1,004 research outputs found

    Review of creativity factors in final year design projects in China

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    This paper focuses on investigating a common phenomenon that emerges in the reformative process of China’s design education system from the traditional to ‘creativity-directed’. The investigation is explored through the following aspects: the circumstance of conducting Final Year Design Projects (FYDPs) in China, with a review of relevant pedagogical theories of Project-based learning (PjBL); and a review of cross-cultural understanding of creativity. Through conducting the literature survey, it is proposed that some product design students’ lack creative abilities that could affect their learning performance in FYDPs. It is proposed that this is evident through their difficulty in applying subject specific knowledge in an effective way. Analyses suggest that underpinning the problem might be the educational and social cultures within China’s product design programmes and how the final year projects are implemented. In conclusion it is suggested that design students’ creative abilities are influenced by the adopted problem solving processes that involve knowledge application

    Aspects of a study of creative thinking and knowledge application

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    An empirical study in the form of survey was conducted investigating design students’ cognitive processes. The aim of this study was to identify specific knowledge for application by students classified as creative in product design. We specifically collected data from China and the UK, representing the Western and Eastern cultures. The results identified six knowledge items, e.g. knowledge of user trails, ergonomics, which were applied at a high frequency in FYDP by creative students, measured by the Metacognition Awareness Inventory (MAI), in both China and the UK. Moreover, we found that Chinese participants with higher creative thinking ability may tend to apply more knowledge of aesthetics, organisation, marketing, and skills to operate relevant machines in a design process, whereas the UK’s participants with higher creative thinking ability would be more likely to apply knowledge of client needs and information processing to a larger degree

    Insights on how metacognition influences knowledge application in product design education

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    Insights on how metacognition influences knowledge application in product design educatio

    RMSD versus C-score-ln() of the I-TASSER models for 500 test proteins (open circles)

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    <p><b>Copyright information:</b></p><p>Taken from "I-TASSER server for protein 3D structure prediction"</p><p>http://www.biomedcentral.com/1471-2105/9/40</p><p>BMC Bioinformatics 2008;9():40-40.</p><p>Published online 23 Jan 2008</p><p>PMCID:PMC2245901.</p><p></p> The dashed curve is from Equation 5 which is fit from the 300 training proteins and used for estimating RMSD of the I-TASSER models. The solid circles are the root mean squared RMSD deviation (RMSRD) from the estimated RMSD values. The solid curve is from Equation 6 which is fit from the 300 training proteins

    TM-score (a) and RMSD (b) versus C-score of the I-TASSER models for 500 testing proteins

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    <p><b>Copyright information:</b></p><p>Taken from "I-TASSER server for protein 3D structure prediction"</p><p>http://www.biomedcentral.com/1471-2105/9/40</p><p>BMC Bioinformatics 2008;9():40-40.</p><p>Published online 23 Jan 2008</p><p>PMCID:PMC2245901.</p><p></p> The dashed curve in (a) is from Equation 3 which is fit from the 300 training proteins and used for estimating the TM-score of the I-TASSER models. The solid circles are the root mean squared deviation from the estimated TM-score values (RMSTD). The solid curve is from Equation 4 which is fit from the 300 training proteins. The dotted lines are the TM-score and C-score cutoffs for correct folds

    TM-score (a) and RMSD (b) of the I-TASSER models versus the length of target proteins

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    <p><b>Copyright information:</b></p><p>Taken from "I-TASSER server for protein 3D structure prediction"</p><p>http://www.biomedcentral.com/1471-2105/9/40</p><p>BMC Bioinformatics 2008;9():40-40.</p><p>Published online 23 Jan 2008</p><p>PMCID:PMC2245901.</p><p></p> The numbers indicate the Pearson correlation coefficients

    The definition of relative orientation of two vector pairs <i>A</i> and <i>B</i>.

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    <p> is the direction vector from <i>A</i> to <i>B</i>. is the direction vector from <i>B</i> to <i>A</i>. Ω is the torsion angle between plane <i>A<sub>1</sub>AB</i> and plane <i>ABB<sub>1</sub></i>.</p

    Average RMSD (Ã…) and TM-score (in parenthesis) of models selected from I-TASSER and ROSETTA decoy sets.

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    <p>The highlights are the highest value in each category.</p

    The ratio of reference state at a distance <i>R</i> to that at 15 Ã… versus <i>R</i> for FIRE, DOPE and RW potentials for a protein of 100 AA.

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    <p>The ratio of reference state at a distance <i>R</i> to that at 15 Ã… versus <i>R</i> for FIRE, DOPE and RW potentials for a protein of 100 AA.</p
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