1,721,127 research outputs found
Role of character structure in judgments of visual similarity of Chinese characters for children in elementary school.
No full textSimultaneous determination of some sweeteners and preservatives in Chinese preserved fruits by micellar electrokinetic capillary chromatography.
No full textInAs0.97N0.03/InGaAs/InP multiple quantum well lasers with emission wavelength λ=2.38 μm
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Strained InAsN/InGaAs/InP multiple quantum well structures grown by RF-plasma assisted GSMBE for mid-infrared laser applications
No full textLandslide mapping with multi-scale object-based image analysis – a case study in the Baichi watershed, Taiwan
Full text linkWe developed a multi-scale OBIA (object-based image analysis) landslide detection technique to map shallow landslides in the Baichi watershed, Taiwan, after the 2004 Typhoon Aere event. Our semi-automated detection method selected multiple scales through landslide size statistics analysis for successive classification rounds. The detection performance achieved a modified success rate (MSR) of 86.5% with the training dataset and 86% with the validation dataset. This performance level was due to the multi-scale aspect of our methodology, as the MSR for single scale classification was substantially lower, even after spectral difference segmentation, with a maximum of 74%. Our multi-scale technique was capable of detecting landslides of varying sizes, including very small landslides, up to 95 m<sup>2</sup>. The method presented certain limitations: the thresholds we established for classification were specific to the study area, to the landslide type in the study area, and to the spectral characteristics of the satellite image. Because updating site-specific and image-specific classification thresholds is easy with OBIA software, our multi-scale technique is expected to be useful for mapping shallow landslides at watershed level
A pump control index for reducing suction and backflow effect caused by the portable centrifugal blood pump
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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