Usually, monolithic bulk metallic glasses undergo inhomogeneous plastic deformation and exhibit poor ductility (< 1%) at room temperature. We present a new class of bulk metallic glass, which exhibits high strength of up to 2265 MPa together with extensive "work hardening" and large ductility of 18%. Significant increase in the flow stress was observed during deformation. The "work-hardening" capability and ductility of this class of metallic glass is attributed to a unique structure correlated with atomic-scale inhomogeneity, leading to an inherent capability of extensive shear band formation, interactions, and multiplication of shear bands.

Microstructural investigation of an as-cast Cu47.5Zr47.5Al5 bulk metallic glass (BMG) reveals two amorphous phases formed by liquid phase separation. The morphology of the phase separated amorphous regions is spherical with 10–20nm in size. These areas are homogeneously distributed throughout the sample. Moreover, a macroscopic heterogeneity also occurs along with the nano-scale liquid phase separation. The macroscopic heterogeneity can be distinguished from the different degree of the chemical fluctuations in the sample, and the existence of nano-scale crystals of less than 5nm in size. Presumably, both the macroscopic heterogeneity and the nano-scale phase separation enhance branching of the shear bands during deformation in the Cu47.5Zr47.5Al5 BMG.

Collaborative Filtering (CF) is widely employed for making Web service recommendation. CF-based Web service recommendation aims to predict missing QoS (Quality-of-Service) values of Web services. Although several CF-based Web service QoS prediction methods have been proposed in recent years, the performance still needs significant improvement. Firstly, existing QoS prediction methods seldom consider personalized influence of users and services when measuring the similarity between users and between services. Secondly, Web service QoS factors, such as response time and throughput, usually depends on the locations of Web services and users. However, existing Web service QoS prediction methods seldom took this observation into consideration. In this paper, we propose a location-aware personalized CF method for Web service recommendation. The proposed method leverages both locations of users and Web services when selecting similar neighbors for the target user or service. The method also includes an enhanced similarity measurement for users and Web services, by taking into account the personalized influence of them. To evaluate the performance of our proposed method, we conduct a set of comprehensive experiments using a real-world Web service dataset. The experimental results indicate that our approach improves the QoS prediction accuracy and computational efficiency significantly, compared to previous CF-based methods.

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