32,691 research outputs found

    Cognitive Styles and Adaptive Web-based Learning

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    Adaptive hypermedia techniques have been widely used in web-based learning programs. Traditionally these programs have focused on adapting to the user’s prior knowledge, but recent research has begun to consider adapting to cognitive style. This study aims to determine whether offering adapted interfaces tailored to the user’s cognitive style would improve their learning performance and perceptions. The findings indicate that adapting interfaces based on cognitive styles cannot facilitate learning, but mismatching interfaces may cause problems for learners. The results also suggest that creating an interface that caters for different cognitive styles and gives a selection of navigational tools might be more beneficial for learners. The implications of these findings for the design of web-based learning programs are discussed

    Learning Motion Predictors for Smart Wheelchair using Autoregressive Sparse Gaussian Process

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    Constructing a smart wheelchair on a commercially available powered wheelchair (PWC) platform avoids a host of seating, mechanical design and reliability issues but requires methods of predicting and controlling the motion of a device never intended for robotics. Analog joystick inputs are subject to black-box transformations which may produce intuitive and adaptable motion control for human operators, but complicate robotic control approaches; furthermore, installation of standard axle mounted odometers on a commercial PWC is difficult. In this work, we present an integrated hardware and software system for predicting the motion of a commercial PWC platform that does not require any physical or electronic modification of the chair beyond plugging into an industry standard auxiliary input port. This system uses an RGB-D camera and an Arduino interface board to capture motion data, including visual odometry and joystick signals, via ROS communication. Future motion is predicted using an autoregressive sparse Gaussian process model. We evaluate the proposed system on real-world short-term path prediction experiments. Experimental results demonstrate the system's efficacy when compared to a baseline neural network model.Comment: The paper has been accepted to the International Conference on Robotics and Automation (ICRA2018

    Improving the Trust of Users on Social Networking Sites via Self-Construal Traits

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    The ability to remove trust concerns for online users is crucial for sustainable online development, especially relating to social networking sites. This study examines independent self-construal and interdependent self-construal as pertinent factors to increase trust on social networking sites. The classification of trust broken down into calculation-, familiarity-, structural assurance-, and situational normality-based trust was adopted in this study. Data was collected from 398 members of the leading social network site: Facebook. Regression analysis was adopted to test the data against the casual relationship among the four trust constructs. Data analysis indicates that the constructs of interdependent self-construal and independent selfconstrual individually, and together, can account for the increase of trust on a social networking site; however interdependent self-construal has the largest explanatory power. These results suggest that social networking sites continuously increase the degree of interdependence of users and develop new applications to engage users to stay longer for each visit. As a result of these measures, social networking sites can sustain the trust of users

    Function of the Signal Peptide and N- and C-terminal Propeptides in the Leucine Aminopeptidase from \u3cem\u3eAeromonas proteolytica\u3c/em\u3e

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    The leucine aminopeptidase from Aeromonas proteolytica (also known as Vibrio proteolyticus) (AAP) is a metalloenzyme with broad substrate specificity. The open reading frame (ORF) for AAP encodes a 54 kDa enzyme, however, the extracellular enzyme has a molecular weight of 43 kDa. This form of AAP is further processed to a mature, thermostable 32 kDa form but the exact nature of this process is unknown. Over-expression of different forms of AAP in Escherichia coli (with AAP\u27s native leader sequence, with and without the N- and/or C-terminal propeptides, and as fusion protein) has allowed a model for the processing of wild-type AAP to be proposed. The role of the A. proteolytica signal peptide in protein secretion as well as comparison to other known signal peptides reveals a close resemblance of the A. proteolytica signal peptide to the outer membrane protein (OmpA) signal peptide. Over-expression of the full 54 kDa AAP enzyme provides an enzyme that is significantly less active, due to a cooperative inhibitory interaction between both propeptides. Over-expression of AAP lacking its C-terminal propeptide provided an enzyme with an identical kcat value to wild-type AAP but exhibited a larger Km value, suggesting competitive inhibition of AAP by the N-terminal propeptide (Ki∼0.13 nM). The recombinant 32 kDa form of AAP was characterized by kinetic and spectroscopic methods and was shown to be identical to mature, wild-type AAP. Therefore, the ease of purification and processing of rAAP along with the fact that large quantities can be obtained now allow new detailed mechanistic studies to be performed on AAP through site-directed mutagenesis

    Living on the edge of chaos: minimally nonlinear models of genetic regulatory dynamics

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    Linearized catalytic reaction equations modeling e.g. the dynamics of genetic regulatory networks under the constraint that expression levels, i.e. molecular concentrations of nucleic material are positive, exhibit nontrivial dynamical properties, which depend on the average connectivity of the reaction network. In these systems the inflation of the edge of chaos and multi-stability have been demonstrated to exist. The positivity constraint introduces a nonlinearity which makes chaotic dynamics possible. Despite the simplicity of such minimally nonlinear systems, their basic properties allow to understand fundamental dynamical properties of complex biological reaction networks. We analyze the Lyapunov spectrum, determine the probability to find stationary oscillating solutions, demonstrate the effect of the nonlinearity on the effective in- and out-degree of the active interaction network and study how the frequency distributions of oscillatory modes of such system depend on the average connectivity.Comment: 11 pages, 5 figure
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