Porous Topography Dependence of Mechanical properties and Biological Responses for 3D Printed Stainless Steel and Modified Bioimplant Devices

Materials used in biomedical engineering should contain certain properties in order to satisfy their roles, and orthopedic implant materials, commonly metals, required some specific properties, including sufficient mechanical strength, good durability, good biocompatibility and less cytotoxicity, to function in the animal or human body. Stainless steel was and continues to be one of the choices to be used as implant material for its relatively low cost, excellent strength, good corrosion resistance and relatively good biocompatibility. Additive layer manufacturing (ALM) allows the precise manufacture of implant in certain material, and porous structure, usually lattice, is found to be benefit to bone recovery. In this work, selective laser melting (SLM) is used to produce stainless steel lattices with different pore size in order to evaluate their capability to be used as orthopedic implant material. It was found that the surface of stainless steel lattices contains voids and partially melted stainless steel particles to affect their mechanical properties, but the strength and porosity of lattices are sufficient to be used to be implanted in human body. Study also found that the mechanical properties have a close relationship between pore size and unit cell size of lattices, which the lower the unit cell size, the higher the elastic modulus and ultimate tensile strength. A long-term submersion of lattices in stimulated body fluid is used to evaluate its durability in a stimulated body environment, and the results shows that there is no damage on sample surface and change in mechanical strength. Cytotoxicity tests and osteogenic characterizations show the stainless steel samples and their calcium sulphate modified samples have relatively good biocompatibility. At last, the lattice samples are implanted into rabbit distal femur, and a qualitative analysis on femur using Dual Energy Computed Tomography (DECT), Computed Tomography (CT), and Volume Rendering Technology (VRT) shows a relatively good bone growth after implantation of both lattice samples and modified samples. Tissues are also sliced and evaluated by pathology staining including HE, Masson and Von Kossa staining. Results suggest that the stainless steel lattice have sufficient mechanical strength, durability and biocompatibility, and have great potential to be used as orthopedic implants

Maintaining stability while boosting growth? The long-term impact of environmental accreditations on firms’ financial risk and sales growth

202105 bchyAccepted ManuscriptRGCOthersRGC: 156050/17B,Others: National Natural Science Foundation of China under grant number 71525005, 71821002, and 71961137004Publishe

Improving Cross-Domain Chinese Word Segmentation with Word Embeddings

Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite recent progress in neural-based CWS. The limited amount of annotated data in the target domain has been the key obstacle to a satisfactory performance. In this paper, we propose a semi-supervised word-based approach to improving cross-domain CWS given a baseline segmenter. Particularly, our model only deploys word embeddings trained on raw text in the target domain, discarding complex hand-crafted features and domain-specific dictionaries. Innovative subsampling and negative sampling methods are proposed to derive word embeddings optimized for CWS. We conduct experiments on five datasets in special domains, covering domains in novels, medicine, and patent. Results show that our model can obviously improve cross-domain CWS, especially in the segmentation of domain-specific noun entities. The word F-measure increases by over 3.0% on four datasets, outperforming state-of-the-art semi-supervised and unsupervised cross-domain CWS approaches with a large margin. We make our code and data available on Github

Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face Recognition

Classical adversarial attacks for Face Recognition (FR) models typically generate discrete examples for target identity with a single state image. However, such paradigm of point-wise attack exhibits poor generalization against numerous unknown states of identity and can be easily defended. In this paper, by rethinking the inherent relationship between the face of target identity and its variants, we introduce a new pipeline of Generalized Manifold Adversarial Attack (GMAA) to achieve a better attack performance by expanding the attack range. Specifically, this expansion lies on two aspects - GMAA not only expands the target to be attacked from one to many to encourage a good generalization ability for the generated adversarial examples, but it also expands the latter from discrete points to manifold by leveraging the domain knowledge that face expression change can be continuous, which enhances the attack effect as a data augmentation mechanism did. Moreover, we further design a dual supervision with local and global constraints as a minor contribution to improve the visual quality of the generated adversarial examples. We demonstrate the effectiveness of our method based on extensive experiments, and reveal that GMAA promises a semantic continuous adversarial space with a higher generalization ability and visual qualityComment: Accepted by CVPR202

