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The purpose of this study is to estimate the convergence order of the Aumann–Serrano Riskiness Index.

This study uses the equivalent relation between the Aumann–Serrano Riskiness Index and the moment generating function and aggregately compares between each two statistical moments for statistical significance. Thus, this study enables to find the convergence order of the index to its stable value.

This study finds that the first-best estimation of the Aumann–Serrano Riskiness Index is reached in no less than its seventh statistical moment. However, this study also finds that its second-best approximation could be achieved with its second statistical moment.

The implications of this research support the standard deviation as a statistically sufficient approximation of Aumann–Serrano Riskiness Index, thus strengthening the CAPM methodology for asset pricing in the financial markets.

This research sheds a new light, both in theory and in practice, on understanding of the risk’s structure, as it may improve accuracy of asset pricing.

Research has shown that the much-anticipated technology revolution in higher education has failed to come to fruition. The arrival of ‘digital natives’ millennial students to higher education was presume to present even greater challenge concerning technology use. In light of these gaps, this research aims to capture higher education students' choice, use and preferences of technology in learning and teaching.

A paper-based questionnaire was distributed to third and fourth year students of industrial engineering and management at an engineering college in Israel. The students were asked to indicate their use of devices and technologies for learning, their frequency of use and their purpose of using.

Students extensively use a variety of technologies for learning. They prefer to use the same technologies for learning that they use in their personal lives – mainstream, commercially available technologies – rather than those offered by the institute. They perceive technology as a learning tool more than as a logistic/administrative tool, they would like technology to be more easily accessible and that it not be used as a facilitator of pedagogical change.

The results indicate that technologies intended for use in teaching should be designed similar to commercially available alternatives that are simpler to use and more appealing.

This study provides an up-to-date view of students' perceptions of technology for learning that can be used to more effectively implement teaching technologies in higher education.

The purpose of this study is to explore the effect of different post-curing times on the mechanical properties, specifically the visco-elastic characteristics, Poisson’s ratio and modulus, of three dimensional (3D) printed photopolymer composites (PPCs) reinforced with short glass fibres (SGF).

This research uses digital light processing-based Vat-photopolymerization process to 3D print PPCs reinforced with SGF at volume fractions of 2%, 4%, 6% and 8%. An inter-stage stirring process was used to reinforce the SGFs in a layer wise fashion. After printing, the parts undergo post-curing for 20, 60 and 100 min. The mechanical properties are then analyzed using dynamic mechanical analysis and in situ optical measurements. In addition, two-dimensional strain mapping from digital image correlation techniques is used to assess the structural behavior.

This study found that composites with 4% SGF reinforcement achieved the highest storage modulus, approximately 1,550 MPa, after 60 and 100 min of post-curing. In addition, the Poisson’s ratio of these composites increased from 0.2 to 0.41 with rising temperature. By applying Poisson’s ratio correction, the modulus was observed to be 1,800 MPa. These results indicate that optimal SGF content and post-curing times significantly enhance the mechanical properties of 3D-printed PPCs.

This research uniquely combines the reinforcement of photopolymers with SGFs at varying volume fractions and the detailed analysis of post-curing times to enhance the mechanical properties of 3D printed PPCs.