Factors Influencing Aggressive Adolescent Behavior: An Analysis Using the Decision Tree Method

Aggression among adolescents is a significant public health concern worldwide. To safeguard adolescents' physical and mental health, controlling the incidence of aggressive behavior and its triggers and drivers is necessary. Different risk factors in each environment drive adolescent aggression. These factors are interconnected, making the environment an ecosystem of aggression. This study aims to analyze the factors that influence adolescents' aggressive behavior and provide a basis for formulating measures to reduce such behavior in the future. This multicenter cross-sectional study was conducted in five middle schools in Lingbao City. The participants were junior high school students aged 11-15 years. Data were analyzed from January to March 2020 using the decision tree analysis method. The aggressive behavior scale, adolescent emotion regulation scale, family intimacy and adaptation scale, parental education participation scale, and a simple parenting style questionnaire were used for the investigation. Of the 581 participants, 25.5% demonstrated a tendency for aggressive behavior, while the remaining did not. The decision tree analysis showed that the incidence of adolescent aggression was the highest (54.8%) when the maternal overprotective score was greater than or equal to 17, the adolescent had low emotional control, and the mother's intelligence score was higher than 30. The decision tree model reflects the influencing factors of adolescent aggressive behavior from multiple levels and provides a reference for formulating effective intervention measures to control such behavior and its influencing factors.

Risk Factors for High-Risk Adenoma on the First Lifetime Colonoscopy Using Decision Tree Method: A Cross-Sectional Study in 6,047 Asymptomatic Koreans

Background/Aims: As risk of colorectal neoplasm is varied even in persons with “average-risk,” risk evaluation and tailored screening are needed. This study aimed to evaluate the risk factors of high-risk adenoma (HRA) in healthy individuals and determine the characteristics of advanced neoplasia (AN) among individual polyps.

Methods: Asymptomatic adults who underwent the first lifetime screening colonoscopy at the Seoul National University Hospital Healthcare System Gangnam Center (SNUH GC) were recruited from 2004 to 2007 as SNUH GC Cohort and were followed for 10 years. Demographic and clinical characteristics were compared between the subjects with and without AN (≥10 mm in size, villous component, and/or high-grade dysplasia and/or cancer) or HRA (AN and/or 3 or more adenomas). For individual polyps, correlations between clinical or endoscopic features and histologic grades were evaluated using a decision tree method.

Results: A total of 6,047 subjects were included and 5,621 polyps were found in 2,604 (43%) subjects. Advanced age, male sex, and current smoking status were statistically significant with regards to AN and HRA. A lower incidence of AN was observed in subjects taking aspirin. In the decision tree model, the location, shape, and size of the polyp, and sex of the subject were key predictors of the pathologic type. A weak but significant association was observed between the prediction of the final tree and the histological grouping (Kendall's tau-c = 0.142, p < 0001).

Conclusions: Advanced neoplasia and HRA can be predicted using several individual characteristics and decision tree models.

Marine Icing Sensor with Phase Discrimination

In this paper, a novel approach is presented to the measurement of marine icing phenomena under the presence of a two-phase condition. We have developed a sensor consisting of an electrostatic array and a signal processing based on a decision tree method. A three-element electrostatic array is employed to derive signals having linearly decoupled characteristics from which two key parameters, ice and water accretion layer dimension, can be determined for the purpose of environmental monitoring. The quantified characteristics revealed a correlation with the ice layer thickness in spite of the strong influence from the top water phase layer. The decision tree model established a relationship between the signal characteristics and the two accretion thickness parameters of water and ice layer. Through experimental verification, it has been observed that our sensor array in combination with the decision tree model based signal processing provides a simple practical solution to the challenging field of a two phase composition measurement such as in the marine icing considered in this study.

[Personality traits of Japanese patients with focal dystonia-Variable classification by affected body part and occupation]

We administered the Revised Neo Personality Inventory (NEO-PI-R) to 92 Japanese patients with focal dystonia and analyzed the relation of their personality traits, affected body parts, and occupations using a decision tree method. Results show that participants' personality traits were related to their occupation rather than the body part. Occupational dystonia patients including musicians exhibited neurotic tendencies and a high level of anxiety. Moreover, patients with occupational upper limb dystonia had realistic thinking patterns. Patients with musicians' dystonia were open about their emotional experiences: both positive and negative.