Smoking, ADHD, and Problematic Video Game Use: A Structural Modeling Approach

Problematic video game use (PVGU), or addiction-like use of video games, is associated with negative physical and mental health problems as well as problems in social and occupational functioning. Possible contributors to PVGU include frequency of play, cigarette smoking, and ADHD. The aim of the current study was to explore the relationships among PVGU, cigarette smoking, ADHD, and frequency of play simultaneously using a structural modeling approach. Secondary data analysis was conducted on 2,801 video game users (Mage = 22.43 years, SDage = 4.7; 93% male) who completed an online survey comprising measures of PVGU, ADHD symptomatology, smoking behavior, and hours of video game use. The full model fit the data well: χ2 (2) = 2.017, p \u3e .05; RMSEA = 0.002 (90% CI [.000, .038]); CFI = 1.000; SRMR = .004. Absolute values of all standardized residuals were less than 0.1. All freely estimated paths were statistically significant. ADHD symptomatology, smoking behavior, and hours of video game use explained 41.8% of variance in PVGU. ADHD symptomatology, cigarette use, and video game use may all contribute to PVGU, which is consistent with past studies that examined these variables independently. Tracking these variables may be useful for PVGU prevention and assessment. The measurement model fit well, suggesting that Young’s Internet Addiction Scale, adapted for video game use, and Problem Videogame Playing Scale measure the same construct. Findings using either measures may be compared to each other, and both measures may be used as a screener of PVGU. The field of video game research may benefit from studying additional variables that help explain PVGU, specific treatment protocols for PVGU, and the effect of ADHD or smoking treatment on PVGU

STORYBOOK TO ENGAGE IN LITERACY PRACTICES IN ELEMENTARY SCHOOL IN KOREA

This study investigates ways to promote English literacy through storytelling methods based on sociocultural perspectives of literacy in Korean public elementary school settings. As a teacher researcher, I ran a storytelling afterschool program to develop English literacy using English storybooks. 14 of 3rd and 4th graders including 6 focal students participated in the study. The research findings show that storybook was useful to engage students in literacy practices in Korean elementary school context where English is taught as a foreign language. While implementing English storybooks, strengthening affective aspects within ZPD was significant. Also, scaffolding should be done in various ways. Even though the class was pursuing literacy development, oral language development was also followed. Storybook made it possible to implement literacy knowledge with ease. In teaching English storybooks in Korean context, teacher needs to consider characteristics of foreign language learners, take advantage of teaching strategies used by regular classes, and make students reflective on themselves

Estimating δ15N and δ13C in Barley and Pea Mixtures Using Near-Infrared Spectroscopy with Genetic Algorithm Based Partial Least Squares Regression

Stable isotope measurements have been increasingly used as a method to obtain information on relationships between plants and their environment (Dawson et al., 2002). Stable isotopes are seen as a powerful tool for advancing our knowledge on stock cycling and, nitrogen and carbon isotopic compositions have provided key insights into biogeochemical interactions between plants, soils and the atmosphere (Robinson, 2001). For the stable isotope measurements, the δ13C isotopic signature has been used successfully to disentangle physiological, ecological and biogeochemical processes and, δ15N studies have significantly improved our knowledge on nitrogen cycling pathways and nitrogen acquisition by plants (Vallano and Sparks, 2008). For the stable isotope measurements, traditional laboratory methods using isotope analysis are accurate and reliable, but usually time-consuming and expensive. Near-infrared spectroscopy (NIRS) analysis provides rapid, accurate and less expensive estimation. NIRS have been made to estimate herbage parameters using statistical methods such as multiple linear regression and partial least square regression (PLSR). PLSR uses all available wavebands in multivariate calibration for quantitative analysis of the spectral data. However, previous studies indicated that PLSR with waveband selection might improve their predictive accuracy in multivariate calibration at laboratory (Leardi, 2000) and the selection of appropriate wavelengths can refine the predictive accuracy of the PLS model by optimizing important spectral wavebands both in laboratory NIRS (Jiang et al., 2002). To optimize important spectral wavebands by wavelength selection, genetic algorithms (GA) is widely used, because GA has the ability to simulate the natural evolution of an individual and GA is well suited for solving variable subset selection problems (Ding et al., 1998). Barley and pea mixture is one of the most important forage species for livestock farming in Korea. To investigate nitrogen fixation and transfer in barley and pea mixture, stable isotope measurements was widely used. However, there was no research to estimate stable isotope in barley and pea mixture using NIRS in Korea. The aim of this study was to investigate performance of NIRS with PLSR using genetic algorithms based wavelength selection (GA-PLSR) and compare with PLSR without wavelength selection (FS-PLSR) for the estimation of δ15N and δ13C in barley and pea mixture

