Comparison in studying approaches of first-year Australian and overseas Chinese university students

This research investigates the studying approaches of first-year Australian and overseas Chinese university students. It is also designed to determine the robustness of Entwistle and Ramsden's (1983) Approaches to Studying Inventory (ASI). Two hundred and two first-year Australian students and two hundred and forth eight first-year overseas Chinese students, drawn from Deakin University and Swinburne University of Technology, were tested using the ASL The data obtained from the two groups were subjected to factor analysis (with orthogonal rotation). For Australian students, a four-factor structure in studying approaches, which accounts for 55.6% of the total variance, was obtained. The factors are Meaning Orientation; Non-Academic Orientation; Anxious-Rigid Orientation; and Goal Orientation. For overseas Chinese students, a three-factor structure in studying approaches which accounts for 52.8% of the total variance was obtained. The factors are Anxious-Surface Orientation; Self-Motivated, Reflective Orientation; and Efficiency Orientation, Cattell's (1949) salient similarity S index indicates a close resemblance between factors obtained for Australian students and the original factors obtained by Entwistle and Ramsden (1983). Similarities are also indicated between factors obtained for Australian and overseas Chinese students* Two main conclusions are drawn. First, the studying approaches of first-year Australian and overseas Chinese university students are described by different factor structures in learning. Second, Entwistle and Ramsden's (1983) Approaches to Studying Inventory is a robust tool from which reliable and meaningful factors in student studying approaches can be obtained. Several implications of the research findings are discussed.

Microstructural characterization and mechanical properties of Mg-Zr-Ca alloys prepared by hot-extrusion for biomedical applications

This paper investigated the microstructural characterization and mechanical properties of Mg-Zr-Ca alloys prepared by hot-extrusion for potential use in biomedical applications. Mg-Zr-Ca alloys were fabricated by commercial pure Mg (99.9%), Ca (99.9%), and master Mg-33% Zr alloy (mass%). The microstructural characterization of the hot-extruded Mg-Zr-Ca alloys was examined by X-ray diffraction analysis and optical microscopy, and the mechanical properties were determined from tensile tests. The experimental results indicate that the hot-extruded Mg-Zr-Ca alloys with 1 mass% Ca are composed of one single phase and those alloys with 2 mass% Ca consist of both Mg2Ca and α phase. The hot-extruded Mg-Zr-Ca alloys exhibit equiaxed granular microstructures and the hot-extrusion process can effectively increase both the tensile strength and ductility of Mg-Zr-Ca alloys. The hot-extruded Mg-1Zr-1Ca alloy (mass%) exhibits the highest strength and best ductility among all the alloys, and has much higher strength than the human bone, suggesting that it has a great potential to be a good candidate for biomedical application.

Asymmetric self-learning for tackling Twitter spam drift

Spam has become a critical problem on Twitter. In order to stop spammers, security companies apply blacklisting services to filter spam links. However, over 90% victims will visit a new malicious link before it is blocked by blacklists. To eliminate the limitation of blacklists, researchers have proposed a number of statistical features based mechanisms, and applied machine learning techniques to detect Twitter spam. In our labelled large dataset, we observe that the statistical properties of spam tweets vary over time, and thus the performance of existing ML based classifiers are poor. This phenomenon is referred as 'Twitter Spam Drift'. In order to tackle this problem, we carry out deep analysis of 1 million spam tweets and 1 million non-spam tweets, and propose an asymmetric self-learning (ASL) approach. The proposed ASL can discover new information of changed tweeter spam and incorporate it into classifier training process. A number of experiments are performed to evaluate the ASL approach. The results show that the ASL approach can be used to significantly improve the spam detection accuracy of using traditional ML algorithms.