• 아동학회지
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• 제24권3호
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• pp.15-26
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• 2003

This research investigated young children's social interaction and task performance in dyadic collaboration. The independent variables were gender and social skill level(high/low groups). Twenty-four pairs each 4-year-old boys and 4-year-old girls(total 96 subjects) worked at a block reconstruction. Two researchers judged degree of dyadic collaboration. Data were processed by 2 way ANOVA and Pearson's product correlation. While no main effect for gender was found, the effect for social skill level(high/low groups)was significant for social interaction. There was positive correlation between social interaction and task performance.

• Genomics & Informatics
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• 제3권3호
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• pp.53-62
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• 2005

MicroRNAs play an important role in regulating gene expression, but their target identification is a difficult task due to their short length and imperfect complementarity. Burge and coworkers developed a program called TargetScan that allowed imperfect complementarity and established a procedure favoring targets with multiple binding sites conserved in multiple organisms. We improved their algorithm in two major aspects - (i) using well-defined UTR (untranslated region) database, (ii) examining the extent of conservation inside the 3' UTR specifically. Average length in our UTR database, based on the ECgene annotation, is more than twice longer than the Ensembl. Then, TargetScan was used to identify putative binding sites. The extent of conservation varies significantly inside the 3' UTR. We used the 'tight' tracks in the UCSC genome browser to select the conserved binding sites in multiple species. By combining the longer 3' UTR data, TargetScan, and tightly conserved blocks of genomic DNA, we identified 107 putative target genes with multiple binding sites conserved in multiple species, of which 85 putative targets are novel.

• 감성과학
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• 제3권2호
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• pp.17-28
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• 2000

Psychoacoustic characteristics of automobile horns play significant role in resulting subjective evaluation and psychphysiological reactions. However, comparison and differentiation of physiological responses to commercially available horns is a complicated task due to the small contrast in technical features of horns and the influence of such processes as habituation on physiological outcome with the increased number of auditory stimulation trials. In a study on 10 college students, there was performed comparative analysis of reactivity of physiological responses mediated by central and autonomic nervous systems in order to identify the role of habituation on decrement of psychophysiological responsivity and assess the ability to differentiate subjectively most and least preferred, as well as most and least appropriate horns according to physiological manifestations. The EEG and autonomic responses to 7 automobile horns were analyzed during 3 blocks of trials, with varying order of stimuli and changed acoustic parameters of horns in each block. Thus, responses were analyzed for totally 21 trials of auditory stimulation. It was shown that electrodermal and cardiovascular responses have different reactivity patterns to repeated stimulation: skin conductance measures habituated, cardiac reactivity showed no signs of habituation, and the vascular response demonstrated sensitization. The temporal EEG exhibited marked habituation of fast beta band power, while alpha-blocking effect did not habituate during the course of experiment. Differentiation of physiological responses of most and least preferred and appropriate horns was possible in our study, however, some cardiovascular reactivity measures differentiated during the entire course of the experiment, while EEG and electrodermal parameters showed significant differences only during first block of trials, and were later affected by the habituation.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.125-134
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• 2024

The novel coronavirus 2019 is called COVID-19 has outspread swiftly worldwide. An early diagnosis is more important to control its quick spread. Medical imaging mechanics, chest calculated tomography or chest X-ray, are playing a vital character in the identification and testing of COVID-19 in this present epidemic. Chest X-ray is cost effective method for Covid-19 detection however the manual process of x-ray analysis is time consuming given that the number of infected individuals keep growing rapidly. For this reason, it is very important to develop an automated COVID-19 detection process to control this pandemic. In this study, we address the task of automatic detection of Covid-19 by using a popular deep learning model namely the VGG19 model. We used 1300 healthy and 1300 confirmed COVID-19 chest X-ray images in this experiment. We performed three experiments by freezing different blocks and layers of VGG19 and finally, we used a machine learning classifier SVM for detecting COVID-19. In every experiment, we used a five-fold cross-validation method to train and validated the model and finally achieved 98.1% overall classification accuracy. Experimental results show that our proposed method using the deep learning-based VGG19 model can be used as a tool to aid radiologists and play a crucial role in the timely diagnosis of Covid-19.