Reality-based interaction affecting mental workload in virtual reality mental arithmetic training

ABSTRACT

The concept of digital game-based learning (DGBL) evolves rapidly together with technological enhancements of virtual reality (VR) and smart phones. However, the mental workload (MWL) that VR-training applications demand and motivational qualities originating from user experience (UX) should be identified in order to create effective and enjoyable training/learning challenges that fit with individual users’ capabilities. This study examined the effects of reality-based interaction (RBI) and VR on measures of student motivation and MWL, in a mental arithmetic game for secondary school pupils. In a randomised controlled trial with sixty school children, a mental arithmetic game was tested with three different interaction and two different presentation methods – VR RBI, VR head-mounted-display tapping and tablet flick-gesture. Results found a significant effect of RBI on MWL but no differences in enjoyment of training were found between VR-experience and tablet training-experience. In fact, adding the gaming-context to the mental arithmetic task created an enjoyable, motivating experience regardless of presentation or interaction-style.     

Comprehensiveness and uniqueness of commercial databases and open access systems

Scientometrics - In this study, scholarly communication systems provided by commercial services and open access systems are examined on the basis of the comprehensiveness and uniqueness of their...     

The influence of processing conditions on the mechanical properties of poly(aryl-ether-ketone)/polybenzimidazole blends

Interest in developing high-performance blends for niche applications has grown significantly in efforts to meet ever-increasing harsh environment demands. In this work, four model poly(aryl-ether-ketone)/polybenzimidazole (PAEK/PBI) blends were chosen to study the influence of premixing methods, processing, and matrix polymers, on their mechanical properties. Among the model poly(ether ether ketone) (PEEK) and PBI blends, mechanical properties are greatly enhanced by melt premixing. The molding process mainly affects the matrix crystallinity, which in turn greatly influences fracture toughness of the blend. Poly(ether ketone ketone) (PEKK) and PBI blend exhibits a slightly lower tensile strength and fracture toughness than PEEK/PBI due to the differences in inherent properties of PEEK and PEKK matrices and their interfacial interaction with PBI. The processing−structure–property relationship of PAEK/PBI blends is established to help guide optimal design of high-performance polymer blends for structural applications. © 2020 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48966.     

Assessing stem cell research productivity

Honour Index (HoI), a method to evaluate research performance within different research fields, was derived from the impact factor (IF). It can be used to rate and compare different categories of journals. HoI was used in this study to determine the scientific productivity of stem cell research in the Asian Four Dragons (Hong Kong, Singapore, South Korea and Taiwan) from 1981 to 2001. The methodology applied in this study represents a synthesis of universal indicator studies and bibliometric analyses of subfields at the micro-level. We discuss several comparisons, and conclude the developmental trend in stem cell research for two decades. This revised version was published online in August 2006 with corrections to the Cover Date.     

Learning concepts in parallel based upon the strategy of versionspace

Applies the technique of parallel processing to concept learning. A parallel version-space learning algorithm based upon the principle of divide-and-conquer is proposed. Its time complexity is analyzed to be O(k log₂n) with n processors, where n is the number of given training instances and k is a coefficient depending on the application domains. For a bounded number of processors in real situations, a modified parallel learning algorithm is then proposed. Experimental results are then performed on a real learning problem, showing that our parallel learning algorithm works, and being quite consistent with the results of theoretical analysis. We conclude that when the number of training instances is large, it is worth learning in parallel because of its faster execution     

Dynamic bandwidth allocation for multimedia traffic with rate guarantee and fair access in WCDMA systems

Packet scheduling in a WCDMA system poses a new challenge due to its nature of variable bit rates and location-dependent, time-varying channel conditions. In this work, three new downlink scheduling algorithms for a WCDMA base station are proposed to support multimedia transmissions. Using a credit management and a compensation mechanism, our algorithms provide rate guarantee and fair access to mobile terminals. In particular, we propose to allow a user to simultaneously use multiple OVSF codes in a time-sharing manner, which we call a multicode, shared model. Using multiple codes allows us to compensate those users suffering from bad communication quality or even errors. The proposed schemes can tolerate a multistate link condition (compared to the typically assumed two-state, or good-or-bad, link condition) by adjusting the number of OVSF codes and the spreading factor of each code. Simulation results show that the proposed schemes do achieve higher bandwidth utilization while keeping transmission delay low.     

Scene-Change Aware Dynamic Bandwidth Allocation for Real-Time VBR Video Transmission Over IEEE 802.15.3 Wireless Home Networks

IEEE 802.15.3, an emerging wireless technology, was designed to provide high-quality multimedia services at home. Dynamic bandwidth allocation for a multimedia connection should be considered in order to achieve higher channel utilization, less buffer and less delay, especially for variable bit rate (VBR) multimedia connections. For real-time VBR videos, the bandwidth requirement should be predicted adaptively for effective channel-time requests. Previously, the adaptive least-mean square (LMS) algorithm with fixed step size was applied to predict channel time requirements due to its simplicity and relatively good performance. However, the performance might degrade when scene changes occurred. In this paper, we modify the variable step-size LMS algorithm and apply it as our predictor (VSSNLMS) so that the prediction errors on scene changes can be effectively reduced. Using the prediction results of VSSNLMS, we propose a dynamic bandwidth-allocation scheme that is scene-change aware and can guarantee the delay bound of real-time VBR videos. Simulation results show that the VSSNLMS predictor is superior to previous LMS-type predictors in performance, and the proposed scheme has better performance in channel utilization, buffer usage, and packet loss