일상생활에서 흔히 접하는 재미난 유머 속에 깃들인 철학을 통해 우리의 잠재된 생각을 키워내, 깊이 있고 고차원적인 철학의 기쁨을 맛보게 하는 유익한 인문교양서.

Modern schooling in Korea, which was officially established by law in 1949, is well known for its function as an engine of economic success in modern Korea. Although this fact seems to be world-wid...

BackgroundAttention deficit hyperactivity disorder is clinically diagnosed; however, quantitative analysis to statistically analyze the symptom severity of children with ADHD via the measurement of head movement is still in progress. Studies focusing on the cues that may influence the attention of children with ADHD in classroom settings, where children spend a considerable amount of time, are relatively scarce. Virtual reality allows real-life simulation of classroom environments and thus provides an opportunity to test a range of theories in a naturalistic and (...) controlled manner. The objective of this study was to investigate the correlation between participants’ head movements and their reports of inattention and hyperactivity, and to investigate how their head movements are affected by different social cues of different sensory modalities.MethodsThirty-seven children and adolescents with and without ADHD were recruited for this study. All participants were assessed for diagnoses, clinical symptoms, and self-reported symptoms. A virtual reality-continuous performance test was conducted under four conditions: control, no-cue, visual cue, and visual/audio cue. A quantitativecomparison of the participants’ head movements was conducted in three dimensions using a head-mounted display in a VR classroom environment. Task-irrelevant head movements were analyzed separately, considering the dimension of movement needed to perform the VR-CPT.ResultsThe magnitude of head movement, especially task-irrelevant head movement, significantly correlated with the current standard of clinical assessment in the ADHD group. Regarding the four conditions, head movement showed changes according to the complexity of social cues in both the ADHD and healthy control groups.ConclusionChildren and adolescents with ADHD showed decreasing task-irrelevant movements in the presence of social stimuli toward the intended orientation. As a proof-of-concept study, this study preliminarily identifies the potential of VR as a tool to understand and investigate the classroom behavior of children with ADHD in a controlled, systematic manner. (shrink)

With regard to facial emotion recognition, previous studies found that specific facial regions were attended more in order to identify certain emotions. We investigated whether a preferential search for emotion-specific diagnostic regions could contribute toward the accurate recognition of facial emotions. Twenty-three neurotypical adults performed an emotion recognition task using six basic emotions: anger, disgust, fear, happiness, sadness, and surprise. The participants’ exploration patterns for the faces were measured using the Moving Window Technique (MWT). This technique presented a small window (...) on a blurred face, and the participants explored the face stimuli through a mouse-controlled window in order to recognize the emotions on the face. Our results revealed that when the participants explored the diagnostic regions for each emotion more frequently, the correct recognition of the emotions occurred at a faster rate. To the best of our knowledge, this current study is the first to present evidence that an exploration of emotion-specific diagnostic regions can predict the reaction time of accurate emotion recognition among neurotypical adults. Such findings can be further applied in the evaluation and/or training (regarding emotion recognition functions) of both typically and atypically developing children with emotion recognition difficulties. (shrink)

This study assessed the demographic characteristics of Koreans engaged in leisure sports activities during the COVID-19 pandemic and the differences in their preventive health behaviors and constraints on leisure activities. For this study, the demographic characteristics of 544 leisure sport participants, who were recruited on a nationwide basis, were examined through an online survey. Then, comparisons between groups were performed using independent t-tests, one-way analysis of variance, and multivariate analysis of variance. Women who participated in both indoor and outdoor leisure (...) sports showed higher adoption of health prevention behaviors than their male counterparts, and married individuals who participated in indoor leisure sports showed higher adoption of health prevention behaviors than unmarried participants. Moreover, individuals who participated in both indoor and outdoor leisure sports by themselves had many interpersonal constraints overall, and the group of married individuals who participated in indoor leisure sports showed structural constraints. In conclusion, leisure sports participants have adopted many health prevention behaviors during the COVID-19 pandemic, but this had led to some interpersonal constraints. These results indicate that, in the case of future pandemics, personal and institutional efforts will need to be made to promote participation in leisure sports and prevent excessive social isolation. (shrink)

Forecasting blood glucose values for patients can help prevent hypoglycemia and hyperglycemia events in advance. To this end, this study proposes an intelligent ensemble deep learning system to predict BG values in 15, 30, and 60 min prediction horizons based on historical BG values collected via continuous glucose monitoring devices as an endogenous factor and carbohydrate intake and insulin administration information as exogenous factors. Although there are numerous deep learning algorithms available, this study applied five algorithms, namely, recurrent neural network, (...) which is optimized for sequence data, and RNN-based algorithms, stacked LSTM, bidirectional LSTM, and gated recurrent unit). Then, a genetic algorithm was applied to the five prediction models to optimize their weights through ensemble techniques and to yield the final predicted BG values. The performance of the proposed model was compared to that of the autoregressive integrated moving average model as a baseline. The results show that the proposed model significantly outperforms the baseline in terms of the root mean square error and continuous glucose error grid analysis. For the valid 29 diabetic patients for the multivariate models, the RMSE was 11.08, 19.25, and 31.30 mg/DL for 15, 30, and 60 min PH, respectively. When the same data were applied to univariate models, the RMSE was 11.28, 19.99, and 33.13 mg/DL for 15, 30, and 60 min PH, respectively. Both the univariate and multivariate models showed a statistically significant difference compared with the baseline at a 5% statistical significance level. Instead of using a model with a single algorithm, applying a GA based on each output of a model with multiple algorithms was found to play a significant role in improving model performance. (shrink)