Students’ Motivations For Using Contemporary Technologies In Learning: A Structural Approach.

This study investigates university students’ motivations for using contemporary information technologies in learning from a Uses and Gratifications (U&G) perspective. The Repertory Grid Interview technique (RGT) is used to interview 16 participants and capture their technology use motivations and the relationship between motivations, with grounded theory used to determine categories. Interpretive Structural Modelling (ISM) technique is used to identify a structural hierarchical framework of motivations. Eleven categories are found: Access and Content Control, Accessibility, Communication Efficiency, Communication Mode, Communication Quality, Course Management, Information Seeking, Interaction, Learning Capability, Managing Contents, and Self-Disclosure. ISM developed in this study reveals that Access and Content Control, Communication Mode, and Course Management are the most important influencing motivations. In contrast, Communication Efficiency, Communication Quality, and Learning Capability are the three most important influenced motivations. This study has made significant contributions to both IS research, university policy makers, and educators by developing a student-specific motivation scale, and a hierarchical motivation framework

On the stability of a superspinar

The superspinar proposed by Gimon and Horava is a rapidly rotating compact entity whose exterior is described by the over-spinning Kerr geometry. The compact entity itself is expected to be governed by superstringy effects, and in astrophysical scenarios it can give rise to interesting observable phenomena. Earlier it was suggested that the superspinar may not be stable but we point out here that this does not necessarily follow from earlier studies. We show, by analytically treating the Teukolsky equations by Detwiler's method, that in fact there are infinitely many boundary conditions that make the superspinar stable, and that the modes will decay in time. It follows that we need to know more on the physical nature of the superspinar in order to decide on its stability in physical reality.Comment: 5 page

Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization

We introduce Auto-Surprise, an Automated Recommender System library. Auto-Surprise is an extension of the Surprise recommender system library and eases the algorithm selection and configuration process. Compared to out-of-the-box Surprise library, Auto-Surprise performs better when evaluated with MovieLens, Book Crossing and Jester Datasets. It may also result in the selection of an algorithm with significantly lower runtime. Compared to Surprise's grid search, Auto-Surprise performs equally well or slightly better in terms of RMSE, and is notably faster in finding the optimum hyperparameters.Comment: To be presented at RecSys '20 Fourteenth ACM Conference on Recommender Systems, September 21-26, 2020, Virtual Even

Development of a model of ischemic heart disease using cardiomyocytes differentiated from human induced pluripotent stem cells

Ischemic heart disease remains the largest cause of death worldwide. Accordingly, many researchers have sought curative options, often using laboratory animal models such as rodents. However, the physiology of the human heart differs significantly from that of the rodent heart. In this study, we developed a model of ischemic heart disease using cardiomyocytes differentiated from human induced pluripotent stem cells (hiPS-CMs). After optimizing the conditions of ischemia, including the concentration of oxygen and duration of application, we evaluated the consequent damage to hiPS-CMs. Notably, exposure to 2% oxygen, 0 mg/ml glucose, and 0% fetal bovine serum increased the percentage of nuclei stained with propidium iodide, an indicator of membrane damage, and decreased cellular viability. These conditions also decreased the contractility of hiPS-CMs. Furthermore, ischemic conditioning increased the mRNA expression of IL-8, consistent with observed conditions in the in vivo heart. Taken together, these findings suggest that our hiPS-CM-based model can provide a useful platform for human ischemic heart disease research