Silicon-Encapsulated Hollow Carbon Nanofiber Networks as Binder-Free Anodes for Lithium Ion Battery

Silicon-encapsulated hollow carbon nanofiber networks with ample space around the Si nanoparticles (hollow Si/C composites) were successfully synthesized by dip-coating phenolic resin onto the surface of electrospun Si/PVA nanofibers along with the subsequent solidification and carbonization. More importantly, the structure and Si content of hollow Si/C composite nanofibers can be effectively tuned by merely varying the concentration of dip solution. As-synthesized hollow Si/C composites show excellent electrochemical performance when they are used as binder-free anodes for Li-ion batteries (LIBs). In particular, when the concentration of resol/ethanol solution is 3.0%, the product exhibits a large capacity of 841 mAh g−1 in the first cycle, prominent cycling stability, and good rate capability. The discharge capacity retention of it was ~90%, with 745 mAh g−1 after 50 cycles. The results demonstrate that the hollow Si/C composites are very promising as alternative anode candidates for high-performance LIBs

Fermion-boson many-body interplay in a frustrated kagome paramagnet

Kagome-net, appearing in areas of fundamental physics, materials, photonic and cold-atom systems, hosts frustrated fermionic and bosonic excitations. However, it is extremely rare to find a system to study both fermionic and bosonic modes to gain insights into their many-body interplay. Here we use state-of-the-art scanning tunneling microscopy and spectroscopy to discover unusual electronic coupling to flat-band phonons in a layered kagome paramagnet. Our results reveal the kagome structure with unprecedented atomic resolution and observe the striking bosonic mode interacting with dispersive kagome electrons near the Fermi surface. At this mode energy, the fermionic quasi-particle dispersion exhibits a pronounced renormalization, signaling a giant coupling to bosons. Through a combination of self-energy analysis, first-principles calculation, and a lattice vibration model, we present evidence that this mode arises from the geometrically frustrated phonon flat-band, which is the lattice analog of kagome electron flat-band. Our findings provide the first example of kagome bosonic mode (flat-band phonon) in electronic excitations and its strong interaction with fermionic degrees of freedom in kagome-net materials.Comment: To appear in Nature Communications (2020

Proceedings of the 17th European Chapter of the Association for Computational Linguistics

Non-neural approaches to argument mining (AM) are often pipelined and require heavy feature-engineering. In this paper, we propose a neural end-to-end approach to AM which is based on dependency parsing, in contrast to the current state-of-the-art which relies on rela- tion extraction. Our biaffine AM dependency parser significantly outperforms the state-of- the-art, performing at F1 = 73.5% for com- ponent identification and F1 = 46.4% for re- lation identification. One of the advantages of treating AM as biaffine dependency parsing is the simple neural architecture that results. The idea of treating AM as dependency parsing is not new, but has previously been abandoned as it was lagging far behind the state-of-the-art. In a thorough analysis, we investigate the fac- tors that contribute to the success of our model: the biaffine model itself, our representation for the dependency structure of arguments, differ- ent encoders in the biaffine model, and syntac- tic information additionally fed to the model. Our work demonstrates that dependency pars- ing for AM, an overlooked idea from the past, deserves more attention in the future.Toshib

The impact of human capital on supply chain integration and competitive performance

With the rapid development of theories and practices in supply chain management (SCM), supply chain integration (SCI) has become a popular research topic. Many studies have examined the relationship between SCI and firm performance; however, few have investigated the enablers of SCI. Considering the important role of people in SCM, investigation of the antecedents of SCI from a human resources perspective is needed. Using the resource-based view as a theoretical lens, this study investigates the impact of human capital (e.g., organizational commitment and multi-skilling) on SCI (e.g., internal integration, supplier integration, and customer integration) and competitive performance. On the basis of data collected from 317 manufacturers in 10 countries, we test the proposed model using structural equation modeling and regression analysis. We find that organizational commitment is positively related to the three dimensions of SCI. Manager’s multi-skilling and employee’s multi-skilling are positively related to internal integration. We also find several interactive effects. The results show that internal integration is related to customer and supplier integration and that internal and customer integration are related to competitive performance. This study contributes to the SCM and human resources literature and has managerial implications for the implementation of SCI