Correlates of Problematic Gambling as Correlates of Problematic Video Game Use

Problematic video game use (PVGU), or addiction-like use of video games, affects a significant portion of the population and is associated with various negative physical and mental health problems. Given existing research regarding PVGU and gambling disorder, as well as the recent convergence of gambling and video gaming activities, studying correlates of gambling disorder in the context of PVGU may help identify novel correlates of PVGU that can be used to improve assessment and intervention. The aim of the current study was to examine correlates of gambling disorder, such as gaming fallacies and perceived locus of control, as potential predictors of PVGU using structural equation modeling. Data were collected through an online survey comprising measures of PVGU and various potential correlates of PVGU. The sample included 3,481 adults between the ages 18 and 74 (M = 25.08, SD = 7.02; 79.8% cisgender male; 11.1% Hispanic; 77.4% Caucasian, 8.5% Asian or Asian American, 5.4% identifying as mixed race, 5.2% identifying as Other, 2.2% Black or African American, 1.1% Native American or Alaskan Native, and 0.4% Native Hawaiian or other Pacific Islander). The final model fit the data very well, χ2 (69) = 278.846, p \u3c .05; RMSEA = 0.034 (90% CI [.030, .038]); CFI = 0.959; SRMR = .027. As hypothesized, vi Gaming Fallacies, Locus of Control, and participants\u27 video game use all had significant, positive relationships with PVGU. The large effect size of the combination of predictors suggests a clinically significant relationship, and considering these multiple correlates in combination may result in more effective assessment and treatment of PVGU

Numerical Sensitivity Tests of Volatile Organic Compounds Emission to PM2.5 Formation during Heat Wave Period in 2018 in Two Southeast Korean Cities

A record-breaking severe heat wave was recorded in southeast Korea from 11 July to 15 August 2018, and the numerical sensitivity simulations of volatile organic compound (VOC) to secondarily generated particulate matter with diameter of less than 2.5 mu m (PM2.5) concentrations were studied in the Busan and Ulsan metropolitan areas in southeast Korea. A weather research and forecasting (WRF) model coupled with chemistry (WRF-Chem) was employed, and we carried out VOC emission sensitivity simulations to investigate variations in PM2.5 concentrations during the heat wave period that occurred from 11 July to 15 August 2018. In our study, when anthropogenic VOC emissions from the Comprehensive Regional Emissions Inventory for Atmospheric Transport Experiment-2015 (CREATE-2015) inventory were increased by approximately a factor of five in southeast Korea, a better agreement with observations of PM2.5 mass concentrations was simulated, implying an underestimation of anthropogenic VOC emissions over southeast Korea. The simulated secondary organic aerosol (SOA) fraction, in particular, showed greater dominance during high temperature periods such as 19-21 July, 2018, with the SOA fractions of 42.3% (in Busan) and 34.3% (in Ulsan) among a sub-total of seven inorganic and organic components. This is considerably higher than observed annual mean organic carbon (OC) fraction (28.4 +/- 4%) among seven components, indicating the enhancement of secondary organic aerosols induced by photochemical reactions during the heat wave period in both metropolitan areas. The PM2.5 to PM10 ratios were 0.69 and 0.74, on average, during the study period in the two cities. These were also significantly higher than the typical range in those cities, which was 0.5-0.6 in 2018. Our simulations implied that extremely high temperatures with no precipitation are significantly important to the secondary generation of PM2.5 with higher secondary organic aerosol fraction via photochemical reactions in southeastern Korean cities. Other possible relationships between anthropogenic VOC emissions and temperature during the heat wave episode are also discussed in this study