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This paper reports a comprehensive study on the mechanical performance of large size superelastic shape memory alloy (SMA) bars, with the main focus given to their potential applications for seismic-resistant connections. A series of practical issues, including heat treatment, mechanical property assessment, and connection design/evaluation, were discussed aiming to benefit both material and civil engineering communities. The study commenced with a detailed discussion on the heat treatment strategy for SMA bars and the resulting mechanical properties including strength/stiffness, self-centring ability, energy dissipation, and fractural resistance. It was observed that the mechanical performance of the bars were quite sensitive to both annealing temperature and duration, and size effect was also evident, resulting in different appropriate heat treatment procedures for the bars with varying diameters. The optimally heattreated SMA bars were machined to the bolt form and were then used for two types of practical self-centring connections, namely, connection with all SMA bars and that with combined angles and SMA bars. Through conducting full-scale tests, both connections were shown to have stable and controllable hysteretic responses till 5% loading drift. Up to 3% drift, the self-centring performance was satisfactory for both connection types, but beyond that the presence of the angles could lead to accumulated residual rotation. Importantly, for both connections, the deformation was accommodated by the SMA bolts or angles, whereas no plastic deformation was observed at any other structural members. This confirmed the feasibility of using such connections for highly resilient structures where minimal repair work is required after earthquakes.

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Web service is one of the key communications software services for the Internet. Web phishing is one of many security threats to web services on the Internet. Web phishing aims to steal private information, such as usernames, passwords, and credit card details, by way of impersonating a legitimate entity. It will lead to information disclosure and property damage. This paper mainly focuses on applying a deep learning framework to detect phishing websites. This paper first designs two types of features for web phishing: original features and interaction features. A detection model based on Deep Belief Networks (DBN) is then presented. The test using real IP flows from ISP (Internet Service Provider) shows that the detecting model based on DBN can achieve an approximately 90% true positive rate and 0.6% false positive rate.