PURPOSE: This study was done to investigate quality of sleep and heart rate variability by the physical activity in high school students. METHODS: A survey that measures physical activity and quality of sleep was distributed to 118 students at Y High School. Heart rate variability was obtained using the LXC3203 heart rate monitor. The data of 105 students were analyzed using descriptive statistics, t-test, x2-test, and ANOVA with Scheffe test. RESULTS: Boys and students with part-time jobs had significantly higher physical activity. The quality of sleep was significantly high when the students were non-smokers, felt healthy, happy, less stressed, and satisfied with their school lives. Mean heart rate was significantly higher among girls, and standard deviation from normal to normal R-R intervals were high in boys. Physical activity had no significant relationships with quality of sleep and heart rate variability. CONCLUSIONS: Physical activity should be encouraged for high school students, especially for girls. An experimental study with different intensity and time of physical activity is recommended to examine the relationships with quality of sleep and heart rate variability in the future.
Adolescent ; Analysis of Variance ; Female ; Heart Rate* ; Heart* ; Humans ; Motor Activity*

A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n=34) of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of training time and classification accuracy. The results showed that some processes such as class code prefix, redundancy code suffix and smoothing effect of the classifiers' outputs could improve the accuracy of classification of robot control commands for a brain-machine interface system.
Aniline Compounds ; Animals ; Brain-Computer Interfaces ; Hippocampus ; Learning ; Neural Prostheses ; Neurons ; Rats ; Machine Learning

Background: There is little information about the airborne hazardous agents released during the heat treatment when manufacturing a welding material. This study aimed to evaluate the airborne hazardous agents generated at welding material manufacturing sites through area sampling. Methods: concentration of airborne particles was measured using a scanning mobility particle sizer and optical particle sizer. Total suspended particles (TSP) and respirable dust samples were collected on polyvinyl chloride filters and weighed to measure the mass concentrations. Volatile organic compounds and heavy metals were analyzed using a gas chromatography mass spectrometer and inductively coupled plasma mass spectrometer, respectively. Results: The average mass concentration of TSP was 683.1 ± 677.4 μg/m3, with respirable dust accounting for 38.6% of the TSP. The average concentration of the airborne particles less than 10 μm in diameter was 11.2–22.8 × 104 particles/cm3, and the average number of the particles with a diameter of 10–100 nm was approximately 78–86% of the total measured particles (<10 μm). In the case of volatile organic compounds, the heat treatment process concentration was significantly higher (p < 0.05) during combustion than during cooling. The airborne heavy metal concentrations differed depending on the materials used for heat treatment. The content of heavy metals in the airborne particles was approximately 32.6%. Conclusions Nanoparticle exposure increased as the number of particles in the air around the heat treatment process increases, and the ratio of heavy metals in dust generated after the heat treatment process is high, which may adversely affect workers' health.