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Background/Objectives: Adolescent obesity is highly likely to lead to adult obesity and is associated with dietary habits, subjective health, and body image perception. This study aimed to analyze the relationships between BMI, dietary habits, subjective health perception, and body image perception among Korean adolescents using data from the 18th Korea Youth Risk Behavior Survey conducted in 2022 to explore strategies for reducing adolescent obesity rates. Methods: Data from 50,427 participants were analyzed, including BMI, seven lifestyle factors (intake frequencies of water, milk, fruit, soft drinks, vegetables, breakfast, and late-night snacks), and responses to one item each for subjective health perception and body image perception. Results: Higher intake frequencies of breakfast, fruits, soft drinks, and late-night snacks were associated with lower BMI (p < 0.001). However, among high school students, those with lower water and breakfast intake but higher soft drink and late-night snack intake exhibited an increasing trend in BMI. Subjective health perception and body image perception were interrelated, with subjective health perception influencing body image perception (p < 0.001). Conclusions: Addressing nutritional issues within schools, including improving school meals, regulating accessible products, and providing nutritional intake guidelines, is essential. Additionally, developing tailored health education programs to promote healthy body image perceptions is necessary. This study can serve as a foundational resource for analyzing adolescent health and developing strategies to improve health behaviors in changing environments. [ABSTRACT FROM AUTHOR]

The promulgation of the Biogas Act in South Korea has increased the number of organic waste treatment facilities and the amount of food waste digestion sludge (FWDS), a byproduct of the biogas process. FWDS recovery involves various challenges, which leads to the accumulation or improper disposal of sludge. Hence, FWDS needs to be treated in environmentally sound and safe ways. In this study, anaerobic digestion sludges were mixed with unused forest biomass to produce fuel. The results showed that pellets produced via mixing of FWDS with unused forest biomass had improved durability, bulk density, and fine particle performance compared to surface-carbonized wood pellets. Carbonized pellets manufactured with 30% FWDS had a moisture content of 11.746% and met all biosolid waste fuel (SRF) standards, except for moisture content. Carbonized pellets prepared with 15% FWDS met the L2 wood pellet standards for ash content (less than 3.0%) and bulk density (greater than 550 kg/m3), as well as all other standard values in both the industrial wood pellet quality standards and bio-SRF criteria. This study confirmed the potential and suitability of digestion sludge and unused forest biomass for fuel utilization by addressing their respective limitations. [ABSTRACT FROM AUTHOR]

A probabilistic estimation model for forest biomass using unmanned aerial vehicle (UAV) photography was developed. We utilized a machine-learning-based object detection algorithm, a mask region-based convolutional neural network (Mask R-CNN), to detect trees in aerial photographs. Subsequently, Bayesian regression was used to calibrate the model based on an allometric model using the estimated crown diameter (CD) obtained from aerial photographs and analyzed the diameter at breast height (DBH) data acquired through terrestrial laser scanning. The F1 score of the Mask R-CNN for individual tree detection was 0.927. Moreover, CD estimation using the Mask R-CNN was acceptable (rRMSE = 10.17%). Accordingly, the probabilistic DBH estimation model was successfully calibrated using Bayesian regression. A predictive distribution accurately predicted the validation data, with 98.6% and 56.7% of the data being within the 95% and 50% prediction intervals, respectively. Furthermore, the estimated uncertainty of the probabilistic model was more practical and reliable compared to traditional ordinary least squares (OLS). Our model can be applied to estimate forest biomass at the individual tree level. Particularly, the probabilistic approach of this study provides a benefit for risk assessments. Additionally, since the workflow is not interfered by the tree canopy, it can effectively estimate forest biomass in dense canopy conditions. [ABSTRACT FROM AUTHOR]

Study aim: The purpose of this study was to confirm the effectiveness of tele-rehabilitation mental practice (TRMP) on balanc­ing performance for patients with hemiplegia. Material and methods: This study included five stroke patients, and all participants were trained to help to recover balancing performance. TRMP was applied for a total of 15 training sessions for 3 weeks, and pre- and post-intervention evaluations were carried out during this study. The Berg Balance Scale was used to confirm the balance ability, the Modified Falls Efficacy Scale to confirm the falls efficacy, and the Wii program to confirm the right and left weight distribution ratio. Results: As a result of this study, we could not confirm a significant difference between pre- and post-intervention balance ca­pacity and fall efficacy. However, weight distribution showed a significant difference after the intervention. Conclusion: TRMP may be applied in novel balance training for stroke patients.

Tree detection and fuel amount and distribution estimation are crucial for the investigation and risk assessment of wildfires. The demand for risk assessment is increasing due to the escalating severity of wildfires. A quick and cost-effective method is required to mitigate foreseeable disasters. In this study, a method for tree detection and fuel amount and distribution prediction using aerial images was proposed for a low-cost and efficient acquisition of fuel information. Three-dimensional (3D) fuel information (height) from light detection and ranging (LiDAR) was matched to two-dimensional (2D) fuel information (crown width) from aerial photographs to establish a statistical prediction model in northeastern South Korea. Quantile regression for 0.05, 0.5, and 0.95 quantiles was performed. Subsequently, an allometric tree model was used to predict the diameter at the breast height. The performance of the prediction model was validated using physically measured data by laser distance meter triangulation and direct measurement from a field survey. The predicted quantile, 0.5, was adequately matched to the measured quantile, 0.5, and most of the measured values lied within the predicted quantiles, 0.05 and 0.95. Therefore, in the developed prediction model, only 2D images were required to predict a few of the 3D fuel details. The proposed method can significantly reduce the cost and duration of data acquisition for the investigation and risk assessment of wildfires. [ABSTRACT FROM AUTHOR]