1.34i to 2.73-1.42i from 532 nm to 633 nm)